title: Noise and interlocking signaling pathways promote distinct transcription factor dynamics in response to different stresses.
abstract: All cells perceive and respond to environmental stresses through elaborate stress-sensing networks. Yeast cells sense stress through diverse signaling pathways that converge on the transcription factors Msn2 and Msn4, which respond by initiating rapid, idiosyncratic cycles into and out of the nucleus. To understand the role of Msn2/4 nuclear localization dynamics, we combined time-lapse studies of Msn2-GFP localization in living cells with computational modeling of stress-sensing signaling networks. We find that several signaling pathways, including Ras/protein kinase A, AMP-activated kinase, the high-osmolarity response mitogen-activated protein kinase pathway, and protein phosphatase 1, regulate activation of Msn2 in distinct ways in response to different stresses. Moreover, we find that bursts of nuclear localization elicit a more robust transcriptional response than does sustained nuclear localization. Using stochastic modeling, we reproduce in silico the responses of Msn2 to different stresses, and demonstrate that bursts of localization arise from noise in the signaling pathways amplified by the small number of Msn2 molecules in the cell. This noise imparts diverse behaviors to genetically identical cells, allowing cell populations to "hedge their bets" in responding to an uncertain future, and to balance growth and survival in an unpredictable environment.
authors.affiliation: Department of Molecular Biology, Princeton University, Princeton, NJ 08544, USA.
authors.forename: Natalia
authors.initials: N
authors.lastname: Petrenko
authors.forename: Razvan V
authors.initials: RV
authors.lastname: Chereji
authors.forename: Megan N
authors.initials: MN
authors.lastname: McClean
authors.forename: Alexandre V
authors.initials: AV
authors.lastname: Morozov
authors.forename: James R
authors.initials: JR
authors.lastname: Broach
mesh_terms: D000465:Algorithms; D002467:Cell Nucleus / Q000378:metabolism; D017868:Cyclic AMP-Dependent Protein Kinases / Q000235:genetics / Q000378:metabolism; D003593:Cytoplasm / Q000378:metabolism; D004268:DNA-Binding Proteins / Q000235:genetics* / Q000378:metabolism; D053263:Gene Regulatory Networks; D005947:Glucose / Q000494:pharmacology; D049452:Green Fluorescent Proteins / Q000235:genetics / Q000378:metabolism; D007700:Kinetics; D008856:Microscopy, Fluorescence; D008954:Models, Biological; D008957:Models, Genetic; D009154:Mutation; D009584:Nitrogen / Q000494:pharmacology; D009994:Osmolar Concentration; D017346:Protein Serine-Threonine Kinases / Q000235:genetics / Q000378:metabolism; D012441:Saccharomyces cerevisiae / Q000187:drug effects / Q000235:genetics / Q000378:metabolism; D029701:Saccharomyces cerevisiae Proteins / Q000235:genetics* / Q000378:metabolism; D015398:Signal Transduction / Q000235:genetics* / Q000502:physiology; D013012:Sorbitol / Q000494:pharmacology; D013312:Stress, Physiological / Q000235:genetics* / Q000502:physiology; D059008:Time-Lapse Imaging; D014157:Transcription Factors / Q000235:genetics* / Q000378:metabolism; D014158:Transcription, Genetic / Q000187:drug effects
chemical_list: D004268:DNA-Binding Proteins; C081935:MSN2 protein, S cerevisiae; D029701:Saccharomyces cerevisiae Proteins; D014157:Transcription Factors; D049452:Green Fluorescent Proteins; D013012:Sorbitol; C071570:SNF1-related protein kinases; D017346:Protein Serine-Threonine Kinases; D017868:Cyclic AMP-Dependent Protein Kinases; D005947:Glucose; D009584:Nitrogen