Literature DB >> 30782397

A Two-Pulse Cellular Stimulation Test Elucidates Variability and Mechanisms in Signaling Pathways.

Madhuresh Sumit1, Andreja Jovic2, Richard R Neubig3, Shuichi Takayama4, Jennifer J Linderman5.   

Abstract

Mammalian cells respond in a variable manner when provided with physiological pulses of ligand, such as low concentrations of acetylcholine present for just tens of seconds or TNFα for just tens of minutes. For a two-pulse stimulation, some cells respond to both pulses, some do not respond, and yet others respond to only one or the other pulse. Are these different response patterns the result of the small number of ligands being able to only stochastically activate the pathway at random times or an output pattern from a deterministic algorithm responding differently to different stimulation intervals? If the response is deterministic in nature, what parameters determine whether a response is generated or skipped? To answer these questions, we developed a two-pulse test that utilizes different rest periods between stimulation pulses. This "rest-period test" revealed that cells skip responses predictably as the rest period is shortened. By combining these experimental results with a mathematical model of the pathway, we further obtained mechanistic insight into potential sources of response variability. Our analysis indicates that in both intracellular calcium and NFκB signaling, response variability is consistent with extrinsic noise (cell-to-cell variability in protein levels), a short-term memory of stimulation, and high Hill coefficient processes. Furthermore, these results support recent works that have emphasized the role of deterministic processes for explaining apparently stochastic cellular response variability and indicate that even weak stimulations likely guide mammalian cells to appropriate fates rather than leaving outcomes to chance. We envision that the rest-period test can be applied to other signaling pathways to extract mechanistic insight.
Copyright © 2019 Biophysical Society. Published by Elsevier Inc. All rights reserved.

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Year:  2019        PMID: 30782397      PMCID: PMC6400859          DOI: 10.1016/j.bpj.2019.01.022

Source DB:  PubMed          Journal:  Biophys J        ISSN: 0006-3495            Impact factor:   4.033


  4 in total

1.  Computational methods for characterizing and learning from heterogeneous cell signaling data.

Authors:  Patrick C Kinnunen; Kathryn E Luker; Gary D Luker; Jennifer J Linderman
Journal:  Curr Opin Syst Biol       Date:  2021-05-04

2.  A predictive model of gene expression reveals the role of network motifs in the mating response of yeast.

Authors:  Amy E Pomeroy; Matthew I Peña; John R Houser; Gauri Dixit; Henrik G Dohlman; Timothy C Elston; Beverly Errede
Journal:  Sci Signal       Date:  2021-02-16       Impact factor: 8.192

3.  Pre-existing Cell States Control Heterogeneity of Both EGFR and CXCR4 Signaling.

Authors:  Phillip C Spinosa; Patrick C Kinnunen; Brock A Humphries; Gary D Luker; Kathryn E Luker; Jennifer J Linderman
Journal:  Cell Mol Bioeng       Date:  2020-07-27       Impact factor: 2.321

4.  Computer vision reveals hidden variables underlying NF-κB activation in single cells.

Authors:  Parthiv Patel; Nir Drayman; Ping Liu; Mustafa Bilgic; Savaş Tay
Journal:  Sci Adv       Date:  2021-10-22       Impact factor: 14.136

  4 in total

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