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Operating regimes in a single enzymatic cascade at ensemble-level

Single enzymatic cascade, ubiquitously found in cellular signaling networks, is a phosphorylation-dephosphorylation reaction cycle causing a transition between inactive and active states of a protein catalysed by kinase and phosphatase, respectively. Steady-state information processing ability of su...

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Autores principales: Parundekar, Akshay, Kalantre, Girija, Khadpekar, Akshada, Viswanathan, Ganesh A.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6675077/
https://www.ncbi.nlm.nih.gov/pubmed/31369598
http://dx.doi.org/10.1371/journal.pone.0220243
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author Parundekar, Akshay
Kalantre, Girija
Khadpekar, Akshada
Viswanathan, Ganesh A.
author_facet Parundekar, Akshay
Kalantre, Girija
Khadpekar, Akshada
Viswanathan, Ganesh A.
author_sort Parundekar, Akshay
collection PubMed
description Single enzymatic cascade, ubiquitously found in cellular signaling networks, is a phosphorylation-dephosphorylation reaction cycle causing a transition between inactive and active states of a protein catalysed by kinase and phosphatase, respectively. Steady-state information processing ability of such a cycle (e.g., MAPK cascade) has been classified into four qualitatively different operating regimes, viz., hyperbolic (H), signal-transducing (ST), threshold-hyperbolic (TH) and ultrasensitive (U). These four regimes represent qualitatively different dose-response curves, that is, relationship between concentrations of input kinase (e.g., pMEK) and response activated protein (e.g., pERK). Regimes were identified using a deterministic model accounting for population-averaged behavior only. Operating regimes can be strongly influenced by the inherently present cell-to-cell variability in an ensemble of cells which is captured in the form of pMEK and pERK distributions using reporter-based single-cell experimentation. In this study, we show that such experimentally acquired snapshot pMEK and pERK distribution data of a single MAPK cascade can be directly used to infer the underlying operating regime even in the absence of a dose-response curve. This deduction is possible primarily due to the presence of a monotonic relationship between experimental observables R(IQR), ratio of the inter-quartile range of the pERK and pMEK distribution pairs and R(M), ratio of the medians of the distribution pair. We demonstrate this relationship by systematic analysis of a quasi-steady state approximated model superimposed with an input gamma distribution constrained by the stimulus strength specific pMEK distribution measured on Jurkat-T cells stimulated with PMA. As a first, we show that introduction of cell-to-cell variability only in the upstream kinase achieved by superimposition of an appropriate input pMEK distribution on the dose-response curve can predict bimodal response pERK distribution in ST regime. Implementation of the proposed method on the input-response distribution pair obtained in stimulated Jurkat-T cells revealed that while low-dosage PMA stimulation preserves the H regime observed in resting cells, high-dosage causes H to ST regime transition.
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spelling pubmed-66750772019-08-06 Operating regimes in a single enzymatic cascade at ensemble-level Parundekar, Akshay Kalantre, Girija Khadpekar, Akshada Viswanathan, Ganesh A. PLoS One Research Article Single enzymatic cascade, ubiquitously found in cellular signaling networks, is a phosphorylation-dephosphorylation reaction cycle causing a transition between inactive and active states of a protein catalysed by kinase and phosphatase, respectively. Steady-state information processing ability of such a cycle (e.g., MAPK cascade) has been classified into four qualitatively different operating regimes, viz., hyperbolic (H), signal-transducing (ST), threshold-hyperbolic (TH) and ultrasensitive (U). These four regimes represent qualitatively different dose-response curves, that is, relationship between concentrations of input kinase (e.g., pMEK) and response activated protein (e.g., pERK). Regimes were identified using a deterministic model accounting for population-averaged behavior only. Operating regimes can be strongly influenced by the inherently present cell-to-cell variability in an ensemble of cells which is captured in the form of pMEK and pERK distributions using reporter-based single-cell experimentation. In this study, we show that such experimentally acquired snapshot pMEK and pERK distribution data of a single MAPK cascade can be directly used to infer the underlying operating regime even in the absence of a dose-response curve. This deduction is possible primarily due to the presence of a monotonic relationship between experimental observables R(IQR), ratio of the inter-quartile range of the pERK and pMEK distribution pairs and R(M), ratio of the medians of the distribution pair. We demonstrate this relationship by systematic analysis of a quasi-steady state approximated model superimposed with an input gamma distribution constrained by the stimulus strength specific pMEK distribution measured on Jurkat-T cells stimulated with PMA. As a first, we show that introduction of cell-to-cell variability only in the upstream kinase achieved by superimposition of an appropriate input pMEK distribution on the dose-response curve can predict bimodal response pERK distribution in ST regime. Implementation of the proposed method on the input-response distribution pair obtained in stimulated Jurkat-T cells revealed that while low-dosage PMA stimulation preserves the H regime observed in resting cells, high-dosage causes H to ST regime transition. Public Library of Science 2019-08-01 /pmc/articles/PMC6675077/ /pubmed/31369598 http://dx.doi.org/10.1371/journal.pone.0220243 Text en © 2019 Parundekar et al http://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
spellingShingle Research Article
Parundekar, Akshay
Kalantre, Girija
Khadpekar, Akshada
Viswanathan, Ganesh A.
Operating regimes in a single enzymatic cascade at ensemble-level
title Operating regimes in a single enzymatic cascade at ensemble-level
title_full Operating regimes in a single enzymatic cascade at ensemble-level
title_fullStr Operating regimes in a single enzymatic cascade at ensemble-level
title_full_unstemmed Operating regimes in a single enzymatic cascade at ensemble-level
title_short Operating regimes in a single enzymatic cascade at ensemble-level
title_sort operating regimes in a single enzymatic cascade at ensemble-level
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6675077/
https://www.ncbi.nlm.nih.gov/pubmed/31369598
http://dx.doi.org/10.1371/journal.pone.0220243
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