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Molecular signaling network motifs provide a mechanistic basis for cellular threshold responses

Increasingly, there is a move toward using in vitro toxicity testing to assess human health risk due to chemical exposure. As with in vivo toxicity testing, an important question for in vitro results is whether there are thresholds for adverse cellular responses. Empirical evaluations may show consi... Full description

Journal Title: Environmental health perspectives December 2014, Vol.122(12), pp.1261-70
Main Author: Zhang, Qiang
Other Authors: Bhattacharya, Sudin , Conolly, Rory B , Clewell, Harvey J , Kaminski, Norbert E , Andersen, Melvin E
Format: Electronic Article Electronic Article
Language: English
Subjects:
ID: E-ISSN: 1552-9924 ; PMID: 25117432 Version:1 ; DOI: 10.1289/ehp.1408244
Link: http://pubmed.gov/25117432
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recordid: medline25117432
title: Molecular signaling network motifs provide a mechanistic basis for cellular threshold responses
format: Article
creator:
  • Zhang, Qiang
  • Bhattacharya, Sudin
  • Conolly, Rory B
  • Clewell, Harvey J
  • Kaminski, Norbert E
  • Andersen, Melvin E
subjects:
  • Environmental Exposure
  • Models, Biological
  • Environmental Pollutants -- Toxicity
ispartof: Environmental health perspectives, December 2014, Vol.122(12), pp.1261-70
description: Increasingly, there is a move toward using in vitro toxicity testing to assess human health risk due to chemical exposure. As with in vivo toxicity testing, an important question for in vitro results is whether there are thresholds for adverse cellular responses. Empirical evaluations may show consistency with thresholds, but the main evidence has to come from mechanistic considerations. Cellular response behaviors depend on the molecular pathway and circuitry in the cell and the manner in which chemicals perturb these circuits. Understanding circuit structures that are inherently capable of resisting small perturbations and producing threshold responses is an important step towards mechanistically interpreting in vitro testing data. Here we have examined dose-response characteristics for several biochemical network motifs. These network motifs are basic building blocks of molecular circuits underpinning a variety of cellular functions, including adaptation, homeostasis, proliferation, differentiation, and apoptosis. For each motif, we present biological examples and models to illustrate how thresholds arise from specific network structures. Integral feedback, feedforward, and transcritical bifurcation motifs can generate thresholds. Other motifs (e.g., proportional feedback and ultrasensitivity)produce responses where the slope in the low-dose region is small and stays close to the baseline. Feedforward control may lead to nonmonotonic or hormetic responses. We conclude that network motifs provide a basis for understanding thresholds for cellular responses. Computational pathway modeling of these motifs and their combinations occurring in molecular signaling networks will be a key element in new risk assessment approaches based on in vitro cellular assays.
language: eng
source:
identifier: E-ISSN: 1552-9924 ; PMID: 25117432 Version:1 ; DOI: 10.1289/ehp.1408244
fulltext: fulltext
issn:
  • 15529924
  • 1552-9924
url: Link


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titleMolecular signaling network motifs provide a mechanistic basis for cellular threshold responses
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descriptionIncreasingly, there is a move toward using in vitro toxicity testing to assess human health risk due to chemical exposure. As with in vivo toxicity testing, an important question for in vitro results is whether there are thresholds for adverse cellular responses. Empirical evaluations may show consistency with thresholds, but the main evidence has to come from mechanistic considerations. Cellular response behaviors depend on the molecular pathway and circuitry in the cell and the manner in which chemicals perturb these circuits. Understanding circuit structures that are inherently capable of resisting small perturbations and producing threshold responses is an important step towards mechanistically interpreting in vitro testing data. Here we have examined dose-response characteristics for several biochemical network motifs. These network motifs are basic building blocks of molecular circuits underpinning a variety of cellular functions, including adaptation, homeostasis, proliferation, differentiation, and apoptosis. For each motif, we present biological examples and models to illustrate how thresholds arise from specific network structures. Integral feedback, feedforward, and transcritical bifurcation motifs can generate thresholds. Other motifs (e.g., proportional feedback and ultrasensitivity)produce responses where the slope in the low-dose region is small and stays close to the baseline. Feedforward control may lead to nonmonotonic or hormetic responses. We conclude that network motifs provide a basis for understanding thresholds for cellular responses. Computational pathway modeling of these motifs and their combinations occurring in molecular signaling networks will be a key element in new risk assessment approaches based on in vitro cellular assays.
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3Integral feedback, feedforward, and transcritical bifurcation motifs can generate thresholds. Other motifs (e.g., proportional feedback and ultrasensitivity)produce responses where the slope in the low-dose region is small and stays close to the baseline. Feedforward control may lead to nonmonotonic or hormetic responses. We conclude that network motifs provide a basis for understanding thresholds for cellular responses. Computational pathway modeling of these motifs and their combinations occurring in molecular signaling networks will be a key element in new risk assessment approaches based on in vitro cellular assays.
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abstractIncreasingly, there is a move toward using in vitro toxicity testing to assess human health risk due to chemical exposure. As with in vivo toxicity testing, an important question for in vitro results is whether there are thresholds for adverse cellular responses. Empirical evaluations may show consistency with thresholds, but the main evidence has to come from mechanistic considerations.
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