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Time‐series transcriptomics and proteomics reveal alternative modes to decode p53 oscillations

The cell stress‐responsive transcription factor p53 influences the expression of its target genes and subsequent cellular responses based in part on its dynamics (changes in level over time). The mechanisms decoding p53 dynamics into subsequent target mRNA and protein dynamics remain unclear. We sys...

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Autores principales: Jiménez, Alba, Lu, Dan, Kalocsay, Marian, Berberich, Matthew J, Balbi, Petra, Jambhekar, Ashwini, Lahav, Galit
Formato: Online Artículo Texto
Lenguaje:English
Publicado: John Wiley and Sons Inc. 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8919251/
https://www.ncbi.nlm.nih.gov/pubmed/35285572
http://dx.doi.org/10.15252/msb.202110588
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author Jiménez, Alba
Lu, Dan
Kalocsay, Marian
Berberich, Matthew J
Balbi, Petra
Jambhekar, Ashwini
Lahav, Galit
author_facet Jiménez, Alba
Lu, Dan
Kalocsay, Marian
Berberich, Matthew J
Balbi, Petra
Jambhekar, Ashwini
Lahav, Galit
author_sort Jiménez, Alba
collection PubMed
description The cell stress‐responsive transcription factor p53 influences the expression of its target genes and subsequent cellular responses based in part on its dynamics (changes in level over time). The mechanisms decoding p53 dynamics into subsequent target mRNA and protein dynamics remain unclear. We systematically quantified p53 target mRNA and protein expression over time under two p53 dynamical regimes, oscillatory and rising, using RNA‐sequencing and TMT mass spectrometry. Oscillatory dynamics allowed for a greater variety of dynamical patterns for both mRNAs and proteins. Mathematical modeling of empirical data revealed three distinct mechanisms that decode p53 dynamics. Specific combinations of these mechanisms at the transcriptional and post‐transcriptional levels enabled exclusive induction of proteins under particular dynamics. In addition, rising induction of p53 led to higher induction of proteins regardless of their functional class, including proteins promoting arrest of proliferation, the primary cellular outcome under rising p53. Our results highlight the diverse mechanisms cells employ to distinguish complex transcription factor dynamics to regulate gene expression.
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spelling pubmed-89192512022-03-24 Time‐series transcriptomics and proteomics reveal alternative modes to decode p53 oscillations Jiménez, Alba Lu, Dan Kalocsay, Marian Berberich, Matthew J Balbi, Petra Jambhekar, Ashwini Lahav, Galit Mol Syst Biol Articles The cell stress‐responsive transcription factor p53 influences the expression of its target genes and subsequent cellular responses based in part on its dynamics (changes in level over time). The mechanisms decoding p53 dynamics into subsequent target mRNA and protein dynamics remain unclear. We systematically quantified p53 target mRNA and protein expression over time under two p53 dynamical regimes, oscillatory and rising, using RNA‐sequencing and TMT mass spectrometry. Oscillatory dynamics allowed for a greater variety of dynamical patterns for both mRNAs and proteins. Mathematical modeling of empirical data revealed three distinct mechanisms that decode p53 dynamics. Specific combinations of these mechanisms at the transcriptional and post‐transcriptional levels enabled exclusive induction of proteins under particular dynamics. In addition, rising induction of p53 led to higher induction of proteins regardless of their functional class, including proteins promoting arrest of proliferation, the primary cellular outcome under rising p53. Our results highlight the diverse mechanisms cells employ to distinguish complex transcription factor dynamics to regulate gene expression. John Wiley and Sons Inc. 2022-03-14 /pmc/articles/PMC8919251/ /pubmed/35285572 http://dx.doi.org/10.15252/msb.202110588 Text en © 2022 The Authors. Published under the terms of the CC BY 4.0 license https://creativecommons.org/licenses/by/4.0/This is an open access article under the terms of the http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.
spellingShingle Articles
Jiménez, Alba
Lu, Dan
Kalocsay, Marian
Berberich, Matthew J
Balbi, Petra
Jambhekar, Ashwini
Lahav, Galit
Time‐series transcriptomics and proteomics reveal alternative modes to decode p53 oscillations
title Time‐series transcriptomics and proteomics reveal alternative modes to decode p53 oscillations
title_full Time‐series transcriptomics and proteomics reveal alternative modes to decode p53 oscillations
title_fullStr Time‐series transcriptomics and proteomics reveal alternative modes to decode p53 oscillations
title_full_unstemmed Time‐series transcriptomics and proteomics reveal alternative modes to decode p53 oscillations
title_short Time‐series transcriptomics and proteomics reveal alternative modes to decode p53 oscillations
title_sort time‐series transcriptomics and proteomics reveal alternative modes to decode p53 oscillations
topic Articles
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8919251/
https://www.ncbi.nlm.nih.gov/pubmed/35285572
http://dx.doi.org/10.15252/msb.202110588
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