Literature DB >> 24229407

PECA: a novel statistical tool for deconvoluting time-dependent gene expression regulation.

Guoshou Teo1, Christine Vogel, Debashis Ghosh, Sinae Kim, Hyungwon Choi.   

Abstract

Protein expression varies as a result of intricate regulation of synthesis and degradation of messenger RNAs (mRNA) and proteins. Studies of dynamic regulation typically rely on time-course data sets of mRNA and protein expression, yet there are no statistical methods that integrate these multiomics data and deconvolute individual regulatory processes of gene expression control underlying the observed concentration changes. To address this challenge, we developed Protein Expression Control Analysis (PECA), a method to quantitatively dissect protein expression variation into the contributions of mRNA synthesis/degradation and protein synthesis/degradation, termed RNA-level and protein-level regulation respectively. PECA computes the rate ratios of synthesis versus degradation as the statistical summary of expression control during a given time interval at each molecular level and computes the probability that the rate ratio changed between adjacent time intervals, indicating regulation change at the time point. Along with the associated false-discovery rates, PECA gives the complete description of dynamic expression control, that is, which proteins were up- or down-regulated at each molecular level and each time point. Using PECA, we analyzed two yeast data sets monitoring the cellular response to hyperosmotic and oxidative stress. The rate ratio profiles reported by PECA highlighted a large magnitude of RNA-level up-regulation of stress response genes in the early response and concordant protein-level regulation with time delay. However, the contributions of RNA- and protein-level regulation and their temporal patterns were different between the two data sets. We also observed several cases where protein-level regulation counterbalanced transcriptomic changes in the early stress response to maintain the stability of protein concentrations, suggesting that proteostasis is a proteome-wide phenomenon mediated by post-transcriptional regulation.

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Year:  2013        PMID: 24229407     DOI: 10.1021/pr400855q

Source DB:  PubMed          Journal:  J Proteome Res        ISSN: 1535-3893            Impact factor:   5.370


  9 in total

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2.  Identification of stress responsive genes by studying specific relationships between mRNA and protein abundance.

Authors:  Shimpei Morimoto; Koji Yahara
Journal:  Heliyon       Date:  2018-03-08

3.  Quantifying post-transcriptional regulation in the development of Drosophila melanogaster.

Authors:  Kolja Becker; Alina Bluhm; Nuria Casas-Vila; Nadja Dinges; Mario Dejung; Sergi Sayols; Clemens Kreutz; Jean-Yves Roignant; Falk Butter; Stefan Legewie
Journal:  Nat Commun       Date:  2018-11-26       Impact factor: 14.919

4.  A network of RNA-binding proteins controls translation efficiency to activate anaerobic metabolism.

Authors:  J J David Ho; Nathan C Balukoff; Phaedra R Theodoridis; Miling Wang; Jonathan R Krieger; Jonathan H Schatz; Stephen Lee
Journal:  Nat Commun       Date:  2020-05-29       Impact factor: 14.919

5.  Integration of absolute multi-omics reveals dynamic protein-to-RNA ratios and metabolic interplay within mixed-domain microbiomes.

Authors:  F Delogu; B J Kunath; P N Evans; M Ø Arntzen; T R Hvidsten; P B Pope
Journal:  Nat Commun       Date:  2020-09-18       Impact factor: 14.919

6.  Differential dynamics of the mammalian mRNA and protein expression response to misfolding stress.

Authors:  Zhe Cheng; Guoshou Teo; Sabrina Krueger; Tara M Rock; Hiromi W L Koh; Hyungwon Choi; Christine Vogel
Journal:  Mol Syst Biol       Date:  2016-01-20       Impact factor: 11.429

7.  The interdependence of transcript and protein abundance: new data--new complexities.

Authors:  Yansheng Liu; Ruedi Aebersold
Journal:  Mol Syst Biol       Date:  2016-01-20       Impact factor: 11.429

8.  PECAplus: statistical analysis of time-dependent regulatory changes in dynamic single-omics and dual-omics experiments.

Authors:  Guoshou Teo; Yun Bin Zhang; Christine Vogel; Hyungwon Choi
Journal:  NPJ Syst Biol Appl       Date:  2017-12-19

9.  New insights into the cellular temporal response to proteostatic stress.

Authors:  Justin Rendleman; Zhe Cheng; Shuvadeep Maity; Nicolai Kastelic; Mathias Munschauer; Kristina Allgoewer; Guoshou Teo; Yun Bin Matteo Zhang; Amy Lei; Brian Parker; Markus Landthaler; Lindsay Freeberg; Scott Kuersten; Hyungwon Choi; Christine Vogel
Journal:  Elife       Date:  2018-10-12       Impact factor: 8.140

  9 in total

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