PECA: A Novel Statistical Tool for Deconvoluting Time-Dependent Gene Expression Regulation
Open accessAbstract
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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PECA dissects mRNA- and protein-level regulation in time-resolved analyses and allows consistent comparisons of the two levels of gene expression regulation.
“PECA dissects mRNA‐ and protein‐level regulation in time‐resolved analyses and allows for consistent comparisons of the two levels of gene expression regulation (Teo et al, 2014).”
From Differential dynamics of the mammalian mRNA and protein expression response to misfolding stress (2016) · cites this paper as Teo et al, 2014
The PECA model estimates the ratio of synthesis and degradation over each time period given the data of X and Y at the beginning and end of each time period and computes the posterior probability that the rate ratio has changed between two adjacent time periods given the paired data (X, Y) at three consecutive time points (two time periods).
“Specifically, the model estimates the ratio of synthesis and degradation over each time period given the data of X and Y at the beginning and end of each time period and also computes the posterior probability that the rate ratio has changed between two adjacent time periods given the paired data (X, Y) at three consecutive time points (two time periods).”
From Differential dynamics of the mammalian mRNA and protein expression response to misfolding stress (2016) · cites this paper as Teo et al, 2014
PECA constructs a probabilistic model for the kinetic parameters governing the synthesis and degradation of an outcome molecule (Y) given the precursor molecule data (X).
“As described in the original publication, PECA constructs a probabilistic model for the kinetic parameters governing the synthesis and degradation of an outcome molecule (Y) given the precursor molecule data (X).”
From Differential dynamics of the mammalian mRNA and protein expression response to misfolding stress (2016) · cites this paper as Teo et al, 2014
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- Authors
- Teo, G., Vogel, C., Ghosh, D., Kim, S., & Choi, H.
- Affiliations
- National University of Singapore (SG)
- New York University (US)
- Pennsylvania State University (US)
- Rutgers, The State University of New Jersey (US)
- Corresponding author
- Choi, Hyungwon
- Venue
- Journal of Proteome Research
- Publisher
- American Chemical Society (ACS)
- Published
- 2013-11-26
- Volume
- 13
- Issue
- 1
- Pages
- 29-37
- ISSN
- 1535-3893 (ISSN-L)
- 1535-3907
- PMC
- PMC10332356
- OpenAlex
- W2323041333
- Cited by
- 30
- References
- 25
- Citations by year
- 2026: 1 · 2025: 1 · 2024: 1 · 2023: 5 · 2021: 3 · 2020: 4 · 2019: 2 · 2018: 6 · 2017: 2 · 2016: 4 · 2015: 1
- FWCI
- 0.75
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Locations
- Journal of Proteome Research (publishedVersion)
- PubMed (publishedVersion)
- PubMed Central (acceptedVersion)
- National University of Singapore (submittedVersion)
- Funders
- York University
- National Institute of General Medical Sciences (R01GM113237, R35GM127089, R01GM072007)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Gene Regulatory Network Analysis
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Fungal and yeast genetics research
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Bioinformatics and Genomic Networks
- Keywords
- gene expression regulation, protein synthesis and degradation, multi-omics integration, time-course proteomics, time-course transcriptomics, rate ratios, false discovery rate, hyperosmotic stress, oxidative stress, yeast stress response, proteostasis, post-transcriptional regulation, protein stability
- MeSH terms
- Gene Expression Regulation, RNA Processing, Post-Transcriptional, RNA, Messenger, Schizosaccharomyces, Models, Statistical, Oxidative Stress
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