PECA: a novel statistical tool for deconvoluting time-dependent gene expression regulation.
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.
Department of Statistics and Applied Probability, National University of Singapore , Block S16, Level 7, 6 Science Drive 2, 117546 Singapore.
Guoshou
G
Teo
Christine
C
Vogel
Debashis
D
Ghosh
Sinae
S
Kim
Hyungwon
H
Choi
D005786:Gene Expression Regulation; D015233:Models, Statistical; D018384:Oxidative Stress; D012323:RNA Processing, Post-Transcriptional; D012333:RNA, Messenger / Q000235:genetics; D012568:Schizosaccharomyces / Q000378:metabolism
D012333:RNA, Messenger