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.

[1]  D. Botstein,et al.  Genomic expression programs in the response of yeast cells to environmental changes. , 2000, Molecular biology of the cell.

[2]  Ronald W. Davis,et al.  Quantitative Monitoring of Gene Expression Patterns with a Complementary DNA Microarray , 1995, Science.

[3]  Julian N. Selley,et al.  Global Translational Responses to Oxidative Stress Impact upon Multiple Levels of Protein Synthesis* , 2006, Journal of Biological Chemistry.

[4]  David Botstein,et al.  Diverse and specific gene expression responses to stresses in cultured human cells. , 2004, Molecular biology of the cell.

[5]  D. Botstein,et al.  Cluster analysis and display of genome-wide expression patterns. , 1998, Proceedings of the National Academy of Sciences of the United States of America.

[6]  E. Powers,et al.  Diversity in the origins of proteostasis networks — a driver for protein function in evolution , 2013, Nature Reviews Molecular Cell Biology.

[7]  Christian Baumgartner,et al.  Profiling the human response to physical exercise: a computational strategy for the identification and kinetic analysis of metabolic biomarkers , 2011, Journal of Clinical Bioinformatics.

[8]  A. Gasch,et al.  Molecular Systems Biology Peer Review Process File a Dynamic Model of Proteome Changes Reveals New Roles for Transcript Alteration in Yeast Transaction Report , 2022 .

[9]  T. Speed,et al.  A multivariate empirical Bayes statistic for replicated microarray time course data , 2006, math/0702685.

[10]  J. Thevelein,et al.  Osmotic Stress-Induced Gene Expression in Saccharomyces cerevisiae Requires Msn1p and the Novel Nuclear Factor Hot1p , 1999, Molecular and Cellular Biology.

[11]  Bernhard Pfeifer,et al.  A new data mining approach for profiling and categorizing kinetic patterns of metabolic biomarkers after myocardial injury , 2010, Bioinform..

[12]  K. Davies Oxidative Stress, Antioxidant Defenses, and Damage Removal, Repair, and Replacement Systems , 2000, IUBMB life.

[13]  John D. Storey,et al.  Significance analysis of time course microarray experiments. , 2005, Proceedings of the National Academy of Sciences of the United States of America.

[14]  Y. Uesono,et al.  Transient Inhibition of Translation Initiation by Osmotic Stress* , 2002, The Journal of Biological Chemistry.

[15]  Christine Vogel,et al.  Protein Expression Regulation under Oxidative Stress* , 2011, Molecular & Cellular Proteomics.

[16]  K. Davies Degradation of oxidized proteins by the 20S proteasome. , 2001, Biochimie.

[17]  T. Grune Oxidative stress, aging and the proteasomal system , 2004, Biogerontology.

[18]  Brad T. Sherman,et al.  Systematic and integrative analysis of large gene lists using DAVID bioinformatics resources , 2008, Nature Protocols.

[19]  K. Voelkerding,et al.  Next-generation sequencing: from basic research to diagnostics. , 2009, Clinical chemistry.

[20]  Jürg Bähler,et al.  Regulation of transcriptome, translation, and proteome in response to environmental stress in fission yeast , 2012, Genome Biology.

[21]  Ana Conesa,et al.  maSigPro: a Method to Identify Significantly Differential Expression Profiles in Time-Course Microarray Experiments , 2006, Spanish Bioinformatics Conference.

[22]  Norman Pavelka,et al.  Delayed Correlation of mRNA and Protein Expression in Rapamycin-treated Cells and a Role for Ggc1 in Cellular Sensitivity to Rapamycin* , 2009, Molecular & Cellular Proteomics.

[23]  Taesung Park,et al.  Statistical tests for identifying differentially expressed genes in time-course microarray experiments , 2003, Bioinform..