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

Area of Science:

• Molecular Biology
• Systems Biology
• Genomics

Background:

• Protein expression is dynamically regulated by synthesis and degradation of messenger RNAs (mRNAs) and proteins.
• Existing methods lack statistical approaches to integrate multi-omics time-course data for dissecting gene expression control.
• Understanding these regulatory mechanisms is crucial for deciphering cellular responses.

Purpose of the Study:

• To develop a novel statistical method, Protein Expression Control Analysis (PECA), for quantitatively dissecting protein expression variation.
• To differentiate contributions from mRNA and protein synthesis/degradation (RNA-level and protein-level regulation).
• To identify dynamic regulatory changes at specific time points.

Main Methods:

• PECA calculates rate ratios of synthesis versus degradation for mRNA and protein levels over time.
• It determines the probability of regulatory changes between adjacent time intervals.
• The method incorporates false-discovery rates for robust identification of regulated genes.

Main Results:

• PECA analysis of yeast stress response data revealed significant RNA-level up-regulation of stress genes early in response.
• Protein-level regulation showed concordance with a time delay, but temporal patterns differed between hyperosmotic and oxidative stress.
• Observed protein-level regulation counterbalancing transcriptomic changes suggests widespread post-transcriptional control for proteostasis.

Conclusions:

• PECA provides a comprehensive description of dynamic gene expression control at both RNA and protein levels.
• The findings highlight distinct regulatory strategies employed by cells under different stress conditions.
• Protein-level regulation plays a critical role in maintaining proteome stability, indicating proteostasis is a fundamental proteome-wide process.