Computational screening of potential regulators for mRNA-protein expression level discrepancy

Area of Science:

• Molecular Biology
• Genomics
• Proteomics

Background:

• Post-transcriptional and translational regulation create discrepancies between mRNA and protein expression levels.
• The precise mechanisms driving these expression differences remain incompletely understood.
• RNA binding proteins (RBPs) are critical regulators at the post-transcriptional level.

Purpose of the Study:

• To quantify the extent of mRNA-protein expression discrepancy in human tissues.
• To identify specific RBPs that contribute to this expression discrepancy.
• To develop a predictive model for mRNA-protein expression differences based on RBP binding.

Main Methods:

• Utilized RNA-seq transcriptome and quantitative proteome data from human healthy tissues.
• Integrated CLIP-seq datasets (POSTAR2, ENCODE, GEO) to map RBP binding sites.
• Developed a logistic regression model incorporating RBP-binding features to predict expression discrepancy.

Main Results:

• A significant fraction of genes exhibited notable differences in expression rankings between transcriptome and proteome data.
• The logistic regression model demonstrated acceptable performance in predicting mRNA-protein expression discrepancy.
• Identified a consensus set of known and putative translation regulators, including G3BP1, DGCR8, LARP4B, EIF4A3, and FXR2, as key contributors.

Conclusions:

• RBPs play a substantial role in mediating mRNA-protein expression level discrepancies.
• The identified RBPs are potential key regulators influencing translation and expression levels.
• This research provides insights into the complex regulatory networks governing gene expression post-transcriptionally.