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

Complicated post-transcriptional and translational regulation processes contribute to the expression discrepancy between mRNA and protein in many tissues, but the underlying mechanisms have not been fully understood. In this study, we assessed to what extent and which RNA binding proteins (RBPs) contribute to mRNA-protein expression discrepancy. To this end, we exploited the RNA-seq transcriptome data and corresponding quantitative proteome data from the same set of human healthy tissues to estimate the mRNA-protein expression discrepancy, and observed that a considerable fraction of genes show obvious difference in expression rankings between transcriptome and proteome. We further assembled the latest CLIP-seq datasets from POSTAR2, ENCODE and GEO to map the binding profiles of known RBPs. A logistic regression model based on the RBP-binding features was established, which could predict the mRNA-protein expression discrepancy with acceptable performance. Finally, by applying two different feature selection methods on this logistic regression model, we identified a consensus set of known and putative translation regulators which may account for the expression level discrepancy, such as G3BP1, DGCR8, LARP4B, EIF4A3 and FXR2.