Quantification and discovery of sequence determinants of protein‐per‐mRNA amount in 29 human tissues

• DOI: 10.15252/msb.20188513
• Publication Date: 2019-02-18T16:25:34Z
• Authors: Basak Eraslan; Dongxue Wang; Mirjana Gusic; Holger Prokisch; Björn M Hallström; Mathias Uhlén; Anna Asplund; Frederik Pontén; Thomas Wieland; Thomas Hopf; Hannes Hahne; Bernhard Kuster; Julien Gagneur
• Keywords: Proteomics; 0301 basic medicine; Medicine (General); Proteome; QH301-705.5; Biochemistry; Codon Usage ; Mrna Sequence Motifs ; Proteomics ; Transcriptomics ; Translational Control; Mass Spectrometry; transcriptomics; 03 medical and health sciences; proteomics; R5-920; Humans; Tissue Distribution; RNA, Messenger; Biology (General); Biokemi; Molecular Biology; Molekylärbiologi; codon usage; Genome, Human; Sequence Analysis, RNA; Biochemistry and Molecular Biology; translational control; Proteins; Articles; ddc:; 3. Good health; Gene Expression Regulation; mRNA sequence motifs; Transcriptome; Biokemi och molekylärbiologi

AbstractDespite their importance in determining protein abundance, a comprehensive catalogue of sequence features controlling protein-to-mRNA (PTR) ratios and a quantification of their effects is still lacking. Here we quantified PTR ratios for 11,575 proteins across 29 human tissues using matched transcriptomes and proteomes. We analyzed the contribution of known sequence determinants of protein synthesis and degradation and 15 novel mRNA and protein sequence motifs that we found by association testing. While the dynamic range of PTR ratios spans more than 2 orders of magnitude, our integrative model predicts PTR ratios at a median precision of 3.2-fold. A reporter assay provided significant functional support for two novel UTR motifs and a proteome-wide competition-binding assay identified motif-specific bound proteins for one motif. Moreover, our direct comparison of protein to RNA levels led to a new metrics of codon optimality. Altogether, this study shows that a large fraction of PTR ratio variance across genes can be predicted from sequence and identified many new candidate post-transcriptional regulatory elements in the human genome.