Comparing Protein and mRNA Abundances to Protein Expression Regulation

作者
Jon M. Laurent,Taeho Kwon,Stephanie A. Craig,Daniel R. Boutz,Holly Huse,Kazunari Nozue,Harkamal Walia,Marvin Whiteley,Pamela C. Ronald,S. Abreu Rde,Daijin Ko,Siyuan Le,Bruce A. Shapiro,Suzanne Burns,Devinder Sandhu,Luiz O. F. Penalva,Edward M. Marcotte,Christine Vogel
出处
期刊:PubMed Central - Europe PMC [European Bioinformatics Institute]
卷期号:22
摘要

Transcription, mRNA decay, translation and protein degradation are essential processes during eukaryotic gene expression, but their relative global contributions to steady-state protein concentrations in multi-cellular eukaryotes are largely unknown. Using measurements of absolute protein and mRNA abundances in cellular lysate from the human Daoy medulloblastoma cell line, we quantitatively evaluate the impact of mRNA concentration and sequence features implicated in translation and protein degradation on protein expression. Sequence features related to translation and protein degradation have an impact similar to that of mRNA abundance, and their combined contribution explains two-thirds of protein abundance variation. mRNA sequence lengths, amino-acid properties, upstream open reading frames and secondary structures in the 5' untranslated region (UTR) were the strongest individual correlates of protein concentrations. In a combined model, characteristics of the coding region and the 3'UTR explained a larger proportion of protein abundance variation than characteristics of the 5'UTR. Further, we used data from human and six other organisms (bacteria, yeast, worm, fly, and plant) and established that steady-state abundances of proteins show significantly higher correlation across these diverse phylogenetic taxa than the abundances of their corresponding mRNAs (p=0.0008, paired Wilcoxon). These data suggest strong selective pressure to maintain protein abundances during evolution, even when mRNA abundances diverge. The absolute protein and mRNA concentration measurements for >1000 human genes and for other organisms represent one of the largest datasets currently available, and reveal both general trends and specific examples of post-transcriptional regulation.

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