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Abstract
The advent of ribosome profiling and other tools to probe mRNA translation has revealed
that codon bias - the uneven use of synonymous codons in the transcriptome - serves
as a secondary genetic code: a code that guides the efficiency of protein production,
the fidelity of translation and the metabolism of mRNAs. Recent advancements in our
understanding of mRNA decay have revealed a tight coupling between ribosome dynamics
and the stability of mRNA transcripts; this coupling integrates codon bias into the
concept of codon optimality, or the effects that specific codons and tRNA concentrations
have on the efficiency and fidelity of the translation machinery. In this Review,
we first discuss the evidence for codon-dependent effects on translation, beginning
with the basic mechanisms through which translation perturbation can affect translation
efficiency, protein folding and transcript stability. We then discuss how codon effects
are leveraged by the cell to tailor the proteome to maintain homeostasis, execute
specific gene expression programmes of growth or differentiation and optimize the
efficiency of protein production.
Strikingly consistent correlations between rates of coding-sequence evolution and gene expression levels are apparent across taxa, but the biological causes behind the selective pressures on coding-sequence evolution remain controversial. Here, we demonstrate conserved patterns of simple covariation between sequence evolution, codon usage, and mRNA level in E. coli, yeast, worm, fly, mouse, and human that suggest that all observed trends stem largely from a unified underlying selective pressure. In metazoans, these trends are strongest in tissues composed of neurons, whose structure and lifetime confer extreme sensitivity to protein misfolding. We propose, and demonstrate using a molecular-level evolutionary simulation, that selection against toxicity of misfolded proteins generated by ribosome errors suffices to create all of the observed covariation. The mechanistic model of molecular evolution that emerges yields testable biochemical predictions, calls into question the use of nonsynonymous-to-synonymous substitution ratios (Ka/Ks) to detect functional selection, and suggests how mistranslation may contribute to neurodegenerative disease.
We report a method for large-scale absolute protein expression measurements (APEX) and apply it to estimate the relative contributions of transcriptional- and translational-level gene regulation in the yeast and Escherichia coli proteomes. APEX relies upon correcting each protein's mass spectrometry sampling depth (observed peptide count) by learned probabilities for identifying the peptides. APEX abundances agree with measurements from controls, western blotting, flow cytometry and two-dimensional gels, as well as known correlations with mRNA abundances and codon bias, providing absolute protein concentrations across approximately three to four orders of magnitude. Using APEX, we demonstrate that 73% of the variance in yeast protein abundance (47% in E. coli) is explained by mRNA abundance, with the number of proteins per mRNA log-normally distributed about approximately 5,600 ( approximately 540 in E. coli) protein molecules/mRNA. Therefore, levels of both eukaryotic and prokaryotic proteins are set per mRNA molecule and independently of overall protein concentration, with >70% of yeast gene expression regulation occurring through mRNA-directed mechanisms.
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