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      The Ccr4-Not complex monitors the translating ribosome for codon optimality

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          Abstract

          Control of messenger RNA (mRNA) decay rate is intimately connected to translation elongation, but the spatial coordination of these events is poorly understood. The Ccr4-Not complex initiates mRNA decay through deadenylation and activation of decapping. We used a combination of cryo–electron microscopy, ribosome profiling, and mRNA stability assays to examine the recruitment of Ccr4-Not to the ribosome via specific interaction of the Not5 subunit with the ribosomal E-site in Saccharomyces cerevisiae. This interaction occurred when the ribosome lacked accommodated A-site transfer RNA, indicative of low codon optimality. Loss of the interaction resulted in the inability of the mRNA degradation machinery to sense codon optimality. Our findings elucidate a physical link between the Ccr4-Not complex and the ribosome and provide mechanistic insight into the coupling of decoding efficiency with mRNA stability.

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          Evolutionary conservation of codon optimality reveals hidden signatures of co-translational folding

          Sebastian Pechmann, Judith Frydman (2012)
          The choice of codons can influence local translation kinetics during protein synthesis. Whether codon preference is linked to co-translational regulation of polypeptide folding remains unclear. Here, we derive a revised translational efficiency scale that incorporates the competition between tRNA supply and demand. Applying this scale to ten closely related yeasts, we uncover the evolutionary conservation of codon optimality in eukaryotes. This analysis reveals universal patterns of conserved optimal and nonoptimal codons, often in clusters, which associate with the secondary structure of the translated polypeptides independent of the levels of expression. Our analysis suggests an evolved function for codon optimality in regulating the rhythm of elongation to facilitate co-translational polypeptide folding, beyond its previously proposed role of adapting to the cost of expression. These findings establish how mRNA sequences are generally under selection to optimize the co-translational folding of corresponding polypeptides.
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            Exome sequencing identifies mutation in CNOT3 and ribosomal genes RPL5 and RPL10 in T-cell acute lymphoblastic leukemia.

            Roel Vandepoel, Peter Vandenberghe, Stein Aerts … (2013)
            T-cell acute lymphoblastic leukemia (T-ALL) is caused by the cooperation of multiple oncogenic lesions. We used exome sequencing on 67 T-ALLs to gain insight into the mutational spectrum in these leukemias. We detected protein-altering mutations in 508 genes, with an average of 8.2 mutations in pediatric and 21.0 mutations in adult T-ALL. Using stringent filtering, we predict seven new oncogenic driver genes in T-ALL. We identify CNOT3 as a tumor suppressor mutated in 7 of 89 (7.9%) adult T-ALLs, and its knockdown causes tumors in a sensitized Drosophila melanogaster model. In addition, we identify mutations affecting the ribosomal proteins RPL5 and RPL10 in 12 of 122 (9.8%) pediatric T-ALLs, with recurrent alterations of Arg98 in RPL10. Yeast and lymphoid cells expressing the RPL10 Arg98Ser mutant showed a ribosome biogenesis defect. Our data provide insights into the mutational landscape of pediatric versus adult T-ALL and identify the ribosome as a potential oncogenic factor.
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              A single determinant dominates the rate of yeast protein evolution.

              D. Drummond, Alpan Raval, Claus Wilke (2006)
              A gene's rate of sequence evolution is among the most fundamental evolutionary quantities in common use, but what determines evolutionary rates has remained unclear. Here, we carry out the first combined analysis of seven predictors (gene expression level, dispensability, protein abundance, codon adaptation index, gene length, number of protein-protein interactions, and the gene's centrality in the interaction network) previously reported to have independent influences on protein evolutionary rates. Strikingly, our analysis reveals a single dominant variable linked to the number of translation events which explains 40-fold more variation in evolutionary rate than any other, suggesting that protein evolutionary rate has a single major determinant among the seven predictors. The dominant variable explains nearly half the variation in the rate of synonymous and protein evolution. We show that the two most commonly used methods to disentangle the determinants of evolutionary rate, partial correlation analysis and ordinary multivariate regression, produce misleading or spurious results when applied to noisy biological data. We overcome these difficulties by employing principal component regression, a multivariate regression of evolutionary rate against the principal components of the predictor variables. Our results support the hypothesis that translational selection governs the rate of synonymous and protein sequence evolution in yeast.
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                Author and article information

                Journal
                Title: Science
                Abbreviated Title: Science
                Publisher: American Association for the Advancement of Science (AAAS)
                ISSN (Print): 0036-8075
                ISSN (Electronic): 1095-9203
                Publication date Created: April 16 2020
                Publication date Created: April 17 2020
                Publication date (Electronic): April 16 2020
                Publication date (Print): April 17 2020
                Volume: 368
                Issue: 6488
                Page: eaay6912
                Affiliations
                [1 ]Gene Center and Department of Biochemistry, University of Munich, 81377 Munich, Germany.
                [2 ]Graduate School of Pharmaceutical Sciences, Tohoku University, Sendai 980-8578, Japan.
                [3 ]Center for RNA Science and Therapeutics, School of Medicine, Case Western Reserve University, Cleveland, OH 44106, USA.
                Article
                DOI: 10.1126/science.aay6912
                PubMed ID: 32299921
                SO-VID: 3816b3fc-ef9c-4602-ba85-5fa102927191
                Copyright © © 2020
                License:

                http://www.sciencemag.org/about/science-licenses-journal-article-reuse

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