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      Stop codon context influences genome-wide stimulation of termination codon readthrough by aminoglycosides

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          Abstract

          Stop codon readthrough (SCR) occurs when the ribosome miscodes at a stop codon. Such readthrough events can be therapeutically desirable when a premature termination codon (PTC) is found in a critical gene. To study SCR in vivo in a genome-wide manner, we treated mammalian cells with aminoglycosides and performed ribosome profiling. We find that in addition to stimulating readthrough of PTCs, aminoglycosides stimulate readthrough of normal termination codons (NTCs) genome-wide. Stop codon identity, the nucleotide following the stop codon, and the surrounding mRNA sequence context all influence the likelihood of SCR. In comparison to NTCs, downstream stop codons in 3′UTRs are recognized less efficiently by ribosomes, suggesting that targeting of critical stop codons for readthrough may be achievable without general disruption of translation termination. Finally, we find that G418-induced miscoding alters gene expression with substantial effects on translation of histone genes, selenoprotein genes, and S-adenosylmethionine decarboxylase (AMD1).

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          Many genes provide a set of instructions needed to build a protein, which are read by structures called ribosomes through a process called translation. The genetic information contains a short, coded instruction called a stop codon which marks the end of the protein. When a ribosome finds a stop codon it should stop building and release the protein it has made.

          Ribosomes do not always stop at stop codons. Certain chemicals can actually prevent ribosomes from detecting stop codons correctly, and aminoglycosides are drugs that have exactly this effect. Aminoglycosides can be used as antibiotics at low doses because they interfere with ribosomes in bacteria, but at higher doses they can also prevent ribosomes from detecting stop codons in human cells. When ribosomes do not stop at a stop codon this is called readthrough. There are different types of stop codons and some are naturally more effective at stopping ribosomes than others.

          Wangen and Green have now examined the effect of an aminoglycoside called G418 on ribosomes in human cells grown in the laboratory. The results showed how ribosomes interacted with genetic information and revealed that certain stop codons are more affected by G418 than others. The stop codon and other genetic sequences around it affect the likelihood of readthrough. Wangen and Green also showed that sequences that encourage translation to stop are more common in the area around stop codons.

          These findings highlight an evolutionary pressure driving more genes to develop strong stop codons that resist readthrough. Despite this, some are still more affected by drugs like G418 than others. Some genetic conditions, like cystic fibrosis, result from incorrect stop codons in genes. Drugs that promote readthrough specifically in these genes could be useful new treatments.

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              Heavy-tailed prior distributions for sequence count data: removing the noise and preserving large differences

              Abstract Motivation In RNA-seq differential expression analysis, investigators aim to detect those genes with changes in expression level across conditions, despite technical and biological variability in the observations. A common task is to accurately estimate the effect size, often in terms of a logarithmic fold change (LFC). Results When the read counts are low or highly variable, the maximum likelihood estimates for the LFCs has high variance, leading to large estimates not representative of true differences, and poor ranking of genes by effect size. One approach is to introduce filtering thresholds and pseudocounts to exclude or moderate estimated LFCs. Filtering may result in a loss of genes from the analysis with true differences in expression, while pseudocounts provide a limited solution that must be adapted per dataset. Here, we propose the use of a heavy-tailed Cauchy prior distribution for effect sizes, which avoids the use of filter thresholds or pseudocounts. The proposed method, Approximate Posterior Estimation for generalized linear model, apeglm, has lower bias than previously proposed shrinkage estimators, while still reducing variance for those genes with little information for statistical inference. Availability and implementation The apeglm package is available as an R/Bioconductor package at https://bioconductor.org/packages/apeglm, and the methods can be called from within the DESeq2 software. Supplementary information Supplementary data are available at Bioinformatics online.
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                Author and article information

                Contributors
                Role: Reviewing Editor
                Role: Senior Editor
                Journal
                eLife
                Elife
                eLife
                eLife
                eLife Sciences Publications, Ltd
                2050-084X
                23 January 2020
                2020
                : 9
                : e52611
                Affiliations
                [1]deptDepartment of Molecular Biology and Genetics Howard Hughes Medical Institute, Johns Hopkins University School of Medicine BaltimoreUnited States
                McGill University Canada
                Columbia University United States
                McGill University Canada
                University of Alabama at Birmingham United States
                Stanford University School of Medicine United States
                University of California, San Francisco United States
                Author information
                https://orcid.org/0000-0001-7008-5749
                https://orcid.org/0000-0001-9337-2003
                Article
                52611
                10.7554/eLife.52611
                7089771
                31971508
                c86a5fda-bd73-4349-9856-2b12f90c0223
                © 2020, Wangen and Green

                This article is distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use and redistribution provided that the original author and source are credited.

                History
                : 09 October 2019
                : 22 January 2020
                Funding
                Funded by: FundRef http://dx.doi.org/10.13039/100000897, Cystic Fibrosis Foundation;
                Award ID: GREEN16G0
                Award Recipient :
                Funded by: FundRef http://dx.doi.org/10.13039/100000002, National Institutes of Health;
                Award ID: T32 GM007445
                Award Recipient :
                The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.
                Categories
                Research Article
                Chromosomes and Gene Expression
                Custom metadata
                Ribosome profiling reveals genome-wide stimulation of stop codon readthrough by aminoglycosides that is influenced sequence context.

                Life sciences
                aminoglycoside,ribosome,stop codon readthrough,ribosome profiling,translation termination,g418,human

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