Abstract
The codon stabilization coefficient (CSC) is derived from the correlation between each codon frequency in transcripts and mRNA half-life experimental data. In this work, we used this metric as a reference to compare previously published Saccharomyces cerevisiae mRNA half-life datasets and investigate how codon composition related to protein levels. We generated CSCs derived from nine studies. Four datasets produced similar CSCs, which also correlated with other independent parameters that reflected codon optimality, such as the tRNA abundance and ribosome residence time. By calculating the average CSC for each gene, we found that most mRNAs tended to have more non-optimal codons. Conversely, a high proportion of optimal codons was found for genes coding highly abundant proteins, including proteins that were only transiently overexpressed in response to stress conditions. We also used CSCs to identify and locate mRNA regions enriched in non-optimal codons. We found that these stretches were usually located close to the initiation codon and were sufficient to slow ribosome movement. However, in contrast to observations from reporter systems, we found no position-dependent effect on the mRNA half-life. These analyses underscore the value of CSCs in studies of mRNA stability and codon bias and their relationships with protein expression.
Highlights
MRNA levels are determined by the rates of mRNA synthesis and degradation(Parker, 2012)
In Saccharomyces cerevisiae, mRNA half-lives have been globally measured through three main classes of methods, namely, transcriptional inhibition, gene activation control and in vivo metabolic labeling
For metabolic in vivo labeling, modified nucleotides are introduced into cell media that are incorporated by cells into to the RNA(Baudrimont et al, 2017) the mRNA modified can be recovered by immunoprecipitation or pulled down by streptavidin beads
Summary
MRNA levels are determined by the rates of mRNA synthesis and degradation(Parker, 2012). Genomewide mRNA half-life measurements showed poor correlation across different datasets and different methods (and, in some cases, even between similar methods), classifying the same mRNA molecule as stable and unstable. It makes the identification of stability sequence motifs and a global view of transcription and translation dynamics in yeast a problematic task(Baudrimont et al, 2017; Harigaya and Parker, 2016). Several features, such as mRNA secondary structure, sequence and structural elements located within 5`and 3`UTR as well as the lengths of the transcripts can affect mRNA stability(Parker, 2012). Yeast, and metazoans indicate that codon optimality is a major determinant of mRNA stability(Boël et al, 2016; Mishima and Tomari, 2016; Presnyak et al, 2015; Radhakrishnan et al, 2016)
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