Translational readthrough goes unseen by natural selection.
Kleppe, A. S.; Bornberg-Bauer, E.
Show abstract
Occasionally during protein synthesis, the ribosome bypasses the stop codon and continues translation to the next stop codon in frame. This error is called translational readthrough (TR). Earlier research suggest that TR is a relatively common error, in several taxa, yet the evolutionary relevance of this translational error is still unclear. By analysing ribosome profiling data, we have conducted species comparisons between yeasts to infer conservation of TR between orthologs. Moreover, we infer the evolutionary rate of error prone and canonically translated proteins to deduct differential selective pressure. We find that about 40% of error prone proteins in Schizosaccharomyces pombe do not have any orthologs in Saccharomyces cerevisiae, but that 60% of error prone proteins in S. pombe are undergoing canonical translation in S. cerevisiae. Error prone proteins tend to have a higher GC-content in the 3-UTR, unlike their canonically translated ortholog. We do not find the same trends for GC-content of the CDS. We discuss the role of 3-UTR and GC-content regarding translational readthrough. Moreover, we find that there is neither selective pressure against or for TR. We suggest that TR is a near-neutral error that goes unseen by natural selection. We speculate that TR yield neutral protein isoforms that are not being purged. We suggest that isoforms, yielded by TR, increase proteomic diversity in the cell, which is readily available upon sudden environmental shifts and which therefore may become adaptive. Author SummaryThere is an evolutionary balance act between adaptation and selection against change. Any system needs to be able to adapt facing novel environmental conditions. Simultaneously, biological systems are under selection to maintain fitness and thus undergo selection against mutations. Phenotypic mutations - translational errors during protein synthesis - have been suggested to play a role in protein evolvability by enabling quick assessment of viable phenotypes and thus enable quick adaptation. Here we test this hypothesis, by inferring evolutionary rate of proteins prone to a specific case of phenotypic mutations: translational readthrough (TR). By making use of publicly available data of yeasts, we find that TR goes unseen by natural selection and appear as a neutral event. We suggest that TR goes unseen by selection and occurs as "permissive wallflowers", which may become relevant and yield adaptive benefits. This work highlights that stochastic processes are not necessarily under stringent selection but may prevail. In conclusion, we suggest that TR is a neutral non-adaptive process that can yield adaptive benefits.
Matching journals
The top 6 journals account for 50% of the predicted probability mass.
Similar papers in this journal
- Functional and transcriptional profiling of non-coding RNAs in yeast reveal context-dependent phenotypes and widespread in trans effects on the protein regulatory network 95%
- Cell volume homeostatically controls the rDNA repeat copy number and rRNA synthesis rate in yeast 95%
- Inheritance bias of deletion-harbouring mtDNA in yeast: the role of copy number and intracellular selection 94%
Similar papers in this journal
- Mapping the architecture of regulatory variation provides insights into the evolution of complex traits 94%
- The amino acid substitution affects cellular response to mistranslation 93%
- Genome annotation of Caenorhabditis briggsae by TEC-RED identifies new exons, paralogs, and conserved and novel operons 92%
Similar papers in this journal
- Global Transcriptome Characterization and Assembly of Thermophilic Ascomycete Chaetomium thermophilum 92%
- 25 years of propagation in suspension cell culture results in substantial alterations of the Arabidopsis thaliana genome 91%
- The effects of sequence length and composition of random sequence peptides on the growth of E. coli cells 91%
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.