The effect of programmed ribosomal frameshifting on codon usage bias
At a glance
The discovery of synonymous codon usage bias (CUB) –the unequal
use of codons that code for the same amino-acid– has strengthened
the notion that synonymous mutations can alter the fitness of
organisms. Synonymous or silent mutations are mutations in DNA that
do not alter the encoded amino acid sequence. Since CUB cannot be
explained by neutral mutational biases alone, it has been concluded
that it arises from the fitness effects of silent mutations.
However, the target of the selection pressure giving rise to CUB
remains debated. The translation efficiency hypothesis states that
CUB emerges from selection for codons that match the most abundant
iso-accepting tRNA, whose use improves translational efficiency and
accuracy. In this way, protein expression may be fine tuned by
appropriate codon choice.
Other mechanisms also alter translation to regulate protein expression, such as -1 programmed ribosomal frameshifting (-1 PRF). -1 PRF is an evolutionarily ancient mechanism found across all domains of the tree of life, in which ribosomes are induced to ’slip’ or frameshift at particular slippery sites. Such slipping leads to the discarding of the protein product and mRNA, thereby diminishing protein expression. In this project, we aim to study how -1 PRF signals in mRNA affect CUB, and what these modifications of CUB reveal about the nature of translational selection.
Building on well-established theory, we aim to develop a mathematical framework to quantify the costs of translation in terms of the time required to translate a synonymous codon, the codon’s probability of mistranslation as well as mutational biases within codon families. We will then integrate the -1 PRF phenomenon into this theory. Programmed ribosomal frameshifting is expected to lead to pronounced translational cost discontinuities across a gene, and may therefore produce salient CUB signals in its wake. We will then set out to test these predictions in genomic data from Saccharomyces cerevisae. Lastly, we will run extensive statistical analyses to investigate associations for -1 PRF presence with other genomic features of genes, such as function, length, and metabolic pathway involvement.
The results of this project will improve our understanding of how -1 PRF affects CUB, using a previously unstudied path of influence on CUB to study its nature. They will provide precise, mathematically informed hypotheses about the processes that give rise to CUB across gene length and function, both by -1 PRF and by translational selection for efficiency and accuracy. Conversely, established causal relations between -1 PRF and codon usage patterns may elucidate how -1 PRF efficiency is determined. Currently, measuring -1 PRF efficiency is a computationally and experimentally expensive task. With these insights, our results will advance our understanding of both CUB pattern emergence as well as -1 PRF protein expression regulation.
Ostash, Bohdan; Anisimova, Maria ,
Srinivasa, K. G.; Siddesh, G. M.; Manisekhar, S. R., eds. ,
Statistical Modelling and Machine Learning Principles for Bioinformatics Techniques, Tools, and Applications.
Algorithms for Intelligent Systems.
Available from : https://doi.org/10.1007/978-981-15-2445-5_13