Codon optimality in cancer

Abstract

A key characteristic of cancer cells is their increased proliferative capacity, which requires elevated levels of protein synthesis. The process of protein synthesis involves the translation of codons within the mRNA coding sequence into a string of amino acids to form a polypeptide chain. As most amino acids are encoded by multiple codons, the nucleotide sequence of a coding region can vary dramatically without altering the polypeptide sequence of the encoded protein. Although mutations that do not alter the final amino acid sequence are often thought of as silent/synonymous, these can still have dramatic effects on protein output. Because each codon has a distinct translation elongation rate and can differentially impact mRNA stability, each codon has a different degree of 'optimality' for protein synthesis. Recent data demonstrates that the codon preference of a transcriptome matches the abundance of tRNAs within the cell and that this supply and demand between tRNAs and mRNAs varies between different cell types. The largest observed distinction is between mRNAs encoding proteins associated with proliferation or differentiation. Nevertheless, precisely how codon optimality and tRNA expression levels regulate cell fate decisions and their role in malignancy is not fully understood. This review describes the current mechanistic understanding on codon optimality, its role in malignancy and discusses the potential to target codon optimality therapeutically in the context of cancer.

Fig. 1. Determinants of codon optimality.

A The availability of tRNAs is a combination of their supply determined by tRNA gene expression and aminoacylation of the tRNAs, and the demand within the expressed transcriptome for each tRNA. B tRNA availability combined with the efficiency of the base-pairing between a given codon and anticodon provides a measure of the optimality of a given codon in a particular cellular context. There are some C-ending codons that require A-to-I modification of the anticodon to be decoded. C Tables depict the types of anticodon:codon base pairing that can decode each codon by either cognate, wobble or inosine interactions. Anticodons are in black and are written 3ʹ to 5ʹ. Codons are coloured according to the 3rd nucleotide position and are written 5ʹ to 3ʹ. “>” is used to indicate differences in efficiencies of decoding of inosine containing anticodons with codons that it can base-pair.

Fig. 2. Consequences of codon optimality differences.

A Elongation rates are greater at optimal codons, leading to increased protein synthesis rates. B Stretches of non-optimal codons can lead to a pile-up of paused ribosomes which can trigger recruitment of mRNA decay factors. C Short stretches of non-optimal codons can also act to slow translation elongation rates in specific regions to provide time for correct protein folding co-translationally.

Fig. 3. Codon usage differences in proliferation.

Synonymous codon usage preferences for mRNAs associated with the GO term: “pattern specification” and ‘mitotic cell cycle’ for mouse (245 and 653 mRNAs respectively) and human (418 and 900 mRNAs respectively). Conducted in a similar manner to Gingold et al. 2014, but with the most recent gene ontology annotation obtained using the R package GO.db. Colours indicate the nucleotide at the 3rd position of the codon and the single letter code is used to indicate the corresponding amino acids.

Fig. 4. tRNA changes in proliferation.

There are several models proposed as to how the translational upregulation of mRNAs preferentially expressed in proliferation occurs. These include a specific increase in the tRNAs that decode A/U-ending codons, a global increase in tRNA expression to overcome limiting elongation rates at non-optimal codons and increased mcm⁵s² U34 modification at the wobble position to increase the elongation rates at specific A-ending codons: CAA, AAA, GAA.