Limitations of codon adaptation index and other coding DNA‐based features for prediction of protein expression in Saccharomyces cerevisiae

Abstract

The relationship between codon usage and protein/mRNA expression in S. cerevisiae has been extensively studied. Recently, protein expression data for the whole yeast genome was published. We investigate which properties of coding DNA sequences can be used to predict expression levels. The new algorithm by Carbone et al. for computing dominating codon bias in a genome is evaluated. It is concluded that it works at least as well as existing methods, and eliminates the need to arbitrarily choose a set of highly expressed genes. Also, the hypothesis that information on codon pair frequencies can be used to predict expression is investigated. Our conclusion is that codon pairs do not contribute more information than do single codon frequencies. Overall correlation between predicted and actual expression data using properties of coding DNA sequences is around 0.65. Hence, while being a useful source of information, the expression levels predicted by these methods should only be used as a rule of thumb.

Metabolic engineering of the non-conventional yeast Pichia ciferrii for production of rare sphingoid bases

“…Also, heterologous expression of the maCER gene from M. musculus in P. ciferrii only led to improved TriASo titers if a codon-optimized allele was used. Though the predictive value of the Codon Adaptation Index (CAI; Sharp and Li, 1987) as a measure of synonymous codon usage bias for prediction of functional heterologous gene expression has been discussed controversially (Friberg et al, 2004), we observed that the CAI values of the various heterologous genes to be expressed in P. ciferrii were much lower than the corresponding values for heterologous expression in S. cerevisiae. While genes from M. musculus, H. sapiens, A. gossypii and S. noursei had CAI values for expression in P. ciferrii between 0.057 and 0.233, the very same genes had CAI values for expression in S. cerevisiae of 0.431 to 0.931.…”

Predicted highly expressed genes in the genomes of Streptomyces coelicolor and Streptomyces avermitilis and the implications for their metabolism

“…Because of this limited dataset, it has been difficult to evaluate the correlation of codon usage and expression potential in a global context. However, with recent progress in whole-genome analysis technologies, such as DNA microarray and proteomics, it is now possible to compare predictions based on codon usage data with experimental data on protein and mRNA expression in a Reis et al, 2003;Jansen et al, 2003;Friberg et al, 2004). In this study, various approaches to estimating gene expression levels based on codon usage were applied to two industrially important Streptomyces strains with the objectives of testing this alternative method of studying whole-genome gene expression.…”

“…Among these, the 'codon adaptation index' (CAI) was proposed as a measure of codon usage in a gene relative to that in a reference set of genes (Sharp & Li, 1987). This index has been shown to correlate better with mRNA expression levels than other codon usage indices, such as the frequency of optimal codons (Ikemura, 1985) or the effective number of codons (Wright, 1990;Friberg et al, 2004). Therefore, CAI has been widely applied to the prediction of highly expressed genes in various organisms (Pan et al, 1998;Coghlan & Wolfe, 2000;dos Reis et al, 2003;Martin-Galiano et al, 2004).…”

Tackling codon usage bias for heterologous expression in Rhodobacter sphaeroides by supplementation of rare tRNAs

“…In fact, most of the widely used gene optimization tools to date aim at maximizing CAI by referring to the codon usage table. However, several studies have contradicted the correlation between CAI and gene expression levels [44,45]. Gene optimization is far more complicated than codons and may involve multiple variables, such as mRNA structures and potential deleterious motifs [46].…”