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Selection on codon bias in yeast: a transcriptional hypothesis.

Trotta E - Nucleic Acids Res. (2013)

Bottom Line: We show that the most used codons in highly expressed genes can be predicted by mRNA structural data and that the codon choice at each synonymous site within an mRNA is not random with respect to the local secondary structure.Consistent with this, we report evidence supporting the adaptation of the tRNA pool to the codon profile of the most expressed genes rather than vice versa.We show that the correlation of codon usage with the gene expression level also includes the stop codons that are normally not decoded by aminoacyl-tRNAs.

View Article: PubMed Central - PubMed

Affiliation: Institute of Translational Pharmacology, Consiglio Nazionale delle Ricerche (CNR), Roma 00133, Italy.

ABSTRACT
Codons that code for the same amino acid are often used with unequal frequencies. This phenomenon is termed codon bias. Here, we report a computational analysis of codon bias in yeast using experimental and theoretical genome-wide data. We show that the most used codons in highly expressed genes can be predicted by mRNA structural data and that the codon choice at each synonymous site within an mRNA is not random with respect to the local secondary structure. Because we also found that the folding stability of intron sequences is strongly correlated with codon bias and mRNA level, our results suggest that codon bias is linked to mRNA folding structure through a mechanism that, at least partially, operates before pre-mRNA splicing. Consistent with this, we report evidence supporting the adaptation of the tRNA pool to the codon profile of the most expressed genes rather than vice versa. We show that the correlation of codon usage with the gene expression level also includes the stop codons that are normally not decoded by aminoacyl-tRNAs. The results reported here are consistent with a role for transcriptional forces in driving codon usage bias via a mechanism that improves gene expression by optimizing mRNA folding structures.

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Comparison of the variation of the relative frequency of synonymous codons. Pearson’s correlation coefficients (Rp) between the fraction change of major codons with increasing mRNA level and the fraction changes with increasing coding sequence properties: GC-content, CAI, Fop, CBI, cellular protein abundance, protein/mRNA ratio, codon complementarity, Pi (three-base periodicity index), PARS score, intrinsic nucleosome occupancy and thermodynamic stability of DNA/DNA and RNA/DNA duplexes (see ‘Materials and Methods’ section). For protein level and protein/mRNA ratio, we used a high-confidence data set of 408 genes (see ‘Materials and Methods’ section).
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gkt740-F5: Comparison of the variation of the relative frequency of synonymous codons. Pearson’s correlation coefficients (Rp) between the fraction change of major codons with increasing mRNA level and the fraction changes with increasing coding sequence properties: GC-content, CAI, Fop, CBI, cellular protein abundance, protein/mRNA ratio, codon complementarity, Pi (three-base periodicity index), PARS score, intrinsic nucleosome occupancy and thermodynamic stability of DNA/DNA and RNA/DNA duplexes (see ‘Materials and Methods’ section). For protein level and protein/mRNA ratio, we used a high-confidence data set of 408 genes (see ‘Materials and Methods’ section).

Mentions: PARS is an experimental technique for the analysis of RNA secondary structure on a genome-wide scale (27). PARS measures the likelihood of a nucleotide in an mRNA molecule to be in a double-stranded conformation. Distinct from nucleosome occupancy, the average PARS score of an mRNA is strongly and positively correlated with the transcript level (16) (Rs = 0.642 and R2 = 0.426, N = 2993, P < 10−10) and weakly with the GC-content (Rs = 0.311 and R2 = 0.100, N = 3000; P < 10−10). Indeed, changes of codon fraction with increasing PARS score are positively coherent with those of mRNA levels (Figure 5 and Supplementary Figure S3).Figure 5.


Selection on codon bias in yeast: a transcriptional hypothesis.

Trotta E - Nucleic Acids Res. (2013)

Comparison of the variation of the relative frequency of synonymous codons. Pearson’s correlation coefficients (Rp) between the fraction change of major codons with increasing mRNA level and the fraction changes with increasing coding sequence properties: GC-content, CAI, Fop, CBI, cellular protein abundance, protein/mRNA ratio, codon complementarity, Pi (three-base periodicity index), PARS score, intrinsic nucleosome occupancy and thermodynamic stability of DNA/DNA and RNA/DNA duplexes (see ‘Materials and Methods’ section). For protein level and protein/mRNA ratio, we used a high-confidence data set of 408 genes (see ‘Materials and Methods’ section).
© Copyright Policy - creative-commons
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC3814355&req=5

gkt740-F5: Comparison of the variation of the relative frequency of synonymous codons. Pearson’s correlation coefficients (Rp) between the fraction change of major codons with increasing mRNA level and the fraction changes with increasing coding sequence properties: GC-content, CAI, Fop, CBI, cellular protein abundance, protein/mRNA ratio, codon complementarity, Pi (three-base periodicity index), PARS score, intrinsic nucleosome occupancy and thermodynamic stability of DNA/DNA and RNA/DNA duplexes (see ‘Materials and Methods’ section). For protein level and protein/mRNA ratio, we used a high-confidence data set of 408 genes (see ‘Materials and Methods’ section).
Mentions: PARS is an experimental technique for the analysis of RNA secondary structure on a genome-wide scale (27). PARS measures the likelihood of a nucleotide in an mRNA molecule to be in a double-stranded conformation. Distinct from nucleosome occupancy, the average PARS score of an mRNA is strongly and positively correlated with the transcript level (16) (Rs = 0.642 and R2 = 0.426, N = 2993, P < 10−10) and weakly with the GC-content (Rs = 0.311 and R2 = 0.100, N = 3000; P < 10−10). Indeed, changes of codon fraction with increasing PARS score are positively coherent with those of mRNA levels (Figure 5 and Supplementary Figure S3).Figure 5.

Bottom Line: We show that the most used codons in highly expressed genes can be predicted by mRNA structural data and that the codon choice at each synonymous site within an mRNA is not random with respect to the local secondary structure.Consistent with this, we report evidence supporting the adaptation of the tRNA pool to the codon profile of the most expressed genes rather than vice versa.We show that the correlation of codon usage with the gene expression level also includes the stop codons that are normally not decoded by aminoacyl-tRNAs.

View Article: PubMed Central - PubMed

Affiliation: Institute of Translational Pharmacology, Consiglio Nazionale delle Ricerche (CNR), Roma 00133, Italy.

ABSTRACT
Codons that code for the same amino acid are often used with unequal frequencies. This phenomenon is termed codon bias. Here, we report a computational analysis of codon bias in yeast using experimental and theoretical genome-wide data. We show that the most used codons in highly expressed genes can be predicted by mRNA structural data and that the codon choice at each synonymous site within an mRNA is not random with respect to the local secondary structure. Because we also found that the folding stability of intron sequences is strongly correlated with codon bias and mRNA level, our results suggest that codon bias is linked to mRNA folding structure through a mechanism that, at least partially, operates before pre-mRNA splicing. Consistent with this, we report evidence supporting the adaptation of the tRNA pool to the codon profile of the most expressed genes rather than vice versa. We show that the correlation of codon usage with the gene expression level also includes the stop codons that are normally not decoded by aminoacyl-tRNAs. The results reported here are consistent with a role for transcriptional forces in driving codon usage bias via a mechanism that improves gene expression by optimizing mRNA folding structures.

Show MeSH