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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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Fraction of synonymous codons in native and simulated sequences. Scatter plot of codon fractions in synonymous sets of lowly (closed circles) and highly transcribed (open triangles) genes versus codon fractions expected by coding sequences with GC-content equal to that of intergenic sequences.
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gkt740-F3: Fraction of synonymous codons in native and simulated sequences. Scatter plot of codon fractions in synonymous sets of lowly (closed circles) and highly transcribed (open triangles) genes versus codon fractions expected by coding sequences with GC-content equal to that of intergenic sequences.

Mentions: We compared the relative frequency of the synonymous codons expected on the basis of the GC-content of intergenic regions with the relative codon frequencies of the lowly transcribed genes. As illustrated in Figure 3 (closed circles), the correlation analysis clearly shows that the codon bias of the lowly transcribed genes is accurately predicted by the GC-content of the intergenic regions (Rp = 0.98). In contrast, the codon bias of highly transcribed genes is scarcely correlated with that expected by the intergenic GC-content (Figure 3, open triangles).Figure 3.


Selection on codon bias in yeast: a transcriptional hypothesis.

Trotta E - Nucleic Acids Res. (2013)

Fraction of synonymous codons in native and simulated sequences. Scatter plot of codon fractions in synonymous sets of lowly (closed circles) and highly transcribed (open triangles) genes versus codon fractions expected by coding sequences with GC-content equal to that of intergenic sequences.
© Copyright Policy - creative-commons
Related In: Results  -  Collection

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

gkt740-F3: Fraction of synonymous codons in native and simulated sequences. Scatter plot of codon fractions in synonymous sets of lowly (closed circles) and highly transcribed (open triangles) genes versus codon fractions expected by coding sequences with GC-content equal to that of intergenic sequences.
Mentions: We compared the relative frequency of the synonymous codons expected on the basis of the GC-content of intergenic regions with the relative codon frequencies of the lowly transcribed genes. As illustrated in Figure 3 (closed circles), the correlation analysis clearly shows that the codon bias of the lowly transcribed genes is accurately predicted by the GC-content of the intergenic regions (Rp = 0.98). In contrast, the codon bias of highly transcribed genes is scarcely correlated with that expected by the intergenic GC-content (Figure 3, open triangles).Figure 3.

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