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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 codon complementarity and Pi scores in simulated and native sequences. Two sets of genomic coding sequences were simulated from native mRNA: in the first set, the frequencies of synonymous codons within each gene were equalized; in the second set, the identities of synonymous codons were exchanged randomly and uniformly in all genes. The scatter plots report the comparison of codon complementarity (panel A) and Pi (panel C) scores of the gene in the two simulated sets of sequences. The scatter plots of panel B (codon complementarity) and D (Pi score) show the comparison between native and codon exchanged sequences. Circle and point markers indicate highly and lowly transcribed genes, respectively. The broken line corresponds to equal values on both axes.
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gkt740-F7: Comparison of codon complementarity and Pi scores in simulated and native sequences. Two sets of genomic coding sequences were simulated from native mRNA: in the first set, the frequencies of synonymous codons within each gene were equalized; in the second set, the identities of synonymous codons were exchanged randomly and uniformly in all genes. The scatter plots report the comparison of codon complementarity (panel A) and Pi (panel C) scores of the gene in the two simulated sets of sequences. The scatter plots of panel B (codon complementarity) and D (Pi score) show the comparison between native and codon exchanged sequences. Circle and point markers indicate highly and lowly transcribed genes, respectively. The broken line corresponds to equal values on both axes.

Mentions: As reported earlier in the text, the codon complementarity score of coding sequences is negatively correlated with the transcript level and with the codon bias associated with selective forces. To investigate the relationship between codon complementarity and codon bias, we simulated two different sets of CDSs from native mRNAs. In the first set, we simulated a genome without codon bias by equalizing the frequencies of synonymous codons within each native CDS. In the second set, we preserved the codon bias of each synonymous set but randomly exchanged the identities of synonymous codons uniformly in all coding sequences. For example, in a simulation, we substituted all codons of glycines in the following way: all GGA were changed to GGG, all GGC to GGT, all GGG to GGC and all GGT to GGA. The differences between the two simulations are attributable to the codon bias but not to the identity of minor and major codons because, in the sequences with exchanged codons, the score is averaged over 20 different simulations. Figure 7A reports the comparison of the complementarity scores calculated for the CDSs of the two simulations. From the graph in Figure 7A, the complementarity scores of the two simulated sets of CDSs are strongly and positively correlated. This is more obvious if we consider only the lowly transcribed genes (points in Figure 7A). This shows that a consistent part of the complementarity is not due to codon bias but is generated by the combination between the structure of the genetic code and the amino acid composition of a protein. From the plot of Figure 7A, it is also observable that, in all CDSs, codon bias determines a decrease of codon complementarity that, in agreement with the above correlational studies, is higher in highly than in lowly transcribed CDSs. Figure 7B compares the complementarity of native CDSs with the average complementarity of simulated sequences with codon identity exchanged. In this case, the differences are only attributable to the specific identities of major and minor codons used in the native sequences and not to the codon bias that is preserved in the simulated sequences. The result shows that the choice of minor and major codons in native sequences produces a higher increase of complementarity in highly than in lowly transcribed CDSs. In summary, although an increase of codon bias generally produces a reduction in codon complementarity of CDSs, the choice of major codons opposes this reduction in highly expressed CDSs. The result suggests that the need to maintain an optimal number of complementary codons in the most expressed genes may affect the choice of major codons.Figure 7.


Selection on codon bias in yeast: a transcriptional hypothesis.

Trotta E - Nucleic Acids Res. (2013)

Comparison of codon complementarity and Pi scores in simulated and native sequences. Two sets of genomic coding sequences were simulated from native mRNA: in the first set, the frequencies of synonymous codons within each gene were equalized; in the second set, the identities of synonymous codons were exchanged randomly and uniformly in all genes. The scatter plots report the comparison of codon complementarity (panel A) and Pi (panel C) scores of the gene in the two simulated sets of sequences. The scatter plots of panel B (codon complementarity) and D (Pi score) show the comparison between native and codon exchanged sequences. Circle and point markers indicate highly and lowly transcribed genes, respectively. The broken line corresponds to equal values on both axes.
© Copyright Policy - creative-commons
Related In: Results  -  Collection

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

gkt740-F7: Comparison of codon complementarity and Pi scores in simulated and native sequences. Two sets of genomic coding sequences were simulated from native mRNA: in the first set, the frequencies of synonymous codons within each gene were equalized; in the second set, the identities of synonymous codons were exchanged randomly and uniformly in all genes. The scatter plots report the comparison of codon complementarity (panel A) and Pi (panel C) scores of the gene in the two simulated sets of sequences. The scatter plots of panel B (codon complementarity) and D (Pi score) show the comparison between native and codon exchanged sequences. Circle and point markers indicate highly and lowly transcribed genes, respectively. The broken line corresponds to equal values on both axes.
Mentions: As reported earlier in the text, the codon complementarity score of coding sequences is negatively correlated with the transcript level and with the codon bias associated with selective forces. To investigate the relationship between codon complementarity and codon bias, we simulated two different sets of CDSs from native mRNAs. In the first set, we simulated a genome without codon bias by equalizing the frequencies of synonymous codons within each native CDS. In the second set, we preserved the codon bias of each synonymous set but randomly exchanged the identities of synonymous codons uniformly in all coding sequences. For example, in a simulation, we substituted all codons of glycines in the following way: all GGA were changed to GGG, all GGC to GGT, all GGG to GGC and all GGT to GGA. The differences between the two simulations are attributable to the codon bias but not to the identity of minor and major codons because, in the sequences with exchanged codons, the score is averaged over 20 different simulations. Figure 7A reports the comparison of the complementarity scores calculated for the CDSs of the two simulations. From the graph in Figure 7A, the complementarity scores of the two simulated sets of CDSs are strongly and positively correlated. This is more obvious if we consider only the lowly transcribed genes (points in Figure 7A). This shows that a consistent part of the complementarity is not due to codon bias but is generated by the combination between the structure of the genetic code and the amino acid composition of a protein. From the plot of Figure 7A, it is also observable that, in all CDSs, codon bias determines a decrease of codon complementarity that, in agreement with the above correlational studies, is higher in highly than in lowly transcribed CDSs. Figure 7B compares the complementarity of native CDSs with the average complementarity of simulated sequences with codon identity exchanged. In this case, the differences are only attributable to the specific identities of major and minor codons used in the native sequences and not to the codon bias that is preserved in the simulated sequences. The result shows that the choice of minor and major codons in native sequences produces a higher increase of complementarity in highly than in lowly transcribed CDSs. In summary, although an increase of codon bias generally produces a reduction in codon complementarity of CDSs, the choice of major codons opposes this reduction in highly expressed CDSs. The result suggests that the need to maintain an optimal number of complementary codons in the most expressed genes may affect the choice of major codons.Figure 7.

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
Related in: MedlinePlus