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Causal signals between codon bias, mRNA structure, and the efficiency of translation and elongation.

Pop C, Rouskin S, Ingolia NT, Han L, Phizicky EM, Weissman JS, Koller D - Mol. Syst. Biol. (2014)

Bottom Line: We present a robust method to extract codon translation rates and protein synthesis rates from these data, and identify causal features associated with elongation and translation efficiency in physiological conditions in yeast.Deletion of three of the four copies of the heavily used ACA tRNA shows a modest efficiency decrease that could be explained by other rate-reducing signals at gene start.We also show a correlation between efficiency and RNA structure calculated both computationally and from recent structure probing data, as well as the Kozak initiation motif, which may comprise a mechanism to regulate initiation.

View Article: PubMed Central - PubMed

Affiliation: Computer Science Department, Stanford University, Stanford, CA, USA cpop@cs.stanford.edu.

No MeSH data available.


Comparison between translation efficiency in wild-type and mutantsLeft: Wild-type TE compared to mutant TE for the three mutant samples. Strong Spearman correlations shown suggest TE is generally unaffected by tRNA manipulation.Right: Spearman correlation, for each codon, between the ratio of mutant TE to wild-type TE and the percent of codon per gene. Significant correlations are shown as filled dots. For AGG mutants, the correlation is not higher for the manipulated codon (highlighted) than for other codons, indicating that optimizing codon usage does not affect TE. For ACA-K, the correlation is negative for the ACA codon, suggesting a mild effect.Source data are available online for this figure.
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fig04: Comparison between translation efficiency in wild-type and mutantsLeft: Wild-type TE compared to mutant TE for the three mutant samples. Strong Spearman correlations shown suggest TE is generally unaffected by tRNA manipulation.Right: Spearman correlation, for each codon, between the ratio of mutant TE to wild-type TE and the percent of codon per gene. Significant correlations are shown as filled dots. For AGG mutants, the correlation is not higher for the manipulated codon (highlighted) than for other codons, indicating that optimizing codon usage does not affect TE. For ACA-K, the correlation is negative for the ACA codon, suggesting a mild effect.Source data are available online for this figure.

Mentions: One of the major goals of codon optimization in biotechnology is an increase in protein yield. Studies done on transgenes expressed at a large fraction of cellular mRNA abundance report increased protein abundance when the mRNA was optimized for codon bias (Gustafsson et al, 2004; Lavner & Kotlar, 2005; Burgess-Brown et al, 2008), suggesting that codon usage contributes to efficiency (Supek & Smuc, 2010; Tuller et al, 2010b). However, other studies observed that optimizing codon adaptation of a reporter does not significantly improve TE or protein yield (Wu et al, 2004; Kudla et al, 2009; Welch et al, 2009; Hense et al, 2010; Letzring et al, 2010; Shah et al, 2013). Our experiments likewise provide support for the view that the TE of endogenous mRNAs is unchanged by effective codon optimization achieved by changes in the tRNA pool (Fig4). We find that increasing tRNA abundance or replacing the tRNA body sequence by one with higher tAI does not improve efficiency: Most genes remain unchanged in TE between the wild-type and mutant samples (Pearson r = 0.96 for AGG-OE and r = 0.95 for AGG-QC). Further, the top 200 genes that do deviate most in TE relative to the wild-type sample have mutant TE that is both lower (reduced TE genes) and higher (increased TE genes) compared to wild-type, with bias toward reduced TE genes (123 reduced versus 77 increased for AGG-OE and 133 versus 67 for AGG-QC). In AGG-OE, we observe no correlation between the fraction of AGG codons per message and the change between mutant and wild-type TE (Spearman r = 0.00002, P = 0.99); we would expect a positive correlation if increasing tRNA abundance increased TE. Further, despite the many-fold overexpression of tRNA, the correlation between TE and fraction of codon per message for AGG is not higher than the correlation for any of the other codons (Fig4). AGG-QC behaves similarly, such that manipulating the tRNA to be “faster” does not lead to a scenario where AGG outperforms other codons in affecting translation efficiency. Finally, these observations also hold if we look at protein synthesis rates instead of TE (Supplementary Fig S5).


Causal signals between codon bias, mRNA structure, and the efficiency of translation and elongation.

Pop C, Rouskin S, Ingolia NT, Han L, Phizicky EM, Weissman JS, Koller D - Mol. Syst. Biol. (2014)

Comparison between translation efficiency in wild-type and mutantsLeft: Wild-type TE compared to mutant TE for the three mutant samples. Strong Spearman correlations shown suggest TE is generally unaffected by tRNA manipulation.Right: Spearman correlation, for each codon, between the ratio of mutant TE to wild-type TE and the percent of codon per gene. Significant correlations are shown as filled dots. For AGG mutants, the correlation is not higher for the manipulated codon (highlighted) than for other codons, indicating that optimizing codon usage does not affect TE. For ACA-K, the correlation is negative for the ACA codon, suggesting a mild effect.Source data are available online for this figure.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4300493&req=5

fig04: Comparison between translation efficiency in wild-type and mutantsLeft: Wild-type TE compared to mutant TE for the three mutant samples. Strong Spearman correlations shown suggest TE is generally unaffected by tRNA manipulation.Right: Spearman correlation, for each codon, between the ratio of mutant TE to wild-type TE and the percent of codon per gene. Significant correlations are shown as filled dots. For AGG mutants, the correlation is not higher for the manipulated codon (highlighted) than for other codons, indicating that optimizing codon usage does not affect TE. For ACA-K, the correlation is negative for the ACA codon, suggesting a mild effect.Source data are available online for this figure.
Mentions: One of the major goals of codon optimization in biotechnology is an increase in protein yield. Studies done on transgenes expressed at a large fraction of cellular mRNA abundance report increased protein abundance when the mRNA was optimized for codon bias (Gustafsson et al, 2004; Lavner & Kotlar, 2005; Burgess-Brown et al, 2008), suggesting that codon usage contributes to efficiency (Supek & Smuc, 2010; Tuller et al, 2010b). However, other studies observed that optimizing codon adaptation of a reporter does not significantly improve TE or protein yield (Wu et al, 2004; Kudla et al, 2009; Welch et al, 2009; Hense et al, 2010; Letzring et al, 2010; Shah et al, 2013). Our experiments likewise provide support for the view that the TE of endogenous mRNAs is unchanged by effective codon optimization achieved by changes in the tRNA pool (Fig4). We find that increasing tRNA abundance or replacing the tRNA body sequence by one with higher tAI does not improve efficiency: Most genes remain unchanged in TE between the wild-type and mutant samples (Pearson r = 0.96 for AGG-OE and r = 0.95 for AGG-QC). Further, the top 200 genes that do deviate most in TE relative to the wild-type sample have mutant TE that is both lower (reduced TE genes) and higher (increased TE genes) compared to wild-type, with bias toward reduced TE genes (123 reduced versus 77 increased for AGG-OE and 133 versus 67 for AGG-QC). In AGG-OE, we observe no correlation between the fraction of AGG codons per message and the change between mutant and wild-type TE (Spearman r = 0.00002, P = 0.99); we would expect a positive correlation if increasing tRNA abundance increased TE. Further, despite the many-fold overexpression of tRNA, the correlation between TE and fraction of codon per message for AGG is not higher than the correlation for any of the other codons (Fig4). AGG-QC behaves similarly, such that manipulating the tRNA to be “faster” does not lead to a scenario where AGG outperforms other codons in affecting translation efficiency. Finally, these observations also hold if we look at protein synthesis rates instead of TE (Supplementary Fig S5).

Bottom Line: We present a robust method to extract codon translation rates and protein synthesis rates from these data, and identify causal features associated with elongation and translation efficiency in physiological conditions in yeast.Deletion of three of the four copies of the heavily used ACA tRNA shows a modest efficiency decrease that could be explained by other rate-reducing signals at gene start.We also show a correlation between efficiency and RNA structure calculated both computationally and from recent structure probing data, as well as the Kozak initiation motif, which may comprise a mechanism to regulate initiation.

View Article: PubMed Central - PubMed

Affiliation: Computer Science Department, Stanford University, Stanford, CA, USA cpop@cs.stanford.edu.

No MeSH data available.