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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.


Correlation between codon translation rates and measures of codon usage biasLeft: Insignificant Spearman correlation between estimated codon translation rates (scaled up by a factor of 1,000) and tRNA abundance from microarray measurements using either fluorophore Cy3 or Cy5 (Dittmar et al, 2004) on 39 codons with measured levels. Right: The same correlation but to tAI is also not significant.
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fig02: Correlation between codon translation rates and measures of codon usage biasLeft: Insignificant Spearman correlation between estimated codon translation rates (scaled up by a factor of 1,000) and tRNA abundance from microarray measurements using either fluorophore Cy3 or Cy5 (Dittmar et al, 2004) on 39 codons with measured levels. Right: The same correlation but to tAI is also not significant.

Mentions: A number of studies in Escherichia coli initially identified codon usage and the availability of tRNA as the dominant force for codon translation rate (Varenne et al, 1984; Sorensen et al, 1989). Later studies found no correlation between measured rates and tRNA abundance or codon frequency (Bonekamp et al, 1989; Curran & Yarus, 1989; Sorensen & Pedersen, 1991). However, all of these studies measured translation speed indirectly, on individual and potentially idiosyncratic reporter systems. We explore these competing hypotheses in the physiological conditions of our yeast dataset. If tRNA abundance were rate-limiting for elongation, we would expect a positive correlation between codon translation rate and tRNA abundance. However, as shown in Fig2, the correlation is insignificant (Spearman r = 0.144, P = 0.380 for Cy5 and r = 0.133, P = 0.417 for Cy3 from microarray tRNA measurements (Dittmar et al, 2004)). A similar result (r = 0.210, P = 0.104) is also obtained when comparing to tAI, a measure of codon bias based on tRNA gene copy number relative to the overall collection of isoacceptor tRNAs (Dos Reis et al, 2004). If we restrict the analysis to the slowest synonymous codon (in terms of tAI), to the fastest, or to the average per amino acid, the correlation with tAI does not improve: r = −0.12 (P = 0.61), r = −0.29 (P = 0.22), and r = −0.32 (P = 0.18), respectively. Finally, the same insignificant correlation exists in the raw footprint data (r = 0.112, P = 0.392; baseline method for rate described in Materials and Methods) and was also observed in another analysis of the yeast dataset from Ingolia et al (2009), in which codon dwell time was estimated as the ratio of observed codon frequencies in the footprint data relative to expected codon frequencies in the mRNA fragment data (Qian et al, 2012).


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)

Correlation between codon translation rates and measures of codon usage biasLeft: Insignificant Spearman correlation between estimated codon translation rates (scaled up by a factor of 1,000) and tRNA abundance from microarray measurements using either fluorophore Cy3 or Cy5 (Dittmar et al, 2004) on 39 codons with measured levels. Right: The same correlation but to tAI is also not significant.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

fig02: Correlation between codon translation rates and measures of codon usage biasLeft: Insignificant Spearman correlation between estimated codon translation rates (scaled up by a factor of 1,000) and tRNA abundance from microarray measurements using either fluorophore Cy3 or Cy5 (Dittmar et al, 2004) on 39 codons with measured levels. Right: The same correlation but to tAI is also not significant.
Mentions: A number of studies in Escherichia coli initially identified codon usage and the availability of tRNA as the dominant force for codon translation rate (Varenne et al, 1984; Sorensen et al, 1989). Later studies found no correlation between measured rates and tRNA abundance or codon frequency (Bonekamp et al, 1989; Curran & Yarus, 1989; Sorensen & Pedersen, 1991). However, all of these studies measured translation speed indirectly, on individual and potentially idiosyncratic reporter systems. We explore these competing hypotheses in the physiological conditions of our yeast dataset. If tRNA abundance were rate-limiting for elongation, we would expect a positive correlation between codon translation rate and tRNA abundance. However, as shown in Fig2, the correlation is insignificant (Spearman r = 0.144, P = 0.380 for Cy5 and r = 0.133, P = 0.417 for Cy3 from microarray tRNA measurements (Dittmar et al, 2004)). A similar result (r = 0.210, P = 0.104) is also obtained when comparing to tAI, a measure of codon bias based on tRNA gene copy number relative to the overall collection of isoacceptor tRNAs (Dos Reis et al, 2004). If we restrict the analysis to the slowest synonymous codon (in terms of tAI), to the fastest, or to the average per amino acid, the correlation with tAI does not improve: r = −0.12 (P = 0.61), r = −0.29 (P = 0.22), and r = −0.32 (P = 0.18), respectively. Finally, the same insignificant correlation exists in the raw footprint data (r = 0.112, P = 0.392; baseline method for rate described in Materials and Methods) and was also observed in another analysis of the yeast dataset from Ingolia et al (2009), in which codon dwell time was estimated as the ratio of observed codon frequencies in the footprint data relative to expected codon frequencies in the mRNA fragment data (Qian et al, 2012).

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.