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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.


All codons show negative correlation between outlier strength and proximity to gene startCorrelation between slow outlier strength and position per length from 5′ end, conditioned by the codon, plotted against codon tAI. For each codon c, we calculate the Spearman correlation for outlier strength Δmk and position per length from 5′ end (k/Lm) but restricted to the (m,k) that satisfy codon(m,k) = c. All codons except one (hollow circle), which has the lowest abundance in the genome, have a significant negative correlation. This indicates that 5′ end outliers are slower even independent of codon bias.
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fig05: All codons show negative correlation between outlier strength and proximity to gene startCorrelation between slow outlier strength and position per length from 5′ end, conditioned by the codon, plotted against codon tAI. For each codon c, we calculate the Spearman correlation for outlier strength Δmk and position per length from 5′ end (k/Lm) but restricted to the (m,k) that satisfy codon(m,k) = c. All codons except one (hollow circle), which has the lowest abundance in the genome, have a significant negative correlation. This indicates that 5′ end outliers are slower even independent of codon bias.

Mentions: The strongest correlation with outlier strength for slow outliers is proximity to the 5′ end, with larger pauses occurring closer to the beginning of a message, even relative to gene length or even when aligned by stop codon as opposed to start codon (position from 5′ correlates to Δmk with Spearman r = −0.043; position from 5′ per length with r = −0.144; and position from 3′ end with r = 0.162, P ≈ 0 for all). Similar observations of increased ribosome occupancy at the 5′ end have produced various hypotheses for the causal basis. In the “ramp” model (Tuller et al, 2010a), the presence of more slow codons (low tAI) at the beginning of a message is thought to separate ribosomes early to avoid the wasteful expenditure of resources on stacked, idling ribosomes. However, we observe a correlation between position from 5′ end and slow outlier strength even when conditioning on the codon (Fig5) and thereby controlling for differences in codon usage at different positions within the gene, suggesting that there is an initial low translation speed, regardless of codon usage, which gradually increases as translation proceeds. Additionally, our model helps account for length, position, and abundance biases when calculating outliers in a particular message in two ways: First, we include message-specific codon dwell times, and, second, we exclude the first 100 codons from each gene during model learning (see Materials and Methods) to avoid inflating or otherwise biasing the expected rates and μc. Our analysis indicates that pausing occurs at the 5′ end, even after accounting for major factors such as codon bias and gene length.


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)

All codons show negative correlation between outlier strength and proximity to gene startCorrelation between slow outlier strength and position per length from 5′ end, conditioned by the codon, plotted against codon tAI. For each codon c, we calculate the Spearman correlation for outlier strength Δmk and position per length from 5′ end (k/Lm) but restricted to the (m,k) that satisfy codon(m,k) = c. All codons except one (hollow circle), which has the lowest abundance in the genome, have a significant negative correlation. This indicates that 5′ end outliers are slower even independent of codon bias.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

fig05: All codons show negative correlation between outlier strength and proximity to gene startCorrelation between slow outlier strength and position per length from 5′ end, conditioned by the codon, plotted against codon tAI. For each codon c, we calculate the Spearman correlation for outlier strength Δmk and position per length from 5′ end (k/Lm) but restricted to the (m,k) that satisfy codon(m,k) = c. All codons except one (hollow circle), which has the lowest abundance in the genome, have a significant negative correlation. This indicates that 5′ end outliers are slower even independent of codon bias.
Mentions: The strongest correlation with outlier strength for slow outliers is proximity to the 5′ end, with larger pauses occurring closer to the beginning of a message, even relative to gene length or even when aligned by stop codon as opposed to start codon (position from 5′ correlates to Δmk with Spearman r = −0.043; position from 5′ per length with r = −0.144; and position from 3′ end with r = 0.162, P ≈ 0 for all). Similar observations of increased ribosome occupancy at the 5′ end have produced various hypotheses for the causal basis. In the “ramp” model (Tuller et al, 2010a), the presence of more slow codons (low tAI) at the beginning of a message is thought to separate ribosomes early to avoid the wasteful expenditure of resources on stacked, idling ribosomes. However, we observe a correlation between position from 5′ end and slow outlier strength even when conditioning on the codon (Fig5) and thereby controlling for differences in codon usage at different positions within the gene, suggesting that there is an initial low translation speed, regardless of codon usage, which gradually increases as translation proceeds. Additionally, our model helps account for length, position, and abundance biases when calculating outliers in a particular message in two ways: First, we include message-specific codon dwell times, and, second, we exclude the first 100 codons from each gene during model learning (see Materials and Methods) to avoid inflating or otherwise biasing the expected rates and μc. Our analysis indicates that pausing occurs at the 5′ end, even after accounting for major factors such as codon bias and gene length.

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.