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

Abstract

Ribosome profiling data report on the distribution of translating ribosomes, at steady-state, with codon-level resolution. 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. We show that neither elongation rate nor translational efficiency is improved by experimental manipulation of the abundance or body sequence of the rare AGG tRNA. 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. This suggests that correlation between codon bias and efficiency arises as selection for codons to utilize translation machinery efficiently in highly translated genes. 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.

Keywords: codon usage bias; elongation; mRNA structure; translation efficiency.

Figure 1. Model of protein synthesis

Ribosomes initiate translation with a protein synthesis rate or flow (J) of ribosomes. This is conserved across the strand, so that at each residue (m,k) the flow depends on the dwell time of the ribosome (μ) and the ribosome occupancy (proportional to footprint count d). Slower positions, for example, (m,2) compared to (m,1), can inflate the average footprint count per gene and must be accounted for when estimating flow. Dwell times and flow are correlated with local and global cis-features.

Figure 2. Correlation between codon translation rates and measures of codon usage bias

Left: 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.

Figure 3. Comparison between codon translation rates in wild-type and mutants

Correlation between estimated codon translation rates in wild-type versus mutant for the three mutant samples (the manipulated codon is highlighted in red). Rates are normalized by the minimum one in each sample. Pearson correlations are nearly exact, indicating that the mutant rates are generally unaffected.Source data are available online for this figure.

Figure 4. Comparison between translation efficiency in wild-type and mutants

Left: 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.

Figure 5. All codons show negative correlation between outlier strength and proximity to gene start

Correlation 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 Δ_mk and position per length from 5′ end (k/L_m) 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.

Figure 6. RNA structure energy and its relationship to translation efficiency

Left: Energy averaged in sliding windows of 40 nt (see Materials and Methods) across all genes for in vitro and in vivo measures of energy via DMS probing (Rouskin et al, 2013). The second red line corresponds to the first window with lowest energy (˜60 nt for in vitro and ˜80 nt in vivo).Right: Spearman correlation between the energy windows and TE. The first red line corresponds to the first window with significant correlation (9 nt for in vitro and 18 nt for in vivo).

Figure 7. Estimated Kozak motif for efficient genes

Estimated TE-driven Kozak motif based on a regression model (see Materials and Methods). The original Kozak consensus for yeast (Hamilton et al, 1987) is WAMAMAATGTCY.