Differential Selection for Translation Efficiency Shapes Translation Machineries in Bacterial Species

Abstract

Different bacterial species have dramatically different generation times, from 20-30 min in Escherichia coli to about two weeks in Mycobacterium leprae. The translation machinery in a cell needs to synthesize all proteins for a new cell in each generation. The three subprocesses of translation, i.e., initiation, elongation, and termination, are expected to be under stronger selection pressure to optimize in short-generation bacteria (SGB) such as Vibrio natriegens than in the long-generation Mycobacterium leprae. The initiation efficiency depends on the start codon decoded by the initiation tRNA, the optimal Shine-Dalgarno (SD) decoded by the anti-SD (aSD) sequence on small subunit rRNA, and the secondary structure that may embed the initiation signals and prevent them from being decoded. The elongation efficiency depends on the tRNA pool and codon usage. The termination efficiency in bacteria depends mainly on the nature of the stop codon and the nucleotide immediately downstream of the stop codon. By contrasting SGB with long-generation bacteria (LGB), we predict (1) SGB to have more ribosome RNA operons to produce ribosomes, and more tRNA genes for carrying amino acids to ribosomes, (2) SGB to have a higher percentage of genes using AUG as the start codon and UAA as the stop codon than LGB, (3) SGB to exhibit better codon and anticodon adaptation than LGB, and (4) SGB to have a weaker secondary structure near the translation initiation signals than LGB. These differences between SGB and LGB should be more pronounced in highly expressed genes than the rest of the genes. We present empirical evidence in support of these predictions.

Keywords: Mycobacterium leprae; Mycobacterium tuberculosis; RNA secondary structure; rrn operons; tRNA; translation efficiency; translation elongation; translation initiation; translation termination.

Figure 1

Fitted nonlinear equations to the observed data (blue dots). (A) Relationship between N_rrn and RankGT in Table 2. (B) Relationship between N_tRNA and RankGT in Table 2.

Figure 2

Selection for codon optimization, measured by ranked $D_{I_{TE}}$($={\overline{I}}_{TE.HEG}-{\overline{I}}_{TE.REST})$ in Equation (7), decreases with increasing generation time in nine bacterial species.

Figure 3

The ranked effective number of anticodons (Rank N_AC) increases with ranked generation time (RankGT) in (A), and decreases with increasing codon adaptation (Rank $D_{I_{TE}}$) in (B).

Figure 4

Change in MFE (minimum folding energy) over a sliding window of 40 nt in HEGs of the nine bacterial species (one sub-figure for each species). The start codon occupies sites 61–63. The mid-window site (horizontal axis) indicates the middle of the sliding window of 40 nt. The middle blue curve is the mean MFE of all HEGs (e.g., each point in the mean curve for V. nitriegens is the average of 76 HEGs). The two curves above and below the mean curve are the 95% upper and lower limits (UL and LL).

Figure 5

Dramatic reduction in nucleotide C near the Shine–Dalgarno sequence (shaded). Plotted are position weight matrix (PWM) scores in sequences immediately upstream of the start codon (at sites 61–63) in HEGs (A) and REST genes (B) in Bacillus subtilis, and in HEGS (C) and REST genes (D) in Vibrio cholerae.

Figure 6

Changes in nucleotide frequencies in sequences immediately upstream of the start codon (at sites 61–63) in highly expressed genes (HEGs). Note the sharp increase in nucleotide G and concurrent decrease in nucleotides C and U near site 50 corresponding to the Shine–Dalgarno (SD) sequence (shaded). (A) AT-rich H. influenzae. (B) GC-rich M. smegmatis.

Figure 7

Change in MFE (minimum folding energy) over a sliding window of 40 nt in HEGs of the nine bacterial species. The stop codon occupies sites 58–60. Other annotations are identical to those in Figure 4.