Prokaryotic rRNA-mRNA interactions are involved in all translation steps and shape bacterial transcripts
The well-established Shine-Dalgarno model suggests that translation initiation in bacteria is regulated via base-pairing between ribosomal RNA (rRNA) and mRNA. We used novel computational analyses and modelling of 823 bacterial genomes coupled with experiments to demonstrate that rRNA-mRNA interactions are diverse and regulate all translation steps from pre-initiation to termination. Previous research has reported the significant influence of rRNA-mRNA interactions, mainly in the initiation phase of translation. The results reported in this paper suggest that, in addition to the rRNA-mRNA interactions near the start codon that trigger initiation in bacteria, rRNA-mRNA interactions affect all sub-stages of the translation process (pre-initiation, initiation, elongation, termination). As these interactions dictate translation efficiency, they serve as an evolutionary driving force for shaping transcripts in bacteria while considering trade-offs between the effects of different interactions across different transcript regions on translation efficacy and efficiency. We observed selection for strong interactions in regions where such interactions are likely to enhance initiation, regulate early elongation, and ensure translation termination fidelity. We discovered selection against strong interactions and for intermediate interactions in coding regions and presented evidence that these patterns maximize elongation efficiency while also enhancing initiation. These finding are relevant to all biomedical disciplines due to the centrality of the translation process and the effect of rRNA-mRNA interactions on transcript evolution.
(a) Three statistical tests detect evolutionary selection for different rRNA-mRNA interaction strengths:
Enrichment of sub-sequences with weak interactions (right tail of the distribution).
Enrichment of sub-sequences with intermediate interactions.
Enrichment of sub-sequences with strong interactions (left tail of the distribution).
(b) Statistical questions explained: tests for weak, intermediate, and strong interactions are independent, not complementary.
(c) Results: Rows represent bacteria, clustered by phyla; columns represent transcript positions. Red/green indicates significant selection for/against strong interactions, black indicates no significant selection. Bacteria with significant positions in the 5ʹUTR or first coding nucleotides are highlighted in the last two columns.
(d) Illustration: Strong rRNA-mRNA interactions upstream of the start codon align the ribosomal subunit to initiate translation.
(e) Suggested model: Strong interactions in early elongation slow ribosome movement, influencing translation efficiency.
(f) Z-scores of interaction strengths for highly and lowly expressed E. coli genes at the last 20 nucleotides of the 5ʹUTR and first 20 nucleotides of the coding region. Insets show bar graphs comparing Z-scores in these regions for highly vs. lowly expressed genes.
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