Prokaryotic rRNA-mRNA interactions are involved in all translation steps and shape bacterial transcripts

ABSTRACT

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.

KEYWORDS:

• Shine-Dalgarno
• rRNA-mRNA interaction
• translation initiation
• translation elongation
• translation termination
• protein translation in bacteria

Acknowledgments

We thank Hadas Zur for helpful comments.

Disclosure statement

The author(s) declare no conflict of interest.

Supplementary material

Supplemental data for this article can be accessed here

Additional information

Funding

This study was supported in part by a fellowship from the Edmond J. Safra Center for Bioinformatics at Tel-Aviv University, by the Ofakim research fellowship by Miri and Efraim and The Koret-UC Berkeley-Tel Aviv University Initiative in Computational Biology and Bioinformatics; the Koret-UC Berkeley-Tel Aviv University Initiative in Computational Biology and Bioinformatics.; the Ofakim research fellowship by Miri and Efraim.