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Review
. 2023 Nov 16;83(22):3953-3971.
doi: 10.1016/j.molcel.2023.09.016. Epub 2023 Oct 5.

tRNA renovatio: Rebirth through fragmentation

Affiliations
Review

tRNA renovatio: Rebirth through fragmentation

Bernhard Kuhle et al. Mol Cell. .

Abstract

tRNA function is based on unique structures that enable mRNA decoding using anticodon trinucleotides. These structures interact with specific aminoacyl-tRNA synthetases and ribosomes using 3D shape and sequence signatures. Beyond translation, tRNAs serve as versatile signaling molecules interacting with other RNAs and proteins. Through evolutionary processes, tRNA fragmentation emerges as not merely random degradation but an act of recreation, generating specific shorter molecules called tRNA-derived small RNAs (tsRNAs). These tsRNAs exploit their linear sequences and newly arranged 3D structures for unexpected biological functions, epitomizing the tRNA "renovatio" (from Latin, meaning renewal, renovation, and rebirth). Emerging methods to uncover full tRNA/tsRNA sequences and modifications, combined with techniques to study RNA structures and to integrate AI-powered predictions, will enable comprehensive investigations of tRNA fragmentation products and new interaction potentials in relation to their biological functions. We anticipate that these directions will herald a new era for understanding biological complexity and advancing pharmaceutical engineering.

Keywords: AI-based RNA structure prediction; RNA structure; biological complexity; deep learning; evolution; non-canonical tRNA functions; tRNA; tRNA fragments; tRNA-derived small RNA.

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Conflict of interest statement

Declaration of interests The authors declare no competing interests.

Figures

Figure 1.
Figure 1.. Canonical and non-canonical tRNA functionality.
Illustration of canonical tRNA function in decoding the genetic code of mRNAs and deliver cognate amino acids to the ribosome for protein synthesis. Shown also is the expanding non-canonical tRNA functions in the cell. These non-canonical functions leverage the structural features of tRNA to interact with specific proteins, thereby regulating cellular physiology and mediating critical viral-host interactions across various biological processes.
Figure 2.
Figure 2.. Overview of the expanding field of tsRNA research.
This illustration depicts the three main branches of current tsRNA research. These branches encompass the study of tsRNA biogenesis, which involves various tRNA modifications, RNases, and regulatory proteins. The molecular mechanisms of actions, and the roles of tsRNAs in both physiological functions and disease associations, is based on previous research and review articles,,,,–.
Figure 3.
Figure 3.. Structural plasticity in tRNAs and tsRNAs and their modulation by RNA modifications and cofactor binding.
(A) Illustration showing how post-transcriptional RNA modifications and tRNA-binding proteins act to regulate RNase-mediated tRNA cleavage and tsRNA formation. (B) Schematic presentation of the conformational free-energy landscapes of tRNAs (left) and tsRNAs (right) and their modulation in response to changes e.g., in modification state or ligand binding. (C) Structural view of predicted tsRNAs derived from a single tRNAAla precursor.
Figure 4.
Figure 4.. Interchangeable structures of tRNAs and tsRNAs depending on the environment and local context.
(A) Illustration showing predicted tsRNAAla secondary structure with differential MFE ranking (using MCfold), or with different temperature settings (using RNAstructure). These predicted structures do not consider the impact of RNA modifications and the changing local context, and therefore need further experimental validations. (B) Illustration showing that heat stress triggers conformational changes in bacterial tRNAAla, increasing the proportion of rod-like shapes while decreasing the prevalence of the classic cloverleaf tRNA structure. These structures are based on experimental data from tRNA structure-seq.
Figure. 5
Figure. 5. Integrating multi-layers of tsRNA information enables diverse tsRNA functionality.
Multi-faceted data obtained from various technologies regarding tsRNA sequence/fragmentation pattern, site-specific RNA modifications, RNA structures, tsRNA binding proteins and subcellular compartmentalization would enable future high-dimensional analyses with the advancement of deep learning algorism, leading to better understand of the fundamental modes of tsRNA action, and thus their biological roles in specific contexts. RNase: ribonuclease; RNPs: ribonucleoprotein particles; EV: extracellular vesicle; Cyto: cytoplasma; Nuc: nucleus.

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