Functions of Bacterial tRNA Modifications: From Ubiquity to Diversity

Abstract

Modified nucleotides in tRNA are critical components of the translation apparatus, but their importance in the process of translational regulation had until recently been greatly overlooked. Two breakthroughs have recently allowed a fuller understanding of the importance of tRNA modifications in bacterial physiology. One is the identification of the full set of tRNA modification genes in model organisms such as Escherichia coli K12. The second is the improvement of available analytical tools to monitor tRNA modification patterns. The role of tRNA modifications varies greatly with the specific modification within a given tRNA and with the organism studied. The absence of these modifications or reductions can lead to cell death or pleiotropic phenotypes or may have no apparent visible effect. By linking translation through their decoding functions to metabolism through their biosynthetic pathways, tRNA modifications are emerging as important components of the bacterial regulatory toolbox.

Keywords: decoding efficiency; post-transcriptional regulation; translation.

Figure 1.. Full set of tRNA modification genes in the model gram-negative E. coli K12 MG1655.

The references for the functional role of every gene can be found in UniProt release 2020_04 (August 12, 2020) [114] using the gene locus tags as input.

Figure 2.. Minimal tRNA modification sets.

(A) Full set of tRNA modifications genes in the minimal gram-positive Mycoplasma capricolum, in red are the ones conserved in Mycoplasma JCVI-Syn3 (see Table S2 for gene list) and are starred (*) the ones considered that are most resistant to gene loss in Molllicute evolution [33]; (B) Predicted tRNA modifications in Ca. Riesia pediculicola and corresponding gene, updated from [41].

Figure 3.. Current pipelines to identify tRNA modifications.

Summary of methods currently used to identify and map tRNA modifications that are tRNA-seq, mass spectrometry (LC-MS/MS or MALDI-based), and comparative genomics (using Hidden Markov Models or HMM to predict the presence/absence of tRNA modification). The major issues with each method are listed in red.

Figure 4.. Poor coverage of bacterial species with sequenced tRNAs.

Organisms in red have available sequence data for tRNAs. It can vary from the full set to just a few The number of known tRNA sequences for each species was transformed into log2 and is represented by a color gradient to the left of each species name. The color gradient range goes from 0 (blue) to 6 (red)., The full data is available in Table S1. The species tree was created in iTol [115] using the list of 120 reference bacteria of the Patric database [116] merged with the organisms that had tRNA sequenced data to give a total of 131 species in the trees.

Figure 5.

Possible regulatory consequences of changes in tRNA modification levels that affect decoding efficiency.