# Are Bacterial Processes Dependent on Global Ribosome Pausing Affected by tRNA Modification Defects?

Valérie de Crécy-Lagard, Zeynep Baharoglu, Yifeng Yuan, Grégory Boël, Jill Babor, Jo Marie Bacusmo, Peter Dedon, Peiying Ho, Katherine Hummels, Daniel Kearns

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Valérie de Crécy-Lagard, Zeynep Baharoglu, Yifeng Yuan, Grégory Boël, Jill Babor, et al.. Are Bacterial Processes Dependent on Global Ribosome Pausing Affected by tRNA Modification Defects?. Journal of Molecular Biology, 2025, 437 (16), pp.169107. 10.1016/j.jmb.2025.169107. hal-05029033

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# Are bacterial processes dependent on global ribosome pausing affected by tRNA modification defects?

Valérie de Crécy-Lagard¹,²*, Zeynep Barahoglu³,⁴*, Yifeng Yuan¹, Grégory Boël⁴*, Jill Babor¹%, Jo Marie Bacusmo¹, Peter C. Dedon⁵,⁶, Peiying Ho⁵, Katherine R. Hummels⁷§, Daniel Kearns⁷

¹Department of Microbiology and Cell Science, University of Florida, Gainesville, Florida 32611, USA

²Genetics Institute, University of Florida, Gainesville, Florida 32611, USA

³Institut Pasteur, Université Paris Cité, Epitranscriptomic and translational responses to anti-bacterial stress, 75015, Paris, France

⁴Expression Génétique Microbienne, CNRS, Université Paris Cité, Institut de Biologie Physico-Chimique, F-75005 Paris, France.

⁵Singapore-MIT Alliance for Research and Technology, Antimicrobial Resistance IRG, Singapore 138602

⁶Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, USA

⁷Department of Biology, Indiana University, Bloomington, Indiana 47405, USA

The sequence of authors was established based on the extent of effort and time dedicated to composing this review.

Present Addresses: %Molecular Biology Program, Sloan-Kettering Institute, New York, NY 10065; §Department of Microbiology, University of Georgia, Athens, Georgia, USA

Valérie de Crécy-Lagard: [vcrecy@ufl.edu](mailto:vcrecy@ufl.edu); Zeynep Barahoglu: [zeynep.baharoglu@pasteur.fr](mailto:zeynep.baharoglu@pasteur.fr); Grégory Boël: [boel@ibpc.fr](mailto:boel@ibpc.fr)

The author's order reflects the contributions to the manuscript in decreasing order

<footnote>*Corresponding authors</footnote>
**Abstract**

By integrating a literature review with transcriptomic, proteomic, and phenotypic data from two model bacteria, *Escherichia coli* and *Vibrio cholerae*, we put forward the hypothesis that defects in tRNA modification broadly impact processes that are evolutionarily tuned to be sensitive to translation speed. These include the translation of regulatory proteins associated with motility, iron homeostasis, and leader peptide-driven attenuation mechanisms. Some of these translation speed-dependent processes are influenced by the absence of a single modification, while others are affected by the absence of multiple modifications. Although further experiments are needed to clarify the mechanisms involved in each case, this work provides a foundational framework to guide future research.

**Keywords**

tRNA modification, leader peptide, iron homeostasis, motility, *Escherichia coli*, *Vibrio cholerae*, translation
Introduction

It is now well established that the pace at which the translation machinery decodes mRNAs to produce proteins is not uniform and greatly affects protein expression levels and folding patterns [1,2]. The rapid expansion of genomic data facilitating global codon usage bias analyses, the development of ribosome profiling techniques to measure in vivo ribosome pausing, and experimental advancements enabling the systematic study of synonymous mutations on protein function have collectively advanced our ability to dissect the diverse forces influencing mRNA translation speed [3–6].

As molecular adaptors linking mRNA codons to their corresponding amino acids during ribosomal elongation, tRNA molecules play a crucial role in regulating translation dynamics [1,2,7]. More than half [12 in the model *Escherichia coli* (Fig. 1)] of the 20 canonical amino acids are encoded by synonymous codons that different tRNAs can decode. The intracellular concentrations of different tRNA pools vary and correspond to the codon usage frequency of their cognate aminoacylated tRNAs [8,9]. Furthermore, highly expressed genes tend to utilize the most frequent codons [9]. Based on this, it has been proposed that the size of aminoacylated-tRNA isoacceptor pools influences ribosome pausing at specific codons by increasing the likelihood that cognate tRNAs are efficiently sampled at the A site, thereby facilitating the next step in elongation. [10,11]. However, recent studies indicate that variations in cognate tRNA concentrations have only a minimal impact on protein expression under physiological conditions [12–15]. These observations suggest that less abundant tRNAs primarily impact translation when their aminoacylated pools become depleted. Additionally, under physiological conditions, they function as a reserve of aminoacylated tRNAs during amino acid starvation [16].

For amino acids encoded by codons read by a single tRNA [8 in *E. coli* (Fig. 1)], codon choice can influence the strength of the codon-anticodon interaction. This is determined by the presence of Watson-Crick base pairs (GC and AU) at the third codon position rather than weaker GU wobble pairs [7,17]. Synonymous codon usage can be subject to selective pressure both between and within genes [2], although some controversy remains on this issue [18]. Several studies have shown that, particularly in organisms with low growth rates, highly expressed genes are enriched in abundant codons [19], while the first 30-50 bases of coding sequences are enriched in rare
codons [20]. The presence of rare codons might allow a ramping up of translation speed [20], but it seems that this effect is mainly attributable to base composition because these codons are enriched in A/U versus G/C [14,21,22] Codon usage in mRNAs encoding membrane proteins appears to be specifically optimized for the coupled translation and secretion process, with alternating regions of slow and fast-translating codons playing a crucial role in ensuring proper protein secretion [2,4].

Beyond its impact on translation dynamics, ribosome pausing has distinct consequences in eukaryotes and bacteria, particularly in relation to mRNA degradation and ribosome quality control mechanisms that are crucial for managing extreme pausing events, such as ribosome stalling and collisions [1,23–25]. Some of these stalling events are caused by combinations of amino acids, which induce conformational stress within the ribosome peptidyl transferase center and the nascent peptide exit tunnel of the ribosome (NPET). The best-characterized case is the polyproline stretches that induce ribosome stalling and prevent the formation of the peptidyl bound. Another example is the repeat of acidic residues (Glutamate and Aspartate) early in the encoding sequence that can provoke pausing of the ribosome that can lead to ribosome stalling or abortive synthesis [26–29]. In both cases specialized factors, EF-P/eIF-5A for polyproline [30–32] and EttA for repeat of acidic residues [29] can resolve the pausing and restore elongation.

Gene expression can be regulated by mechanisms that depend on ribosome stalling. Indeed, because transcription and translation are coupled in some bacteria, sophisticated regulatory mechanisms have evolved to monitor translation stalling using a leader peptide, a small coding sequence that encodes a short peptide prone to ribosome stalling [33] (Fig. 2). When stalling occurs during the synthesis of the leader peptide, it prevents premature transcription termination by inhibiting the formation of a transcriptional terminator (Fig. 2A) or blocking access to the Rho termination site (Fig. 2B), both of which overlap with the ORF of the leader peptide [34]. Ribosomal stalling can be induced by uncharged tRNA, as seen in many amino acid synthesis operons where the leader peptide is enriched with corresponding codons, such as the TrpL or HisL leader peptides [35]. Stalling can also be induced by metabolites that bind between the NPET of the ribosome and the nascent peptide[36]. For example, the regulation of the *tnaAB* operon by the leader peptide TnaC (Fig. 2B) occurs when intracellular concentrations of tryptophan (Trp) increase. A single Trp molecule binds
within the NPET of the ribosome by interacting with the TnaC nascent peptide. This interaction prevents the termination of TnaC peptide translation, leading to ribosome stalling. Ribosome stalling, in turn, prevents Rho-dependent termination of transcription and allows transcription of the *tnaAB* operon [37]. This type of regulation by metabolites is frequently observed in the regulation of antibiotic resistance genes [38]. In some regulatory elements, the leader peptide can directly regulate the translation of downstream genes by using a translational attenuator, which, like a transcriptional terminator, is inactivated by ribosome stalling on the leader peptide, as seen in ErmC, SecM, and VemP [39–41] (Fig. 2C). Interestingly, the stimulus sensed by SecM and VemP is the mechanical force exerted on the leader peptide as it exits the NPET of the ribosome [40,41]. Another regulatory mechanism similar to the leader peptide uses overlapping genes, where the translation of the first gene dictates the expression of the downstream one (Fig. 2D).

By design, leader peptides are hence excellent sensors of translational stress, monitoring translation dynamics within the cell. For example, HisL has been used to study the elongation dynamics of synonymous codons [42], and the SecM sequence has been employed for its ribosome-stalling ability in genetic screens [25]. In eukaryotes, similar regulatory systems use small ORFs, called upstream open reading frames (uORFs), to regulate downstream genes. The regulation mainly occurs through the uORF's ability to titrate ribosomes, which can initiate translation of the downstream ORF [43] (Fig. 2D). This difference in regulation arises from the distinct mechanisms of translation initiation between eukaryotes and prokaryotes.

tRNAs are heavily post-transcriptionally modified molecules with any given tRNA harboring 5 to 10 modifications [44]. Modifications affect many aspects of tRNA functions including recognition by tRNA-synthetase or other components of the translation apparatus and provide quality control mechanisms to ensure correct structure and stability [45,46]. The most complex modifications are found in the anticodon-stem-loop (ASL) involved in decoding [17]. These ASL modifications can expand or restrict the decoding capacity of tRNAs and can play crucial roles in harmonizing the speed of decoding of different synonymous codons [47]. The absence of tRNA modifications can lead to changes in the translation speed of a given codon directly resulting in the misfolding of the translated protein but can also lead to the indirect misfolding of other proteins by overwhelming the protein homeostasis
machinery [48–50]. The final concentrations of translated proteins can also be affected by the absence/presence of modifications even if they are correctly folded, hence the phenotypes of mutations affecting tRNA modifications can be very pleiotropic and difficult to pin down to unique molecular mechanisms [51]. For example, the absence of t⁶A37, a universal modification located just upstream of the anticodon/codon first base pair, can lead to cell death in some organisms [52], high levels of protein aggregation in others [50], and multiple cellular phenotypes including genetic disease in humans [53]. On the other end of the spectrum, the absence of many conserved modifications does not lead to any specific growth defects under standard growth conditions and phenotypes can sometimes be revealed under specific stresses or growth conditions [54]. For example, it was postulated for over 40 years that the absence of Queuosine (Q), a wobble base modification found in tRNAs with GUN anticodons led to no dramatic growth phenotypes in *E. coli* beyond a fitness cost in stationary phase [55] until metal-related growth phenotypes and biofilm defects were reported in the same organism [56,57].

Some organisms use tRNA modifications to regulate the translation of specific genes [51,58]. Codon usage analysis focusing on modification-dependent codons can identify groups of genes whose translation is affected by modification levels [58]. Modification tunable transcripts (MOTTs) involved in regulatory cycles have been identified in both eukaryotes and prokaryotes [51,58]. Bacterial examples are found in the areas of Mg²⁺ homeostasis, oxidative stress resistance[59] or iron homeostasis [60] but as more studies integrating codon usage, ribosome profiling (RiboSeq), and proteomics analysis of tRNA modification deficient mutants are published, other examples of regulatory roles should emerge.

In the work described here, we compared the whole genome expression data generated for ten *E. coli* and three *Vibrio cholerae* mutants as well as proteomics data from four *V. cholerae* mutants to their WT controls. Extracting the commonalities and the differences between these expression profiles led us to propose that the processes that are built to sense translation speed are particularly sensitive to tRNA modification defects.

## Material and methods
## Media, Strains and Plasmids

Luria-Bertani (LB) broth and agar (tryptone 10 g/L, yeast extract 5 g/L, sodium chloride 10 g/L, Fisher Scientific BP1426-2 and BP1425-2) were routinely used for growth of *E. coli* cells at 37°C from frozen stocks. When required, 50 µg/mL of kanamycin (Kan), 100 µg/mL Ampicillin or Arabinose (0.02%) were added to the medium. M9 minimal medium (12.8 g/L Na₂HPO₄·7H₂O, 3.0 g/L KH₂PO₄, 0.5 g/L NaCl, 1.0 g/L NH₄Cl, 2.0 mM MgSO₄, 0.1 mM CaCl₂) was used in the *E. coli* motility assays, with 0.3% agar.

*V. cholerae* cells were grown in LB or Mueller-Hinton (MH) (Sigma, Catalog number 70192) and 0.2 µg/mL of tobramycin (20% of Minimum Inhibitory concentrations (MIC)) was sometimes added. The strains used in this study are listed in Table S1. Some of the *E. coli* strains used here were constructed by transferring the Kan⁴ allele from the Keio collection strains [61] to a fresh background by P1 transduction [62]. The initial Keio strains and the transductants were checked by PCR for the presence of correct mutant allele as described in Bacusmo et al [63]. Deletions of *V. cholerae* tRNA modifications genes were performed as previously described [64]. Details are given in Table S1 and Table S2.

## Motility assay

For *E. coli*, overnight cultures were prepared in LB at 37°C with agitation. The OD(A600nm) was normalized to 10, and 2 µL of cells was plated on the surface of 0.3 % agar plate (M9). Motility diameters were measured for biological triplicates after 22 hours of incubation at 37°C. For *V. cholerae*, overnight cultures were prepared in LB at 37°C with agitation. 5 µl of overnight cultures were deposited in the center of a 0.3 % agar plate (LB). Motility diameters were measured for biological triplicates after 6 to 12 hours incubation at 37°C and compared to WT *V. cholerae* N16961 *hapR+*.

## Data mining

*E. coli* TnSeq data was accessed from [http://fit.genomics.lbl.gov](http://fit.genomics.lbl.gov) [65]. Information on *E. coli* genes was extracted from EcoCyc [66] and the resources of NCBI including Pubmed [67]. Mapping of identifiers was performed at EcoCyc for *E. coli* and BioCyc [68] for *V. cholerae* biovar El Tor str. N16961. Transmembrane helices were predicted by TMHMM2.0 [69].
RNA seq of *E. coli* strains

RNA was isolated from each of the different strains at grown in LB at 37°C up to mid-exponential phase (OD(A600nm) ~0.6) in three biological replicates and were extracted and purified as previously described [70]. Samples were sequenced using an Illumina HiSeq2000 Platform without depletion of rRNA prior to sequencing.

RNA-seq analysis was carried out using the KBase platform transcriptomics and expression analysis tools [71]. Paired-end reads were assembled from corresponding FASTQ files, trimmed of their adaptor sequences using Trimmomatic (v0.36). Quality of the paired-end reads was assessed using FastQC (v0.11.9) (https://www.bioinformatics.babraham.ac.uk/projects/fastqc/) for each sample and analysis confirmed that all samples had uniform sequence quality scores and GC content, ranging from 39.36-39.77% and 51.0-52.0% across all paired-end reads, respectively and deposited in Kbase [72].

The wildtype *E. coli* BW25113 genome assembly (GCA_000750555.1/ASM75055v1) was annotated using RASTtk (v1.073) to give "BW25113_Annotated" as the reference genome for transcriptome analysis. Trimmed reads were aligned to the reference genome using HISAT2 (v2.1.0)[73] and aligned reads were assembled using StringTie (v2.1.5)[74]. Differential expression matrices were created comparing each mutant strain to the reference genome as well as each other using DESeq2 (v1.20.0)[75]. Up- and down-regulated feature sets were then created, filtering for genes with significant differential expression between each mutant and the wildtype reference as well as each mutant versus *dusB*, using a false discovery rate (FDR; q-value) of < 0.05 and an absolute log2 fold change greater than 1 as significance cutoffs. Total differential expression data with no significance filtering was also compiled for quality control and spot-checking. The results of the comparison of each strain vs the WT and the *dusB* mutant are available in Supplementary Data Set 1 and Supplementary Data Set 2 respectively.

RNA seq of *V. cholerae* strains
Overnight cultures of the different *V. vibrio* strains were diluted 1:1000 in mild stress conditions in MH + tobramycin 0.2 µg/ml medium (20% of the MIC) and grown with agitation at 37°C until an OD(A600nm) of 0.3-0.4 (exponential phase). 0.5 mL of these cultures were centrifuged, and the supernatant removed. Pellets were homogenized by resuspension with 1.5 mL of room temperature TRIzol Reagent. Next, 300 µL chloroform were added to the samples following mix by vortexing. Samples were then centrifuged at 4°C for 10 minutes. Upper (aqueous) phase was transferred to a new 2mL tube and mixed with 1 volume of 70% ethanol. From this point, the homogenate was loaded into a RNeasy Mini kit (Qiagen) column and RNA purification proceeded according to the manufacturer's instructions. Samples were then subjected to DNase treatment using TURBO DNA-free Kit (Ambion) according to the manufacturer's instructions. The libraries were constructed using the Illumina® Stranded Total RNA Prep, Ligation with Ribo-Zero Plus.

For Sequencing, quality control was performed on iSeq100 and the production
runs were performed on NextSeq 2000 yielding 5M reads/sample. The RNA-seq
analysis was performed with Sequana 0.16.11 [76]. We used the RNA-seq pipeline
0.19.2 (https://github.com/sequana/sequana rnaseq) built on top of Snakemake
7.32.4 [77] Briefly, reads were trimmed from adapters using Fastp 0.22.0 [78] then
mapped to the Vibrio cholerae N16961 genome assembly (accession numbers:
chromosome 1, NC_002505, chromosome 2, NC_002506 using bowtie2 2.4.5 [79].
FeatureCounts 2.0.1 [80] was used to produce the count matrix. Quality control
statistics were summarized using MultiQC 1.17 [81]. Statistical analysis on the count
matrix was performed to identify differentially regulated genes. The clustering of
transcriptomic profiles was assessed using a Principal Component Analysis (PCA).
Differential expression testing was conducted using DESeq2 library 1.34.0 [75] scripts
indicating the significance (Benjamini-Hochberg adjusted p-values, false discovery
rate FDR < 0.05) and the effect size (fold-change) for each comparison. The results of
comparing each strain vs the WT are available in Supplementary Data Set 3.

**Proteomics analyses**

Overnight cultures of *V. cholerae* were diluted 1:100 in MH medium and grown
under the same conditions as the RNAseq experiment until an OD 600 nm of 0.3
(exponential phase). 50 mL of these cultures were centrifuged for 10 min at 4°C and
supernatant removed. Lysis was achieved by incubating cells in the presence of lysis buffer (10 mM Tris-HCl pH 8, 150 mM NaCl, 1% Triton 100X) supplemented with 0.1 mg/mL lysozyme and complete EDTA-free Protease Inhibitor Cocktail (Roche) for 1 hour on ice. Resuspensions were sonicated 3x50 sec (power: 6, pulser: 90%), centrifuged for 1 h at 4°C at 5000 rpm and supernatants were quantified using Pierce™ BCA Protein Assay Kit (Cat. No 23225) following the manufacturer's instructions. Proteins were then stored at -80°C. Proteomics MS and analysis were performed as described [59]. The results of the comparison of each strain vs the WT are available in Supplementary Data Set 4.

## GO enrichment analysis

Gene Ontology (GO) enrichment was analyzed using R packages `clusterProfiler[82]`. Briefly, the GO-annotation of genes of *E. coli* and *Vibrio cholerae* O1 biovar El Tor str. N16961 were retrieved from the EcoCyc/BioCyc databases (biocyc.org)[66,68] The GO class description was retrieved from the GO Consortium (geneontology.org)[83]. Genes with an absolute log2-fold change greater than 1 and adjusted p-values less than 0.05 were selected for the `clusterProfiler` enricher function. The enriched GO terms with p-values less than 0.05 and Benjamini-Hochberg adjusted p-values less than 0.2 were plotted. Heatmaps were generated using R package `ComplexHeatmap` [84]. The differentially expressed genes (DEGs) with their GO terms in the different *E. coli* tRNA modification mutants compared with the WT and the *dusB* mutant are listed in Supplementary Data Set 5A and Set 5B, respectively. The DEGs with GO terms in the *V. cholerae dusB*, *tgt* and *truA* strains are listed in Supplementary Data Set 5C. The enriched GO terms in each comparison of *E. coli* and *V. cholerae* are listed in Supplementary Data Set 5D.

## Synonymous codon usage and correlation analyses

First, every codon in every *V. cholerae* CDS was counted excluding the start and stop codon (Supplementary Data Set 6A and Set 6B). Synonymous codons were hypothesized to follow a binomial distribution with the number of a synonymous codon in a CDS as the number of successful outcomes and the frequency of the codon within its synonymous codons of all proteins as the probability of success. Then, the lower
and upper bounds of the binomial distribution with 95% confidence interval (CI) for each codon in each CDS were calculated using the BINO.INV function. Finally, whether a codon lies within the 95% confidence interval for a binomial distribution was summarized (Supplementary Data Set 6C). When the hypothesis was rejected, the significance of biased synonymous codon usage was indicated by the difference of the observation of a synonymous codon to the bound of 95% CI normalized ($\frac{\text{observed occurrence of a codon-expected occurrence with 95% CI}}{\text{expected occurrence with 95% CI}}$) (Supplementary Data Set 6C and 9C). The codon usage data was combined with other features for the *V. cholerae* *tgt*, *truA* and *dusB* mutants Supplementary Data Set 7), and Pearson correlation between any two features was calculated using the cor function in R (Supplementary Data Set 8). The codon usage for every *E. coli* CDS was analyzed in similar fashion (Supplementary Data Set 9A-C). The transcription factors (TFs) that contain a helix-turn-helix (HTH) domain in *E. coli* and *V. cholerae* were predicted/retrieved from pantherdb.org[85]. The synonymous codon usage of TFs was listed in Supplementary Data Set 10.

# Results and discussion

## Motility can be increased or decreased by the absence of tRNA modifications in *E. coli* and *V. cholerae*

When conducting a synthetic lethal screen combining deletions in 20 different tRNA modifications genes in *E. coli*, we identified several mutations that when combined gave rise to growth defects (Fig. 2 of [63]). We performed an RNAseq analysis of 9 of these mutants (*dusB*, *truA*, *truD*, *thil*, *miaA*, *rlmN*, *mnmA*, *mnmC*, *mnmG*) as described in the method section. When analyzing the differentially expressed genes in these mutant strains, we realized that the WT BW25113 laboratory collection strain used in the RNAseq study was not isogenic with the Keio collection strains [61] (Table S1). The main difference was the repression of motility genes, such as flagellar synthesis genes, in the laboratory WT strain (Table S3 and Fig. S2 and S3), suggesting that the WT strain used was non-motile. This hypothesis was confirmed in a motility assay (Fig. S1B). This phenomenon had already been reported as resting strains of *E. coli* can accumulate mutations that increase motility overnight and Keio collection strains vary widely in their motility phenotypes [86]. The *dusB* data was therefore used as a
reference for comparisons and we found that the expression of flagella synthesis genes still varied greatly between strains (Fig. 3 and Fig. S4). As these phenotypes could still be due to suppressor mutations, we constructed an isogenic set by transducing all the mutant alleles in a novel WT BW2113 obtained from the Coli Genetic Stock Center (CGSC) (https://ecgrc.net/) that was motile (Fig. S1B). As shown in Fig. 4A and 4B, correlations between the expression data and the mobility phenotypes were observed with some deletions reducing motility (*dusB*, *miaA*, *tgt* and *mnmA*) and others increasing motility (*thil*, *truA*).

Reduced motility phenotypes could also be caused by growth rate defects, but previous analysis had shown that *tgt* mutants grow like WT in most standard conditions [56]. In addition, we had previously conducted a comprehensive proteomics and transcriptomic analysis of *tgt* strain lacking the wobble base modification Q and found a global downregulation of genes involved in flagella synthesis and chemotaxis (see Fig. 3 of [56]). Analysis of *E. coli* data from a Tn-seq fitness study [87] revealed that genes with similar profiles (high cofitness scores) to the *tgt* gene were related to Q biosynthesis as expected but also to motility (Table S3). We therefore tested if the motility phenotype of the *tgt* could be complemented. As shown in Fig. S1A, the *tgt* mutant was slightly less motile than the WT strain. Although subtle, this difference was significant and consistently observed in repeated experiments. This phenotype was complemented upon overexpression of the *tgt* gene, confirming the *tgt* gene absence as the cause of the observed motility phenotype (Fig. S1).

We had previously shown that mutations in different types of tRNA modification genes led to decreased (such as *tgt* or *miaA* deletions) or increased (such as *dusB*, *rlmN* or *truA* deletions) fitness of *V. cholerae* cells grown in the presence of sub-MIC concentrations of tobramycin [64]. This led us to perform RNAseq analyses of the *tgt*, *truA*, *dusB* strains as well as proteomic studies of the *tgt*, *truA*, *dusB* and *rlmN* strains as described in the methods section. Analysis of the *V. cholerae* RNAseq and proteomics data also showed a downregulation of the expression of genes involved in the synthesis of flagella in the *truA* mutant and to a lesser extent in *tgt* strain whereas expression of these genes was not affected in the *dusB* strain (Fig. S5 and Table S4). The changes in flagella gene expression correlated with the reduced motility of the *V. cholerae truA* and *tgt* mutants and the absence of any motility phenotype of the *dusB* strain (Fig. 4C). We then surveyed 19 other tRNA modifications genes deletion strains and found that several others were less motile. The most severely affected were the
*truD*, *mnmE* and *trmJ* mutants (Fig. 4C). If the absence of tRNA modifications led to motility phenotypes in both *E. coli* and *V. cholerae*, the phenotypes hardly overlapped except for the *tgt* mutants that were less motile in both organisms (Fig.4A and C).

Motility is regulated by multiple input signals and regulatory networks, including the master regulator FlhDC, which integrates with metabolic pathways and cell envelope systems [88]. Several mechanisms can be invoked to explain how the translation defects caused by the absence of tRNA modifications might impact motility. Of course, levels of key regulators such as FlhD or FlgM could be affected [89]. Flagellar biosynthesis genes are often organized in operons with translationally coupled genes [89,90] where tRNA modifications may influence translation efficiency, thereby affecting the proper stoichiometry required for flagellar assembly [91,92]. Additionally, the transcription of flagellar genes is tightly regulated by feedback mechanisms that respond to the state of flagellar assembly [88,90].

In *Salmonella enterica* serovar Typhimurium, synonymous codon changes significantly alter the translation of *flgM*, a key regulator of flagellar synthesis [93], suggesting that tRNA modifications may also influence flagellar biogenesis through codon-dependent translation control. Furthermore, the absence of certain tRNA modifications can lead to mistranslation, which has been linked to reduced motility via increased expression of the small RNA DsrA, though the underlying mechanism remains unknown [94].

Further studies will be required to elucidate the specific roles of tRNA modifications in motility. However, this analysis reinforces the observation that motility is highly sensitive to perturbations in the translation machinery. For *V. cholerae*, with the availability of proteomics datasets, we further explored the correlation between protein expression and codon usage, which is analyzed in the final section of this report.

## Genome wide transcriptomic and/or proteomic analyses of 9 *E. coli* and 4 *V. cholerae* tRNA modification deficient strains reveal common cellular responses

In addition to the dysregulation of motility genes observed in the absence of the majority of tRNA modification genes tested both in *E. coli* and *V. cholerae*, several other global changes were observed in several tRNA modification deficient mutants, and a few were consistent between the two species as detailed below.
First, in *E. coli*, a partial clustering of the genome-wide expression profiles of strains lacking modifications of the ASL from those lacking modifications in the bodies of tRNAs was observed as the *miaA*, *truA*, *rlmN* and *mnmG* formed a clear cluster (Figs. S6 and S7). Second, in addition to genes involved in motility, other physiological areas were enriched in both the *E. coli* and *V. cholerae* tRNA modification mutants analyzed (Fig. 3, Fig. S4 and Fig. 5). Two processes were constitutively enriched: amino acid (particularly histidine) metabolism, and iron homeostasis. Investigating the potential molecular mechanisms underlying these similar responses across different bacterial species revealed a unifying theme. These were process processes that are regulated by ribosome pausing or translation speed, as elaborated in the following sections.

**'Ribosome-mediated' transcription attenuation systems are affected by the absence of modifications**

A historical example of the importance of tRNA modifications in ribosome-mediated transcription attenuation systems is the identification by Bruce Ames of *truA* [95]. The corresponding enzyme inserts Ψ at position 38-40 in the ASL of around half the bacterial tRNAs including those decoding His codons as *hisT* [95]. Mutations in *hisT/truA* increase the expression of the *his* operon genes. Like fully modified uncharged tRNAs, charged tRNAs lacking Ψ38-40 increase ribosome pausing at His codons when translating the *hisL* leader peptide mRNA, not allowing the attenuator structure to form (as shown Fig. 2B), thus allowing the transcription of the downstream *his* operon genes. RNAseq analysis of a *truA* mutant reproduces in *E. coli* K12 the initial observation made in S. Typhimurium LT2, with a log2 fold change >3 of the *his* synthesis genes occurring in the mutant (Fig. 6A and Fig. S4). A similar activation was observed with RNA-seq and proteomic analyses in *V. cholerae* (Fig. 6C and Fig. 5B). Two other modifications are present in the tRNA<sup>His</sup>GUG ASL, m<sup>2</sup>A and Queuosine (Q) (Fig. 1). In *E. coli* ([56] and Fig.6A) and partly in *V. cholerae* (Fig. 6C), the absence of Q (*tgt* mutants) and m<sup>2</sup>A (*rlmN* mutants) also leads to the activation of *his* operon genes but less than in the *truA* strain.

The Ames group also showed that mutations in *hisT/truA* increased the expression of the *ilv* and *leu* operons in S. Typhimurium, two other systems regulated
by ribosome-mediated transcription attenuation [96]. We reproduced these findings in *E. coli* and *V. cholerae* for the *leu* operon (Fig. 6B and 6D). In *E. coli*, expression of the *ile* biosynthesis genes is unexpectedly down in the *truA* strain and most other mutants (Fig. S8A), and no trend was seen in *V. cholerae* (Fig. S8B). These variations might reflect previously reported differences between species and/or tRNA sequences on how Ψ38-40 deficiency affects reading efficiency, reading fidelity, and frameshifting [97,98]. In addition, the organization of the *ile* biosynthesis genes in *E. coli* is very complex with multiple regulators[99]. In addition, the IlvL peptide sequence contains a di-proline (PP) that might influence the role of modifications in complex ways because of the increased stalling that requires the specialized EF-P factor [31].

Other amino acid biosynthesis genes/operons are known to be controlled by
'ribosome-mediated' transcription attenuation through leader peptides [34](Table 1).
Several of these, including those under the control of *pheM*, *thrL* and *trpL*, are activated
by modification defects (Table 1) as expected if these promote ribosome pausing. For
example, we confirmed in *E. coli* the upregulation of the *trp* and *phe* genes in the
absence ms²i⁶A (*miaA* mutant) previously reported in *Salmonella* [100]. Like in *E. coli*,
two TrpE leader sequences (VC1175 and VC1176, Table 1) are found in front of the
*V. cholerae trp* operon, but unexpectedly, Trp proteins (but not transcripts) are
downregulated in *V. cholerae truA* and *rlmN* mutants (Supplementary Data Set 4). To
a lesser extent, ThrB also appears to be down in *V. cholerae truA* the proteomic dataset
(Supplementary Data Set 4). Increased ribosomal frameshifting has been observed In
the absence of *truA* at leucine CUN codons [101]. When analyzing CUN/UUN ratios in
*V. cholerae*, an increased usage of CUN codons is found in *thrB* and *trp* transcripts [in
particular *trpA*, *trpB*, *trpE*, *trpD* and *trpG* (Table S5)]. This codon bias could explain the
lower levels of proteins detected for these transcripts in the *truA* deletion mutant.

An elegant example of a homeostatic response that includes a tRNA
modification is the regulation of the Mg²+ transporter encoded by *mgtA*. In this case,
transcription attenuation is mediated by leader peptide (MgtL) sensing ribosome
pausing at Pro codons that can be affected by the amount of m¹G37 in the tRNAs
decoding Pro codons inserted by the TrmD enzyme that is dependent on Mg²+ [102].

Some of the major transcriptomic changes resulting from tRNA modification
defects are not aligned with the expected outcomes, as illustrated by the *ilv* operon
example. These discrepancies may stem from indirect effects, as discussed in the
conclusion section, but more broadly, they highlight the extensive unknowns regarding
the fine-tuning of elongation speed [42,93]. For example, the poly-phenylalanine
stretch in the PyrL leader peptide (encoded by TTTTTCTTT) is not regulated by
ribosome-mediated transcription attenuation but by an RNA polymerase-dependent
process dependent on a polyT track [103]. Hence, the PyrL regulated does not seem
to be affected by the absence of MiaA (Table 1). However, the same stretch of Ts in
PheM leader peptide seems to be affected by the absence of the corresponding
modification (Table 1). In a seminal study using the HisL peptide linked to a reporter
lacZ gene that was used as a "speedometer", Chevance and Hughes showed that
ribosome pausing at a given codon was greatly influenced by the neighboring codons
proposing a triplet of triplet code [42,93]. This could explain how these same codons
do not always elicit the same responses.

The TnaC leader peptide regulatory system that responds to ribosome pausing is sensitive to many tRNA modification defects

The pioneering work of the Yanofsky group dissected in the 1990's the complex
regulatory mechanisms of the tryptophanase (*tna*) operon of *E. coli* [104]. Not only is
the expression of the *tnaABC* operon repressed by catabolite repression but it is
induced by tryptophan-dependent transcription antitermination. Induction requires
translation of the leader peptide (TnaC) that acts on the translating ribosome to stall in
the presence of high tryptophan levels [37]. A similar regulation presumably also
occurs in *V. cholerae* since the TnaC leader is also found in this species (Table 1). The
absence of *i*⁶*A* in the *miaA* mutant had been shown to repress the expression of
*tnaABC* already by Yanofsky [105]. We reproduced this finding but also showed that
the absence of many other modifications, including mnm⁵s²U34 (*mnmAGC* mutants)
and m²A37 (*rlmN* mutants), led to a strong repression of the *tnaABC* operon in *E. coli*
(Fig. 7A and 7B). In *V. cholera* deleting *tgt* led to repression of the *tna* operon (Fig. 7C)
but this was not seen in *E. coli* [56]. In summary, this data suggests that, at least in *E.
coli*, the TnaC regulatory system is very sensitive to the absence of tRNA
modifications.
Iron homeostasis is sensitive to tRNA modification defects

Over 15 years ago, Bläsi and colleagues showed that the translation of *fur*, the master regulator of iron homeostasis, was coupled to the translation of an upstream peptide (Uof, Upstream of fur) in a region that also binds the regulatory RNA RyhB [106] (Fig. 2D). They reported that the translation of a Ser codon in Uof was sensitive to the presence of the ms²i⁶A37 modification in the decoding tRNA inserted by MiaB, an iron-sulfur cluster enzyme. This led to a model for *fur* regulation partly dependent on the translation speed of *uof* (Fig. 2D). The same study proposed that other iron-dependent proteins harbor ms²i⁶A37-dependent codons at the beginning of the coding sequence. Consistent with this model, it was reported that the levels of the enterobactin siderophore are increased twofold in a *miaA* mutant [107]. We did observe that genes repressed by Fur, the master regulation of iron homeostasis (compiled in [108]), were upregulated in the *miaA*, *rlmN* and *thiI* backgrounds (Fig. 8 and Fig. S4). The molecular mechanisms underlying this regulation are not yet understood. However, it is tempting to propose that these could be caused by inefficient translation or pausing of the *uof* coding sequence, which leads to lower Fur levels. That said the same genes were downregulated in the *tgt* mutant [56] as well as the *mnmA* and *dusB* strains (Fig. 8), indicating that multiple mechanisms must be at play.

In *V. cholerae*, the Fur regulon has been characterized by Chip-seq [109]. We found that genes in the *V. cholerae* Fur regulon are downregulated in the *truA* and *tgt* deletion mutants (Fig. S9), particularly the vibriobactin/enterobactin genes (Fig. S9). No *uof* homolog is found upstream of *V. cholerae fur*, suggesting a different regulation mechanism from *E. coli*. One possibility involves reactive oxygen species (ROS) formation. In *V. cholerae*, proteomics data show that OxyR expression is induced in *Δtgt*, indicating the presence of ROS [59]. The link between TGT and oxidative stress has also been observed in other species [56, 110, 111]. ROS can damage iron-sulfur clusters within proteins, leading to the release of free iron inside the cell. Fur expression and activity are regulated by iron. When intracellular levels of iron increase, Fur repressor is activated, which is consistent with decreased expression of iron transporters in *Δtgt*.

Is the expression of membrane proteins sensitive to tRNA modification defects?
It has been proposed that the positions of slow or fast codons along the coding sequence could be important for the localization of membrane proteins [2]. Hence, the absence of tRNA modifications could lead to defects in membrane protein folding and localization by affecting translation speed. Indeed, a study from the Hou laboratory showed that levels of m¹G37 affect the expression of efflux pumps such as TolC due to the slow decoding of proline codons located at the beginning of the coding sequences [112]. Transcriptomic analyses are not adapted to monitor the integrity of membrane proteins, and proteomic analyses can be challenging because membrane proteins are often insoluble and, thus, not detected by Mass-Spectrometry. With this caveat in mind, we analyzed the available proteomic datasets of *V. cholerae* strains deficient in two ASL modifications (Q34, Ψ38-40) and one tRNA body modification (D20) to evaluate if membrane proteins are enriched in the down-regulated set. First, protein and transcript differential expression levels, fractions of transmembrane domains, amino acid, and codon usage frequencies were gathered for every CDS in the *V. cholerae* genome (Supplementary Data Set 7), and the correlations between all these features were analyzed (Supplementary Data Set 8). No correlation was observed between transmembrane domain fraction and protein expression but as expected, the fraction of transmembrane helices slightly correlated with the frequency of specific amino acids (Supplementary Data Set 8A), as more hydrophobic and less charged amino acids are enriched in transmembrane helices [113]. However, synonymous codons showed similar correlation coefficients: for example, F1 (UUC) and F2 (UUU) are positively correlated with the proportion of transmembrane helices with coefficients of 0.31 and 0.39 respectively (Supplementary Data Set 8A). Hence, in this analysis, differences in synonymous codon usage did not affect the translation of transmembrane helices.

## Analyses of codon usage of motility genes in tRNA modification mutants of *V. cholerae*

To further investigate the mechanism how tRNA modifications affect the motility of *V. cholerae* (Fig. 4C), we calculated the correlation coefficient between the proteome and other features in three mutants, Δ*tgt*, Δ*truA* and Δ*dusB* that showed severe, moderate, and no motility phenotype for motility related genes (Table S4). We found
that translation of two flagellar proteins, FlgP and FlgT, was significantly
downregulated in the *tgt* mutant (at least 3-fold less and adjusted p-value <0.05). The
levels of nine motility-related proteins were reduced by 2 to 4 folds in the *truA* mutant.
None of those genes were downregulated at the transcriptional level (Table S4). To
investigate whether the usage of NAU and NAC codons played a role in translation in
the *tgt* mutant, we compared the distribution of NAU and NAC in each protein to that
of the genome-wide NAU/NAC ratios. We did find that FlgT, which decreased in levels
in the *tgt* and *truA* mutant, showed a weak bias towards CAC over CAU (Table S4).
This could be explained by the slower decoding speed of NAC compared to NAU with
the loss of Q [59].

**Conclusions and perspectives**

The field of tRNA modification has witnessed a renaissance in the last 10 years
fueled by technical advances that have improved their detection [114] and by the
characterization of the full set of tRNA modification genes in a few model
organisms[44,51]. Many new researchers are being drawn into the field because of the
wide array of phenotypes caused by the absence of tRNA modifications with some that
can have great consequences for human health [115,116].

In this work, we emphasize that some of the common phenotypes observed in
the absence of many different tRNA modifications are related to processes that are
regulated by ribosome pausing such as regulation by leader peptides of amino acid
biosynthesis genes (Fig. 2). In view of the known roles of tRNA modifications in
affecting translation speed, this observation was not unexpected but had yet to be
explicitly formulated. This proposed hypothesis can now be specifically tested using
ribosome profiling even if it is still difficult to obtain single nucleotide resolution of
ribosome pausing events in bacteria compared to eukaryotes [59,117].

Another difficulty in this field lies in interpreting the consequence of tRNA
modifications in "multi-omics" data sets (transcriptomic, proteomic, metabolomic and
more) because of compounding of direct and indirect effects. Indeed, tRNA
modifications act as determinants or anti-determinants for many players of the
translation machinery, including other tRNA modification enzymes and aminoacyl-
tRNA synthetases[118,119]. For example, the upregulation of *his* operon genes in the
*truA* deletion mutants of both *E. coli* and *V. cholerae* (Fig. 6) is due to ribosome pausing
in the translation of the HisL leader peptide that triggers the antitermination and
subsequent transcription of the downstream *his* genes (Fig. 2B). However, it is not clear why defects in tRNA modifications that do not modify tRNA<sup>His</sup> such as ms<sup>2</sup>i<sup>6</sup>A37 in the *miaA* strain or XM<sup>5</sup>S<sup>2</sup>U in the *mnmG* strain repressed the same *his* operon in *E. coli* (Fig. 6A). Were histidine pool levels higher in these mutants? Was there a global stress response that triggers a downregulation of amino acids synthesis? [56,59]. In summary, dissecting the molecular consequences of tRNA modification defects can take many years and requires combining different types of omics data and some serendipity as some phenotypes are revealed only in specific species or growth conditions as we recently showed when comparing the phenotypes of Queuosine deficient mutants in *E. coli* and *V. cholerae* [56,59]. That said, with the recent advancements in nanopore RNA modification detection methodologies and in light of the recent report from the US National Academies of Sciences [120], which underscores the importance of establishing "concerted efforts to deepen understanding of RNA modifications and their role in living systems", research into dissecting the multiple and often subtle roles of tRNA modifications in bacterial physiology and stress adaptation is likely to increase in the coming years.

**Supplementary material**

**Data Set 1.** The differential expression analysis of *E. coli dusB*, *truA*, *truD*, *thil*, *miaA*, *rlmN mnmA*, *mnmC*, *mnmG* strain vs the WT.

**Data Set 2.** The differential expression analysis of *E. coli truA*, *truD*, *thil*, *miaA*, *rlmN mnmA*, *mnmC*, *mnmG* mutants and WT vs the *dusB* mutant.

**Data Set 3.** The differential expression analysis of the *V. cholerae tgt*, *truA* and *dusB* strains vs the WT.

**Data Set 4.** Proteomics analyses of *V. cholerae* WT, *tgt*, *truA*, *rlmN* with sub-MIC tobramycin (TOB) treatment or no treatment.

**Data Set 5.** GO enrichment of RNAseq data. (A) *E. coli dusB*, *truA*, *truD*, *thil*, *miaA*, *rlmN mnmA*, *mnmC*, *mnmG* mutants vs WT; (B) *E. coli truA*, *truD*, *thil*, *miaA*, *rlmN mnmA*, *mnmC*, *mnmG* mutants vs *dusB*; (C) *V. cholerae tgt*, *truA* and *dusB* mutants.

**Data Set 6.** Codon usage data for every coding sequence of *V. cholerae*.
**Data Set 7.** Proteome, transcriptome, transmembrane domain %, and codon usage for each protein in *tgt*, *dusB* and *truA* *Vibrio cholerae* mutants.

**Data Set 8.** Correlations between the codon frequency, the fraction of transmembrane helices and the proteome expression data.

**Data Set 9.** Codon usage data for every coding sequence of *E. coli*.

**Data Set 10.** Codon usage for transcription factors in *E. coli* and *V. cholerae*.

## Supplemental tables

**Table S1.** Strains used in this study.

**Table S2.** Oligonucleotides used for *V. cholerae* strain constructions.

**Table S3.** Top cofit genes for *tgt* gene (b0406) from *Escherichia coli* BW25113.

**Table S4.** Differential Expression (RNASeq and proteomics) data of *V. cholerae* mobility genes in *dusB*, *tgt* and *truA* mutants and their corresponding usage of NAC and NAU codons.

**Table S5.** Codon usage data for *V. cholerae trp* operon genes.

**Table S6.** Sequenced of the leader peptides listed in Table 1.

**Table S7.** Q-related synonymous codons with significant codon usage biases.

## Data statement

All mass spectrometry proteomics data have been deposited at the ProteomeXchange Consortium via the PRIDE partner repository with the dataset identifiers PXD035297 and PXD057061. The raw reads of *E. coli* RNA-seq are available at https://doi.org/10.2598/2/197146.10/2475943. For the RNAseq of *V. cholerae*, the accession numbers are GSE275883 for the *dusB*, *truA* mutant datasets and GSE214520 for the *tgt* mutant datasets. The scripts for GO analysis and heatmap plot and GO annotation files are available at https://github.com/vdclab/published scripts/tree/main/tRNAmod_speed.
## Author contributions

V.d.C.-L., Conceptualization, Methodology, Data curation, Supervision, Writing – original draft, Writing – review and editing, Funding acquisition; Z.B., Methodology, Data curation, Supervision, Writing – original draft, Writing – review and editing, Funding acquisition; Y.Y., Formal analysis, Software, Data curation, Visualization, Writing – review and editing; G.B., Conceptualization, Writing – original draft, Writing – review and editing; J.B., Methodology, Formal analysis, Data curation, Investigation; J.M.B., Formal analysis, Data curation, Investigation; P.C.D, Supervision, Writing – review and editing; P.H.: Data analysis, Investigation; K.R.H., Investigation. Visualization; D.K., Supervision, Funding acquisition.

## Acknowledgements and Funding source

This work was funded by the National Institute of General Medical Sciences (NIGMS) (grant GM70641 de VdC-L and R35 GM131783 to DK), by the National Science Foundation (NSF) (grant MCB1412379 to VdC-L, and Graduate Research Fellowship 1342962 to KRH), by National Research Foundation of Singapore through the SMART Antimicrobial Resistance IRG (Grant to PCD). Funding to ZB was also provided by the Institut Pasteur, the Centre National de la Recherche Scientifique (CNRS-UMR 3525), ANR ModRNAntibio (grant ANR-21-CE35-0012) and by the Fondation pour la Recherche Médicale (grant FRM EQU202103012569). GB was supported by the CNRS, Université Paris Cité, and ANR grants ANR-11-LABX-0011, EZOtrad/ANR-14-ACHN-0027 and ABCF/ANR-18-CE35-0010.

For V. cholerae RNAseq data, we thank Elodie Turc, Laure Lemée and Etienne Kornobis from Biomics Platform, C2RT, Institut Pasteur, Paris, France, supported by France Génomique (ANR-10-INBS-09) and IBISA. For proteomics data, we thank Mariette Matondo, Quentin Giai-Gianetto and Magali Duchateau from the UtechS MSBio proteomics platform.

## Declaration of generative AI and AI-assisted technologies in the writing process.
References

[1] E. Samatova, J. Daberger, M. Liutkute, M. V. Rodnina, Translational Control by Ribosome Pausing in Bacteria: How a Non-uniform Pace of Translation Affects Protein Production and Folding, Front Microbiol 11 (2021). https://doi.org/10.3389/fmicb.2020.619430.

[2] M.J. Moss, L.M. Chamness, P.L. Clark, The Effects of Codon Usage on Protein Structure and Folding, Annu Rev Biophys 53 (2024) 87–108. https://doi.org/10.1146/annurev-biophys-030722-020555.

[3] M. Davyt, N. Bharti, Z. Ignatova, Effect of mRNA/tRNA mutations on translation speed: Implications for human diseases, J Biol Chem 299 (2023) 105089. https://doi.org/10.1016/j.jbc.2023.105089.

[4] P. Oelschlaeger, Molecular Mechanisms and the Significance of Synonymous Mutations, Biomolecules 14 (2024) 132. https://doi.org/10.3390/biom14010132.

[5] I.M. Walsh, M.A. Bowman, I.F. Soto Santarriaga, A. Rodriguez, P.L. Clark, Synonymous codon substitutions perturb cotranslational protein folding in vivo and impair cell fitness, Proc Natl Acad Sci U S A 117 (2020) 3528–3534. https://doi.org/10.1073/pnas.1907126117.

[6] K. Chyżyńska, K. Labun, C. Jones, S.N. Grellscheid, E. Valen, Deep conservation of ribosome stall sites across RNA processing genes, NAR Genom Bioinform 3 (2021) lqab038. https://doi.org/10.1093/nargab/lqab038.

[7] W. Ran, P.G. Higgs, The influence of anticodon-codon interactions and modified bases on codon usage bias in bacteria, Mol Biol Evol 27 (2010) 2129–2140. https://doi.org/10.1093/molbev/msq102.

[8] H. Dong, L. Nilsson, C.G. Kurland, Co-variation of tRNA abundance and codon usage in *Escherichia coli* at different growth rates, J Mol Biol 260 (1996) 649–663. https://doi.org/http://dx.doi.org/10.1006/jmbi.1996.0428.

[9] T. Ikemura, Correlation between the abundance of *Escherichia coli* transfer RNAs and the occurrence of the respective codons in its protein genes: a
proposal for a synonymous codon choice that is optimal for the *E. coli*
translational system, J Mol Biol 151 (1981) 389-409.

[10] M. V. Rodnina, The ribosome in action: Tuning of translational efficiency and protein folding, Protein Science 25 (2016) 1390–1406.
https://doi.org/10.1002/pro.2950.

[11] H. Gingold, Y. Pilpel, Determinants of translation efficiency and accuracy, Mol Syst Biol 7 (2011) 481. https://doi.org/10.1038/msb.2011.14.

[12] G.-W. Li, E. Oh, J.S. Weissman, The anti-Shine-Dalgarno sequence drives translational pausing and codon choice in bacteria, Nature 484 (2012) 538–541.
https://doi.org/http://www.nature.com/nature/journal/v484/n7395/abs/nature10965.html#supplementary-information.

[13] C. Pop, S. Rouskin, N.T. Ingolia, L. Han, E.M. Phizicky, J.S. Weissman, D. Koller, Causal signals between codon bias, mRNA structure, and the efficiency of translation and elongation, Mol Syst Biol 10 (2014) 770.
https://doi.org/10.15252/msb.20145524.

[14] G. Boël, R. Letso, H. Neely, W.N. Price, K.-H. Wong, M. Su, J.D. Luff, M. Valecha, J.K. Everett, T.B. Acton, R. Xiao, G.T. Montelione, D.P. Aalberts, J.F. Hunt, Codon influence on protein expression in *E. coli* correlates with mRNA levels, Nature 529 (2016) 358–363. https://doi.org/10.1038/nature16509.

[15] S. Höllerer, M. Jeschek, Ultradeep characterisation of translational sequence determinants refutes rare-codon hypothesis and unveils quadruplet base pairing of initiator tRNA and transcript, Nucleic Acids Res 51 (2023) 2377–2396.
https://doi.org/10.1093/nar/gkad040.

[16] J. Elf, D. Nilsson, T. Tenson, M. Ehrenberg, Selective charging of tRNA isoacceptors explains patterns of codon usage, Science (1979) 300 (2003) 1718–1722. https://doi.org/10.1126/science.1083811.

[17] H. Grosjean, E. Westhof, An integrated, structure- and energy-based view of the genetic code, Nucleic Acids Res 44 (2016) 8020–8040.
http://dx.doi.org/10.1093/nar/gkw608.

[18] L. Kruglyak, A. Beyer, J.S. Bloom, J. Grossbach, T.D. Lieberman, C.P. Mancuso, M.S. Rich, G. Sherlock, C.D. Kaplan, Insufficient evidence for non-neutrality of synonymous mutations, Nature 616 (2023) E8–E9.
https://doi.org/10.1038/s41586-023-05865-4.
[19] P.M. Sharp, L.R. Emery, K. Zeng, Forces that influence the evolution of codon bias, Philos Trans R Soc Lond B Biol Sci 365 (2010) 1203-1212.
https://doi.org/10.1098/rstb.2009.0305.

[20] T. Tuller, A. Carmi, K. Vestsigian, S. Navon, Y. Dorfan, J. Zaborske, T. Pan, O. Dahan, I. Furman, Y. Pilpel, An evolutionarily conserved mechanism for controlling the efficiency of protein translation, Cell 141 (2010) 344-354.
https://doi.org/10.1016/j.cell.2010.03.031.

[21] D.B. Goodman, G.M. Church, S. Kosuri, Causes and effects of N-terminal codon bias in bacterial genes, Science (1979) (2013) 475-79.
https://doi.org/10.1126/science.1241934.

[22] K. Bentele, P. Saffert, R. Rauscher, Z. Ignatova, N. Blüthgen, Efficient translation initiation dictates codon usage at gene start, Mol Syst Biol 9 (2013) 675.
https://doi.org/10.1038/msb.2013.32.

[23] T. Inada, R. Beckmann, Mechanisms of Translation-coupled Quality Control, J Mol Biol 436 (2024) 168496.
https://doi.org/10.1016/j.jmb.2024.168496.

[24] M.A. Collart, B. Weiss, Ribosome pausing, a dangerous necessity for co-translational events, Nucleic Acids Res 48 (2020) 1043-1055.
https://doi.org/10.1093/nar/gkz763.

[25] K. Saito, H. Kratzat, A. Campbell, R. Buschauer, A.M. Burroughs, O. Berninghausen, L. Aravind, R. Green, R. Beckmann, A.R. Buskirk, Ribosome collisions induce mRNA cleavage and ribosome rescue in bacteria, Nature 603 (2022) 503-508.
https://doi.org/10.1038/s41586-022-04416-7.

[26] Y. Chadani, N. Sugata, T. Niwa, Y. Ito, S. Iwasaki, H. Taguchi, Nascent polypeptide within the exit tunnel stabilizes the ribosome to counteract risky translation, EMBO J 40 (2021) e108299.
https://doi.org/10.15252/embj.2021108299.

[27] Y. Chadani, T. Niwa, T. Izumi, N. Sugata, A. Nagao, T. Suzuki, S. Chiba, K. Ito, H. Taguchi, Intrinsic ribosome destabilization underlies translation and provides an organism with a strategy of environmental sensing, Mol Cell 68 (2017) 528-539 e5.
https://doi.org/10.1016/j.molcel.2017.10.020.

[28] Y. Chadani, S. Yamanouchi, E. Uemura, K. Yamasaki, T. Niwa, T. Ikeda, M. Kurihara, W. Iwasaki, H. Taguchi, The ABCF proteins in *Escherichia coli* individually cope with 'hard-to-translate' nascent peptide sequences, Nucleic Acids Res 52 (2024) 5825-5840.
https://doi.org/10.1093/nar/gkae309.
[29] F. Ousalem, S. Ngo, T. Oiffer, A. Omairi-Nasser, M. Hamon, L. Monlezun, G. Boël, Global regulation via modulation of ribosome pausing by the ABC-F protein EttA, Nat Commun 15 (2024) 6314. https://doi.org/10.1038/s41467-024-50627-z.

[30] E. Gutierrez, B.S. Shin, C.J. Woolstenhulme, J.R. Kim, P. Saini, A.R. Buskirk, T.E. Dever, eIF5A promotes translation of polyproline motifs, Mol Cell 51 (2013) 35-45. https://doi.org/10.1016/j.molcel.2013.04.021.

[31] L.K. Doerfel, I. Wohlgemuth, C. Kothe, F. Peske, H. Urlaub, M. V Rodnina, EF-P is essential for rapid synthesis of proteins containing consecutive proline residues, Science (1979) 339 (2013) 85-88. https://doi.org/10.1126/science.1229017.

[32] S. Ude, J. Lassak, A.L. Starosta, T. Kraxenberger, D.N. Wilson, K. Jung, Translation elongation factor EF-P alleviates ribosome stalling at polyproline stretches, Science (1979) 339 (2013) 82-85. https://doi.org/10.1126/science.1228985.

[33] P.S. Lovett, E.J. Rogers, Ribosome regulation by the nascent peptide, Microbiol Rev 60 (1996) 366-385. https://doi.org/10.1128/mr.60.2.366-385.1996.

[34] C.L. Turnbough Jr., Regulation of bacterial gene expression by transcription attenuation, Microbiol Mol Biol Rev 83 (2019). https://doi.org/10.1128/mmbr.00019-19.

[35] B.N. Ames, T.H. Tsang, M. Buck, M.F. Christman, The leader mRNA of the histidine attenuator region resembles tRNHis: possible general regulatory implications, Proc Natl Acad Sci U S A 80 (1983) 5240-5242. https://doi.org/10.1073/pnas.80.17.5240.

[36] A. Bhattacharya, T.T. Renault, C.A. Innis, The ribosome as a small-molecule sensor, Curr Opin Microbiol 77 (2024) 102418. https://doi.org/10.1016/j.mib.2023.102418.

[37] A.-X. van der Stel, E.R. Gordon, A. Sengupta, A.K. Martínez, D. Klepacki, T.N. Perry, A. Herrero del Valle, N. Vázquez-Laslop, M.S. Sachs, L.R. Cruz-Vera, C.A. Innis, Structural basis for the tryptophan sensitivity of TnaC-mediated ribosome stalling, Nat Commun 12 (2021) 5340. https://doi.org/10.1038/s41467-021-25663-8.
[38] C.R. Fostier, L. Monlezun, F. Ousalem, S. Singh, J.F. Hunt, G. Boël, ABC-F translation factors: from antibiotic resistance to immune response, FEBS Lett 595 (2021) 675-706. https://doi.org/10.1002/1873-3468.13984.

[39] S. Arenz, H. Ramu, P. Gupta, O. Berninghausen, R. Beckmann, N. Vázquez-Laslop, A.S. Mankin, D.N. Wilson, Molecular basis for erythromycin-dependent ribosome stalling during translation of the ErmBL leader peptide, Nat Commun 5 (2014) 3501. https://doi.org/10.1038/ncomms4501.

[40] T. Su, J. Cheng, D. Sohmen, R. Hedman, O. Berninghausen, G. von Heijne, D.N. Wilson, R. Beckmann, The force-sensing peptide VemP employs extreme compaction and secondary structure formation to induce ribosomal stalling, Elife 6 (2017) e25642. https://doi.org/10.7554/eLife.25642.

[41] F. Gersteuer, M. Morici, S. Gabrielli, K. Fujiwara, H.A. Safdari, H. Paternoga, L. V Bock, S. Chiba, D.N. Wilson, The SecM arrest peptide traps a pre-peptide bond formation state of the ribosome, Nat Commun 15 (2024) 2431. https://doi.org/10.1038/s41467-024-46762-2.

[42] F.F. V. Chevance, S. Le Guyon, K.T. Hughes, The Effects of Codon Context on In Vivo Translation Speed, PLoS Genet 10 (2014) e1004392. https://doi.org/10.1371/journal.pgen.1004392.

[43] T.E. Dever, I.P. Ivanov, M.S. Sachs, Conserved Upstream Open Reading Frame Nascent Peptides That Control Translation, Annu Rev Genet 54 (2020) 237-264. https://doi.org/10.1146/annurev-genet-112618-043822.

[44] B. El Yacoubi, M. Bailly, V. de Crécy-Lagard, Biosynthesis and function of posttranscriptional modifications of Transfer RNAs, Annu Rev Genet 46 (2012) 69-95. https://doi.org/10.1146/annurev-genet-110711-155641.

[45] S.K. Schultz, U. Kothe, RNA modifying enzymes shape tRNA biogenesis and function, Journal of Biological Chemistry 300 (2024) 107488. https://doi.org/10.1016/j.jbc.2024.107488.

[46] T. Suzuki, The expanding world of tRNA modifications and their disease relevance, Nat Rev Mol Cell Biol 22 (2021) 375-392. https://doi.org/10.1038/s41580-021-00342-0.

[47] H. Grosjean, V. de Crécy-Lagard, C. Marck, Deciphering synonymous codons in the three domains of life: Co-evolution with specific tRNA modification enzymes, FEBS Lett 584 (2010) 252. http://www.sciencedirect.com/science/article/B6T36-4XRCRVX-6/2/a050f4da43ffb942c1f3f6f3df3451ac.
[48] D.D. Nedialkova, S.A. Leidel, Optimization of codon translation rates via tRNA modifications maintains proteome integrity, Cell 161 (2015) 1606–18. https://doi.org/10.1016/j.cell.2015.05.022.

[49] F. Tuorto, C. Legrand, C. Cirzi, G. Federico, R. Liebers, M. Müller, A.E. Ehrenhofer-Murray, G. Dittmar, H. Gröne, F. Lyko, Queuosine-modified tRNAs confer nutritional control of protein translation, EMBO J 37 (2018) e99777. http://emboj embopress.org/content/early/2018/08/09/embj.201899777 abstrac.

[50] L. Pollo-Oliveira, R. Klassen, N. Davis, A. Ciftci, J.M. Bacusmo, M. Anderson, M.S. DeMott, P.C. Dedon, R. Schaffrath, V. Crécy-Lagard, Loss of Elongator- and KEOPS-dependent tRNA modifications leads to severe growth phenotypes and protein aggregation in yeast., Biomolecules 10 (2020) 322. DOI: 10.3390/biom10020322

[51] V. de Crécy-Lagard, M. Jaroch, Functions of bacterial tRNA modifications: from ubiquity to diversity, Trends Microbiol 29 (2021) 41–53. https://doi.org/10.1016/j.tim.2020.06.010.

[52] P.C. Thiaville, B. El Yacoubi, C. Köhrer, J.J. Thiaville, C. Deutsch, D. Iwata-Reuyl, J.M. Bacusmo, J. Armengaud, Y. Bessho, C. Wetzel, X. Cao, P.A. Limbach, U.L. Rajbhandary, V. de Crécy-Lagard, Essentiality of threonylcarbamoyladenosine (t6A), a universal tRNA modification, in bacteria, Mol Microbiol 98 (2015) 1199–221. https://doi.org/10.1111/mmi.13209.

[53] C. Su, M. Jin, W. Zhang, Conservation and diversification of tRNA t6A-modifying enzymes across the three domains of life, Int J Mol Sci 23 (2022) 13600. https://doi.org/10.3390/ijms232113600.

[54] A. Kouvela, A. Zaravinos, V. Stamatopoulou, Adaptor Molecules Epitranscriptome Rep programs Bacterial Pathogenicity, Int J Mol Sci 22 (2021) 8409. https://doi.org/10.3390/ijms22168409.

[55] S. Noguchi, Y. Nishimura, Y. Hirota, S. Nishimura, Isolation and characterization of an *Escherichia coli* mutant lacking tRNA-guanine transglycosylase. Function and biosynthesis of queuosine in tRNA, J Biol Chem 257 (1982) 6544–6550.

[56] L. Pollo-Oliveira, N.K. Davis, I. Hossain, P. Ho, Y. Yuan, P. Salguero García, C. Pereira, S.R. Byrne, J. Leng, M. Sze, C.E. Blaby-Haas, A. Sekowska, A. Montoya, T. Begley, A. Danchin, D.P. Aalberts, A. Angerhofer, J. Hunt, A. Conesa, P.C. Dedon, V. de Crécy-Lagard, The absence of the queuosine tRNA modification leads to pleiotropic phenotypes revealing perturbations of metal and
oxidative stress homeostasis in *Escherichia coli* K12, Metallomics 14 (2022).
https://doi.org/10.1093/mtomcs/mfac065.

[57] J. Díaz-Rullo, J.E. González-Pastor, tRNA queuosine modification is involved in biofilm formation and virulence in bacteria, Nucleic Acids Research 51 (2023) 9821–9837. https://doi.org/10.1093/nar/gkad667.

[58] M.M. Mitchener, T.J. Begley, P.C. Dedon, Molecular Coping Mechanisms: Reprogramming tRNAs To regulate codon-biased translation of stress response proteins, *Acc Chem Res* 56 (2023) 3504–3514. https://doi.org/10.1021/acs.accounts.3c00572.

[59] L. Fruchard, A. Babosan, A. Carvalho, M. Lang, B. Li, M. Duchateau, Q. Giai-Gianetto, M. Matondo, F. Bonhomme, C. Fabret, O. Namy, V. de Crécy-Lagard, D. Mazel, Z. Baharoglu, Queuosine modification of tRNA-Tyrosine elicits translational reprogramming and enhances growth of *Vibrio cholerae* with aminoglycosides, *Elife* (2024) RP96317. https://doi.org/10.1101/2022.09.26.509455.

[60] L. Pollo-Oliveira, V. de Crécy-Lagard, Can protein expression be regulated by modulation of tRNA Modification profiles?, Biochemistry 58 (2019) 355–362. https://doi.org/10.1021/acs.biochem.8b01035.

[61] T. Baba, T. Ara, M. Hasegawa, Y. Takai, Y. Okumura, M. Baba, K.A. Datsenko, M. Tomita, B.L. Wanner, H. Mori, Construction of *Escherichia coli* K-12 in-frame, single-gene knockout mutants: the Keio collection, Mol Syst Biol 2 (2006) 2006.0008. https://doi.org/10.1038/msb4100050.

[62] J.H. Miller, Experiments in Molecular Genetics, Cold Spring Harbor Laboratory Press, Cold Spring Harbor, NY, 1972.

[63] J.M. Bacusmo, J. Babor, J. Hu, B. Cao, S. Kellner, S. Skrybalo, Y. Yuan, S. Sander, Kuipers. Paul, M. deMott, H. Mori, P.C. Dedon, V. de Crécy-Lagard, Synergistic effects of tRNA modification defects in *Escherichia coli*, (2024).
bioRxiv 2024.11.12.622971; doi: https://doi.org/10.1101/2024.11.12.622971

[64] A. Babosan, L. Fruchard, E. Krin, A. Carvalho, D. Mazel, Z. Baharoglu, Nonessential tRNA and rRNA modifications impact the bacterial response to sub-MIC antibiotic stress, *MicroLife* 3 (2022) uqac019. https://doi.org/10.1093/femsml/uqac019.

[65] K.M. Wetmore, M.N. Price, R.J. Waters, J.S. Lamson, J. He, C.A. Hoover, M.J. Blow, J. Bristow, G. Butland, A.P. Arkin, A. Deutschbauer, Rapid quantification
of mutant fitness in diverse bacteria by sequencing randomly bar-coded
transposons, MBio 6 (2015) e00306-e315. https://doi.org/10.1128/mBio.00306-15.

[66] I.M. Keseler, S. Gama-Castro, A. Mackie, R. Billington, C. Bonavides-Martínez, R. Caspi, A. Kothari, M. Krummenacker, P.E. Midford, L. Muñiz-Rascado, W.K. Ong, S. Paley, A. Santos-Zavaleta, P. Subhraveti, V.H. Tierrafría, A.J. Wolfe, J. Collado-Vides, I.T. Paulsen, P.D. Karp, The EcoCyc Database in 2021, Front Microbiol 12 (2021) 711077. https://doi.org/10.3389/fmicb.2021.711077.

[67] E.W. Sayers, J. Beck, E.E. Bolton, D. Bourexis, J.R. Brister, K. Canese, D.C. Comeau, K. Funk, S. Kim, W. Klimke, A. Marchler-Bauer, M. Landrum, S. Lathrop, Z. Lu, T.L. Madden, N. O'Leary, L. Phan, S.H. Rangwala, V.A. Schneider, Y. Skripchenko, J. Wang, J. Ye, B.W. Trawick, K.D. Pruitt, S.T. Sherry, Database resources of the National Center for Biotechnology Information, Nucleic Acids Res 49 (2021) D10–D17. https://doi.org/10.1093/nar/gkaa892.

[68] P.D. Karp, R. Billington, R. Caspi, C.A. Fulcher, M. Latendresse, A. Kothari, I.M. Keseler, M. Krummenacker, P.E. Midford, Q. Ong, W.K. Ong, S.M. Paley, P. Subhraveti, The BioCyc collection of microbial genomes and metabolic pathways, Brief Bioinform 20 (2019) 1085–1093. https://doi.org/10.1093/bib/bbx085.

[69] A. Krogh, B. Larsson, G. von Heijne, E.L.L. Sonnhammer, Predicting transmembrane protein topology with a hidden Markov model: application to complete genomes, J Mol Biol 305 (2001) 567–580. https://doi.org/10.1006/jmbi.2000.4315.

[70] F. Hia, Y.H. Chionh, Y.L.J. Pang, M.S. De Mott, M.E. McBee, P.C. Dedon, Mycobacterial RNA isolation optimized for non-coding RNA: high fidelity isolation of 5S rRNA from Mycobacterium bovis BCG reveals novel post-transcriptional processing and a complete spectrum of modified ribonucleosides, Nucleic Acids Res 43 (2014) e32. https://doi.org/10.1093/NAR/GKU1317.

[71] A.P. Arkin, R.W. Cottingham, C.S. Henry, N.L. Harris, R.L. Stevens, S. Maslov, P. Dehal, D. Ware, F. Perez, S. Canon, M.W. Sneddon, M.L. Henderson, W.J. Riehl, D. Murphy-Olson, S.Y. Chan, R.T. Kamimura, S. Kumari, M.M. Drake, T.S. Brettin, E.M. Glass, D. Chivian, D. Gunter, D.J. Weston, B.H. Allen, J. Baumohl, A.A. Best, B. Bowen, S.E. Brenner, C.C. Bun, J.-M. Chandonia, J.-M. Chia, R.
Colasanti, N. Conrad, J.J. Davis, B.H. Davison, M. DeJongh, S. Devoid, E. Dietrich, I. Dubchak, J.N. Edirisinghe, G. Fang, J.P. Faria, P.M. Frybarger, W. Gerlach, M. Gerstein, A. Greiner, J. Gurtowski, H.L. Haun, F. He, R. Jain, M.P. Joachimiak, K.P. Keegan, S. Kondo, V. Kumar, M.L. Land, F. Meyer, M. Mills, P.S. Novichkov, T. Oh, G.J. Olsen, R. Olson, B. Parrello, S. Pasternak, E. Pearson, S.S. Poon, G.A. Price, S. Ramakrishnan, P. Ranjan, P.C. Ronald, M.C. Schatz, S.M.D. Seaver, M. Shukla, R.A. Sutormin, M.H. Syed, J. Thomason, N.L. Tintle, D. Wang, F. Xia, H. Yoo, S. Yoo, D. Yu, KBase: The United States Department of Energy Systems Biology Knowledgebase, Nature Biotechnology 7 36 (2018) 566–569. https://doi.org/https://doi.org/10.1038/nbt.4163.

[72] V. de Crécy-Lagard, J. Babor, J.M. Bacusmo, P.C. Dedon, RNA-Seq analysis of nine tRNA-Modification deficient mutants in *E. coli.*, Kbase (2024). https://doi.org/.https://doi.org/10.25982/197146.10/2475943.

[73] D. Kim, J.M. Paggi, C. Park, C. Bennett, S.L. Salzberg, Graph-based genome alignment and genotyping with HISAT2 and HISAT-genotype, Nat Biotechnol 37 (2019) 907–915. https://doi.org/10.1038/s41587-019-0201-4.

[74] M. Pertea, G.M. Pertea, C.M. Antonescu, T.-C. Chang, J.T. Mendell, S.L. Salzberg, StringTie enables improved reconstruction of a transcriptome from RNA-seq reads, Nat Biotechnol 33 (2015) 290–295. https://doi.org/10.1038/nbt.3122.

[75] M.I. Love, W. Huber, S. Anders, Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2, Genome Biol 15 (2014) 550. https://doi.org/10.1186/s13059-014-0550-8.

[76] T. Cokelaer, D. Desvillechabrol, R. Legendre, M. Cardon, “Sequana”: a Set of Snakemake NGS pipelines, The Journal of Open Source Software 2 (2017) 352. https://doi.org/10.21105/joss.00352.

[77] J. Köster, S. Rahmann, Snakemake—a scalable bioinformatics workflow engine, Bioinformatics 28 (2012) 2520–2522. https://doi.org/10.1093/bioinformatics/bts480.

[78] S. Chen, Y. Zhou, Y. Chen, J. Gu, fastp: an ultra-fast all-in-one FASTQ preprocessor, Bioinformatics 34 (2018) i884–i890. https://doi.org/10.1093/bioinformatics/bty560.

[79] B. Langmead, S.L. Salzberg, Fast gapped-read alignment with Bowtie 2, Nat Methods 9 (2012) 357–359. https://doi.org/10.1038/nmeth.1923.
[80] Y. Liao, G.K. Smyth, W. Shi, featureCounts: an efficient general purpose program for assigning sequence reads to genomic features, Bioinformatics 30 (2014) 923–930. https://doi.org/10.1093/bioinformatics/btt656.

[81] P. Ewels, M. Magnusson, S. Lundin, M. Käller, MultiQC: summarize analysis results for multiple tools and samples in a single report, Bioinformatics 32 (2016) 3047–3048. https://doi.org/10.1093/bioinformatics/btw354.

[82] S. Xu, E. Hu, Y. Cai, Z. Xie, X. Luo, L. Zhan, W. Tang, Q. Wang, B. Liu, R. Wang, W. Xie, T. Wu, L. Xie, G. Yu, Using clusterProfiler to characterize multiomics data, Nat Protoc (2024) 3292-3320. https://doi.org/10.1038/s41596-024-01020-z.

[83] Gene Ontology Consortium, S.A. Aleksander, J. Balhoff, S. Carbon, J.M. Cherry, H.J. Drabkin, D. Ebert, M. Feuermann, P. Gaudet, N.L. Harris, D.P. Hill, R. Lee, H. Mi, S. Moxon, C.J. Mungall, A. Muruganugan, T. Mushayahama, P.W. Sternberg, P.D. Thomas, K. Van Auken, J. Ramsey, D.A. Siegele, R.L. Chisholm, P. Fey, M.C. Aspromonte, M.V. Nugnes, F. Quaglia, S. Tosatto, M. Giglio, S. Nadendla, G. Antonazzo, H. Attrill, G. Dos Santos, S. Marygold, V. Strelets, C.J. Tabone, J. Thurmond, P. Zhou, S.H. Ahmed, P. Asanitthong, D. Luna Buitrago, M.N. Erdol, M.C. Gage, M. Ali Kadhum, K.Y.C. Li, M. Long, A. Michalak, A. Pesala, A. Pritazahra, S.C.C. Saverimuttu, R. Su, K.E. Thurlow, R.C. Lovering, C. Logie, S. Oliferenko, J. Blake, K. Christie, L. Corbani, M.E. Dolan, H.J. Drabkin, D.P. Hill, L. Ni, D. Sitnikov, C. Smith, A. Cuzick, J. Seager, L. Cooper, J. Elser, P. Jaiswal, P. Gupta, P. Jaiswal, S. Naithani, M. Lera-Ramirez, K. Rutherford, V. Wood, J.L. De Pons, M.R. Dwinell, G.T. Hayman, M.L. Kaldunski, A.E. Kwitek, S.J.F. Laulederkind, M.A. Tutaj, M. Vedi, S.-J. Wang, P. D'Eustachio, L. Aimo, K. Axelsen, A. Bridge, N. Hyka-Nouspikel, A. Morgat, S.A. Aleksander, J.M. Cherry, S.R. Engel, K. Karra, S.R. Miyasato, R.S. Nash, M.S. Skrzypek, S. Weng, E.D. Wong, E. Bakker, T.Z. Berardini, L. Reiser, A. Auchincloss, K. Axelsen, G. Argoud-Puy, M.-C. Blatter, E. Boutet, L. Breuza, A. Bridge, C. Casals-Casas, E. Coudert, A. Estreicher, M. Livia Famiglietti, M. Feuermann, A. Gos, N. Gruaz-Gumowski, C. Hulo, N. Hyka-Nouspikel, F. Jungo, P. Le Mercier, D. Lieberherr, P. Masson, A. Morgat, I. Pedruzzi, L. Pourcel, S. Poux, C. Rivoire, S. Sundaram, A. Bateman, E. Bowler-Barnett, H. Bye-A-Jee, P. Denny, A. Ignatchenko, R. Ishtiaq, A. Lock, Y. Lussi, M. Magrane, M.J. Martin, S. Orchard, P. Raposo, E. Speretta, N. Tyagi, K. Warner, R. Zaru, A.D. Diehl, R.
Lee, J. Chan, S. Diamantakis, D. Raciti, M. Zarowiecki, M. Fisher, C. James-Zorn, V. Ponferrada, A. Zorn, S. Ramachandran, L. Ruzicka, M. Westerfield, The Gene Ontology knowledgebase in 2023., Genetics 224 (2023) iyad031. https://doi.org/10.1093/genetics/iyad031.

[84] Z. Gu, Complex heatmap visualization, IMeta 1 (2022) e43. https://doi.org/10.1002/imt2.43.

[85] H. Mi, D. Ebert, A. Muruganujan, C. Mills, L.-P. Albou, T. Mushayamaha, P.D. Thomas, PANTHER version 16: a revised family classification, tree-based classification tool, enhancer regions and extensive API, Nucleic Acids Res 49 (2021) D394–D403. https://doi.org/10.1093/nar/gkaa1106.

[86] D.J. Parker, P. Demetci, G.-W. Li, Rapid accumulation of motility-activating mutations in resting liquid culture of *Escherichia coli*, J Bacteriol 201 (2019) e00259-19. https://doi.org/10.1128/JB.00259-19.

[87] M.N. Price, K.M. Wetmore, R.J. Waters, M. Callaghan, J. Ray, H. Liu, J. V. Kuehl, R.A. Melnyk, J.S. Lamson, Y. Suh, H.K. Carlson, Z. Esquivel, H. Sadeeshkumar, R. Chakraborty, G.M. Zane, B.E. Rubin, J.D. Wall, A. Visel, J. Bristow, M.J. Blow, A.P. Arkin, A.M. Deutschbauer, Mutant phenotypes for thousands of bacterial genes of unknown function, Nature 557 (2018) 503–509. https://doi.org/10.1038/s41586-018-0124-0.

[88] F.F. V. Chevance, K.T. Hughes, Coordinating assembly of a bacterial macromolecular machine, Nat Rev Microbiol 6 (2008) 455–465. https://doi.org/10.1038/nrmicro1887.

[89] R.M. Macnab, Genetics and biogenesis of bacterial flagella, Annu Rev Genet 26 (1992) 131-58. https://doi.org/10.1146/annurev.ge.26.120192.001023.

[90] S. Mukherjee, D.B. Kearns, The structure and regulation of flagella in *Bacillus subtilis*, Annu Rev Genet 48 (2014) 319-40. https://doi.org/10.1146/annurev-genet-120213-092406.

[91] K.R. Hummels, D.B. Kearns, Suppressor mutations in ribosomal proteins and FliY restore Bacillus subtilis swarming motility in the absence of EF-P, PLoS Genet 15 (2019) e1008179. https://doi.org/10.1371/journal.pgen.1008179.

[92] R.T. Oshiro, S. Rajendren, H.A. Hundley, D.B. Kearns, Robust stoichiometry of FliW-CsrA governs flagellin homeostasis and cytoplasmic organization in *Bacillus subtilis*, MBio 10 (2019) e00533-19. https://doi.org/10.1128/mBio.00533-19.
[93] F.F. V. Chevance, K.T. Hughes, Case for the genetic code as a triplet of triplets, Proc Natl Acad Sci USA 114 (2017) 4745-4750.
https://doi.org/10.1073/pnas.1614896114.

[94] Y. Fan, C.R. Evans, J. Ling, Reduced protein synthesis fidelity inhibits flagellar biosynthesis and motility, Sci Rep 6 (2016) 30960.
https://doi.org/10.1038/srep30960.

[95] J.A. Lewis, B.N. Ames, Histidine regulation in *Salmonella typhimurium*, J Mol Biol 66 (1972) 131-142.
https://doi.org/10.1016/S0022-2836(72)80011-1.

[96] R. Cortese, R. Landsberg, R.A. Vonder Haar, H.E. Umbarger, B.N. Ames, Pleiotropy of *hisT* mutants blocked in pseudouridine synthesis in tRNA: *Leucine* and *Isoleucine-Valine* operons, Proc Natl Acad Sci USA 71 (1974) 1857-1861.
https://doi.org/10.1073/pnas.71.5.1857.

[97] K. Jürgenstein, M. Tagel, H. Ilves, M. Leppik, M. Kivisaar, J. Remme, Variance in translational fidelity of different bacterial species is affected by pseudouridines in the tRNA anticodon stem-loop, RNA Biol 19 (2022) 1050-1058.
https://doi.org/10.1080/15476286.2022.2121447.

[98] J. Li, B. Esberg, J.F. Curran, G.R. Björk, Three modified nucleosides present in the anticodon stem and loop influence the in vivo aa-tRNA selection in a tRNA-dependent manner, J Mol Biol 271 (1997) 209-221.
https://doi.org/10.1006/jmbi.1997.1176.

[99] K.A. Salmon, C.-R. Yang, G.W. Hatfield, Biosynthesis and Regulation of the Branched-Chain Amino Acids†, EcoSal Plus 2 (2006).
https://doi.org/10.1128/ecosalplus.3.6.1.5.

[100] M. Buck, E. Griffiths, Regulation of aromatic amino acid transport by tRNA: role of 2-methylthio-N<sup>6</sup>-(Δ<sup>2</sup>-isopentenyl)-adenosine, Nucleic Acids Res 9 (1981) 401-414.
https://doi.org/10.1093/nar/9.2.401.

[101] J. Urbonavicius, Q. Qian, J.M.B. Durand, T.G. Hagervall, G.R. Björk, Improvement of reading frame maintenance is a common function for several tRNA modifications, EMBO Journal 20 (2001) 4863.
http://dx.doi.org/10.1093/emboj/20.17.4863.

[102] A.R. Gall, K.A. Datsenko, N. Figueroa-Bossi, L. Bossi, I. Masuda, Y.-M. Hou, L.N. Csonka, Mg<sup>2+</sup> regulates transcription of *mgtA* in *Salmonella Typhimurium* via translation of proline codons during synthesis of the MgtL peptide, Proc Natl
Acad Sci USA 113 (2016) 15096-15101.
https://doi.org/10.1073/pnas.1612268113.

[103] C.L. Turnbough, R.L. Switzer, Regulation of pyrimidine biosynthetic gene expression in bacteria: repression without repressors, Microbiol Mol Biol Rev 72 (2008) 266-300. https://doi.org/10.1128/MMBR.00001-08.

[104] R. Landick, J. Carey, C. Yanofsky, Translation activates the paused transcription complex and restores transcription of the trp operon leader region, Proc Natl Acad Sci U S A 82 (1985) 4663-4667. https://doi.org/10.1073/pnas.82.14.4663.

[105] P. Gollnick, C. Yanofsky, tRNA(Trp) translation of leader peptide codon 12 and other factors that regulate expression of the tryptophanase operon, J Bacteriol 172 (1990) 3100-3107. https://doi.org/10.1128/jb.172.6.3100-3107.1990.

[106] B. Večerek, I. Moll, U. Bläsi, Control of Fur synthesis by the non-coding RNA RyhB and iron-responsive decoding, EMBO J 26 (2007) 965-975. https://doi.org/10.1038/sj.emboj.7601553.

[107] U. Schweizer, S. Bohleber, N. Fradejas-Villar, The modified base isopentenyladenosine and its derivatives in tRNA, RNA Biol 14 (2017) 1197-1208. https://doi.org/10.1080/15476286.2017.1294309.

[108] S.W. Seo, D. Kim, H. Latif, E.J. O'Brien, R. Szubin, B.O. Palsson, Deciphering Fur transcriptional regulatory network highlights its complex role beyond iron metabolism in *Escherichia coli*, Nat Commun 5 (2014) 4910. https://doi.org/10.1038/ncomms5910.

[109] B.W. Davies, R.W. Bogard, J.J. Mekalanos, Mapping the regulon of Vibrio cholerae ferric uptake regulator expands its known network of gene regulation, Proc Natl Acad Sci USA 108 (2011) 12467-12472. https://doi.org/10.1073/pnas.1107894108.

[110] S.M. Huber, U. Begley, A. Sarkar, W. Gasperi, E.T. Davis, V. Surampudi, M. Lee, J.A. Melendez, P.C. Dedon, T.J. Begley, Arsenite toxicity is regulated by queuine availability and oxidation-induced reprogramming of the human tRNA epitranscriptome, Proc Natl Acad Sci USA 119 (2022) e2123529119. https://doi.org/10.1073/pnas.2123529119.

[111] S. Nagaraja, M.W. Cai, J. Sun, H. Varet, L. Sarid, M. Trebicz-Geffen, Y. Shaulov, M. Mazumdar, R. Legendre, J.-Y. Coppée, T.J. Begley, P.C. Dedon, S. Gourinath, N. Guillen, Y. Saito-Nakano, C. Shimokawa, H. Hisaed, S. Ankri, Queuine Is a Nutritional Regulator of *Entamoeba histolytica* Response to
Oxidative Stress and a Virulence Attenuator, MBio 12 (2021) e03549-20.
https://doi.org/10.1128/mBio.03549-20.

[112] I. Masuda, R. Matsubara, T. Christian, E.R. Rojas, S.S. Yadavalli, L. Zhang, M. Goulian, L.J. Foster, K.C. Huang, Y.-M. Hou, tRNA Methylation Is a Global Determinant of Bacterial Multi-drug Resistance, Cell Syst 8 (2019) 302-314.e8.
https://doi.org/10.1016/j.cels.2019.03.008.

[113] M. Saidijam, S. Azizpour, S.G. Patching, Comprehensive analysis of the numbers, lengths and amino acid compositions of transmembrane helices in prokaryotic, eukaryotic and viral integral membrane proteins of high-resolution structure, J Biomol Struct Dyn 36 (2018) 443-464.
https://doi.org/10.1080/07391102.2017.1285725.

[114] A. Sarkar, W. Gasperi, U. Begley, S. Nevins, S.M. Huber, P.C. Dedon, T.J. Begley, Detecting the epitranscriptome, WIREs RNA 12 (2021) e1663.
https://doi.org/10.1002/wrna.1663.

[115] S. Delaunay, M. Helm, M. Frye, RNA modifications in physiology and disease: towards clinical applications, Nat Rev Genet (2023) 104-122.
https://doi.org/10.1038/s41576-023-00645-2.

[116] L. Antoine, R. Bahena-Ceron, H. Devi Bunwaree, M. Gobry, V. Loegler, P. Romby, S. Marzi, RNA modifications in pathogenic bacteria: impact on host adaptation and virulence, Genes (Basel) 12 (2021) 1125.
https://doi.org/10.3390/genes12081125.

[117] F. Mohammad, A. Buskirk, Protocol for ribosome profiling in bacteria, Bio Protoc 9 (2019). https://doi.org/10.21769/BioProtoc.3468.

[118] P. Barraud, C. Tisné, To be or not to be modified: Miscellaneous aspects influencing nucleotide modifications in tRNAs, IUBMB Life 71 (2019) 1126-1140.
https://doi.org/10.1002/iub.2041.

[119] R. Giegé, G. Eriani, The tRNA identity landscape for aminoacylation and beyond, Nucleic Acids Res 51 (2023) 1528-1570. https://doi.org/10.1093/nar/gkad007.

[120] Charting a Future for Sequencing RNA and Its Modifications, National Academies Press, Washington, D.C., 2024. https://doi.org/10.17226/27165.

[121] E. Choi, D. Nam, J. Choi, S. Park, J. Lee, E. Lee, Elongation factor P controls translation of the *mgtA* gene encoding a Mg²⁺ transporter during *Salmonella* infection, Microbiology open 8 (2019) e680. https://doi.org/10.1002/mbo3.680.
[122] G.R. Björk, T.G. Hagervall, Transfer RNA modification, in: A. Böck, R. Curtis, J.B. Kaper, F.C. Neidhardt, T. Nyström, C.L. Squires (Eds.), *Escherichia coli* and Salmonella. Cellular and Molecular Biology, ASM. Press, Washington DC, 2005: http://www.ecosal.org.

[123] P. Boccaletto, M.A. MacHnicka, E. Purta, P. Pitkowski, B. Baginski, T.K. Wirecki, V. de Crécy-Lagard, R. Ross, P.A. Limbach, A. Kotter, M. Helm, J.M. Bujnicki, MODOMICS: A database of RNA modification pathways. 2017 update, Nucleic Acids Res 46 (2018) D303–D307. https://doi.org/10.1093/nar/gkx1030.

[124] Y. Sakai, S. Kimura, T. Suzuki, Dual pathways of tRNA hydroxylation ensure efficient translation by expanding decoding capability, Nat Commun 10 (2019) 2858. https://doi.org/10.1038/s41467-019-10750-8.

[125] K. Nilsson, G. Jäger, G.R. Björk, An unmodified wobble uridine in tRNAs specific for Glutamine, Lysine, and Glutamic acid from Salmonella enterica Serovar Typhimurium results in nonviability-due to increased missense errors?, PLoS One 12 (2017) e0175092-e0175092. https://doi.org/10.1371/journal.pone.0175092.
<table><caption>**Table 1.** How the absence of tRNA modifications affects expression of genes regulated by leader peptide-dependent processes in *E. coli* and *V. cholerae*. The sequences of the leader peptides are given in Table S6. Analysis of expression data present in Supplemental data sets 1 and 3. Most Information was mined from EcoCyc and Pubmed.</caption><thead><tr><th>Gene</th><th>Product</th><th>Effects of modification deficiencies</th><th>regulated genes</th><th>codon</th></tr></thead><tbody><tr><td colspan="5"><strong>E. coli</strong></td></tr><tr><td>pyrL</td><td>Pyr operon leader peptide</td><td>No effect</td><td>pyrBI</td><td>None, poly-T tract</td></tr><tr><td>tnaC</td><td>Tryptophanase operon leader peptide<br>Phenylalanyl-tRNA synthetase (pheST)</td><td>Operon downregulated in any mutants</td><td>tnaAB</td><td>W</td></tr><tr><td>pheM</td><td>operon leader peptide</td><td>Operon upregulated in miaA and dusB</td><td>pheST</td><td>F</td></tr><tr><td>leuL</td><td>Leu operon leader peptide</td><td>Operon up-regulated only in truA</td><td>leuABCD</td><td>L</td></tr><tr><td>ilvL</td><td>IlvGEDA operon leader peptide</td><td>Operon downregulated in many mutants but not in expected direction</td><td>ilvGEDA</td><td>I, L, V</td></tr><tr><td>ivbL</td><td>IlvB operon leader peptide</td><td>Gene downregulated in 7 out of the 10 mutants analyzed.</td><td>ilvLBN</td><td>L, V</td></tr><tr><td>hisL</td><td>His operon leader peptide</td><td>Operon upregulated in truA, tgt and rlmN</td><td>hisGDCBHAFI</td><td>H</td></tr><tr><td>thrL</td><td>Thr operon leader peptide</td><td>Operon upregulated in truA</td><td>thrLABC</td><td>T</td></tr><tr><td>trpL</td><td>Trp operon leader peptide</td><td>Operon upregulated in miaA as expected</td><td>trpECBA</td><td>W</td></tr><tr><td>pheL</td><td>Leader peptide of chorismate mutase-P-prephenate dehydratase</td><td>Operon upregulated in dusB but not miaA, downregulated in a few other mutants such as mnmA</td><td>pheLA</td><td>F</td></tr><tr><td>mgtL</td><td>MgtLA operon leader peptide</td><td>Operon upregulated when trmD is depleted [102] or EFP is depleted [121] and downregulated in thil.</td><td>mgtLA</td><td>P</td></tr><tr><td>uof</td><td>RyhB-regulated fur leader peptide</td><td>Many modifications activate Fur-dependent genes</td><td>fur</td><td>S</td></tr><tr><td colspan="5"><strong>V. cholerae</strong></td></tr><tr><td>VC_1175</td><td>?</td><td>No data</td><td>trp operon?</td><td></td></tr><tr><td>VC_1176</td><td>predicted TrpL</td><td>Operon downregulated in truA and rlmN</td><td>trp operon? pheST</td><td>W</td></tr><tr><td>VC_1218</td><td>hypothetical PheM</td><td>Operon downregulated in truA</td><td>VC_1219 possibly guaB</td><td></td></tr><tr><td>VC_1030</td><td>?</td><td>No data</td><td>VC_1031 possibly mgtA/zntA</td><td>P?</td></tr><tr><td>VC_1032</td><td>Hypothetical MgtL?</td><td>No data</td><td>VC_1033</td><td></td></tr><tr><td>VC_2509</td><td>PyrL</td><td>Operon downregulated in truA</td><td>pyrB VC_2510</td><td>None, poly-T tract</td></tr><tr><td>VC_A0162</td><td>TnaC</td><td>Operon upregulated in truA but down regulated in tgt</td><td>tnaAB</td><td></td></tr></tbody></table>
## Figure Legends

**Figure 1.** The tRNA decoding capacities and ASL modifications of *E. coli*. Letters from inside out indicate the codon's first, second, and third position (5'- to 3'-end) as the subscripts indicate. The circles outside of the wheel connected or not by a line represent one tRNA species. A filled circle indicates the capacity of that tRNA to base pair with the indicated codon by Watson-Crick pairing. An open (white) circle suggests base pairing by the wobble hypothesis [122]. The base at position 34 of the anticodon and the anticodon stem loop (ASL) modifications in positions 32 to 40 are shown next to each tRNA circle. Amino acids that are decoded by a single tRNA are highlighted in red. Data are compiled from references [122–125]. Discrepancies among references are shown in parentheses, suggesting partial modifications or modifications at certain conditions.

**Figure 2:** Gene regulation mediated by the translation efficiency of small ORFs.
In prokaryotes, three major mechanisms of regulation are known: (A) Rho-independent transcription termination, where translation stalling on the leader peptide alters the mRNA conformation to prevent the formation of a Rho-independent terminator; (B) Rho-dependent transcription termination, where translation stalling or ribosomal drop-off on the leader peptide alters the mRNA conformation to block Rho accessibility to the Rho utilization site (RUT); and (C) Transcriptional attenuation, where translation stalling on the leader peptide alters the mRNA conformation, allowing the initiation by the ribosome of the regulated gene. **A to C**, Dark blue and sky blue represent sequences that can form a stem-loop structure. (D) Proposed regulation of the *fur* gene by the small ORF *uof*, the two genes overlap, and a putative stalling or drop-off of the ribosome during translation of *uof* regulates *fur* expression. In eukaryotes (E), uORFs also mediate the expression of regulated genes, but the regulation occurs through the ability of uORFs to prevent ribosome scanning, thereby blocking the initiation of the regulated gene.

**Figure 3.** Differentially expressed genes in nine *E. coli* tRNA modification mutants. (A) Heatmap showing DEGs and their GO terms observed in three and more out of ten comparisons between the WT and nine tRNA modification mutants to the *dusB* mutant. The data is extracted from Supplementary Data Set 5B. The GO terms
associated with each gene are arbitrarily grouped. (B) The enriched GO terms with p-values less than 0.05 and Benjamini-Hochberg adjusted p-values less than 0.2 were shown in three of the mutants (the full analysis is shown in Fig. S4).

**Figure 4. The absence of different tRNA modification genes can increase or decrease motility.** Motility assays were performed as described in the method section for isogenic sets of (A) *E. coli* or (C) *V. cholerae* WT and deletion strains. Error bars represent the standard deviation of biological triplicates. (B) heatmap showing the differentially expressed motility genes in the *E. coli* tRNA modification mutants compared to the *dusB* strain. Data was extracted from Supplementary Data Set 5B.

**Figure 5. DEGs in three *V. cholerae* tRNA modification mutants.** (A) A heatmap showing DEGs and their GO terms in comparisons between the *dusB*, *tgt* and *truA* strains to the WT. The data is extracted from Supplementary Data Set 5C. (B) The enriched GO terms with p-values less than 0.05 and Benjamini-Hochberg adjusted p-values less than 0.2 were shown.

**Figure 6. HisL and LeuL-regulated genes are upregulated specifically by the absence of Ψ38-40.** Log2 fold change of transcripts of HisL- (A) and LeuL- (B) regulated genes in *E. coli* deletion mutants compared to the WT. Data extracted from Supplementary Data Set 1. Log2 fold change of transcripts and peptides of HisL- (C) and LeuL- (D) regulated genes in *V. cholerae* compared to the WT (light colors RNA seq, dark colors proteomics). The deletion mutants are indicated at the bottom in corresponding colors. Data extracted from Supplementary Data Sets 3 and 4.

**Figure 7. TnaC-regulated genes are greatly affected by tRNA modification deficiency.** (A) and (B) Log2 fold change of transcripts of *tnaABC* genes in *E. coli* BW25113 deletion mutants compared to the WT.(A) Data extracted from Supplementary Data Set 1. (B) Data extracted from NCBI Gene Expression Omnibus database (accession GSE237609). (C) Log2 fold change of transcripts of *tnaABC* genes and encoded peptides in *V. cholerae* (light colors RNA seq, dark colors proteomics). Data was extracted from Supplementary Data Sets 3 and 4. The deletion mutants are indicated at the bottom in corresponding colors.
**Figure. 8.** Iron homeostasis genes can be up or down-regulated in *E. coli* tRNA modification mutants. Log2 fold changes of transcripts of iron homeostasis genes in *E. coli* deletion mutants compared to the WT strain. The deletion mutants are indicated at the bottom in corresponding colors. Data extracted from Supplementary Data Set 1.
<figure><img src="image_2.png" /></figure>
A

Rho-independent transcription termination

<figure><img src="image_3.png" /></figure>

Repression

<figure><img src="image_4.png" /></figure>

Expression

Example:

PheM
LeuL
IlvL
IvbL
HisL
ThrL
PheL

B

Rho-dependent transcription termination

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Repression

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Expression

TnaC

Or

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MgtL

C

Translational attenuation

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Repression

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Expression

SecM
VemP

D

Overlapping gene regulation

<figure><img src="image_11.png" /></figure>

Expression

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Repression

Uof

E

Scanning inhibition of the downstream initiation codon

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Expression

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Repression
A

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B

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A

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B

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A

<figure><img src="image_23.png" /></figure>

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B

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C

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D

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