Skip to main content
NCBI home page
ACS AuthorChoice logoLink to ACS AuthorChoice
. 2023 Nov 22;56(23):3504–3514. doi: 10.1021/acs.accounts.3c00572

Molecular Coping Mechanisms: Reprogramming tRNAs To Regulate Codon-Biased Translation of Stress Response Proteins

Michelle M Mitchener †, Thomas J Begley ‡,§,*, Peter C Dedon †,∥,*
PMCID: PMC10702489  PMID: 37992267

Conspectus

graphic file with name ar3c00572_0006.jpg

As part of the classic central dogma of molecular biology, transfer RNAs (tRNAs) are integral to protein translation as the adaptor molecules that link the genetic code in messenger RNA (mRNA) to the amino acids in the growing peptide chain. tRNA function is complicated by the existence of 61 codons to specify 20 amino acids, with most amino acids coded by two or more synonymous codons. Further, there are often fewer tRNAs with unique anticodons than there are synonymous codons for an amino acid, with a single anticodon able to decode several codons by “wobbling” of the base pairs arising between the third base of the codon and the first position on the anticodon. The complications introduced by synonymous codons and wobble base pairing began to resolve in the 1960s with the discovery of dozens of chemical modifications of the ribonucleotides in tRNA, which, by analogy to the epigenome, are now collectively referred to as the epitranscriptome for not changing the genetic code inherent to all RNA sequences. tRNA modifications were found to stabilize codon–anticodon interactions, prevent misinitiation of translation, and promote translational fidelity, among other functions, with modification deficiencies causing pathological phenotypes. This led to hypotheses that modification-dependent tRNA decoding efficiencies might play regulatory roles in cells. However, it was only with the advent of systems biology and convergent “omic” technologies that the higher level function of synonymous codons and tRNA modifications began to emerge.

Here, we describe our laboratories’ discovery of tRNA reprogramming and codon-biased translation as a mechanism linking tRNA modifications and synonymous codon usage to regulation of gene expression at the level of translation. Taking a historical approach, we recount how we discovered that the 8–10 modifications in each tRNA molecule undergo unique reprogramming in response to cellular stresses to promote translation of mRNA transcripts with unique codon usage patterns. These modification tunable transcripts (MoTTs) are enriched with specific codons that are differentially decoded by modified tRNAs and that fall into functional families of genes encoding proteins necessary to survive the specific stress. By developing and applying systems-level technologies, we showed that cells lacking specific tRNA modifications are sensitized to certain cellular stresses by mistranslation of proteins, disruption of mitochondrial function, and failure to translate critical stress response proteins. In essence, tRNA reprogramming serves as a cellular coping strategy, enabling rapid translation of proteins required for stress-specific cell response programs. Notably, this phenomenon has now been characterized in all organisms from viruses to humans and in response to all types of environmental changes. We also elaborate on recent findings that cancer cells hijack this mechanism to promote their own growth, metastasis, and chemotherapeutic resistance. We close by discussing how understanding of codon-biased translation in various systems can be exploited to develop new therapeutics and biomanufacturing processes.

Key References

  • Chan C. T.; Dyavaiah M.; DeMott M. S.; Taghizadeh K.; Dedon P. C.; Begley T. J.. A quantitative systems approach reveals dynamic control of tRNA modifications during cellular stress. PLoS Genet. 2010, 6, e1001247. This work describes an LC-MS/MS method for quantifying the dozens of tRNA modifications in an organism and the first observations of stress-induced reprogramming of tRNA modifications.1

  • Doyle F.; Leonardi A.; Endres L.; Tenenbaum S. A.; Dedon P. C.; Begley T. J.. Gene- and genome-based analysis of significant codon patterns in yeast, rat and mice genomes with the CUT Codon Utilization tool. Methods 2016, 107, 98–109. This article details the construction of a codon-utilization database to help identify transcripts with biased codon usage patterns that might be subject to translational control.2

  • Chionh Y. H.; Babu I. R.; Hia F.; Lin W.; Dziergowska A.; Malkiewicz A.; Begley T. J.; Alonso S.; Dedon P. C.. tRNA-mediated codon-biased translation in mycobacterial hypoxic persistence. Nat. Commun. 2016, 7, 13302. This article presents the first complete multiomic demonstration of stress-induced tRNA reprogramming and codon-biased translation, with genetic validation of the role of codon biases in translational regulation of gene expression. The article was featured in a special collection on biological mass spectrometry by Nature Communications: https://www.nature.com/collections/idihhjdbga.3

  • Hu J. F.; Yim D.; Ma D.; Huber S. M.; Davis N.; Bacusmo J. M.; Vermeulen S.; Zhou J.; Begley T. J.; DeMott M. S.; Levine S. S.; de Crecy-Lagard V.; Dedon P. C.; Cao B. Quantitative mapping of the cellular small RNA landscape with AQRNA-seq. Nat. Biotechnol. 2021, 39, 978–988. This article presents a novel next-generation sequencing technology for absolute quantification of all small RNAs, including tRNAs, in a sample, with application to changes in the tRNA pool during stress-induced tRNA reprogramming.4

Introduction

The existence of secondary chemical modifications of DNA, RNA, and histone proteins has been known for nearly a century, with the discovery of 5-methylcytosine in DNA in 1925.5 The catalog of modifications has now grown to >30 DNA modifications6 and >20 histone protein modifications7,8 comprising the epigenome, while the epitranscriptome9 consists of more than 170 modifications found on all forms of RNA, with the greatest diversity on tRNA.10 While the chemical structure discovery phase of the epigenome and epitranscriptome continues, understanding the function of these modifications has only begun to emerge in the past two decades with the advent of the convergent computational and analytical tools of systems biology.11,12

For the past 15 years, our laboratories have focused on defining the function of the epitranscriptome of transfer RNAs (tRNAs), the 75–90 nucleotide-long adaptor molecules linking the genetic code in messenger RNA (mRNA) to the amino acids in the growing peptide chain (Figure 1a,b). tRNA modifications are installed post-transcriptionally by highly specific enzymes—writers—at conserved locations, and some modifications are also enzymatically removed, albeit this is a more nascent area of study.13,14 Modifications decorate tRNA from all three kingdoms of life, with eukaryotes bearing the largest abundance and variety of chemistries, including methylation, hydroxylation, acetylation, deamination, transglycosylation, and others (Figure 1c). These chemistries greatly expand the information content and functionality of tRNAs in terms of structure, recognition by proteins, and translational fidelity, as has been expertly reviewed elsewhere.15 By increasing the surface area of the tRNA molecule by 20% and changing local physicochemical properties, modified ribonucleosides affect tRNA recognition by both proteins and nucleic acids.16 Modifications outside of the anticodon stem loop (ASL) help maintain tRNA structure by restricting nonfunctional alternative folding and promoting cooperative Mg2+ binding and thermal stabilization.16,17 RNA modifications also affect tRNA charging efficiency and alter aminoacylation kinetics.18

Figure 1.

Figure 1

Open in a new tab

tRNA modification and translation. (a) tRNA anticodons interact with mRNA codons to promote translation. (b) Codon usage table revealing codon degeneracy and wobbling by tRNA-Arg. (c) Examples of modification structures and locations in tRNA.

The highest frequency and diversity of modifications occur within the ASL (Figure 1a), specifically at positions 34 and 37. Among other functions, these modifications fine tune codon–anticodon interactions. Watson–Crick base pairing generally applies to positions 35 and 36 of the anticodon (which pair with the second and first base of the codon, respectively). However, nonstandard hydrogen bonding (wobble pairing) can occur at the anticodon wobble position 34, which pairs with the third base of the codon (Figure 1b). Wobble modifications thus enhance regulation of translation efficiency and fidelity in the face of multiple synonymous codons for most amino acids (61 codons for 20 amino acids) and often fewer tRNAs with unique anticodons than there are synonymous codons. For example, in bacteria, the presence of the cytidine derivative lysidine at position 34 of tRNA-Ile-CAU shifts the tRNA from reading AUG to AUA and changes the amino acid specificity of the tRNA from methionine to isoleucine,19 whereas N4-acetylcytidine at the wobble position of tRNA-Met-CAU shifts reading from AUA to AUG.20 Modifications of the ASL and other tRNA regions thus explain how codon–anticodon interactions can be both relaxed enough to allow multiple codons to be decoded by a single anticodon yet stringent enough to discriminate between closely related codons that specify different amino acids.16

These arguments point to one of the functions of the tRNA epitranscriptome in differential reading of synonymous codons. With a three-letter genetic code that provides 61 codons for 20 amino acids, the functional significance of synonymous codons has largely eluded definition. The codon composition of mRNA is well established to affect both RNA stability and translation efficiency in part by regulating RNA secondary structure.21 However, two sets of correlations have led to a model in which a static tRNA pool drives translation efficiency, with the most highly expressed proteins arising from mRNAs enriched with codons read by the most abundant tRNAs. First, there is a weak correlation between the number of copies of each tRNA gene in organisms such as yeast and the level of the tRNA in the pool.22 Second, the most abundant proteins are often enriched with codons read by the most abundant isoacceptors in the tRNA pool.23,24 These correlations, albeit weak, led to the concept of “codon optimality”, with the most frequently used of several synonymous codons in an organism termed the “optimal codon”. The optimal codons are then decoded by the most abundant tRNA species, and mRNAs possessing optimal codons translated more efficiently than mRNAs with other codons. Although these models predict expression of the most abundant proteins, such as ribosomal proteins, they do not predict the shifts in translational efficiency that are associated with environmental changes (i.e., stresses).

The links between tRNA modifications, translation, and cell stress response began to emerge in the last several decades as researchers observed how modification deficiencies affected cellular phenotypes. For example, certain modifications were found to be altered under conditions of high temperature25,26 or lack of oxygen.27 Other modifications were linked to cellular processes such as bacterial sporulation28 or cell division.29 Significantly, the importance of specific RNA modifications tended to be “revealed under pressure”, so to speak, for example, during host–pathogen interactions, in conditions of cellular stress, or in the absence of another protein. Thus, many scientists postulated that the types and degree of tRNA modifications might play a regulatory role in the cell, given the differential decoding efficiency of the various tRNAs. In 1973, Weiss stated this regulatory hypothesis perhaps the most clearly when he wrote that weak codon–tRNA interactions might serve to adjust the protein concentrations in the cell since “relative rates of synthesis could be controlled by the number of modulator codons in each mRNA”.30 Likewise, Kirino et al., upon discovery of the importance of a tRNA taurine modification in translation of select codons in human mitochondrial disease, hypothesized that perhaps a specific mitochondrial protein enriched in these codons and essential for human health might be poorly translated in diseased patients.31 Yet, there was no experimental support for these models until Begley et al. found that the wobble U-modifying enzyme tRNA methyltransferase 9 (Trm9) modulated the toxicity of alkylating agents32,33 by installing modifications on specific tRNAs that read codons enriched in alkylation response genes.34 These studies led to a concerted effort by the Begley and Dedon laboratories to understand the systems-level function of tRNA modifications in regulating cellular stress responses.

In this Account, we describe our laboratories’ discovery that tRNA modifications are dynamically reprogrammed in response to stress to promote translation of mRNAs with distinct codon usage patterns—modification tunable transcripts (MoTTs)—thus upregulating key survival proteins. Here, we highlight key studies from our laboratories that reveal tRNA reprogramming and codon-biased translation across the range of living organisms. We reveal how advances in systems technologies both enabled and arose out of efforts to better understand this universal mechanism. Finally, we explore the implications of codon-biased translation and the resulting innovations for health and biotechnology and discuss future directions for the field amid renewed interest in RNA modifications. We apologize at the outset for not being able to cite all of the many publications that both supported our discoveries and built upon them during the rapid growth of the tRNA epitranscriptome community.

Discovery of Modification Tunable Transcripts

The story begins with high-throughput studies of yeast gene deletion libraries, which identified Trm9 as an enzyme important for the DNA damage response; loss of Trm9 increased cell sensitivity to methylmethanesulfonate, hydroxyurea, and γ-irradiation exposure.32,33,35 Given the role of Trm9 in forming the mcm5U and mcm5s2U wobble bases in tRNA-Arg-UCU and tRNA-Glu-UUC, respectively, Begley et al. postulated that Trm9 played a role in the translational response to DNA damage by altering tRNA anticodon–mRNA codon interactions. To test this hypothesis, the group designed a codon-specific reporter system in S. cerevisiae in which reporter proteins contained identical peptide sequences but different patterns of synonymous codon usage.34 Significantly, deletion of trm9 led to reduced translation of the reporter peptide ∼6- and 7-fold with AGA- and GAA-enriched reporter genes, respectively. This established the requirement of Trm9-dependent wobble modifications for efficient translation of transcripts bearing codons read by the modified tRNAs.

Begley et al. then hypothesized that endogenous mRNAs enriched in these codons might be preferentially translated by tRNAs with Trm9 modifications.34 To test this idea, they developed the gene-specific codon usage algorithm (now termed Codon Utilization Tool2) to profile codon usage in each gene across an entire genome (Figure 2).34 Hierarchical clustering of yeast codon usage data revealed 425 genes overusing AGA, GAA, and other codons and associated with protein synthesis, energy metabolism, and stress and damage responses. Genes encoding the translation elongation factor Yef3 and ribonucleotide reductases 1–4 (Rnr1–4) were particularly enriched in AGA and GAA codons, as were other genes decoded by anticodons bearing wobble mcm5U and mcm5s2U. Loss of Trm9 resulted in decreased levels of Yef3-TAP, Rnr1-TAP, and Rnr3-TAP proteins despite unchanged mRNA levels.34 Polysome profiling revealed that these transcripts were in the process of being translated and that the abundance of YEF3 and RNR1 transcripts in polysome fractions was higher in trm9Δ cells, suggesting that translation was perturbed in a codon-specific manner in the absence of Trm9. Moreover, loss of Rnr rendered trm9Δ yeast sensitive to DNA damage induced by hydroxyurea (Rnr inhibitor), a phenotype that could be rescued by overexpression of Trm9.34 Finally, [3H] labeling of the Trm9 methyl donor S-adenosylmethionine revealed a 4-fold decrease in tRNA methylation in trm9Δ cells, confirming tRNA modification in the absence of Trm9 is indeed substantially reduced.34

Figure 2.

Figure 2

Open in a new tab

Application of omic tools to discover and validate tRNA reprogramming and codon-biased translation. Codon Utilization Tool: heat map shows codon overusage (yellow) and underusage (purple) relative to a genome average in S. cerevisiae. Adapted with permission from ref (34). Copyright 2007 Elsevier. AQRNA-seq: time-dependent changes in levels of a subset of tRNAs in starved M. bovis BCG. Adapted with permission from ref (4). Copyright 2021 Springer Nature. LC-MS/MS epitranscriptomics: heat map shows toxicant-specific signatures of tRNA modifications (rows) in yeast. Adapted with permission from ref (36). Copyright 2015 American Chemical Society. Proteomics: downregulation of proteins from genes enriched with AGA and GAA codons in yeast lacking Trm9. Adapted with permission from ref (37). Copyright 2015 PLoS.

Collectively, these findings led to a model in which Trm9-catalyzed modification of select tRNAs gives rise to preferential translation of mRNAs enriched in codons read by the modified tRNAs. The fact that the regulatory impact of specific tRNA modifications occurs at the level of translation is supported by the lack of changes in mRNA levels of modification-dependent response genes. At this point, the Dedon and Begley laboratories began their collaboration to elaborate the tRNA reprogramming and codon-biased translation model depicted in Figure 3 at the systems level in yeast and then test its generality across the range of organisms.

Figure 3.

Figure 3

Open in a new tab

tRNA reprogramming and codon-biased translation enables cells to respond and adapt to stresses caused by environmental changes. Regulatory deficits or surpluses contribute to dysfunction and disease.

tRNA Reprogramming and Codon-Biased Translation in Yeast

Testing the tRNA reprogramming and codon-biased translation model proposed by Begley et al. required a variety of new systems-level technologies in addition to codon analytics, as illustrated in Figure 2. The first of these was a method to quantify the full set of tRNA modifications in an organism. Building on the DNA damage analytics experience of the Dedon laboratory, Chan et al. developed a chromatography-coupled tandem quadrupole (LC-MS/MS) method to quantify tRNA modifications and applied it to analyze changes in modification levels in yeast exposed to a variety of alkylating and oxidizing agents (Figure 2).1,36 Hierarchical clustering and machine learning models of modification changes revealed toxicant- and dose-dependent patterns that could distinguish SN1 from SN2 alkylating agents and that were >80% predictive of the toxicant.36 Further, mutants lacking Trm and other RNA-modifying enzymes were sensitive to killing by the chemical stressors that altered the levels of specific modifications.1,36

Following up on their observation that hydrogen peroxide (H2O2) increased the level of m5C in tRNA,1,36 Chan et al. found that loss of the m5C writer Trm4 caused yeast to become hypersensitive to this form of reactive oxygen species (ROS).38 m5C is present in multiple tRNA isoacceptors but is a wobble modification only in tRNA-Leu-CAA, where it is also present at position 48 at the junction between the variable and TΨC loops. To quantify wobble m5C, tRNA-Leu-CAA was affinity purified from H2O2-exposed and unexposed cells, and an m5C-containing ASL loop oligonucleotide was quantified by mass spectrometric analysis.38 This revealed a significant increase in m5C at the wobble position but not at position 48, suggesting that a modification deficiency in the anticodon might be responsible for the observed H2O2 sensitivity of trm4Δ yeast. We then used a codon-specific reporter system containing a run of UUG codons read by m5C-modified tRNA-Leu-CAA and found a 10-fold reduction in reporter activity in trm4Δ cells that was exacerbated following H2O2 treatment.38 Genome-wide codon usage analysis revealed an enrichment of UUG codons in 38 genes, 26 of which coded for ribosomal protein paralogs (i.e., interchangeable pairs) and had 90% of their leucines coded by UUG. In one set of paralogs, 100% of the leucines in RPL22A were coded with UUG, whereas this value fell to 34% for RPL22B. Subsequent proteomics analysis of polysomes purified from wild-type and trm4Δ yeast exposed to H2O2 revealed Trm4- and H2O2-dependent increases in Rpl22a but not Rpl22b with loss of RPL22A causing increased sensitivity to killing by H2O2.38

This Trm4-UUG link supported the notion that tRNA modifications promote the selective translation of specific codon-biased mRNAs and suggested that changing synonymous codons in a transcript could alter the translation efficiency of the protein. Patil et al. circled this idea back to Trm9 in a study of its role in cell cycle progression during the DNA damage response in yeast.39 As mentioned earlier, Rnr1 levels are reduced in trm9Δ cells relative to wild type in asynchronous populations. In this study, the group noticed that Rnr1 protein levels are especially reduced during hydroxyurea-induced S-phase in trm9Δ cells. Consistent with the role of ribonucleotide reductase activity in supporting DNA synthesis in S-phase following DNA damage, trm9Δ cells showed a delayed entrance into S-phase following treatment with DNA-damaging reagents, a phenotype that could be reversed by overexpression of Rnr1. Given these findings and the observation that Trm9-mediated mcm5U-based modifications are increased in S-phase in wild-type yeast, the group tested the idea that changing Trm9-dependent codons to other synonymous codons would rescue the DNA damage-induced trm9Δ cell cycle block. Indeed, when the endogenous RNR1 gene was replaced by a codon-optimized version, both wild-type and trm9Δ cells showed increased Rnr1 protein levels and transitioned more rapidly into S-phase following DNA damage. The codon-based phenotypic rescue of trm9Δ cells revealed RNR1 to be a bona fide MoTT and provided yet another example of how tRNA modifications promote codon-biased translation to allow organisms to survive environmental challenges. Similarly, Lamichhane et al. extended this translational regulation of gene expression to the anticodon-adjacent position 37 modification N6-isopentenyladenosine (i6A) in Schizosaccharomyces pombe.40 i6A-modified tRNA-Cys and tRNA-Tyr read codons enriched in high-abundance mRNAs for ribosome subunits and energy metabolism, with polysome profiles confirming decreased translational efficiency of mRNAs in cells lacking i6A.40

A variety of other studies in yeast further illuminated the correlation between tRNA modifications and codon-biased translation of mRNA transcripts encoding proteins required for cell survival (Figure 3). In their original study in trm9Δ yeast, Begley et al. noticed MoTTs were more abundant in polysome fractions, suggesting a deficiency in translation of these transcripts in trm9Δ cells.34 Indeed, quantitative proteomics coupled with codon analysis of dysregulated proteins revealed that loss of Trm9 impairs expression of proteins from genes enriched with Tmr9-dependent AGA and GAA codons (Figure 2).37 Importantly, this study showed a lack of correlation between changes in protein levels and changes in mRNA expression, suggesting regulation of gene expression was occurring post-transcriptionally.37 Patil et al. further showed that loss of Trm9 caused translational infidelity characterized by increased amino acid misincorporation and −1 frameshifting specific to Trm9-dependent codons.41 Transcripts for genes associated with heat shock and the untranslated protein response were upregulated in trm9Δ cells, pointing to unfolded or damaged proteins as a result of increased translational errors. Similarly, S. cerevisiae lacking t6A and mcm5/s2U34 modifications (Figure 1) were marked by protein aggregation and displayed +1 frameshift phenotypes, including shape defects, mis-segregated nuclei, and sensitivity to temperature and nutrient deprivation.42 In perhaps the most comprehensive tRNA-modifying enzyme knockout screen to date, Tavares et al. profiled 70 yeast strains each lacking a single tRNA modification gene to assess protein aggregation.43 In addition to increased size of aggregate foci in mutants lacking specific modification enzymes, the aggregates were statistically enriched in proteins whose genes overused modification-dependent codons. Taken together, these data support a model in which a lack of tRNA modifications leads to codon-specific translational pausing, which then increases translational errors and promotes cotranslational protein misfolding.

As depicted in Figure 4, these foundational studies in yeast opened the door to the discovery of the mechanism of tRNA reprogramming and codon-biased translation in other living organisms and as a major driver of many human diseases, including cancer.

Figure 4.

Figure 4

Open in a new tab

Organisms in which tRNA modifications have been shown to play a role in the cellular response to stress and environmental change.

tRNA Reprogramming and Codon-Biased Translation in Bacteria and Parasites

While the yeast studies provided the foundation for the mechanism of tRNA reprogramming and codon-biased translation in cell responses to environmental changes, subsequent studies demonstrated the universality of this regulatory mechanism for both the type of organism and the variety of cellular stressors that triggered the response (Figure 4). Perhaps the most complete studies of tRNA-mediated codon-biased translation have been performed in the Mycobacterium bovis BCG model for the stress-induced nonreplicative and drug-resistant state of infection by Mycobacterium tuberculosis.3,4,44 Both hypoxia and nutrient deprivation caused reprogramming of tRNA modifications and the tRNA pool (Figure 2) with subsequent selective translation of survival proteins. Hypoxia caused 100% of the tRNA-Thr-CGT isoacceptors to convert a wobble mo5U to cmo5U and, as measured by mass spectrometric analysis, increased the level of the modified isoacceptor. These changes forced reading of the threonine ACG codon enriched in genes required for converting the log-growing cell to a nonreplicative and drug-resistant state.3 Most notable among these genes was that for DosR, the transcriptional regulator of a 48-gene regulon required for hypoxic dormancy.3 Starvation of BCG, on the other hand, caused the opposite effect on tRNAs for threonine. Here, we used a new next-generation RNA sequencing method termed AQRNA-seq to quantify all RNA species <200 nucleotides in each BCG RNA sample (Figure 2). Unlike standard NGS methods that are not quantitative, AQRNA-seq maximizes ligation of novel adapters onto the ends of the RNA targets and uses a two-step adaptor ligation strategy to minimize loss of information caused by reverse-transcriptase falloff at tRNA modifications. This provides a direct correlation between sequencing read count and the number of copies of each RNA species.4 Using AQRNA-seq, we observed that, among many other tRNA pool changes, starvation reduced levels of cmo5U-modified tRNA-Thr-CGT and increased levels of tRNA-Thr-GGT that reads the ACC codon, the most abundant (i.e., optimal) of the four codons for threonine. These opposing pathways in hypoxia and starvation stresses illustrate the complexity of translational regulation of gene expression.

Among other pathogenic microbes, Vibrio cholerae displays C to Ψ editing that facilitates tyrosine decoding in the organism.45 This editing process is promoted by yet another modification, ms2io6A (Figure 1), at position 37, the levels of which are subject to environmental iron concentrations. In the unicellular parasite Entamoeba histolytica, the wobble modification queuosine stimulates methylation of C38 to m5C (Figure 1) in the tRNA-Asp-GUC by the enzyme DNMT2,46 a tRNA involved in translation of mRNAs for stress response genes involved in heat shock and DNA repair.47 Like other eukaryotes, E. histolytica cannot synthesize the queuine precursor to queuosine and must obtain it from the environment, including ingested bacteria. Queuine was shown to protect the parasite from oxidative stress by upregulating oxidative stress response genes and downregulating virulence proteins.46 Finally, DNMT2 also methylates C38 in tRNA-Asp-GTC in the malaria parasite Plasmodium falciparum, which protects the tRNA from endonucleolytic cleavage and promotes translation of stress response mRNAs enriched in the cognate codon GAC. Loss of DNMT2 shifted metabolism and increased gametocyte production by >6-fold, revealing an epitranscriptomic mechanism regulating protein translation and homeostasis of sexual commitment in malaria parasites.48 We found that tRNA modifications were more generally involved in parasite biology with a highly coordinated behavior of tRNA modifications throughout the intraerythrocytic developmental cycle (IDC) of P. falciparum.49 We observed a synchronized increase in 22 of 28 modifications from ring to trophozoite stage, consistent with tRNA maturation during translational upregulation. Analysis of >2000 proteins across the IDC revealed that up- and downregulated proteins in late but not early stages showed strong codon biases that directly correlated with parallel changes in tRNA modifications and enhanced translational efficiency.49 Here, we see yet again a translational regulation system in which tRNA modifications modulate the abundance of stage-specific proteins by enhancing translation efficiency of codon-biased transcripts for stage-critical genes.

tRNA Reprogramming and Codon-Biased Translation in Mice and Humans

The importance of tRNA modifications in translation extends beyond microbes to mice and humans, where they also play a critical role in both cell and tissue survival. One of the most well studied of these is mcm5U (Figure 1), a wobble modification generated by the mammalian ortholog of yeast Trm9, AlkB homologue 8 (ABH8/ALKBH8), in conjunction with Elp1–6.50,51 Building on our observation of ALKBH8-dependent sensitivity of human HEK293T cells to alkylation stress,50 we later showed in mice that the presence of mcm5U in the wobble position of tRNA-Sec-UGA positioned ALKBH8 as a regulator of expression of selenocysteine-containing proteins important for responding to oxidative stresses. ROS induced ALKBH8 expression, increased mcm5U levels, and promoted the translation of selenocysteine-containing glutathione peroxidase and thioreductase ROS-detoxification enzymes in mouse embryonic fibroblasts (MEFs).52 MEFs deficient in ALKBH8 were characterized by increased ROS and damage to DNA and lipids, decreased levels of selenoproteins, defective proliferation, and reliance on glycolytic metabolism.53 Analogous phenotypes were observed in mice lacking ALKBH8: elevated protein damage and decreased thioredoxin reductase in unstressed tissues,54 hypersensitivity to redox-active naphthalene and selenoprotein-detoxified acetaminophen,55 and increased levels of necrotic cells, macrophages, and oxidative stress.54

In another parallel with our studies in yeast,36 we found that different stresses uniquely reprogrammed tRNA modifications in human cells.56,57 Changes in the tRNA epitranscriptome of HepG2 liver cells distinguished alkylating from oxidizing agents, with oxidative stressors and arsenite exposure uniquely increasing levels of the wobble queuosine, which is present on tRNAs that read GUN codons.56,57 Arsenite exposure caused upregulation of proteins from codon-biased genes involved in glycolysis, which paralleled a shift from mitochondrial respiration to glycolytic metabolism and markers of mitochondrial toxicity. Limiting the amount of queuine, the microbially produced micronutrient precursor to queuosine in eukaryotes,58 increased arsenite-induced cell death, altered translation, increased ROS levels, and caused mitochondrial dysfunction. In addition to reiterating the mechanistic link between epitranscriptome reprogramming and translational control of cell response, these results have implications for the emerging role of queuine as a micronutrient that determines the human cell response to toxic stresses.

tRNA Reprogramming and Codon-Biased Translation in Cancer

Given ubiquity of tRNA reprogramming and codon-biased translation for normal cell physiology, it is not surprising that corruption of any of the hundreds of genes involved in the epitranscriptome is rapidly being recognized as a major cause of disease in humans.59−61 Of particular interest in recent years has been the role of tRNA modifications in cancers, which we recently reviewed.61 For example, Delauney et al. showed that a subset of breast cancer cells overexpress ELP3, CTU1, and CTU2 that catalyze the wobble modifications cm5U, mcm5U, and mcm5s2U (Figure 1), respectively, to drive tumor growth.62 This tumorigenic behavior was shown to involve a network driven by the DEK trans-acting factor, the gene for which is strongly biased with mcm5s2-regulated codons, and its protein levels were reduced by Elp3 knockdown.62 Elp3-dependent DEK overexpression in tumor cells drives translation of the LEF1 transcription factor that then drives expression of PI3K-AKT, KRAS, and WNT oncogenic pathways.62 Expression of a codon-reprogrammed DEK gene, in which U34 modification-dependent codons were replaced by synonymous codons, resulted in reduced ELP3- or CTU1-dependent translation and cell growth. The same team showed that U34 tRNA modifications also play a role in mutant BRAFV600E-expressing melanoma cell growth.63 U34 modifications were shown to promote glycolysis by supporting codon-biased translation of the transcription factor HIF1A, driving tumorigenic metabolism and drug resistance.64 Once again, introduction of a codon-reprogrammed HIF1A gene into BRAFV600E melanoma cells abolished the dependence of melanoma cells on ELP3- and CTU1-mediated U34 modifications.

Another well-developed example of dysfunctional tRNA reprogramming and codon-biased translation in cancer involves the work of Orellana et al. with METTL1, the methyltransferase catalyzing m7G modification (Figure 1) at position 46 in the variable loop region of several tRNAs. The gene for METTL1 is frequently amplified and overexpressed in cancers, and its levels are correlated with poor patient survival.65 Depletion of METTL1 inhibited cancer cell growth in vitro and cancer progression in mice by impairing the G1/S phase transition. Overexpression of METTL1 led to increased expression of select tRNA substrates, most notably tRNA-Arg-UCU. They also noted higher translational efficiencies for mRNAs bearing the corresponding AGA codons, many of which have functions in cell cycle progression. Further illustrating the interplay between modifications and tRNA copy numbers, ectopic overexpression of a single tRNA, tRNA-Arg-UCU 4–1, in human AML cells increased tumor growth in mice and phenocopied METTL1 overexpression in terms of overall animal survival. Interestingly, the tRNA-Arg-UCU modified by METTL1 at position 46 to m7G also contains the mcm5U modification catalyzed by ELP1–6 and ALKBH8, which raises the potential for dysfunctional tRNA reprogramming crosstalk among different cancers and cancer subtypes. Altogether, these studies point to a role for tRNA modifications in cancer progression and put forth tRNA-modifying enzymes as viable anticancer drug targets.

Conclusions and Perspectives

Changes in tRNA modifications in response to stress have now been identified in all kingdoms of life. Although the stressors and affected organisms vary widely, the mechanism by which these modifications induce their effects appears common—stress-specific reprogramming of tRNA modifications promotes translation of stress-responsive mRNAs possessing modification-dependent codons. The discovery of this mechanism required the development of multiple omic technologies involving mass spectrometry, next-generation sequencing, genomics, and informatics. These tools were used to measure >50 RNA modifications, hundreds of small RNAs, proteome changes, and codon usage patterns. Given that codon-biased translational regulation is frequently hijacked to promote cancer proliferation and chemotherapeutic resistance in many cancer subtypes,61 not only do the enzymes involved in tRNA reprogramming and codon-biased translational regulation represent potential targets for developing personalized therapeutics but also the pathways have applications as biomarkers of disease state and therapeutic response (Figure 5). In other realms, tRNA reprogramming and codon-biased translation also have direct applications in optimizing protein levels. For example, cultured cells used to produce therapeutic and industrial proteins face numerous stresses, including nutrient and oxygen deprivation, that are managed by tRNA modifications and codon biases. This mechanism can thus be exploited to improve biomanufacturing processes. Similarly, mRNA therapeutics are ideally directed toward specific tissues or cells, each of which experiences different physiological stressors. Optimizing codon usage with an understanding of cell type-specific tRNA reprogramming and codon-biased translation therefore has the potential to improve the efficacy of mRNA-based medicines.

Figure 5.

Figure 5

Open in a new tab

Applications of tRNA reprogramming and codon-biased translation concepts and tools for therapeutics and manufacturing.

Acknowledgments

The authors thank all past and present RNA scientists for characterizing the chemistry, enzymology, and biology of the epitranscriptome and for all of their collective efforts to define the complex cellular roles of tRNA modifications. The authors are also grateful for generous financial support from the National Institutes of Health (ES026856, ES031529, GM070641, ES024615), the US National Science Foundation (CHE 1308839), the US Department of Defense (W81XWH-17-1-0185), the National Research Foundation of Singapore through the Singapore-MIT Alliance for Research and Technology Antimicrobial Resistance Interdisciplinary Research Group, the MIT-Spain “la Caixa” Foundation Seed Fund, and the Agilent Foundation. Figures were generated using BioRender.com.

Biographies

Michelle M. Mitchener received her B.S. degree in Chemistry and Molecular and Cellular Biology from Cedarville University and her Ph.D. degree in Chemistry at Vanderbilt University under the direction of Lawrence J. Marnett. Following postdoctoral studies with Tom W. Muir at Princeton University, she joined the Singapore-MIT Alliance for Research and Technology. She is currently investigating the role of tRNA modifications and codon-biased translation in Enterococcus faecalis biofilm formation as part of the Dedon laboratory.

Thomas J. Begley is a Distinguished Professor of Biological Sciences and Director of Training in RNA Science and Technology at the RNA Institute at the University at Albany, SUNY. He received his Ph.D. degree from the University at Albany-SUNY and held NIH- and Merck-sponsored fellowships in Cancer Biology and Biological Engineering at the Harvard School of Public Health and the Massachusetts Institute of Technology. The Begley lab works at the interfaces of nucleic acid biology, chemistry, and computation. A major focus of current studies centers on how RNA modifications, which comprise the epitranscriptome, are collectively reprogrammed in response to environmental stress, pharmaceuticals, and disease states to translationally regulate genes with specific codon usage patterns.

Peter C. Dedon is the Singapore Professor of Biological Engineering at MIT and the Lead Principal Investigator of the Antimicrobial Resistance Interdisciplinary Research Group of the Singapore-MIT Alliance for Research and Technology. His group has developed a variety of analytical and informatic platforms for studying the systems biology of epigenetics, with discovery of phosphorothioate and 7-deazaguanine modifications in bacteria, bacteriophage, and the human microbiome, as well as for understanding epitranscriptomics in infectious disease and cancer. He and Tom Begley are now leveraging these epigenetic and epitranscriptomic discoveries to develop enzymatic tools for biotechnology, methods for industrial microbiology and protein production, and new antimicrobial and anticancer agents.

Author Contributions

All authors contributed equally to this manuscript in terms of research, drafting, and final writing. CRediT: Michelle M. Mitchener conceptualization, writing-original draft, writing-review & editing.

The authors declare the following competing financial interest(s): M.M. declares no competing financial interest. P.D. and T.B. are co-founders of and hold equity in Hovana Inc., Codomax Inc., and Proteomax Lte. Ptd., and they have patents pending on technologies discussed in this paper.

Special Issue

Published as part of the Accounts of Chemical Research special issue “RNA Modifications”.

References

  1. Chan C. T.; Dyavaiah M.; DeMott M. S.; Taghizadeh K.; Dedon P. C.; Begley T. J. A quantitative systems approach reveals dynamic control of tRNA modifications during cellular stress. PLoS Genet 2010, 6, e1001247 10.1371/journal.pgen.1001247. [DOI] [PMC free article] [PubMed] [Google Scholar]
  2. Doyle F.; Leonardi A.; Endres L.; Tenenbaum S. A.; Dedon P. C.; Begley T. J. Gene- and genome-based analysis of significant codon patterns in yeast, rat and mice genomes with the CUT Codon UTilization tool. Methods 2016, 107, 98–109. 10.1016/j.ymeth.2016.05.010. [DOI] [PMC free article] [PubMed] [Google Scholar]
  3. Chionh Y. H.; McBee M.; Babu I. R.; Hia F.; Lin W.; Zhao W.; Cao J.; Dziergowska A.; Malkiewicz A.; Begley T. J.; Alonso S.; Dedon P. C. tRNA-mediated codon-biased translation in mycobacterial hypoxic persistence. Nat. Commun. 2016, 7, 13302. 10.1038/ncomms13302. [DOI] [PMC free article] [PubMed] [Google Scholar]
  4. Hu J. F.; Yim D.; Ma D.; Huber S. M.; Davis N.; Bacusmo J. M.; Vermeulen S.; Zhou J.; Begley T. J.; DeMott M. S.; Levine S. S.; de Crecy-Lagard V.; Dedon P. C.; Cao B. Quantitative mapping of the cellular small RNA landscape with AQRNA-seq. Nat. Biotechnol. 2021, 39, 978–988. 10.1038/s41587-021-00874-y. [DOI] [PMC free article] [PubMed] [Google Scholar]
  5. Johnson T. B.; Coghill R. D. Researches on pyrimidines. C111. The discovery of 5-methyl-cytosine in tuberculinic acid, the nucleic acid of the tubercle bacillus. J. Am. Chem. Soc. 1925, 47, 2838–2844. 10.1021/ja01688a030. [DOI] [Google Scholar]
  6. Sood A. J.; Viner C.; Hoffman M. M. DNAmod: the DNA modification database. J. Cheminform 2019, 11, 30. 10.1186/s13321-019-0349-4. [DOI] [PMC free article] [PubMed] [Google Scholar]
  7. Zhao Y.; Garcia B. A. Comprehensive Catalog of Currently Documented Histone Modifications. Cold Spring Harb Perspect Biol. 2015, 7, a025064. 10.1101/cshperspect.a025064. [DOI] [PMC free article] [PubMed] [Google Scholar]
  8. Diehl K. L.; Muir T. W. Chromatin as a key consumer in the metabolite economy. Nat. Chem. Biol. 2020, 16, 620–629. 10.1038/s41589-020-0517-x. [DOI] [PMC free article] [PubMed] [Google Scholar]
  9. Saletore Y.; Meyer K.; Korlach J.; Vilfan I. D.; Jaffrey S.; Mason C. E. The birth of the Epitranscriptome: deciphering the function of RNA modifications. Genome Biol. 2012, 13, 175. 10.1186/gb-2012-13-10-175. [DOI] [PMC free article] [PubMed] [Google Scholar]
  10. Boccaletto P.; Stefaniak F.; Ray A.; Cappannini A.; Mukherjee S.; Purta E.; Kurkowska M.; Shirvanizadeh N.; Destefanis E.; Groza P.; Avsar G.; Romitelli A.; Pir P.; Dassi E.; Conticello S. G.; Aguilo F.; Bujnicki J. M. MODOMICS: a database of RNA modification pathways. 2021 update. Nucleic Acids Res. 2022, 50, D231–D235. 10.1093/nar/gkab1083. [DOI] [PMC free article] [PubMed] [Google Scholar]
  11. Kitano H. Systems biology: a brief overview. Science 2002, 295, 1662–1664. 10.1126/science.1069492. [DOI] [PubMed] [Google Scholar]
  12. Sharp P. A.; Langer R. Research agenda. Promoting convergence in biomedical science. Science 2011, 333, 527. 10.1126/science.1205008. [DOI] [PubMed] [Google Scholar]
  13. Liu F.; Clark W.; Luo G.; Wang X.; Fu Y.; Wei J.; Wang X.; Hao Z.; Dai Q.; Zheng G.; Ma H.; Han D.; Evans M.; Klungland A.; Pan T.; He C. ALKBH1-Mediated tRNA Demethylation Regulates Translation. Cell 2016, 167, 816–828. 10.1016/j.cell.2016.09.038. [DOI] [PMC free article] [PubMed] [Google Scholar]
  14. Ohira T.; Minowa K.; Sugiyama K.; Yamashita S.; Sakaguchi Y.; Miyauchi K.; Noguchi R.; Kaneko A.; Orita I.; Fukui T.; Tomita K.; Suzuki T. Reversible RNA phosphorylation stabilizes tRNA for cellular thermotolerance. Nature 2022, 605, 372–379. 10.1038/s41586-022-04677-2. [DOI] [PMC free article] [PubMed] [Google Scholar]
  15. Agris P. F. The importance of being modified: roles of modified nucleosides and Mg2+ in RNA structure and function. Prog. Nucleic Acid Res. Mol. Biol. 1996, 53, 79–129. 10.1016/S0079-6603(08)60143-9. [DOI] [PubMed] [Google Scholar]
  16. El Yacoubi B.; Bailly M.; de Crecy-Lagard V. Biosynthesis and function of posttranscriptional modifications of transfer RNAs. Annu. Rev. Genet 2012, 46, 69–95. 10.1146/annurev-genet-110711-155641. [DOI] [PubMed] [Google Scholar]
  17. Lorenz C.; Lunse C. E.; Morl M. tRNA Modifications: Impact on Structure and Thermal Adaptation. Biomolecules 2017, 7, 35. 10.3390/biom7020035. [DOI] [PMC free article] [PubMed] [Google Scholar]
  18. Bjork G. R.; Ericson J. U.; Gustafsson C. E.; Hagervall T. G.; Jonsson Y. H.; Wikstrom P. M. Transfer RNA modification. Annu. Rev. Biochem. 1987, 56, 263–287. 10.1146/annurev.bi.56.070187.001403. [DOI] [PubMed] [Google Scholar]
  19. Soma A.; Ikeuchi Y.; Kanemasa S.; Kobayashi K.; Ogasawara N.; Ote T.; Kato J.; Watanabe K.; Sekine Y.; Suzuki T. An RNA-modifying enzyme that governs both the codon and amino acid specificities of isoleucine tRNA. Mol. Cell 2003, 12, 689–698. 10.1016/S1097-2765(03)00346-0. [DOI] [PubMed] [Google Scholar]
  20. Stern L.; Schulman L. H. The role of the minor base N4-acetylcytidine in the function of the Escherichia coli noninitiator methionine transfer RNA. J. Biol. Chem. 1978, 253, 6132–6139. 10.1016/S0021-9258(17)34590-8. [DOI] [PubMed] [Google Scholar]
  21. Hanson G.; Coller J. Codon optimality, bias and usage in translation and mRNA decay. Nat. Rev. Mol. Cell Biol. 2018, 19, 20–30. 10.1038/nrm.2017.91. [DOI] [PMC free article] [PubMed] [Google Scholar]
  22. Rocha E. P. Codon usage bias from tRNA’s point of view: redundancy, specialization, and efficient decoding for translation optimization. Genome Res. 2004, 14, 2279–2286. 10.1101/gr.2896904. [DOI] [PMC free article] [PubMed] [Google Scholar]
  23. Novoa E. M.; Ribas de Pouplana L. Speeding with control: codon usage, tRNAs, and ribosomes. Trends Genet 2012, 28, 574–581. 10.1016/j.tig.2012.07.006. [DOI] [PubMed] [Google Scholar]
  24. Duret L. Evolution of synonymous codon usage in metazoans. Curr. Opin Genet Dev 2002, 12, 640–649. 10.1016/S0959-437X(02)00353-2. [DOI] [PubMed] [Google Scholar]
  25. Agris P. F.; Koh H.; Soll D. The effect of growth temperatures on the in vivo ribose methylation of Bacillus stearothermophilus transfer RNA. Arch. Biochem. Biophys. 1973, 154, 277–282. 10.1016/0003-9861(73)90058-1. [DOI] [PubMed] [Google Scholar]
  26. Watanabe K.; Shinma M.; Oshima T.; Nishimura S. Heat-induced stability of tRNA from an extreme thermophile, Thermus thermophilus. Biochem. Biophys. Res. Commun. 1976, 72, 1137–1144. 10.1016/S0006-291X(76)80250-1. [DOI] [PubMed] [Google Scholar]
  27. Buck M.; Ames B. N. A modified nucleotide in tRNA as a possible regulator of aerobiosis: synthesis of cis-2-methyl-thioribosylzeatin in the tRNA of Salmonella. Cell 1984, 36, 523–531. 10.1016/0092-8674(84)90245-9. [DOI] [PubMed] [Google Scholar]
  28. Buu A.; Menichi B.; Heyman T. Thiomethylation of tyrosine transfer ribonucleic acid is associated with initiation of sporulation in Bacillus subtilis: effect of phosphate concentration. J. Bacteriol. 1981, 146, 819–822. 10.1128/jb.146.2.819-822.1981. [DOI] [PMC free article] [PubMed] [Google Scholar]
  29. Ortwerth B. J.; Lin V. K.; Lewis J.; Wang R. J. Lysine tRNA and cell division: a G1 cell cycle mutant is temperature sensitive for the modification of tRNA5Lys to tRNA4Lys. Nucleic Acids Res. 1984, 12, 9009–9023. 10.1093/nar/12.23.9009. [DOI] [PMC free article] [PubMed] [Google Scholar]
  30. Weiss G. B. Translational control of protein synthesis by tRNA unrelated to changes in tRNA concentration. J. Mol. Evol 1973, 2, 199–204. 10.1007/BF01654000. [DOI] [PubMed] [Google Scholar]
  31. Kirino Y.; Yasukawa T.; Ohta S.; Akira S.; Ishihara K.; Watanabe K.; Suzuki T. Codon-specific translational defect caused by a wobble modification deficiency in mutant tRNA from a human mitochondrial disease. Proc. Natl. Acad. Sci. U. S. A. 2004, 101, 15070–15075. 10.1073/pnas.0405173101. [DOI] [PMC free article] [PubMed] [Google Scholar]
  32. Begley T. J.; Rosenbach A. S.; Ideker T.; Samson L. D. Damage recovery pathways in Saccharomyces cerevisiae revealed by genomic phenotyping and interactome mapping. Mol. Cancer Res. 2002, 1, 103–112. [PubMed] [Google Scholar]
  33. Begley T. J.; Rosenbach A. S.; Ideker T.; Samson L. D. Hot spots for modulating toxicity identified by genomic phenotyping and localization mapping. Mol. Cell 2004, 16, 117–125. 10.1016/j.molcel.2004.09.005. [DOI] [PubMed] [Google Scholar]
  34. Begley U.; Dyavaiah M.; Patil A.; Rooney J. P.; DiRenzo D.; Young C. M.; Conklin D. S.; Zitomer R. S.; Begley T. J. Trm9-catalyzed tRNA modifications link translation to the DNA damage response. Mol. Cell 2007, 28, 860–870. 10.1016/j.molcel.2007.09.021. [DOI] [PMC free article] [PubMed] [Google Scholar]
  35. Bennett C. B.; Lewis L. K.; Karthikeyan G.; Lobachev K. S.; Jin Y. H.; Sterling J. F.; Snipe J. R.; Resnick M. A. Genes required for ionizing radiation resistance in yeast. Nat. Genet. 2001, 29, 426–434. 10.1038/ng778. [DOI] [PubMed] [Google Scholar]
  36. Chan C. T.; Deng W.; Li F.; DeMott M. S.; Babu I. R.; Begley T. J.; Dedon P. C. Highly Predictive Reprogramming of tRNA Modifications Is Linked to Selective Expression of Codon-Biased Genes. Chem. Res. Toxicol. 2015, 28, 978–988. 10.1021/acs.chemrestox.5b00004. [DOI] [PMC free article] [PubMed] [Google Scholar]
  37. Deng W.; Babu I. R.; Su D.; Yin S.; Begley T. J.; Dedon P. C. Trm9-Catalyzed tRNA Modifications Regulate Global Protein Expression by Codon-Biased Translation. PLoS Genet 2015, 11, e1005706 10.1371/journal.pgen.1005706. [DOI] [PMC free article] [PubMed] [Google Scholar]
  38. Chan C. T.; Pang Y. L.; Deng W.; Babu I. R.; Dyavaiah M.; Begley T. J.; Dedon P. C. Reprogramming of tRNA modifications controls the oxidative stress response by codon-biased translation of proteins. Nat. Commun. 2012, 3, 937. 10.1038/ncomms1938. [DOI] [PMC free article] [PubMed] [Google Scholar]
  39. Patil A.; Dyavaiah M.; Joseph F.; Rooney J. P.; Chan C. T.; Dedon P. C.; Begley T. J. Increased tRNA modification and gene-specific codon usage regulate cell cycle progression during the DNA damage response. Cell Cycle 2012, 11, 3656–3665. 10.4161/cc.21919. [DOI] [PMC free article] [PubMed] [Google Scholar]
  40. Lamichhane T. N.; Blewett N. H.; Crawford A. K.; Cherkasova V. A.; Iben J. R.; Begley T. J.; Farabaugh P. J.; Maraia R. J. Lack of tRNA modification isopentenyl-A37 alters mRNA decoding and causes metabolic deficiencies in fission yeast. Mol. Cell. Biol. 2013, 33, 2918–2929. 10.1128/MCB.00278-13. [DOI] [PMC free article] [PubMed] [Google Scholar]
  41. Patil A.; Chan C.; Dyavaiah M.; Rooney J. P.; Dedon P. C.; Begley T. J. Translational infidelity-induced protein stress results from a deficiency in Trm9-catalyzed tRNA modifications. RNA Biol. 2012, 9, 990–1001. 10.4161/rna.20531. [DOI] [PMC free article] [PubMed] [Google Scholar]
  42. Pollo-Oliveira L.; Klassen R.; Davis N.; Ciftci A.; Bacusmo J. M.; Martinelli M.; DeMott M. S.; Begley T. J.; Dedon P. C.; Schaffrath R.; de Crecy-Lagard V. Loss of Elongator- and KEOPS-Dependent tRNA Modifications Leads to Severe Growth Phenotypes and Protein Aggregation in Yeast. Biomolecules 2020, 10, 322. 10.3390/biom10020322. [DOI] [PMC free article] [PubMed] [Google Scholar]
  43. Tavares J. F.; Davis N. K.; Poim A.; Reis A.; Kellner S.; Sousa I.; Soares A. R.; Moura G. M. R.; Dedon P. C.; Santos M. tRNA-modifying enzyme mutations induce codon-specific mistranslation and protein aggregation in yeast. RNA Biol. 2021, 18, 563–575. 10.1080/15476286.2020.1819671. [DOI] [PMC free article] [PubMed] [Google Scholar]
  44. Hia F.; Chionh Y. H.; Pang Y. L.; DeMott M. S.; McBee M. E.; Dedon P. C. 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. 2015, 43, e32 10.1093/nar/gku1317. [DOI] [PMC free article] [PubMed] [Google Scholar]
  45. Kimura S.; Srisuknimit V.; McCarty K. L.; Dedon P. C.; Kranzusch P. J.; Waldor M. K. Sequential action of a tRNA base editor in conversion of cytidine to pseudouridine. Nat. Commun. 2022, 13, 5994. 10.1038/s41467-022-33714-x. [DOI] [PMC free article] [PubMed] [Google Scholar]
  46. Nagaraja S.; Cai M. W.; Sun J.; Varet H.; Sarid L.; Trebicz-Geffen M.; Shaulov Y.; Mazumdar M.; Legendre R.; Coppee J. Y.; Begley T. J.; Dedon P. C.; Gourinath S.; Guillen N.; Saito-Nakano Y.; Shimokawa C.; Hisaeda H.; Ankri S.. Queuine Is a Nutritional Regulator of Entamoeba histolytica Response to Oxidative Stress and a Virulence Attenuator. mBio 2021, 12. 10.1128/mBio.03549-20 [DOI] [PMC free article] [PubMed] [Google Scholar]
  47. Hertz R.; Tovy A.; Kirschenbaum M.; Geffen M.; Nozaki T.; Adir N.; Ankri S. The Entamoeba histolytica Dnmt2 homolog (Ehmeth) confers resistance to nitrosative stress. Eukaryot Cell 2014, 13, 494–503. 10.1128/EC.00031-14. [DOI] [PMC free article] [PubMed] [Google Scholar]
  48. Hammam E.; Sinha A.; Baumgarten S.; Nardella F.; Liang J.; Miled S.; Bonhomme F.; Erdmann D.; Arcangioli B.; Arimondo P. B.; Dedon P.; Preiser P.; Scherf A. Malaria Parasite Stress Tolerance Is Regulated by DNMT2-Mediated tRNA Cytosine Methylation. mBio 2021, 12, e0255821 10.1128/mBio.02558-21. [DOI] [PMC free article] [PubMed] [Google Scholar]
  49. Ng C. S.; Sinha A.; Aniweh Y.; Nah Q.; Babu I. R.; Gu C.; Chionh Y. H.; Dedon P. C.; Preiser P. R. tRNA epitranscriptomics and biased codon are linked to proteome expression in Plasmodium falciparum. Molecular Systems Biology 2018, 14, e8009 10.15252/msb.20178009. [DOI] [PMC free article] [PubMed] [Google Scholar]
  50. Fu D.; Brophy J. A.; Chan C. T.; Atmore K. A.; Begley U.; Paules R. S.; Dedon P. C.; Begley T. J.; Samson L. D. Human AlkB homolog ABH8 Is a tRNA methyltransferase required for wobble uridine modification and DNA damage survival. Mol. Cell. Biol. 2010, 30, 2449–2459. 10.1128/MCB.01604-09. [DOI] [PMC free article] [PubMed] [Google Scholar]
  51. Huang B.; Johansson M. J.; Bystrom A. S. An early step in wobble uridine tRNA modification requires the Elongator complex. RNA 2005, 11, 424–436. 10.1261/rna.7247705. [DOI] [PMC free article] [PubMed] [Google Scholar]
  52. Endres L.; Begley U.; Clark R.; Gu C.; Dziergowska A.; Malkiewicz A.; Melendez J. A.; Dedon P. C.; Begley T. J. Alkbh8 Regulates Selenocysteine-Protein Expression to Protect against Reactive Oxygen Species Damage. PLoS One 2015, 10, e0131335 10.1371/journal.pone.0131335. [DOI] [PMC free article] [PubMed] [Google Scholar]
  53. Lee M. Y.; Leonardi A.; Begley T. J.; Melendez J. A. Loss of epitranscriptomic control of selenocysteine utilization engages senescence and mitochondrial reprogramming(☆). Redox Biol. 2020, 28, 101375. 10.1016/j.redox.2019.101375. [DOI] [PMC free article] [PubMed] [Google Scholar]
  54. Leonardi A.; Kovalchuk N.; Yin L.; Endres L.; Evke S.; Nevins S.; Martin S.; Dedon P. C.; Melendez J. A.; Van Winkle L.; Zhang Q. Y.; Ding X.; Begley T. J. The epitranscriptomic writer ALKBH8 drives tolerance and protects mouse lungs from the environmental pollutant naphthalene. Epigenetics 2020, 15, 1121–1138. 10.1080/15592294.2020.1750213. [DOI] [PMC free article] [PubMed] [Google Scholar]
  55. Evke S.; Lin Q.; Melendez J. A.; Begley T. J. Epitranscriptomic Reprogramming Is Required to Prevent Stress and Damage from Acetaminophen. Genes (Basel) 2022, 13, 421. 10.3390/genes13030421. [DOI] [PMC free article] [PubMed] [Google Scholar]
  56. Huber S. M.; Begley U.; Sarkar A.; Gasperi W.; Davis E. T.; Surampudi V.; Lee M.; Melendez J. A.; Dedon P. C.; Begley T. J. Arsenite toxicity is regulated by queuine availability and oxidation-induced reprogramming of the human tRNA epitranscriptome. Proc. Natl. Acad. Sci. U.S.A. 2022, 119, e2123529119 10.1073/pnas.2123529119. [DOI] [PMC free article] [PubMed] [Google Scholar]
  57. Wang X.; Matuszek Z.; Huang Y.; Parisien M.; Dai Q.; Clark W.; Schwartz M. H.; Pan T. Queuosine modification protects cognate tRNAs against ribonuclease cleavage. RNA 2018, 24, 1305–1313. 10.1261/rna.067033.118. [DOI] [PMC free article] [PubMed] [Google Scholar]
  58. Fergus C.; Barnes D.; Alqasem M. A.; Kelly V. P. The queuine micronutrient: charting a course from microbe to man. Nutrients 2015, 7, 2897–2929. 10.3390/nu7042897. [DOI] [PMC free article] [PubMed] [Google Scholar]
  59. Jonkhout N.; Tran J.; Smith M. A.; Schonrock N.; Mattick J. S.; Novoa E. M. The RNA modification landscape in human disease. RNA 2017, 23, 1754–1769. 10.1261/rna.063503.117. [DOI] [PMC free article] [PubMed] [Google Scholar]
  60. Suzuki T. The expanding world of tRNA modifications and their disease relevance. Nat. Rev. Mol. Cell Biol. 2021, 22, 375–392. 10.1038/s41580-021-00342-0. [DOI] [PubMed] [Google Scholar]
  61. Dedon P. C.; Begley T. J. Dysfunctional tRNA reprogramming and codon-biased translation in cancer. Trends Mol. Med. 2022, 28, 964–978. 10.1016/j.molmed.2022.09.007. [DOI] [PMC free article] [PubMed] [Google Scholar]
  62. Delaunay S.; Rapino F.; Tharun L.; Zhou Z.; Heukamp L.; Termathe M.; Shostak K.; Klevernic I.; Florin A.; Desmecht H.; Desmet C. J.; Nguyen L.; Leidel S. A.; Willis A. E.; Buttner R.; Chariot A.; Close P. Elp3 links tRNA modification to IRES-dependent translation of LEF1 to sustain metastasis in breast cancer. J. Exp Med. 2016, 213, 2503–2523. 10.1084/jem.20160397. [DOI] [PMC free article] [PubMed] [Google Scholar]
  63. Rapino F.; Delaunay S.; Rambow F.; Zhou Z.; Tharun L.; De Tullio P.; Sin O.; Shostak K.; Schmitz S.; Piepers J.; Ghesquiere B.; Karim L.; Charloteaux B.; Jamart D.; Florin A.; Lambert C.; Rorive A.; Jerusalem G.; Leucci E.; Dewaele M.; Vooijs M.; Leidel S. A.; Georges M.; Voz M.; Peers B.; Buttner R.; Marine J. C.; Chariot A.; Close P. Codon-specific translation reprogramming promotes resistance to targeted therapy. Nature 2018, 558, 605–609. 10.1038/s41586-018-0243-7. [DOI] [PubMed] [Google Scholar]
  64. Denko N. C. Hypoxia, HIF1 and glucose metabolism in the solid tumour. Nat. Rev. Cancer 2008, 8, 705–713. 10.1038/nrc2468. [DOI] [PubMed] [Google Scholar]
  65. Orellana E. A.; Liu Q.; Yankova E.; Pirouz M.; De Braekeleer E.; Zhang W.; Lim J.; Aspris D.; Sendinc E.; Garyfallos D. A.; Gu M.; Ali R.; Gutierrez A.; Mikutis S.; Bernardes G. J. L.; Fischer E. S.; Bradley A.; Vassiliou G. S.; Slack F. J.; Tzelepis K.; Gregory R. I. METTL1-mediated m(7)G modification of Arg-TCT tRNA drives oncogenic transformation. Mol. Cell 2021, 81, 3323–3338. 10.1016/j.molcel.2021.06.031. [DOI] [PMC free article] [PubMed] [Google Scholar]

Articles from Accounts of Chemical Research are provided here courtesy of American Chemical Society

RESOURCES