Development of an LC-MS/MS method to quantify modified ribonucleosides

As shown in Figure 1, we developed an LC-MS/MS method capable of quantifying 23 of the ∼25 known ribonucleoside modifications in cytoplasmic tRNA in S. cerevisiae [2], [3]. The method begins with isolation of small RNA species (<200 nt) and quantification of the tRNA content (∼80–90% of small RNA species). Individual ribonucleosides in enzymatic hydrolysates of tRNA were resolved by HPLC and identified by high mass accuracy mass spectrometry, by fragmentation patterns with collision-induced dissociation (CID) and by comparison to chemical standards. Each ribonucleoside was subsequently quantified by pre-determined molecular transitions during CID in the LC-MS/MS system. We were able to quantify 23 of the 25 tRNA modifications in yeast, with 2′-O-ribosyladenosine phosphate (Ar(p)) not detected in positive ion mode, possibly due to the negatively charged phosphate, and only tentative identification of ncm⁵Um by CID due to weak signal intensities.

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Figure 1. Total ion chromatogram from LC-MS/MS analysis of yeast tRNA ribonucleosides, as described in Materials and Methods.

https://doi.org/10.1371/journal.pgen.1001247.g001

A critical feature of our approach is quantitative rigor given the need for highly precise measurement of even small changes in the relative quantities of ribonucleosides. To this end, we used an Agilent Bioanalyzer (microfluidics-based sizing and quantification against an internal standard) for quantification of total tRNA species in the mixture of small RNA (85±5%, N = 39) and an internal standard ([¹⁵N₅]-2′-deoxyriboadenosine) to minimize variation in the levels of the individual ribonucleosides. One caveat here is low-level contamination (a few percent) with 5S rRNA that also contains ribonucleoside modifications. We were able to obtain highly reproducible data for the signal intensity associated with each ribonucleoside (see Figure S1 for linearity of signal intensity for the 23 ribonucleosides). Multiple reaction monitoring (MRM) mode yielded no detectable background signal in the absence of tRNA hydrolysates except for i⁶A (9±2%). The method proved to be highly precise: 3±1% intra-day variance in average signal intensity and 12±10% inter-day variance in average fold-change values for each ribonucleoside in treated and untreated cells (294 analyses in three biological replicates over several weeks).

Analysis of tRNA from wild type cells revealed a three-log range of signal intensity, with I and ac⁴C producing the highest intensity and ncm⁵Um the lowest (Figure 1). In general, modifications can be categorized in high (I, ac⁴C, m¹A, m²₂G, Am, Y), medium (Cm, m⁵C, Gm, m¹G, t⁶A, m⁷G, m²G, m³C, i⁶A) and low signal intensities (m¹I, D, m⁵U, ncm⁵Um, mcm⁵U, mcm⁵s²U, Um, yW, ncm⁵U), with signal intensity reflecting both the abundance and mass spectrometric sensitivity for each ribonucleoside.

Yeast exposure parameters

To quantify the dynamics of tRNA modifications in cellular responses, we selected four well studied chemicals that possess distinct mechanisms of toxicity: MMS, hydrogen peroxide (H₂O₂), sodium arsenite (NaAsO₂), and sodium hypochlorite (NaOCl, pK_a 7.5; ref. [21]). The behavior of yeast upon exposure to MMS, NaAsO₂ and H₂O₂ has been extensively studied in terms of transcriptional response and cytotoxicity phenotyping [11], [22], [23]. We also chose NaOCl since it produces an oxidative stress distinct from that of H₂O₂ and could thus affect the tRNA modification spectrum differently. We then performed cytotoxicity dose-response studies in S. cerevisiae exposed to agents (Figure S2), choosing concentrations (Figure 2) that produced ∼20%, 50% and 80% cytotoxicity to ensure a common phenotypic endpoint for comparison.

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Figure 2. Hierarchical cluster analysis of toxicant-induced changes in tRNA modification spectra in wild-type yeast exposed to concentrations of MMS, H₂O₂, NaOCl, and NaAsO₂ producing 20%, 50%, and 80% cytotoxicity.

LC-MS/MS quantification of ribonucleosides was performed as described in Materials and Methods and data expressed as fold-change relative to unexposed controls (Table S2), with hierarchical cluster analysis performed on mean-centered data. The top-left color bar indicates the range of fold-change values.

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One important issue with the methylating agent, MMS, was the possibility that changes in methyl-based modifications in tRNA could be due to both enzymatic methylation and direct chemical methylation. Literature precedent indicates that MMS reacts with DNA to form adducts mainly at guanine N⁷ (68%), adenine N¹ (18%) and cytosine N³ (10%) [24], [25]. To address the extent of direct methylation of RNA by MMS, control studies were performed and revealed that direct alkylation by MMS contributes <25% to the cellular burden of m⁷G in small RNA, with the bulk of m⁷G arising by enzymatic methylation of tRNA (Figure S3). No other agent affected tRNA modifications in this manner, with changes in the relative quantities of the modifications resulting from alterations in biosynthesis, tRNA gene transcription or tRNA degradation.

Reprogramming tRNA modifications during the stress response

With exposure and analytical parameters established, we tested the hypothesis that the spectrum of tRNA modifications would dynamically change as a function of the S. cerevisiae stress response. In addition, we predicted that these changes would serve as biomarkers of each exposure. Cells were exposed to three concentrations of each chemical and 23 tRNA modifications were quantified by LC-MS/MS, with the results shown in Tables S1 and S2, the latter as the ratio of treated to control signal intensities. A crude analysis of the data shows fold-changes ranging from 0.2 to 4, with 25% and 36% of the exposure data significantly different from control values by Student's t-test at p<0.05 and p<0.1, respectively (Table S2). These results point to the non-random and regulated nature of the exposure-induced changes in the levels of the tRNA modifications.

Multivariate statistical analyses revealed important patterns or signatures in the toxicant-induced changes in tRNA modifications. As shown in Figure 2, hierarchical clustering distinguished both agent- and dose-specific changes in the modification spectra, with unique patterns of increase and decrease apparent in all cases. H₂O₂ consistently increased the levels of m⁵C, Cm and m₂²G and, at the highest concentration, t⁶A, with dose-dependent decreases in m⁵U, m¹G, m²G, mcm⁵s²U, i⁶A, yW and m¹A. MMS consistently increased the level of m⁷G, and decreased Am, m⁵C, Cm, mcm⁵s²U, i⁶A, and yW. NaAsO₂ caused only decreases in modification levels at the highest concentration, most notably for mcm⁵U, m³C, m⁷G, mcm⁵s²U, i⁶A, yW, m⁵C, and Cm. Interestingly, the dose-response for NaOCl showed an inverse correlation between concentration and increased levels of Am and Um and decreased levels of m⁵C. Given the reproducibility of the data, the changes in tRNA modification spectra can be considered signature biomarkers of exposure for these four classes of chemical stressor.

Principal component analysis (PCA) creates a model that reduces the complexity of a data set by identifying hidden correlations (the principal components) comprised of weighted, linear combinations of the original variables, with the first principal component (P1) accounting for the largest portion of the variation of the data and so on. The results of PCA of the dataset of nucleoside fold-change values (Table S2) are shown in Figure 3. With 88% of the variability expressed in the first 3 principal components (56%, 22% and 10%, respectively), individual agents contributed variance to each as shown in Table S3, with H₂O₂ contributing 74% in P1, MMS and NaOCl each contributing >40% in P2 and NaAsO₂ contributing 53% in P3. The scores plots (Figure 3A, 3C) clearly distinguish the four agents, with H₂O₂-induced changes as the major determinant of P1 and with MMS, NaOCl and NaAsO₂ distinguished best in P2. While H₂O₂ and NaOCl are negatively correlated in P1, they are more closely grouped in P2 and P3, which suggests that the changes in tRNA modifications reflect both common and unique facets of the toxic mechanism of each agent. For example, H₂O₂ and NaOCl are both oxidizing agents, but H₂O₂ generates hydroxyl radicals by Fenton chemistry while the protonated form of NaOCl yields hydroxyl radicals, chloramines and singlet oxygen [26]–[29]. Similarly, MMS and NaAsO₂ are negatively correlated in P3 and more positively correlated in P2, with the latter consistent with recent evidence for alkylation-like adduction of arsenic to DNA and proteins following its metabolism [30], [31]. This would also explain the negative correlation of NaAsO₂ and H₂O₂ in P1, while the recognized oxidative stress caused by arsenite [32] is consistent with a positive correlation between NaAsO₂ and H₂O₂ in P2.

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Figure 3. Principal component analysis (PCA) of changes in the levels of tRNA modifications caused by exposure to MMS, H₂O₂, NaOCl, and NaAsO₂.

Toxicant-induced changes in the relative quantities of 23 tRNA ribonucleoside modifications (Table S2) were subjected to PCA following mean centering and normalization of the fold-change data.

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Both PCA (Figure 3B, 3D) and cluster analysis (Figure 2) revealed that m⁵C, m₂²G, Cm and t⁶A are major features of the H₂O₂ response, while m¹A, m³C and m⁷G were associated with MMS. Increases in Gm, Um, I and Am were responsible for the variance induced by NaOCl, which is consistent with the inversely related doses and levels for Am and Um observed in cluster analysis. NaAsO₂ was poorly distinguished in P2, with only m²G accounting for variance only at the highest concentrations (Figure 2).

tRNA modification biosynthetic pathways are critical to the stress response

The observation of toxicant- and dose-dependent changes in the levels of the 23 tRNA modifications is consistent with a model in which cells respond to toxicant exposure by modifying tRNA structure to enhance the synthesis of proteins critical to cell survival, as has been proposed in our earlier work with yeast exposure to MMS [11]. In this case, the conversion of cm⁵U to mcm⁵U by Trm9 was found to be critical for surviving MMS exposure [11]. To define the roles of specific tRNA modifications in the toxicant response, cytotoxicity phenotypic analyses were performed with yeast mutants lacking each of 13 trm tRNA methyltransferase genes and 3 other types of RNA modification biosynthetic genes. As shown in Figure 4, heightened sensitivity to H₂O₂ was observed in mutants lacking Trm4 and Trm7, which catalyze formation of two modifications elevated by H₂O₂ exposure: m⁵C and Cm, respectively [33], [34]. The simple explanation is that the increase in a specific tRNA modification is needed to promote an efficient stress response. However, m²₂G was also elevated by H₂O₂ (Figure 2, Figure 3), yet loss of an enzyme involved in its biosynthesis, Trm1 [35], [36], did not confer H₂O₂ sensitivity (Figure 4). This behavior draws a comparison to mRNA, as it has been reported that many of the transcripts induced in response to a stress are not essential for viability during a challenge from that stress [37], [38]. MMS sensitivity was identified in trm1, trm4 and trm9 mutants, the latter as shown previously [11], whose corresponding proteins synthesize m²₂G, m⁵C and mcm⁵U/mcm⁵s²U, respectively. However, these modifications were not strongly associated with MMS exposure in PCA (Figure 2, Figure 3). Somewhat surprisingly, loss of Trm1, Trm4, Trm7 and Trm9 conferred NaAsO₂ sensitivity. These methyltransferases are responsible for m₂²G, m⁵C, m¹G (position 37) and mcm⁵u/mcm⁵s²U, respectively, of which only m²G was found to vary significantly in PCA (Figure 3). For NaOCl, only trm4 was sensitive to exposure and the m⁵C product of Trm4 was not associated with NaOCl exposure (Figure 3). Again, this behavior parallels that of mRNA transcripts the levels of which do not change after exposure but that encode proteins important for viability after exposure [37], [38].

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Figure 4. Phenotypic analysis of cytotoxicity induced by MMS, NaOCl, H₂O₂, and NaAsO₂ in yeast mutants lacking trm tRNA methyltransferase and other modification genes.

Data represent mean ± SD for three biological replicates. Asterisks denote values statistically different from unexposed controls by Student's t-test, p<0.05. Associated RNA modifications are listed below each enzyme.

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Potential mechanisms linking tRNA modifications and the stress response

These results reveal a complex and dynamic control of tRNA modifications in cellular survival responses and suggest models for homeostasis of the modifications. One example involves modifications for which the biosynthetic mutant is sensitive to exposure but the modification level does not change in wild type cells following exposure (e.g., MMS exposure and trm1/m²₂G, trm4/m⁵C, trm9/mcm⁵U or mcm⁵s²U; Figure 2, Figure 3, Figure 4). The simplest explanation here is that the modification change occurs in a single tRNA species and the change is masked by an inverse change in the level of the modification in the larger population of tRNA molecules. As noted in Table S4, both m²₂g and m⁵C occur in multiple tRNAs. A second explanation parallels the idea of both pre-existing mRNA and stressor-induced transcription during a stress response. We have observed stress-induced increases in the levels of several modifications required for the survival response (Figure 2, Figure 3; ref. [11]). However, other modifications may already exist on tRNA molecules involved in selective translation of stress response messages. In both cases, the modifications are absolutely required for survival, but some are already present in unstressed cells and others are induced. Finally, it is possible that a modification, though its level may not change, is required for the subsequent synthesis of other modifications that are critical to the survival response. Such “cooperativity” is suggested by data from mod5-deficient cells, in which i⁶A decreases by ∼75-fold while D is reduced by ∼2-fold. The presence of i⁶A may signal downstream biosynthetic events, with deficiencies promoting a general reprogramming of tRNA. Similarly, cells deficient in Trm82, a subunit of m⁷G methyltransferase, had a ∼7-fold reduction in m⁷G and a >1.5-fold increase in m³C, mcm⁵U, m¹G, m²G, t⁶A, mcm⁵s²U and m₂²G (Figure 5), which raises the possibility that Trm82 itself or m⁷G inhibits other tRNA modifying enzymes. With the caveat of possible increases in tRNA copy number, the ∼50% increase in these modifications suggests a pool of unmodified tRNA molecules, an observation supported by increases in m³C after exposure to MMS, mcm⁵U after exposure to NaOCl, and both t⁶A and m₂²G after exposure to H₂O₂ (Figure 2, Figure 3).

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Figure 5. Cluster analysis visualization of changes in the relative levels of tRNA ribonucleoside modifications in mutants lacking ribonucleoside-modifying enzymes.

The ratios of ribonucleoside levels from Table S5 were subjected to hierarchical cluster analysis. Red – increases; green – decreases. The top-left color bar indicates the range of fold-change values.

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Cooperativity could also explain the case in which the level of a modification changes significantly following exposure yet the mutant strain is not sensitive to the exposure. For example, loss of trm1 did not confer sensitivity to H₂O₂ but its product, m²₂G, rose significantly with H₂O₂ exposure (Figure 2, Figure 3, Figure 4). The stress-induced change in m²₂G may be a response to a change occurring with another modification for which the mutant strain might be sensitive to the exposure. In support of this argument, m⁵C modifications increase along with m²₂G after H₂O₂ exposure and deficiencies in the m⁵C-producing methyltransferase Trm4 confer sensitivity to H₂O₂. Wohlgamuth-Benedum et al. have also demonstrated such cooperativity among RNA modifications in their observation of the negative regulation of wobble position C-to-U editing by thiolation of a U at position 33 outside the anticodon in T. brucei [39].

Finally, there is the case in which a modification decreases with exposure to a stressor and a deficiency in the enzyme responsible for that modification confers sensitivity, as in the case of m⁵C, trm4 and NaOCl (Figure 2, Figure 3, Figure 4). The population level of m⁵C may decrease with NaOCl exposure in spite of a protective increase in the level of m⁵C at some critical tRNA location. This may reflect a decrease in the transcription of tRNA substrates of Trm4 or the targeted degradation of specific tRNA species. It is important to note that biosynthetic redundancy, as in the case of Gm with Trm3 and Trm7, could mask any major changes in tRNA modification levels that are associated with mutational loss of one enzyme (Figure 5), yet loss of one of the redundant enzymes can induce sensitivity, such as the case of H₂O₂ and trm7 (Figure 2, Figure 3, Figure 4). These observations lead to many questions that obviously require more mechanistic study to define the precise role of tRNA modifications in cellular responses to stress.

One consistent feature that arose from our studies of modifications affected by or protecting against toxicant exposure was the frequent involvement of the wobble position, 34 (Tables S4, S6). The correlation between the wobble modification and the importance of a corresponding enzyme after toxicant exposure is not surprising in light of recent observations of the critical role played by these modifications and anticodon loop ribonucleosides in translational fidelity and efficiency [4]. Controlled alteration of ribonucleoside structure at position 34, and that at the conserved purine at position 37, is proposed to allow reading of degenerate codons by modulating the structure of the anticodon domain to facilitate correct codon binding [4]. As the most frequently modified ribonucleosides, positions 34 and 37 also have the largest variety of modifications [40], [41], so it is reasonable that they would be extensively involved in translational control of the survival response. This is also consistent with our previous observation that mcm⁵U at the wobble position was critical to the translation of key protein synthesis and DNA damage response genes [11].

Perhaps more interesting is a potential role for putative non-anticodon loop ribonucleoside modifications in the survival response. For example, Trm44 is the 2′-O-methyltransferase in yeast responsible for formation of 2′-O-methyl-U (Um), which occurs only at position 44 in yeast tRNA [42], [43]. Loss of Trm44 conferred sensitivity to NaAsO₂ exposure. This observation suggests three possibilities: (1) that Trm44 synthesizes or influences the synthesis of modifications at other positions in tRNA; (2) that Um occurs in positions other than 44 (e.g., anticodon loop); or (3) that Um(44) plays a role in modulating translation in response to NaAsO₂ exposure. Another example involves Trm1 and m²₂G at position 26. Current evidence suggests that m²₂G occurs only at position 26 in yeast tRNA [43] and that Trm1 is the methyltransferase responsible for its formation [44]. The fact that loss of Trm1 conferred sensitivity to MMS and NaAsO₂ exposure and that H₂O₂ exposure increased the level of m²₂G again suggest the three possibilities analogous to those for Trm44 and Um. Similar arguments can be made for Trm3 and Gm at position 18 with NaOCl exposure, for Trm11 and m²G at position 10 with NaOCl and NaAsO₂ exposure, and for Trm8/82 and m⁷G at position 46 with MMS exposure.

All of these observations point to participation of wobble and non-wobble RNA modifications in a complex and dynamic network of translational mechanisms in cellular responses. This expands the repertoire of translational control mechanisms, which includes recent discoveries about the effect of ribonucleoside modifications on tRNA stability [8], [9]. In this model, cell stress leads to rapid degradation of specific tRNAs and subsequent effects on translational efficiency. Another similar stress response involves cleavage of cytoplasmic transfer RNAs by ribonucleases released during the stress [10]. One consequence of these degradation pathways would be to decrease the amount of modified ribonucleoside detected in our assay, which may explain some of our observations with the toxicant stresses. Our approach to quantifying tRNA modifications provides information only about population-level changes, so the observed changes could result from modification of existing tRNA molecules or changes in the number of tRNA copies. Of particular importance here is the observation by Phizicky and coworkers that loss of m⁷G at position 46 leads to degradation of specific tRNAs [9], which suggests that our observation of changes in the levels of RNA modifications could be amplified by both reduction in the activity of modifying enzymes and by tRNA degradation. On the other hand, one argument against large increases in tRNA copy number arises from recent observations of repressed tRNA transcription during S-phase and, of direct relevance to the present studies, during replication stress induced by MMS, hydroxyurea and likely other toxicants [45]. Finally, our findings may also parallel recent work on tRNA charging. Reactive oxygen species have been implicated as a methionine misacylation trigger and modification status could help promote these programmed changes to the genetic code [12]. As we are beginning to appreciate the precision and coordinated nature by which cells mount a regulated stress-response, it is most likely the observed changes in tRNA modification levels promote multiple biological responses.

Novel biosynthetic pathways for tRNA modifications

As recognized by several groups [19], [20], the LC-MS/MS platform facilitates definition of biosynthetic pathways for RNA modifications. This is illustrated in Table S5, which contains ratios of the basal levels of tRNA modifications in yeast mutants lacking various tRNA modification enzymes compared to wild type yeast, and in a heat map visual depiction of these ratios in Figure 5. These data corroborate known substrate/enzyme pairs [43] and further demonstrate the highly quantitative nature of our approach. For example, the level of m¹I drops to nearly undetectable levels with loss of Tad1, the adenosine deaminase producing the inosine precursor to m¹I [46]. That a diploid heterozygous mutant of trm5, the product of which catalyzes N-methylation of I [47], caused a ∼40% reduction in total m¹I attests to the accuracy of our assay and demonstrate that gene dosage effects alter the level of tRNA modification. A similar ∼50% reduction in yW occurred in the trm5 mutant due to the absence of the m¹G(37) precursor to yW [47], while complete loss of Trm12, which methylates the 4-demethylwyosine precursor of yW, made yW undetectable. Other pathways critical to yW are apparent in the smaller decreases in yW (0.3– to 0.5-fold) occurred in cells deficient in other enzymes (Trm8, Trm82, Tad1, Mod5, Tan1, Trm11, Trm5; Figure 5, Table S5).

The data in Figure 5 also reveal several novel observations. Pintard et al. observed that Trm7 catalyzes 2′-O-methylation of G and C nucleosides at positions 32 and 34, but they could not detect the ncm⁵Um product of 2′-O-methylation of ncm⁵U [34]. While we could only tentatively identify ncm⁵Um, we observed a quantifiable signal for a species with the correct molecular transition for ncm⁵Um and observed that loss of Trm7 led to a lowering of putative ncm⁵Um to undetectable levels (Figure 5, Table S5). This supports their prediction that Trm7 catalyzes formation of ncm⁵Um in yeast.

Another example involves the formation of Um. While Trm44 catalyzes synthesis of Um at position 44 in tRNA(ser) [42], analysis of trm mutants in Figure 5 and Table S5 suggests a redundancy in methyltransferase activity capable of 2′-O-methylation of U(44), including Trm7, which methylates U at positions 32 and 34 [34], and Trm13 methylation of C and A at position 4 in several yeast tRNAs. Cells lacking Trm44, Trm7 or Trm13 have 53%, 50% and 76% of wild type levels of Um, respectively. More striking evidence for this redundancy arises in correlation analysis that revealed a strong covariance in the levels of tRNA modifications in cells lacking either Trm 44 or Trm 13 (Table S7; C = 0.87). This correlation ranks second highest in our analysis behind the two subunits of the m⁷G methyltransferase (Trm8 and Trm82; C = 0.95), which suggests possible functional redundancy for Trm44 and Trm13, with broader substrate specificities for either or both enzymes.

In summary, a quantitative bioanalytical approach to the study of tRNA modifications has revealed several novel biosynthetic pathways for RNA modifications and has led to the discovery of signature changes in the spectrum of tRNA modifications in the damage response to different toxicant exposures. The results support a general model of dynamic control of tRNA modifications in cellular response pathways and add to the growing repertoire of mechanisms controlling translational responses in cells [8]–[10], [13]. Further, these cellular response mechanisms almost certainly involve parallel changes in spectrum of ribonucleoside modifications in rRNA and perhaps other RNA species.

Materials

All chemicals and reagents were of the highest purity available and were used without further purification. 2′-O-Methyluridine (Um), pseudouridine (Y), N¹-methyladenosine (m¹A), N²,N²-dimethylguanosine (m²₂G), and 2′-O-methylguanosine (Gm) were purchased from Berry and Associates (Dexter, MI). N⁶-Threonylcarbamoyladenosine (t⁶A) was purchased from Biolog (Bremen, Germany). N⁶-Isopentenyladenosine (i⁶A) was purchased from International Laboratory LLC (San Bruno, CA). 2′-O-Methyladenosine (Am), N⁴-acetylcytidine (ac⁴C), 5-methyluridine (m⁵U), inosine (I), 2-methylguanosine (m²G), N⁷-methylguanosine (m⁷G), 2′-O-methylcytidine (Cm), 3-methylcytidine (m³C), 5-methylcytidine (m⁵C), alkaline phosphatase, lyticase, RNase A, ammonium acetate, geneticine and desferrioxamine were purchased from Sigma Chemical Co. (St. Louis, MO). Nuclease P1 was purchased from Roche Diagnostic Corp. (Indianapolis, IN). Phosphodiesterase I was purchased from USB (Cleveland, OH). PureLink miRNA Isolation Kits were purchased from Invitrogen (Carlsbad, CA). Acetonitrile and HPLC-grade water were purchased from Mallinckrodt Baker (Phillipsburg, NJ). All strains of S. cerevisiae BY4741 were purchased from American Type Culture Collections (Manassas, VA).

Exposure of S. cerevisiae

Cultures of S. cerevisiae BY4741 were grown to mid-log phase followed by addition of toxicants to the noted final concentrations (cytotoxicity of ∼20%, 50% and 80%): H₂O₂, 2, 5 or 12 mM; MMS, 6, 12 or 24 mM; NaAsO₂, 20, 40 or 60 mM; NaOCl, 3.2, 4.0 or 4.8 mM. The sensitivity of the following mutant strains to toxicant exposure was also determined (doses producing ∼80% cytotoxicity in wild-type: 12 mM H₂O₂, 24 mM MMS, 60 mM NaAsO₂, or 4.8 mM NaOCl): trm1, trm2, trm3, trm4, trm7, trm8, trm9, trm10, trm11, trm12, trm13, trm44, trm82, tad1, mod5, and tan1. Since trm5 is essential, a diploid strain (GBY1) lacking one copy of trm5 was used. After a 1 h, cells were collected and viability determined by plating.

tRNA isolation

Following lyticase treatment (50 units) in the presence of deaminase inhibitors (5 µg/ml coformycin, 50 µg/ml tetrahydrouridine) and antioxidants (0.1 mM desferrioxamine, 0.1 mM butylated hydroxytoluene), tRNA-containing small RNA species were isolated (Invitrogen PureLink miRNA kit) and the tRNA quantified (Agilent Series 2100 Bioanalyzer).

Quantification of cytoplasmic tRNA modifications

Following addition of deaminase inhibitors, antioxidants and [¹⁵N]₅-2-deoxyadenosine internal standard (6 pmol), tRNA (6 µg) in 30 mM sodium acetate and 2 mM ZnCl₂ (pH 6.8) was hydrolyzed with nuclease P1 (1 U) and RNase A (5 U) for 3 h at 37°C and dephosphorylated with alkaline phosphatase (10 U) and phosphodiesterase I (0.5 U) for 1 h at 37°C following addition of acetate buffer to 30 mM, pH 7.8. Proteins were removed by filtration (Microcon YM-10). Ribonucleosides were resolved with a Thermo Scientific Hypersil GOLD aQ reverse-phase column (150×2.1 mm, 3 µm particle size) eluted with the following gradient of acetonitrile in 8 mM ammonium acetate at a flow rate of 0.3 ml/min and 36°C: 0–18 min, 1–2%; 18–23 min, 2%; 23–28 min, 2–7%; 28–30 min, 7%; 30–31 min, 7–100%; 31–41 min, 100%. The HPLC column was coupled to an Agilent 6410 Triple Quadrupole LC/MS mass spectrometer with an electrospray ionization source where it was operated in positive ion mode with the following parameters for voltages and source gas: gas temperature, 350°C; gas flow, 10 l/min; nebulizer, 20 psi; and capillary voltage, 3500 V. The first and third quadrupoles (Q1 and Q3) were fixed to unit resolution and the modifications were quantified by pre-determined molecular transitions. Q1 was set to transmit the parent ribonucleoside ions and Q3 was set to monitor the deglycosylated product ions, except for Y for which the stable C-C glycosidic bond led to fragmentation of the ribose ring; we used the m/z 125 ion for quantification [48], [49]. The dwell time for each ribonucleoside was 200 ms. The retention time, m/z of the transmitted parent ion, m/z of the monitored product ion, fragmentor voltage, and collision energy of each modified nucleoside and ¹⁵N-labeled internal standard are as follow: D, 1.9 min, m/z 247→115, 80 V, 5 V; Y, 2.5 min, m/z 245→125, 80 V, 10 V; m⁵C, 3.3 min, m/z 258→126, 80 V, 8 V; Cm, 3.6 min, m/z 258→112, 80 V, 8 V; m⁵U, 4.2 min, m/z 259→127, 80 V, 7 V; ncm⁵U, 4.3 min, m/z 302→170, 90 V, 7 V; ac⁴C, 4.4 min, m/z 286→154, 80 V, 6 V; m³C, 4.4 min, m/z 258→126, 80 V, 8 V; ncm⁵Um, 5.5 min, m/z 316→170, 90 V, 7 V; Um, 5.1 min, m/z 259→113, 80 V, 7 V; m⁷G, 5.1 min, m/z 298→166, 90 V, 10 V; m¹A, 5.7 min, m/z 282→150, 100 V, 16 V; mcm⁵U, 6.4 min, m/z 317→185, 90 V, 7 V; m¹I, 7.3 min, m/z 283→151, 80 V, 10 V; Gm, 8.0 min, m/z 298→152, 80 V, 7 V; m¹G, 8.3 min, m/z 298→166, 90 V, 10 V; m²G, 9.4 min, m/z 298→166, 90 V, 10 V; I, 10.9 min, m/z 269→137, 80 V, 10 V; mcm⁵s²U, 14.2 min, m/z 333→201, 90 V, 7 V; [¹⁵N]₅-dA, 14.4 min, m/z 257→141, 90 V, 10 V; m²₂G, 15.9 min, m/z 312→180, 100 V, 8 V; t⁶A, 17.2 min, m/z 413→281, 100 V, 8 V; Am, 19 min, m/z 282→136, 100 V, 15 V; yW, 34.2 min, m/z 509→377, 80 V, 5 V, and i⁶A, 34.4 min, m/z 336→204, 100 V, 17 V. The mass spectrometer monitored ions with the molecular transitions of D, Y, m⁵C, and Cm from 1 to 4 min; molecular transitions of m⁵U, ncm⁵U, ac⁴C, m³C, ncm⁵Um, Um, m⁷G, m¹A, and mcm⁵U from 4 to 7 min; molecular transitions of m¹I, Gm, m¹G, and m²G from 7 to 10 min; molecular transitions of I, mcm⁵s²U, [¹⁵N]₅-dA, m²₂G, t⁶A, and Am from 10 to 30 min; molecular transitions of yW and i⁶A from 30 to 40 min. The identities of individual ribonucleosides were established by comparison to commercially available synthetic standards, high mass accuracy mass spectrometry, fragmentation patterns generated by collision-induced dissociation (CID) in a quadrupole time-of-flight mass spectrometer (QTOF) or MSⁿ analysis by ion trap mass spectrometry, with comparison to literature data (e.g., ref. [48]).

Quantification of m⁷G in control and MMS-treated yeast

To assess the direct and indirect effects of MMS on levels of methylated ribonucleosides, the absolute levels of m⁷G were quantified in small RNA hydrolysates isolated from MMS-exposed and unexposed mutant and wild type strains of yeast by the LC-MS/MS method described above. Calibration curves were generated by mixing variable amounts of m⁷G (final concentrations of 0, 5, 50, 300, 600, 1000, and 2000 nM) with a fixed concentration of [¹⁵N]₅-dA (40 nM). A volume of 10 µl of each solution was analyzed with the LC-MS/MS system described earlier.

Statistical analysis of changes in the levels of tRNA modifications

Differences in the levels of ribonucleosides in exposed versus unexposed and in mutant versus wild-type yeast were analyzed by Student's t-test. Hierarchical clustering analyses were performed using Cluster 3.0. Data were transformed to log₂ ratios of modification levels in treated cells relative to unexposed controls. Clustering was carried out using the centroid linkage algorithm based on the distance between each dataset measured using the Pearson correlation, with heat map representations produced using Java Treeview. Principal component analysis was performed using XLStat (Addinsoft SARL, Paris, France), with a Pearson correlation matrix consisting of data that were mean-centered and normalized to the standard deviation. Correlation analysis was used to assess the degree of covariance among the various sets of fold-change values for each mutant (Table S5), with correlation coefficients calculated using Excel (Microsoft).