cells respond to environmental 
 stressors and xenobiotic exposures 
 by controlling gene expression with multilayered , complex regulatory 
 networks . the emergence of tools to quantify the molecular changes 
 associated with these networks has led to an appreciation for the 
 mechanistic insights and predictive power derived from transcriptional 
 profiling , proteomics , and metabolomics . using a novel bioanalytical platform , we recently described an example 
 of another network that regulates the cellular response to xenobiotic 
 exposures and other stresses : stress - induced reprogramming of a system 
 of two - dozen post - transcriptional modifications on trna ( trna ) , which 
 promotes selective translation of codon - biased mrnas for critical 
 response proteins . most forms of rna contain modified ribonucleosides , 
 with more than 120 different chemical structures across all organisms 
 and 23 dozen in any one organism . trna 
 is the most extensively modified rna species , with the presence of 
 specific ribonucleoside structures affecting the rate and fidelity 
 of translation , trna stability , cellular stress responses , and cell growth . to define a systems - level 
 behavior for trna modifications , we developed a chromatography - coupled 
 tandem quadrupole mass spectrometry ( lc - ms / ms ) method to quantify 
 stress - induced changes in the system of modified ribonucleosides in 
 a population of cellular trna molecules or in individual , purified 
 trna species . we applied this approach to budding yeast exposed 
 to four mechanistically different toxicants ( hydrogen peroxide , h2o2 ; methylmethanesulfonate , mms ; sodium arsenite 
 naaso3 ; and sodium hypochlorite , naocl ) and observed agent - specific 
 changes in the relative quantities of 23 modified ribonucleosides 
 in trna . these results pointed to the 
 spectrum of modified ribonucleosides in trna as a predictive biomarker 
 of exposure . furthermore , stress - specific reprogramming of trna wobble 
 modifications was found to result in selective translation of mrnas 
 containing biased use of the codons corresponding to these trnas , 
 with the mrnas representing critical stress response proteins . these observations were made with four chemically distinct 
 toxicants , 
 which raised questions about the specificity of the trna reprogramming 
 signatures for classes of chemical toxicants ( e.g. , all oxidants ) 
 and the generality of the translational response mechanism . to test 
 the hypothesis that cells respond to mechanistically similar toxicants 
 with similar patterns of trna modification reprogramming and that 
 the patterns are predictive of the chemical class of the toxicant , 
 we analyzed changes in the levels of 23 modified ribonucleosides in 
 trna from saccharomyces cerevisiae exposed 
 to four oxidizing agents ( hydrogen peroxide ( h2o2 ) , t - butyl hydroperoxide ( tbhp ) , -radiation 
 ( -rad ) , and peroxynitrite ( onoo ) ) and five 
 alkylating agents ( methylmethanesulfonate ( mms ) , ethylmethanesulfonate 
 ( ems ) , isopropyl methanesulfonate ( ims ) , n - methyl - n-nitro - n - nitrosoguanidine ( mnng ) , 
 and n - nitro - n - methylurea ( nmu ) ) . multivariate statistical analysis and data - driven modeling of the 
 rna modification patterns proved to be highly predictive of toxicant 
 chemistry , with bioinformatic and proteomic analyses establishing 
 a mechanistic link among exposure , trna modifications , and codon - biased 
 translation of response proteins . the results support a 30 year old 
 model for one of the mechanisms by which sn2 alkylating 
 agents promote cell death , by damaging membrane proteins , and establish 
 a novel regulatory mechanism linking exposure to translational response 
 in cells , which is summarized in figure 1 . proposed 
 model for stress - specific reprogramming of trna modifications 
 leading to selective translation of codon - biased response proteins . the specific model shown here is based on exposure to the sn2 alkylating agent , methylmethanesulfonate ( mms ) , which leads to 
 upregulation of the modified ribonucleoside mc in trna 
 species coding for threonine and serine , with their cognate codons 
 enriched ( acc , act ) in membrane - associated proteins . this model is 
 consistent with broad mechanisms for mms - induced toxicity proposed 
 by smith and grisham . all chemicals and reagents were of the highest 
 purity available and were used without further purification . mms , 
 ems , nmu , tbhp , h2o2 , rnase a , and alkaline 
 phosphatase were obtained from sigma chemical co. ( st . louis , mo ) . sodium peroxynitrite 
 was obtained from cayman chemical co. ( ann arbor , mi ) . hplc - grade water and acetonitrile were obtained from mallinckrodt 
 baker ( phillipsburg , nj ) . s. cerevisiae by4741 was obtained from american type culture collections ( manassas , 
 va ) . s. cerevisiae by4741 
 was cultured in ypd ( yeast extract - peptone - dextrose ) media with 200 
 g / ml of geneticine at 30 c with shaking at 220 rpm . each 
 culture was grown to mid log phase ( od660  0.60.8 ) 
 followed by exposure to the following toxicants and concentrations : 
 h2o2 ( 0 , 2 , 3.5 , 5 , 10 , 15 , and 20 mm ) , tbhp 
 ( 0 , 0.7 , 2 , 4 , 7 , 14 , 22 , 25 , and 29 mm ) , onoo ( 0 , 
 0.3 , 0.5 , 0.8 , 1.0 , 1.5 , and 2.0 mm ) , -rad ( 116 gy / min ) ( 0 , 
 21.3 , 168 , 327 , 513 , and 606 gy ) , mms ( 0 , 1.2 , 6 , 12 , 24 , 36 , and 
 48 mm ) , ems ( 0 , 0.19 , 0.29 , 0.39 , 0.49 , and 0.58 m ) , ims ( 0 , 8 , 17 , 
 33 , 50 , and 66 mm ) , mnng ( 0 , 41 , 61 , 82 , 102 , and 136 mm ) , and nmu 
 ( 0 , 1.3 , 2.3 , 3.2 , and 4.2 mm ) . after 1 h at 30 c , these cultures 
 were diluted 10-fold with ypd medium , and 50 l 
 was plated on ypd agar . survival rates of exposed cells were determined 
 by comparing colony counts for untreated and treated samples after 
 2 days of growth . cultures of s. cerevisiae at mid log 
 phase ( od660  
 0.6 ; 2  10 cells ) were treated with 5 mm 
 h2o2 , 25 mm tbhp , 0.8 mm onoo , 500 gy -rad ( 116 gy / min ) , 24 mm mms , 190 mm ems , 50 mm ims , 
 82 mm mnng , or 3.2 mm nmu and incubated for 1 h at 30 c alongside 
 untreated control cultures . cells were pelleted by centrifugation 
 and resuspended in trizol reagent with the addition of antioxidants 
 ( 0.1 mm desferrioxamine and 0.1 mm butylated hydroxytoluene ) and deaminase 
 inhibitors ( 5 g / ml coformycin and 50 g / ml tetrahydrouridine ) . cells in this solution were lysed by three cycles of bead beating 
 in a thermo fp120 bead beater at 6.5 m / s for 20 s each cycle , with 
 1 min of cooling on ice between cycles . the lysates was mixed with 
 one - volume of chloroform , and phases were separated by centrifugation . small rna in the aqueous phase was isolated using using the purelink 
 mirna isolation kit according to manufacturer s instructions . each sample yielded 6 g of rna composed of 90% 
 trna , as judged by bioanalyzer analysis ( agilent corporation ) . trna modifications 
 were quantified using an established lc - ms / ms method , which starts with hydrolysis of trna ( 6 g ) in a 
 solution ( 50 l ; ph 6.8 ) containing 30 mm sodium acetate , 2 
 mm zncl2 , 0.02 unit/l of nuclease p1 , 0.1 unit/l 
 of rnase a , 5 g / ml coformycin , 50 g / ml tetrahydrouridine , 
 0.1 mm deferoxamine mesylate , 0.1 mm butylated hydroxytoluene , and 
 6 pmol of [ n]5 - 2-deoxyadenosine 
 ( [ n]5-da ) as an internal standard . following 
 incubation at 37 c for 3 h , the solution was augmented ( additional 
 50 l volume ) with an additional 30 mm sodium acetate , 0.2 unit/l 
 of alkaline phosphatase , and 0.01 unit/l of phosphodiesterase 
 i at a final ph of 7.8 and incubated 37 c overnight . an aliquot of filtrate containing 0.4 g of 
 ribonucleosides was loaded on a thermo scientific hypersil gold aq 
 reverse - phase hplc column ( 150  2.1 mm , 3 m particle 
 size ) , and ribonucleosides were eluted with the following gradient 
 of acetonitrile in 8 mm ammonium acetate at a flow rate of 0.3 ml / min 
 at 36 c : 018 min , 0% ; 1823 min , 01% ; 
 2328 min , 16% ; 2830 min , 6% ; 3040 
 min , 6100% ; 4050 min , 100% . the hplc column was coupled 
 to an agilent 6410 triple quadrupole lc / ms mass spectrometer with 
 an electrospray ionization source operated in positive ion mode with 
 the following parameters : gas temperature , 350 c ; gas flow , 
 10 l / min ; nebulizer , 20 psi ; and capillary voltage , 3500 v. the first 
 and third quadrupoles ( q1 , q3 ) were fixed to unit resolution , and 
 the modifications were quantified by predetermined molecular transitions . q1 was set to transmit the parent ribonucleoside ions , and q3 was 
 set to monitor the deglycosylated product ions , except for pseudouridine 
 ( y ) , which was detected by setting q1 to transmit the parent ion and 
 q3 set to monitor the m / z = 125 
 product ion resulting from pyrimidine ring fragmentation . 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 n - labeled internal standard are as follows : d , 2.2 
 min , m / z 247  115 , 80 v , 
 5 v ; y , 2.3 min , m / z 245  
 125 , 80 v , 10 v ; mc , 5.4 min , m / z 258  126 , 80 v , 8 v ; cm , 6.4 min , m / z 258  112 , 80 v , 8 v ; mu , 
 7.9 min , m / z 259  127 , 90 
 v , 7 v ; ncmu , 8.7 min , m / z 302  170 , 90 v , 7 v ; acc , 19.0 min , m / z 286  154 , 80 v , 6 v ; mc , 5.0 min , m / z 258  
 126 , 80 v , 8 v ; um , 10.7 min , m / z 259  113 , 90 v , 7 v ; mg , 8.5 min , m / z 298  166 , 90 v , 10 v ; ma , 
 6.9 min , m / z 282  150 , 100 
 v , 16 v ; mcmu , 14.6 min , m / z 317  185 , 90 v , 7 v ; mi , 16.0 min , m / z 283  151 , 80 v , 10 v ; gm , 17.2 min , m / z 298  152 , 80 v , 7 v ; mg , 18.8 min , m / z 298  
 166 , 90 v , 10 v ; mg , 22.2 min , m / z 298  166 , 90 v , 10 v ; i , 7.8 min , m / z 269  137 , 80 v , 10 v ; mcmsu , 31.3 min , m / z 333 
  201 , 90 v , 7 v ; [ n]5-da , 30.0 min , m / z 257  141 , 90 v , 10 v ; m2 g , 31.7 min , m / z 312  180 , 100 v , 8 v ; ta , 32.8 min , m / z 413  281 , 100 v , 8 v ; am , 33.1 
 min , m / z 282  136 , 100 v , 
 15 v ; yw , 34.1 min , m / z 509  
 377 , 120 v , 10 v , and ia , 34.5 min , m / z 336  204 , 120 v , 17 v. the signal from 
 each modified nucleoside was normalized by dividing by the signal 
 from [ n]5-da for the purpose of comparison 
 between samples . complete analysis was performed as technical duplicates 
 of each of five biological replicates , with samples from each biological 
 replicate analyzed as a single batch to minimize the influence of 
 day - to - day variation in instrument performance . exposure - induced changes in the quantities 
 of modified ribonucleosides were calculated as fold - change values 
 using the normalized ms signal intensities for the treated and unexposed 
 samples . this was accomplished by averaging the normalized ms signal 
 intensity for each ribonucleoside in the three control samples and 
 then dividing the corresponding normalized ms signal intensity for 
 each ribonucleoside in each treated sample by this average control 
 value . these fold - change values are presented in table s1 , and the mean ( standard deviation ) fold - change 
 values for the five replicates are presented in table s2 . the fold - change values were then analyzed by hierarchical 
 clustering using the centroid linkage algorithm in cluster 3.0 following 
 log2 transformation of the fold - change data ; heat map representations 
 were produced using java treeview , as described elsewhere . the heat map for the entire data set is shown 
 in figure s3 , and that for the averaged 
 data , in figure 3 . to assess the predictive power 
 of the stress - altered trna modification patterns , the normalized ms 
 signal intensities for each biological replicate of treated and control 
 cells was used to develop a data - driven model ( figure 4a ) . the data for individual samples were labeled as one of 
 three classes of toxicant exposure : unexposed control ( ct ) , alkylating 
 agent - exposed ( aa ) , and oxidizing agent - exposed ( ox ) . a classification 
 model was then developed using k - nearest neighbor classification . 
 from each exposure class , normalized ribonucleoside signal intensities 
 were compared to the other classes using multiple t - tests with bonferroni correction , and those with p values < 0.01 were assigned as unique features of that exposure 
 group . all of these unique features were then set as parameters to 
 construct a data - driven model using the programming software r , in 
 which all data were randomly assigned into two groups : a training 
 set to build the model and a testing set to evaluate the prediction 
 accuracy of the model . for this evaluation , the confusion matrix method 
 ( figure 4b ) was used to determine prediction 
 sensitivity and prediction specificity . mms - induced changes in protein levels 
 were determined by silac proteomics . to prepare 
 isotopically labeled proteins , lys1 yeast 
 cells were grown in yeast nitrogen base ( ynb ) liquid medium containing 
 30 mg / l of l - lysine - u-[c]6 , [ n]2 ( isotec - sigma , miamisburg , oh ) for at least 
 10 generations , until they reached log - phase ( od600  
 0.7 ) . wild - type yeast cells were grown 
 to log phase ( od600  0.7 ) in ynb medium containing 
 30 mg / l of l - lysine . cells were harvested by centrifugation 
 at 1500 g for 10 min at 4 c and washed twice 
 with ice - cold water . cells were then lysed by suspension in alkaline 
 buffer ( 2 m naoh , 8% 2-mercaptoethanol v / v ) , and proteins were precipitated 
 by adding 50% tca and incubating on ice for 10 min , followed by centrifugation 
 at 15  000 g for 15 min at 4 c . the pellet 
 was resuspended in lysis buffer ( 8 m urea , 75 mm nacl , 50 mm tris , 
 ph 8.2 , 50 mm naf , 50 mm -glycerophosphate , 1 mm sodium orthovanadate , 
 10 mm sodium pyrophosphate , 1 mm phenylmethylsulfonyl fluoride ) . heavy silac - labeled lys1 yeast proteins were used as a global internal 
 standard . for the mms exposure studies , 
 cells ( od600  0.7 ) were treated for 1 h with 0.0125% 
 mms in ynb media at 30 c , which caused less than 5% cell death 
 based on a cytotoxicity assay . internal standard was added 
 to all treated and untreated protein samples ( 1:1 ) , and the protein 
 mixture was subjected to disulfide reduction by incubation for 2.5 
 h at 37 c in 1 mm dithiothreitol , followed by thiol alkylation 
 by 5.5 mm iodoacetamide for 40 min at ambient temperature in the dark . proteins were then digested with 50:1 ( w / w ) trypsin overnight at 37 
 c . peptide mixtures were loaded onto a vydac c18 trap column 
 ( 150 m  10 mm ; 5 m diameter , 300  pore - size 
 particle ; grace , deerfield , il ) at flow rate of 5 l / min and 
 eluted onto a vydac c18 analytical column ( 75 m  150 
 mm , 5 m diameter , 300  pore - size particle ) at 200 nl / min 
 with a gradient of 298% acetonitrile with 0.1% formic acid 
 over 180 min . eluted peptides were analyzed by mass spectrometric 
 analysis on a qstar - xl system ( applied biosystems / mds sciex , foster 
 city , ca ) . ms / ms spectra were parsed by spectrum mill ( agilent technologies , 
 foster city , ca ) and searched against saccharomyces genome database 
 ( sgd ) . silac peptide and protein quantitation was performed with differential 
 expression quantitation , and silac protein ratios were determined 
 as the average of all peptide ratios assigned to the protein . a total of 2381 high - confidence proteins 
 with a false - discovery 
 rate of 0.5% were identified in control and mms - treated samples . to control for protein expression regulated 
 by transcription , rather than by translational control mechanisms , the protein expression data were corrected using microarray data from 
 our previous studies with the same yeast strain and mms treatment 
 conditions . after removing genes that show the same expression change ( up , down , 
 or no change ) for both mrna and protein , we identified 222 upregulated 
 and 438 downregulated genes whose expression is significantly regulated 
 by translational machinery ( table s3 ) . gene functional categorization 
 and pathway analysis were performed with david bioinformatics resources 
 2011 . the annotated proteins are clustered 
 according to the biological process branch of the gene ontology ( go ) 
 annotation . the statistical significance of over- or under - representation 
 of proteins in each go category was assessed using a hypergeometric 
 distribution , and the significance indicated by the p values for each go category . a quantitatively meaningful comparative 
 analysis of stress responses requires that cells experience equivalent 
 levels of stress . to this end , we chose cytotoxicity as a common end 
 point , with doses producing 80% lethality for a 1 h exposure to the 
 four oxidizing agents ( onoo , h2o2 , -rad , tbhp ) and five alkylating agents ( mms , ems , 
 ims , mmng , mnu ) , the structures of which are shown in figure 2 . the dose  response curves for the nine agents 
 are shown in figure s1 , from which we determined 
 ld80 doses of 5 mm h2o2 , 25 mm tbhp , 
 0.8 mm onoo , 510 g -radiation ( 116 gy / min ) , 
 24 mm mms , 190 mm ems , 50 mm ims , 82 mm mnng , and 3 mm nmu . to assess 
 the role of trm140 , which methylates c3 of cytosine at position 32 
 in several trnas ( see below ) , in the cellular response to alkylating 
 agent exposures , cell survival was determined for wild - type and trm140 mutant s. cerevisiae exposed to the alkylating agents at ld80 doses determined 
 for wild - type cells . as shown in figure s3 , loss of trm140 modestly but significantly increased the sensitivity 
 of the cells to sn2 alkylating agents mms and ems but not 
 the other three sn1 alkylating agents . having defined equivalent exposure conditions , 
 the effect of exposure to nine different agents on the relative quantities 
 of 23 trna modifications was determined using lc - ms / ms . the analysis 
 consisted of five biological replicate sets of samples each comprised 
 of three unexposed controls and one exposure for each of the nine 
 agents . normalized mass spectrometric ( ms ) signal intensities for 
 each modification in the three unexposed controls were averaged , and 
 changes in the ms signal intensities for the exposed samples were 
 determined relative to this control value . table 
 s1 shows the complete set of 1380 data elements for the five 
 biological replicates of this analysis , with the mean fold - change 
 for each ribonucleoside and each exposure shown in table s2 . the results reveal that the levels of 22 of the 
 23 modified ribonucleosides were changed significantly by the various 
 exposures ( p < 0.05 by student s t test ) ; only mu did not change significantly 
 ( table s2 ) . to identify exposure - dependent 
 patterns in the relative quantities of trna modifications in cells 
 exposed to the nine toxicants , the fold - change data in tables s1 and s2 were subjected to hierarchical 
 clustering analysis , with the heat map representations shown in figures 3 and s3 . analysis of the averaged data sets in figure 3 shows clear class - specific distinctions for the 
 groups of oxidizing agents and alkylating agents . all alkylating agents 
 caused significant ( p < 0.05 ) increases in um , 
 mg , mcmsu , mcmu , and 
 ma , but these modifications were not significantly affected 
 by the oxidizing agents . furthermore , levels of yw and mi decreased significantly in all alkylator - exposed cells , but the 
 changes in oxidant - exposed cells were not significant . in contrast , 
 the levels of ncmu , mc , and ia 
 did not change in cells exposed to any of the alkylating agents , but 
 they increased significantly in response to all oxidizing agents . in addition to these class - specific signature patterns of modified 
 ribonucleosides , there were also  sub - signatures  apparent 
 for both classes of toxicant . this is most apparent for the alkylating 
 agents , for which sn2 alkylating agents ( ems , mms ) cause increases in mc and mg , whereas sn1 alkylating agents ( ims , mnng , mnu ) uniquely increase am ( figure 3 ) . hierarchical clustering analysis of average fold - change 
 values 
 for trna modifications in total trna from cells exposed to equitoxic 
 ( ld80 ) doses of different alkylating agents and oxidizing 
 agents . on the basis of the dose  response curves shown in figure s1 , budding yeast cells were exposed to 
 ld80 doses of the various agents , and trna modifications 
 were quantified by lc - ms / ms . the fold - change values ( table s2 , with significance testing ) were derived from the 
 average of normalized ms signal intensity data from five biological 
 replicates ( figure s2 and table s1 ) , and 
 hierarchical clustering analysis was performed in log space ( log2 ) . the class- and agent - specific distinctions revealed 
 by clustering analysis suggested that the stress - specific patterns 
 of trna modifications could be used to predict exposure chemistry . 
 to test this hypothesis , we developed a data - driven model ( figure 4a ) using the k - nearest 
 neighbor classification method to classify and predict the agent classes 
 in term of trna modification spectra . following 20 training and test 
 cycles of supervised learning , a stable model ( standard error < 2% ) 
 was established . as shown in the confusion matrix in figure 4b , which reports the average number of false positives , false negatives , true positives , and true negatives , the model proved 
 to have sensitivities of 95% for the alkylating agent - exposed group 
 ( aa ) , 94% for oxidant exposures ( ox ) , and 78% for unexposed cells the predictive specificities are 95% for aa , 76% for ox , and 
 98% for ct ( figure 4c ) . on the basis of this 
 model , a set of 14 modified ribonucleosides was identified as contributing 
 the most to distinguishing the exposures , with the group for predicting 
 alkylating agents composed of mc , mg , yw , 
 mcmu , am , gm , mc , mcmsu , and mi , and those for identifying oxidizing agent - exposures 
 consisting of mc , mc , mg , gm , 
 ncmu , m2 g , ia , yw , 
 and cm ( table 1 ) . these features match those 
 revealed in the hierarchical clustering analysis ( figure 3 ) . a data - driven model to define the ability of trna modification 
 spectra to distinguish different stresses . k - nearest neighbor classification 
 was used to develop a data - driven model ( a ) for the normalized ms 
 signal intensities for each biological replicate of treated and control 
 cells ( table s1 ; hierarchical clustering 
 analysis of data sets shown in figure s3 ) . a confusion matrix ( b ) shows the number of false positives , false 
 negatives , true positives , and true negatives that were used to determine 
 prediction sensitivity ( c ) and prediction specificity ( d ) of the model , 
 based on a total of 20 training and test cycles . a low standard error 
 ( < 2% ) demonstrates the stability of the data - driven model . the modified ribonucleosides 
 ( rn ) 
 are organized into three groups , depending on whether their levels 
 increased or decreased in response to the two classes of chemical 
 stress ( alkylating agents ; oxidizing agents ) . specific trnas are noted 
 if the modification is located in a single trna species . information 
 was from modomics database , saccharomyces 
 genome database , and dsilva et 
 al . the link between mc and 
 cell response to sn2 alkylating agents mms and ems is shown 
 in figure s3 , in which it is shown that 
 loss of trm140 confers sensitivity these agents . the observation 
 that sn2 alkylating agents caused differential upregulation 
 of mc in trna is reminiscent of h2o2-induced increases in mc ( figure 3 ) that led to selective translation of mrnas enriched in the leu 
 codon ttg . in addition , the increase in 
 mcmu following mms exposure has been linked to trm9 and 
 to the selective translation of mrnas enriched with the arg codon 
 aga . following this line of logic with mms - treated 
 cells , mc is present at position 32 of the anticodon loop 
 of trna , trna , and trna in s. cerevisiae , with 
 these trnas reading codons act , tca and tcg , respectively . position 32 is adjacent to the anticodon ( positions 
 3436 ) and has the potential to modulate codon it was thus hypothesized that mms treatment would cause 
 upregulation of proteins containing high levels of thr and ser . to 
 test this hypothesis , we performed a comparative analysis of mms - altered 
 protein expression in s. cerevisiae using silac proteomics to quantify soluble proteins . this analysis yielded 2381 high - confidence proteins 
 with a false - discovery rate of 0.5% . to 
 control for protein expression regulated by transcription , rather 
 than by translational control mechanisms , we corrected the protein 
 expression data using microarray data from our previous studies with 
 the same yeast strain and mms treatment conditions . after removing genes that show the same 
 expression change ( up , down , or no change ) for both mrna and protein , 
 we identified 222 upregulated and 438 downregulated genes whose expression 
 is significantly regulated by translational machinery ( table s3 ) . this corrected index of protein expression 
 was then evaluated for codon usage to test the hypothesis that mms 
 alters translational efficiency of genes with biased use of codons 
 related to mc . to minimize the possibility that the influence 
 of mc is counterbalanced by other functional constraints , 
 we considered only proteins with high usage of optimal codons ( each 
 being > 3% of the total codons in the gene for the protein ) . among 
 the thr codons regulated by trna ( thr , thr , thr , thr ) and ser 
 codons regulated by trna and trna ( ser , ser ) , only thr and 
 thr are optimal codons , which are more efficiently translated 
 and more frequently used by yeast genome in comparison with other 
 synonymous codons ( thr , thr ) . as a result indeed , less than 20 of 660 
 mms - altered proteins identified in this study have significantly high 
 usage of these nonoptimal codons ( hypergeometric distribution , p < 0.01 ; yeast genome as background ) . however , as shown 
 in figure 5a , b , genes upregulated by mms treatment 
 are significantly skewed to higher usage of thr and 
 thr codons in comparison with downregulated genes ( student s t test ; thr , p = 3.8  
 10 ; thr , p = 8.0 
  10 ) . this is also apparent as a shift in 
 the distribution of proteins with different acc and act codon content 
 ( figures 5c , d ; kolmogorov  smirnov test : 
 thr , p = 3.7  10 , thr , 1.9  10 ) . ( 29 of 222 ; hypergeometric distribution , p < 0.01 , 
 with yeast genome as background ) , whereas only 7.5% of downregulated 
 genes ( 33 of 438 ) have high usage of this codon ( chi - squared test : 
  = 5.29 , p = 0.021 ) ( figure 5e ) . similarly , the thr codon is overrepresented 
 in 9.0% of upregulated genes ( 20 of 222 ; hypergeometric distribution , p < 0.01 ) but only in 4.1% of downregulated genes ( 18 
 of 438 ) ( chi - squared test :  = 6.52 , p = 0.011 ) ( figure 5f ) . furthermore , if we 
 consider the summed usage of the four thr codons and two ser codons 
 from trnas possessing mc , then no significant difference 
 is observed between up- and downregulated genes , indicating the over - representation 
 of thr and thr codons in upregulated genes 
 is not due to the overall high usage of thr and ser in the proteins . these results support the idea that the four thr codons are differentially 
 recognized by mc - modified trna and that 
 mc - modified trna predominantly interacts 
 with acu and acc to enhance translation . an analysis of statistically 
 significant use of acc and act codons in genes in gene ontology categories 
 reveals highly significant enrichment of translation functions in 
 genes upregulated by mms and in intermediary metabolism categories 
 in genes downregulated by mms , as shown in figure 
 s4 . proteomic analysis reveals that exposure to the sn2 
 alkylating agent , mms , causes selective translation of mrnas possessing 
 biased use of the threonine codons acc and act that are decoded by 
 of trna , one of three trnas possessing mms - regulated 
 mc at position 34 ( figure 3 ) . silac - based 
 proteomic analysis revealed that mms treatment induced up- or downregulation 
 of 694 of 2381 proteins independent of changes in mrna levels ( table s3 ) . the upper box plots show that the 
 threonine codons acc ( a ) and act ( b ) are used significantly more frequently 
 in genes for proteins upregulated by mms exposure than in downregulated 
 proteins , with significance demonstrated by a student s t test ( p = 3.8  10 and p = 8.0  10 , respectively ) . similarly , the distribution plots in middle panels c and d demonstrate 
 a significant mms - induced shift in the population of proteins enriched 
 in codons acc ( c ) and act ( d ) , with significance demonstrated by a 
 kolmogorov smirnov test ( p = 3.7  10 and 1.9  10 , respectively ) . the lower tables show that the threonine codons acc ( e ) and act ( f ) 
 are significantly enriched in genes upregulated by mms exposure ( 29 
 of 222 ; hypergeometric distribution , p < 0.01 ) , 
 whereas the codons are under - represented relative to genome averages 
 in genes downregulated by mms ( chi - squared test : acc ,  = 5.29 , p = 0.021 ; act ,  = 6.52 , p = 0.011 ) . figure 
 s4 shows an analysis of statistically significant use of acc 
 and act codons in mms - regulated genes ( table s3 ) in gene ontology categories , and table s4 lists proteins with threonine content > 10% . we recently discovered a system of translational 
 control of the 
 cellular stress response , in which stress - induced reprogramming of 
 the two - dozen modified ribonucleosides in trna causes the selective 
 translation of codon - biased mrnas for critical response proteins . the studies revealed that mechanistically 
 different chemical stressors produced signature patterns of changes 
 in the relative quantities of the various trna modifications . for 
 example , h2o2 treatment increased the levels 
 of cm , mc and m2 g , whereas hocl , 
 naaso2 and mms either did not affect these modifications 
 or lowered their levels . these results 
 raised the question of the predictive power of trna modification patterns 
 to identify a specific stressor and to distinguish among chemically 
 similar stressors . to address these questions , we developed a convergent 
 platform of bioanalytical and mathematical tools to perform a systems - level 
 analysis of patterns of change in the trna modifications in cells 
 exposed to four oxidizing agents and five alkylating agents . this 
 highly precise analysis ( 12% variance among biological replicates ; 
 see ref ( 7 ) ) revealed 
 significant class - specific features that distinguished oxidizing agents 
 from alkylating agents ( figure 3 ) , with 14 
 modifications forming the basis for a data - driven model that predicted 
 toxicant chemistry with > 80% sensitivity and specificity ( figure 4 ) . furthermore , trna modification spectra distinguished 
 sn1 from sn2 alkylating agents , with sn2 agents causing a coordinated increase in mc and selective 
 translation of threonine - rich membrane proteins derived from act - rich 
 genes . these results establish trna modifications as predictive biomarkers 
 of exposure and illustrate a novel regulatory mechanism for translational 
 control of cell stress response . the class - specific patterns in the spectrum of trna 
 modifications 
 revealed by hierarchical clustering analysis ( figure 3 ) motivated the development of a data - driven model to assess 
 the predictive power of the system of modified ribonucleosides . such 
 a model would provide insights into biological function of the system 
 and the utility of trna modifications as biomarkers of exposure . the 
 resulting model identified 14 of 25 trna modifications in s. cerevisiae as contributing most significantly 
 to distinguishing the two classes of agents . as discussed shortly , 
 several of these modified ribonucleosides have been observed to play 
 roles in the cellular response to h2o2 and mms 
 exposures , including mc , mcmu , and mcmsu , which supports the biological relevance of 
 the model . the predictive power of the model , as revealed by confusion 
 matrix analysis , was remarkably strong , with overall sensitivity and 
 specificity over 80% . examination of the individual specificity and 
 sensitivity for oxidants and alkylating agents , however , revealed 
 that the model had greater predictive power for the latter ( > 95% 
 sensitivity 
 and specificity versus 7678% for oxidants ; figure 4 ) . although the basis for this difference is unclear , 
 it is possible that alkylating agents produce a more complicated or 
 extensive response in terms of activating more gene expression or 
 requiring a broader set of responses than oxidizing agents for the 
 same level of cytotoxicity . by any mechanism , it is striking that 
 changes in the levels of 14 modified ribonucleosides are highly predictive 
 of exposure chemistry in a manner similar to that with transcriptional 
 profiling , proteomics , and metabolomics . the predictive power of the trna 
 modifications likely lies in their link to the system of biased codon 
 use in families of stress response genes , for which we have observed 
 that different stresses uniquely upregulate specific sets of survival 
 and damage mitigation genes . while further 
 study is needed to compare the predictive power of trna modification 
 patterns relative to other large  omic data sets following 
 exposure to these toxicants , the apparent mechanistic interdependence 
 of trna modifications , selective translation of survival proteins , 
 and the resulting changes in cell phenotype suggests the potential 
 for strongly parallel and complementary biomarker signatures . while some of the stress - regulated modified ribonucleosides in 
 table 1 are located at more than one position 
 in a trna and in multiple trna species , which complicates interpretation 
 of their biological function , 10 of them are found in the anticodon 
 loop , which suggests possible involvement in codon recognition and 
 interactions with aminoacyl - trna synthetases . a mechanistic link between stress - induced trna reprogramming and 
 cell stress response is illustrated by oxidation - induced increases 
 in the level of mc ( figure 3 ) . 
 among other positions in several trnas , mc is located 
 at the wobble position of trna that reads the uug 
 in mrna , and it confers resistance to h2o2 by 
 regulating translation of critical stress response proteins from ttg - enriched 
 genes . so , it is not surprising that mc appears in the basis set of 14 ribonucleosides in the model . similar arguments can be made for other ribonucleosides in the basis 
 set ( table 1 ) , several of which are located 
 in a single species of trna ( mcmu , mcmsu , mc , am , yw , mi ) . for example , alkylating 
 agent - induced increases in the relative levels of mcmu 
 and mcmsu ( figure 3 ) , which are part of the 14 ribonucleoside basis set for the exposure 
 model , are consistent with our observation that modification of the 
 wobble position of trna with mcmu confers 
 resistance to mms by promoting the translation of proteins from genes 
 enriched with the cognate aga codon , which represent a specific group 
 of dna damage - response genes . so , again , 
 it is not surprising to see elevations of mcmu and mcmsu in mms - exposed cells . while changes in the 
 number of copies of specific trnas could contribute to the stress - induced 
 changes in levels of trna modifications , along with increased modifying 
 enzyme activity , our previous trna - seq analysis revealed that h2o2 and mms induced nearly identical patterns of 
 up- and downregulation for 58 trnas , with 18 trnas showing opposing 
 changes for the stresses . analogous 
 arguments may provide insights into the role of other stress - regulated 
 ribonucleosides in table 1 . for example , yw 
 and mi are trm5-dependent modifications that are downregulated 
 by exposure to the alkylating agents ( figure 3 ) , with yw located at position 37 of trna and 
 mi located at the wobble position 34 of trna , respectively . niu and co - workers have shown that trm5 is an essential gene involved in cell cycle regulation , which raises the possibility that yw and mi play a role in regulating the translation of cell cycle 
 proteins during the alkylation stress response . further evidence for 
 mechanistic linkage between stress - regulated trna modifications and 
 the stress response arises in the observation of subclass signatures 
 for both oxidizing ( am changes with onoo exposure ) 
 and alkylating agents ( mc changes with sn2 
 alkyllators mms and ems ) , as discussed next . while the behavior 
 is more striking for the alkylating agents , 
 the four oxidizing agents each showed unique patterns of change in 
 the 23 trna modifications ( figure 3 and table s2 ) . for example , onoo uniquely and significantly caused an increase in am , whereas no 
 significant change in this ribonucleoside was observed with the other 
 three oxidizing agent samples ( table s2 ) . the basis for this behavior may lie in the different chemistries 
 of reactive oxygen species ( ros ) and reactive nitrogen species ( rns ) 
 in terms of molecular damage . am is found only at 
 position 4 of trna in s. cerevisiae . analysis of the yeast genome using the codon 
 counting database reveals that the cognate 
 codon for this trna , cac , is found to be significantly enriched in 
 410 genes in the yeast genome ( hypergeometric distribution , p value < 0.01 ; table s4 ) . 
 of these cac - enriched genes , yeast flavohemoglobin is a nitric oxide 
 oxidoreductase , which directly interacts with onoo and is involved in no detoxification . it is well - known to play a 
 role in the oxidative and nitrosative stress responses . interestingly , among spectra of the other 
 three oxidizing agents , h2o2 exposure is more 
 similar to -rad than to another peroxide , tbhp . this may be 
 due to the fact that h2o2 and -rad generate 
 hydroxyl radicals that lead to chemically distinct types of molecular 
 damage than the peroxyl radicals arising from tbhp . thus , there is 
 likely to be a discrete biological mechanism underlying the trna modification 
 patterns for different chemical stresses , with the subclass signatures 
 for sn1 and sn2 alkylating agents providing 
 a striking illustration of the phenomenon . the hierarchical clustering analysis 
 in figure 3 reveals a significant differential 
 alteration of modified ribonucleosides by the two chemically distinct 
 types of alkylating agents . mms and ems represent one class of alkylating 
 agent that appears to react with nucleophilic sites in nucleic acids , 
 proteins , and other cellular molecules by a bimolecular 
 nucleophilic substitution reaction ( sn2 ) , whereas ims , 
 mnng and nmu appear to react by a unimolecular mechanism ( sn1 ) . the relevance of this mechanistic 
 classification for genetic toxicology has been challenged but the two groups of alkylating agents produce 
 distinctly different types of molecular damage , and we use the sn1/sn2 nomenclature 
 for the sake of clarity . the analyses in figure 3 and table s2 clearly and significantly 
 distinguish mms and ems from the other three agents in terms of trna 
 modification spectra . for example , the relative levels of mg and mc in mms- and ems - exposed cells increased more 
 significantly than in ims- , mnng- , and nmu - exposed cells . in contrast , 
 the levels of am and um did not change in response to mms and ems , 
 but they were elevated when cells were treated with the other three 
 alkylating agents . these results suggest that the cells are responding 
 to the damage caused by two classes of alkylating agents with different 
 translational responses that reflect activation of different cellular 
 survival pathways . this point was demonstrated in 1983 by smith and 
 grisham in studies of cytotoxicity in 
 yeast . they observed that the toxicity of sn2 alkylating 
 agents , such as mms , arises mainly by damage to membrane proteins , 
 whereas the toxicity of sn1 alkylating agents , such as 
 mnng , results from interference with dna replication . the sn2 agents , mms and ems , increased the level of mc , which is known to occur at position 32 of trna , trna , and trna and in the 
 variable stem of a ser trna . the link between these trnas 
 and the proposed membrane protein target of sn2 agents 
 may lie in the fact that thr and ser are significantly enriched in 
 membrane proteins in eukaryotes . analysis of the s. cerevisiae genome reveals that , on average , 5.9% 
 of each yeast protein is composed of thr , whereas only 83 of 6000 
 genes in the yeast genome code for proteins composed of > 10% thr ( tables s4 and s5 ) . functional analysis of the thr - enriched proteins by annotation 
 with gene ontology functional attributes using the david bioinformatics 
 resources revealed that 61 of these 83 
 genes code for cell wall and cell membrane proteins ( tables s5 and s6 ) . while membrane proteins were not well represented 
 in our proteomic analysis , which is to be expected given the predominance 
 of soluble proteins in our isolation method , there was a significant 
 upregulation of proteins from genes enriched with thr and the optimal codon thr in cells exposed to mms 
 ( figure 5 ) . along with the observation that 
 loss of the ability synthesize mc confers sensitivity 
 to ems and mms in trm140 cells ( figure s2 ) , these results are consistent with 
 the idea that the ems- and mms - induced increase in mc 
 reflects the need for more efficient translation of thr - enriched membrane 
 proteins in the response of s. cerevisiae to exposure to the sn2 alkylating agents . there are certainly 
 factors other than reprogramming of mc - cointaining trnas 
 and selective translation of act- and acc - enriched genes in the translational 
 control of the mms stress response , as suggested by the downregulation 
 of some proteins from act- and acc - enriched genes ( figure 5 ) , such as contributions from trna modifications 
 and biased codon usage , as well as factors regulating trna charging , 
 ribosome loading , and translational elongation rates . we employed novel bioanalytical and computational tools to demonstrate 
 that cells respond to exposure to different classes of chemical stressors 
 by reprogramming a system of modified ribonucleosides in trna , with 
 unique patterns distinguishing the class of chemicals , as well as 
 the subclass . multivariate statistical analysis and data - driven modeling 
 of the trna modification patterns proved to be highly predictive of 
 toxicant chemistry , with bioinformatic and proteomic analyses establishing 
 a mechanistic link among exposure , trna modifications , and codon - biased 
 translation of response proteins . with implications for other organisms , 
 including humans , these results establish trna modifications as predictive 
 biomarkers of exposure and illustrate a novel regulatory mechanism 
 for translational control of cell stress response .
cells respond to stress by controlling 
 gene expression at several 
 levels , with little known about the role of translation . here 
 , we 
 demonstrate a coordinated translational stress response system involving 
 stress - specific reprogramming of trna wobble modifications that leads 
 to selective translation of codon - biased mrnas representing different 
 classes of critical response proteins . 
 in budding yeast exposed to 
 four oxidants and five alkylating agents , trna modification patterns 
 accurately distinguished among chemically similar stressors , with 
 14 modified ribonucleosides forming the basis for a data - driven model 
 that predicts toxicant chemistry with > 80% sensitivity and specificity . 
 trna modification subpatterns also distinguish sn1 from 
 sn2 alkylating agents , with sn2-induced increases 
 in m3c in trna mechanistically linked to selective translation 
 of threonine - rich membrane proteins from genes enriched with acc and 
 act degenerate codons for threonine . 
 these results establish trna 
 modifications as predictive biomarkers of exposure and illustrate 
 a novel regulatory mechanism for translational control of cell stress 
 response .