systematic detection of amino acid substitutions in ... · starving bacteria for serine resulted in...

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SystematicdetectionofaminoacidsubstitutionsinproteomerevealsamechanisticbasisofribosomeerrorsErnestMordret, Avia Yehonadav, Georgina D Barnabas, Jürgen Cox, Orna Dahan, TamarGeiger*,ArielBLindner*,YitzhakPilpel**correspondingauthorsTranslationerrors limit theaccuracyof information transmission fromDNA toproteins.Selectivepressuresshapethewaycellsproducetheirproteins:thetranslationmachineryand themRNAsequences it decodes co-evolved to ensure that translationproceeds fastand accurately in a wide range of environmental conditions. Our understanding of thecauses of amino acid misincorporations and of their effect on the evolution of proteinsequencesislargelyhinderedbythelackofexperimentalmethodstoobserveerrorsatthefullproteomelevel.Here,wesystematicallydetectandquantifyerrorsinentireproteomesfrommassspectrometrydata.FollowingHPLCMS-MSdataacquisition,weidentifyE.coliandS.cerevisiaepeptideswhosemassandfragmentionspectrumareconsistentwiththatofapeptidebearingasingleaminoacidsubstitution,andverifythatsuchspectrumcannotresultfromapost-translationalmodification.Ouranalysesconfirmthatmostsubstitutionsoccurduetocodon-to-anticodonmispairingwithintheribosome.Patternsoferrorsduetomispairing were similar in bacteria and yeast, suggesting that the error spectrum ischemically constrained. Treating E. coli cells with a drug known to affect ribosomalproofreading increased the error rates due tomispairing at thewobble codon position.Starving bacteria for serine resulted in specific patterns of substitutions reflecting theaminoaciddeficiency.Overall, translationerrors tend tooccuratpositions thatare lessevolutionarily conserved,and thatminimallyaffectproteinenergetic stability, indicatingthat they are selected against. Genomewide ribosome density data suggest that errorsoccuratsiteswhereribosomevelocityisrelativelyhigh,supportingthenotionofatrade-off between speed and accuracy as predicted by proofreading theories. Together ourresultsrevealamechanisticbasisforribosomeerrorsintranslation.

.CC-BY-NC 4.0 International licensenot peer-reviewed) is the author/funder. It is made available under aThe copyright holder for this preprint (which was. http://dx.doi.org/10.1101/255943doi: bioRxiv preprint first posted online Jan. 29, 2018;

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IntroductionGenetic information propagation along the central dogma is subject to errors in

DNA replication, RNA transcription and protein translation. DNA replication typicallymanifests thehighest fidelityamong theseprocesses, featuringgeneticmutation rateontheorderof10-9-10-10pernucleotidepergenomedoubling1,2.“Phenotypicmutations”,i.e.errorsinRNAtranscriptionandproteintranslation,inwhichthewrongRNAnucleotideoraminoacidarerespectivelyincorporated,occuratconsiderablyhigherrate.BacterialRNApolymerases misincorporate nucleotides every 104 to 105 positions in vivo3. In E. coli,aminoacidsubstitutionsrateswerepreciselymeasuredatdefinedsitesusingaluciferasereporterconstruct4,totypicallyresidewithinthe10-4–10-3perpositionrange.

SincethetranslationmachineryreliesontRNAstomatchaminoacidswithcodons,errors can result either from the charging of a tRNA with a non cognate amino acid(synthetaseerror,or“mischarging”),orfromtheribosomefailingtodiscriminateagainstimperfectcodon-anticodoncomplexesinitsA-site(ribosomeerror,or“mispairing”).Theaccuracy of both processes is amplified by kinetic proofreading5,6, a generalmechanismduringwhichtheadditionofanirreversible,energyconsumingsteptoareactionpermitsthe system to reach discrimination levels that are inaccessible at thermodynamicequilibrium. Theoretical models demonstrated that processes relying on kineticproofreading to increase their accuracywere necessarily subject to a trade-off betweenincorporationspeed,accuracyandenergeticcost7–9.

Theamountofresourcesthatcellsinvesttoensurethatproteinsfunctionproperlyindicatesthaterrorsduringproteinsynthesiscanstronglyaffectfitness.Indeed,proteinsthat contain amino acid substitutions tend to misfold and aggregate, promote spuriousprotein-proteininteractions,andsaturatetheproteinqualitycontrolmachinery,resultinginproteotoxicstress10,andpotentially indiseases11.Conversely,someerrorsmightevenprove to be advantageous:moderate levels ofmethioninemisacylation on various non-methionine-tRNAs were shown to provide a fitness advantage in response to oxidativestressfrombacteriatohumans12–14.Asexample,higherrorlevelsacrossinstancesofrarecodonallowaparasiticyeasttoincreaseitsadherenceanditsabilitytoevadetheimmuneresponse15.Itthiscase,mistranslationisbeneficialinresponsetoenvironmentalstressesasitcanhelpsustainanddisseminatecellularphenotypicviability,e.g.asitmayenhancepathogenssurvivalbycreatingantigenicdiversityinsurfaceproteins15Onanevolutionarytimescalestoo,phenotypicerrorsmightopenevolutionarypathsotherwiseprecludedbyepistatic interactions16, and facilitate the purging of deleterious mutations17. Acomputational analysis of codon usage patterns across genomes revealed that naturalselection constrains the identity of codons at evolutionarily conserved positions,suggestingthatevolutionfavorsmoreaccuratecodonsatthesesites18.

Whereas the rates of DNA mutation and RNA polymerase errors are nowquantifiable across the genome and the transcriptome thanks to next-generationsequencing, errors in protein translation have remained elusive. An early effort byEdelmannandGallant19,whoquantitativelytrackedtheinsertionofradioactivelylabeledcysteine in E. coli’s flagellin, a cysteine free protein, revealed a first global estimate ofmistranslation,withmisincorporationshappeningonaverageevery10,000aminoacids.Sincethen,theuseofluminescentreporterconstructsallowedthequantitativetrackingof


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specific types of mistranslation, at defined positions within these probes4,20. Thesemethods have highlighted the importance of codon-anticodon recognition and tRNAcompetitionasdeterminantsoftheseerrorrates,andwereusedtocharacterizetheeffectsofaminoglycosideantibioticsandribosomeambiguitymutations(ram)4,20.

However,majorquestionsstillremainopen.Whileerrorratescouldbequantifiedat specific sites, the overall error spectrum across the proteome has not yet beencharacterized. Such measurements would shed light on the relative contribution ofaminoacyl tRNA Synthetase (aaRS) and ribosomes to translation errors. Furthermore,detecting and quantifying amino acid substitutions across the proteome may revealevolutionary constraints imposed by translation infidelity and allow us to ask howorganisms locallymodulatetheirerror levels,andthestrategiestheyemploytomitigatetheirdeleteriouseffects.

Mass Spectrometry (MS)-based proteomics, enables routine, high throughputcharacterization of canonical proteomes and common post translational modifications(PTMs),andwasdescribedasanupcomingtoolforthestudyofproteinmistranslationforalmost a decade10. Recently, proteomics was harnessed to detect various substitutionsfromseveralpurifiedrecombinantproteins21andtotracktheincorporationofnorvalineat leucine positions across the proteome of E. coli mutants22. Yet, MS has yet to beharnessedforthesystematicstudyofaminoacidsubstitutionsonaproteomewidescale.Such study was hitherto hindered by the low abundance of substitutions compared toother natural and post-translational protein modifications, and a much larger searchspace.

Toidentifytheseraretranslationerrors,weperformedadeepproteomicanalysisofE.colicells,andassessedtheeffectsofanaminoglycosideantibiotic(paromomycin)onthebacteria’stranslationerrorratesandspectrum.Inaddition,wealsotestedtheeffectofserine starvation on the mistranslation rate of its cognate codons. We carried out ouranalysiswithMaxQuant23, repurposing itsdependentpeptidealgorithmto identifymassshifts consistent with amino acid substitutions, and stringently filtering out potentialmethodologyartifacts.WethenvalidatedtheseidentificationsusingasetofindependentanalysesthatincludeashiftinHPLCretentiontimeduetochangeinhydrophobicityoftheencoded amino acid. Performing these experiments and analyses on E. coli in severalgrowthconditionsandanalyzingsimilardatainS.cerevisiae24,wecoulddetectover3,500independentsubstitutionevents.

Thisdataset,unprecedentedinitstypeandextent,allowedustobegintounravelthe mechanistic basis of translation errors. We found that most errors result frommispairing between codons and near-cognate tRNAs, mostly within the A site of theribosome.We could derive the amino acid error spectrum of each codon in the geneticcode, and deduce patterns ofmispairing between codons and anticodons at each of thethreecodonpositions.Wecouldalsomeasuretheeffectoftheaminoglycosideontheerrorpattern and revealed that it mainly causes errors due tomispairing at the third codonposition.Wefoundthaterrorspectrainyeastandbacteriaaresimilar,probablyreflectingsharedchemicalconstraints.Wefoundthaterrorsareallowedtooccurmorefrequentlyatevolutionarily rapidly evolving sites, at protein structure sites in which mutations aremore tolerated energetically, and in positions in which the ribosome progresses morerapidly. Ourresultsdemonstratethatnaturalselectionhastunedtranslationerrorrates


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atspecificproteinpositions,actinguponthenucleotidesequenceofproteinstoreducethecostburdenoftranslationerrorswhenmostdetrimental.

Results

Apipeline toconfidently identifyaminoacid substitutions inaproteome

Mass spectrometry enables the fast, large-scale identification of peptides withincomplex samples. From a computational point of view, amino acid substitutions can beregarded as aparticular caseof post-translationalmodifications (PTMs),manyofwhicharenowroutinelystudiedat theproteomelevel.However, thestandarddatabasesearchalgorithmisnotadaptedtothelargescaledetectionofsubstitutions:assumingpeptidesofaveragelengthof10aminoacids,therewouldbeontheorderof200timesmoresingly-modifiedthancanonicalpeptidestosearchfor, leadingtoimpracticalsearchtimesandaconsiderablelossofstatisticalpower.

Blindmodificationsearches25–27offerawaytoidentifymodifiedpeptideswithoutrequiringtheusertoinputalistofpredefinedmodifications.Theyrelyontheobservationthatmodifiedpeptidesareusuallylessabundantthantheirunmodifiedcounterparts,andarethereforeonlylikelytobepresentinthesampleiftheunmodifiedpeptidehasalreadybeen detected. We thus adapted MaxQuant23, developed previously for the analysis ofpost-translational modifications in order to identify mistranslated peptides using its“dependent peptide search” algorithm; see outline of our pipeline in Figure 1A.“DependentPeptides”aredefinedaspeptidesthatshowmassshiftsincomparisontotheunmodified,genome-encoded“BasePeptides”(Fig.1B).Wethenappliedaseriesoffilterstothelistofdependentpeptides,inordertostringentlyremoveknownPTMsandknownartifacts and conservatively retain only amino acid substitutions. For a detaileddescriptionofthepipeline,seeMethods.

The detection of translation errors, rare events by definition, required specificexperimentalsettings.MassspectrometerssampleinprioritythemostabundantpeptidesforMS2 fragmentation. Sincewe expect translation errors to be present atmuch lowerlevelthanerror-freepeptides,wefractionatedoursamplestoreducetheircomplexityandincrease thechancesof sampling lowabundancepeptides.Weseparatedproteins intoahighsolubilityandalowsolubilityfraction28,whichwefurtherfractionatedusingstrongcationexchange(SCX)chromatography.

Wegeneratedtwodeep-coverage,high-resolutionmapsoftheE.coliproteome.Inourfirstdataset,wemeasuredtheeffectsofparomomycinatsub-lethalconcentrationontheribosome'saccuracy, in richconditions (LB,37°C),andcompared it toanon-treatedsample.Intheseconddataset,westarvedaserineauxotrophforserine(MOPS,37°C)andcompared its substitution pattern to the parental unstarved wild type. In total wegenerated errormapsof nine samples, each in two replicates (seeMethods).Altogetherwedetected3,596 independentaminoacidsubstitutions(eachdefinedherebyauniquepositionwithinaspecificproteinandauniqueaminoacidsubstitutiondestination))intheE.coliproteome.Inadditiontothesixteensamplesfromthebacteriumwealsoanalyzed


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an existing proteomedataset24 from the yeastS. cerevisiaeat a single type, non-treated,conditionthatyielded225independentsubstitutions.

Figure 1: a computational pipeline to confidently identify amino acid substitutions from Mass Spectrometry data. A: Overview of the pipeline. For a detailed description of the different steps, see Material & Methods. B: MaxQuant Dependent Peptide search performs exhaustive pairing of unidentified spectra to a spectral library derived from the identified spectra. For each pair of (identified, unidentified) spectra of the same charge z, and found in the same fraction, the algorithm first computes the mass difference Δm = munidentified - midentified. It simulates in silico, and sequentially, the addition of a single moiety of mass Δm at any position in the identified peptide, and generates the corresponding theoretical spectrum for the modified peptide. These spectra are then compared to the experimental spectrum using MaxQuant Andromeda’s score formula. The pair with the highest score is retained, and the significance of the match is assessed using a target-decoy FDR procedure. C: The observed retention time shift induced by our set of substitutions is accurately predicted by a simple sequence-based retention time model.

Most of the high quality hits are bona fide amino acidsubstitutions.

First, we grew wild type E. coli cells (MG1655) in defined medium (MOPScomplete, 37°C) in biological duplicates, harvested cells at three time points, roughlycorresponding to early exponential phase, late exponential phase, and stationary phase,andusedourpipelinetodetectaminoacidsubstitutions.Givenmassdifferencesdetectedbetweenbaseanddependentpeptideswemust first establish that they representbona-fideaminoacidsubstitutions,andnotmethodologicalartifacts.Wetookadvantageofthe

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factthatmanyaminoacidsubstitutionsresultinachangeofpeptidehydrophobicityandthey should hence result in retention time shifts during liquid chromatography. Theretentiontimeofapeptidecanbepredictedwithhighaccuracy(R2>0.9)asthesumofthehydrophobicitycoefficientsof itsaminoacids29.Therefore, thepredictedHPLCretentiontime of the substituted amino acid can be computed and compared to the observedretention time recorded for the substituted peptide. We trained a retention timeprediction tool30 on a list of confidently identified unmodified peptides, and used it togenerate an expectation of the retention time shift induced by the substitutions. Wecompared this expectation to the observed retention time shift for each of the detectedsubstitutions (Fig. 1C).We observed a good agreement between the observed retentiontimeshiftsandourmodel, supporting thenotion thatmostof thesubstitutionsdetectedaregenuineaminoacidreplacements.

Note that, since MS2 spectra are systematically recorded for highly abundantparent ions, our sampling strategy is biased towards the detection of substitutionsoriginatingfromhighlyexpressedproteins.

Wedefineasubstitutionasacombinationofaposition inaprotein,characterizedbyan“origin” amino acid (and its associated codon), and a “destination” amino acid.We thendivide all substitutions in two sets: a substitution is classified as a Near Cognate Error(NeCE)iftheerror-bearingcodonoftheoriginaminoacidmatchestwooutofthreebasesof oneof the codonsof thedestination amino acid, and asNonCognateError (NoCE) ifmoremismatcheswouldberequired.Forexample,asubstitutionfromSerineencodedbyAGCintoanAsparagineisconsideredNeCEsinceofthecodonsencodingthisaminoaciddestination AAC, represents a single mismatch (at the 2nd position); in contrastsubstitutionofaCysteineencodedbyaUGUintoanAlaninemustbeaNoCEsincenoneoftheAlanine codons (GCN)matcheswith a singlemismatch. The structure of the geneticcode dictates that only a minority of the substitutions would be classified as NeCE. Inparticular,ofalldetectablecodontoaminoacidsubstitutiontypes30%areexpectedtobeof the NeCE type. In stark contrast, 88% of the unique substitutions detected by ourmethod with the full E. coli dataset are classified as NeCE. Thus, the great majority ofobservedsubstitutionsinourdatacanberationalizedbyasimilaritybetweentheorigin’scodonandacodonofthedestinationaminoacid.SuchenrichmentforNeCEservesasanindicationthatweinspectgenuineaminoacidsubstitutions. Theribosomeusessmalldifferencesinfreeenergybetweencorrectandincorrectcodon-anticodon matches to select tRNAs, and was shown to generate NeCE at muchhigherrates thanNoCE4.During loadingofanaminoacidbyanaaRS,anerrorcanstemfromthechoiceofanincorrecttRNAortheloadingofanincorrectaminoacid.Sincethemajority of synthetases assess the identity of the tRNA by probing the bases of theanticodon,thisfirstmechanismoferrorisalsolikelytogeneratemostlyNeCE.However,ifthe error results from the loading of thewrong amino acid, there should be a priori nopreference for NeCE over NoCE.We will consider NoCE as more likely to stem from asynthetase error, and examine the notion that the majority of NeCE indeed representmRNA-tRNAmispairingevents.


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OverviewofE.coli’saminoacidsubstitutionlandscapeWegenerated64*20 codon to aminoacidmatrices thatdepict theprevalenceof

eachtypeofaminoaciderrorinadataset.Notethatthesematricesdonotrepresentrealrelative probabilities of errors as they are affected by biases such as codon usage. Thenumbers of unique peptides supporting any codon to amino acid substitution type ispresentedinFig.2A;theintensityoftheshadeisproportionaltothelogarithm(base2)ofthenumberofuniquegenomepositionsinwhichagivensubstitutiontypewasobserved.Because leucine and isoleucine are isomers and thus share the exact same mass, ourmethodisnotabletodistinguishthetwoaminoacidsasdestinationsofasubstitution,andwe treat them as a single destination amino acid. Furthermore, substitution types thattransformacodonintoitscognateaminoacid,involveastopcodon,orsubstitutionsthatcannot be detected using our method because they represent a mass shift thatcorrespondsprecisely to themass shift andspecificityof at leastoneknownPTM,weregrayedout,anddiscardedfromsubsequentanalyses(seeMethods).


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Figure 2: overview of the substitution profile of E. coli in MOPS complete medium. A: Matrix of identified substitutions. Each entry in the matrix represents the number of independent substitutions detected for the corresponding (original codon, destination amino acid) pair, in the MOPS-complete dataset. The logarithmic color bar highlights the dynamic range of detection. Grey squares indicate substitutions from a codon to its cognate amino acid, substitutions from stop codons, and substitutions undetectable via our method because they are indistinguishable from one of the PTMs or artifacts in the unimod.org database. Substitutions to Leu and Ile are a priori undistinguishable, and thus grouped together. B: Left panel: For each of the top 10 most frequently detected substitution types, we fetched the quantification profile of the dependent peptide and the base peptide. Each dot represents the ratio of intensities IDP/IBP for each of the samples, when both peaks have been detected and quantified. The black line indicates the medians of the distributions. Right panel: we inferred the most likely mismatch for each of the substitution types, using a procedure described in the Material and Methods. This allows us to guess that the V -> I/L substitutions are likely substitutions from Val to Ile, enabled by a G:U mismatch at the 1st position.

Thematrix ishighlystructured,andsomesubstitutionsappear tobemuchmoreprevalentthanothers. Inparticular,weobservethatthecodonusedtoencodeanaminoacidpositiondeterminesthemostlikelyerrorspatternsatthecorrespondingsite.ThisisnicelyillustratedwithsubstitutionsfromGlytoAspandGlu.WhenGlyisencodedbytheGGC codon, the frequent substitution destination is the near-cognate Asp (that can beencodedby the near cognate codonGAC),while encodingGlywithGGAoften results insubstitutionofGlybyGlu(presumablyduetoitsnearcognatecodonGAA).Similarcases

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in which different codons for the same amino acid tend to show different amino acidsubstitutionpatternscanbefoundinthematrix(Fig.2A).

We calculated the observed error rate (i.e. the ratio of intensity between thedependent and base peptide) for abundantly detected substitutions. As an example, theSerAGCàAsnsubstitutionwasdetectedamongatotalof81differentpeptidesacrosstheE.coli proteome of the non-treated samples. Figure 2B summarizes the error rateestimations in each of these substitutions – each dots in the plot corresponds to onespecificSerAGCàAsnsubstitutiononaparticulargenomicposition,andtheerrorrateison the y-axis. Likewise the 10 most frequent substitutions types in the proteome areshown.Themajorityofthesubstitutionsthatareobservableinourdatasetspantheerrorraterangearound10-3,withthemosthighlyabundantsubstitutionstypesshowingslightlyhighererrorrates.Due to theMSacquisitionstrategy,positions that featurea lowerrorrate are less likely to be detected,which could lead to an over-estimation of the actualerror rates. We note that for most substitution types, error rates seem to consistentlydecline as cells progress from exponential to stationary phase – out of the 10 mostprevalent substitution types, only two follow the opposite trend, and both involve thecommonglycineGGCcodon,perhapsreflectinganintracellularshortageofglycine

AglobalnucleotidemispairingpatternfortranslationerrorsWefurtherclassifiedNeCEbasedonthetypeofcodon-anticodonmismatchwithin

the codon and thenucleotide types they involved, characterizedby a position along thecodon,acodonnucleotideandananticodonnucleotide.Wedefinethecountdensityforagivenmismatchtypeasthenumberofindependentsubstitutionsthatcanbeexplainedbythat type of mismatch, divided by the number of classes of substitutions that can beexplainedbythesamemismatch.Thesecountdensitiesarepresentedunderthe formofthree 4*4 “nucleotide mismatch matrices” (fig 3A), which reflect, for each of the threecodonpositions,therelativeprevalenceofthevariousmismatchtypes.Substitutionsthatcouldnotbeunambiguously tracedbacktoasinglemismatchtypewereassignedto themost likely mismatch using a greedy algorithm (see Material and Methods). The mostfrequentlyobservedsubstitutiontypesinvolvemismatchesbetweenuracilandguanineinthefirstorthesecondpositionofthecodon. Interestingly, thisruleholdsmainlyforG:Umismatches, where the codon base is G and the anticodon base in U; theoverrepresentationofthismismatchtypecomparedtothesymmetricalU:Gtypecouldnotbe explained by the numerous modifications affecting the anticodon of tRNAs, as theyseldomaffecttheanticodons’2ndand3rdbase(involvedinthe1stand2ndcodonpositionmismatches)31


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Figure 3: Comparing the error profiles for E. coli and S. cerevisiae reveals a shared signature of errors. A: the substitutions identifications matrices of S. cerevisiae (green channel, left) and E. coli (red channel, right) are compared and overlaid (middle). The intensity of the color is proportional to the logarithm of the number of independent identification, with one pseudo-count. Values are normalized by the highest entry in the matrix for each of the two organisms. The blue box highlights the recently described property of eukaryotic AlaRS to mischarge tRNA. B: NeCE are classified by the mismatch most likely to generate them. The shade intensity reflects the ratio of independent substitution to number of substitution types associated with the corresponding mismatch. Grey boxes are either correct base-pairings, or mismatches to which no substitutions could be unambiguously mapped. Upper panel indicates results obtained from E. coli, lower panel was generated based on S. cerevisiae data.

E.coliandS.cerevisiaesharesimilarerrorprofilesWhilebothcharacterizedbyamostlyplanktoniclifestyleandhighgrowthrates,E.coliandS.cerevisiaehavebeendivergingfromoneanotherforatleast2.7billionyears.Comparingthe error profiles of these twoorganisms, thus, allowsus to look at how strongly theseerrors are constrained, both by chemical and evolutionary necessities.We reanalyzed apreviouslypublishedmassspectrometrydatasetofstronganionexchange(SAX)andSCXfractionatedproteomeofS. cerevisiae grown in a single condition, a richmedium (30°C,YPD)24usingourpipeline.

Wedetected a total of 225unique substitutions in the yeastproteome, from thetwosamplesanalyzed.SimilarlytotheE.colidataset,themajorityoftheerrors,143,wereclassifiedasNeCE(63%).Comparingtheerrorspectrumbetweentheeukaryoteandtheprokaryoteweobservedahighoverlapbetweenthesetofsubstitutiontypesseeninthetwoorganisms.Forexample,themosthighlyfrequentsubstitutiontypes(e.g.MetAUG→Thr,SerAGC→Asn,ValGUU→Ile)aresharedbetween the twospecies.Among the34substitutiontypes observed more than once in the yeast dataset, 25 had been seen in the E. colisamples.Sinceonly25.6%ofsubstitutiontypeswereobservedatleastonceintheE.colidataset,weexpectedonly9ofthese34substitutiontypestobedetectedintheE.colidata.Amongthe25substitutiontypesalsodetectedinE.coli,16wereNeCE.Converselyamong

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theremaining9substitutiontypesthatwerenotseeninthebacterialsamples,only4wereNeCE. This observation reveals a universal error pattern formistakes that are likely tooccurwithintheribosome,whilemostNoCElikelyoriginatefromseparatefactorsuniquetoeachofthespecies.ThemostnotabledifferencebetweenthetwospeciesisinthemostfrequentlyobservedsubstitutionofAlatoCysinyeast,whichisnotseeninthebacterium.Arecentreport32revealedthebasisforthisobservation-eukaryotic,butnotprokaryoticAlanyl-tRNAsynthetase(AlaRS)tendtomischargetRNACyswithAlanine.

FortheyeastdatatoowecomputednucleotidemismatchmatricesandobservedthatinsimilaritytotheE.colimatricestheyalsopredominantlyfeatureG:Umismatchesatthe firstor secondpositions (Fig.4B).Observing such levelsof error similaritybetweensuchlooselyrelatedorganisms,exhibitingdistinctcodonusagebiases,andrelyingonverydifferent translation machineries, suggests that these errors depend on universalconstraints.Whether these constraints are of a purely chemical nature, or the observedsubstitutionshappentobemoretolerablebytheseorganismsremainstobedetermined.

Aminoglycoside treatment and amino acid starvation perturbtheerrorspectrum

We characterized the responseof errorpatterns to external perturbations. First,wegrewE.colicellsinLBsupplementedwithsub-lethalconcentrationsofparomomycin,an aminoglycoside antibiotic known to interfere with the ribosome’s proofreadingactivity4,33,andrecordeditseffectontheproteome,incomparisontothatofanuntreatedcontrol. For both conditions,we again inspected the codon to amino acidmatrices (Fig.4A), the error rate profiles (Fig. 4B) and the nucleotide mispairing matrices (Fig. 4C).Comparing the codon to amino acid substitution matrices between the paromomycintreated and untreated samples reveals a clear pattern – the drug increased error rates,especially at 3rd codon wobble positions (Fig. 4C; greyed examples in 4B), while othermismatch positions remained relatively unaffected. The increased error rate at the 3rdpositioncanbequantifiedusingMS1information,asreportedinFig.4B.


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Figure 4: the error spectrum is affected by external perturbation. A: the substitutions identifications matrices of E. coli in LB (green channel, left), or LB supplemented with paromomycin (red channel, right), are compared and overlaid (middle). The intensity of the color is proportional to the logarithm of the number of independent identification, with one pseudo-count. Values are normalized by the highest entry in the matrix for each of the two organisms. The blue boxes highlight errors involving 3rd position mismatches. B: Quantification of the top 10 most frequent substitutions in the paromomycin dataset. Errors involving 3rd position mismatches are shaded in light blue. C: NeCE are classified using the same procedure as in Fig. 2B, for the LB samples, with or without paromomycin. D: Effect of serine starvation on errors at serine codons, for the three most frequently detected substitutions affecting serine codons.

Next, we examined the impact of amino acid starvation on the cell’s errorspectrum. We starved a serine auxotroph for serine, and measured its proteome. Wepredicted that, upon starvation to serine, we would observe elevated level of errorsleading from serine to other amino acids. Indeed, the rate of SerAGCàAsn markedlyincreaseduponstarvation,andintensifiedasthecellsapproachedstationaryphase,whentheeffectsofstarvationaresupposedtobethestrongest.Thisresult indicatestoaclearmechanismthataccountsformistakesintranslationinwhichashortageofanaminoaciddeterminesitsprobabilitytobereplacedbyothers.Whileamodel34predictsthattheUCNserine codons should be affected more strongly by starvation than the AGC and AGUcodons,ourmethodwasmostlyabletofetchsubstitutionsstemmingfromthetwolattercodons. This observation does not necessarily contradict the theory, since the AGC andAGUerrorsarealsothosemostseenintheMOPScompletemedium,anditispossiblethattheUCXcodonsdosuffermorefromstarvation,butremainatlowlevelsinabsoluteterms.

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Inaddition,weobservedthatallsamplesintheserinestarvationexperimentpresentedastrong signal forThreonineàSerine substitutions,whichdidnot seem tobe affectedbythe nature of the threonine codon. Replacing threonine for serine in an environmentwhere serine is scarce seems particularlymaladaptive. However, Ling& Söll35 reportedthatE.coli’sThrRStendedtomisincorporateserineforthreonineduringoxidativestress.Sincethestarvationexperimentwasperformedonaserineauxotroph,,wecannotexcludethatthemodificationofthebacteria’smetabolisminducedoxidativestressandthereforeTàSsubstitutions.

Misincorporationsoccuraterror-tolerantandrapidlytranslatedpositions

Drummond&Wilke18 posited thatcells, inorder toavoid the fitness lossdue toproteinmisfoldingandaggregation,manage theirerrorsbyselectingerror-proofcodonsat positions where inserting the correct amino acid is critical to a protein’s folding orfunction.Theywereable to support that theoryusingcomputationalmeans,butas theywerelackingdataonactualerrorsinproteometheyhadtorelyonproxies.Inparticular,theyusedevolutionaryconservationasaproxyforsensitivitytophenotypicerrors.Theyderived the identity of error prone and error proof codons from conservation data.Correspondingly,fastevolvingpositionswithinproteinarepredictedtobelesscriticalforprotein foldingandfunction,andshouldthereforetolerateaminoacidsubstitutions.Yet,thelackofasystematicsetoftranslationerroreventswithinaproteomeprecludedsofarexamination of the notion that they occur in rapidly evolving sites, or in positions thatminimally affect protein structure and function. A theoretical argument, kineticproofreading5,6,predictsthatribosomeswouldbemorelikelytomisincorporateanaminoacid at sites where they translate rapidly36. This trade-off between speed and accuracywasdemonstratedbymutantsthatfeaturedmodifiedtranslationspeed37,andbyin-vitrousing conditions that affect ribosome velocity38. Even though indirect evidence suggeststhat, in yeast, downstream mRNA structures are used to modulate the speed of theribosome when it decodes evolutionarily conserved sites39, this theory has not beenexperimentallytested.Wethereforederivedtherelativespeedofribosomesaterrorsitesviaribosomeprofiling40,andlookedfordirectevidenceofthistrade-off.


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Figure 5: general properties of substitutions. A. The sampling strategy: In order to test if the set of detected substitutions differs from expectations in any way, we first need to account for the fact that many local properties of proteins are affected by the protein’s expression level, and so is our ability to detect substitutions from that protein. First, the local property of interest (‘score’) is recorded at all the positions bearing a substitution. The average score for that set of positions is plotted as a red dashed line. To compare this average to an appropriate control, we devised three strategies to eliminate the potential contributions of protein level, amino acid identity and codon identity on the score. In each of these strategies, we draw 1000 sets of positions, and plot the average score of each of these sets as a blue dot. In the first strategy, for every bona fide distribution, we draw the score from any position within the same protein. In the second strategy, we draw the score from any position within the same protein that shares the same amino acid as the one bearing the bona fide substitution. In the third control, the codon for the sampled position also needs to be the same as the substituted codon. B. Amino acid conservation: We derived amino acid conservation scores for E. coli proteins, using the COGs database to fetch 50 homologs, MUSCLE to align them, and rate4site to estimate the evolutionary rate at each site. The resulting scores are standardized per proteins, and a high score indicates low conservation. The empirical p-values are computed by dividing the number of blue dots above the red line, divided by 1000. n indicates the number of positions considered in this analysis. C. Ribosome density: Ribosome profiling data were processed (see methods) to estimate the ribosome density at positions along the E. coli transcriptome. Since a ribosome would have to rely on local cues to modulate its speed, we do not expect misloading errors to be influenced by the ribosome density. Therefore, this analysis was restricted to NeCE. D. Effect of substitutions on protein stability: for proteins whose 3D structure is known, we evaluated the effect of NeCE on protein stability using FoldX. In control 1, we test if the observed substitutions are on average less destabilizing than those stemming from other single-nt mismatches between the codon and the anticodon, at the same position. In control 2 and 3, we test if the observed substitution type observed was less destabilizing on average at

A general sampling strategy to evaluate global properties of substitutions

Substitutions are observed at less conserved sites Substitutions minimally affect protein stability

Substitutions are observed at sites with low ribosome density

more conserved

less conserved

slower ribosome

faster ribosome

more destabilising

less destabilising

A

B D

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the observed position than at other positions sharing the same AA, or the same codon.

We computed normalized conservation scores for each position in the E. coliproteome(seeMaterialandMethods),suchthatahighscoreindicatesthatanaminoacidpositionispoorlyconservedcomparedtootherpositionsinthesameprotein.Inordertoaccountforthefactthatsomeaminoacidstendtobemoreconservedthanother,andthatsomecodonsareover-representedatconservedpositions,wedevisedthreestrategiestogenerateadequatenegative controls (Fig.5A). In the first control, that is least stringent,for each observed substitution, we sampled a normalized conservation score from anypositioninthesameprotein.Inthesecondcontrolstrategy,therandomre-samplingwascarriednotonlywithinthesameprotein,butalsowiththeadditionalconstraintthattheaminoacidtypeintherandomlysampledpositionhastomatchthesameaminoacidtypeobservedatthepositionatwhichthesubstitutionoccurred.Finally,inthemoststringentofthesenegativecontrols,weperformedarandomre-samplingwithinthesameprotein,at sites sharing the samecodonas theobservedpositions.Wegenerated1,000 such re-samplings ineachof thethreetypesofnegativecontrols,andcomparedthemeanof theobserved distribution of scores at the observed substitution positions to those of therandomcontroldistributionstoobtainempiricalp-values.Themeanrateofevolutionatsubstitutionsitesissimilartothatofrandomsetsofpositionsgeneratedthoughthefirstmodel,butsignificantlyhigherthanthatoftherandomsetsgeneratedwiththeothertwo(Fig. 5B). Consistent with a previous prediction18, controlling for the codon identityreduced the magnitude of the difference between the real error sites and randomlyselectedsites(Fig.5B,“samecodon”vs“sameAA”),supportingthenotionthatevolutionallows or precludes error-prone codons from sites that are correspondingly tolerant orintolerant to errors. However, codon identity did not fully account for the poorconservation at substituted sites, suggesting that other factors allow cells to locallymodulatetheirerrorlevels.

Similarly, we examined the related possibility that observed amino acids

substitutionsintheE.coliproteometendtominimallyaffecttheenergeticfoldingstabilityofproteininwhichtheyoccur.Tothisend,wecomputationallypredictedtheeffectofeachof the observed substitutions on its protein’s folding stability (ΔΔG).We compared thedistributionofΔΔGtomockdistributionsobtainedviathreecontrolstrategies,describedinFig.5D.Thefirstofthesestrategies(identitycontrol)allowsustotest if theobservedNeCEarelessdestabilizing,onaverage,thanothersubstitutiontypeswouldbeatthesamepositions. As before, we generate 1000 random sets of ΔΔG values, such that for eachobservedsubstitutionwerandomlysampleadestinationaminoacidaccessibleviaasinglemutation from the error bearing codon, and compute the difference in free energyresulting froma substitution towards thataminoacid.ThemeanΔΔGof the setofbonafide substitutions, 1.454 kcal/mol, was lower than that of each of the 1000 mock setssampled under the identity control, suggesting that error rates are tuned so thatsubstitutionspreferentiallyreplacetheoriginalaminoacidwithonethatwouldminimallyaffect protein folding at the same site. Our control strategy accounts for the fact thatsubstitutiontypesclassifiedasNeCEtendtoswapchemicallysimilaraminoacidsduetotheorganizationofthegeneticcode.


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Next, we tested if the observed errors were preferentially mapped to proteinpositionsweretheyminimallydestabilizedfolding.Wegenerated1000setssuchthatforeach observed NeCE, we estimated the ΔΔG that would have resulted from that samesubstitutionoccurringatarandomlychosenpositionof thesameprotein,sharingeitherthe same amino acid (“amino acid control”) or the same codon (“codon control”). ThemeanΔΔG of our set of observed substitutionswas lower than that of 98% of the setsgenerated with the amino acid control, and 97% of those generated with the codoncontrol, suggesting that substitutions preferentially occur at protein sites where theywouldnot disturb folding.We cannot presently exclude the alternative explanation thatsome substitutions destabilize protein structure, promote degradation, and are thusprecludedfrombeingsampledinourmethod.

The classical model of kinetic proofreading suggests that the ribosome mustbalancebetweenspeedandaccuracyduringtheaa-tRNAselectionstepbytheribosome,and that it ismore likely tomisincorporateaminoacidswhendecodingat ahigh rate36.This trade-off was experimentally observed in vitro: ribosomes tested in increasingmagnesiumconcentrations translate their templates faster, but less accurately41. In vivo,hyperandhypo-accurateribosomalmutantstranslaterespectivelyslowerandfasterthanthewild type ribosome37. Yang et al. proposed that yeast cells take advantage ofmRNAstructures downstream of the ribosome to tip this trade-off towards faster or moreaccurate decoding39. The speed at which ribosomes translate mRNAs can be estimatedfrom ribosome footprints density42, which allowed us to test if substitutions arisepreferentiallyatquicklytranslatedpositions.

WeestimatedtheA-siteribosomedensityacrosstheproteomeofMG1655usingapublisheddatasetacquiredundersimilarconditions40,andstandardizedthisdensityscoreper protein to control for intergenic differences in mRNA abundance and translationinitiation levels (seeMaterial &Methods).We compared themean ribosome density aterrorsitestothatofsetsofrandomsamplesgeneratedusingthestrategydescribedinFig.5A.Ineachofthecontrols,themeanribosomedensityofthebonafidesubstitutionswaslower than that of most of the random control samples (Fig. 5C) – error sites are lessdense, and hence translated faster than expected by chance. In particular, the meandensity at sites associated with a substitution was significantly (p = 0.025) lower thanexpectedunderournullmodelcontrollingforbiasesassociatedwithcodonidentity.TheseresultssuggestthatE.colicanlocallytuneitsribosomestotradeaccuracyforspeed.

DiscussionHerewereportonanewmethodtoobservesingleaminoacidmisincorporations,whichweusedtodetectover3500distincttranslationerrorsacrosstheproteomeofE.coli.Wetakeadvantageoftheveryhighaccuracyofmodernmassspectrometertogeneratehighconfidence identifications. Orbitrap mass spectrometers can be tuned to detect massdifferences on the order of thousandth of Daltons, during both the MS1 and MS2acquisition phases. This accuracy in turn allows us to distinguish peptides and peptidefragments of almost identicalmasses, but of different atomic and isotopic compositions,andthusgreatlyimprovestheperformanceofdatabasesearchalgorithms.Ourmethodistherefore able to distinguish amino acid substitutions from PTMs of similar masses.Despitethefalsediscoverrate(FDR)procedureappliedattheendourpipeline,wecannot


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excludewithabsolutecertaintythatsomeofthesubstitutiontypeswedetectcorrespondin fact to annotated PTMs that cannot be distinguished from amino acid substitutions.However,theretentiontimeshiftsinHPLCobservedforoursetofidentificationscorrelateverywell theexpected retention timeshiftspredicted fromsequence informationalone,anobservationthatcouldnotbeexplainedbytheidentificationofspuriousPTMs.Onecannotguaranteeapriorithatthesesubstitutionsstemfromerrorsinthetranslationmachinery,becausenon-synonymouserrorsattheDNAorRNAlevelscouldgeneratethesame mistakes at the protein level. However, our samples originate from clonalpopulations,whichimpliesthatDNAmutationsareunlikelytoreachadetectablelevelinthe absence of strong adaptive selection, and would be very rarely observed to occuracrossmultiplesamples.Asecondpossibility is thatwearealsoobserving transcriptionerrors. Yet, since we analyze samples in which the number of cells (~109) is greatlysuperior to the inverseof theobserved lowerboundof transcriptionerrorrates (~105),andtheaveragenumberofmRNApercellforthegeneswedetectisgreaterthanone,therelativeabundanceoferrors isexpected tocorrespondto the transcriptionerrorrateatany examined site, and should not fluctuate from sample to sample thanks to theassumptionof ergodicity. Since this estimate is twoordersofmagnitude lower than theaverage observed error rates quantified by our method, substitutions likely arise fromtranslationerrors.Twodistinctprocesseshavebeenshowntogeneratehigh levelsoferrors in translation:aaRScanmistakenlyloadanaminoacidtoanon-cognatetRNA,andtheribosomecanpairacorrectlychargedaa-tRNAcomplextoanon-cognatecodon.Bothprocessesrelyonsmallenergetic differences between correct an incorrect pairings. For the ribosome, therecognition process exploits the difference of free energy between cognate and non-cognate codon-anticodonpairs.Most aaRSalsoprobe thenatureof theanticodonof thetRNAbeforeloading,andadditionallyrelyoncluesfromthetRNAbackbonetoachieveahigh specificity43.Theaminoacid recognition step canbe challengingdue to similaritiesbetweenaminoacidtypes,andasubsetoftheseenzymeshavetorelyonaneditingsteptoachievehigherspecificity.DifferentialbindingofEF-TutomisacylatedtRNAswasshowntodiscriminateagainstcommonaaRSmistakes44,andthusprovidesanadditionallayerofspecificity.Wearguethatmostofthesubstitutionsdetectedinourworkstemfromerrorsin the ribosome i.e. wrong pairing between codons and anticodons. Indeed, theoverwhelmingmajority(88%)ofthesubstitutionscouldbeexplainedbyasinglecodon-anticodon mismatch, a fraction much higher than expected by chance due to theorganization of the genetic code (30%). Additionally, we treated the cells withparomomycin, an aminoglycoside antibiotic known to perturb the accuracy of theribosome. This drug increased the rate of several substitution types, particular thoseinvolving mismatches at the 3rd codon position. These substitution types are thereforedominated by ribosome errors. However, we were able to identify several instancesCysàAlasubtitutions(NoCE)intheS.cerevisiaesamples,consistentwitharecentreportthat eukaryotic, but not prokaryotic AlaRS had a tendency to mischarge non-cognatecysteinetRNAs32.Comparing the error spectrum of the E. coli and S. cerevisiae in untreated conditionsrevealed a large overlap between the set of observed substitution types, and a strikingprevalence of Gcodon:Uanticodon mismatches at the first and second positions. Structuralanalysis of G:U and U:G mismatches within the ribosome revealed that they typically


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adopted aWatson-Crick G:C like geometry rather than the expectedwobble one due tospatialconstraintsinthedecodingcenter.Theseerrorsarethereforebelievedtooriginatefromrare,transientgeometriesofnucleobases,asproposed46.Theseerrorsarethereforebelieved tooriginate from rare, transient geometries of nucleobases, asproposed45. Thesurprisingobservationthat1stand2ndpositionG:Umismatchesaretypicallymuchmoreprevalent than the symmetrical U:G conformation could not be explained by the manyenzymes modifying the uracil on the anticodons of tRNAs, as they mostly assist thedecodingofthethirdcodonposition.TheE. coli MOPS complete data allowed us to quantify a large number of substitutionsoccurringduringfastgrowth.Themeanerrorratedetectedwasontheorderof10-3,inthehigherendoftherangeofpreviouslyreportedestimates.Severalreasonscanbeinvokedto explain this observation. First, MS detectability is intimately linked to MS1 intensitylevels: since themass spectrometer systematically samples themost intensepeptides ineachscan,weareboundtopreferentiallydetectandquantifysubstitutionsassociatedtohigh error rates. Similarly, a peptide’s MS1 intensity depends on its abundance in thesampleandon its ability to ionizewell.Theabundanceof the correctpeptide isusuallymuchhigherthanthatoftheerror-bearingone,whichmeansthatitwillbesampledmoreoften.Thequantificationdependsonthesamplingofthelowerabundance,error-bearingpeptide.Substitutionsthatincreasethepeptide’sionizationefficiencyarethereforeboundto increase itsdetectability,andwill result inan inflatederrorrate.While it isgenerallyaccepted that ionizationefficiencydependsonapeptide’s sequence inaverynon-linearfashion, we trained a linear regressor to evaluate the mean effects of amino acidcomposition on ionization efficiency.Ourmodel gave satisfactory results (see appendix:IonizationEfficiencyprediction),andsuggeststhatsubstitutionsshouldrarelyresultinadramatic change in ionizationefficiency. It remainsdifficult toassess towhatextent thevariance of the error rate distribution for each substitution type reflects biologicalvariabilityortechnicalbiases.Comparing the median error rates of several substitutions across the differentphysiological states during bacterial growth revealed that they react dynamically to thechanging environment: substitutions rate from valine codons tended to decrease withtime, while glycine codons becamemore error-prone in later stages. The extent of thischangemightbeunderestimateddue to the fact thatwe arenot specificallyquantifyingthe error rate of newly synthesized protein, but rather quantifying the errors in batch,integratingatanygiventimepointonproteinsthatweresynthesizedtill thattimepointthatwerenotdegradedalready.Starvingthecellsforserinerevealedastrikingincreaseintheerrorrateoftwosubstitutionsinvolvingserinecodons,SerAGCàAsnandSerAGUàAsn.Themedianerror rate for thesecodonsrose toalmost10-2 in thestationaryphase timepoint,withsomesitesreachinganerrorrateapproaching10-1.Otherserinecodonswerealso affected, but the scarcity of sampling for these rarer errors precluded a reliablequantification of the process. Theory predicts that the 4-box codons of serine (UCN)should suffer more from serine depletion than the 2-box codons (AGY) because of adifferential chargingof the tRNA isoacceptors34.Our failure todetect a largequantityoferrorsatTCNsitesmightbepartiallyexplainedbythepreferentialusageofAGYcodonsingenesover-expressedduringserinestarvation34.


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Translation errors have been hypothesized to affect protein evolution, and to drive thelongknownanti-correlationbetweengeneexpressionandevolutionaryrateattheproteinlevel18. According to this theory, the selective pressure to prevent translation errorsconstrains the synonymous encoding of amino acids critical to protein folding, andorganisms must select preferred, error-proof codons at positions where errors woulddisturbproteinstability.Thesesensitivesitesarecharacterizedbyahigherevolutionaryconservation,andaslowrateofevolution.Oursetofsubstitutionsenabledustodirectlytest if errors are preferentially observed at fast evolving sites. Our analysis carefullycontrolled for the effects of protein expression level on the detectability of translationerrors,thecodonusageofproteins,andtheirevolutionaryconservation.Itconfirmedthatindeed,substitutionsoccuronaverageat lessconservedsites,butalsothatthechoiceofcodons could not entirely explain this effect, suggesting that other factors might affecttranslation accuracy in cis. Similarly, simulating the effects of the set of observedsubstitutionsonproteinstabilityrevealedthattheytendedtobelocalizedatsiteswheretheyminimallyaffectedprotein folding.ObservedNeCEwerealso lessdestabilizingthanrandomly sampled NeCE at the corresponding sites, suggesting that the spectrum ofribosomeerrorsisevenmoreconservativethantheeffectofnaïvesinglesubstitutionsattheDNAlevel.Together,theseresultsconfirmthatthecellsencodetheirproteinsandtunetheir translationmachinery inways thatminimize the deleterious effects of amino acidmisincorporations.Since codon identity does not entirely account for the fact that substitutions arepreferentiallyobservedatsiteswheretheyaretolerated,wetestediftheribosomeitselfmight modulate its accuracy locally. Several lines of evidence indicate that ribosomesoptimizebothspeedandaccuracy,andmustthereforeperformatrade-offbetweenthesetwoconstraints.Inparticular,decreasingtheribosome’sGTPhydrolysisrateshouldresultin an lower processing speed, but a better discrimination between cognate and non-cognate aa-tRNAs.36We hypothesized that the ribosomemight rely on external clues tolocally slow-down in order to increase its accuracy at critical sites. Our analysis of apublished ribosome profiling dataset indeed revealed a subtle but significant shift inribosomedensity: the sites atwhichweobserved substitutionswere characterizedbyalowerribosomedensity,i.e.ahigherspeed.Our method provides a way to scan the proteome of organisms in various growthconditions,andcanbeusedtounveilnewtypesofadaptivetranslation46.Inmulticellularorganisms,onecouldextendthisanalysis todifferent tissues,anddiseasesassociated toproteostasisdefects,suchasAlzheimer’s47.Indeed,therecentreportthattranslationerrorrates are inversely correlated to themaximum lifespan of rodent species indicates thatmaintaininghigh translationaccuracy iscriticalduringaging. Inmodelorganisms, itcanbe coupled with genetic manipulations to probe how the different components of theproteostasis network contribute tomaintain accurate translation, and how errors affectthe physiology and fate of proteins. This, in turn, will provide a newway to study theinterplaybetweentranslationerrorratesandproteinevolution.


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Material and Methods Strains and growth conditions TogeneratetheE.colidrugsdataset,MG1655cellswereplatedonLBagarandincubatedat30°Covernight.6coloniesofMG1655werepickedandgrownuntilstationaryphasein3mlLB,30°C.All8cellcultureswerediluted1/100andgrownaerobicallyin100mlLBsupplementedwith the relevant antibiotics (see tableX) in500mlErlenmeyer flasks at37°C until they reach mid-log phase (OD ≃ 0.5). For the serine starvation dataset,BW25113(WT)andJW2880-1(ΔserA,obtainedfromtheKeiodeletionlibrary)cellswereplatedonLBagarandincubatedat37°Covernight.2coloniesofeachstrainwerepickedandgrownin3mlofmodifiedMOPSrichdefinedmediummadeaccordingtoCluzeletalrecipe(SIAppendix)andincubatedat37°Cuntilstationaryphase.BW25113andJW2880-1 cell cultureswerediluted1/1000andgrownaerobically in220ml ofmodifiedMOPSrich defined medium and MOPS serine starvation medium accordingly in 500 mlErlenmeyer flasks at 37°C (mediums were made according to Cluzel et al 2012 SIAppendix).

ProteomeextractionWe adapted our proteome extraction protocol from Khan et al., 201128. Samples wereeach split into two 50ml falcon tubes, centrifuged at 4000 rpm for 5min, andwashedtwice with PBS (add 10 ml PBS, vortex, centrifuge for 5 min). Remaining PBS wasvacuumedandthepelletswerefrozeninethanol-dryice.Pelletswerere-suspendedin1ml of B-PER bacterial protein extraction buffer (Thermo Fisher Scientific), pooledtogether,andvortexedvigorouslyfor1min.Themixturewascentrifugedat13,000rpmfor 5 min. The supernatant (high solubility fraction) was collected and frozen in anethanol-dryicebath.Thepelletwasre-suspendedin2mlof1:10dilutedB-PERreagent.The suspension was centrifuged and washed one more time with 1:10 diluted B- PERreagent. The pellet was re-suspended in 1 ml of Inclusion Body Solubilization Reagent(ThermoFisherScientific).Thesuspensionwasvortexedfor1min,shakenfor30min,andplacedinasonicbathfor10minatmaximumintensity.Cellulardebriswasremovedfromthesuspensionbycentrifugationat13,000rpmfor15min.Thesupernatantwasfrozeninanethanol-dryicebath(lowsolubilityfraction).

SCXfractionation,HPLCandMassSpectrometry400μgofproteinwastakenforin-solutiondigestionandprocessedbyFilteraidedsamplepreparation(FASP)48protocolusing30kMicrocon filtrationdevices(Millipore).Proteinsweresubjectedtoon-filter trypticdigestion forovernightat37°Candthepeptideswerefractionatedusingstrongcationexchange(SCX)followedbydesaltingonC18StageTips49(3M EmporeTM, St. Paul, MN, USA). Peptides were analyzed by liquid-chromatographyusingtheEASY-nLC1000HPLCcoupledtohigh-resolutionmassspectrometricanalysisonthe Q-Exactive Plus mass spectrometer (ThermoFisher Scientific, Waltham, MA, USA).Peptideswereseparatedon50cmEASY-spraycolumns(ThermoFisherScientific)witha140 min gradient of water and acetonitrile. MS acquisition was performed in a data-


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dependent mode with selection of the top 10 peptides from each MS spectrum forfragmentationandanalysis

ComputationalmethodsRawfileswereprocessedwithMaxQuantv.1.5.5.1.The listofparameters isavailable inthe supplementarymaterials.HighandLowsolubility fractionswerealigned separately.The amino acid substitutions identification procedure relies on the built-in dependentpeptidealgorithmofMaxQuant.

TheDependentPeptidesearchExperimental spectra are first searched using a regular database search algorithm,withoutanyvariablemodification,andthesignificanceofidentificationsiscontrolledtoa1%FDRvia a targetdecoyprocedure. Identified spectra are then turned into a spectrallibrary,andadecoyspectrallibraryiscreatedbyreversingthesequencesoftheidentifiedspectra. For each possible pair consisting of an identified spectrum in the concatenatedspectral libraries and an unidentified experimental spectrum of the same charge, andrecorded in the same raw file,we apply the following steps: firstwe compute themassshift Δmby subtracting themass of the identified (unmodified) spectrum to that of theunidentified (modified) spectrum, thenwe simulatemodified versionsof the theoreticalspectrumbyaddinginsilicothismassshiftateverypositionalongthepeptide,andfinallyweevaluatethematchbetweenthetheoreticalspectrumandtheexperimentalspectrumusingaformulasimilartoAndromeda'sbinomialscore.Foreachunidentifiedpeptide,thematchwiththebestscoreisreported,thenatureofthematch(targetordecoy)isrecorded,andatarget-decoyprocedure50isappliedtokeeptheFDRat1%.Peptides identifiedusingthisprocedurearecalledDependentPeptides(DP),whereastheirunmodifiedcounterpartsarenamedBasePeptides(BP).Additionally, the confidence of themass shift's localization is estimated using amethodsimilartoMaxQuant/Andromeda'sPTMScorestrategy,whichreturnstheprobabilitythatthemodificationisharboredbyanyofthepeptide'saminoacid.

DPidentificationsfilteringThe list of all knownmodifications was downloaded from www.unimod.org, and thosemarkedasAAsubstitution,IsotopiclabelorChemicalderivativewereexcluded.Entriesinthislistarecharacterizedbyamonoisotopicmassshift,andasitespecificity(i.e.theycanonlyoccuron a specific amino acidor onpeptides' andproteins' termini).We removedfromouranalysis anyDP identification that couldbeexplainedbyanyof the remainingmodifications,usingthefollowingcriteria:therecordedΔmandtheknownmodification'smassshiftmustnotdifferbymorethan0.01Da,andthemodificationmustbeharboredbya site consistent with the uniprot entry with a probability p ≥ 0.05. Conversely, wecomputedthelistofallpossibleaminoacidsubstitutionsandtheirassociatedmassshifts.Foreverysubstitution,weonlyretainedDPidentificationssuchthattheobservedΔmandtheAAsubstitution'smassshiftdidnotdifferbymorethan0.005Da,andthemassshiftwaslocalizedonthesubstitution'soriginalAAwithp≥0.95.


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Among the remaining DP identifications, those such that the peptide sequence aftersubstitution was a substring of the proteome (allowing Ile-Leu ambiguities), were alsoremoved,topreventpairingofdependentpeptidesandbasepeptidesbetweenparalogs.Finally,theFDRwascontrolledonceagainat1%usingthesameprocedureasabove.

ErrorratequantificationInorder to assess the error ratewequantify and comparepairs of base anddependentpeptides across many samples. For each independent substitution, we fetched thequantificationprofileofthebasepeptidefromMaxQuant’speptides.txttable,andsimilarlyfetch the dependent peptide’s quantification profile from thematchedFeatures.txt table.Wheneverapeakhasbeendetectedandquantifiedforboththedependentandthebasepeptide,weestimatethetranslationerrorrateastheratiooftheirMS1intensities.

AssignmentofNeCEtotheirmostlikelynucleotidemismatchFirst, for each amino acid substitution type, we fetch all the possible nucleotide mismatches that could have lead to this substitution (e.g. G:U, 1st position). Then for each nucleotide mismatch type, we count all the unique amino acid substitutions that could be unambiguously mapped to that type, and divide that number by the number of substitutions types that could be unambiguously mapped to that same nucleotide mismatch type. We call the resulting quantity the "count density". In subsequent iterations of the algorithm, we repeat the previous assignment procedure, but in cases where a substitution can be mapped to more than one nucleotide mismatch type, we break the tie based on the count density of the corresponding nucleotide mismatch types, and repeat until convergence.

EvolutionaryratescomputationForeachoftheproteinsassociatedtoasubstitutionintheMOPSdataset,wefetchedalistof orthologous protein sequences from the COG database51, excluding partial matches(membershipclass=3).Proteinswhoselistoforthologscontainedlessthan50sequenceswere excluded from this analysis. For the remaining proteins,we randomly selected 50sequences from the list, and created evolutionary alignments using MUSCLE52. Thealignmentswere then used to compute normalized evolutionary rates per sitewith therate4site program53. The mean evolutionary rate of sites associated with detectedsubstitutions was compared to that of a 1000 randomly sampled positions, using thestrategydescribedinFig.5A

EffectofsubstitutionsonproteinstabilityForeachoftheproteinsassociatedtoasubstitutionintheMOPSdataset,weattemptedtofetchthebest3DstructureforitsbiologicalassemblyinthePDBdatabasetoestimatetheeffect of our substitutions on protein stability using the FoldX software54.We excludedmembraneproteins,whosestabilityispoorlymodeledbyFoldX,andexcludedribosomalproteinbecausetheribosomeistoobigtobemodeledentirely.WerestrictedouranalysistoWTproteins fromE.coli,excludingstructuresdetermined fromorthologs.Amongtheremainingstructures,weselectedthosewiththelowestR-freescore.


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Thesestructureswerefirst“repaired”usingtherepairPDBcommand.Wethenevaluatedtheeffectof a setof aminoacid substitutions comprising thedetected substitutionsandthe controls described in Fig. 5D on protein stability (ΔΔG), using the PositionScancommand. Finally the mean(ΔΔG) of our set of substitutions was compared to themean(ΔΔG)of1000randomlysampledsubstitutions,usingthestrategydescribedinFig.5D.

RibosomedensitycomputationRibosome profiling data for the MOPS complete experiments were downloaded fromWoolstenhulmeetal.,201540(GSM1572266,GSM1572267).Readswerealignedusingthe3’mappingmethoddescribedinthecorrespondingarticle,andshiftedby12nttoobtainthedensityattheA-site.Readcountsfrombothreplicatesweresummedtoobtainmorerobustestimates,and20codonswereexcludedfromboththe3’andthe5’endstoavoidknownbiases.Fortheremainingpositions,weappliedthetransformationx:log2(x+1)tostabilizethevariance,andstandardizedtheresultingscoretoobtainthenormalizedreaddensity(NRD),sothatthemeanoftheNRDperproteinis0anditsstandarddeviationis1.Themean(NRD)of thesetofobservedsubstitutionswas thencompared to thatof1000randomlysampledsubstitutions,usingthestrategydescribedinFig.5A.

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