Selective silencing of antibiotic-tethered ribosomes as a resistance mechanism against aminoglycosides

Selective silencing of antibiotic-tethered ribosomes as a resistance mechanism against aminoglycosides | bioRxiv /* */ /* */ <!-- <!-- /*! * yepnope1.5.4 * (c) WTFPL, GPLv2 */ (function(a,b,c){function d(a){return"[object Function]"==o.call(a)}function e(a){return"string"==typeof a}function f(){}function g(a){return!a||"loaded"==a||"complete"==a||"uninitialized"==a}function h(){var a=p.shift();q=1,a?a.t?m(function(){("c"==a.t?B.injectCss:B.injectJs)(a.s,0,a.a,a.x,a.e,1)},0):(a(),h()):q=0}function i(a,c,d,e,f,i,j){function k(b){if(!o&&g(l.readyState)&&(u.r=o=1,!q&&h(),l.onload=l.onreadystatechange=null,b)){"img"!=a&&m(function(){t.removeChild(l)},50);for(var d in y[c])y[c].hasOwnProperty(d)&&y[c][d].onload()}}var j=j||B.errorTimeout,l=b.createElement(a),o=0,r=0,u={t:d,s:c,e:f,a:i,x:j};1===y[c]&&(r=1,y[c]=[]),"object"==a?l.data=c:(l.src=c,l.type=a),l.width=l.height="0",l.onerror=l.onload=l.onreadystatechange=function(){k.call(this,r)},p.splice(e,0,u),"img"!=a&&(r||2===y[c]?(t.insertBefore(l,s?null:n),m(k,j)):y[c].push(l))}function j(a,b,c,d,f){return q=0,b=b||"j",e(a)?i("c"==b?v:u,a,b,this.i++,c,d,f):(p.splice(this.i++,0,a),1==p.length&&h()),this}function k(){var a=B;return a.loader={load:j,i:0},a}var l=b.documentElement,m=a.setTimeout,n=b.getElementsByTagName("script")[0],o={}.toString,p=[],q=0,r="MozAppearance"in l.style,s=r&&!!b.createRange().compareNode,t=s?l:n.parentNode,l=a.opera&&"[object Opera]"==o.call(a.opera),l=!!b.attachEvent&&!l,u=r?"object":l?"script":"img",v=l?"script":u,w=Array.isArray||function(a){return"[object Array]"==o.call(a)},x=[],y={},z={timeout:function(a,b){return b.length&&(a.timeout=b[0]),a}},A,B;B=function(a){function b(a){var a=a.split("!"),b=x.length,c=a.pop(),d=a.length,c={url:c,origUrl:c,prefixes:a},e,f,g;for(f=0;f<d;f++)g=a[f].split("="),(e=z[g.shift()])&&(c=e(c,g));for(f=0;f<b;f++)c=x[f](c);return c}function g(a,e,f,g,h){var i=b(a),j=i.autoCallback;i.url.split(".").pop().split("?").shift(),i.bypass||(e&&(e=d(e)?e:e[a]||e[g]||e[a.split("/").pop().split("?")[0]]),i.instead?i.instead(a,e,f,g,h):(y[i.url]?i.noexec=!0:y[i.url]=1,f.load(i.url,i.forceCSS||!i.forceJS&&"css"==i.url.split(".").pop().split("?").shift()?"c":c,i.noexec,i.attrs,i.timeout),(d(e)||d(j))&&f.load(function(){k(),e&&e(i.origUrl,h,g),j&&j(i.origUrl,h,g),y[i.url]=2})))}function h(a,b){function c(a,c){if(a){if(e(a))c||(j=function(){var a=[].slice.call(arguments);k.apply(this,a),l()}),g(a,j,b,0,h);else if(Object(a)===a)for(n in m=function(){var b=0,c;for(c in a)a.hasOwnProperty(c)&&b++;return b}(),a)a.hasOwnProperty(n)&&(!c&&!--m&&(d(j)?j=function(){var a=[].slice.call(arguments);k.apply(this,a),l()}:j[n]=function(a){return function(){var b=[].slice.call(arguments);a&&a.apply(this,b),l()}}(k[n])),g(a[n],j,b,n,h))}else!c&&l()}var h=!!a.test,i=a.load||a.both,j=a.callback||f,k=j,l=a.complete||f,m,n;c(h?a.yep:a.nope,!!i),i&&c(i)}var i,j,l=this.yepnope.loader;if(e(a))g(a,0,l,0);else if(w(a))for(i=0;i (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0];var j=d.createElement(s);var dl=l!='dataLayer'?'&l='+l:'';j.src='//www.googletagmanager.com/gtm.js?id='+i+dl;j.type='text/javascript';j.async=true;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-M677548'); Skip to main content Home About Submit ALERTS / RSS Search for this keyword Advanced Search New Results Selective silencing of antibiotic-tethered ribosomes as a resistance mechanism against aminoglycosides View ORCID Profile Nilanjan Ghosh Dastidar , View ORCID Profile Nicola S. Freyer , View ORCID Profile Valentyn Petrychenko , View ORCID Profile Ana C. de A. P. Schwarzer , View ORCID Profile Bee-Zen Peng , View ORCID Profile Ekaterina Samatova , Christina Kothe , Marlen Schmidt , View ORCID Profile Frank Peske , View ORCID Profile Antonio Z. Politi , View ORCID Profile Henning Urlaub , View ORCID Profile Niels Fischer , View ORCID Profile Marina V. Rodnina , View ORCID Profile Ingo Wohlgemuth doi: https://doi.org/10.1101/2025.07.08.660097 Nilanjan Ghosh Dastidar 1 Department for Physical Biochemistry, Max Planck Institute for Multidisciplinary Sciences , Göttingen, Germany 2 Project Group Fidelity of Protein Synthesis in vivo, Department for Physical Biochemistry, Max Planck Institute for Multidisciplinary Sciences , Göttingen, Germany Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Nilanjan Ghosh Dastidar Nicola S. Freyer 1 Department for Physical Biochemistry, Max Planck Institute for Multidisciplinary Sciences , Göttingen, Germany 2 Project Group Fidelity of Protein Synthesis in vivo, Department for Physical Biochemistry, Max Planck Institute for Multidisciplinary Sciences , Göttingen, Germany Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Nicola S. Freyer Valentyn Petrychenko 3 Project Group Molecular Machines in Motion, Department for Physical Biochemistry, Max Planck Institute for Multidisciplinary Sciences , Göttingen, Germany Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Valentyn Petrychenko Ana C. de A. P. Schwarzer 3 Project Group Molecular Machines in Motion, Department for Physical Biochemistry, Max Planck Institute for Multidisciplinary Sciences , Göttingen, Germany Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Ana C. de A. P. Schwarzer Bee-Zen Peng 1 Department for Physical Biochemistry, Max Planck Institute for Multidisciplinary Sciences , Göttingen, Germany Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Bee-Zen Peng Ekaterina Samatova 1 Department for Physical Biochemistry, Max Planck Institute for Multidisciplinary Sciences , Göttingen, Germany Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Ekaterina Samatova Christina Kothe 1 Department for Physical Biochemistry, Max Planck Institute for Multidisciplinary Sciences , Göttingen, Germany Find this author on Google Scholar Find this author on PubMed Search for this author on this site Marlen Schmidt 4 Genetic Engineering Heidelberg GmbH , Heidelberg, Germany Find this author on Google Scholar Find this author on PubMed Search for this author on this site Frank Peske 1 Department for Physical Biochemistry, Max Planck Institute for Multidisciplinary Sciences , Göttingen, Germany Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Frank Peske Antonio Z. Politi 5 Facility for Light Microscopy, Max Planck Institute for Multidisciplinary Sciences , Göttingen, Germany Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Antonio Z. Politi Henning Urlaub 6 Bioanalytical Mass Spectrometry Group, Max Planck Institute for Multidisciplinary Sciences , Göttingen, Germany 7 Institute of Clinical Chemistry , Bioanalytics, University Medical Center Göttingen , Göttingen, Germany Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Henning Urlaub Niels Fischer 3 Project Group Molecular Machines in Motion, Department for Physical Biochemistry, Max Planck Institute for Multidisciplinary Sciences , Göttingen, Germany Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Niels Fischer Marina V. Rodnina 1 Department for Physical Biochemistry, Max Planck Institute for Multidisciplinary Sciences , Göttingen, Germany Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Marina V. Rodnina For correspondence: Ingo.Wohlgemuth{at}mpinat.mpg.de rodnina{at}mpinat.mpg.de Ingo Wohlgemuth 2 Project Group Fidelity of Protein Synthesis in vivo, Department for Physical Biochemistry, Max Planck Institute for Multidisciplinary Sciences , Göttingen, Germany Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Ingo Wohlgemuth For correspondence: Ingo.Wohlgemuth{at}mpinat.mpg.de rodnina{at}mpinat.mpg.de Abstract Full Text Info/History Metrics Preview PDF Abstract Antibiotic resistance is a growing threat, underscoring the need to understand the underlying mechanisms. Aminoglycosides kill bacteria by disrupting translation fidelity, leading to the synthesis of aberrant proteins. Surprisingly, mutations in fusA , a gene encoding translation elongation factor G (EF-G), frequently confer resistance, even though EF-G neither participates in mRNA decoding nor blocks aminoglycoside binding. Here, we show that EF-G resistance variants selectively slow ribosome movement along mRNA when aminoglycosides are bound. This delay increases the chance that the drug dissociates before misreading occurs. Over several elongation cycles, this selective silencing of drug-bound ribosomes prevents error cluster formation, preserving proteome and membrane integrity. As a result, fusA mutations confer resistance early in treatment by preventing self-promoted aminoglycoside uptake. Translation on drug-free ribosomes remains sufficiently rapid to sustain near-normal bacterial growth. This previously unrecognized resistance mechanism— selective silencing of corrupted targets—reveals a novel antibiotic resistance strategy with potential therapeutic implications. Introduction Infections with antibiotic-resistant bacteria pose an increasing threat to human health, ranking globally as the third leading cause of death in 2019 1 . Most aminoglycoside antibiotics (AGAs) are bactericidal, broad-spectrum antimicrobials that reduce the speed and fidelity of translation ( Supplementary Fig. 1a-c ) 2 , 3 , 4 . In Gram-negative bacteria, AGAs must cross both the outer and inner membranes to reach the ribosome, with the inner membrane representing a major permeability barrier for these cationic, hydrophilic compounds. According to the current uptake model 5 , 6 , 7 , AGAs enter the periplasm through porin channels or by disrupting the outer membrane. Aided by the membrane potential they then seep into the cytoplasm where they bind to a few ribosomes and induce mRNA misreading 2 , 8 , 9 by stabilizing an error-prone ribosome conformation 10 . Even single amino acid substitutions can render proteins nonfunctional; however, when AGA-bound ribosomes continue translation in the error-prone conformation 3 , 11 they can accumulate consecutive amino acid substitutions known as error clusters 12 , further compounding the loss of protein function. Error clusters potentiate proteotoxic error burden at the onset of AGA treatment, when only a small fraction of ribosomes is corrupted by the drug. The accumulation of faulty membrane proteins 13 , 14 , 15 and misreading-induced metabolic by-products 16 , 17 makes the inner membrane permeable to AGAs. As more and more AGAs enter the cell, more ribosomes bind AGAs and become corrupted. Over time, this self-promoted uptake leads to a massive influx of AGAs, resulting in a burst of translation errors, proteostasis collapse, and an accumulation of reactive by-products. These factors damage macromolecular structures 16 , 18 , disrupt metabolism 19 and cause membrane voltage dysregulation 20 , ultimately leading to cell death. In general, AGA resistance can be achieved through intrinsic, adaptive, or acquired mechanisms 21 . Gram-negative bacteria, such as Escherichia coli, are naturally protected against rapid AGA uptake due to their membrane architecture and efflux pumps, such as AcrAD 22 . Bacteria can also develop tolerance by adopting a different lifestyle as persisters or within biofilms 23 . Acquired resistance is typically mediated by drug-modifying enzymes or ribosomal methyltransferases spread by horizontal gene transfer. While these enzymes provide high-level resistance, they impose a metabolic and genetic burden and are specific to a narrow range of drugs 21 , 24 . AGA resistance can also arise as a result of mutations in the translational apparatus. Although 16S rRNA mutations in the ribosomal decoding center can confer high-level resistance 25 , their impact on antibiotic resistance is limited by the redundancy of rRNA operons in most bacteria. AGA resistance mutations can also arise in ribosomal proteins ( Supplementary Fig. 1a ), however, most AGA resistance mutations occur outside of the ribosome itself. Unexpectedly, a major hotspot of AGA resistance mutations is the fusA gene, which encodes elongation factor G (EF-G), a GTPase that promotes translocation of mRNA and tRNAs on the ribosome during each elongation cycle of protein synthesis ( Supplementary Fig. 1d and Supplementary Table 1). This is surprising, because EF-G is not directly involved in decoding and therefore cannot counteract AGA-induced misreading. Additionally, EF-G does not interact with ribosome-bound AGA, so it cannot directly displace AGA from its binding site ( Fig. 1a ). Nevertheless, fusA mutations confer resistance in various bacteria (Supplementary Table 1) as well as in parasites such as Leishmania 26 . For instance, clinical isolates of Pseudomonas aeruginosa lacking AGA-inactivating enzymes often harbor fusA1 mutations 27 . In adaptive laboratory evolution experiments, fusA mutations rapidly evolve in various pathogens, including Mycobacterium tuberculosis 28 and all ESKAPE pathogens ( E nterococcus faecium , S taphylococcus aureus , K lebsiella pneumoniae , A cinetobacter baumannii , P seudomonas aeruginosa , and E nterobacter cloacae) 29 , 30 , which are responsible for most multidrug-resistant infections. In E. coli , fusA mutations emerge under selection by various AGAs such as amikacin (Amk), apramycin (Apr), gentamicin (Gen), kanamycin A (KanA) and tobramycin (Tob) ( Supplementary Fig. 1d ) 30 , 31 , 32 , 33 , 34 , 35 , 36 , 37 , 38 , 39 , 40 , 41 , but not with AGAs like neamine (Nea), streptomycin (Str), or spectinomycin (Spc) ( Supplementary Fig. 1a ) 40 . In many cases, fusA mutations alone are sufficient to confer AGA resistance 37 , 42 , 43 with most mutations imposing minimal to moderate fitness costs 37 , 39 , and complementing these strains with wild type (wt) EF-G restores AGA sensitivity 37 , 44 . Download figure Open in new tab Fig. 1 l fusA mutations confer resistance to a specific set of AGAs a Resistance mutations in EF-G domains I to V. Mutations are mapped onto the structure of EF-G bound to the ribosome. AGA Apr and the key structural elements at the decoding center are shown (PDB entry 7PJV) 46 . Mutations that evolve during AGA treatment in E. coli are shown in cyan (Supplementary Table 1). Resistance variants F593L (orange), A608E (olive), and P610L (red) were chosen for in-vivo experiments using chromosomally-encoded fusA mutations. These and several other mutations (dark blue) were also introduced into the plasmid-encoded fusA gene for expression of EF-G variants, which were then used in translocation experiments in vitro . Notably, none of the residues at the mutated positions is close to the ribosome-bound Apr (> 20 Å in all cases) and none is involved in direct interactions with the ribosome. b Effect of chromosomally-encoded fusA mutations on cell growth. Shown are mean doubling times ± SD of 8 biological replicates. Significance levels were calculated using Tukeýs multiple comparison test. c Spectrum of antibiotic resistance. Minimal inhibitory concentrations (MIC) were measured by broth microdilution. Bars represent the MIC ratio of fusA mutant and wt strain. Mean values ± SD of 8-15 biological replicates are shown. d Volcano plots representing log 2 -fold changes in protein abundance (X-axis) and negative log 10 adjusted (Benjamini-Hochberg) p values (Y-axis) for fusA mutants F593L (left panel), A608E (center panel), and P610L (right panel) compared to the wt (n = 4 biological replicates). Colored dots represent proteins which are significantly ( q -value < 0.01, more-saturated colors; q -value < 0.05, less-saturated colors) and more than 2-fold regulated. Horizontal dashed lines indicate 1% and 5% false discovery rate (FDR) thresholds; vertical dashed lines indicate a two-fold change threshold. Despite their clinical relevance, the mechanism by which fusA mutations confer resistance remains unclear, as EF-G neither blocks AGA binding nor contacts the decoding center ( Supplementary Fig. 2 ). In this study, we used a combination of quantitative mass spectrometry (MS), live-cell imaging, kinetic analysis, and cryo-EM to identify a novel type of resistance mechanism by which fusA mutations silence corrupted ribosomes and shield cells from AGAs. Results Effect of fusA mutations on bacterial growth, antibiotic resistance, and proteome composition To explore how fusA mutations confer resistance to AGAs, we constructed E. coli MG1655 strains harboring three frequently reported laboratory-evolved EF-G variants (F593L, A608E, and P610L) ( Fig. 1a ), which belong to a prominent cluster of resistance mutations in EF-G domain IV (Supplementary Table 1), along with an isogenic wild-type control ( Supplementary Fig. 3a-d ). Resistance mutations in the same region of EF-G have been identified in ESKAPE pathogens, suggesting a shared resistance mechanism against AGAs across different bacterial species 29 . EF-G variants F593L and A608E caused only a slight reduction in the growth rate, whereas P610L had a stronger effect ( Fig. 1b ). All three mutations conferred resistance to a range of AGAs, including Apr, Gen, KanA, KanB, neomycin (Neo), ribostamycin (Rib), and sisomicin (Sis) ( Fig. 1c ). Despite belonging to different structural classes of AGAs ( Supplementary Fig. 1a ), these AGAs share a common mode of action: they slow down translocation and induce misreading and error cluster formation 12 . The P610L mutant has a stronger negative impact on cell growth ( Fig. 1b ), yet it conferred the same level of resistance as the F593L and A608E mutants. This suggests that the reduced growth rate alone cannot fully explain the observed resistance, thus challenging models that attribute resistance to just a slower translation rate, which would provide the cell with more time to pump out AGAs (see Supplementary Fig. 2 for details of putative resistance mechanisms; models 1 and 2). Furthermore, the mutations did not confer significant resistance to neamine (Nea), which slows translocation and induces misreading at high concentrations, but does not induce error clusters 12 . The mutant strains also remained sensitive to streptomycin (Str), which induces errors and error clusters 12 but does not strongly inhibit translocation 45 . These observations show that the mutant strains are not generally tolerant towards translational misreading and proteotoxic stress. Sensitivity to spectinomycin (Spc), which inhibits translocation without inducing misreading, and kasugamycin (Ksg), which does not bind to the ribosomal A site, remained unchanged, further supporting the notion that fusA mutant strains do not exhibit broad resistance to all AGAs. Together, these observations suggest that fusA mutations specifically counteract the effects of AGAs that both disrupt translocation and induce misreading with error cluster formation. Furthermore, the fusA mutations did not confer resistance to other bactericidal antibiotics such as carbenicillin (Car), a cell wall synthesis inhibitor, or norfloxacin (Nor), a gyrase inhibitor. Since these antibiotics—as well as some AGAs—have been reported to kill bacteria by escalating metabolic stress 14 , 47 , the lack of resistance suggests that fusA mutations do not mitigate this common metabolic death pathway. Instead, their resistance appears to be specific to a subset of AGAs, likely through a more targeted mechanism affecting translation. To determine whether fusA mutations might confer resistance to AGAs indirectly by inducing expression of resistance proteins or redirecting existing pathways ( Supplementary Fig. 2 , model 2), we analyzed the proteomes of the three mutant strains compared to the parental MG1655 and wt strains. The fusA mutations did not alter the expression patterns of proteins directly associated with AGA entry (e.g., porins, sugar and peptide transporters), AGA removal (e.g., efflux pumps), AGA-induced stress adaptation (e.g., ribosome silencing factors, global stress modulators), AGA response regulation (e.g., stress-associated transcription factors), and AGA-induced proteostasis maintenance (e.g., proteases and chaperones) ( Supplementary Fig. 4a ). Overall, the proteomic changes induced by the mutations were minimal for F593L and A608E, and moderate for P610L, with at most 63 out of 2527 proteins showing more than a 2-fold change ( Fig. 1d ) and correlated with growth rate differences ( Fig. 1b and Supplementary Fig. 4b-d ) but not with resistance patterns ( Fig. 1c and Supplementary Fig. 4e ). Thus, these adaptions are likely due to changes in translation rates ( Fig. 1d and Supplementary Fig. 4b-f ), but do not account for the observed AGA resistance, further challenging the validity of models suggesting an indirect effect of fusA mutations on adaptation ( Supplementary Fig. 2 , model 2). Notably, the most upregulated proteins, such as enzymes of the Leu and Ile synthesis pathway ( Fig. 1d ), are regulated by translation attenuation mechanisms normally activated during starvation. Under starvation, translation slows down due to low aminoacyl-tRNA levels, promoting the formation of alternative mRNA secondary structures that enhance full-length transcription of Leu and Ile biosynthetic operons, resulting in increased expression levels of enzymes involved in Leu and Ile synthesis 48 . The same regulatory pathway is activated in fusA mutant strains presumably due to slower translation, stimulating the production of these anabolic enzymes under non-starvation conditions. A similar misregulation seems to occur in the pyrimidine synthesis pathway in fusA mutant strains. Under nutrient-rich conditions, high UTP levels speed up transcription of uridine-rich regulatory sequences, resulting in the loss of transcription-translation coupling and downregulation of key enzymes of pyrimidine biosynthesis, PyrB and PyrI. The observed down regulation of PyrB and PyrI in fusA mutants ( Fig. 1d ) is consistent with previous reports on the effects of ribosome variants 49 , which showed that slower translation leads to a transcription-translation decoupling and a misregulation of the expression of the pyrBI operon. While fusA mutations activate attenuation pathways, suggesting alterations in local translation speed, they have little effect on cell growth, implying that EF-G mutants still promote efficient mRNA translocation. EF-G mutations can selectively slow down translation on AGA-bound ribosomes To test how fusA mutations affect translation we performed in-vitro experiments with EF-G variants F593L, A608E, and P610L, as well as additional variants within the prominent mutation cluster in domain IV (F593C, F605I, A608E, and P610Q). We also tested five additional mutations (G117C, R371L, T674A, A678V, and Y680C) located throughout different domains of EF-G ( Fig. 1a and Supplementary Table 1). Notably, the last three mutations (T674A, A678V, and Y680C) belong to a cluster of mutations that are frequently found in P. aeruginosa (residues 668-680). We analyzed the in-vitro translocation activity of the purified EF-G variants in the absence of AGAs using a time-resolved assay that monitors the kinetics of individual translocation events 50 . The EF-G variants exhibited a moderate reduction in translocation rates, ranging from 1.3-to 4-fold ( Fig. 2a ). For the F593L, A608E, and P610L variants, the results align with the observed changes in growth rates and the proteome changes ( Supplementary Fig. 5a ). Similarly, a translation assay monitoring the time course of in-vitro synthesis of the model protein SlyD revealed that EF-G variants have only minor effects on translation speed in the absence of AGA ( Fig. 2b and Supplementary Fig. 5b ). Download figure Open in new tab Fig. 2 l EF-G variants selectively slow down AGA-bound ribosomes a Translocation rates in the absence of AGAs. Top panel: Schematics of translocation assay. Ribosome movement by one codon was measured under single round translocation conditions on synchronized ribosomes in excess of EF-G by tracking the fluorescence change of fluorescein-labeled model mRNA in a stopped-flow apparatus. SSU, small subunit; LSU, large subunit. Bottom panel: Bar graph showing translocation rates calculated by exponential fitting of the stopped-flow time courses shown as mean values ± SD of 5-8 technical replicates. b In-vitro translation in the absence and presence of AGA. Top panel: Schematics of translation assay. Ribosome initiation complexes with slyD mRNA and Bodipy-FL-labeled Met-tRNA fMet were mixed with EF-G (P610L or wt), and ternary complexes of aminoacyl-tRNAs, GTP and elongation factor Tu (EF-Tu, not shown). The reaction was stopped after indicated time intervals and translation products were separated by Tris-Tricine SDS-PAGE and visualized using the N-terminal BodipyFL fluorescence reporter. Bottom panel: Time courses of slyD translation with wt EF-G and P610L in the absence and presence of Apr (1 µM). c Translation yield of full-length SlyD at increasing Apr concentrations after 30 min of in-vitro translation. Mean values ± SD of 3 biological replicates are shown. d Effect of AGAs on single-round translocation rates at saturating concentrations of Apr (left panel) or KanA (right panel) (both 250 µM). Bars represent mean values ± SD of 5-8 technical replicates. Dotted line indicates the AGA-induced rate reduction for EF-G wt. In the presence of low, subsaturating Apr concentrations, translation with wt EF-G was slower, as expected 12 , 51 . Notably, the EF-G resistance variants did not alleviate the inhibitory effect of Apr; instead, translation was even slower than with wt EF-G. This suggests that EF-G resistance variants neither displace the drug from the ribosome nor enable efficient translation on AGA-bound ribosomes, ruling out the ‘drug displacement’ and ‘gain-of-function’ models proposing that variant EF-G accelerates translocation in the presence of the drug ( Supplementary Fig. 2 , models 3,4). With increasing Apr concentration, more ribosomes bound Apr, leading to a dramatic decrease in the yield of full-length SlyD ( Fig. 2c and Supplementary Fig. 5c ). While translation with wt EF-G continued to produce SlyD even at high Apr concentrations, the EF-G resistance variants substantially reduced SlyD formation at concentrations above 10 µM, indicating that these variants specifically impair translation on the AGA-bound ribosomes. To better assess the underlying kinetic effect on translocation on AGA-bound ribosomes, we measured the rates of individual translocation events at high Apr and KanA concentrations (250 µM), at which nearly all ribosomes carry AGAs ( Fig. 2d ). As expected, Apr and KanA substantially reduced translocation rates for wt EF-G, without completely blocking it. Notably, none of the twelve EF-G variants alleviated the translocation inhibition ( Fig. 2d ), effectively ruling out resistance mechanisms in which EF-G variants compensate for the inhibitory AGA effect ( Supplementary Fig. 2 , model 3) or displace the drug allosterically ( Supplementary Fig. 2 , model 4). Instead, for most EF-G variants, especially the frequently observed variants in EF-G domain IV including F593L, A608E, and P610L, translocation rates substantially decreased in the presence of AGAs, supporting the idea that these mutants are selectively hindered in promoting translocation on AGA-bound ribosomes ( Supplementary Fig. 2 , model 5). Next, we used time-resolved cryo-EM to visualize the mechanism of translation inhibition and confirm that the AGA is not displaced. To capture various states of translocation, we slowed the reaction by lowering the temperature and by adding polyamines 46 . Our cryo-EM analysis of EF-G P610L-promoted translocation reveals that the EF-G variant and Apr bind simultaneously to the ribosome ( Fig. 3a-c , Supplementary Fig. 6a-g and Supplementary Table 3). Superimposing ribosome complexes with EF-G wt and P610L shows no significant structural changes in the decoding center or drug-binding site ( Fig. 3c ). These findings further support the notion that EF-G variants do not displace the drug from the ribosome ( Supplementary Fig. 2 , model 3). Download figure Open in new tab Fig. 3 l EF-G variant P610L selectively impedes translocation on Apr-bound ribosomes a Cryo-EM map of EF-G P610L bound to 70S ribosome in the presence of Apr at 3.0 Å resolution. SSU (dark gray) and LSU (light gray), small and large ribosomal subunit, respectively. A, P, E are tRNA binding sites on SSU and LSU with the two tRNAs (deacylated tRNA in the P site (pink) and peptidyl-tRNA in the A site (magenta) in hybrid states and EF-G in the GDP-Pi form. Close-up: Nucleotide binding pocket of EF-G P610L with GDP-Pi; residues of EF-G taking part in stabilization of GDP-Pi, including H91 which is a key residue for GTP hydrolysis, are indicated. SRL, sarcin-ricin loop of LSU. b Comparison of the SSU conformations in complexes with EF-G P610L-GDP-Pi (left) and wt EF-G-GDP-Pi (right) 46 . The mRNA and tRNAs were omitted for visual clarity. Pink dot, location of P610L substitution in EF-G. c Superposition of Apr-binding site from structures shown in (B) , demonstrating that EF-G wt and P610L do not displace the antibiotic. A1492 and A1493 are key 16S rRNA residues in the decoding center 4 , 10 ; RMSD, root-mean-square deviation. d Mechanism of translocation inhibition. Schematic of translocation in the presence of Apr with EF-G wt (blue) or P610L (red). b and h, body and head of the SSU, respectively. The ribosome-tRNA complexes are shown in three conformations: classical C state; hybrid H state with the SSU rotated relative to LSU; and chimeric CHI state with the head and body domains of the SSU swiveled relative to each other and tRNAs partially translocated relative to the SSU body domain. Both EF-G wt and P610L bind to the ribosome 46 , stabilize tRNA hybrid states and hydrolyze GTP. Upon Pi release, wt EF-G promotes tRNA movement relatively to the SSU resulting in the formation of the CHI state, a bona-fide translocation intermediate. In contrast, in EF-G P610L the key reaction of Pi release is slowed down, inhibiting the conformational rearrangement required to promote the tRNA movement into the CHI state. e Cryo-EM particle distribution. In contrast to wt EF-G 46 , EF-G P610L impedes transition to chimeric (CHI) tRNA states in presence of Apr. With wt EF-G, Apr does not interfere with the early steps of translocation, including tRNA hybrid state stabilization upon EF-G binding, GTP hydrolysis, inorganic phosphate (Pi) release, up to the movement of tRNAs into chimeric states on the small ribosomal subunit (SSU) ( Fig. 3d ) 46 . However, Apr significantly slows down the final transition of the tRNAs on the SSU to the post-translocation state 46 . With EF-G P610L, the early stages of translocation up to GTP hydrolysis are unaffected by Apr but the later steps appear to be inhibited, particularly Pi release and the tRNA movement into chimeric states. This contradicts the ‘gain-of-function’ model ( Supplementary Fig. 2 , model 4). Together with the kinetic analysis ( Fig. 2 ), these results demonstrate that EF-G variants are impaired in facilitating translation on AGA-bound ribosomes. Furthermore, this also suggests that EF-G resistance variants can promote efficient translation only when intracellular AGA concentrations are low and most ribosomes are drug-free, whereas at higher AGA concentrations, translation on all ribosomes would cease, abolishing the resistance phenotype. fusA mutations help to preserve proteome integrity Next, we asked whether selective silencing of AGA-bound ribosomes by EF-G variants affects proteome integrity. To measure translation errors, we treated E. coli cultures (wt, F593L, A608E and P610L) with Apr ( Fig. 4a ) and quantified amino acid misincorporation using MS. After Apr addition, the cultures continued to grow at similar rates initially, likely due to the lag phase in self-promoted AGA uptake ( Fig. 4a ). After 60 min, wt cells stopped growing, while mutant cells remained unaffected by the Apr exposure, consistent with their resistance phenotypes ( Fig. 1c ). In wt cells, the Apr treatment led to a burst of misreading within 60 min ( Fig. 4b and Supplementary Fig. 7a-c ). After that, error frequencies did not increase further, likely because proteostasis collapsed and cell growth ceased. In contrast, error levels in mutant strains increased gradually and remained lower than in wt cells, suggesting that fusA mutations establish resistance early in the initial phase of AGA uptake. As the accumulation of aberrant membrane proteins is crucial for AGA uptake 5 , we analyzed error frequencies specifically in membrane proteins (see Methods). Similar to their effect on cytosolic proteins, fusA mutations helped to maintain the integrity of the membrane proteome, including diverse proteins involved in protein export, quality control, respiration, metabolic transport, and cell structure maintenance ( Fig. 4c and Supplementary Fig. 7d ). Download figure Open in new tab Fig. 4 l fusA mutations preserve proteome integrity a-b fusA mutations preserve cell growth (a) and prevent translational misreading (b) in the presence of Apr. Cells were treated with Apr (16 µM) at 0.25 OD 600 and cell growth was recorded photometrically. Peptides with missense errors in the abundant cytosolic protein EF-Tu were quantified by targeted MS using label-free Parallel Reaction Monitoring (PRM). Bars represent the mean ± SD of 3 technical replicates of median error frequencies of at least 25 independent misreading events. Note that in (b) , untreated wt and mutants are indistinguishable. c fusA mutations preserve the inner membrane proteome integrity. Inverted inner membrane vesicles were prepared from untreated and Apr-treated (16 µM, 60 min) wt and P610L cells and error frequencies in inner membrane proteins (indicated in the cartoon) were quantified by targeted MS (label-free PRM). Bars represent the relative difference of error frequencies in wt and P610L cells after Apr treatment. Values represent the mean ± SD of 3 biological replicates based on 1-21 missense peptides/protein (as indicated). One type of errors that crucially depends on the translation velocity is error clusters: Faster translation in wt strain should allow AGA-bound ribosomes to decode multiple codons before the drug dissociates, thereby increasing the probability of long error clusters. In contrast, selective silencing of AGA-bound ribosomes in fusA mutant strains increases the chance that AGA will dissociate from the ribosome before several elongation cycles are completed, thereby suppressing error cluster formation. To assess relative translation speed of AGA-bound ribosomes in wt and mutant strains in vivo , we measured error cluster formation using targeted MS 12 . In wt cells, Apr treatment led to a rapid increase in single translation errors and error clusters, and to growth arrest ( Fig. 5a,b and Supplementary Fig. 8a ). In contrast, in the P610L strain the level of single errors increased gradually, reaching levels comparable to the wt only at very high Apr concentrations (128-256 µM), reflecting the attenuated AGA uptake at lower concentrations. Across all concentrations and incubation times, error clusters were dramatically reduced, with some falling below the of the mass spectrometer’s detection limit. Download figure Open in new tab Fig. 5 l fusA mutations substantially reduce error cluster formation a Misreading errors and error clusters in Apr-treated wt and P610L cells. Cells were treated at 0.25 OD 600 with increasing concentrations of Apr (4-256 µM), and misreading for single errors (examined in 12 peptides) and error cluster formation (double errors in 14 peptides) were quantified at various incubation times by targeted MS. The mean of their median error frequencies from 3 biological replicates are color coded. At high error burden all missense peptides were confidently detected in wt cells (ratio dot products > 0.97, see Supplementary Fig. 8a ). To provide an orthogonal measure of misreading across all conditions, the number of detected missense peptides (ratio dot products > 0.8) for each conditions is represented by the size of the circles, with smaller dots reflecting fewer detected peptides. The apparently lower error frequency in wt cells at high Apr concentration is due to massive cell death and the rapid decline in actively growing cells. b Growth curves of Apr-treated wt and P610L cells used to evaluate translation errors in (a) . Cell growth was recorded photometrically. Shown are mean values ± SD of 3 biological replicates. c Examples of correlations between error frequencies ( E f ) of the initial misreading event and of corresponding error clusters across 189 biological conditions (from (a) ). Notably, the ratio depends on the error pair, but is independent of the incubation time and Apr concentration. d Distance dependence of error cluster frequencies in P610L vs wt. Dots represent individual error clusters compared at conditions of similar error load (wt at 16 µM Apr; P610L at 256 µM), with their median indicated as horizontal line. The distribution of single-error peptides is shown in Supplementary Fig. 8b for comparison. e Distance dependence of error cluster formation ( E f next ) in F593L, A608E and P610L stains normalized to the wt. Dots represent mean values ± SD of 1-4 error clusters per distance. f Correlation between the reduction in error cluster formation and increase in MIC values for different AGAs (from Fig. 1c ) for P610L vs wt strain (Pearson coefficient r = −0.88, p = 0.017). Points represent the mean E f next values of 5 error clusters with a distance of 2-4 residues between misincorporated amino acids, measured at conditions of induced single errors in wt and P610L (wt: at 16 µM Str, 16 µM Apr, 16 µM Neo, 8 µM Dhs, 4 µM Gen, 32 µM Rib, 16 µM Sis, 8 µM KanA and 4 µM KanB; P610L: at 16 µM Str, 256 µM Apr, 128 µM Neo, 8 µM Dhs, 32 µM Gen, 256 µM Rib, 64 µM Sis, 256 µM KanA and 64 µM KanB). The curve represents a linear regression as visual guide; note the logarithmic scale. Error cluster formation depends linearly on the occurrence of the initial error 12 ( Fig. 5c ), with the slope reflecting the probability that an AGA-bound ribosome, after making the first error, will proceed to make a subsequent error 12 . For example, for the wt strain, the probability of misreading the next codon D48E after the initial F47L misincorporation ( E f next ) is ∼0.2 (e.g., 20%), which is a very high error rate ( Fig. 5c , upper panel). Shallower slopes indicate that P610L systematically prevents error cluster formation, e.g. for the same error cluster E f next is ∼0.1, about 2-fold less that in the wt strain. However, the reduction is not uniform for all error clusters (compare examples of F47L-D51E and H67Q-D71E in Fig. 5c ). Notably, the codon distance between the consecutive misreading events matters: for the clusters where the misreading events are separated by 3 codons, the probability to make a second error is decreased about 30-fold, from E f next ∼0.08 to ∼0.003. A systematic analysis for P610L showed that clusters with a longer codon distance decrease dramatically compared to the wt ( Fig. 5d and Supplementary Fig. 8a,b ), while misreading events involving single amino acid substitutions are less affected ( Supplementary Fig. 8b ). Similar length-dependent reductions in error cluster formation were also observed for F593L and A608E mutant strains ( Fig. 5e ), indicating that translocation inhibition of AGA-bound ribosomes and the corresponding reduction in error clusters are hallmarks of fusA mediated resistance. To evaluate whether silencing of AGA-bound ribosomes is associated with resistance, we correlated the effect of different AGAs on P610L-mediated error cluster suppression with their impact on resistance ( Fig. 5f ). For Apr, KanA, KanB, Sis and Rib, error clusters were strongly reduced in P610L compared to wt, correlating with a significant increase in resistance. In contrast, Neo and Gen showed a smaller reduction in error cluster formation and only a slight increase in resistance, while Str and dihydrostreptomycin (Dhs) showed almost no reduction and little resistance. Overall, the strong correlation between error cluster reduction and resistance, together with our biochemical, kinetic and structural data, support the conclusion that EF-G resistance variants selectively silence corrupted ribosomes, thereby reducing the proteotoxic stress ( Supplementary Fig. 2 , model 5). fusA mutations help to maintain proteostasis and membrane integrity Because the proposed resistance mechanism is most effective at low intracellular AGA concentrations, we investigated how proteostasis and membrane integrity are affected in fusA mutant strains at extracellular AGA concentrations that are lethal for wt bacteria. Light microscopy revealed significant protein aggregation at the poles of Apr-treated wt cells ( Fig. 6a ), which is consistent with previous microscopic studies of AGA-treated cells 11 , 52 . These aggregates form due to AGA-induced misreading, which leads to protein misfolding and the activation of the unfolded protein response 12 , 53 . The unfolded protein response then triggers the expression of the small chaperones IbpA and IbpB, which guide unfolded proteins into aggregates 52 , 54 . In contrast, fusA mutant cells appear unaffected by Apr treatment ( Fig. 6a and Supplementary Fig. 9 ). Supporting our microscopic analysis and error burden evaluation ( Figs. 4b,c and 5a ), the quantification of these chaperones along with their transcription factor RpoH revealed that the unfolded protein response is strongly induced in Apr-treated wt cells but remains unaffected in resistance mutant strains ( Fig. 6b ), supporting the notion that fusA mutations confer resistance by maintaining proteostasis. Download figure Open in new tab Fig. 6 l fusA mutations preserve the membrane integrity and delay AGA uptake a Protein aggregation. Brightfield images of wt and fusA mutant cells treated with Apr (16 µM) for 180 min prior to imaging. Yellow arrows point to inclusion bodies formed in the wt cells. Scale bar, 5 µm. Representative images of 3 biological replicates. b Unfolded protein stress response. The expression of small chaperones IbpA/B involved in guiding misfolded proteins into aggregates and their corresponding transcription factor RpoH was quantified by targeted MS (label-free PRM using isotope labeled reference peptides for identification). For each protein, abundance ratios (Apr-treated/untreated) were normalized to the wt 180 min. Bars represent mean values ± SD of 3 biological replicates. c Membrane integrity. Cells were grown in the absence or presence of Apr (16 µM) for 180 min and the uptake of the membrane-permeable dye SYTO 9 and of membrane-impermeable propidium iodide (PI) was quantified by confocal microscopy. Representative images of 3 biological replicates (left panel; scale bar, 5 µm) and quantification (right panel). Bars represent the mean fraction ± SD of PI stained cells from 3 biological replicates. Statistical significance was determined using a two-tailed Student’s t test with Bonferroni correction; *** p adj < 0.001, ns: non-significant ( p adj = 1). d AGA uptake. Representative Airyscan super-resolution images of wt and fusA mutant cells ± Apr (16 µM, for 90 min) and stained with GTTR (9 µM, 90 min staining). Shown images representative of 3 biological replicates. Scale bar, 8 µm. e Quantification of AGA uptake. Fold change in GTTR fluorescence intensity was measured in Apr-treated samples relative to the untreated (-Apr) control for wt and three fusA mutant strains. Bars represent mean GTTR fold change ± SD of 3 biological replicates. Statistical significance was determined using a two-tailed Student’s t test with Bonferroni correction for differences in mean GTTR fold change from wt only; *** p adj < 0.001. Dashed line indicates no change. f Impact of AgNO 3 -mediated membrane permeabilization on Apr resistance. Minimal inhibitory concentrations (MIC) were measured in the absence and presence AgNO 3 (4 µM) by broth microdilution. Bars represent mean values ± SD of 8-9 biological replicates. Statistical significance was determined by one-way ANOVA with Šidák correction. We then investigated whether fusA mutants affect AGA uptake. First, we first assessed the membrane integrity of Apr-treated wt and P610L cells by staining them with propidium iodide, a membrane-impermeable dye. While wt cells took up the dye, indicating membrane damage, P610L cells remained unstained, suggesting that fusA mutations help preserve membrane integrity after AGA treatment ( Fig. 6c ). Second, to investigate the specific effect on AGA uptake, we treated wt and mutant strains with Apr and then used gentamicin-Texas Red (GTTR) as a reporter to visualize AGA uptake ( Fig. 6d,e ). Wt cells showed a strong increase in GTTR fluorescence after AGA treatment, indicating substantial antibiotic accumulation. In contrast, fusA mutant cells showed little or no increase in GTTR fluorescence during AGA treatment, suggesting low AGA uptake ( Fig. 6e ). These findings suggest that fusA mutations, by silencing AGA-bound ribosomes, contribute to resistance by limiting AGA uptake. To test whether reduced antibiotic uptake explains the resistance phenotype of fusA mutants, we measured MIC values in the absence and presence of AgNO 3 , a compound that permeabilizes bacterial membranes and accelerates AGA uptake 55 ( Fig. 6f ). While the fusA mutant strains appeared to maintain a low-level residual AGA resistance after AgNO 3 treatment, the differences to the wt strain were not statistically significant (one-way ANOVA with Šidák correction). The large (25-50-fold) effect of AgNO 3 strongly supports the notion that while selective silencing of corrupted ribosomes is the primary, microscopic effect of fusA mutations, the preservation of the membrane integrity is the dominant macroscopic outcome that ultimately enables the cell to survive at otherwise lethal AGA concentrations. Discussion Our findings reveal a previously unrecognized resistance mechanism: EF-G resistance variants can efficiently promote translation in the absence of AGAs, but selectively silence AGA-bound ribosomes allowing the drug to dissociate before the ribosomes decode the next codon, resulting in fewer translation errors ( Fig. 7 ). Repeated over multiple slow elongation cycles, this mechanism reduces the formation of error clusters, particularly of highly destabilizing long error clusters with multiple amino acid substitutions, which are especially toxic 56 . The resulting lower error burden helps fusA mutant strains to preserve the proteome and membrane integrity, attenuating the self-promoted AGA uptake and keeping intracellular AGA concentrations low. Lower AGA-induced misreading may also reduce the burden on cellular quality control systems (e.g., chaperones and proteases), enabling the cell to remove remaining faulty macromolecules. Overall, these adaptations enable the cell to avoid the disastrous AGA-induced downstream effects such as oxidative stress and to maintain normal growth despite exposure to otherwise lethal drug concentrations. Download figure Open in new tab Fig. 7 l Mechanism of AGA action in fusA mutant strains Upper panel: AGA action in wt strains. At the early stages of AGA treatment, AGA enter the periplasm, but only small amounts seep into the cytoplasm where they bind to a few ribosomes which become corrupted. Corrupted ribosomes produce aberrant cytosolic and membrane proteins leading to membrane disintegration and self-promoted massive influx of AGA. As more ribosomes become corrupted, high error load in newly synthesized proteins leads to a collapse of proteostasis, metabolic and ionic stress, and membrane voltage dysregulation, ultimately leading to cell death. Lower panel: AGA action in fusA mutant strains. As in the wt strain, initially only a few ribosomes become corrupted by AGA. These ribosomes are selectively silenced by the resistance EF-G variant until the drug dissociates, thereby lowering the error burden. This keeps the membrane intact, prevents self-promoted AGA uptake and maintains proteostasis. In addition, slower AGA uptake may allow the cell to clear AGAs from the cytosol, repair or remove damaged macromolecules, adapt to different lifestyles or evolve additional resistance mutations resulting in AGA resistance. Created in BioRender ( https://BioRender.com/40melak ). This mode of resistance clarifies the differential response to distinct AGAs. Resistance to Apr, KanA, KanB, Rib, and Sis correlates with the suppression of error cluster formation ( Fig. 5f ), and these AGAs lead to the selection of fusA mutations across organisms, despite their structural differences ( Supplementary Fig. 1a,d and Supplementary Table 1). This highlights the critical link between the effects of fusA mutations on formation of error clusters and their associated resistance phenotype. In contrast, Nea, which binds to the same site but likely dissociates faster 57 does not induce error clusters 12 and elicits minimal fusA -mediated resistance ( Fig. 1c ). Consequently, Nea selects fidelity-enhancing mutations in ribosomal proteins uS5, uS12, and uS17 ( Supplementary Fig. 1a ). Similarly, Str-treated cells acquire instead of fusA mutations mutations in uS12, consistent with its minor effect on translocation 58 , lack of resistance ( Fig. 1c ) and lack of error cluster formation suppression in P610L ( Fig. 5f ). Likewise, Spc, which does not promote misreading, fails to select fusA mutations as well, which is in line with our proposed mechanism. Although AGA resistance mutations are broadly distributed across EF-G (Supplementary Table 1), they do not occur randomly, and not all of them selectively silence AGA-bound ribosomes. Some less frequent resistance variants, such as G117C, R371L, and T674A, generally slow translocation ( Fig. 2d ), likely also increasing the chance of AGA dissociation and error clusters reduction. However, this general slowdown presumably comes at a higher fitness cost, likely limiting their prevalence. Similarly, fusA mutations that confer resistance to bacillaene 59 or fusidic acid 60 by preventing these drugs from binding to EF-G, also slow down translation and can confer cross-resistance to AGAs, however, these mutations are typically not selected under AGA exposure. In addition to reduced protein production, a general translation slowdown is more likely to cause ribosome stalling, triggering translation termination by the ribosome rescue pathways 61 , or promoting ribosome frameshifting. 50 , 62 . This may explain why mutations of key catalytic residues which could substantially slow down translation (e.g., H583 in E. coli ) 50 are transiently sampled but not maintained 41 . In contrast, domain IV mutations (residues 585-610) avoid these fitness defects. Our structural data reveal how corrupted ribosomes are selectively stalled and explain why spontaneous frameshifting is prevented. Normally, wt EF-G rapidly binds to the ribosome and hydrolyzes GTP to GDP and Pi driving the tRNA movement on the ribosome 63 . The subsequent Pi dissociation from EF-G triggers a conformational rearrangement of the ribosome complex, promoting mRNA-tRNA displacement and the movement of EF-G domain IV into the A site of the ribosome. AGA binding alters the dynamics of the decoding center, stabilizing the mRNA-tRNA codon-anticodon complex in the A site and increasing the energy barrier for translocation 45 , 64 . While wt EF-G can overcome this barrier, it does so with a significant delay in translocation. For EF-G resistance variants, this added energy barrier poses a significant challenge, resulting in a translocation block at an early stage, before the mRNA and tRNA move on the small ribosomal subunit ( Fig. 3 ). Halting translation at the early stage preserves the stabilizing interactions between the ribosome decoding center and the mRNA-tRNA codon-anticodon complex, preventing ribosome slippage. At the conditions where only a minor fraction of ribosomes is corrupted by AGAs, this mechanism likely minimizes the metabolic costs associated with globally slower translation and reduces undesirable side effects on ribosome function. Despite the benefits of domain IV mutations and the high sequence conservation of fusA , resistance patterns vary among pathogens. One likely reason for this is that, apart from the individual AGA and the selection regime, resistance mutations will depend on the specific growth conditions. In P. aeruginosa, fusA mutations mainly arise in cystic fibrosis patients under long-term selection and counter-selection, generating diverse mutation landscapes. Often, different populations of fusA mutations evolve even within the lungs of individual patients 58 , 65 . Species-specific traits also influence the selected mutations: while the mechanism involving silencing of AGA-bound ribosomes appears predominant in E. coli , a dedicated translation attenuation mechanism triggers the subsequent expression of MexXY efflux pumps in P. aeruginosa 66 . Although E. coli lacks this regulatory pathway, our proteome analysis suggests that slower translation caused by fusA mutations may in principle trigger attenuation pathways (e.g. in the Leu, Ile, and Trp operons) ( Supplementary Fig. 4b,f ). In such mechanisms, the mRNA regulatory elements, AGAs, and EF-G variants might act synergistically, resulting in distinct effects of fusA mutations on attenuation at specific mRNAs and on general misreading, thus affecting the selection of predominant resistance mutations. Therefore, we speculate that although the mechanics of EF-G action on the ribosomes resulting in selective silencing of corrupted ribosomes might be conserved, the downstream consequences might be species- and sequence-dependent. Consequently, the general reduction of the error burden might be accompanied by dedicated resistance programs. Classical AGA resistance enzymes preserve proteostasis by altering either the drug or the ribosome to prevent AGA binding 67 . We show that fusA mutations can achieve a similar outcome indirectly by preventing AGA uptake. However, these two antibiotic resistance mechanisms are conceptually distinct in the broader context of combating antibiotic resistance. Drug-modifying enzymes confer resistance by modifying specific functional groups of AGAs. Consequently, selecting AGA that lack these groups 68 or chemically derivatizing the drugs can restore their efficacy. In contrast, fusA mutations impact the mechanics of ribosomal translation itself, providing a more general resistance mechanism against a wide set of structurally diverse AGAs. Thus, evolution of fusA mutations provides a broader resistance mechanism against diverse AGAs. For example, strains harboring widespread resistance cassettes remain sensitive to Apr, a promising drug candidate that is less ototoxic and lacks functional groups common to other AGAs 68 , 69 . However, because Apr kinetically affects translation similarly to other AGAs, fusA mutations can silence Apr-bound ribosomes, thereby compromising its efficacy. Similarly, the activity of newly designed Tob derivatives which have shown potency against AGA-resistant clinical isolates of P. aeruginosa, was compromised by fusA mutations 70 . However, fusA mutations do not confer uniform resistance to all AGAs. This suggests that a deeper understanding of their resistance mechanism might inform the development of new AGAs in the future. The selective silencing mechanism may extend to other ribosomal proteins mutants. Potential candidates include error-restrictive uS12 variants, which not only reduce tRNA selection speed improving translation fidelity but also slow down translocation, resulting in reduced cell growth 71 , 72 , 73 . In addition, C-terminally truncated uL6 variants confer AGA resistance through an unknown mechanism in different bacteria 74 , 75 , 76 . In S. aureus , small-colony-forming variants that survive AGA treatment often have mutations in fusA or rplF 76 , which encodes uL6, suggesting that their resistance mechanisms might be similar. The C-terminal domain of uL6 plays a dual role: it binds the incoming aminoacyl-tRNA early in tRNA decoding 77 and interacts with domain V of EF-G during translocation ( Supplementary Fig. 6h ). Disruption of these interactions may silence corrupted ribosomes similarly to fusA mutations. However, in a cellular context, this may not be favored, as rplF mutations or deletions are often associated with ribosome assembly defects and significant growth defects 75 , 76 , 78 . In summary, we show that fusA mutations silence AGA-bound ribosomes, preserving membrane integrity, and attenuating AGA uptake. This strategy minimizes translation errors without modifying the ribosome itself ― a key advantage, as altering the ribosome could affect all stages of translation, including ribosome assembly. While demonstrated in E. coli , the mechanism offers a framework for understanding evolutionary resistance patterns in the translation systems of various bacteria, including ESKAPE pathogens, suggesting broad applicability across diverse bacterial species. Beyond AGAs, other bactericidal antibiotics ― such as beta-lactams, griselimycins, macrolones, fluoroquinolones, or rifampicin ― corrupt their targets rendering them into killing devices 79 , 80 , 81 . Thus, the novel concept of silencing corrupted targets may offer valuable insights into resistance mechanisms across multiple antibiotic classes. Methods Chemicals and isotope labeled reference peptides Unless otherwise specified, chemicals were purchased from Merck or Sigma. Chemicals used for LC-MS/MS were of HPLC/MS grade and obtained from Thermo Fisher. Samples were handled in low-retention reaction cups (Eppendorf). For the reliable identification of missense peptides, isotope-labeled reference peptides (spikeTidesL, JPT) were used where indicated. Contamination with unlabeled peptides in the reference standards was monitored and found to be negligible at the low concentrations of reference peptides used. Bacterial strains and cell growth E. coli reference strain MG1655 was purchased from the German Collection of Microorganisms and Cell Cultures (Braunschweig, Germany). Construction of EF-G variants F593L, A608E and P610L in the essential fusA gene of E. coli MG1655 was performed according to Kim et al., with minor changes 82 . Red/ET recombination was used to replace 339 bp of the wt fusA gene starting from amino acid F593 with a codon optimized part of fusA in combination with a Flippase Recognition Target (FRT)-flanked selection marker (Zeocin TM ). The wt part of fusA and the artificial codon optimized part generate a fu s A gene chimera, encoding for exactly the same amino acid sequence as the wt fusA gene. The three fusA variants were generated in the same manner but using codon optimized DNA fragments encoding for the desired mutations at amino acid positions 593, 608 and 610 ( Supplementary Fig. 3a ). All clones were analyzed by sequencing of the modified region. Sequences of the synthetic regions and of the oligonucleotides used for sequencing are summarized in Supplementary Table 2. E. coli cells were grown in LB medium at 37°C. In general, 500 ml cultures were grown at 200 rpm to OD 600 of 0.2-0.3 and treated with AGAs for 60 min, 120 min, or 180 min as indicated. To determine doubling times, cells were inoculated to OD 600 of 0.01 and cell growth was recorded between 0.05 and 0.5 by measuring the OD 600 over time and fitting them to the Malthusian growth equation. Minimal Inhibitory Concentrations (MIC) were determined by broth microdilution. Briefly, single bacterial colonies were used to inoculate overnight cultures, which were then diluted to an initial optical density of 0.0005 (OD 600 ) in LB medium. Twofold serial dilutions of the tested antibiotics were prepared in 96-well plates, maintaining a final volume of 200 µl medium per well. Plates were incubated with shaking at 400 rpm in a Thermomixer (Eppendorf) at 37°C for 22 h. Bacterial growth was monitored using a FERAstar FS plate reader (BMG Labtech), and MIC values were defined as the lowest antibiotic concentration that inhibited cell growth by ≥ 90% compared to untreated controls. Purification of inverted inner membrane vesicles To detect amino acid substitutions in inner membrane proteins, it was necessary to first enrich the membrane protein fraction. This was achieved by generating inverted inner membrane vesicles. The process involves mechanically lysing E. coli cells, followed by density gradient centrifugation to separate inverted outer and inner membrane vesicles. In detail, bacteria were grown in the absence and presence of Apr (16 µM, 60 min) as described above and harvested by centrifugation at 5,000 x g for 10 min. Cell pellets were flash frozen in liquid nitrogen and stored at −80°C. Cell pellets were thawed in buffer A (50 mM Tris-HCl pH 7.8, 300 mM NaCl, 10% glycerol (v/v), 5 mM 2-mercaptoethanol containing cOmplete protease inhibitor (1 tablet per 50 ml, Roche Diagnostics)). Cells were lysed using the EmulsiFlex C3 (Avestin). Cell debris was removed by centrifugation. Inverted inner membrane vesicles in the supernatant were collected by ultracentrifugation at 35,000 rpm in Ti50.2 rotor (Beckman Coulter) for 1 h. Membrane vesicles were resuspended in 20% (w/v) sucrose in 20 mM Tris-HCl pH 7.8 using a tissue grinder and inner and outer membrane vesicles were separated by ultracentrifugation through a sucrose step gradient (73% (w/v) and 53% (w/v) in 20 mM Tris-HCl pH 7.8) in a SW32 rotor (Beckman Coulter) at 23,000 rpm for 20 h. Inner membrane vesicle fractions were pooled and inner membranes were collected by ultracentrifugation in Ti50.2 rotor at 35,000 rpm (Beckman Coulter) for 1h. Membrane pellets were washed with 100 mM Na 2 CO 3 and 6 M urea in 50 mM Tris-HCl pH 7.8. Inner membrane vesicle pellets were resuspended in buffer A and stored at −80°C. For the quantification of missense peptides, inner membrane proteins were separated on an anykD Criterion gradient SDS PAGE (BIO RAD). Bands of abundant inner membrane proteins were stained with Coomassie and in-gel proteolyzed with trypsin (Sequencing grade modified, Promega) 83 . Cloning and protein purification of EF-G variants fusA from E. coli MG1655 was cloned into pet24a (Novagen) and mutations were introduced by QuikChange PCR. The EF-G (wt and variants) were overexpressed in E. coli BL21(DE3) and cells for His-tagged EF-G purification were lysed in buffer B (25 mM HEPES-HCl pH 7.5, 400 mM KCl, 10% glycerol (v/v), 5 mM 2-mercaptoethanol containing cOmplete protease inhibitor (1 tablet per 50 ml, Roche Diagnostics) and traces of DNase I (Sigma Aldrich)) using an EmulsiFlex C3 (Avestin). Cell debris was removed by centrifugation and EF-G was purified using the Protino Ni-IDA 2000 kit (Macherey-Nagel) according to the manufactureŕs protocol. EF-G eluted with 200 mM imidazole was rebuffered into buffer C (25 mM HEPES-HCl pH 7.5, 70 mM NH 4 Cl, 30 mM KCl, 7 mM MgCl 2 ) and stored at −80°C. In-vitro translocation: Complex preparation and translocation experiments Ribosome from E. coli (MRE600), initiation factors, EF-Tu, f[ 3 H]Met-tRNA fMet , [ 14 C]Phe-tRNA Phe , fluorescein-labeled mRNA(MF+14Flu), and pre-translocation complex (PRE) were prepared and purified as described 50 . The mRNA used in the translocation experiments was purchased from IBA. The sequence of the model mRNA(MF) was: 5’-GUUAACAGGUAUACAUACU AUG UUUGUUAUUAC-3’ (start codon is underlined). Translocation experiments were carried out in buffer D (50 mM Tris-HCl pH 7.5 at 37°C, 70 mM NH 4 Cl, 30 mM KCl, 7 mM MgCl 2 ). mRNA translocation was measured by monitoring the fluorescence change of MF+14Flu. The MF+14Flu-programmed PRE complexes (0.05 µM) were mixed with EF-G (wt or variants, 5 µM) and Apr or KanA (0 and 250 µM) in a stopped-flow apparatus (SX 20, Applied Photophysics) at 37°C. Fluorescein was excited at 470 nm and emitted light was detected after passing a KV500 cut-off filter. The changes in fluorescence were recorded over 10-100 s and 5-8 replicates were measured for each condition. The rate of mRNA translocation was determined by exponential fitting using GraphPad Prism 45 . In-vitro translation Initiation complex with slyD mRNA were prepared in buffer E (50 mM HEPES-HCl pH 7.5, 70 mM NH 4 Cl, 30 mM KCl, 7 mM MgCl 2 , 2 mM DTT, and 2 mM GTP). Ribosomes (0.5 μM) were incubated with initiation factors (IF1, IF2, and IF3; 2.25 μM each), SlyD mRNA (2 μM), and BodipyFL-[ 3 H]Met-tRNA fMet (1 μM) for 45 min at 37°C. Ternary complex EF-Tu–GTP–aa-tRNA was prepared in buffer E by incubating EF-Tu–GDP (120 μM) with phosphoenolpyruvate (3 mM), and pyruvate kinase (0.05 mg/mL) for 15 min at 37°C, mixing with total aminoacyl-tRNA (200 μM), and further incubation for 1 min at 37°C. In-vitro translation was performed in HiFi buffer (50 mM HEPES-HCl pH 7.5, 70 mM NH 4 Cl, 30 mM KCl, 3.5 mM MgCl 2 , 1 mM DTT, 0.5 mM spermidine, and 8 mM putrescine) 9 . Initiation complexes (80 nM) were mixed with EF-Tu–GTP–aa-tRNA (100 μM) and EF-G (wt or variants, 1 μM), and incubated at 37°C without or with Apr. Translation products were separated by Tris-Tricine PAGE 84 . Fluorescent peptides were detected using Starion IR/FLA-9000 scanner (FUJIFILM) and quantified using Multi Gauge software (FUJIFILM). Microscopic methods To visualize protein aggregates in vivo , 1 OD 600 of untreated and Apr-treated cells (16 µM, 180 min) were harvested by centrifugation and washed with 0.85% NaCl (w/v) in water. Cells were applied to a 1.5% low melt agarose (w/v) (Lonza) in water pad placed on 25 × 75 mm glass slide (Epredia) and covered with a cover glass (Menzel Gläser) for microscopy. Bright-field images were captured on a LSM880 microscope (Zeiss) equipped with a plan apochromat 63x/1.4 NA oil objective. Minimum intensity was set to 112 for visualization and the scale bar was added using Fiji (NIH) software. To assess the impact of fusA mutations on membrane permeability, untreated and Apr-treated cells (16 µM, 180 min treatment) were harvested by centrifugation and washed twice with 0.85% NaCl (w/v). Cells (1 OD 600 ) were stained with SYTO 9 and propidium iodide stains from the LIVE/DEAD® BacLight TM Bacterial Viability Kit L7012 (Invitrogen) following the manufacturer’s instructions. Briefly, equal volumes of SYTO 9 and propidium iodide stains were mixed and 1.5 µl of the mix was used to stain the cells for 15 min in dark at room temperature. To remove excess stain, cells were washed with 0.85% NaCl (w/v). Cells were applied to low-melt agarose pads (as described above) for confocal microscopy. Confocal images were acquired on a Zeiss confocal microscope LSM880 equipped with a 63x/1.40NA Plan Apochromat oil objective. Images were recorded using Zen Black 2.3 software and analyzed in Fiji (NIH) software. Cells were counted manually using the Cell Counter plugin of Fiji (NIH). To monitor the AGA uptake, untreated and Apr-treated (16 µM, 90 min) cells were collected and incubated for 90 min at 37°C with gentamicin Texas Red (GTTR, AAT Bio, 9 µM). Cells were washed twice with 0.5 ml of 500 µM unlabeled gentamicin (Sigma Aldrich), followed by 0.5 ml of LB medium and finally with 0.5 ml of 0.85% NaCl (w/v) to remove excess and non-internalized GTTR. Cells were stained with SYTO 9 stain from the LIVE/DEAD® BacLight TM Bacterial Viability Kit L7012 (Invitrogen) following manufacturer’s instructions (15 min in the dark at RT). To remove excess stain, cells were washed with 0.85% NaCl (w/v). Cells were applied to low-melt agarose pads (as described above) for Airyscan super-resolution microscopy. Airyscan raw images were processed using Zen Black 2.3 software (Zeiss) using default settings. MicrobeJ plugin 85 (version 5.14p) of Fiji version 2.140 (NIH) was used to create closed cell contours around each cell based on the SYTO 9 signal. The mean GTTR fluorescence pixel intensity was measured within the given contour of each cell. The median intensity of all cells was calculated for each image. To compare fold changes, the ratio (Apr-treated/untreated control) of intensities was calculated using the averaged median fluorescence values from three images from three biological replicates. Statistical significance was determined using a two-tailed Student’s t test with Bonferroni correction for differences from wt only; *** p adj < 0.001. Maximum fluorescence values of representative images were readjusted for visualization, not for quantification; GTTR channel: 20000, SYTO 9 channel: 24000. All microscopy experiments were replicated thrice with three independent fields of view acquired per replicate. Cryo-EM analysis Cryo-EM grids were prepared at 4°C in a time-resolved manner with a total time of ∼10 s to vitrification after mixing of EF-G P610L–GTP with PRE complex, using identical conditions as described for wt EF-G 46 . Specifically, 3 µL of 0.8 µM Apr-treated PRE complex were mixed with 3 µL of 2 µM EF-G P610L–GTP in buffer F (50 mM HEPES-HCl pH 7.5, 70 mM NH 4 Cl, 30 mM KCl, 3.5 mM MgCl 2 , 50 µM Apr, 0.6 mM spermine, 0.4 mM spermidine and 1 mM GTP), resulting in the final concentrations of 0.4 µM PRE and 1 µM EF-G P610L. Ribosome–EF-G P610L complexes were then immediately applied to glow-discharged EM grids (Quantifoil 2.7/1.3 µm), manually blotted for 8-10 s using Whatman® Grade 1 Qualitative Filter Paper, and plunge-frozen in liquid ethane at 4°C and 95% humidity using a custom-made device. Cryo-EM data acquisition was performed on a Titan Krios electron microscope (Thermo Fisher Scientific) operating at 300 kV with an XFEG electron source, a spherical aberration corrector (CEOS Heidelberg), and a Falcon III direct electron detector (Thermo Fisher Scientific). Images were recorded in movie mode with an electron dose of 40 ± 5 e-/Ų, a defocus range of −0.3 to −1.2 µm and a nominal magnification of 59,000×, yielding a final pixel size of 1.16 Å. Data processing was performed using RELION 3.1 and 5.0 86 ( Supplementary Fig. 6f ), if not mentioned otherwise. Image stacks were motion-corrected with MotionCorr2 87 , and CTF parameters were estimated with CTFFIND4 88 . Particle selection was performed automatically using GAUTOMATCH 0.56 (K. Zhang, MRC-LMB, Cambridge), followed by extraction and subsequent processing in RELION 3.1 (step 1 in Supplementary Fig. 6f ). Selected particles were sorted for particle quality by 2D classification at a binned pixel size of 4.64 Å, resulting in an initial set of 1,753,901 ribosome particle images (step 2). 3D classification without alignment for particle quality and global ribosome conformation yielded two populations showing rotated and non-rotated ribosomal subunits, respectively (step 3). Further image processing was carried out separately for the two groups at the final pixel size of 1.16 Å. Per-particle motion correction was performed using the Bayesian polishing approach 89 , followed by CTF refinement 90 for per-particle defocus and per-micrograph astigmatism. Subsequently, another round of Bayesian polishing and CTF refinement was performed. In this round, the CTF refinement was executed on a 3 × 3 grid to account also for off-axial aberrations, correcting first for magnification anisotropies, and then for per-particle defocus, per-micrograph astigmatism, beamtilt, trefoil and fourth-order aberrations. The population of rotated ribosomes was sorted by global 3D classification (step 5 in Supplementary Fig. 6f ), which allowed us to identify pre-translocation state ribosomes with both A- and P-site tRNAs bound, and to remove ribosomes with only P-site tRNA, isolated LSUs and low-quality particles. All subsequent sorting steps were performed using focused classification with signal subtraction (FCwSS). Sorting on the A-site tRNA resulted in two ribosome populations (step 6), hybrid state group 1 (H1 group) and hybrid state group 2 (H2 group), which differed in the orientation of the elbow region of the A-site tRNA on the LSU 46 . The H1 group was then sorted on presence of EF-G P610L and A-site tRNA (step 7), resulting in populations of ribosomes without (H1) and with EF-G P610L bound [H1-EF-G P610L], respectively. The particles containing EF-G P610L, were further sorted for EF-G domains 4 and 5 (step 8), yielding two sub-populations with different orientations of domain 4 (‘D4-1’ and D4-2’ in Supplementary Fig. 6f ). For the H2 group, FCwSS on the EF-G P610L core (step 6) identified a population of EF-G-free ribosomes (H2), as well as ribosomal complexes with EF-G P610L (H2-EF-G). The group of non-rotated ribosomes was first sorted by global 3D classification for particle quality and presence of tRNAs, followed by FCwSS focusing on the tRNAs (step 4 in Supplementary Fig. 6f ), resulting in a final population of non-rotated ribosomes with tRNAs bound in classic pre-translocation states, A/A and P/P, respectively. Further extensive sorting trials, both on rotated and non-rotated ribosomes, did not reveal any complexes with EF-G P610L in a state after Pi-release and tRNAs in chimeric state, as observed previously for complexes with wt EF-G 46 . All final particle populations were refined to high resolution following the gold-standard procedure 86 . The final cryo-EM maps were post-processed by global sharpening in PHENIX 1.18 91 and low-pass filtered to the respective final resolutions ( Supplementary Fig. 6c and Supplementary Table 3). Cryo-EM maps of EF-G P610L-bound ribosome complexes, H1-EF-G P610L and H2-EF-G P610L, were resampled to a pixel size of 0.6525 Å for improved visualization and atomic model refinement. Atomic model refinement Initial atomic models for H1-EF-G P610L and H2-EF-G P610L states were created by fitting the atomic coordinates of the corresponding complexes with wt EF-G (PDB entries: 7PJV and 7PJW, respectively) 46 as rigid bodies into the cryo-EM density maps map using ChimeraX 1.8 92 . The P610L residue substitution was introduced and minor structural refinements were done in WinCoot 0.9.8.95 93 . Subsequent atomic model refinement was performed in PHENIX 1.20 91 . Secondary structure and metal coordination restraints for atomic model refinement were prepared with phenix.ready_set, and real-space refinement was performed using phenix.real_space_refine with automated weighting over 300 iterations and five macrocycles. Two such rounds of refinement were conducted for each state, with manual optimizations of the structural models in WinCoot between cycles (Supplementary Table 3). Proteolysis and chromatographic peptide separation E. coli cells (1 OD 600 ) were lysed in 1x Laemmli sample buffer (Bio-Rad). For proteome analysis, proteins (corresponding to 0.1 OD 600 ) were desalted by a 10% Criterion TGX SDS PAGE (Biorad). For the analysis of individual proteins, proteins (corresponding to 0.1 OD 600 cells) were separated using Criterion TGX SDS PAGE (10% or anykD, Bio-Rad). Proteins were stained with Coomassie and in-gel proteolyzed with trypsin (Sequencing grade modified, Promega) 83 . Peptides were analyzed on a Vanquish Neo UHPLC system coupled to an Orbitrap Exploris 480 mass spectrometer (Thermo Fisher). Tryptic peptides were loaded on a PepMap Neo C18 trap column (5 µm particle size, 5 mm length, 300 µm inner diameter (Thermo Fisher). Bound peptides were eluted and separation was performed on a C18 capillary column (31 cm length, 75 μm inner diameter, Reprosil-Pur 120 Å, 3 μm (Dr. Maisch GmbH)) at a flow rate of 300 nL/min with a acetonitrile gradient in 0.1% formic acid. Proteome analysis by data-independent acquisition (DIA) Peptides eluting from the Vanquish Neo UHPLC system coupled to an Orbitrap Exploris 480 mass spectrometer (Thermo Fisher) were analyzed in positive mode over a 118min runtime using a data-independent acquisition (DIA) method. Orbitrap resolution setting was set to 120,000 for MS and 30,000 for MS/MS (Full Width at Half Maximum). The MS scan range was 350-1650 m/z , with automatic gain control (AGC) targets were set to 300% for MS and 1000% for MS/MS. Maximum injection times were set to 20 ms for MS and 55 ms for MS/MS. Precursor fragmentation was performed using normalized higher-energy collision-induced dissociation (HCD) at 30%. MS/MS scans were acquired after each MS scan in 40 isolation windows (Supplementary Table 2). For each strain (MG1655, wt, F593L, A608E, P610L) four biological replicates were acquired, each in two technical replicates. DIA data were processed using Spectronaut (version 17.1.22, Biognosys) with the spectral library-free directDIA workflow. Data were searched against the E. coli (K-12, MG1655) reference proteome from Uniprot (UP000000625, download on 03.04.2023, 4,401 protein entries). Trypsin/P was set as digestion enzyme, allowing up to two missed cleavages. For directDIA database search and data extraction, default settings provided by Biognosys were used. Protein abundance estimates generated by Spectronaut (PG.ProteinQuantity) were analyzed using the Perseus software platform (v.1.6.15.0). Technical replicates were averaged, while biological replicates (n=4) were kept separate for statistical analysis. Raw values were log 2 -transformed, and a filtering threshold was set to retain proteins observed in at least 70% of all MS runs. Missing values were replaced with random numbers drawn from a normal distribution (default settings: width 0.3, and downshift 1.8). To identify significantly altered proteins in the fusA mutants relative to the wt, protein abundances were first compared pairwise between wt and each mutant respectively ( Fig. 1d ) using an unpaired two-sample Student’s t-test (S 0 = 0.5; Benjamini-Hochberg FDR = 0.05) and the corresponding significance values are reported as q -values. Significance thresholds were set to q < 0.05—or q < 0.01 for higher stringency—and a minimum fold change of ± 2. In addition, to globally identify proteomic changes between all strains ( Supplementary Fig. 4a-f ), significantly altered proteins were identified by ANOVA statistics applying a permutation-based FDR (S 0 = 1; FDR = 0.05; 250 randomizations). The log 2 -transformed intensities of significantly regulated proteins were Z -score normalized, and Z -scores of biological replicates were averaged. Co-regulated proteins were identified by hierarchical clustering based on their Euclidian distance and grouped into 5 clusters. Enrichment of biological processes within individual clusters was determined using Fisher’s exact test. Targeted quantification of unfolded protein stress response Due to the low abundance of small chaperones under unstressed conditions, we employed targeted mass spectrometry (PRM) to comparatively analyze their expression levels across biological states. Proteolysis and sample preparation was performed as described above. For each target protein (IbpA, IbpB, RpoH, EF-Tu, and uL10), a panel of at least two proteotypic, tryptic peptides was selected to serve as quantitative reporters. Peptides were separated using an Ultimate 3000 RSLC system (Thermo Fisher Scientific) coupled to an Orbitrap Fusion Lumos Tribrid mass spectrometer (Thermo Fisher Scientific). In detail, peptides were initially loaded on a C18 precolumn (2.5 cm, 150 µm ID, Reprosil-Pur 120 Å, 5 µm), eluted and then separated on a C18 capillary column (31 cm, 75 µm ID, packed with Reprosil-Pur 120 Å, 1.9 µm) at a flow rate of 300 nl/min, with a 68 min linear gradient from 5 to 42% ACN in 0.1% formic acid. PRM data acquisition was used for label-free quantification using isotope-labeled peptides for peptide identification. In PRM acquisitions, precursor ions were isolated using 1 m/z isolation windows, AGC target set to standard and maximum injection time set to ‘auto’ for fragmentation by HCD with a normalized collision energy setting of 30%. MS/MS transients were acquired at resolution setting of 60,000 (FWHM) in the Orbitrap mass analyzer. Sets of interference-free fragment chromatograms were extracted at a resolution of 60,000 using the Skyline software and abundance differences were estimated based on the sum of the individual integrated fragment intensities. To account for potential sample loading variability, signal intensities were normalized to the levels of two constitutively expressed proteins which are unaffected by AGA treatment, EF-Tu and ribosomal protein uL10. Means of peptide intensity ratios (Apr treated / untreated) for each protein were compared between the different strains. Identification of missense peptides in survey runs For the identification of missense peptides, data acquisition was performed in positive ion mode using a Top 30 data-dependent acquisition (DDA) method. Typically, MS spectra were acquired at a resolution setting of 120,000 FWHM over a range of 350-1600 m/z . The normalized automatic gain control (AGC) target was set to 300%, with a maximum injection time of 50 ms. Precursors with charge states 2-6 and intensities above 1e 4 were selected for fragmentation at an isolation width of 1.6 m/z. Fragmentation was carried out using HCD with a normalized collision energy of 30%. Precursors with undetermined charge states were excluded from fragmentation selection, and masses of fragmented precursors were dynamically excluded for 22 s with a mass tolerance of ± 10 ppm. MS/MS transitions were acquired at a resolution setting of 15,000 FWHM, using a normalized AGC target of 100% and a maximum injection time of 200 ms. Acquisition parameters optimized for individual experiments are reported in Supplementary Table 2. To gain additional missense peptide identifications that are aligned to the other identification runs, MS gas-phase fractionation was performed (380-500; 500-580; 580-660; 660-740; 740-1200 m/z ). For the identification of missense peptides with single errors (illustrated in Supplementary Fig. 7a ), raw data files were first processed using MaxQuant (version 2.0.1.0) 94 . Unless stated otherwise, standard software settings were applied. Protein identification was performed using the E. coli proteome as the reference database, along with a database of known lab contaminants. To identify partial peptide spectrum matches, we utilized the MaxQuant Dependent Peptide search algorithm 95 , which defines a dependent peptide as an encoded peptide carrying an additional, localized delta mass. Dependent peptide identifications were subsequently filtered for missense peptides with single amino acid substitutions using a Python script modified from Mordret et al. (2019) 96 . Missense peptides whose delta masses could be attributed to known post-translational or chemical modifications were excluded based on the Unimod database 97 . Additionally, substitutions attributed to non-cognate codon-anticodon mispairing (involving two or three base mismatches) as well as atryptic peptides were excluded from analysis. Furthermore, a mass tolerance of 0.005 Da and a localization probability threshold of 0.95 were applied. Missense peptide candidates whose sequences are also found in unrelated proteins were excluded. Lastly, a FDR of 0.01 was applied using a target-decoy approach, and the remaining dependent peptide identifications were accepted as missense peptide identifications. For the exploratory comparison of misreading events in P610L and wt ( Supplementary Fig. 8b ), the MaxQuant search database was supplemented with missense peptides carrying X to K/R and K/R to X substitutions, which are not systematically identified using the dependent peptide approach. These peptides were identified using the PEAKS software (version 10.5) with the SPIDER algorithm. A MS/MS peptide spectrum library was generated from validated missense peptide identifications and imported into the Skyline software for quantification by targeted MS or MS1 filtering. Selection of missense peptides as reporter peptides To select suitable reporter missense peptides with single amino acid substitutions for quantification in targeted MS, MS/MS spectra of identified missense peptides were matched against PROSIT-predicted MS/MS spectra 98 and only matches with ratio dot products ≥ 0.7 were included in the analysis. Additionally, at least four fragment ions were required to confirm peptide identity, including fragments covering the substituted amino acid position. If reliable identifications of cognate peptides were not possible, all corresponding missense peptides were excluded. Similarly, identifications were excluded if they could not be assigned to a chromatographic peak (ion dot products ≤ 0.7). Peptides with missed cleavages were excluded from analysis. In order to set up a readout that reflects AGA-induced misreading, only missense peptides with low background levels in untreated cells and a > 3-fold induction upon AGA-treatment were considered for analysis. For a relative comparison of missense peptides with single errors across various biological states, peptides with a high signal intensity, moderate hydrophobicity, and good chromatographic behavior were selected. For the analysis of missense peptides with error clusters, we chose candidates with two amino acid substitutions per peptide, which were initially identified using the PEAKS software using the SPIDER algorithm 12 . All missense peptides with error clusters were observed and validated previously. In general, AGA-induced error clusters were observed in various proteins and shared similar properties (such as the their linear dependence on the first error) 12 . Thus, we selected missense peptides with error clusters from EF-Tu as the highest abundant protein in the E. coli cytosol, because this allows their confident quantification in AGA-treated cells across multiple biological states even for fusA mutants with diminished levels of error clusters. Furthermore, we selected error clusters for which both errors were also observed individually in single substituted peptides and all three peptides were induced by AGA treatment > 5-fold. We further kept only candidates for which the net delta mass ≠ 0. From the list of all possible missense peptides with error clusters, we selected candidates for targeting based on their intensity, their hydrophobicity, and distance between the individual misreading events. All missense peptides that were selected for targeting and that were unambiguously identified by targeted MS using isotope-labeled peptides as reference were included in the analysis. Detection of missense peptides by Parallel Reaction Monitoring (PRM) Samples were analyzed on a Vanquish Neo UHPLC system coupled to an Orbitrap Exploris 480 mass spectrometer (Thermo Fisher) as described above. Acquisition parameters were tuned for the individual experiments and performance of the mass spectrometer, and can be found in Supplementary Table 2. In general, the highest populated charge states of correct and missense peptides were targeted. In general, sets of interference-free fragment chromatograms were extracted using the Skyline software. Because amino acid substitutions can alter the fragmentation pattern of peptides, the chosen set of extracted fragments can differ for correct peptides, single error peptides, and peptides with error clusters. Because we interpret the difference between wt and mutants, these differences have no impact on our conclusions. Abundance differences were estimated based on the sum of the individual integrated fragment intensities. Quantification of missense peptides For the quantification of missense peptides by spectral counting ( Supplementary Fig. 7c ), missense peptides were identified in DDA runs as described above and their corresponding MS/MS spectra were counted. For a more comprehensive analysis, all types of amino acid substitutions were included if their respective counts were induced > 3-fold by AGA-treatment. For the intensity-based, label-free quantification of missense peptides, we applied different acquisition and analysis strategies: First, for the exploratory profiling of misreading events at high error load ( Supplementary Fig. 8b ), we identified missense peptides as described above and quantified them using MS1 filtering in Skyline. Identifications were imported into Skyline, and precursor ion signals were extracted at a resolution of 60,000. Missense peptides were identified as the peptide with the highest scoring identification across all aligned chromatographic runs. Missense peptide abundances were estimated based on integrated MS1 signal relative to their correct parental peptide. Second, for the comparative analysis of missense peptides with single errors in EF-Tu and membrane proteins ( Fig. 4 and Supplementary Fig. 7 ), we targeted missense peptides (see above) by PRM. Correct and missense peptides were identified by the high sequence coverage of their co-eluting ion fragments and their high dot product scores, reflecting the similarity between their fragmentation spectra and those derived from databse search results (see above). Third, in all experiments involving the quantification of error clusters, we spiked in isotope-labeled reference peptides ( Fig. 5a-f and Supplementary Fig. 8a ) (JPT-L grade, JPT Peptide technologies, estimated < 5 fmol on column). The reference peptides were not used for quantification but helped to select interference-free pseudo-transitions and to identify missense peptides by their close to one ratio dot product. In general, peptide abundances were quantified by integration over consistent elution windows in all quantification runs. Integrated peak areas were exported from Skyline and error frequencies ( E f ) were calculated as the intensity ratio of the missense peptide over the cognate peptide. E f next values were calculated in two different ways. E f next values derived from multiple biological states were estimated as the slope of the regression line when plotting the frequency of error clusters against the frequency of the initial misreading event ( Fig. 5c ). Alternatively, E f next was estimated from point measurements at a fixed time and AGA concentration ( Fig. 5a, d-f ). Here, E f next was estimated as the ratio of the abundances of the error cluster and the first misreading event. In cases where fixed integration windows led to infinitely low error frequencies due to the absence of detectable peaks or noise, small constant values were imputed. Especially for long error clusters, detection in the fusA mutant strains was sometimes not possible due to their low abundance. Although in some cases this prevents the exact quantification of E f next reduction, it clearly shows how strongly error clusters are reduced. Amino acid substitutions can alter the physico chemical properties and ionization propensities of peptides significantly, thereby potentially affecting observed error frequencies. However, because we use error frequencies only for relative comparisons between wt and mutant strains, such effects have no impact on our conclusions. In a few cases—such as when Arg or Lys was substituted or introduced, altering the tryptic cleavage pattern—the corresponding parental peptide could not be detected. This was likely due to poor fragmentation or exclusion during the database search, for instance because of its small size. In these instances, the median intensity of correctly identified peptides was used as a reference. In general, similar amounts of target protein-derived peptides were analyzed and controls of non-treated cells were performed to exclude false-positive identifications. Chromatographic carryover of peptides with misreading events between runs was evaluated and found to be below the detection limit of the mass spectrometer. Statistics and reproducibility All central conclusions (e.g. EF-G variants silence AGA-bound ribosomes and fusA mutations shield bacteria against AGAs) are supported by orthogonal methods. All experiments were reproduced as stated in the figure legends. Sample sizes were selected based on standard practice in microbiology and proteomics to ensure reproducibility and statistical robustness. No statistical methods were used to predetermine sample size. For MS, technical replicates are derived from repeats in the analysis of the same sample. Biological replicates derived from analysis of separate, biologically distinct samples produced independently of each other starting from culture inoculation from different clones, and followed by individual cell growth, drug treatment, sample processing, and data acquisition. When representative results are shown, the experiment has been repeated three times with similar results. Quantitative proteomics data were analysed using MaxQuant (v2.1.4.0) with a false discovery rate (FDR) of 1% at the peptide and protein levels. Statistical testing for differential protein abundance was performed using two-sided Student’s t-tests implemented in Perseus (v1.6.15.0), with significance thresholds and multiple testing corrections indicated in the respective figure legends. Missing values were imputed from a normal distribution as described in the Methods. For imaging experiments, image fields were chosen at random and differences in fluorescence intensity were evaluated using two-tailed Student’s t-test. All attempts at replication were successful and no data were excluded from analysis. Experiments were not randomized and investigators were not blinded, owing to the mechanistic nature of the study. Moreover, randomization and blinding were not applied because sample identity could be inferred from data structure, and key results (such as the reduction or length distribution of error clusters) can be independently derived from each individual dataset. All attempts at replication were successful and no data were excluded from analysis. Author contributions Conceptualization, MVR and IW; chromosomal mutants, MS; MIC measurements, CK; in-vitro kinetics, BZP and ES; Cryo-EM, VP, AS, NF using ribosomes complexes from BZP and FP; mass spectrometry NGD, NSF and IW; light microscopy, NGD; supervision, FP, AZP, HU, NF, MVR, IW. NGD, NSF, MVR and IW wrote the manuscript with input from all authors. All authors contributed to the interpretation of the data and approved the final manuscript. Competing interests The authors declare no competing interests. Material & Correspondence Engineered fusA strains will be provided upon reasonable request. Further information and requests for resources and reagents should be directed and will be fulfilled by Ingo Wohlgemuth ( Ingo.Wohlgemuth{at}mpinat.mpg.de ). Additional information Supplementary Table S1: fusA mutations across various pathogens Supplementary Table S2: Source data file Supplementary Table S3: Cryo-EM structure determination Supplementary Information : Supplementary Figs 1-9, Supplementary Table 4 Supplementary Information for Supplementary Figures Download figure Open in new tab Supplementary Figure 1. Action of DOS aminoglycosides on the ribosome, related to Figure 1 (a) Chemical classification of aminoglycoside antibiotics (AGAs). Most AGAs belong to the class of 2-deoxystreptamine (2-DOS) AGAs. Neomycin (Neo) and ribostamycin (Rib) share a 4,5 disubstituted 2-DOS ring (II). Kanamycin (KanA, KanB), gentamicin (Gen) and sisomicin (Sis) belong to the 4,6 disubstituted 2-DOS family, whereas in apramycin (Apr) the 2-DOS ring is monosubstituted at position 4. All these AGAs target helix 44 of 16S rRNA at the decoding center (DC) of the ribosome. They induce misreading, resulting in single errors and error clusters 1 , and slow down tRNA-mRNA translocation. Treatment with DOS AGAs promotes fusA (coding for EF-G) resistance mutations in many pathogens 2 , 3 , 4 , 5 , 6 , 7 , 8 , 9 , 10 , 11 , 12 , 13 , 14 , 15 , 16 , 17 , 18 , 19 , 20 , 21 , 22 , 23 , 24 , 25 , 26 , 27 , 28 , 29 , 30 , 31 , 32 , 33 , 34 , 35 , 36 , 37 , 38 , 39 , 40 , 41 . Less frequently, mutations are found in rpsL (coding for uS12) and rplF (coding for uL6) 42 . Neamine (Nea) is the smallest 2-DOS AGA with only two rings that are necessary to recognize the binding site in the DC. It has a lower affinity to the ribosome 43 , higher Minimal Inhibitory Concentrations (MIC) 44 , and does not promote error cluster formation 1 . Neamine resistance mutations were reported in rpsL and rpsQ (coding for uS17) 45 , 46 , 47 . Streptomycin (Str) and dihydrostreptomycin (Dhs) have a streptamine ring; they induce misreading and error cluster formation 1 but have only a moderate effect on translocation 48 and induce resistance mutations in rpsL 12 . Non-restrictive resistance mutations in uS12 prevent drug binding, confer high Str resistance and are associated with moderate fitness costs. In contrast, restrictive rpsL mutations confer a higher translation fidelity than the wt but only moderate resistance, and have higher fitness costs. Kasugamycin (Ksg) is a small, structurally unrelated AGA that inhibits initiation 49 and the transition to elongation in a sequence-dependent manner 50 . Resistance mutations evolve in the methyltransferases RsmA and RsmG. Spectinomycin (Spc) is structurally similar to AGAs and belongs to the class of aminocyclitols; it inhibits translocation but has no effect on the decoding fidelity of the ribosome. Spc resistance mutations evolve in rpsE (encoding uS5) 12 , 51 , 52 . FusA mutations confer little or no resistance against Nea, Str or Spc ( Figure 1C ) 24 , 53 , consistent with the fact that fusA mutation only evolve upon 2-DOS-AGA treatment 12 . (b) Superposition of drug binding sites on the small ribosomal subunit (SSU). Shown is the binding site for Apr (gold) and other 2-DOS AGAs at the top of helix 44 of 16S rRNA (PDB entry 7PJS) 54 . At elevated drug concentrations 2-DOS AGAs can also bind to secondary binding sites, which synergistically inhibits translation 54 . However, because rRNA mutations in helix 44 alone confer high AGA resistance by preventing AGA binding 44 , secondary binding sites are less important for AGA uptake and misreading and are not considered here. Unlike the 2-DOS AGAs, Str interacts with the phosphate backbones of helices 44, 27 and 18 of 16S rRNA as well as the ribosomal protein S12; the latter is the hotspot for Str resistance mutations (PDB entry 8cgj) 55 . Ksg binds within the mRNA channel (PDB entry 8CEP) 55 . Spc binds to the end of helix 34 of 16S rRNA at the neck region of the SSU (PDB entry 8ca7) 55 . The nonaminoglycoside antibiotic tetracycline binds to the minor groove of helix 34 and the loop of helix 34 (PDB entry 8cf1) 55 . (c) Apr (dark grey with DOS ring orange) primary binding site at the top of helix 44 (PDB entry 7PJS) 54 . DOS rings I and II are essential for the decoding site recognition and the stabilization of the error-prone conformation of the ribosome. (d) Rank plot of genes with resistance mutations evolved in laboratory evolution experiments upon treatment with 2-DOS AGAs. Mutational analysis of 108 AGA-treated Escherichia coli strains were retrieved from the literature 2 , 3 , 4 , 5 , 6 , 7 , 8 , 12 , 14 , 15 , 16 . Categories of mutated genes are indicated. fusA is by far the most sampled gene in laboratory evolution experiments. Download figure Open in new tab Supplementary Figure 2. Potential models of fusA- mediated resistance mechanisms, related to Figure 1 Models 1 and 2 suggest that EF-G resistance variants could indirectly contribute to resistance by affecting translation velocity. While the effect of EF-G resistant variants on translation have not been tested so far, at least two different models of resistance can be envisioned: Model 1. Direct slowdown of self-promoted AGA uptake . If AGA-induced misreading is unchanged, slower translation with mutant EF-G might lead to a slower accumulation of faulty proteins. As a result, quality control machinery and efflux pumps could better cope with proteotoxic stress, resulting in a slower AGA uptake 56 , 57 , 58 and allowing cells to grow at otherwise lethal concentrations. Model 2. Adaption to slower translation. Slower translation might change the proteome composition. Such changes could directly contribute to the resistome, for example, in Pseudomonas aeruginosa , fusA1 mutations were reported to lead to elevated levels of mexXY efflux pumps and Type III secretion system (T3SS) 28 . However, the AGA resistance correlates poorly with mexXY expression levels 59 . In Salmonella enterica , fusA mutations were associated with lower levels of HemA, the enzyme which catalyzes the first committed step in heme biosynthesis, resulting in lower levels of respiration 60 , which is often associated with higher AGA resistance. Alternatively, fusA mutations could indirectly render bacteria resistant by changes in the re-allocation of resources and metabolic fluxes, e.g., fusA mutations were proposed to disturb the levels of ppGpp 61 , a global transcription and translation regulator often associated with antibiotic resistance 62 . Alternatively, models 3-5 suggest that EF-G resistance variants could directly interfere with AGA action on the ribosome: Model 3. Allosteric displacement of the drug . None of the amino acid substitutions in EF-G are in close proximity to AGA on the ribosome, making the direct displacement of the drug unlikely. However, EF-G variants have been proposed to induce allosteric changes that lower the affinity of the drug to the ribosome 8 , 24 , similar to the mechanism of the tetracycline resistance protein Tet(O), an EF-G homolog, which uses its GTPase activity to remove tetracycline from the A site of the ribosome 63 . Model 4. Gain of function. EF-G variants were suggested to facilitate translocation in the presence of AGA 11 , although such gain-of-function mechanism seems unlikely considering the widespread distribution of resistance mutations across EF-G. Model 5. Ribosome silencing mechanism. EF-G variants could affect the error load in the AGA-affected cell. While EF-G variants do not restore the fidelity of AGA-bound ribosomes, because EF-G is not bound to the ribosome during decoding, they may affect formation of error clusters. If EF-G mutants slow down translation, AGA-bound ribosomes would perform fewer elongation cycles before the drug dissociates. Both the allosteric displacement (model 3) and the translation deceleration (model 5) could in principle alleviate error cluster formation and thus lower the proteotoxic burden that drives the self-promoted AGA uptake. Download figure Open in new tab Supplementary Figure 3. Generation and characterization of chromosomal fusA mutants, related to Figure 1 (a) Schematic of the operon structure after insertion of fusA mutations. Correct insertion was confirmed by DNA sequencing. (b) Targeted MS-based detection of EF-G variants. Each row corresponds to a particular peptide (Zeocin-binding protein, wt EF-G, or EF-G variant carrying one of the indicated mutations), while each column represents an E. coli strain (MG1655, wt, F593L, A608E, and P610L). A colored square indicates that the corresponding peptide was detected in that strain. Revertants or cross contaminations were not observed. The analysis confirms identity of the strains on the protein level. (c) Proteome comparison of wt and MG1655 by quantitative MS (data independent acquisition). Apart from the expression of the introduced zeocin resistance protein, the cloning strategy did not introduce significant proteomic changes. Notably, expression levels of EF-Tu, uS7 and uS12, which are members of the same operon, are not significantly altered. (d) Comparison of the drug susceptibility of wt and MG1655. The minimal inhibitory concentrations (MIC) of all drugs tested are not significantly altered. Bars represent the mean ± SD of 5 biological replicates. Download figure Open in new tab Supplementary Figure 4. Proteome changes induced by fusA mutations, related to Figure 1 (a) Subset of data from Figure 1D showing the regulation of proteins involved in AGA action or resistance. No systematic upregulation of porins or transporters that facilitate AGA entry into bacterial cell was observed 64 . Efflux pumps, including AcrA, AcrD, TolC (RND transporters), which promote AGA efflux in E. coli 65 , 66 were also not upregulated. Proteins involved in ppGpp-responsive regulation —which could potentially confer resistance via global reallocation of resources and major transcription factors orchestrating global stress responses—showed no significant changes. Similarly, chaperones and proteases linked to proteostasis 67 , 68 , 69 were not systematically upregulated. Finally, proteins previously identified in systematic screens as conferring AGA resistance upon overexpression 70 showed no consistent upregulation. These findings indicate that none of the known AGA resistance pathways are activated in fusA mutants. (b) Hierarchical clustering of significantly regulated proteins in MG1655, wt, and fusA mutants. Significantly regulated proteins were identified by multiple sample ANOVA test and compared by their Z -scores. Biological replicates (n = 4) were averaged. Proteins and strains were hierarchically clustered by their Euclidean distance and proteins were grouped in 5 clusters of co-regulated proteins. In the three minor clusters (saddle brown, dark orange and magenta), proteins show no consistent regulation between fusA mutants and thus these proteins cannot constitute a common resistance mechanism. In contrast, in the two major clusters proteins were consistently down-(dark green, 234 proteins) or upregulated (blue, 163 proteins) in fusA mutant strains. (c) Profile view of changes in expression levels of downregulated (upper panel) and upregulated (lower panel) proteins. (d) Correlation of cell growth and proteome adaptions. While overall the fusA mutants show only minor changes in their proteomes relative to the wt (Figure 1D), subtle adaptions of protein levels in the major clusters detected by hierarchical clustering correlate well with the growth rate differences. (e) Correlation matrix of Pearson coefficients. Mutual correlation analysis of cell growth ( Figure 1B ), MIC values ( Figure 1C ), translocation rates ( Figure 2A ), and proteome regulation patterns (Figure S4B-D) across wt and fusA mutants (e.g. see Figures S4D, S5A). MIC values of different AGAs showed strong correlation, consistent with the idea that different fusA mutations confer resistance through a common mechanism. Cell growth, translocation rates, and proteome regulation patterns also correlate well, supporting the notion that proteome changes reflect subtle adaptions to the slower translocation rates and the resulting reduced growth. However, poor correlation between MICs values and growth-related metrics indicates that translocation rate differences, growth and proteome differences alone are not the primary drivers of AGA resistance. (f) Pathway enrichment analysis of protein clusters. Pathway enrichment analysis of the five protein clusters was performed using Fisher’s exact test, with false discovery rate (FDR) correction by the Benjamini-Hochberg procedure. Members of only three pathways were significantly enriched in the clusters, all related to amino acid biosynthesis and upregulated in response to slower translation, which triggers translation attenuation mechanisms. These findings suggest that changes in translation rate drive misadaptive responses typically observed under starvation conditions, but do not explain the observed resistance pattern (Figure S4E). Download figure Open in new tab Supplementary Figure 5. Effect of EF-G variants and Apr on single-codon translocation and translation in vitro , related to Figure 2 (a) Correlation of the translocation rates measured in single-codon assay with growth rates (left panel) and proteome changes (center and right panel) for wt EF-G (blue symbol), F593L (orange), A608E (olive), and P610L (red). Data replotted from Figures 1B , 2A and S4B-D. Pearson coefficients as indicated. (b) Time courses of slyD in-vitro translation with EF-G variants in the absence and presence of Apr (1 µM). (c) Dependence of SlyD formation on Apr concentration. Full length product was quantified after 30 min of translation in the absence and presence of increasing Apr concentrations. Download figure Open in new tab Supplementary Figure 6. Cryo-EM analysis, related to Figure 3 (a) Cryo-EM analysis of EF-G P610L-promoted translocation in presence of Apr. Top: Schematics of tRNA positions. The arrows indicate dynamics of the tip of EF-G domain IV, similar to that observed for EF-G wt 54 . Bottom: Cryo-EM maps with respective overall resolution in Å and number of cryo-EM particle images. In Figure 3 panels A-D, hybrid state (H) refers to the major tRNA hybrid state H1, while in Figure 3E hybrid state (H) summarizes H1 and H2 particle populations. (b) Nucleotide binding pocket of EF-G P610L with GDP-Pi in H2. (c) Fourier-Shell-Correlation curves of final cryo-EM reconstructions, computed between half-maps (half1 vs. half2), and between full maps and atomic models (model vs. map), respectively. Atomic models were built for EF-G P610L-bound complexes only. (d) Repositioning of the elbow of A/P tRNA and helix 38 of 23S rRNA (H38) upon transition between tRNA hybrid states. (e) Per-residue RMSDs of 16SrRNA between EFG P610L- and wt-bound complexes, revealing generally negligible structural differences, particularly at the Apr-binding site (Apr-site, orange circle). H1-EF-G wt: PDB 7PJV; H2-EF-G wt: PDB 7PJW 54 . (f) Multi-step sorting of cryo-EM data. EF-G refers here to EF-G P610L. See Methods for details. (g) Masks used for focused classification. (h) Interaction of LSU protein uL6 with domain V of EF-G. In the fusidic acid stalled ribosome-EF-G complex, residues K172 and K175 of uL6 form salt-bridges with D619 of EF-G (PDB 4V5F 71 ); residues numbered according to Thermus thermophilus components here. Download figure Open in new tab Supplementary Figure 7. Preservation of proteome integrity in fusA mutant strains, related to Figure 4 (a) Workflow for the identification, validation, and quantification of missense peptides with individual amino acid substitutions. Using the MaxQuant Dependent Peptide feature 72 , missense peptide candidates are identified by comparing MS/MS spectra of correct and missense peptides ensuring the confident localization of the amino acid substitution (shown is a peptide with the substitution E308D, corresponding to a −14 delta mass). Missense peptide candidates are validated by matching their MS/MS spectra with fragmentation spectra predicted in-silico by the deep neural network Prosit 73 . Established fragmentation pattern and chromatographic retention times are used to quantify missense peptides across various biological states by label-free Parallel Reaction Monitoring (PRM). PRM traces are extracted in Skyline 74 . Extracted fragment ions are color coded; amino acid substitutions indicated in red. Peptide abundances are calculated by integrating the signal of their co-eluting peptide fragments and error frequencies are estimated as the ratio of missense peptide and correct peptides abundances (see Methods). (b) fusA mutations prevent Apr-induced (16 µM) translational misreading. Bars represent the mean error frequencies ± SD of 3 biological replicates of the median of 28 misreading events. (c) Semiquantitative analysis of the error burden in the proteome through spectral counting. Untreated and Apr-treated wt and P610L cells were lysed and proteins fractionated by SDS-PAGE. Missense peptide identifications in various proteins were counted (detection and identification as described above). Only types of substitutions whose counts are induced > 3-fold by AGA treatment were included. Position of the mismatch in the codon-anticodon interaction as indicated. (d) Error frequencies in inner membrane proteins in the absence and presence of Apr (16 µM, 60 min). Error frequencies of 109 misreading events in 28 proteins were determined by label free PRM. Bars represent mean error frequencies ± SD of three biological replicates of the medians of 1-21 misreading events per protein, as indicated above the bars). Download figure Open in new tab Supplementary Figure 8. fusA mutations prevent error cluster formation, related to Figure 5 (a) PRM traces of missense peptides with first errors and corresponding error clusters at induced conditions (wt: at 8-16 µM Apr; P610L: at 128-256 µM Apr; data are shown in Figure 5A,C ). The conditions were chosen to ensure similar levels of single substituted peptides in wt and P610L. Extracted fragment ions are color-coded; amino acid substitutions indicated in red. Missense peptides were identified by the high sequence coverage, the coelution and identical fragmentation as of the spiked-in isotope-labeled reference peptides (ratio dot products ≥ 0.97). While missense peptides with short error clusters are often high abundant in wt and P610L cells, error clusters with more amino acid residues between the consecutive errors were strongly suppressed in P610L. To study this length effect in detail we extended set of error clusters from 14 to 38 in Figures 5D and S8B. (b) Comparison of the error load with single errors and error clusters between wt and P610Lat conditions of maximal misreading (wt at 16 µM Apr, P610L at 256 µM Apr). Missense peptides with error clusters are less abundant in P610L than in wt cells (red circles, quantified by PRM). This effect depends on the distance between the first and the consecutive amino acid substitution (see replot in Figure 5D). In contrast to error cluster formation, EF-G (P610L) had no significant effect on other misreading events: Grey circles, single errors quantified by MS1-filtering in DDA runs; black circles, single errors quantified by targeted MS (PRM). The type of data acquisition had no impact on this conclusion. This shows that fusA mutations prevent error cluster formation, but have otherwise no significant impact on the proteome integrity indicating that the intrinsic ribosome fidelity is unaltered, consistent with the fact that EF-G does not affect decoding per se . Because error frequencies are influenced by the concentrations of competing aminoacyl-tRNAs 75 , this also suggests that stoichiometries of cellular tRNAs are not substantially altered. Download figure Open in new tab Supplementary Figure 9. FusA mutations prevent protein aggregation in vivo , related to Figure 6 Upper panel: brightfield images of wt and fusA mutant cells after 60 min and 180 min in the absence of Apr. Lower panel: same for cells after 60 min and 180 min of Apr treatment (16 µM). Images for 180 min time point same as Figure 6A . Yellow arrows point to inclusion bodies. Scale bar, 5 µm. View this table: View inline View popup Supplementary Table 4 RESOURCES Acknowldgements This research was supported by the German Science Foundation (Deutsche Forschungsgemeinschaft, DFG) through SFB1565 (project-ID 469281184 to MVR and IW) and Leibniz Prize (to MVR), and by the Max Planck Society. We express our sincere gratitude to Holger Stark (MPI-NAT) for providing access to the infrastructure of his department at the MPI-NAT, Peter Lenart (Facility for Light microscopy) for helpful discussions on light miscroscopy experiments, and Michele Felletti (MPI-NAT, Department Rodnina) for critically reading the manuscript. We acknowledge Mario Lüttich and Tobias Koske (MPI-NAT, Department Stark) for support in high-performance computation and Ralf Pflanz (MPI-NAT, Bioanalytical Mass Spectrometry Group) for his help in mass spectrometry maintenance and acquisition. We thank Olaf Geintzer, Vanessa Herold, Franziska Hummel, Jasmin Jakobi, Sandra Kappler, Anna Pfeifer, Monika Raabe, and Michael Zimmermann for expert technical assistance. Funder Information Declared Deutsche Forschungsgemeinschaft , SFB1565 project-ID 469281184 , Leibniz prize Max Planck Society, https://ror.org/01hhn8329 References 1. ↵ Antimicrobial Resistance Collaborators . Global burden of bacterial antimicrobial resistance in 2019: a systematic analysis . Lancet 399 , 629 – 655 ( 2022 ). OpenUrl CrossRef PubMed 2. ↵ Davies J. , Gorini L. , Davis B. D . Misreading of RNA codewords induced by aminoglycoside antibiotics . Mol. Pharmacol . 1 , 93 – 106 ( 1965 ). OpenUrl Abstract / FREE Full Text 3. ↵ Cabañas M. J. , Vázquez D. , Modolell J . Inhibition of ribosomal translocation by aminoglycoside antibiotics . Biochem. Biophys. Res. Commun . 83 , 991 – 997 ( 1978 ). OpenUrl CrossRef PubMed Web of Science 4. ↵ Carter A. P. , Clemons W. M. , Brodersen D. E. , Morgan-Warren R. 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