Johansen, S. Cell 23, — Jonker, H. NMR structures of thiostrepton derivatives for characterization of the ribosomal binding site. Kelly, W. Thiostrepton biosynthesis: prototype for a new family of bacteriocins. Knappe, D. Bactericidal oncocin derivatives with superior serum stabilities.
Agents 37, — Kozak, M. Migration of 40 S ribosomal subunits on messenger RNA in the presence of edeine. Krizsan, A.
Influence of the yjiL-mdtM gene cluster on the antibacterial activity of proline-rich antimicrobial peptides overcoming Escherichia coli resistance induced by the missing SbmA transporter system. Insect-derived proline-rich antimicrobial peptides kill bacteria by inhibiting bacterial protein translation at the 70S ribosome. Li, Q. Modular, scalable synthesis of group A streptogramin antibiotics. Lin, J. Conformational changes of elongation factor G on the ribosome during tRNA translocation.
Lin, Y. The antituberculosis antibiotic capreomycin inhibits protein synthesis by disrupting interaction between ribosomal proteins L12 and L Liou, Y. Dual actions of viomycin on the ribosomal functions. Lopez-Alonso, J.
Structure of a 30S pre-initiation complex stalled by GE reveals structural parallels in bacterial and eukaryotic protein synthesis initiation pathways. Ly, C. Single-molecule study of viomycin's inhibition mechanism on ribosome translocation.
Biochemistry 49, — Maio, A. The oligopeptide permease opp mediates illicit transport of the bacterial P-site decoding inhibitor GE Antibiotics 5:E Manzella, J. Quinupristin-dalfopristin: a new antibiotic for severe gram-positive infections. Mardirossian, M. The host antimicrobial peptide Bac binds to bacterial ribosomal proteins and inhibits protein synthesis. The dolphin proline-rich antimicrobial peptide Tur1A inhibits protein synthesis by targeting the bacterial ribosome.
Cell Chem. Marrero, P. Induction of translational errors misreading by tuberactinomycins and capreomycins. Mattiuzzo, M. Role of the Escherichia coli SbmA in the antimicrobial activity of proline-rich peptides.
Maus, C. Molecular analysis of cross-resistance to capreomycin, kanamycin, amikacin, and viomycin in Mycobacterium tuberculosis. Metelev, M. Klebsazolicin inhibits 70S ribosome by obstructing the peptide exit tunnel. Mikolajka, A. Differential effects of thiopeptide and orthosomycin antibiotics on translational GTPases. Modolell, J. The inhibition of ribosomal translocation by viomycin. Monshupanee, T. Capreomycin susceptibility is increased by TlyA-directed 2'-O-methylation on both ribosomal subunits.
Nicolaou, K. Recent advances in the chemistry and biology of naturally occurring antibiotics. Nissen, P. The structural basis of ribosome activity in peptide bond synthesis. Science , — Noeske, J. Synergy of streptogramin antibiotics occurs independently of their effects on translation. Ogle, J. Insights into the decoding mechanism from recent ribosome structures. TIBS 28, — Olivier, N.
Negamycin induces translational stalling and miscoding by binding to the small subunit head domain of the Escherichia coli ribosome. Omura, S. New Antibiotic, Am Osterman, I. Madumycin II inhibits peptide bond formation by forcing the peptidyl transferase center into an inactive state. Otvos, L. Interaction between heat shock proteins and antimicrobial peptides. Biochemistry 39, — Pan, D. Kinetically competent intermediates in the translocation step of protein synthesis. Cell 25, — Pankuch, G.
Time-kill activity of the streptogramin NXL against Gram-positive and -negative bacteria. Pantel, L. Odilorhabdins, antibacterial agents that cause miscoding by binding at a new ribosomal site. Cell 70, 83— Parfait, R. A spectrofluorimetric study of the interaction between virginiamycin S and bacterial ribosomes. Peske, F. Pestka, S. Studies on the formation of transfer ribonucleic acid-ribosome complexes.
Antibiotic effects on phenylalanyl-oligonucleotide binding to ribosomes. Pioletti, M. Crystal structures of complexes of the small ribosomal subunit with tetracycline, edeine and IF3.
Podda, E. Dual mode of action of Bac7, a proline-rich antibacterial peptide. Acta , — Polikanov, Y. Cell 56, — Rodnina, M. Thiostrepton inhibits the turnover but not the GTPase of elongation factor G on the ribosome. Roy, R. The mechanism of inhibition of protein synthesis by the proline-rich peptide oncocin. Savelsbergh, A. Distinct functions of elongation factor G in ribosome recycling and translocation. Schmeing, T. An induced-fit mechanism to promote peptide bond formation and exclude hydrolysis of peptidyl-tRNA.
Schmidt, A. The quantitative and condition-dependent Escherichia coli proteome. Schneider, M. Differential infectivity of two Pseudomonas species and the immune response in the milkweed bug, Oncopeltus fasciatus Insecta: Hemiptera. Scocchi, M. Proline-rich antimicrobial peptides: converging to a non-lytic mechanism of action. Life Sci. Seefeldt, A. Structure of the mammalian antimicrobial peptide Bac7 bound within the exit tunnel of a bacterial ribosome. The proline-rich antimicrobial peptide Onc inhibits translation by blocking and destabilizing the initiation complex.
Seo, H. Conformational change and fusidic acid inhibition. Shoji, S. Reverse translocation of tRNA in the ribosome. Cell 24, — Skerlavaj, B. Rapid membrane permeabilization and inhibition of vital functions of gram-negative bacteria by bactenecins.
Stanley, R. The structures of the anti-tuberculosis antibiotics viomycin and capreomycin bound to the 70S ribosome. Thomas, M. Deciphering tuberactinomycin biosynthesis: isolation, sequencing, and annotation of the viomycin biosynthetic gene cluster. Thompson, C. Biochemical characterization of resistance determinants cloned from antibiotic-producing streptomycetes. Travin, D. Biosynthesis of translation inhibitor Klebsazolicin proceeds through heterocyclisation and N-terminal amidine formation catalysed by a single YcaO enzyme.
Tu, D. Structures of MLSBK antibiotics bound to mutated large ribosomal subunits provide a structural explanation for resistance. Vannuffel, P. Mechanism of action of streptogramins and macrolides. Drugs 51 Suppl. The role of rRNA bases in the interaction of peptidyltransferase inhibitors with bacterial ribosomes.
Walter, J. Thiostrepton inhibits stable 70S ribosome binding and ribosome-dependent GTPase activation of elongation factor G and elongation factor 4. Wang, L. Allosteric control of the ribosome by small-molecule antibiotics. Weixlbaumer, A. Crystal structure of the ribosome recycling factor bound to the ribosome. Westman, E. Self resistance to the atypical cationic antimicrobial peptide edeine of Brevibacillus brevis Vm4 by the N-acetyltransferase EdeQ.
Wieland Brown, L. Thirteen posttranslational modifications convert a residue peptide into the antibiotic thiocillin. Wilson, D. The A-Z of bacterial translation inhibitors. Ribosome-targeting antibiotics and bacterial resistance mechanisms. Translation regulation via nascent polypeptide-mediated ribosome stalling. Woodcock, J. Wurmbach, P. The inhibition pattern of antibiotics on the extent and accuracy of tRNA binding to the ribosome, and their effect on the subsequent steps in chain elongation.
Yamada, T. Resistance to viomycin conferred by RNA of either ribosomal subunit. Yates, J. Virginiamycin as an antibiotic for poultry feeds. Role of ribosomal protein S12 in discrimination of aminoacyl-tRNA.
Zeng, F. Peptide release promoted by methylated RF2 and ArfA in nonstop translation is achieved by an induced-fit mechanism. RNA 22, 49— Keywords: proline-rich antimicrobial peptides, ribosome, translation, inhibitor, antibiotic.
As discussed, the combination of trimethoprim and sulfamethoxazole is an example of antibacterial synergy. When used alone, each antimetabolite only decreases production of folic acid to a level where bacteriostatic inhibition of growth occurs.
However, when used in combination, inhibition of both steps in the metabolic pathway decreases folic acid synthesis to a level that is lethal to the bacterial cell.
Because of the importance of folic acid during fetal development, sulfa drugs and trimethoprim use should be carefully considered during early pregnancy. The drug isoniazid is an antimetabolite with specific toxicity for mycobacteria and has long been used in combination with rifampin or streptomycin in the treatment of tuberculosis. It is administered as a prodrug, requiring activation through the action of an intracellular bacterial peroxidase enzyme, forming isoniazid-nicotinamide adenine dinucleotide NAD and isoniazid-nicotinamide adenine dinucleotide phosphate NADP , ultimately preventing the synthesis of mycolic acid, which is essential for mycobacterial cell walls.
Possible side effects of isoniazid use include hepatotoxicity , neurotoxicity , and hematologic toxicity anemia. Figure 4. Click for a larger image. Sulfonamides and trimethoprim are examples of antimetabolites that interfere in the bacterial synthesis of folic acid by blocking purine and pyrimidine biosynthesis, thus inhibiting bacterial growth.
Bedaquiline, representing the synthetic antibacterial class of compounds called the diarylquinolones , uses a novel mode of action that specifically inhibits mycobacterial growth. Although the specific mechanism has yet to be elucidated, this compound appears to interfere with the function of ATP synthases, perhaps by interfering with the use of the hydrogen ion gradient for ATP synthesis by oxidative phosphorylation , leading to reduced ATP production.
Due to its side effects , including hepatotoxicity and potentially lethal heart arrhythmia, its use is reserved for serious, otherwise untreatable cases of tuberculosis. The doctor prescribed ciprofloxacin. In the meantime, her urine was cultured to grow the bacterium for further testing. Which of the following terms refers to the ability of an antimicrobial drug to harm the target microbe without harming the host?
Skip to main content. Antimicrobial Drugs. Search for:. Mechanisms of Antibacterial Drugs Learning Objectives Describe the mechanisms of action associated with drugs that inhibit cell wall biosynthesis, protein synthesis, membrane function, nucleic acid synthesis, and metabolic pathways. Think about It Compare and contrast the different types of protein synthesis inhibitors. Think about It How do polymyxins inhibit membrane function?
Think about It Why do inhibitors of bacterial nucleic acid synthesis not target host cells? Think about It How do sulfonamides and trimethoprim selectively target bacteria? What types of antimicrobials are typically prescribed for UTIs?
Based upon the antimicrobial drugs she was given in Vietnam, which of the antimicrobials for treatment of a UTI would you predict to be ineffective? Key Concepts and Summary Antibacterial compounds exhibit selective toxicity , largely due to differences between prokaryotic and eukaryotic cell structure. There are a variety of broad-spectrum, bacterial protein synthesis inhibitors that selectively target the prokaryotic 70S ribosome, including those that bind to the 30S subunit aminoglycosides and tetracyclines and others that bind to the 50S subunit macrolides , lincosamides , chloramphenicol , and oxazolidinones.
Polymyxins are lipophilic polypeptide antibiotics that target the lipopolysaccharide component of gram-negative bacteria and ultimately disrupt the integrity of the outer and inner membranes of these bacteria. The nucleic acid synthesis inhibitors rifamycins and fluoroquinolones target bacterial RNA transcription and DNA replication, respectively.
Some antibacterial drugs are antimetabolites , acting as competitive inhibitors for bacterial metabolic enzymes. Sulfonamides and trimethoprim are antimetabolites that interfere with bacterial folic acid synthesis.
Isoniazid is an antimetabolite that interferes with mycolic acid synthesis in mycobacteria. Multiple Choice Which of the following terms refers to the ability of an antimicrobial drug to harm the target microbe without harming the host? Show Answer Answer b. Show Answer Answer a. Tetracyclines does not bind to the 50S ribosomal subunit. Show Answer Answer c. Nalidixic acid inhibits the activity of DNA gyrase.
Show Answer Selective toxicity antimicrobials are easier to develop against bacteria because they are prokaryotic cells, whereas human cells are eukaryotic. Show Answer False. Think about It If human cells and bacterial cells perform transcription, how are the rifamycins specific for bacterial infections?
What bacterial structural target would make an antibacterial drug selective for gram-negative bacteria? Provide one example of an antimicrobial compound that targets this structure. In considering the cell structure of prokaryotes compared with that of eukaryotes, propose one possible reason for side effects in humans due to treatment of bacterial infections with protein synthesis inhibitors.
Licenses and Attributions. CC licensed content, Shared previously. Mycobacterial adenosine triphosphate ATP synthase inhibitor. Interact directly with PBPs and inhibit transpeptidase activity.
Narrow-spectrum against gram-positive and a few gram-negative bacteria. Narrow-spectrum against gram-positive bacteria but with increased gram-negative spectrum. Narrow-spectrum against gram-positive bacteria only, including strains producing penicillinase. Narrow-spectrum similar to penicillin but with increased gram-negative spectrum.
Narrow-spectrum but with increased gram-negative spectrum compared with first generation. Broad-spectrum against gram-positive and gram-negative bacteria, including MRSA. Large molecules that bind to the peptide chain of peptidoglycan subunits, blocking transglycosylation and transpeptidation.
Narrow spectrum against gram-positive bacteria only, including multidrug-resistant strains. Block transport of peptidoglycan subunits across cytoplasmic membrane. Certain drug interactions were correctly predicted by this approach e. For the latter, the assumption that the formation of the doubly-bound ribosome population is prohibited, which yields an additive response surface, offers even better agreement with the experimental data Fig.
Occasionally, drug interactions are better explained if competitive binding is assumed e. Other drug interactions clearly deviated from the model predictions. Such clear deviations could originate from direct molecular interactions of the drugs on the ribosome, and thus be specific for every drug pair; we explore this situation theoretically in ref. Alternatively, these drug interactions could result from the multi-step structure of the translation cycle itself, which our model does not take into account.
Simple partitioning of ribosomes into different populations that are susceptible to different antibiotics does not alter the drug interaction Supplementary Methods. In the most complex cases, drug interactions could result from drug effects that are unrelated to the primary drug target 11 , in particular from effects on drug uptake or efflux We focused on the plausible hypothesis that drug interactions are caused by the interplay of ribosomes halted in different stages of the translation cycle such as initiation, translocation, recycling, etc.
To test this hypothesis, we developed a technique for measuring how halting ribosomes in different stages of the translation cycle affects the efficacy of various antibiotics. Specifically, we imposed artificial bottlenecks in translation by genetically limiting the expression of translation factors that catalyze well-defined translation steps We constructed E.
These genes were integrated into the chromosome outside of their endogenous loci and the endogenous copy of the gene was disrupted Fig. This procedure yielded six strains that enable continuous control of key translation processes Fig. Higher expression alleviates the artificial bottleneck. Thicker lines or arrows indicate higher rates.
Full induction of the translation factor rescues wild type growth; increasing bottleneck severity leads to a smooth decrease in growth rate to zero. Comparison of the response surfaces with independent expectation dashed purple line identify alleviation orange line or aggravation dark blue line. Circles show dependency vectors projected onto the first two principal components PC1, and PC2 ; colors indicate cluster identity. The extended cluster areas shown are convex hulls of bootstrapped projections denoted by dots.
See Supplementary Equations 19 — 20 and Supplementary Fig. Reducing translation factor expression by varying the inducer concentration resulted in a gradual decrease in growth which stopped at almost complete cessation of growth, reflecting the essentiality of translation factors Fig. Since the endogenous regulation of translation factors generally follows that of the translation machinery 29 , 30 , 31 , 32 , limiting the expression of a single translation factor imposes a highly specific bottleneck as all other components get upregulated.
Any global feedback regulation is left intact as we removed the factor from its native operon. Similar genetic perturbations further conform to bacterial growth laws 13 , 20 , 21 , supporting that translation factor deprivation is a suitable means of assessing responses to targeted perturbations of translation. While antibiotics often have secondary targets and other non-specific effects on the cell, thus obfuscating experiments, translation factor deprivation is highly specific.
Our synthetic strains offer precise control over artificial translation bottlenecks that determine the rates of different translation steps and enable disentangling phenomena that are caused by the primary mode of action of antibiotics from those that result from other effects of these drugs. We used these synthetic strains to assess the impact of bottlenecks on antibiotic efficacy. We measured growth rates over a two-dimensional matrix of concentrations of inducer and antibiotic for each of the six strains Fig.
To assess if the action of the antibiotic is independent of the translation bottleneck, we analyzed these experiments using a multiplicative null expectation. Note that additivity, as used for antibiotics Fig.
However, if antibiotic action is independent of the translation bottleneck, the growth rate should be a product of the relative growth rates of each of the two perturbations acting individually.
Independence implies that the dose—response surface is obtained as a multiplication of the antibiotic dose—response and the translation factor induction curve. Deviations from independence indicate a nontrivial interaction between the bottleneck and the antibiotic action. We systematically identified interactions between translation inhibitors and bottlenecks by their deviation from independence. In general, antibiotic action can be alleviated or aggravated by a given bottleneck, i.
We quantified the magnitude of these effects by bottleneck dependency BD scores Supplementary Methods and collected them into a single bottleneck dependency vector per antibiotic. The components of this vector describe the interactions between the antibiotic and all six translation bottlenecks. Bottleneck dependency vectors were diverse Fig. These results are consistent with the hypothesis that the high diversity of drug interactions between translation inhibitors Fig.
The bottleneck dependency vector of a given antibiotic provides a quantitative, functional summary of its interactions with the translation cycle.
Drug interactions between antibiotics from the same cluster were strictly additive Figs. These results show that interactions of antibiotics with translation bottlenecks have explanatory power for drug mode of action and can expose antibiotics acting as substitutes for one another. While the clustering of certain antibiotics can be rationalized from their presumed modes of action, this is more challenging for others.
To further assess the value of this analysis, we measured bottleneck dependencies for three additional antibiotics: lamotrigine LAM , trimethoprim TMP , and nitrofurantoin NIT. As we elaborate in the Supplementary Discussion , using drugs with a defined mode of action LAM and TMP corroborates the utility of clustering by bottleneck dependencies, while the similarity of STR to NIT, which has multiple modes of action, suggests a plausible reason for the separation of STR from other clusters of translation inhibitors.
We reasoned that the effects of translation bottlenecks on antibiotic action should also have predictive power for drug interactions between translation inhibitors. We, therefore, sought a quantitative way of probing the contribution of translation bottlenecks to drug interactions between translation inhibitors. Ribosomes progress through the translation cycle in a sequence of steps Fig.
Antibiotics and genetic translation bottlenecks hinder this progression by reducing the transition rates between these steps. If an antibiotic specifically targets a single translation step and reduces the same transition rate as a genetic translation bottleneck, the effects of the drug and the bottleneck should be equivalent, i.
To establish the equivalence of specific translation bottlenecks and antibiotic action, we first transformed the measurements of growth rate as a function of translation factor induction into dose—response curves of a corresponding idealized antibiotic that targets a single translation step with perfect specificity. In essence, this procedure converts inducer concentrations into equivalent antibiotic concentrations: the two concentrations are identified as equivalent if they lead to the same relative growth rate Fig.
If the perturbations of factor and antibiotic are equivalent, then the true and the idealized antibiotic should act as substitutes for each other, and exhibit an additive drug interaction. Conversely, we can use this comparison Fig.
In the absence of perturbations, ribosomes progress through the steps unimpeded, resulting in unperturbed growth. Top: as the abundance of factor F1 is lowered smaller factor symbol , the rate of step 1 decreases thinner arrows and ribosomes queue in front of the bottleneck. Bottom: the same rate is reduced by an antibiotic. The effects of factor deprivation and antibiotic action on growth are equivalent. Purple dashed line shows isobole for multiplicative responses at relative growth rate 0.
The remapped response surface is additive, corroborating the equivalence of CRY and translocation factor deprivation. The bottlenecks and antibiotics are shown on the bottom right, respectively. Errors in LI and in expected and remapped responses were evaluated by bootstrapping Supplementary Methods , Supplementary Fig. The large and statistically significant discrepancy in LI from 0 indicates that CRY and a recycling bottleneck are not equivalent.
We found that the effect of certain translation inhibitors is almost perfectly mimicked by translation bottlenecks.
Within our selection of antibiotics, several strong candidates for equivalent perturbations exist Fig. In contrast, if the bottleneck is not equivalent to the drug, remapping does not yield an additive response surface; an example is CRY and the recycling bottleneck Fig. Occasionally, marginal effects dominate the apparent equivalence: STR lowers translocation rate only two-fold 33 , but inhibiting translocation by deprivation of EF-G is still the best mimic of STR.
In general, demonstrating that the action of an antibiotic is equivalent to a specific translation bottleneck provides strong quantitative evidence for its primary mode of action, since translation factors control individual steps with high specificity. In contrast, the common approach of overexpressing the drug target does not provide useful insights into the mode of action of ribosome-targeting antibiotics.
Simple overexpression requires a well-defined drug target like a single protein; overexpressing the ribosome is impractical 34 and would not help distinguish the precise action of different ribosome-targeting antibiotics.
Even for less complex drug targets, the interpretation of overexpression assays is challenging Still, we tested if simple overexpression of translation factors can provide similar insights into the mode of action of TET as translation bottlenecks. Overexpression of translation factors only weakly affected antibiotic efficacy Supplementary Fig.
The effects of overexpressing different translation factors were not specific for antibiotic mode of action Supplementary Fig. Hence, unlike the depletion of translation factors, their overexpression provides no information about drug interactions with other antibiotics. For antibiotics that are equivalent to specific translation factors Fig.
In practice, this is done by remapping the antibiotic-translation factor response surfaces as described above Fig. Unlike the predictions of the biophysical model Fig. While the biophysical model is only valid for antibiotics that conform to bacterial growth laws, the predictions based on the observed effects of translation bottlenecks are independent of whether or not the growth laws hold for the specific perturbations of translation used.
The resulting prediction will be faithful if the drug interaction originates exclusively from the interplay of two translation bottlenecks. The drug interaction between CHL and an antibiotic that targets initiation can be predicted through mimicking the initiation inhibition by limiting the expression of initiation factor infB.
This response surface contains information about the interaction between CHL and any antibiotic that interferes with initiation. The inducer axis is remapped into mimicked antibiotic concentration Fig. Drug interactions with these antibiotics can be predicted for any antibiotic with a known response to the equivalent bottleneck.
Color-code shows cluster identity from Fig. Top row: scatter plots as in d ; bottom row: predicted and measured response surfaces, respectively. Drug interactions predicted using this procedure were often highly accurate Fig. In particular, some of the most striking cases of antagonistic and suppressive interactions were correctly predicted. The same interactions were correctly predicted for LCY Fig. Remapping qualitatively accounted for nearly all observed interactions of KSG with quantitative agreement in several cases Supplementary Fig.
In this way, several drug interactions with previously elusive mechanisms are explained by the interplay of the specific steps in the translation cycle that are targeted by the antibiotics involved. Remapping correctly predicted additive drug interactions between antibiotics that could not be easily explained by the biophysical model.
For some antibiotic pairs, the predictions based on equivalent translation bottlenecks failed to explain the observed drug interactions e. We expect that these cases are often due to idiosyncrasies and secondary effects of the drugs, which will require separate in-depth characterization in each case.
In contrast, our results show that various non-trivial drug interactions between antibiotics are systematically explained by the interplay of specific translation bottlenecks caused by the antibiotics.
If suppressive drug interactions are caused by the interplay of different translation bottlenecks alone, it should be possible to recapitulate these interactions in a purely genetic way. We thus expanded our translation bottleneck approach by introducing multiple genetic bottlenecks in the same cell. Moreover, the initiation inhibitor KSG alleviated a genetic translocation bottleneck and an initiation bottleneck suppressed the effect of the translocation inhibitor FUS Fig.
These observations suggest that a universal mechanism underlies the suppression between initiation and translocation inhibitors. We constructed a synthetic strain that enables simultaneous independent control of initiation and translocation factor levels.
To maximize the precision of induction that is achievable with different inducer concentrations, we put both factors under negative autoregulatory control by chromosomally integrated repressors 13 , The resulting strain showed virtually no growth when at least one of the inducers was absent but unrestricted wild type growth in the presence of both inducers Fig. These observations confirm that both translation factors are essential and show that their expression can be varied over the entire physiologically relevant dynamic range, thus enabling quantitative genetic control of two key translation processes.
Curtailing translation initiation suppresses the effect of a genetic translocation bottleneck. We determined the bacterial response to varying translocation and initiation factor levels by measuring growth rates over finely resolved two-dimensional concentration gradients of both inducers. The resulting response surface clearly showed that inhibition of initiation alleviates the effect of translocation inhibition Fig.
An all-or-nothing approach Fig. Taken together, these data show that the interplay of translation initiation and translocation alone is sufficient to produce strong suppression: dialing down initiation cranks up growth stalled by translocation bottlenecks.
The widespread suppression between antibiotics targeting initiation and translocation is thus explained as a general consequence of the combined inhibition of specific translation steps alone.
Expression of initiation and elongation factors are controlled by the level of inducer IPTG and aTc, respectively. Right: cross-section of the response surface along the dashed purple line gray circles and at maximal aTc induction white circles ; solid lines are smoothed profiles. Black arrow denotes a decrease in translocation; if initiation is lowered simultaneously with translocation orange arrow , growth reduction is smaller.
Ribosomes advance on transcripts as described by a generalized totally asymmetric simple exclusion process TASEP for particles of size L see a and text. States below and to the right of the green line are in the translocation limiting regime. Right: cross-sections of the response surface. As the initiation factor level is decreased, the critical point of the phase transition green triangle is reached; growth starts increasing after passing the critical point, and decreases again after passing the maximum red square as the number of translating ribosomes becomes limiting.
What is the underlying mechanism of the suppressive interaction between initiation and translocation inhibitors? The traffic of translating ribosomes that move along mRNAs can be dense When a ribosome gets stuck, e. The resulting situation is similar to a traffic jam of cars on a road.
Traffic jams can form due to the asynchronous movement and stochastic progression of particles in discrete jumps, which is a good approximation for the molecular dynamics of a translating ribosome. If particle progression were deterministic and synchronous, no traffic jams would form. A classic model of queued traffic progression, which can be applied to translation 39 , 40 , is the totally asymmetric simple exclusion process TASEP 41 , We developed a generalization of the TASEP that describes the traffic of translating ribosomes on mRNAs and takes into account the laws of bacterial cell physiology.
There are several differences between the classic TASEP and translating ribosomes moving along a transcript. Second, the total number of ribosomes in the cell is finite and varies as dictated by bacterial growth laws 13 , Third, translation steps are mediated by translation factors that bind to the ribosome in a specific state and push the ribosome into another state These transitions are stochastic with rates that depend on the abundance of ribosomes in a specific state and on the abundance of translation factors available to catalyze the step.
Thus, the initiation- and translocation-attempt rates, which are constant in the classic TASEP, depend on the state of the system. We formulated a generalized TASEP that captures these extensions, estimated all of its parameters based on literature, and derived the model equations analytically Methods and Supplementary Information. The resulting growth rate was calculated numerically. In brief, our generalized TASEP model provides a physiologically realistic description of the factor-mediated traffic of ribosomes on multiple transcripts.
Without any free parameters, this generalized TASEP qualitatively reproduced the suppressive effect of lowering the initiation rate under a translocation bottleneck Fig. This suppression results from a phase transition between the translocation- and the initiation-limited regime Supplementary Information.
In the translocation-limited regime black arrow in Fig. Beyond the critical point of this phase transition green triangle in Fig. Hence, ultimately, a non-equilibrium phase transition in which ribosome traffic jams dissolve underlies the suppressive effect. The densification of ribosomes on transcripts has an additional consequence: as the number of ribosomes that are stuck on transcripts increases, more elongation factors are sequestered by ribosomes.
This in turn reduces the probability that an individual ribosome is bound by a factor—a necessary condition for the ribosome to attempt a translocation step. This situation results in a positive feedback loop in which the reduced translocation attempt rate further amplifies ribosome congestion. To compare measured and predicted surfaces, which have different axes, we calculated their respective deviation from independence as for the bottleneck dependency score Fig. By this measure, the model faithfully captured the clear deviation from the multiplicative expectation Fig.
Taken together, these results show that suppressive drug interactions between translation inhibitors are caused by the interplay of two different translation bottlenecks. Close agreement of the experiments with a plausible theoretical model of ribosome traffic, which captures physiological feedback mediated by growth laws, strongly suggests that suppression is caused by ribosome traffic jams.
Such traffic jams result from imbalances between translation initiation and translocation; they dissolve in a phase transition that occurs when one of these processes is slowed, leading to an overall acceleration of translation and growth. Stalled ribosomes facilitate the formation of traffic jams by sequestering elongation factors.
We conclude that a non-equilibrium phase transition in ribosome traffic is at the heart of suppressive drug interactions between antibiotics targeting translation initiation and translocation. We established a framework that combines mathematical modeling, high-throughput growth rate measurements, and genetic perturbations to elucidate the underlying mechanisms of drug interactions between antibiotics inhibiting translation.
This model explained many interactions, but not all, failing specifically for suppressive interactions. Predictions improved by taking into account the step-wise progression of ribosomes through the translation cycle Figs.
This was achieved by mimicking antibiotic perturbations of this progression genetically, which directly identified the contribution of antibiotic-imposed translation bottlenecks to the observed drug interactions. Finally, to explain the origin of suppressive interactions unaccounted for by the biophysical model, we modeled the traffic of translating ribosomes explicitly.
Our results show that translocation inhibition can cause ribosomal traffic jams, which dissolve in a non-equilibrium phase transition when initiation is inhibited simultaneously with translocation, thereby restoring growth Fig. This phase transition explains the suppressive drug interactions between antibiotics targeting initiation and translocation. Taken together, our framework mechanistically explained 20 out of 28 observed drug interactions Fig. While 16 out of 28 interactions were already explained by the biophysical model, these include many weak and additive interactions; in contrast, only the translation bottleneck approach correctly predicted some of the strongest interactions and, in particular, suppression.
Furthermore, we only classified predictions as correct if the majority of growth rates across the dose—response surface quantitatively matched the prediction. As a result, cases where the predicted and observed drug interaction type agree, are often still classified as false because the agreement is not quantitative. If the same stringent criteria are applied to replicate measurements of drug interactions Supplementary Fig.
Notably, even cases rejected as quantitatively different can provide valuable insights. Nevertheless, remapping correctly predicts the occurrence of suppression as well as its direction. Qualitative observations like these still advance our understanding of drug interactions by highlighting drug interaction mechanisms that are distorted by additional effects of unknown origin.
While we focused on translation inhibitors, key elements of our framework can be generalized to drugs with other modes of action. Specifically, when considering a drug that targets a specific process mediated by an essential enzyme, our approach of equating the deprivation of the enzyme with the action of an antibiotic is readily applicable.
Our observations also highlight the advantages of factor deprivation compared to simple overexpression: the former produced a quantitative prediction for drug interactions, while no meaningful prediction could be made from overexpression data Supplementary Fig. The general approach of depleting key accessory proteins is particularly useful for antibiotics targeting multi-component complexes or in cases where the effects of overexpressing the drug target are difficult to interpret Mimicking the effects of two drugs with controllable genetic perturbations generalizes the concept of genetic epistasis to continuous perturbations.
Epistasis studies compare the effects of double gene knockouts to those of single knockouts and identify epistatic interactions—an approach that can reveal functional modules in the cell 6 , 37 , Our results show that continuous genetic perturbations provide valuable additional information on genetic interactions Fig.
Firstly, the direction of epistatic interactions cannot be extracted from measurements of single and double mutants. In particular, continuous epistasis data can be powerful for the development of whole-cell models that describe the interplay of different functional modules in the cell.
Thirdly, this approach allows including essential genes in epistatic interaction networks even for haploid organisms, which otherwise requires the use of less well-defined hypomorphs.
Hence, continuous epistasis measurements augment all-or-nothing genetic perturbations. Continuous epistasis measurements further enable a deeper understanding of previously mysterious antibiotic resistance mutations.
Specifically, translation bottlenecks that alleviate the effect of an antibiotic expose a latent potential for resistance development. Indeed, mutations with effects equivalent to factor-imposed bottlenecks occur under antibiotic selection pressure. For example, resistance to ERM in E. Consistent with this observation, our results indicate that the action of ERM is alleviated by lowering the stability of the 50S subunit Fig. Mutations in recycling factor were observed in Pseudomonas aeruginosa evolved for resistance to the TET derivative tigecycline The observed alleviation of TET action by a recycling bottleneck Fig.
Mutations in other genes predicted based on the effect of translation bottlenecks may be difficult to observe, especially in clinical isolates, due to the associated fitness cost and selection pressure for reverting the mutations in the absence of antibiotic selection.
Beyond mutations conferring resistance to individual drugs, consistent or conflicting dependencies of different antibiotics on translation bottlenecks may further indicate the potential for evolving cross-resistance and collateral sensitivity, respectively Our work also demonstrates the potential of improved null models for drug interactions that are based on generic biophysical and physiological considerations.
The number of parameters is minimal and the biophysical model we presented makes parameter-free predictions. This model is readily extended to capture phenomena such as an inactive fraction of ribosomes Supplementary Information or physical interactions between antibiotics on the ribosome Including more detailed mechanisms, e.
In essence, such a detailed model and its parameters would have to be fine-tuned for every antibiotic combination.
0コメント