首页 » 文章 » 文章详细信息
Molecular Systems Biology Volume 13 ,Issue 10 ,2017-10-23
Drug detoxification dynamics explain the postantibiotic effect
Articles
Jaydeep K Srimani 1 Shuqiang Huang 2 Allison J Lopatkin 1 Lingchong You 1 , 3 , 4
Show affiliations
DOI:10.15252/msb.20177723
Received 2017-04-30, accepted for publication 2017-09-22, Published 2017-09-22
PDF
摘要

Abstract The postantibiotic effect (PAE) refers to the temporary suppression of bacterial growth following transient antibiotic treatment. This effect has been observed for decades for a wide variety of antibiotics and microbial species. However, despite empirical observations, a mechanistic understanding of this phenomenon is lacking. Using a combination of modeling and quantitative experiments, we show that the PAE can be explained by the temporal dynamics of drug detoxification in individual cells after an antibiotic is removed from the extracellular environment. These dynamics are dictated by both the export of the antibiotic and the intracellular titration of the antibiotic by its target. This mechanism is generally applicable for antibiotics with different modes of action. We further show that efflux inhibition is effective against certain antibiotic motifs, which may help explain mixed cotreatment success.

关键词

systems biology;postantibiotic effect;antibiotic tolerance

授权许可

© 2017 EMBO
This is an open access article under the terms of the Creative Commons Attribution 4.0 License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

图表

Recovery time increases exponentially as a function of total antibiotic exposure(Left panel) We define the recovery time (RT) as the time required for a population (red line) to double in response to a transient antibiotic treatment (blue shading). (Right panel) The postantibiotic effect (PAE) induced by an antibiotic treatment refers to the additional time required for a population to recover (red line) in comparison with the untreated control (black line).A microfluidic device for quantitative recovery time measurements. Each PDMS‐fabricated chip consists of six independent channels, one of which is shown, from top view (top row). Green circles indicate media inflow; black circle indicates outflow. Bacteria are manually loaded in inflow ports and are trapped in individual culturing chambers (gray circles), and the height of which ensures that bacteria are imaged in a monolayer (bottom row). Growth conditions (e.g., antibiotic dose profile) are controlled via programmable syringe pumps. Fluorescent images (bottom row) show representative growth of a monolayer of Escherichia coli BW25113 cells constitutively expressing GFP over four hours.Representative time series fluorescence data showing dose‐dependent population recovery in response to transient streptomycin treatment. Here, time zero corresponds to the end of treatment (120 min). Trajectories show mean and standard deviation for five replicates; colors indicate increasing antibiotic concentration (2, 4, 6, 8, 10, 12 μg/ml). Fluorescence values are normalized to those at time zero. Dotted line indicates a twofold increase, corresponding to the recovery time for each population.Recovery time increases with total antibiotic exposure. Inset shows recovery time as a function of dose duration for increasing streptomycin concentrations (as in panel C). When plotted against total antibiotic exposure (calculated as ∫0DA(t)dt), all recovery time values collapse onto a single exponential function (black line, R2 = 0.83). Recovery time was measured in response to streptomycin concentrations of 0, 2, 4, 6, 8, 10, and 12 μg/ml, and treatment durations 30, 60, 90, and 120 min. Error bars indicate standard deviation, calculated from five replicates; y‐axis uses the natural logarithm.

The timescale of overall intracellular antibiotic dictates individual and population recoveryA minimal model of antibiotic action. Antibiotic is transported between the intracellular and extracellular environments (Ain and Aout) by influx and efflux rates (kin and kout). Ain reversibly binds to target ribosomes C to form the complex CA, with binding and dissociation rates kf and kb, respectively. This complex can then be degraded through the intermediate CA′. These intracellular dynamics influence the overall population recovery rate; the maximum growth rate μ is dependent on the ribosome concentration C. Thus, recovery can be quantified both on the individual level, as a function of C, or on the population level, in terms of cell density.Concurrent antibiotic transport and ribosome inhibition dictate recovery dynamics. During treatment, Aout ≫ Ain, and the intracellular antibiotic concentration can be linearly approximated by Ain(D) ≈ kinAoutD, where Aout is assumed to be constant and D represents the treatment duration (leftmost schematic and panel 1). Antibiotic influx is greater than efflux; Ain binds to target ribosomes and strongly inhibits the upregulation of ribosome synthesis. When extracellular antibiotic is removed, Ain decreases (middle and rightmost schematics, green shading and panel 2), and ribosome synthesis resumes when Ain is sufficiently small.Ain accumulates linearly with treatment duration for sufficiently short treatment durations (panel i). After the removal of Aout, efflux and inhibition dynamics combine to delay the synthesis of ribosomes in a concentration‐dependent manner (panel ii). Colors indicate increasing antibiotic concentration, as shown in panel ii.Antibiotic turnover timescale sets intracellular recovery RTcell. Regardless of the antibiotic treatment history, the relationship between intracellular antibiotic Ain and ribosome concentration C approaches the same asymptote, indicating that the timescale of individual detoxification sets the timescale of ribosome synthesis. Colors indicate increasing antibiotic concentration, as in Fig 2C; for these representative trajectories, treatment duration was set to 120 min.Population recovery time is dictated by total antibiotic exposure. Inset shows that RTpop is an increasing function of dose duration; these data collapse onto a single relationship as a function of total antibiotic exposure (main figure).Individual (RTcell) and population recovery time (RTpop) are strongly correlated, suggesting that intracellular dynamics lead to population level recovery (R2 = 0.86).

Key parameter perturbations confirm model validityModeling predicts that decreasing the ribosome degradation rate leads to shorter recovery times in response to equal amounts of total antibiotic exposure. Here, we use kd = 0.1 and kd = 0.2 for high and low degradation, respectively.Modeling predicts that decreasing the antibiotic efflux rate leads to longer recovery times in response to equal amounts of total antibiotic exposure. Here, we use kout = 0.01 and kout = 0.001 for fast and slow efflux, respectively.Modeling predicts that increasing the ribosome synthesis rate leads to shorter recovery times in response to equal amounts of total antibiotic exposure. Here, we use k1 = 0.2 and k1 = 0.4 for slow and fast ribosome synthesis, respectively.Streptomycin treatment (closed data points, R2 = 0.83) results in significantly longer recovery times than chloramphenicol treatment (open data points). Here, the difference between two responses is statistically significant (P < 0.05 by ANOVA). Error bars indicate standard deviation of five replicates.The addition of efflux pump inhibitor (CCCP) (closed data points, R2 = 0.91) increases population recovery time in response to chloramphenicol treatment (open data points, R2 = 0.80). Here, CCCP was added at subinhibitory concentrations (3 μg/ml); in the absence of antibiotic treatment, CCCP alone did not inhibit population recovery. The CCCP‐mediated increase in recovery time is statistically significant (P < 0.01 by ANOVA). Error bars indicate standard deviation of five replicates.The ribosome synthesis rate was increased by increasing the concentration of the casamino acids in the media from 0.01% w/v (closed data points) to 0.05% w/v (open data points). Faster synthesis resulted in lower recovery times (P < 0.001 by ANOVA) in response to streptomycin treatment. Error bars indicate standard deviation of five replicates. In each of these panels, the color scheme indicates increasing antibiotic concentration, as in Fig 1C.

Efflux inhibition is an effective cotreatment strategy for certain antibioticsThree general motifs of intracellular antibiotic action. Left and middle motifs correspond to ribosome‐inhibiting antibiotics that induce rapid and minimal ribosome degradation, respectively. In these motifs, the target molecule is subject to nonlinear positive feedback (i.e., transcription and translation, in the case of ribosomes). Right motif corresponds to antibiotics that inhibit other targets that are not subject to positive feedback, for example, β‐lactams. In each case, recovery time is a function of total antibiotic exposure (closed circles) and inhibiting efflux results in longer recovery times (open circles).Inhibiting antibiotic efflux with CCCP significantly increased recovery time for antibiotics that induced rapid target degradation and involved a positive feedback loop. Antibiotics are as follows: penicillin G (PenG), spectinomycin (Spec), gentamicin (Gent), streptomycin (Str), chloramphenicol (Cm), tetracycline (Tet), and carbenicillin (Carb). Antibiotic concentrations used have been scaled by their respective IC50 values (solid points and open circles show response with no efflux inhibition and 2 μg/ml CCCP, respectively). Colors correspond to the motifs of action shown in Fig 4A.

通讯作者

Lingchong You.Department of Biomedical Engineering, Duke University, Durham, NC, USA;Center for Genomic and Computational Biology, Duke University, Durham, NC, USA;Department of Molecular Genetics and Microbiology, Duke University School of Medicine, Durham, NC, USA.you@duke.edu

推荐引用方式

Jaydeep K Srimani,Shuqiang Huang,Allison J Lopatkin,Lingchong You. Drug detoxification dynamics explain the postantibiotic effect. Molecular Systems Biology ,Vol.13, Issue 10(2017)

您觉得这篇文章对您有帮助吗?
分享和收藏
5

是否收藏?

参考文献
[1] Eagle H, Fleischman R (1950) The bactericidal action of penicillin in vivo: the participation of the host, and the slow recovery of the surviving organisms. Ann Intern Med 33: 544–571
[2] AliAbadi FS, Lees P (2000) Antibiotic treatment for animals: effect on bacterial population and dosage regimen optimisation. Int J Antimicrob Agents 14: 307–313
[3] Amaral L, Engi H, Viveiros M, Molnar J (2007) Comparison of multidrug resistant efflux pumps of cancer and bacterial cells with respect to the same inhibitory agents. In vivo 21: 237–244
[4] Paixão L, Rodrigues L, Couto I, Martins M, Fernandes P, de Carvalho CC, Monteiro GA, Sansonetty F, Amaral L, Viveiros M (2009) Fluorometric determination of ethidium bromide efflux kinetics in Escherichia coli. J Biol Eng 3: 18
[5] Ma D, Cook DN, Hearst JE, Nikaido H (1994) Efflux pumps and drug resistance in gram‐negative bacteria. Trends Microbiol 2: 489–493
[6] Viveiros M, Martins A, Paixão L, Rodrigues L, Martins M, Couto I, Fähnrich E, Kern WV, Amaral L (2008) Demonstration of intrinsic efflux activity of Escherichia coli K‐12 AG100 by an automated ethidium bromide method. Int J Antimicrob Agents 31: 458–462
[7] Li RC, Lee SW, Kong CH (1997) Correlation between bactericidal activity and postantibiotic effect for five antibiotics with different mechanisms of action. J Antimicrob Chemother 40: 39–45
[8] Poole K, Lomovskaya O (2006) Can efflux inhibitors really counter resistance? Drug Discov Today Ther Strateg 3: 145–152
[9] Van Bambeke F, Lee VJ (2006) Inhibitors of bacterial efflux pumps as adjuvants in antibiotic treatments and diagnostic tools for detection of resistance by efflux. Recent Pat Antiinfect Drug Discov 1: 157–175
[10] Papich MG (2014) Pharmacokinetic–pharmacodynamic (PK–PD) modeling and the rational selection of dosage regimes for the prudent use of antimicrobial drugs. Vet Microbiol 171: 480–486
[11] Lomovskaya O, Bostian KA (2006) Practical applications and feasibility of efflux pump inhibitors in the clinic—a vision for applied use. Biochem Pharmacol 71: 910–918
[12] Zundel MA, Basturea GN, Deutscher MP (2009) Initiation of ribosome degradation during starvation in Escherichia coli. RNA 15: 977–983
[13] Toutain PL, del Castillo JRE, Bousquet‐Mélou A (2002) The pharmacokinetic–pharmacodynamic approach to a rational dosage regimen for antibiotics. Res Vet Sci 73: 105–114
[14] Parker RF, Marsh HC (1946) Action of penicillin on Staphylococcus. J Bacteriol 51: 181
[15] Lopatkin AJ, Huang S, Smith RP, Srimani JK, Sysoeva TA, Bewick S, Karig DK, You L (2016) Antibiotics as a selective driver for conjugation dynamics. Nat Microbiol 1: 16044
[16] Zhanel GG, Hoban DJ (1991) The postantibiotic effect: a review of in vitro and in vivo data. DICP 25: 153–163
[17] Eagle H, Fleischman R, Musselman AD (1950) Effect of schedule of administration on the therapeutic efficacy of penicillin: importance of the aggregate time penicillin remains at effectively bactericidal levels. Am J Med 9: 280–299
[18] Edmunds T, Goldberg AL (1986) Role of ATP hydrolysis in the degradation of proteins by protease La from Escherichia coli. J Cell Biochem 32: 187–191
[19] Zhanel GG, Craig WA (1994) Pharmacokinetic contributions to postantibiotic effects. Clin Pharmacokinet 27: 377–392
[20] Tan C, Smith RP, Srimani JK, Riccione KA, Prasada S, Kuehn M, You L (2012) The inoculum effect and band‐pass bacterial response to periodic antibiotic treatment. Mol Syst Biol 8: 617
[21] Fishman N (2006) Antimicrobial stewardship. Am J Infect Control 34: S55–S63
[22] Damper PD, Epstein W (1981) Role of the membrane potential in bacterial resistance to aminoglycoside antibiotics. Antimicrob Agents Chemother 20: 803
[23] Athamna A (2004) In vitro post‐antibiotic effect of fluoroquinolones, macrolides, ‐lactams, tetracyclines, vancomycin, clindamycin, linezolid, chloramphenicol, quinupristin/dalfopristin and rifampicin on Bacillus anthracis. J Antimicrob Chemother 53: 609–615
[24] Odenholt‐Tornqvist I (1993) Studies on the postantibiotic effect and the postantibiotic sub‐MIC effect of meropenem. J Antimicrob Chemother 31: 881–892
[25] Marquez B (2005) Bacterial efflux systems and efflux pumps inhibitors. Biochimie 87: 1137–1147
[26] Talpaert MJ, Gopal Rao G, Cooper BS, Wade P (2011) Impact of guidelines and enhanced antibiotic stewardship on reducing broad‐spectrum antibiotic usage and its effect on incidence of Clostridium difficile infection. J Antimicrob Chemother 66: 2168–2174
[27] MacKenzie FM, Gould IM (1993) The post‐antibiotic effect. J Antimicrob Chemother 32: 519–537
[28] Wiuff C, Zappala RM, Regoes RR, Garner KN, Baquero F, Levin BR (2005) Phenotypic tolerance: antibiotic enrichment of noninherited resistance in bacterial populations. Antimicrob Agents Chemother 49: 1483–1494
[29] Opperman TJ, Nguyen ST (2015) Recent advances toward a molecular mechanism of efflux pump inhibition. Front Microbiol 6: 11086
[30] Okusu H, Ma D, Nikaido H (1996) AcrAB efflux pump plays a major role in the antibiotic resistance phenotype of Escherichia coli multiple‐antibiotic‐resistance (Mar) mutants. J Bacteriol 178: 306–308
[31] Stubbings W (2006) Mechanisms of the post‐antibiotic effects induced by rifampicin and gentamicin in Escherichia coli. J Antimicrob Chemother 58: 444–448
[32] Mahamoud A, Chevalier J, Alibert‐Franco S, Kern WV, Pages JM (2007) Antibiotic efflux pumps in Gram‐negative bacteria: the inhibitor response strategy. J Antimicrob Chemother 59: 1223–1229
[33] zur Wiesch PA, Abel S, Gkotzis S (2015) Classic reaction kinetics can explain complex patterns of antibiotic action. Sci Transl Med 7: 287ra273
[34] Vogelman B, Gudmundsson S, Leggett J, Turnidge J, Ebert S, Craig W (1988) Correlation of antimicrobial pharmacokinetic parameters with therapeutic efficacy in an animal model. J Infect Dis 158: 831–847
[35] Eagle H (1949) The recovery of bacteria from the toxic effects of penicillin. J Clin Invest 28: 832
[36] Askoura M, Mattawa W, Abujamel T, Taher I (2017) Efflux pump inhibitors (EPIs) as new antimicrobial agents against Pseudomonas aeruginosa. Libyan J Med 6: 5870
[37] Stavri M, Piddock LJV, Gibbons S (2007) Bacterial efflux pump inhibitors from natural sources. J Antimicrob Chemother 59: 1247–1260
[38] Andreu N, Zelmer A, Fletcher T, Elkington PT, Ward TH, Ripoll J, Parish T, Bancroft GJ, Schaible U, Robertson BD, Wiles S (2010) Optimisation of bioluminescent reporters for use with mycobacteria. PLoS One 5: e10777
[39] Eagle H, Musselman AD (1949) The slow recovery of bacteria from the toxic effects of penicillin. J Bacteriol 58: 475
[40] Bundtzen RW, Gerber AU, Cohn DL (1981) Postantibiotic suppression of bacterial growth. Rev Infect Dis 3: 28–37
[41] Muir ME, van Heeswyck RS, Wallace BJ (1984) Effect of growth rate on streptomycin accumulation by Escherichia coli and Bacillus megaterium. Microbiology 130: 2015–2022
[42] Mizunaga S (2005) Influence of inoculum size of Staphylococcus aureus and Pseudomonas aeruginosa on in vitro activities and in vivo efficacy of fluoroquinolones and carbapenems. J Antimicrob Chemother 56: 91–96
[43] Kaki R, Elligsen M, Walker S, Simor A, Palmay L, Daneman N (2011) Impact of antimicrobial stewardship in critical care: a systematic review. J Antimicrob Chemother 66: 1223–1230
[44] Mcmurry LM, Oethinger M (1998) Overexpression of marA, soxS, or acrAB produces resistance to triclosan in laboratory and clinical strains of Escherichia coli. FEMS Microbiol Lett 166: 305–309
[45] Meredith HR, Lopatkin AJ, Anderson DJ, You L (2015) Bacterial temporal dynamics enable optimal design of antibiotic treatment. PLoS Comput Biol 11: e1004201
[46] Craig WA, Vogelman B (1987) The postantibiotic effect. Ann Intern Med 106: 900–902
[47] Hurwitz C, Rosano CL (1962) Accumulation of label from C14‐streptomycin by Escherichia coli. J Bacteriol 83: 1193–1201
[48] Craig WA (1993) Post‐antibiotic effects in experimental infection models: relationship to in‐vitro phenomena and to treatment of infections in man. J Antimicrob Chemother 31: 149–158
[49] Cohen SP, Hooper DC, Wolfson JS (1988) Endogenous active efflux of norfloxacin in susceptible Escherichia coli. Antimicrob Agents Chemother 32: 1187–1191
[50] Hancock R (1962) Uptake of 14G‐streptomycin by Bacillus megaterium. J Gen Microbiol 28: 503
[51] Lazzarini RA, Dahlberg AE (1971) The control of ribonucleic acid synthesis during amino acid deprivation in Escherichia coli. J Biol Chem 246: 420–429
[52] Beam TR, Gilbert DN, Kunin CM (1992) General guidelines for the clinical evaluation of anti‐infective drug products. Clin Infect Dis 15: S5–S32
[53] Avent ML, Rogers BA, Cheng AC, Paterson DL (2011) Current use of aminoglycosides: indications, pharmacokinetics and monitoring for toxicity. Intern Med J 41: 441–449
[54] Odenholt‐Tornqvist I, Löwdin E (1991) Pharmacodynamic effects of subinhibitory concentrations of beta‐lactam antibiotics in vitro. Antimicrob Agents Chemother 35: 1834–1839
[55] Nikaido H (1998) Antibiotic resistance caused by gram‐negative multidrug efflux pumps. Clin Infect Dis 27: S32–S41
[56] Prosser J, Killham K, Glover L, Rattray E (1996) Luminescence‐based systems for detection of bacteria in the environment. Crit Rev Biotechnol 16: 157–183
[57] Hanberger H, Nilsson LE, Kihlström E (1990) Postantibiotic effect of beta‐lactam antibiotics on Escherichia coli evaluated by bioluminescence assay of bacterial ATP. Antimicrob Agents Chemother 34: 102–106
[58] Renneberg J, Walder M (1989) Postantibiotic effects of imipenem, norfloxacin, and amikacin in vitro and in vivo. Antimicrob Agents Chemother 33: 1714–1720
[59] Gudmundsson S, Einarsson S (1993) The post‐antibiotic effect of antimicrobial combinations in a neutropenic murine thigh infection model. J Antimicrob Chemother 31: 171–191
[60] Nikaido H (1994) Prevention of drug access to bacterial targets: permeability barriers and active efflux. Science 264: 382–388
[61] Spivey J (1992) The postantibiotic effect. Clin Pharm 11: 865–875
[62] Neidhardt FC (1996) Escherichia coli and Salmonella: cellular and molecular biology. Washington, DC: ASM Press
[63] Singh M, Jadaun G, Srivastava K (2011) Effect of efflux pump inhibitors on drug susceptibility of ofloxacin resistant Mycobacterium tuberculosis isolates. Indian J Med Res 133: 535
[64] Grenier F, Matteau D, Baby V, Rodrigue S (2014) Complete genome sequence of Escherichia coli BW25113. Genome Announc 2: e01038‐14
[65] Gottfredsson M, Erlendsdóttir H (1998) Characteristics and dynamics of bacterial populations during postantibiotic effect determined by flow cytometry. Antimicrob Agents Chemother 42: 1005–1011
[66] Kriengkauykiat J, Porter E, Lomovskaya O, Wong‐Beringer A (2005) Use of an efflux pump inhibitor to determine the prevalence of efflux pump‐mediated fluoroquinolone resistance and multidrug resistance in Pseudomonas aeruginosa. Antimicrob Agents Chemother 49: 565–570
[67] Bryan LE, Van Den Elzen HM (1976) Streptomycin accumulation in susceptible and resistant strains of Escherichia coli and Pseudomonas aeruginosa. Antimicrob Agents Chemother 9: 928–938
[68] Bigger JW (1944) Treatment of staphylococcal infections with penicillin by intermittent sterilisation. Lancet 244: 497–500
[69] Kinoshita N, Unemoto T, Kobayashi H (1984) Proton motive force is not obligatory for growth of Escherichia coli. J Bacteriol 160: 1074–1077
[70] Kaplan R, Apirion D (1975) The fate of ribosomes in Escherichia coli cells starved for a carbon source. J Biol Chem 250: 1854–1863