Towards model-informed design of antibiotic therapies: prediction of bacterial physiology & growth in response to ribosome-targeting antibiotics.
Elena Pascual Garcia1, Prof. Dr. Charlote Kloft2, Dr. Andrea Weiße3, Prof. Dr. Wilhelm Huisinga1
1Universität Potsdam, 2Freie Universität Berlin, 3University of Edinburgh
Introduction and objectives: Antibiotic resistance is a growing global challenge. Increasing levels of resistance have been reported across bacterial species and antibiotic classes [1]. In order to rationally design optimal treatment strategies and extend the effective lifespan of existing drugs, it is essential to understand the relationship between the physiological state of bacteria and their susceptibility to drugs in different growth conditions. Here we present a mechanistic modelling framework that quantitatively predicts the effects of ribosome-targeting antibiotics on bacterial physiology, linking growth dynamics to intracellular processes such as mRNA levels, ribosome availability and translational efficiency. Methods : We use an established mechanistic model of bacterial growth [2] to describe the autoregulation of bacterial physiology in the absence of a drug. We modified the base model to better describe ribosome-limiting conditions that stem from the exposure to ribosome-targeting antibiotics. Specifically, we accounted for polysomes (multiple ribosomes translating an mRNA molecule in parallel), and we re-estimated key physiological parameters integrating a variety of ribosome and mRNA abundance data [3, 4]. We further considered a mechanistic description of the mode of action of different ribosome-binding antibiotics, accounting for drug permeability across the cell membrane and binding dynamics of the drug to translating ribosomes. We estimated antibiotic-specific parameters for four different ribosome-targeting antibiotics (chloramphenicol, tetracycline, streptomycin and kanamycin) from experimental growth-response data across six different growth media [5], adapting Bayesian Optimization for parameter inference. Results: The model successfully captured the observed concentration-response dynamics of all four antibiotics, accurately reproducing IC50 values and inhibitory kinetics across six different growth media. Due to its mechanistic nature, our framework was able to predict the correlation of the drug action with the physiological state of bacteria, revealing that growth inhibition was primarily driven by a reduction in the active ribosome pool rather than a direct impairment of ribosomal efficiency (e.g. a reduction of translational elongation rates). Upon antibiotic exposure, ribosome stalling led to an accumulation of cellular resources (e.g., ATP). This was particularly notable for streptomycin (STR) and kanamycin (KAN), where the model predicted a complete halt of translation. The accumulation of resources resulted in increased translational elongation rates in unaffected ribosomes, highlighting a compensatory adaptation to antibiotic stress. The obtained results align with experimental findings, including recent measurements for chloramphenicol and tetracycline [3, 6], which support the predicted relationship between reduction of the active ribosome pool and an increase in ribosome efficiency. Furthermore, our analysis proposes complete disruption of translation as the primary driver of antibiotic action for streptomycin and kanamycin—a mechanistic hypothesis that, while previously proposed, has not been directly demonstrated [7, 8]. Conclusion: This study presents a mechanistic framework that links bacterial physiology to the action of ribosome-targeting antibiotics, capturing growth inhibition differentially across diverse environmental conditions. By integrating multiple data sources, the model successfully predicts drug effects across growth environments and highlights depletion of the active ribosome pool as the key driver of antibiotic efficacy. The findings align with experimental data for chloramphenicol and tetracycline while supporting the importance of the mechanism for streptomycin and kanamycin. This framework provides a foundation for optimizing treatment strategies and exploring combination therapies that manipulate ribosome dynamics to enhance single antibiotic potency.
[1] O’Neill, J. Tackling Drug-Resistant Infections Globally: Final Report and Recommendations. Review on Antimicrobial Resistance. Wellcome Trust and HM Government; 2016. [2] Weiße, A. Y., Oyarzún, D. A., Danos, V. & Swain, P. S. Mechanistic links between cellular trade-offs, gene expression, and growth. Proceedings of the National Academy of Sciences of the United States of America ;2015. [3] Rohan Balakrishnan et al. Principles of gene regulation quantitatively connect DNA to RNA and proteins in bacteria. Science; 2022. [4] Dai, X., Zhu, M., Warren, M. et al. Reduction of translating ribosomes enables Escherichia coli to maintain elongation rates during slow growth. Nat Microbiol; 2017. [5] Greulich P, Scott M, Evans MR, Allen RJ. Growth-dependent bacterial susceptibility to ribosome-targeting antibiotics. Mol Syst Biol; 2015. [6] Levin BR, McCall IC, Perrot V, Weiss H, Ovesepian A, Baquero F. A Numbers Game: Ribosome Densities, Bacterial Growth, and Antibiotic-Mediated Stasis and Death. mBio; 2017. [7] Webster CM, Shepherd M. A mini-review: environmental and metabolic factors affecting aminoglycoside efficacy. World J Microbiol Biotechnol; 2022. [8] Lang M, Carvalho A, Baharoglu Z, Mazel D. Aminoglycoside uptake, stress, and potentiation in Gram-negative.