Predicting the effects of ribosome-targeting antibiotic combinations

• Applicant: Professor Dr. Tobias Bollenbach
• Subject Area: Medical Microbiology and Mycology, Hygiene, Molecular Infection Biology ; Bioinformatics and Theoretical Biology ; Biophysics
• Term: from 2019 to 2024
• Project identifier: Deutsche Forschungsgemeinschaft (DFG) - Project number 422345533

• Final Report Year: 2024

Final Report Abstract

Drug combinations are increasingly important in treating various diseases and conditions. Discovering new synergistic combinations can potentially revive old antibiotics that were previously withdrawn due to high resistance levels. Thoughtfully designed drug pairs could also help prevent future drug resistance. Despite this great potential, interactions between drugs, such as synergism and antagonism, remain largely unpredictable. Systematically identifying these interactions requires large-scale screenings, which often become impractical due to combinatorial explosion. In this project, we developed a quantitative model that predicts drug interactions between ribosome-targeting antibiotics – a major class of antibiotics that are ideal for quantitative studies. By combining high-throughput growth measurements in two-drug environments with specific synthetic bottlenecks in translation, proteomics, and theoretical modeling, we demonstrated that the effects of multiple translation inhibitors on cell physiology can be explained by bacterial growth laws. Moreover, the interactions between specific translation steps targeted by different antibiotics offer mechanistic insights into drug interactions. We characterized the effects of various genetic and pharmacological perturbations on key translation factors and determined the precise modes of action of individual antibiotics that inhibit different stages of the translation cycle. Using a generalized version of the Totally Asymmetric Simple Exclusion Process (TASEP), we found that the most drastic drug interactions – where one translation inhibitor suppresses the effect of another – are caused by ribosome traffic jams on the transcripts they are translating. Furthermore, we showed that drug interactions between translation inhibitors are robust to changes in the nutrient environment. Reflecting the evolutionary conservation of the bacterial translation machinery, these drug interactions are also highly conserved across different bacterial species. Our findings provide fundamental insights into protein translation and reveal the underlying mechanisms behind most drug interactions between antibiotics that target this essential process.

Publications

• Mechanisms of drug interactions between translation-inhibiting antibiotics. Nature Communications, 11(1).
  Kavčič, Bor; Tkačik, Gašper & Bollenbach, Tobias
  
• Minimal biophysical model of combined antibiotic action. PLOS Computational Biology, 17(1), e1008529.
  Kavčič, Bor; Tkačik, Gašper & Bollenbach, Tobias
  
• Uncovering Key Metabolic Determinants of the Drug Interactions Between Trimethoprim and Erythromycin in Escherichia coli. Frontiers in Microbiology, 12.
  Qi, Qin; Angermayr, S. Andreas & Bollenbach, Tobias
  
• The physiology and genetics of bacterial responses to antibiotic combinations. Nature Reviews Microbiology, 20(8), 478-490.
  Roemhild, Roderich; Bollenbach, Tobias & Andersson, Dan I.
  
• Quantitative approaches to study phenotypic effects of large-scale genetic perturbations. Current Opinion in Microbiology, 74, 102333.
  Müller, Janina & Bollenbach, Tobias