Evaluating the Mechanisms of Synergy Between Gentamicin and Ceftazidime in Klebsiella pneumoniae: Insights into Antimicrobial Resistance and Combination Therapy

Abstract

The rise of multidrug-resistant (MDR) Klebsiella pneumoniae poses a significant public health threat, particularly in low- and middle-income countries where treatment options are limited. This study investigates the potential of antibiotic combination therapy to reverse resistance in MDR K. pneumoniae isolates collected from clinical samples in Bangladesh. Using a combination of phenotypic assays and molecular techniques, five isolates were screened for resistance to Ceftazidime, Amikacin, Meropenem, and Ciprofloxacin. Antibiotic susceptibility testing and Minimum Inhibitory Concentration (MIC) assays confirmed high-level resistance across multiple antibiotics, with partial reversal observed through dual disk diffusion synergy testing. Notably, a combination of Ceftazidime and Gentamicin demonstrated significant synergistic activity against isolate 87.

To understand the molecular basis of this synergy, key genes associated with antimicrobial resistance, oxidative stress, membrane permeability, and DNA repair—including gyrA, phoQ, soxS, ompF, rpoB, mutS, and sodB—were selected for future expression analysis via RT-qPCR. Bioinformatics tools such as Venn diagram overlap analysis and STRING interaction networks were employed to identify genes with dual roles in virulence and resistance. Results suggest that the synergistic effect may involve disruption of redox homeostasis and mismatch repair pathways, triggered by elevated reactive oxygen species and increased membrane permeability.

This integrative approach combining phenotypic assays, gene expression profiling, and computational analysis highlights the potential of strain-specific combination therapies in overcoming drug resistance. Future work will expand this analysis to additional clinical isolates and synergy pairs (Ceftazidime + Chloramphenicol, Norfloxacin, Vancomycin, and Erythromycin) to further validate predictive markers with RT-qPCR and optimize treatment strategies in resource-limited settings.