Molecular characterization of carbapenem-resistance in Gram-negative isolates obtained from clinical samples at Jimma Medical Center, Ethiopia

Background: In resource-constrained settings, limited antibiotic options make treating carbapenem-resistant bacterial infections difficult for healthcare providers. This study aimed to assess carbapenemase expression in Gram-negative bacteria isolated from clinical samples in Jimma, Ethiopia. Methods: A cross-sectional study was conducted to assess carbapenemase expression in Gram-negative bacteria isolated from patients attending Jimma Medical Center. Totally, 846 Gram-negative bacteria were isolated and identified using matrix-assisted laser desorption ionization-time of flight mass spectrometry (MALDI-TOF MS). Phenotypic antibiotic resistance patterns were determined using the Kirby-Bauer disk diffusion method and Etest strips. Extended-spectrum β-lactamase phenotype was determined using MAST disks, and carbapenemases were characterized using multiplex polymerase chain reactions (PCR). Results: Among the isolates, 19% (157/846) showed phenotypic resistance to carbapenem antibiotics. PCR analysis revealed that at least one carbapenemase gene was detected in 69% (107/155) of these strains. The most frequently detected acquired genes were blaNDM in 35% (37/107), blaVIM in 24% (26/107), and blaKPC42 in 13% (14/107) of the isolates. Coexistence of two or more acquired genes was observed in 31% (33/107) of the isolates. The most common coexisting acquired genes were blaNDM + blaOXA-23, detected in 24% (8/33) of these isolates. No carbapenemase-encoding genes could be detected in 31% (48/155) of carbapenem-resistant isolates, with P. aeruginosa accounting for 85% (41/48) thereof. Conclusion: This study revealed high and incremental rates of carbapenem-resistant bacteria in clinical samples with various carbapenemase-encoding genes. This imposes a severe challenge to effective patient care in the context of already limited treatment options against Gram-negative bacterial infections in resource-constrained settings.