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Molecular typing of clinical multidrug-resistant Klebsiella pneumoniae isolates

  • Mehdi Kashefieh,
  • Habib Zeighami,
  • Hossein Samadi Kafil,
  • Pourya Gholizadeh,
  • Javid Sadeghi,
  • Mohammad Hossein Soroush Barhaghi,
  • Hamed Ebrahimzadeh Leylabadlo,
  • Reza Ghotaslou

摘要

Introduction

Klebsiella pneumoniae is an opportunistic pathogen which is an important cause of hospital-acquired and antibiotic resistance infections. Therefore, this study aimed to determine the frequency of resistance to antibiotics, as well as the molecular typing of the associated isolates, and compare multiple-locus VNTR analysis (MLVA) and Enterobacterial Repetitive Intergenic Consensus-Polymerase Chain Reaction (ERIC-PCR) methods to specify the degree to which distinctions can be separated from each other.

Methods and materials

One hundred K. pneumoniae isolates were obtained from different sources of infections from patients admitted to hospitals. Antibiotic susceptibility testing was then performed by applying the Kirby–Bauer disk diffusion method. Typing of K. pneumoniae was done by utilizing MLVA and ERIC-PCR methods.

Results

Eighty-six multidrug-resistant (MDR) K. pneumoniae isolates were identified, which resistance to ampicillin, trimethoprim/sulfamethoxazole, and ceftriaxone was the most frequent in the considered isolates (100, 93, and 93%, respectively). A total of 50 different antibiotic susceptibility patterns were observed among the MDR K. pneumonia, with the most frequent pattern being resistance to all antibiotics (12.79%) and resistance to all antibiotics except amikacin (10.47%). The isolates were then divided into 37 different MLVA types and seven clonal complexes were obtained from the minimum spanning tree analysis. Finally, the isolates were assigned to 38 different ERIC types. The discriminatory power of MLVA and ERIC methods also showed a value of 0.958, and 0.974.

Conclusion

Both PCR-typing methods with phenotypic patterns can be useful for the epidemiological typing of K. pneumoniae isolates with the highest performance in discriminating isolates.