Evaluation of Toxin and Antitoxin System in Acinetobacter Multidrug Resistance Bacteria Isolated From Clinical Specimens
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Abstract:
Introduction: Acinetobacter baumannii is one of the most important nosocomial and community-acquired pathogens that is resistant to many antibiotics. Toxin-antitoxin systems are regulatory systems that maintain bacteria and serve as new targets for Antimicrobial therapies are considered. The prevalence and transcription of these systems in clinical isolates is still unknown. The aim of this study was to evaluate the toxin-antitoxin system (mazEF, relBE and higBA) in Acinetobacter baumannii with multidrug resistance isolated from clinical specimens. Methods: In this study, 60 isolates of Acinetobacter species were collected from 255 clinical specimens. Acinetobacter baumannii was identified by biochemical tests of oxidase, citrate, SIM and TSI. Antimicrobial susceptibility testing was performed by discard method and PCR method to identify genes in the toxin-antitoxin system (mazEF, relBE and higBA). Results: The highest resistance to ampicillin (98.3%) and the lowest resistance to colistin (35%). Resistance of more than 90% was observed in 12 antibiotics out of 15 antibiotics studied. Out of 60 isolates, 98.34% were resistant to more than 8 antibiotics and only one sample was sensitive to all. The mazEF, higBA, and relBE versions were present in approximately half of the Acinetobacter baumannii isolates studied. In all samples, more than 80% of the isolates containing each of the mazEF, higBA, and relBE genes were resistant to the antibiotics tested (with the exception of colistin, which was approximately 40%). The frequencies of mazEF, higBA and relBE genes were 24 (40%), 32 (53.33%), 35 (53.33%), respectively. 6 isolates (10%) were negative for all three genes. Conclusion: Significant antibiotic resistance was observed among the isolates. Based on the presence of TA systems in half of A. baumannii isolates, these could be used as a novel target for antimicrobial therapy.
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Journal title
volume 11 issue 3
pages 349- 364
publication date 2022-07
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