Metabolic response of Klebsiella oxytoca to ciprofloxacin exposure: a metabolomics approach

产酸克雷伯菌对环丙沙星暴露的代谢反应:代谢组学方法

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作者:Shwan Ahmed ,Sahand Shams ,Dakshat Trivedi ,Cassio Lima ,Rachel McGalliard ,Christopher M Parry ,Enitan D Carrol ,Howbeer Muhamadali ,Royston Goodacre

Abstract

Introduction: Rapid detection and identification of pathogens and antimicrobial susceptibility is essential for guiding appropriate antimicrobial therapy and reducing morbidity and mortality associated with sepsis. Objectives: The metabolic response of clinical isolates of Klebsiella oxytoca exposed to different concentrations of ciprofloxacin (the second generation of quinolones antibiotics) were studied in order to investigate underlying mechanisms associated with antimicrobial resistance (AMR). Methods: Metabolomics investigations were performed using Fourier-transform infrared (FT-IR) spectroscopy as a metabolic fingerprinting approach combined with gas chromatography-mass spectrometry (GC-MS) for metabolic profiling. Results: Our findings demonstrated that metabolic fingerprints provided by FT-IR analysis allowed for the differentiation of susceptible and resistant isolates. GC-MS analysis validated these findings, while also providing a deeper understanding of the metabolic alterations caused by exposure to ciprofloxacin. GC-MS metabolic profiling detected 176 metabolic features in the cellular extracts cultivated on BHI broth, and of these, 137 could be identified to Metabolomics Standards Initiative Level 2. Data analysis showed that 40 metabolites (30 Level 2 and 10 unknown) were differentiated between susceptible and resistant isolates. The identified metabolites belonging to central carbon metabolism; arginine and proline metabolism; alanine, aspartate and glutamate metabolism; and pyruvate metabolism. Univariate receiver operating characteristic (ROC) curve analyses revealed that six of these metabolites (glycerol-3-phosphate, O-phosphoethanolamine, asparagine dehydrate, maleimide, tyrosine, and alanine) have a crucial role in distinguishing susceptible from resistant isolates (AUC > 0.84) and contributing to antimicrobial resistance in K. oxtytoca. Conclusion: Our study provides invaluable new insights into the mechanisms underlying development of antimicrobial resistance in K. oxytoca suggests potential therapeutic targets for prevention and identification of AMR in K. oxytoca infections. Keywords: Klebsiella oxytoca; FT-IR; GC-MS antimicrobial resistance; Metabolic fingerprinting; Metabolic profiling; Metabolomics; Sepsis.

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