Rapid detection of antibiotic resistance in Burkholderia pseudomallei using MALDI-TOF mass spectrometry

Rapid detection of antibiotic resistance in Burkholderia pseudomallei using MALDI-TOF mass spectrometry

Abstract Antibiotic resistance in Burkholderia pseudomallei (Bp) is a growing public health concern requiring urgent attention. Matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF MS) has emerged as a rapid bacterial identification tool with potential for future d...

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Main Authors: Nut Nithimongkolchai, Yothin Hinwan, Kanwara Trisakul, Lumyai Wonglakorn, Ploenchan Chetchotisakd, Auttawit Sirichoat, Arnone Nithichanon, Sorujsiri Chareonsudjai, Pisit Chareonsudjai, Jody Phelan, Taane G. Clark, Kiatichai Faksri
Format: Article
Language:English
Published: Nature Portfolio 2025-07-01
Series:Scientific Reports
Subjects:
MALDI-TOF MS
Machine learning
Burkholderia pseudomallei
Minimum inhibitory concentration
Antibiotic resistance
Geographical distribution
Online Access:https://doi.org/10.1038/s41598-025-07687-y
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Summary:Abstract Antibiotic resistance in Burkholderia pseudomallei (Bp) is a growing public health concern requiring urgent attention. Matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF MS) has emerged as a rapid bacterial identification tool with potential for future diagnostic applications. This study investigates differences in MALDI-TOF spectra of antibiotic-resistant Bp strains from northeastern Thailand without any pretreatment steps. A total of 300 Bp isolates were tested for minimum inhibitory concentration (MIC) values against five antibiotics: ceftazidime, meropenem, imipenem, co-amoxiclav, and co-trimoxazole. MALDI-TOF spectra were obtained using the direct colony method and analysed for correlations with antibiotic resistance patterns. Thirteen isolates exhibited resistance based on CLSI breakpoints, with most showing low-level MICs across all tested antibiotics. MALDI-TOF MS analysis revealed clustering patterns associated with resistance. A decision tree algorithm identified ten significant peaks that effectively distinguished resistant isolates. This study highlights MALDI-TOF MS as a rapid and reliable tool for detecting antibiotic resistance, offering timely results to support treatment decisions and streamline microbiology workflows.
ISSN:2045-2322

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