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An improved MALDI-TOF MS data analysis pipeline for the identification of carbapenemase-producing Klebsiella pneumoniae

ABSTRACT: An improved MALDI-TOF MS data analysis pipeline for the identification of carbapenemase-producing Klebsiella pneumoniae

PROVIDER: PRJEB43543 | ENA |

REPOSITORIES: ENA

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Dataset's files

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			Action	DRS
	ERR5456470_1.fastq.gz	Fastqsanger.gz
	ERR5456470_2.fastq.gz	Fastqsanger.gz
	ERR5456480_1.fastq.gz	Fastqsanger.gz
	ERR5456480_2.fastq.gz	Fastqsanger.gz
	ERR5456471_1.fastq.gz	Fastqsanger.gz

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Project description:The spread of carbapenemase-producing Enterobacterales (CPE) is emerging as a significant clinical concern in tertiary hospitals and in particular, long-term care facilities with deficiencies in infection control. This study aims to evaluate an advanced matrix-assisted laser desorption/ionization mass spectrometry (A-MALDI) method for the identification of carbapenemases and further discrimination of their subtypes in clinical isolates. The A-MALDI method was employed to detect CPE target proteins. Enhancements were made to improve detectability and mass accuracy through the optimization of MALDI-TOF settings and internal mass calibration. A total of 581 clinical isolates were analyzed, including 469 CPE isolates (388 KPC, 51 NDM, 40 OXA, and 2 GES) and 112 carbapenemase-negative isolates. Clinical evaluation of the A-MALDI demonstrated 100% accuracy and precision in identifying all the collected CPE isolates. Additionally, A-MALDI successfully discriminated individual carbapenemase subtypes (KPC-2 or KPC-3/4; OXA-48 or OXA-181 or OXA-232; GES-5 or GES-24) and also differentiated co-producing carbapenemase strains (KPC & NDM; KPC & OXA; KPC & GES; NDM & OXA), attributed to its high mass accuracy and simultaneous detection capability. A-MALDI is considered a valuable diagnostic tool for accurately identifying CPE and carbapenemase’s subtypes in clinical isolates. It may also aid in selecting appropriate antibiotics for each carbapenemase subtype. Ultimately, we expect that the A-MALDI method will contribute to preventing the spread of antibiotic resistance and improving human public health.

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