Development and Validation of a Custom Database for Identifying Methicillin-Resistant Staphylococcus aureus (MRSA) Using an Automated Bacterial Identification System with MALDI-TOF MS and a Rapid Protein Extraction Method
Keywords:
ClinProTools , Flex Analysis , MALDI-TOF MS, MRSA, Rapid Protein Extraction MethodAbstract
Methicillin-resistant Staphylococcus aureus (MRSA) poses a critical public health as a predominant agent of hospital-acquired infections, often leading to life-threatening conditions. Therefore, rapid identification of MRSA is crucial for effective treatment and reducing patient mortality. The MALDI-TOF MS (Matrix-Assisted Laser Desorption/Ionization Time-of-Flight Mass Spectrometry), an automated bacterial classification system, is increasingly utilized in clinical settings due to its high reliability and rapid processing time of 2-3 minutes. However, the existing database currently lacks the capacity to discriminate between antibiotic-resistant and non-resistant strains. This study aimed to develop a specialized database for MRSA identification using a rapid protein extraction method with the MALDI-TOF MS system. MRSA and methicillin-susceptible Staphylococcus aureus (MSSA) strains, confirmed by a standard method, were analyzed to identify peptide mass profiles distinct to each strain. Analysis using Flex Analysis and ClinProTools software revealed five significant peptide masses—3041.07, 4310.12, 4857.34, 5527.09, and 6553.92—capable of distinguishing MRSA from MSSA (p<0.0001). These unique peptide profiles were integrated into the MALDI-TOF MS system’s database, which was then validated using patient-derived bacterial samples pre-identified by standard method, achieving 100% accuracy in MRSA versus MSSA differentiation. This study highlights a practical implementation in rapid protein extraction and automated bacterial classification, offering enhanced efficiency and reduced turnaround time for drug-resistant bacterial strain identification in clinical microbiology.
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