Rapid detection of antimicrobial resistance in methicillin-resistant Staphylococcus aureus using MALDI-TOF mass spectrometry
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ABSTRACT:
SUBMITTER: Rensner J
PROVIDER: S-EPMC10702551 | biostudies-literature | 2023 Jan
REPOSITORIES: biostudies-literature
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Project description:Methicillin-resistant Staphylococcus aureus (MRSA) is a serious pathogen in clinical settings and early detection is critical. Here, we investigated the MRSA discrimination potential of matrix-assisted laser desorption ionization time-of-flight mass spectrometry (MALDI-TOF MS) using 320 clinical S. aureus isolates obtained in 2005-2014 and 181 isolates obtained in 2018. We conducted polymerase chain reactions (PCR) for staphylococcal cassette chromosome mec (SCCmec) typing and MALDI-TOF MS to find specific markers for methicillin resistance. We identified 21 peaks with significant differences between MRSA and methicillin-susceptible S. aureus (MSSA), as determined by mecA and SCCmec types. Each specific peak was sufficient to discriminate MRSA. We developed two methods for simple discrimination according to these peaks. First, a decision tree for MRSA based on six MRSA-specific peaks, three MSSA-specific peaks, and two SCCmec type IV peaks showed a sensitivity of 96.5%. Second, simple discrimination based on four MRSA-specific peaks and one MSSA peak had a maximum sensitivity of 88.3%. The decision tree applied to 181 S. aureus isolates from 2018 had a sensitivity of 87.6%. In conclusion, we used specific peaks to develop sensitive MRSA identification methods. This rapid and easy MALDI-TOF MS approach can improve patient management.
| S-EPMC6963962 | biostudies-literature
Project description:Methicillin-resistant Staphylococcus aureus (MRSA) is a serious pathogen in clinical settings and early detection is critical. Here, we investigated the MRSA discrimination potential of matrix-assisted laser desorption ionization time-of-flight mass spectrometry (MALDI-TOF MS) using 320 clinical S. aureus isolates obtained in 2005-2014 and 181 isolates obtained in 2018. We conducted polymerase chain reactions (PCR) for staphylococcal cassette chromosome mec (SCCmec) typing and MALDI-TOF MS to find specific markers for methicillin resistance. We identified 21 peaks with significant differences between MRSA and methicillin-susceptible S. aureus (MSSA), as determined by mecA and SCCmec types. Each specific peak was sufficient to discriminate MRSA. We developed two methods for simple discrimination according to these peaks. First, a decision tree for MRSA based on six MRSA-specific peaks, three MSSA-specific peaks, and two SCCmec type IV peaks showed a sensitivity of 96.5%. Second, simple discrimination based on four MRSA-specific peaks and one MSSA peak had a maximum sensitivity of 88.3%. The decision tree applied to 181 S. aureus isolates from 2018 had a sensitivity of 87.6%. In conclusion, we used specific peaks to develop sensitive MRSA identification methods. This rapid and easy MALDI-TOF MS approach can improve patient management.
| S-EPMC6963962 | biostudies-literature
Project description:Early administration of proper antibiotics is considered to improve the clinical outcomes of Staphylococcus aureus bacteremia (SAB), but routine clinical antimicrobial susceptibility testing takes an additional 24 h after species identification. Recent studies elucidated matrix-assisted laser desorption/ionization time-of-flight mass spectra to discriminate methicillin-resistant strains (MRSA) or even incorporated with machine learning (ML) techniques. However, no universally applicable mass peaks were revealed, which means that the discrimination model might need to be established or calibrated by local strains' data. Here, a clinically feasible workflow was provided. We collected mass spectra from SAB patients over an 8-month duration and preprocessed by binning with reference peaks. Machine learning models were trained and tested by samples independently of the first six months and the following two months, respectively. The ML models were optimized by genetic algorithm (GA). The accuracy, sensitivity, specificity, and AUC of the independent testing of the best model, i.e., SVM, under the optimal parameters were 87%, 75%, 95%, and 87%, respectively. In summary, almost all resistant results were truly resistant, implying that physicians might escalate antibiotics for MRSA 24 h earlier. This report presents an attainable method for clinical laboratories to build an MRSA model and boost the performance using their local data.
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Project description:Rapidly identifying methicillin-resistant Staphylococcus aureus (MRSA) with high integration in the current workflow is critical in clinical practices. We proposed a matrix-assisted laser desorption/ionization-time-of-flight mass spectrometry (MALDI-TOF MS)-based machine learning model for rapid MRSA prediction. The model was evaluated on a prospective test and four external clinical sites. For the data set comprising 20,359 clinical isolates, the area under the receiver operating curve of the classification model was 0.78 to 0.88. These results were further interpreted using shapely additive explanations and presented using the pseudogel method. The important MRSA feature, m/z 6,590 to 6,599, was identified as a UPF0337 protein SACOL1680 with a lower binding affinity or no docking results compared with UPF0337 protein SA1452, which is mainly detected in methicillin-susceptible S. aureus. Our MALDI-TOF MS-based machine learning model for rapid MRSA identification can be easily integrated into the current clinical workflows and can further support physicians in prescribing proper antibiotic treatments. IMPORTANCE Over 20,000 clinical MSSA and MRSA isolates were collected to build a machine learning (ML) model to identify MSSA/MRSA and their markers. This model was tested across four external clinical sites to ensure the model's usability. We report the first discovery and validation of MRSA markers on the largest scale of clinical MSSA and MRSA isolates collected to date, covering five different clinical sites. Our developed approach for the rapid identification of MSSA and MRSA can be highly integrated into the current workflows.
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Project description:Food-borne methicillin resistant Staphylococcus aureus (MRSA) is involved in two-fold higher mortality rate compared to methicillin susceptible S. aureus (MSSA). Eventhough Mysuru recognized as cleanest city in the world, prevalence of food contamination is not detailed. The aim is to screen food samples from Mysuru area and to characterize MRSA strain, employing MALDI-Biotyper, multiplex PCR to distinguish between MRSA and MSSA by PCR-coupled single strand conformation polymorphism (PCR-SSCP). Of all the food-borne pathogens, S. aureus contamination accounts for 94.37 ± 0.02% (P < 0.01), strains characterized by means of nuc genes, followed by species specific identification by Coa, Eap and SpA genes and multiplex PCR to confirm the presence of three methicillin resistant staphylococcal species simultaneously using nuc and phoP genes. Amplification of mecA gene in 159 isolates confirmed that all strains are methicillin resistant, except UOM160 (MSSA) and multi-drug resistant (MDR) in 159 isolates confirmed by 22 sets of β-lactam antibiotics. MSSA and MDR-MRSA were discriminated by PCR-SSCP using nuc gene for the first time. From the present studies, compared to conventional methods MALDI-Biotyper emerged as an effective, sensitive (>99%), robust (<2 min), and alternative tool for pathogen identification, and we developed a PCR-SSCP technique for rapid detection of MSSA and MRSA strains.
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Project description:The aim of the present study was to detect the Staphylococcus aureus delta-toxin using Whole-Cell (WC) Matrix Assisted Laser Desorption Ionization-Time-of-Flight (MALDI-TOF) mass spectrometry (MS), correlate delta-toxin expression with accessory gene regulator (agr) status, and assess the prevalence of agr deficiency in clinical isolates with and without resistance to methicillin and glycopeptides. The position of the delta-toxin peak in the mass spectrum was identified using purified delta-toxin and isogenic wild type and mutant strains for agr-rnaIII, which encodes delta-toxin. Correlation between delta-toxin production and agr RNAIII expression was assessed by northern blotting. A series of 168 consecutive clinical isolates and 23 unrelated glycopeptide-intermediate S. aureus strains (GISA/heterogeneous GISA) were then tested by WC-MALDI-TOF MS. The delta-toxin peak was detected at 3005±5 Thomson, as expected for the naturally formylated delta toxin, or at 3035±5 Thomson for its G10S variant. Multivariate analysis showed that chronicity of S. aureus infection and glycopeptide resistance were significantly associated with delta-toxin deficiency (p?=?0.048; CI 95%: 1.01-10.24; p?=?0.023; CI 95%: 1.20-12.76, respectively). In conclusion, the S. aureus delta-toxin was identified in the WC-MALDI-TOF MS spectrum generated during routine identification procedures. Consequently, agr status can potentially predict infectious complications and rationalise application of novel virulence factor-based therapies.
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