Dataset Information

Detection of methicillin-resistant Staphylococcus aureus using phage amplification combined with matrix-assisted laser desorption/ionization mass spectrometry.

ABSTRACT: Antibiotic resistance continues to contribute significantly to morbidity and mortality across the world. Developing new tests for antibiotic-resistant bacteria is a core action to combat resistant infections. We describe a method that uses phage amplification detection (PAD) combined with matrix-assisted laser desorption/ionization mass spectrometry (MALDI-MS) to rapidly identify Staphylococcus aureus and determine phenotypic susceptibility to cefoxitin. Samples tested for S. aureus are incubated together with bacteriophage in the presence and absence of cefoxitin and subjected to rapid trypsin digestion followed by MALDI-MS analysis. Tryptic peptides derived from amplified phage proteins can be detected by MALDI-MS, as validated by time-of-flight (TOF)/TOF analysis of each peptide combined with database searching. Methicillin-resistant S. aureus show significant phage amplification in the presence of cefoxitin, while methicillin-sensitive S. aureus show no phage amplification relative to a no-antibiotic control. We also show that PAD methodology can be implemented on an FDA-approved commercial MALDI-MS bacterial identification system to identify S. aureus and determine antibiotic susceptibility. The novelty of this assay includes the use of phage-derived tryptic peptides as detected by MALDI-MS to monitor the results of PAD on an instrument common to many modern microbiology laboratories.

SUBMITTER: Rees JC

PROVIDER: S-EPMC5258805 | biostudies-literature | 2017 Feb

REPOSITORIES: biostudies-literature

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Detection of methicillin-resistant Staphylococcus aureus using phage amplification combined with matrix-assisted laser desorption/ionization mass spectrometry.

Rees Jon C JC Barr John R JR

Analytical and bioanalytical chemistry 20161119 5

Antibiotic resistance continues to contribute significantly to morbidity and mortality across the world. Developing new tests for antibiotic-resistant bacteria is a core action to combat resistant infections. We describe a method that uses phage amplification detection (PAD) combined with matrix-assisted laser desorption/ionization mass spectrometry (MALDI-MS) to rapidly identify Staphylococcus aureus and determine phenotypic susceptibility to cefoxitin. Samples tested for S. aureus are incubate ...[more]

PMID: 27866257

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Project description:Recent studies have demonstrated that the matrix-assisted laser desorption ionization-time of flight mass spectrometry (MALDI-TOF MS) could be used to detect superbugs, such as methicillin-resistant Staphylococcus aureus (MRSA). Due to an increasingly clinical need to classify between MRSA and methicillin-sensitive Staphylococcus aureus (MSSA) efficiently and effectively, we were motivated to develop a systematic pipeline based on a large-scale dataset of MS spectra. However, the shifting problem of peaks in MS spectra induced a low effectiveness in the classification between MRSA and MSSA isolates. Unlike previous works emphasizing on specific peaks, this study employs a binning method to cluster MS shifting ions into several representative peaks. A variety of bin sizes were evaluated to coalesce drifted or shifted MS peaks to a well-defined structured data. Then, various machine learning methods were performed to carry out the classification between MRSA and MSSA samples. Totally 4858 MS spectra of unique S. aureus isolates, including 2500 MRSA and 2358 MSSA instances, were collected by Chang Gung Memorial Hospitals, at Linkou and Kaohsiung branches, Taiwan. Based on the evaluation of Pearson correlation coefficients and the strategy of forward feature selection, a total of 200 peaks (with the bin size of 10 Da) were identified as the marker attributes for the construction of predictive models. These selected peaks, such as bins 2410-2419, 2450-2459 and 6590-6599 Da, have indicated remarkable differences between MRSA and MSSA, which were effective in the prediction of MRSA. The independent testing has revealed that the random forest model can provide a promising prediction with the area under the receiver operating characteristic curve (AUC) at 0.8450. When comparing to previous works conducted with hundreds of MS spectra, the proposed scheme demonstrates that incorporating machine learning method with a large-scale dataset of clinical MS spectra may be a feasible means for clinical physicians on the administration of correct antibiotics in shorter turn-around-time, which could reduce mortality, avoid drug resistance and shorten length of stay in hospital in the future.

Dataset Information

Detection of methicillin-resistant Staphylococcus aureus using phage amplification combined with matrix-assisted laser desorption/ionization mass spectrometry.

Publications

Detection of methicillin-resistant Staphylococcus aureus using phage amplification combined with matrix-assisted laser desorption/ionization mass spectrometry.

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