Dataset Information

Evaluation of Staphylococcus aureus Subtyping Module for Methicillin-Resistant Staphylococcus aureus Detection Based on Matrix-Assisted Laser Desorption Ionization Time-of-Flight Mass Spectrometry.

ABSTRACT: A recently developed S. aureus subtyping module for rapidly differentiate methicillin-resistant Staphylococcus aureus (MRSA) from methicillin-susceptible S. aureus (MSSA) had been introduced into China. The principle of this method was to identify the methicillin resistance through detection of a specific phenol soluble modulin-mec peak (PSM-mec) by matrix-assisted laser desorption/ionization time of flight mass spectrometry (MALDI-TOF MS). A total of 347 non-duplicated S. aureus strains were collected from the Second Affiliated Hospital of Zhejiang University School of Medicine during January 2014 to February 2019. The sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) of the automated subtyping module in identifying MRSA were evaluated. The specificity and PPV of this method were both 100%, and the sensitivity was 60.2%. PSM-bearing MRSA was reported with different prevalence from different parts of the world, our sample collection has the highest percentage so far. The repeatability showed that 1.7% (6/347) and 18.4% (64/347) were reported differently in the intra- and inter-batch analysis, respectively, which demonstrated that the threshold of this method could be further optimized to increase the sensitivity of MRSA detection. Overall, Bruker^™ MALDI Biotyper can detect S. aureus isolates with a quite high specificity and expedite the identification of MRSA isolates without using extra reagent, labor, or time. The reduced turnaround time of MRSA identification is essential for appropriate therapeutic management and timely intervention for infection control.

SUBMITTER: Hu Y

PROVIDER: S-EPMC6834645 | biostudies-literature | 2019

REPOSITORIES: biostudies-literature

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Evaluation of Staphylococcus aureus Subtyping Module for Methicillin-Resistant Staphylococcus aureus Detection Based on Matrix-Assisted Laser Desorption Ionization Time-of-Flight Mass Spectrometry.

Hu Yanyan Y Huang Yonglu Y Lizou Yangzi Y Li Jiaping J Zhang Rong R

Frontiers in microbiology 20191031

A recently developed S. aureus subtyping module for rapidly differentiate methicillin-resistant Staphylococcus aureus (MRSA) from methicillin-susceptible S. aureus (MSSA) had been introduced into China. The principle of this method was to identify the methicillin resistance through detection of a specific phenol soluble modulin-mec peak (PSM-mec) by matrix-assisted laser desorption/ionization time of flight mass spectrometry (MALDI-TOF MS). A total of 347 non-duplicated S ...[more]

PMID: 31736927

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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.

Project description:Streptococcus suis, particularly S. suis serotype 2 (SS2), is an important zoonotic pathogen causing meningitis in humans worldwide. Although the proper classification of the causative and pathogenic serotype is salutary for the clinical diagnosis, cross-reactions leading to the indistinguishability of serotypes by the current serotyping methods are significant limitations. In the present study, matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF-MS) analysis of extracted peptides was developed to improve the classification of serotype of S. suis. The peptide mass fingerprint (PMFs) database of S. suis was generated from the whole-cell peptides of 32 reference strains of S. suis isolates obtained from pigs. Thirty-two human S. suis isolates from clinical cases in Thailand were used to validate this alternative serotyping method in direct comparison to the multiplex (m)PCR approach. All reference strains, representing 32 serotypes of S. suis, exhibited their individual PMFs patterns, thus allowing differentiation from one another. Highly pathogenic SS2 and SS14 were clearly differentiated from the otherwise serologically closely related SS1/2 and SS1, respectively. The developed MALDI-TOF-MS serotyping method correctly classified the serotype in 68.8% (22/32) of the same serotype isolates generated from the PMFs database; while the validity for the clinical human isolates was 62.5% (20/32). The agreement between the MALDI-TOF-MS and mPCR serotyping was moderate with a Kappa score of 0.522, considering that mPCR could correctly serotype up to 75%. The present study demonstrated that PMFs from the developed MALDI-TOF-MS-based method could successfully discriminate the previously indistinguishable highly pathogenic SS2 and SS14 from SS1/2 and SS1, respectively. Moreover, this serotyping method distinguished pathogenic SS6, and so is an alternative approach of choice to rapidly and reliably serotype clinically pathogenic S. suis isolates.

Dataset Information

Evaluation of Staphylococcus aureus Subtyping Module for Methicillin-Resistant Staphylococcus aureus Detection Based on Matrix-Assisted Laser Desorption Ionization Time-of-Flight Mass Spectrometry.

Publications

Evaluation of <i>Staphylococcus aureus</i> Subtyping Module for Methicillin-Resistant <i>Staphylococcus aureus</i> Detection Based on Matrix-Assisted Laser Desorption Ionization Time-of-Flight Mass Spectrometry.

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