Large-scale mass spectrometry data combined with demographics analysis rapidly predicts methicillin resistance in Staphylococcus aureus

Abstract Background A mass spectrometry-based assessment of methicillin resistance in Staphylococcus aureus would have huge potential in addressing fast and effective prediction of antibiotic resistance. Since delays in the traditional antibiotic susceptibility testing, methicillin-resistant S. aureus remains a serious threat to human health. Results Here, linking a 7 years of longitudinal study from two cohorts in the Taiwan area of over 20 000 individually resolved methicillin susceptibility testing results, we identify associations of methicillin resistance with the demographics and mass spectrometry data. When combined together, these connections allow for machine-learning-based predictions of methicillin resistance, with an area under the receiver operating characteristic curve of >0.85 in both the discovery [95% confidence interval (CI) 0.88–0.90] and replication (95% CI 0.84–0.86) populations. Conclusions Our predictive model facilitates early detection for methicillin resistance of patients with S. aureus infection. The large-scale antibiotic resistance study has unbiasedly highlighted putative candidates that could improve trials of treatment efficiency and inform on prescriptions.

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Imaging Mass Spectrometry-Based Proteomic Analysis to Differentiate Melanocytic Nevi and Malignant Melanoma

Cancers ◽

10.3390/cancers13133197 ◽

2021 ◽

Vol 13 (13) ◽

pp. 3197

Author(s):

Rita Casadonte ◽

Mark Kriegsmann ◽

Katharina Kriegsmann ◽

Isabella Hauk ◽

Rolf R. Meliß ◽

...

Keyword(s):

Mass Spectrometry ◽

Malignant Melanoma ◽

Operating Characteristic ◽

Characteristic Curve ◽

Imaging Mass Spectrometry ◽

Independent Set ◽

Molecular Techniques ◽

Charge Ratio ◽

Linear Discriminant ◽

Melanocytic Nevi

The discrimination of malignant melanoma from benign nevi may be difficult in some cases. For this reason, immunohistological and molecular techniques are included in the differential diagnostic toolbox for these lesions. These methods are time consuming when applied subsequently and, in some cases, no definitive diagnosis can be made. We studied both lesions by imaging mass spectrometry (IMS) in a large cohort (n = 203) to determine a different proteomic profile between cutaneous melanomas and melanocytic nevi. Sample preparation and instrument setting were tested to obtain optimal results in term of data quality and reproducibility. A proteomic signature was found by linear discriminant analysis to discern malignant melanoma from benign nevus (n = 113) with an overall accuracy of >98%. The prediction model was tested in an independent set (n = 90) reaching an overall accuracy of 93% in classifying melanoma from nevi. Statistical analysis of the IMS data revealed mass-to-charge ratio (m/z) peaks which varied significantly (Area under the receiver operating characteristic curve > 0.7) between the two tissue types. To our knowledge, this is the largest IMS study of cutaneous melanoma and nevi performed up to now. Our findings clearly show that discrimination of melanocytic nevi from melanoma is possible by IMS.

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Investigation of the antibiotic resistance of staphylococcus species isolated from foods

Élelmiszervizsgálati Közlemények ◽

10.52091/evik-2021/2-1-eng ◽

2021 ◽

Vol 67 (2) ◽

pp. 3372-3382

Author(s):

Brigitta Horváth ◽

Ferenc Peles ◽

Judit Gasparikné Reichardt ◽

Edit Pocklán ◽

Rita Sipos ◽

...

Keyword(s):

Staphylococcus Aureus ◽

Antibiotic Resistance ◽

Methicillin Resistance ◽

Multidrug Resistant ◽

Farm Animals ◽

The European Union ◽

Methicillin Resistant ◽

Maldi Tof Ms ◽

Maldi Tof ◽

Tof Ms

The presence of methicillin-resistant Staphylococcus aureus (MRSA) strains in the food chain has been confirmed by several studies in the European Union, but there are only limited data available in Hungary. The objective of the present study was to investigate the antibiotic resistance of Staphylococcus strains isolated from foods, using classical microbiological, molecular biological methods and the MALDI-TOF-MS technique, as well as the multi-locus sequence typing (MLST) of antibiotic resistant strains. During the study, 47 coagulase-positive (CPS) and 30 coagulase-negative (CNS) Staphylococcus isolates were collected. In the course of the MALDI-TOF-MS investigations, all CPS isolates (n=47) were found to be S. aureus species, while 8 different species were identified in the case of the CNS strains. Methicillin resistance was confirmed in two S. aureus strains, one of which had a sequence type not yet known, while the other MRSA strain was type ST398, which is the most common type of MRSA strain isolated from farm animals in the EU/EEA. (The abbreviation “MRSA” is often used in common parlance, but occasionally in the literature to denote “multidrug-resistant Staphylococcus aureus”. In the authors’ manuscript - the methicillin-resistant pathogen is correctly designated as such. Ed.)

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Increased antibiotic resistance of methicillin-resistance Staphylococcus aureus USA300 Δpsm mutants and a complementation study of Δpsm mutants using synthetic phenol soluble modulins

Journal of Microbiology and Biotechnology ◽

10.4014/jmb.2007.07034 ◽

2020 ◽

Author(s):

Hun-Suk Song ◽

Shashi Kant Bhatia ◽

Tae-Rim Choi ◽

Ranjit Gurav ◽

Hyun Joong Kim ◽

...

Keyword(s):

Staphylococcus Aureus ◽

Antibiotic Resistance ◽

Methicillin Resistance ◽

Complementation Study

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MSpectraAI: a powerful platform for deciphering proteome profiling of multi-tumor mass spectrometry data by using deep neural networks

BMC Bioinformatics ◽

10.1186/s12859-020-03783-0 ◽

2020 ◽

Vol 21 (1) ◽

Author(s):

Shisheng Wang ◽

Hongwen Zhu ◽

Hu Zhou ◽

Jingqiu Cheng ◽

Hao Yang

Keyword(s):

Mass Spectrometry ◽

Neural Networks ◽

Large Scale ◽

Deep Neural Networks ◽

Spectral Feature ◽

Mass Spectrometry Data ◽

Learning Approaches ◽

Proteomics Data ◽

Proteome Profiling ◽

Analytical Technique

Abstract Background Mass spectrometry (MS) has become a promising analytical technique to acquire proteomics information for the characterization of biological samples. Nevertheless, most studies focus on the final proteins identified through a suite of algorithms by using partial MS spectra to compare with the sequence database, while the pattern recognition and classification of raw mass-spectrometric data remain unresolved. Results We developed an open-source and comprehensive platform, named MSpectraAI, for analyzing large-scale MS data through deep neural networks (DNNs); this system involves spectral-feature swath extraction, classification, and visualization. Moreover, this platform allows users to create their own DNN model by using Keras. To evaluate this tool, we collected the publicly available proteomics datasets of six tumor types (a total of 7,997,805 mass spectra) from the ProteomeXchange consortium and classified the samples based on the spectra profiling. The results suggest that MSpectraAI can distinguish different types of samples based on the fingerprint spectrum and achieve better prediction accuracy in MS1 level (average 0.967). Conclusion This study deciphers proteome profiling of raw mass spectrometry data and broadens the promising application of the classification and prediction of proteomics data from multi-tumor samples using deep learning methods. MSpectraAI also shows a better performance compared to the other classical machine learning approaches.

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Validating the validation: reanalyzing a large-scale comparison of deep learning and machine learning models for bioactivity prediction

Journal of Computer-Aided Molecular Design ◽

10.1007/s10822-019-00274-0 ◽

2020 ◽

Vol 34 (7) ◽

pp. 717-730 ◽

Cited By ~ 9

Author(s):

Matthew C. Robinson ◽

Robert C. Glen ◽

Alpha A. Lee

Keyword(s):

Machine Learning ◽

Deep Learning ◽

Numerical Experiments ◽

Large Scale ◽

Operating Characteristic ◽

Characteristic Curve ◽

Learning Models ◽

Bioactivity Prediction ◽

Operating Characteristic Curve ◽

Machine Learning Models

Abstract Machine learning methods may have the potential to significantly accelerate drug discovery. However, the increasing rate of new methodological approaches being published in the literature raises the fundamental question of how models should be benchmarked and validated. We reanalyze the data generated by a recently published large-scale comparison of machine learning models for bioactivity prediction and arrive at a somewhat different conclusion. We show that the performance of support vector machines is competitive with that of deep learning methods. Additionally, using a series of numerical experiments, we question the relevance of area under the receiver operating characteristic curve as a metric in virtual screening. We further suggest that area under the precision–recall curve should be used in conjunction with the receiver operating characteristic curve. Our numerical experiments also highlight challenges in estimating the uncertainty in model performance via scaffold-split nested cross validation.

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Carriage Methicillin-Resistant Staphylococcus aureus in Poultry and Cattle in Northern Algeria

Veterinary Medicine International ◽

10.1155/2018/4636121 ◽

2018 ◽

Vol 2018 ◽

pp. 1-5 ◽

Cited By ~ 3

Author(s):

Saliha Bounar-Kechih ◽

Mossadak Taha Hamdi ◽

Hebib Aggad ◽

Nacima Meguenni ◽

Zafer Cantekin

Keyword(s):

Staphylococcus Aureus ◽

Antibiotic Resistance ◽

Broiler Chickens ◽

Methicillin Resistant Staphylococcus Aureus ◽

Laying Hens ◽

Methicillin Resistance ◽

Public Health Problem ◽

Cross Resistance ◽

Isolation And Identification ◽

Methicillin Resistant

Multiresistant and especially Methicillin-Resistant Staphylococcus aureus (MRSA) poses a serious public health problem that requires their immediate identification and antibiotic resistance characteristics. In order to determine antibiotic resistance S. aureus poultry and bovine origin, 8840 samples were collected from slaughterhouses in the northern region of Algeria between years 2009 and 2014. 8375 samples were from an avian origin (1875 from laying hens and 6500 from broiler chickens) and the rest was from bovine origin. Bacteriological isolation and identification were made by classical culture method and antibiotic resistance patterns were determined by disc diffusion test. The prevalence of S. aureus was 42% in laying hens, 12% in broilers, and 55% in bovine samples. The prevalence of MRSA was 57%, 50%, and 31% in laying hens, broiler chickens, and bovine, respectively. While MRSA strains isolated from poultry showed cross-resistance to aminoglycosides, fluoroquinolones, macrolides, sulphonamides, and cyclins, those isolated from bovine also revealed similar multiresistance except for sulphonamide. This high percentage of methicillin resistance and multidrug resistance in S. aureus poultry and bovine origin may have importance for human health and curing of human infections.

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