Dataset Information

Novel physico-chemical diagnostic tools for high throughput identification of bovine mastitis associated gram-positive, catalase-negative cocci.

ABSTRACT:

Background

The routine diagnosis of Streptococcus spp. and other mastitis associated gram-positive, catalase-negative cocci is still based upon biochemical tests and serological methods, which frequently provide ambiguous identification results. We therefore aimed to establish an accurate identification system for differential diagnosis of mastitis associated Streptococcus spp. and related species using biophysical techniques such as Fourier-transform infrared (FTIR) spectroscopy and MALDI - TOF/MS.

Results

Based on a panel of 210 isolates from cases of bovine mastitis, an unsupervised FTIR spectral reference library was established and an artificial neural network (ANN) - assisted identification system was developed. All bacterial isolates were previously identified by species-specific PCR and/or 16S rRNA gene sequence analysis. An overall identification rate of 100% at species level for 173 strains unknown to the ANN and the library was achieved by combining ANN and the spectral database, thus demonstrating the suitability of our FTIR identification system for routine diagnosis. In addition, we investigated the potential of matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF MS) for the identification of mastitis associated Streptococcus spp. and related bacteria. Using the Microflex LT System, MALDI Biotyper software™ (V3.3) we achieved an accuracy rate of 95.2%. A blind study, including 21 clinical samples from dairy cows, revealed a 100% correct species identification rate for FTIR and 90.5% for MALDI-TOF MS, indicating that these techniques are valuable tools for diagnosis.

Conclusions

This study clearly demonstrates that FTIR spectroscopy as well as MALDI-TOF MS can significantly improve and facilitate the identification and differentiation of mastitis associated Streptococcus spp. and related species. Although the FTIR identification system turned out being slightly superior to MALDI-TOF MS in terms of identification on species level, both methods offer interesting alternatives to conventional methods currently used in mastitis diagnosis as both of them provide high accuracy at low operating costs once the instrument is acquired.

SUBMITTER: Schabauer L

PROVIDER: S-EPMC4105049 | biostudies-literature | 2014 Jul

REPOSITORIES: biostudies-literature

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Publications

Novel physico-chemical diagnostic tools for high throughput identification of bovine mastitis associated gram-positive, catalase-negative cocci.

Schabauer Lydia L Wenning Mareike M Huber Ingrid I Ehling-Schulz Monika M

BMC veterinary research 20140711

<h4>Background</h4>The routine diagnosis of Streptococcus spp. and other mastitis associated gram-positive, catalase-negative cocci is still based upon biochemical tests and serological methods, which frequently provide ambiguous identification results. We therefore aimed to establish an accurate identification system for differential diagnosis of mastitis associated Streptococcus spp. and related species using biophysical techniques such as Fourier-transform infrared (FTIR) spectroscopy and MAL ...[more]

PMID: 25015262

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Project description:Simple Summary Monitoring health status and disease outbreak among food animal herds is vitally important to global food safety. Affected animals will experience production losses, and in uncurable cases, operations will need to be modified or animals culled. Producers and veterinary personnel have consistent interest in new diagnostic tools to provide rapid, accurate, and simple testing strategies which do not undermine financial viability of the operation. This manuscript describes a new approach for diagnosis of mastitis in dairy animals. The new method involves first analyzing raw milk from animals by matrix-assisted laser desorption/ionization mass spectrometry and collecting mass spectra. Then, peaks from the mass spectra are imported into a machine learning model, and this software application discovers non-obvious patterns present in the data which coincide with the mastitis condition. Finally, a separate set of milk samples is analyzed (scoring set) to evaluate the diagnostic accuracy of the new model. Results suggest that certain machine learning models offer value to the producer for diagnosis of subclinical mastitis in dairy cows. More generally, the manuscript outlines use of the machine learning approach for diagnoses of animal disease, and we prophesize that this strategy may be applicable to a wide array of animal health concerns. Abstract Novel strategies for diagnostic screening of animal and herd health are crucial to contain disease outbreaks, maintain animal health, and maximize production efficiency. Mastitis is an inflammation of the mammary gland in dairy cows, often resulting from infection from a microorganism. Mastitis outbreaks result in loss of production, degradation of milk quality, and the need to isolate and treat affected animals. In this work, we evaluate MALDI-TOF mass spectrometry as a diagnostic for the culture-less screening of mastitis state from raw milk samples collected from regional dairies. Since sample preparation requires only minutes per sample using microvolumes of reagents and no cell culture, the technique is promising for rapid sample turnaround and low-cost diagnosis. Machine learning algorithms have been used to detect patterns embedded within MALDI-TOF spectra using a training set of 226 raw milk samples. A separate scoring set of 100 raw milk samples has been used to assess the specificity (spc) and sensitivity (sens) of the approach. Of machine learning models tested, the gradient-boosted tree model gave global optimal results, with the Youden index of J = 0.7, sens = 0.89, and spc = 0.81 achieved for the given set of conditions. Random forest models also performed well, achieving J > 0.63, with sens = 0.83 and spc = 0.81. Naïve Bayes, generalized linear, fast large-margin, and deep learning models failed to produce diagnostic results that were as favorable. We conclude that MALDI-TOF MS combined with machine learning is an alternative diagnostic tool for detection of high somatic cell count (SCC) and subclinical mastitis in dairy herds.

Project description:Highlights • SEM-based method can be applied for rapid phenotypic AST on Gram-positive cocci towards 2 vancomycin based on morphological changes 3.• The ratio of septa as a marker of bacterial division and size of grape-like clusters enabled the 4 profiling of E. faecalis, E. faecium and S. aureus after brief incubation with vancomycin 5.• SEM-AST strategy showed the feasibility of detecting antibiotic susceptibility or 6 resistance on Gram-positive cocci within one hour of exposition to vancomycin 7. The rapid detection of resistant bacteria has become a challenge for microbiologists worldwide. Numerous pathogens that cause nosocomial infections are still being treated empirically and have developed resistance mechanisms against key antibiotics. Thus, one of the challenges for researchers has been to develop rapid antimicrobial susceptibility testing (AST) to detect resistant isolates, ensuring better antibiotic stewardship. In this study, we established a proof-of-concept for a new strategy of phenotypic AST on Gram-positive cocci towards vancomycin using scanning electron microscopy (SEM). Our study evaluated the profiling of Enterococcus faecalis, Enterococcus faecium and Staphylococcus aureus after brief incubation with vancomycin. Sixteen isolates were analysed aiming to detect ultrastructural modifications at set timepoints, comparing bacteria with and without vancomycin. After optimising slides preparation and micrographs acquisition, two analytical strategies were used. The high magnification micrographs served to analyse the division of cocci based on the ratio of septa, along with the bacterial size. Susceptible strains with vancomycin showed a reduced septa percentage and the average surface area was consequently double that of the controls. The resistant bacteria revealed multiple septa occurring at advanced timepoints. Low magnification micrographs made it possible to quantify the pixels at different timepoints, confirming the profiling of cocci towards vancomycin. This new phenotypic AST strategy proved to be a promising tool to discriminate between resistant and susceptible cocci within an hour of contact with vancomycin. The analysis strategies applied here would potentially allow the creation of artificial intelligence algorithms for septa detection and bacterial quantification, subsequently creating a rapid automated SEM-AST assay. Graphical abstract Image, graphical abstract

Dataset Information

Novel physico-chemical diagnostic tools for high throughput identification of bovine mastitis associated gram-positive, catalase-negative cocci.

Background

Results

Conclusions

Publications

Novel physico-chemical diagnostic tools for high throughput identification of bovine mastitis associated gram-positive, catalase-negative cocci.

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