Dataset Information

Development of a flow chart for identification of gram-positive anaerobic cocci in the clinical laboratory.

ABSTRACT: Gram-positive anaerobic cocci (GPAC) are a heterogeneous group of organisms that are isolated from clinical specimens more often than any group of anaerobic bacteria except Bacteroides species, yet many strains are still difficult or impossible to identify in the diagnostic laboratory. In this study, a total of 124 strains, including 13 reference strains of GPAC species and 111 isolates that had been recovered from clinical specimens previously and identified by 16S rRNA gene sequencing, were subjected to biochemical characterization. Based on the results, a short biochemical scheme that involves the minimum essential biochemical tests for accurate identification of clinically important GPAC was developed.

SUBMITTER: Song Y

PROVIDER: S-EPMC1829041 | biostudies-literature | 2007 Feb

REPOSITORIES: biostudies-literature

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Development of a flow chart for identification of gram-positive anaerobic cocci in the clinical laboratory.

Song Yuli Y Liu Chengxu C Finegold Sydney M SM

Journal of clinical microbiology 20061129 2

Gram-positive anaerobic cocci (GPAC) are a heterogeneous group of organisms that are isolated from clinical specimens more often than any group of anaerobic bacteria except Bacteroides species, yet many strains are still difficult or impossible to identify in the diagnostic laboratory. In this study, a total of 124 strains, including 13 reference strains of GPAC species and 111 isolates that had been recovered from clinical specimens previously and identified by 16S rRNA gene sequencing, were su ...[more]

PMID: 17135439

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Project description:BackgroundThe 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.ResultsBased 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.ConclusionsThis 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.

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

Development of a flow chart for identification of gram-positive anaerobic cocci in the clinical laboratory.

Publications

Development of a flow chart for identification of gram-positive anaerobic cocci in the clinical laboratory.

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