To investigate whether 16S-23S rDNA (rDNA) spacer region length polymorphisms are suitable for the identification of Staphylococcus strains, the 16S-23S rDNA intergenic spacer region lengths of 221 strains belonging to 31 species were studied by using a PCR-based method. Each species presented a specific 16S-23S pattern made of 1-8 fragments ranging from 104-771 bp, with the exception of the species Staphylococcus warnei, Staphylococcus caprae and Staphylococcus piscifermentans, which presented larger or smaller fragments. Very few species showed more than one pattern, Staphylococcus saprophyticus subsp. saprophyticus and Staphylococcus aureus being the most heterogeneous species (five different patterns for eight strains). Five clinical strains that could not be identified at the species level by phenotypical tests were finally identified using this method. Discrimination between some species that showed close patterns (Staphylococcus aureus/Staphylococcus chromogenes/Staphylococcus equorum, Staphylococcus aureus/staphylococcus intermedius, Staphylococcus delphini/Staphylococcus felis, Staphylococcus gallinarum, Staphylococcus delphini/Staphylococcus felis, Staphylococcus vitulus/Staphylococcus auricularis) was further achieved after Dral digestion of the PCR products. Although it does not allow discrimination of subspecies, the use of 16S-23S spacer region length data determined by PCR-mediated amplification is suitable for the identification of the 31 Staphylococcus species tested in this study. The method is rapid, easy and may be a useful tool for the identification of Staphylococcus species in the clinical microbiology laboratory.
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http://dx.doi.org/10.1099/00207713-48-3-1049 | DOI Listing |
Infect Prev Pract
March 2025
Central Department of Biotechnology, Tribhuvan University, Kirtipur, Nepal.
Introduction: Meticillin resistant (MRSA) is a major contributor to surgical site infections in post-operative patients. Hospital environments harbor MRSA, contributing to higher risk of nosocomial infections. Meticillin resistance is conferred by acquisition of gene, typically carried on mobile genetic element called Staphylococcal Cassette Chromosome (SCC).
View Article and Find Full Text PDF<b>Background and Objective:</b> Peatlands are unique ecosystems rich in microbial diversity, including bacteria with potential antibiotic activity. This study focuses on the isolation and characterization of bacteria from Indonesian peat soil, particularly their potential to produce antibiotics against multidrug-resistant (MDR) pathogens, including Methicillin-Resistant <i>Staphylococcus aureus</i> (MRSA). <b>Materials and Methods:</b> Bacterial isolates were rejuvenated on nutrient agar and subjected to antimicrobial activity testing using the Bauer & Kirby diffusion method against MRSA.
View Article and Find Full Text PDFAnal Chem
January 2025
Institute of Analytical Chemistry, Chemo- and Biosensors, University of Regensburg, Universitaetsstr. 31, Regensburg 93053, Germany.
To ensure high quality of food and water, the identification of traces of pathogens is mandatory. Rapid nucleic acid-based tests shorten traditional detection times while maintaining low detection limits. Challenging is the loss of nucleic acids during necessary purification processes, since elution off solid surfaces is not efficient.
View Article and Find Full Text PDFBiofilm
June 2025
Centre of Biological Engineering, LIBRO - Laboratório de Investigação em Biofilmes Rosário Oliveira, University of Minho, Campus de Gualtar, Braga, 4710-057, Portugal.
Bacterial biofilms formed by and pose significant challenges in treating cystic fibrosis (CF) airway infections due to their resistance to antibiotics. New therapeutic approaches are urgently needed to treat these chronic infections. This study aimed to investigate the antibiofilm potential of various plant extracts, specifically targeting mucoid and small colony variants of and and strains.
View Article and Find Full Text PDFAnal Chem
January 2025
School of Pharmaceutical Sciences, Wenzhou Medical University, Wenzhou 325035, China.
Label-free surface-enhanced Raman spectroscopy (SERS) combined with machine learning (ML) techniques presents a promising approach for rapid pathogen identification. Previous studies have demonstrated that purine degradation metabolites are the primary contributors to SERS spectra; however, generating these distinguishable spectra typically requires a long incubation time (>10 h) at room temperature. Moreover, the lack of attention to spectral variations between strains of the same bacterial species has limited the generalizability of ML models in real-world applications.
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