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Updated: Mar 20, 2026

Heuristic Mining of Hierarchical Genotypes and Accessory Genome Loci in Bacterial Populations
Published on: December 7, 2021
Relationship between super antigenicity, antimicrobial resistance and origin of Staphylococcus aureus isolated
Luisa Fernanda Corredor Arias1, Jenna Samara Luligo Espinal2, José Ignacio Moncayo Ortiz2
1Montana State University. Montana USA.
Introduction:
Staphylococcus aureus is a pathogen that causes food poisoning as well as hospital and community acquired infections.
Objective:
Establish the profile of superantigen genes among hospital isolates in relation to clinical specimen type, susceptibility to antibiotics and hospital or community acquisition.
Methods:
Eighty one isolates obtained from patients at Colombian hospital, were classified by antimicrobial susceptibility, specimen type and hospital or community acquired . The PCR uniplex and multiplex was used for detection of 22 superantigen genes (18 enterotoxins, tsst-1 and three exfoliative toxins).
Results:
Ninety five point one percent of isolates harbored one or more of the genes with an average of 5.6 genes. Prevalence of individual genes was variable and the most prevalent was seg (51.9%). Thirty nine genotypes were obtained, and the genotype gimnou (complete egc cluster) was the most prevalent alone (16.0%) and in association with other genes (13.6%). The correlation between presence of superantigens and clinical specimen or antimicrobial susceptibility showed no significant difference. But there was significant difference between presence of superantigens and the origin of the isolates, hospital or community acquired (p= 0.049).
Conclusions:
The results show the variability of the superantigen genes profile in hospital isolates and shows no conclusive relationship with the clinical sample type and antimicrobial susceptibility, but there was correlation with community and hospital isolates. The analysis of the interplay between virulence, epidemic and antibiotic resistance of bacterial populations is needed to predict the future of infectious diseases.
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