Analysis of phenotypic antibiotic resistance profile of Staphylococcus aureus from community settings of a university campus

Background: The multivariate analysis distinguishes components or factors and establishes associations among antibiotics based on their different levels of correlation. Objectives: A dendrogram analysis utilizing the clustering algorithm and comparative multivariate analysis on the Minimum Inhibitory Concentration (MICs) of eleven antibiotics, including the vancomycin, was performed. Methods: A sum of 37 phenotypic resistance profile of S. aureus [10 methicillin-resistant S. aureus (MRSA)], isolated from various community settings of university campuses including Guru Angad Dev Veterinary and Animal Sciences University (GADVASU) and Punjab Agriculture University (PAU), were preselected. Results: The detection of isolates with elevated MICs against oxacillin, ceftriaxone, tetracycline, and vancomycin suggests the emergence of multidrug-resistant S. aureus isolates resistant to an expanded number of antibiotics. From the dendrogram analysis, an approximate number of unique resistance profiles were 24 and arbitrarily circulated. No relationship or clustering of the resistance profile of isolates was observed. There were no outliers either concerning the sample. On Principal Component Analysis (PCA), the first factor had strong positive loadings on oxacillin, clindamycin, ceftriaxone, erythromycin, ciprofloxacin and trimethoprim-sulphamethoxazole and is largely a concern because of their clinical relevance. The second factor had strong loading on tetracycline and the third factor had a strong loading on Vancomycin (VAN). The first four components defined approximately 71% of the total variance and could easily be represented graphically in a three dimensional scatter plot. In this graphic representation, the eleven antibiotics clustered in four different spatial regions; tetracycline and vancomycin occupied separate spatial positions. Conclusions: The use of dendrogram and multivariate analysis offers a different approach to draw the inferences from antibiotic MICs and draw some new findings on the relationships between different antibiotic classes.