Characterisation of Methicillin-Resistant Staphylococcus aureus from Alexandria, Egypt.
Stefan MoneckeAmira K BedewyElke MüllerSascha D BraunCelia DiezelAmel ElsheredyOla KaderMartin ReinickeAbeer GhazalShahinda RezkRalf EhrichtPublished in: Antibiotics (Basel, Switzerland) (2023)
The present study aims to characterise clinical MRSA isolates from a tertiary care centre in Egypt's second-largest city, Alexandria. Thirty isolates collected in 2020 were genotypically characterised by microarray to detect their resistance and virulence genes and assign them to clonal complexes (CC) and strains. Isolates belonged to 11 different CCs and 14 different strains. CC15-MRSA-[V+ fus ] (n = 6), CC1-MRSA-[V+ fus+tir+ccrA/B-1 ] (PVL+) (n = 5) as well as CC1-MRSA-[V+ fus+tir+ccrA/B-1 ] and CC1153-MRSA-[V+ fus ] (PVL+) (both with n = 3) were the most common strains. Most isolates (83%) harboured variant or composite SCC mec V or VI elements that included the fusidic acid resistance gene fusC . The SCC mec [V+ fus+tir+ccrA/B -1] element of one of the CC1 isolates was sequenced, revealing a presence not only of fusC but also of blaZ , aacA-aphD and other resistance genes. PVL genes were also common (40%). The hospital-acquired MRSA CC239-III strain was only found twice. A comparison to data from a study on strains collected in 2015 (Montelongo et al., 2022) showed an increase in fusC and PVL carriage and a decreasing prevalence of the CC239 strain. These observations indicate a diffusion of community-acquired strains into hospital settings. The beta-lactam use in hospitals and the widespread fusidic acid consumption in the community might pose a selective pressure that favours MRSA strains with composite SCC mec elements comprising mecA and fusC . This is an unsettling trend, but more MRSA typing data from Egypt are required.
Keyphrases
- methicillin resistant staphylococcus aureus
- staphylococcus aureus
- escherichia coli
- healthcare
- genome wide
- genetic diversity
- genome wide identification
- biofilm formation
- pseudomonas aeruginosa
- electronic health record
- big data
- machine learning
- deep learning
- risk factors
- dna methylation
- transcription factor
- multidrug resistant