Enteropathogenic and Multidrug-Resistant bla CTX-M -Carrying E. coli Isolates from Dogs and Cats.
Catherine Biondo FeitosaGabriel Siqueira Dos SantosNatalia Carrillo GaetaGustavo da Silva SchiaviCarla Gasparotto Chande VasconcelosJonas Moraes FilhoMarcos Bryan HeinemannAdriana CortezPublished in: Animals : an open access journal from MDPI (2024)
Enteropathogenic Escherichia coli (EPEC) are pathogens associated with gastrointestinal illnesses. Dogs and cats can harbor EPEC, and antimicrobial resistance may impair necessary treatments. This study characterized E. coli strains from dogs and cats, focusing on phylogroup classification, virulence factors, and antimicrobial resistance profiles. Ninety-seven E. coli isolates from fecal samples of 31 dogs and 3 cats were obtained from a private diagnostic laboratory in Botucatu, Brazil, from March to October 2021. The antimicrobial susceptibility was assessed using the disk diffusion method. Polymerase chain reaction (PCR) was employed to screen for bla CTX-M and genes encoding virulence factors, as well as to classify the isolates into phylogroups. Twenty isolates were positive for intimin encoding gene eae and, consequently, these isolates were classified as EPEC (20.62%). Notably, 5.1% (5/97) of the isolates exhibited extended-spectrum β-lactamase (ESBL) production and 13.4% (13/97) were identified as multidrug-resistant bacteria. Phylogroups A and B2 were the most prevalent, comprising 29.9% (29/97) and 26.8% (26/97) of the bacterial isolates, respectively. This characterization highlights the prevalence of EPEC in domestic animals, emphasizing the potential risk they pose to public health and highlighting the urgency of responsible antimicrobial use in veterinary practices and the important role of laboratories in the surveillance of pathogenic multidrug-resistant bacteria.
Keyphrases
- antimicrobial resistance
- klebsiella pneumoniae
- escherichia coli
- multidrug resistant
- public health
- gram negative
- drug resistant
- genetic diversity
- acinetobacter baumannii
- biofilm formation
- healthcare
- staphylococcus aureus
- genome wide
- risk factors
- deep learning
- high throughput
- cystic fibrosis
- dna methylation
- machine learning
- pseudomonas aeruginosa
- single cell
- transcription factor
- genome wide identification
- human health