Phenotypic and genotypic analyses of antimicrobial resistant bacteria in livestock in Uganda.
Torahiko OkuboMontira YossapolFumito MaruyamaEddie M WampandeSteven KakoozaKenji OhyaSayaka TsuchidaTetsuo AsaiJohn D KabasaKazunari UshidaPublished in: Transboundary and emerging diseases (2018)
Antimicrobial resistant bacteria (ARB) in livestock are a global public health concern, not only because they prolong infectious diseases but also they can be transferred from animals to humans via the food chain. Here, we studied ARB in livestock at commercial and subsistence farms (n = 13) in Wakiso and Mpigi districts, Uganda. We enquired from the farmers about the type and the purpose of antimicrobial agents they have used to treat their livestock. After collecting faeces, we isolated antimicrobial resistant Escherichia coli from livestock faeces (n = 134) as an indicator bacterium. These strains showed resistance to ampicillin (44.8%), tetracycline (97.0%), and sulfamethoxazole-trimethoprim (56.7%). The frequency of ampicillin-resistance was significantly correlated with the usage of penicillins to livestock in the farms (p = 0.04). The metagenomics data detected 911 antimicrobial resistant genes that were classified into 16 categories. Genes for multidrug efflux pumps were the most prevalent category in all except in one sample. Interestingly, the genes encoding third-generation cephalosporins (blaCTX-M ), carbapenems (blaACT ), and colistin (arnA) were detected by metagenomics analysis although these phenotypes were not detected in our E. coli strains. Our results suggest that the emergence and transmission of cephalosporin, carbapenem, and/or colistin-resistant bacteria among livestock can occur in future if these antimicrobial agents are used.
Keyphrases
- escherichia coli
- staphylococcus aureus
- klebsiella pneumoniae
- public health
- gram negative
- multidrug resistant
- acinetobacter baumannii
- drug resistant
- infectious diseases
- genome wide
- pseudomonas aeruginosa
- biofilm formation
- transcription factor
- machine learning
- bioinformatics analysis
- genome wide analysis
- electronic health record
- candida albicans
- cystic fibrosis
- data analysis
- deep learning
- human health
- wastewater treatment