Farming Practice Influences Antimicrobial Resistance Burden of Non-Aureus Staphylococci in Pig Husbandries.
Manonmani SoundararajanGabriella MarincolaOlivia LiongTessa MarciniakFreya D R WenckerFranka HofmannHannah SchollenbruchIris KobuschSabrina LinnemannSilver A WolfMustafa HelalTorsten SemmlerBirgit WaltherChristoph SchoenJustin NyasingaGunturu RevathiMarc BoelhauveWilma ZiebuhrPublished in: Microorganisms (2022)
Non-aureus staphylococci (NAS) are ubiquitous bacteria in livestock-associated environments where they may act as reservoirs of antimicrobial resistance (AMR) genes for pathogens such as Staphylococcus aureus . Here, we tested whether housing conditions in pig farms could influence the overall AMR-NAS burden. Two hundred and forty porcine commensal and environmental NAS isolates from three different farm types (conventional, alternative, and organic) were tested for phenotypic antimicrobial susceptibility and subjected to whole genome sequencing. Genomic data were analysed regarding species identity and AMR gene carriage. Seventeen different NAS species were identified across all farm types. In contrast to conventional farms, no AMR genes were detectable towards methicillin, aminoglycosides, and phenicols in organic farms. Additionally, AMR genes to macrolides and tetracycline were rare among NAS in organic farms, while such genes were common in conventional husbandries. No differences in AMR detection existed between farm types regarding fosfomycin, lincosamides, fusidic acid, and heavy metal resistance gene presence. The combined data show that husbandry conditions influence the occurrence of resistant and multidrug-resistant bacteria in livestock, suggesting that changing husbandry practices may be an appropriate means of limiting the spread of AMR bacteria on farms.
Keyphrases
- antimicrobial resistance
- genome wide
- genome wide identification
- staphylococcus aureus
- multidrug resistant
- copy number
- genome wide analysis
- heavy metals
- healthcare
- primary care
- bioinformatics analysis
- dna methylation
- transcription factor
- big data
- risk assessment
- magnetic resonance
- gram negative
- escherichia coli
- machine learning
- methicillin resistant staphylococcus aureus
- climate change
- pseudomonas aeruginosa
- mental illness
- acinetobacter baumannii
- genetic diversity
- deep learning
- quality improvement
- sensitive detection
- drinking water
- candida albicans
- aqueous solution