Randomized trial assesses antibiotic resistance levels in Escherichia coli from pigs, highlighting ecological complexities.
OBJECTIVES: Livestock are recognized reservoirs of antimicrobial resistance (AMR). However, current surveillance often overlooks key ecological aspects such as spatial-temporal patterns and quantification of shedding levels of resistant bacteria. In particular, early detection of low-level shedding of resistance to critically important antimicrobials (CIAs), including extended-spectrum cephalosporins (ESCs) and fluoroquinolones (FQs), remains limited. Using commensal Escherichia coli as an indicator, we applied a high-throughput Robotic Antimicrobial Susceptibility Platform (RASP) to assess phenotypic resistance in isolates from 900 samples collected across 10 pig herds over 3 years. METHODS: Quantitative assessments of antimicrobial resistance (cfu/g) were performed using selective agars containing antimicrobials, with plating and data capture (colony counting) automated on the RASP. Broth microdilution and whole-genome sequencing were performed on CIA-R E. coli using RASP. RESULTS: Persistent resistance to ampicillin and tetracycline (∼5.8 log10 cfu/g) showed minimal variation between herds and years. Gentamicin resistance declined significantly (-0.23 log10 cfu/g/year, P < 0.0001), while ESC resistance rose significantly (0.16 log10 cfu/g/year, P = 0.015), although some herds showed no ESC shedding. Ciprofloxacin resistance was detected in 58% of samples but generally at lower levels (∼2.1 log10 cfu/g) with herd-level variability. Genomic analysis identified FQ-resistant sequence types ST744 and ST167 with global phylogenetic links, and ESC resistance was associated with blaCTX-M-1 on IncI1 plasmids. CONCLUSIONS: These findings reveal the ecological complexity of AMR in livestock and highlight limitations of standard surveillance in detecting rare resistances. Our study demonstrates how high-throughput robotics integrated with robust field design can enhance AMR monitoring and inform One Health strategies for mitigation.
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Abraham et al. (2026) studied this question.
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