Network Analysis of Antimicrobial MIC Profiles Reveals Extensive Covariation in Chicken-Associated Escherichia coli from Large-Scale Breeding Flocks in Central Hungary

Background: The surveillance of non-clinical bacteria from food-producing animals can reveal antimicrobial selection patterns without conditioning sampling on clinical disease, but categorical resistance estimates are difficult to compare when organism-specific breakpoints are incomplete. We aimed to characterize antimicrobial MIC distributions in chicken-associated Staphylococcus spp., Enterococcus spp., and Escherichia coli from Central Hungary and to determine whether E. coli MIC profiles formed structured covariance networks. Methods: The dataset comprised 133 isolates collected in 2022–2023 (28 Staphylococcus spp., 59 Enterococcus spp., and 46 E. coli). MIC distributions were summarized using MIC50/MIC90, and E. coli MICs were analyzed by Spearman correlation with Benjamini–Hochberg correction. Historical threshold classification was retained only as a sensitivity analysis, and nine E. coli genomes provided contextual genomic information. Results: Staphylococcus spp. showed lower amoxicillin MIC50/MIC90 values (0.25/1 µg/mL) than Enterococcus spp. (1/256 µg/mL), illustrating marked cross-genus differences. In E. coli, MIC50/MIC90 values included 16/512 µg/mL for ceftriaxone, 128/256 µg/mL for neomycin, 32/128 µg/mL for enrofloxacin, and 32/>512 µg/mL for trimethoprim–sulfamethoxazole. Ten associations met |ρ| ≥ 0.40 and adjusted q < 0.05, forming two connected components that persisted after sensitivity analyses for upper-bound accumulation and the amoxicillin–amoxicillin/clavulanic acid relationship. The nine genomes showed heterogeneous resistance-associated gene calls. Conclusions: Continuous MIC analysis revealed phenotypic covariance not captured by categorical summaries alone and identified region-specific targets for stewardship-oriented follow-up. Inference remains isolate-level rather than farm-adjusted, and the genomic panel is not representative of the phenotypic cohort; longitudinal sampling linked to antimicrobial-use data and larger matched phenotype–genotype datasets are therefore warranted.