{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"institution":[{"name":"bioRxiv"}],"indexed":{"date-parts":[[2026,6,24]],"date-time":"2026-06-24T06:04:13Z","timestamp":1782281053023,"version":"3.54.5"},"posted":{"date-parts":[[2025,1,16]]},"group-title":"Microbiology","reference-count":65,"publisher":"openRxiv","license":[{"start":{"date-parts":[[2025,1,16]],"date-time":"2025-01-16T00:00:00Z","timestamp":1736985600000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"accepted":{"date-parts":[[2025,1,16]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                <jats:p>\n                  <jats:italic>Campylobacter spp<\/jats:italic>\n                  . resistant to fluoroquinolones and macrolides are serious public health threats. Studies aiming to identify risk factors for drug-resistant\n                  <jats:italic>Campylobacter<\/jats:italic>\n                  have narrowly focused on antimicrobial use at the farm level. Using chain graphs, we quantified risk factors for fluoroquinolones- and macrolide-resistance in\n                  <jats:italic>Campylobacter coli<\/jats:italic>\n                  isolated from two distinctive swine production systems, conventional and antibiotic-free (ABF). The chain graphs were learned using genotypic and phenotypic resistance data from 1082 isolates and host exposures obtained through surveys for 18 cohorts of pigs. The gyrA T86I point mutation alone explained at least 58 % of the variance in ciprofloxacin minimum inhibitory concentration (MIC) for ABF and 79 % in conventional farms. For macrolides, genotype and host exposures explained similar variance in azithromycin and erythromycin MIC. Among host exposures, heavy metal exposures were identified as risk factors in both conventional and ABF. Chain graph models can generate insights into the complex epidemiology of antimicrobial resistance by characterizing context-specific risk factors and facilitating causal discovery.\n                <\/jats:p>\n                <jats:sec>\n                  <jats:title>Author summary<\/jats:title>\n                  <jats:p>Antimicrobial resistance is influenced by multiple factors, including exposures to selecting agents, such as antibiotics, antiseptics, or heavy metals, and factors affecting the transmission of resistant pathogens, such as biosecurity and hygiene measures. Understanding what specific factors are associated with resistance in a given context is challenging. We developed an approach based on probabilistic graphical models to investigate context-specific antimicrobial resistance risk factors. We applied the approach to Campylobacter coli isolated from pigs in antibiotic-free and conventional farms. We demonstrated how for fluoroquinolones, risk factors were similar across both types of farms, but risk factors for macrolides were different across settings.<\/jats:p>\n                <\/jats:sec>","DOI":"10.1101\/2025.01.16.633345","type":"posted-content","created":{"date-parts":[[2025,1,17]],"date-time":"2025-01-17T02:45:17Z","timestamp":1737081917000},"source":"Crossref","is-referenced-by-count":0,"title":["Risk factors for fluoroquinolone- and macrolide-resistance among swine\n                  <i>Campylobacter coli<\/i>\n                  using multi-layered chain graphs"],"prefix":"10.64898","author":[{"given":"C. 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