{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,27]],"date-time":"2026-10-27T19:37:55Z","timestamp":1793129875435,"version":"4.5.0"},"reference-count":71,"publisher":"American Society for Microbiology","issue":"10","license":[{"start":{"date-parts":[[2026,10,20]],"date-time":"2026-10-20T00:00:00Z","timestamp":1792454400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"},{"start":{"date-parts":[[2026,10,20]],"date-time":"2026-10-20T00:00:00Z","timestamp":1792454400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/journals.asm.org\/non-commercial-tdm-license"}],"funder":[{"DOI":"10.13039\/501100000925","name":"National Health and Medical Research Council","doi-asserted-by":"publisher","award":["APP1178715"],"award-info":[{"award-number":["APP1178715"]}],"id":[{"id":"10.13039\/501100000925","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Department of Education and Training | Australian Research Council","award":["DE230100542"],"award-info":[{"award-number":["DE230100542"]}],"id":[{"id":"https:\/\/ror.org\/05mmh0f86","id-type":"ROR","asserted-by":"crossref"}]},{"name":"National Health & Medical Research Council of Australia","award":["2016330"],"award-info":[{"award-number":["2016330"]}],"id":[{"id":"https:\/\/ror.org\/011kf5r70","id-type":"ROR","asserted-by":"crossref"}]}],"content-domain":{"domain":["journals.asm.org"],"crossmark-restriction":true},"short-container-title":["mSystems"],"published-print":{"date-parts":[[2026,10,20]]},"abstract":"<jats:title>ABSTRACT<\/jats:title>\n                  <jats:sec>\n                    <jats:title\/>\n                    <jats:p>Antimicrobial-resistant (AMR) bacteria and genes are continually exchanged among humans, animals, and environmental reservoirs. Disparate and siloed surveillance methods present a major challenge for tracing and disrupting AMR transmission, with clinical monitoring focusing on detecting specific pathogens or specific genes of interest, and environmental surveys often relying on inferences drawn from indicator organisms. Here, we demonstrate that, following sample-specific pre-processing, common surveillance approaches can be applied consistently to profile AMR abundance, distribution, and phenotypes across diverse reservoirs in both urban and agricultural settings. Across all sample types, three core methods provided complementary insights: (i) quantitative PCR (qPCR) arrays to measure multiple AMR genes, (ii) gene- and genome-centric metagenomics for comprehensive resistome profiling, and (iii) culture-based genomics with susceptibility testing to link genotypes to phenotypes. We applied this approach to profile 1,032 metagenome-assembled genomes, 66 bacterial isolate genomes, and 78 and 6,642 AMR genes\/reference sequences via qPCR and metagenomics, respectively. This integrated framework revealed the prevalence and diversity of resistance mechanisms in both putative pathogens and non-pathogenic bacteria with potentially transmissible genes, with wastewater especially enriched in AMR genes. We detected mismatches between genotype and phenotype predictions and a prevalence of intermediate resistance phenotypes, highlighting how many mechanisms of environmental resistance remain poorly understood. Overall, this study demonstrates that unified, field-leading surveillance methods can be used in diverse environmental and animal samples while highlighting that multiple methods are needed to capture the diverse AMR genotypes and phenotypes in these settings to enable comprehensive monitoring and adaptive solutions to restrict transmission.<\/jats:p>\n                    <jats:sec>\n                      <jats:title>IMPORTANCE<\/jats:title>\n                      <jats:p>Antimicrobial resistance (AMR) is driven by the exchange of resistant bacteria and genes across interconnected human, animal, and environmental reservoirs, yet fragmented surveillance limits our ability to track transmission and intervene effectively. We demonstrate that widely used surveillance methods can be applied cohesively across diverse sample types, including wastewater, soil, water, agricultural environments, and fecal samples, to generate comparable insights into AMR abundance, diversity, and phenotype. By integrating qPCR, metagenomics, and culture-based genomics, we reveal resistance in both pathogenic and non-pathogenic bacteria, reaffirm wastewater as a major AMR reservoir, and uncover frequent mismatches between genetic predictions and observed susceptibility. The prevalence of intermediate resistance further suggests that many environmental resistance mechanisms remain poorly understood. These findings show that no single method captures AMR complexity and highlight the need for unified, multi-method surveillance to support comprehensive monitoring and strategies to control the spread of resistance.<\/jats:p>\n                    <\/jats:sec>\n                  <\/jats:sec>","DOI":"10.1128\/msystems.00741-26","type":"journal-article","created":{"date-parts":[[2026,9,28]],"date-time":"2026-09-28T13:01:31Z","timestamp":1790600491000},"update-policy":"https:\/\/doi.org\/10.1128\/asmj-crossmark-policy-page","source":"Crossref","is-referenced-by-count":0,"title":["Integrated cross-sectoral surveillance of antimicrobial resistance genotypes and phenotypes across disparate reservoirs"],"prefix":"10.1128","volume":"11","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-3657-8790","authenticated-orcid":true,"given":"Thomas 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