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Here, we dissect 935\n                      <jats:italic toggle=\"yes\">Salmonella<\/jats:italic>\n                      isolates, collected from both clinical and food chain sources, through integrated genomics and phenomics to resolve population structure, spatiotemporal dynamics, and evolutionary drivers.\n                      <jats:italic toggle=\"yes\">Salmonella Typhimurium<\/jats:italic>\n                      (18.7%) and\n                      <jats:italic toggle=\"yes\">Salmonella Enteritidis<\/jats:italic>\n                      (17.1%) dominate the serotype landscape, while MLST uncovers ST34 (20.7%) as the pivotal sequence type bridging multiple serotypes. Temporal tracking (2018\u20132022) exposes alarming AMR trajectories: ciprofloxacin resistance doubled (15.3% to 30.4%) by 2020, and tetracycline resistance peaked at 77.3%. The serotype-specific epidemiology reveals that\n                      <jats:italic toggle=\"yes\">S. Typhimurium<\/jats:italic>\n                      declined and then stabilized,\n                      <jats:italic toggle=\"yes\">S. Enteritidis<\/jats:italic>\n                      fluctuated due to vaccination, and\n                      <jats:italic toggle=\"yes\">S. Derby<\/jats:italic>\n                      emerged persistently (+69%). Network analysis reveals two evolutionary clusters: one anchored by\n                      <jats:italic toggle=\"yes\">S. Typhimurium\/S. Enteritidis<\/jats:italic>\n                      -ST34\/ST11 and another harboring diverse STs associated with\n                      <jats:italic toggle=\"yes\">S. Derby<\/jats:italic>\n                      . Notably, ST34 acts as a genetic backbone for serotype switching. Notably,\n                      <jats:italic toggle=\"yes\">S. Typhimurium<\/jats:italic>\n                      exhibits the highest AMR gene burden (median: 4.2 genes\/isolate) and virulence arsenal (\n                      <jats:italic toggle=\"yes\">spvB<\/jats:italic>\n                      : 85.1%;\n                      <jats:italic toggle=\"yes\">pefA<\/jats:italic>\n                      : 75.4%), which correlates with invasive disease. Geographic heterogeneity results in distinct serotype distributions:\n                      <jats:italic toggle=\"yes\">S. Enteritidis<\/jats:italic>\n                      dominates in Xinyu (28.4%),\n                      <jats:italic toggle=\"yes\">S. Typhimurium<\/jats:italic>\n                      prevails in Shangrao (31.5%), and Ganzhou exhibits balanced diversity. Our findings establish that clonal expansion, horizontal gene transfer, and regional ecologies are key factors jointly driving\n                      <jats:italic toggle=\"yes\">Salmonella<\/jats:italic>\n                      evolution. This necessitates genotype-phenotype-integrated surveillance to preempt the emergence and widespread dissemination of resistance and virulence.\n                    <\/jats:p>\n                    <jats:sec>\n                      <jats:title>IMPORTANCE<\/jats:title>\n                      <jats:p>\n                        <jats:italic toggle=\"yes\">Salmonella enterica<\/jats:italic>\n                        is a globally significant foodborne pathogen, whose pathogenicity and antimicrobial resistance (AMR) evolution are driven by complex mechanisms. This study provides a comprehensive analysis of 935\n                        <jats:italic toggle=\"yes\">Salmonella<\/jats:italic>\n                        isolates from clinical and food chain sources, integrating genomic and phenotypic data to elucidate population structure, spatiotemporal dynamics, and key evolutionary drivers. We reveal critical resistance trends, including a concerning doubling of ciprofloxacin resistance by 2020 and sustained high tetracycline resistance. Our comparative analysis of serotypes (e.g.,\n                        <jats:italic toggle=\"yes\">S. Typhimurium<\/jats:italic>\n                        and\n                        <jats:italic toggle=\"yes\">S. Enteritidis<\/jats:italic>\n                        ) highlights associations between AMR gene burden and virulence factors and identifies ST34 as a pivotal genetic element facilitating serotype switching. These findings underscore the imperative for integrated genotypic-phenotypic surveillance to predict resistance evolution and inform \u201cOne Health\u201d-based interventions. By disrupting AMR dissemination across the animal food chain, this research offers novel strategies for global\n                        <jats:italic toggle=\"yes\">Salmonella<\/jats:italic>\n                        control and improved public health outcomes.\n                      <\/jats:p>\n                    <\/jats:sec>\n                  <\/jats:sec>","DOI":"10.1128\/aem.01511-25","type":"journal-article","created":{"date-parts":[[2025,8,29]],"date-time":"2025-08-29T18:32:53Z","timestamp":1756492373000},"update-policy":"https:\/\/doi.org\/10.1128\/asmj-crossmark-policy-page","source":"Crossref","is-referenced-by-count":5,"title":["Integrating serotyping, MLST, and phenotypic data: decoding the evolutionary drivers of\n                    <i>Salmonella<\/i>\n                    pathogenicity and drug resistance"],"prefix":"10.1128","volume":"91","author":[{"given":"Wanting","family":"Hong","sequence":"first","affiliation":[{"id":[{"id":"https:\/\/ror.org\/00dc7s858","id-type":"ROR","asserted-by":"publisher"}],"name":"School of Food Science & Engineering, Jiangxi Agricultural University","place":["Nanchang, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zheng","family":"Yang","sequence":"additional","affiliation":[{"id":[{"id":"https:\/\/ror.org\/00dc7s858","id-type":"ROR","asserted-by":"publisher"}],"name":"School of Food Science & Engineering, Jiangxi Agricultural University","place":["Nanchang, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Guoping","family":"Wu","sequence":"additional","affiliation":[{"id":[{"id":"https:\/\/ror.org\/00dc7s858","id-type":"ROR","asserted-by":"publisher"}],"name":"School of Food Science & Engineering, Jiangxi Agricultural University","place":["Nanchang, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chengwei","family":"Liu","sequence":"additional","affiliation":[{"id":[{"id":"https:\/\/ror.org\/02yr91f43","id-type":"ROR","asserted-by":"publisher"}],"name":"Jiangxi Provincial Center for Disease Control and Prevention","place":["Nanchang, China"]},{"name":"Jiangxi Provincial Key Laboratory of Major Epidemics Prevention and Control","place":["Nanchang, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yuexia","family":"Wang","sequence":"additional","affiliation":[{"id":[{"id":"https:\/\/ror.org\/04epb4p87","id-type":"ROR","asserted-by":"publisher"}],"name":"Academy of Chinese Medicine Science, Zhejiang Chinese Medical University","place":["Hangzhou, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1914-2319","authenticated-orcid":true,"given":"Ningbo","family":"Liao","sequence":"additional","affiliation":[{"id":[{"id":"https:\/\/ror.org\/00dc7s858","id-type":"ROR","asserted-by":"publisher"}],"name":"School of Food Science & Engineering, Jiangxi Agricultural University","place":["Nanchang, China"]},{"name":"Jiangxi Provincial Key Laboratory of Major Epidemics Prevention and Control","place":["Nanchang, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"235","reference":[{"key":"e_1_3_3_2_2","doi-asserted-by":"publisher","DOI":"10.1128\/AEM.02659-20"},{"key":"e_1_3_3_3_2","doi-asserted-by":"publisher","DOI":"10.1128\/spectrum.03485-23"},{"key":"e_1_3_3_4_2","doi-asserted-by":"publisher","DOI":"10.1128\/spectrum.00262-24"},{"key":"e_1_3_3_5_2","doi-asserted-by":"publisher","DOI":"10.1128\/mSphere.00089-19"},{"key":"e_1_3_3_6_2","doi-asserted-by":"publisher","DOI":"10.1016\/S2542-5196(23)00213-9"},{"key":"e_1_3_3_7_2","doi-asserted-by":"publisher","DOI":"10.3390\/applmicrobiol4040116"},{"key":"e_1_3_3_8_2","doi-asserted-by":"publisher","DOI":"10.1186\/1471-2164-13-88"},{"key":"e_1_3_3_9_2","doi-asserted-by":"publisher","DOI":"10.1128\/Spectrum.00155-21"},{"key":"e_1_3_3_10_2","doi-asserted-by":"publisher","DOI":"10.1016\/S1473-3099(17)30705-3"},{"key":"e_1_3_3_11_2","doi-asserted-by":"publisher","DOI":"10.1093\/jac\/dkx217"},{"key":"e_1_3_3_12_2","doi-asserted-by":"publisher","DOI":"10.1186\/s12866-020-01886-5"},{"key":"e_1_3_3_13_2","doi-asserted-by":"publisher","DOI":"10.1099\/mgen.0.000064"},{"key":"e_1_3_3_14_2","doi-asserted-by":"publisher","DOI":"10.3390\/ijms221810003"},{"key":"e_1_3_3_15_2","doi-asserted-by":"publisher","DOI":"10.1128\/AEM.01173-18"},{"key":"e_1_3_3_16_2","doi-asserted-by":"publisher","DOI":"10.1038\/s41467-021-23091-2"},{"key":"e_1_3_3_17_2","doi-asserted-by":"publisher","DOI":"10.1128\/mSphere.00485-21"},{"key":"e_1_3_3_18_2","doi-asserted-by":"publisher","DOI":"10.1038\/s41467-021-22700-4"},{"key":"e_1_3_3_19_2","doi-asserted-by":"publisher","DOI":"10.1101\/gr.232397.117"},{"key":"e_1_3_3_20_2","doi-asserted-by":"publisher","DOI":"10.1128\/AAC.02140-16"},{"key":"e_1_3_3_21_2","doi-asserted-by":"publisher","DOI":"10.1128\/JCM.00213-21"},{"key":"e_1_3_3_22_2","doi-asserted-by":"publisher","DOI":"10.1038\/s43705-022-00179-4"},{"key":"e_1_3_3_23_2","unstructured":"Seemann T. 2020. 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