Draft genome sequences of Salmonella enterica subsp. enterica isolates from fresh produce and agricultural environments in South Korea

Draft genome sequences of Salmonella enterica subsp. enterica isolates from fresh produce and agricultural environments in South Korea | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Data Note Draft genome sequences of Salmonella enterica subsp. enterica isolates from fresh produce and agricultural environments in South Korea Su-Hyeon Kim, Ji Min Han, Gyu-Sung Cho, Erik Brinks, Charles M.A.P. Franz, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6547187/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 13 Oct, 2025 Read the published version in BMC Research Notes → Version 1 posted 7 You are reading this latest preprint version Abstract Objectives: Salmonella enterica is a globally significant foodborne pathogen and a leading cause of gastrointestinal infections, with increasing concern over strains harboring antimicrobial resistance (AMR). This Data Note reports draft genome sequences of six S. enterica subsp. enterica isolates from fresh produce and agricultural environments in South Korea. The objective of this work was to provide genomic data on environmental Salmonella isolates, their serovar diversity, AMR gene profiling, and genetic attributes relevant to Salmonella surveillance and comparative genomics. Data description: Draft genomes of six isolates consisted of 3 to 7 contigs with genome sizes ranging from 4.89 to 5.02 Mbp and mol% GC contents between 51.89% and 52.24%. The isolates were identified as four serovars, such as S . Typhimurium, S . I 4,[5],12:i:-, S . Kentucky, and S . Montevideo, and five MLST types. Among them, three strains representing serovars including Typhimurium, I 4,[5],12:i:-, and Kentucky harbored acquired AMR genes, conferring resistance to aminoglycosides, aminopenicillins, diaminopyrimidines, sulfonamide, and tetracyclines. Our study highlights the genomic diversity and resistance potential of S. enterica isolates derived from preharvest agricultural environments, providing valuable resources for future comparative analyses and AMR surveillance efforts. Salmonella antimicrobial resistance fresh produce and agricultural environment South Korea Objective Salmonella enterica is one of the most significant foodborne pathogens globally, responsible for hundreds of millions of infections and mortality each year [1]. Among various transmission routes, fresh produce and its surrounding environments have recently emerged as a significant source of Salmonella contamination. In the USA, fresh produce-associated Salmonella outbreaks accounted for more than 33.7% of all foodborne Salmonella outbreaks reported between 1998 and 2021 [2]. Among the more than 2,610 known Salmonella ( S .) enterica serovars, several serovars such as S. Typhimurium, S. Enteritidis, S. Bareilly, S. Weltevreden, S . Thompson, and S . Newport have frequently been implicated in fresh produce-related outbreaks [2]. These serovars demonstrate persistence in soil, irrigation water, and organic fertilizers, contributing to their relevance in preharvest contamination [1, 3]. Moreover, recent reports showed an increasing occurrence of AMR among such isolates [2]. In a previous study, our group isolated Salmonella enterica strains from fresh produce and agricultural environment samples in South Korea and assessed their AMR profiles [4]. Thus, this study aimed to present their draft genome sequences to facilitate deeper insight into their serovar composition, acquired AMR genes, and potential genomic characteristics relevant to food safety. The generated data are intended to support comparative genomic analyses and surveillance efforts related to Salmonella contamination in fresh produce and agricultural environments. The detailed isolation, biochemical identification, and sequencing procedures used for these isolates are provided in the Data Description section. Data description A total of six Salmonella strains were isolated from green onion, peach leaves, peach orchard soil, and cow manure collected in Daegu and Gyeonsangbuk-do provinces, South Korea. Each sample (25 g) was pre-enriched in 225 mL of tryptic soy broth (BD Difco, Franklin Lakes, NJ, USA) at 37°C for 18 hours. Then, 1 mL of the pre-enriched culture was transferred into Rappaport-Vassiliadis broth (BD Difco) at 37°C for 24 hours. Aliquots were streaked onto MacConkey and xylose lysine deoxycholate agar (BD Difco), and the plates were incubated at 37°C for 48 hours. Two agar plates with the ideal color were selected and presumptively identified using indole, methyl red, Voges-Proskauer, and citrate tests, as well as 16S rRNA gene sequencing [4]. Genomic DNAs were extracted using the Wizard ® HMW DNA Extraction Kit (Promega Co., Medison, WI, USA) according to the manufacturer’s instructions. DNA libraries were prepared using the ligation sequencing and native barcoding kit (SQK-NBD114.96, Oxford Nanopore Technologies Inc., Oxford, UK) and sequenced using ONT PromethION 2 Solo platform with R10.4.1 flow cell. Basecalling and demultiplexing were conducted using Dorado version 0.9.5. The raw sequence data (Data set 1) [5] were filtered using Chopper v. 0.9.2 with a minimum average quality score of Q15 and minimum length of 600 bp [6]. De novo assembly was then performed using Flye (v. 2.9.5) with the --nano-corr option [7], resulting in a coverage range of 66× to 198× (Data file 1) [8]. The quality of assembled contigs was assessed using QUAST v.5.3.0 [9]. Genomes were annotated using the NCBI Prokaryotic Genome Annotation Pipeline [10] and serotypes were predicted with SeqSero v 1.3.1 [11]. Average nucleotide identity (ANI) and dDDH were calculated using the pyani pipeline [12] and the genome-to-genome distance calculator (GGDC, formula 2) [13], respectively. Identification of acquired AMR genes, plasmid replicon types, and multilocus sequence types (MLST) was conducted using the Staramr pipeline (v.0.11.0) [14], incorporating ResFinder (database version 13.12.2024), PlasmidFinder (database version 14.11.2024), and MLST (v2.23.0). Draft genome assemblies of six Salmonella isolates consisted of 3 to 7 contigs, with genome sizes ranging from 4.89 to 5.02 Mbp and mol% GC contents of approximately 51.9% to 52.2% (Data file 1) [8]. The total number of predicted coding sequences ranged from 4,541 to 4,761, and all genomes contained 22 rRNA genes and 85–88 tRNA genes [8]. In silico MLST analysis revealed that the isolates belonged to distinct sequence types, including ST4, ST19, ST34, ST198, and ST8316, and they were identified as S . enterica based on dDDH analysis (Data file 2) [15]. Their serotypes were further predicted and assigned to four serovars: S . Typhimurium (GOVDG-1, GORGM-1, and PLGS-1), S . I 4,[5],12:i:- (PSGS-1), S . Kentucky (PSCD-1), and S . Montevideo (CMCD-1) (Data file 3) [16]. In addition, 3 of them carried acquired AMR genes (Data file 4) [17], most commonly aph (3")-Ib, aph (6)-Id, and tet (B), as well as at least one plasmid contig (Data file 5) [18]. Virulence genes were detected with the Virulence Factors of Pathogenic Bacteria (Data file 6) [19–20]. The data were deposited in Figshare and NCBI database (Table 1) [8, 15–18, 20]. Table 1 : Overview of data files/data sets. Label Name of data file/data set File types (file extension) Data repository and identifier (DOI or accession number) Data file 1 Table S1, Summary of sequencing and annotation results Portable Document Format file (.pdf) https://doi.org/10.6084/m9.figshare.28815209 [8] Data file 2 Table S2, MLST type, and dDDH and ANI values with reference genomes MS Excel file (.xlsx) https://doi.org/10.6084/m9.figshare.28815287 [15] Data file 3 Table S3, Serotype prediction Portable Document Format file (.pdf) https://doi.org/10.6084/m9.figshare.28815548.v1 [16] Data file 4 Table S4, ResFinder summary MS Excel file (.xlsx) https://doi.org/10.6084/m9.figshare.28829828.v1 [17] Data file 5 Table S5, PlasmidFinder summary MS Excel file (.xlsx) https://doi.org/10.6084/m9.figshare.28829693 [18] Data file 6 Table S6, VFDB summary MS Excel file (.xlsx) https://doi.org/10.6084/m9.figshare.28829870 [20] Data set 1 Raw sequencing data of S . enterica GOVDG-1, S . enterica PLGS-1, S . enterica PSGS-1, S . enterica PSCD-1, S . enterica GORGM-1, and S . enterica CMCD-1 Fastq file (.fastq) PRJNA1251564 [5] Data set 2 Genome assembly of S . enterica GOVDG-1 FASTA file (.fasta) / GenBank file (.gbk) JBNDEH000000000 [21] Data set 3 Genome assembly of S . enterica PLGS-1 FASTA file (.fasta) / GenBank file (.gbk) JBNDEI000000000 [22] Data set 4 Genome assembly of S . enterica PSGS-1 FASTA file (.fasta) / GenBank file (.gbk) JBNDEJ000000000 [23] Data set 5 Genome assembly of S . enterica PSCD-1 FASTA file (.fasta) / GenBank file (.gbk) JBNDEK000000000 [24] Data set 6 Genome assembly of S . enterica GORGM-1 FASTA file (.fasta) / GenBank file (.gbk) JBNDEL000000000 [25] Data set 7 Genome assembly of S . enterica CMCD-1 FASTA file (.fasta) / GenBank file (.gbk) JBNDEM000000000 [26] Limitations The draft nature of the genome assemblies remains fragmented, which may compromise the identification of complete plasmid structures and chromosomal rearrangements. Future studies incorporating hybrid assembly strategies and expanded sampling could enhance genomic resolution and epidemiological interpretations. Abbreviations AMR Antimicrobial Resistance ANI Average Nucleotide Identity dDDH Digital DNA-DNA Hybridization NCBI National Center for Biotechnology Information ONT Oxford Nanopore Technologies VFDB Virulence Factors of Pathogenic Bacteria Declarations Ethics approval and consent to participate Not applicable. Consent for publication Not applicable. Availability of data and materials The data described in this Data note can be freely and openly accessed on the GenBank of NCBI. The genome sequences of S . GOVDG-1, S . PLGS-1, S . PSGS-1, S . PSCD-1, S . GORGM-1, and S . CMCD-1 are available on JBNDEH000000000 [21], JBNDEI000000000 [22], JBNDEJ000000000 [23], JBNDEK000000000 [24], JBNDEL000000000 [25], and JBNDEM000000000 [26]. Associated data files are provided on Figshare [8, 15–18]. Please see table 1 and references [5, 8, 15–18, 20–26] for details and links to the data. Competing interests The authors declare no competing interests. Funding This work was supported by the Korea Institute of Planning and Evaluation for Technology in Food, Agriculture and Forestry (IPET) through the High Value-added Food Technology Development Program, funded by the Ministry of Agriculture, Food and Rural Affairs (MAFRA) (RS-2022-IP322014). Authors’ contributions Su-Hyeon Kim, Investigation, Formal analysis, Writing – original draft | Ji Min Han, Formal analysis | Gyu-Sung Cho, Data curation, Formal analysis, Software | Charles M.A.P. Franz, Supervision, Resources | Mi-Kyung Park, Conceptualization, Funding acquisition, Project administration, Supervision, Writing – review and editing References Oh JH and Park MK Park. Recent trends in Salmonella outbreaks and emerging technology for biocontrol of Salmonella using phages in foods: a review. J Microbiol Biotechnol. 2017;27(12): 2075-2088. Ma J, Dai J, Cao C, Su L, Cao M, He Y, et al. Prevalence, serotype, antimicrobial susceptibility, contamination factors, and control methods of Salmonella spp. in retail fresh fruits and vegetables: A systematic review and meta‐analysis. Compr Rev Food Sci Food Saf. 2024;23(4): e13407. Yang X, Wu Q, Huang J, Wu S, Zhang J, Chen L, et al., Prevalence and characterization of Salmonella isolated from raw vegetables in China. Food Control. 2020; 109 : e106915. Choe J, Kim SH, Han JM, Kim JH, Kwak MS, Jeong DW, et al. Prevalence of indigenous antibiotic-resistant Salmonella isolates and their application to explore a lytic phage vB_SalS_KFSSM with an intra-broad specificity. J Microbiol. 2023;61(12): 1063-1073. NCBI Sequence Read Archive https://www.ncbi.nlm.nih.gov/sra/PRJNA1251564 (2025). De Coster W and Rademakers R. NanoPack2: population-scale evaluation of long-read sequencing data. Bioinformatics. 2023;39(5): btad311. Kolmogorov M, Yuan J, Lin Y, Pevzner PA. Assembly of long, error-prone reads using repeat graphs. Nat Biotechnol. 2019;37(5): 540-546. Kim SH, Han JM, Cho GS, Franz C, Park MK. Table S1, Summary of sequencing and annotation results. Figshare. https://doi.org/10.6084/m9.figshare.28815209 (2025). Mikheenko A, Prjibelski A, Saveliev V, Antipov D, Gurevich A. Versatile genome assembly evaluation with QUAST-LG . Bioinformatics. 2018;34(13):142-150. Tatusova T, DiCuccio M, Badretdin A, Chetvernin V, Nawrocki EP, Zaslavsky L, et al. NCBI prokaryotic genome annotation pipeline. Nucleic Acids Res. 2016;44(14): 6614-6624. Zhang S, Yin Y, Jones M, Zhang Z, Kaiser BD, Dinsmore BA, et al. Salmonella serotype determination utilizing high-throughput genome sequencing data. J Clin Microbiol. 2015;53(5): 1685-1692. Pritchard L, Cock P, Esen Ö, YT. Pyani v0. 2.8: Average nucleotide identity (ANI) and related measures for whole genome comparisons. widdowquinn/pyani: v0.2.8 (v0.2.8). Zenodo. https://doi.org/10.5281/zenodo.2584238 (2019). Auch AF, Jan MV, Klenk HP, Göker M. Digital DNA-DNA hybridization for microbial species delineation by means of genome-to-genome sequence comparison. Stand Genomic Sci. 2010;2(1): 117-134. Bharat A, Petkau A, Avery BP, Chen JC, Folster JP, Carson CA, et al. Correlation between phenotypic and in silico detection of antimicrobial resistance in Salmonella enterica in Canada using Staramr. Microorganisms. 2022;10(2): e292. Kim SH, Han JM, Cho GS, Franz C, Park MK. Table S2, MLST type and dDDH values with reference genomes. https://doi.org/10.6084/m9.figshare.28815287 (2025). Kim SH, Han JM, Cho GS, Franz C, Park MK. Table S3, Serotype prediction. https://doi.org/10.6084/m9.figshare.28815548.v1 (2025). Kim SH, Han JM, Cho GS, Franz C, Park MK. Table S4, ResFinder summary. https://doi.org/10.6084/m9.figshare.28829828.v1 (2025). Kim SH, Han JM, Cho GS, Franz C, Park MK. Table S5, PlasmidFinder summary. https://doi.org/10.6084/m9.figshare.28829693 (2025). Liu B, Zheng D, Zhou S, Chen L, Yang J. VFDB 2022: A general classification scheme for bacterial virulence factors. Nucleic Acids Res. 2022;50(D1):912-917. Kim SH, Han JM, Cho GS, Franz C, Park MK. Table S6, VFDB summary. https://doi.org/10.6084/m9.figshare.28829870 (2025). NCBI. https://www.ncbi.nlm.nih.gov/nuccore/JBNDEH000000000 (2025). NCBI. https://www.ncbi.nlm.nih.gov/nuccore/JBNDEI000000000 (2025). NCBI. https://www.ncbi.nlm.nih.gov/nuccore/JBNDEJ000000000 (2025). NCBI. https://www.ncbi.nlm.nih.gov/nuccore/JBNDEK000000000 (2025). NCBI. https://www.ncbi.nlm.nih.gov/nuccore/JBNDEL000000000 (2025). NCBI. https://www.ncbi.nlm.nih.gov/nuccore/JBNDEM000000000 (2025). Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 13 Oct, 2025 Read the published version in BMC Research Notes → Version 1 posted Editorial decision: Revision requested 15 May, 2025 Reviews received at journal 14 May, 2025 Reviewers agreed at journal 06 May, 2025 Reviewers invited by journal 29 Apr, 2025 Editor assigned by journal 28 Apr, 2025 Submission checks completed at journal 28 Apr, 2025 First submitted to journal 28 Apr, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6547187","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Data Note","associatedPublications":[],"authors":[{"id":449567477,"identity":"d86d7299-2f17-4371-af77-7e51ae5b7034","order_by":0,"name":"Su-Hyeon Kim","email":"","orcid":"","institution":"Kyungpook National University","correspondingAuthor":false,"prefix":"","firstName":"Su-Hyeon","middleName":"","lastName":"Kim","suffix":""},{"id":449567479,"identity":"f6130cf7-9dcf-4a4f-ad33-1ff9df8a029c","order_by":1,"name":"Ji Min Han","email":"","orcid":"","institution":"Kyungpook National University","correspondingAuthor":false,"prefix":"","firstName":"Ji","middleName":"Min","lastName":"Han","suffix":""},{"id":449567481,"identity":"53ebd295-061a-4a2a-9048-d907ffbba028","order_by":2,"name":"Gyu-Sung Cho","email":"","orcid":"","institution":"Max Rubner-Institut","correspondingAuthor":false,"prefix":"","firstName":"Gyu-Sung","middleName":"","lastName":"Cho","suffix":""},{"id":449567483,"identity":"fa3043a2-7c16-4357-a211-77219b0eee8d","order_by":3,"name":"Erik Brinks","email":"","orcid":"","institution":"Max Rubner-Institut","correspondingAuthor":false,"prefix":"","firstName":"Erik","middleName":"","lastName":"Brinks","suffix":""},{"id":449567484,"identity":"08a68ee7-28f1-4553-bd68-bebb32f4d1bc","order_by":4,"name":"Charles M.A.P. 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Among various transmission routes, fresh produce and its surrounding environments have recently emerged as a significant source of \u003cem\u003eSalmonella\u003c/em\u003e contamination. In the USA, fresh produce-associated \u003cem\u003eSalmonella\u003c/em\u003e outbreaks accounted for more than 33.7% of all foodborne \u003cem\u003eSalmonella\u003c/em\u003e outbreaks reported between 1998 and 2021 [2]. Among the more than 2,610 known \u003cem\u003eSalmonella\u0026nbsp;\u003c/em\u003e(\u003cem\u003eS\u003c/em\u003e.)\u003cem\u003e\u0026nbsp;enterica\u003c/em\u003e serovars, several serovars such as \u003cem\u003eS.\u0026nbsp;\u003c/em\u003eTyphimurium, \u003cem\u003eS.\u0026nbsp;\u003c/em\u003eEnteritidis, \u003cem\u003eS.\u0026nbsp;\u003c/em\u003eBareilly, \u003cem\u003eS.\u0026nbsp;\u003c/em\u003eWeltevreden, \u003cem\u003eS\u003c/em\u003e. Thompson, and \u003cem\u003eS\u003c/em\u003e. Newport have frequently been implicated in fresh produce-related outbreaks [2]. These serovars demonstrate persistence in soil, irrigation water, and organic fertilizers, contributing to their relevance in preharvest contamination [1, 3]. Moreover, recent reports showed an increasing occurrence of AMR among such isolates [2].\u003c/p\u003e\n\u003cp\u003eIn a previous study, our group isolated \u003cem\u003eSalmonella enterica\u0026nbsp;\u003c/em\u003estrains from fresh produce and agricultural environment samples in South Korea and assessed their AMR profiles [4]. Thus, this study aimed to present their draft genome sequences to facilitate deeper insight into their serovar composition, acquired AMR genes, and potential genomic characteristics relevant to food safety. The generated data are intended to support comparative genomic analyses and surveillance efforts related to \u003cem\u003eSalmonella\u003c/em\u003e contamination in fresh produce and agricultural environments. The detailed isolation, biochemical identification, and sequencing procedures used for these isolates are provided in the Data Description section.\u0026nbsp;\u003c/p\u003e"},{"header":"Data description","content":"\u003cp\u003eA total of six \u003cem\u003eSalmonella\u003c/em\u003e strains were isolated from green onion, peach leaves, peach orchard soil, and cow manure collected in Daegu and Gyeonsangbuk-do provinces, South Korea. Each sample (25 g) was pre-enriched in 225 mL of tryptic soy broth (BD Difco, Franklin Lakes, NJ, USA) at 37\u0026deg;C for 18 hours. Then, 1 mL of the pre-enriched culture was transferred into Rappaport-Vassiliadis broth (BD Difco) at 37\u0026deg;C for 24 hours. Aliquots were streaked onto MacConkey and xylose lysine deoxycholate agar (BD Difco), and the plates were incubated at 37\u0026deg;C for 48 hours. Two agar plates with the ideal color were selected and presumptively identified using indole, methyl red, Voges-Proskauer, and citrate tests, as well as 16S rRNA gene sequencing [4].\u003c/p\u003e\n\u003cp\u003eGenomic DNAs were extracted using the Wizard\u003csup\u003e\u0026reg;\u003c/sup\u003e HMW DNA Extraction Kit (Promega Co., Medison, WI, USA) according to the manufacturer\u0026rsquo;s instructions. DNA libraries were prepared using the ligation sequencing and native barcoding kit (SQK-NBD114.96, Oxford Nanopore Technologies Inc., Oxford, UK) and sequenced using ONT PromethION 2 Solo platform with R10.4.1 flow cell. Basecalling and demultiplexing were conducted using Dorado version 0.9.5. The raw sequence data (Data set 1) [5] were filtered using Chopper v. 0.9.2 with a minimum average quality score of Q15 and minimum length of 600 bp [6]. \u003cem\u003eDe novo\u003c/em\u003e assembly was then performed using Flye (v. 2.9.5) with the --nano-corr option [7], resulting in a coverage range of 66\u0026times; to 198\u0026times; (Data file 1) [8]. The quality of assembled contigs was assessed using QUAST v.5.3.0 [9]. Genomes were annotated using the NCBI Prokaryotic Genome Annotation Pipeline [10] and serotypes were predicted with SeqSero v 1.3.1 [11]. Average nucleotide identity (ANI) and dDDH were calculated using the pyani pipeline [12] and the genome-to-genome distance calculator (GGDC, formula 2) [13], respectively. Identification of acquired AMR genes, plasmid replicon types, and multilocus sequence types (MLST) was conducted using the Staramr pipeline (v.0.11.0) [14], incorporating ResFinder (database version 13.12.2024), PlasmidFinder (database version 14.11.2024), and MLST (v2.23.0).\u003c/p\u003e\n\u003cp\u003eDraft genome assemblies of six \u003cem\u003eSalmonella\u003c/em\u003e isolates consisted of 3 to 7 contigs, with genome sizes ranging from 4.89 to 5.02 Mbp and mol% GC contents of approximately 51.9% to 52.2% (Data file 1) [8]. The total number of predicted coding sequences ranged from 4,541 to 4,761, and all genomes contained 22 rRNA genes and 85\u0026ndash;88 tRNA genes [8]. \u003cem\u003eIn silico\u003c/em\u003e MLST analysis revealed that the isolates belonged to distinct sequence types, including ST4, ST19, ST34, ST198, and ST8316, and they were identified as \u003cem\u003eS\u003c/em\u003e. \u003cem\u003eenterica\u003c/em\u003e based on dDDH analysis (Data file 2) [15]. Their serotypes were further predicted and assigned to four serovars: \u003cem\u003eS\u003c/em\u003e. Typhimurium (GOVDG-1, GORGM-1, and PLGS-1), \u003cem\u003eS\u003c/em\u003e. I 4,[5],12:i:- (PSGS-1), \u003cem\u003eS\u003c/em\u003e. Kentucky (PSCD-1), and \u003cem\u003eS\u003c/em\u003e. Montevideo (CMCD-1) (Data file 3) [16].\u0026nbsp;In addition, 3 of them carried acquired AMR genes (Data file 4) [17], most commonly \u003cem\u003eaph\u003c/em\u003e(3\u0026quot;)-Ib, \u003cem\u003eaph\u003c/em\u003e(6)-Id, and \u003cem\u003etet\u003c/em\u003e(B), as well as at least one plasmid contig (Data file 5) [18]. Virulence genes were detected with the Virulence Factors of Pathogenic Bacteria (Data file 6) [19\u0026ndash;20]. The data were deposited in Figshare and NCBI database (Table 1) [8, 15\u0026ndash;18, 20].\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 1\u003c/strong\u003e: Overview of data files/data sets.\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"650\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 11.5385%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eLabel\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 37.8462%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eName of data file/data set\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20.3077%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eFile types\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e(file extension)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 30.3077%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eData repository and identifier (DOI or accession number)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 11.5385%;\"\u003e\n \u003cp\u003eData file 1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 37.8462%;\"\u003e\n \u003cp\u003eTable S1, Summary of sequencing and annotation results\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20.3077%;\"\u003e\n \u003cp\u003ePortable Document Format file (.pdf)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 30.3077%;\"\u003e\n \u003cp\u003e\u003cu\u003ehttps://doi.org/10.6084/m9.figshare.28815209\u003c/u\u003e [8]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 11.5385%;\"\u003e\n \u003cp\u003eData file 2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 37.8462%;\"\u003e\n \u003cp\u003eTable S2, MLST type, and dDDH and ANI values with reference genomes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20.3077%;\"\u003e\n \u003cp\u003eMS Excel file (.xlsx)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 30.3077%;\"\u003e\n \u003cp\u003e\u003cu\u003ehttps://doi.org/10.6084/m9.figshare.28815287\u003c/u\u003e [15]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 11.5385%;\"\u003e\n \u003cp\u003eData file 3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 37.8462%;\"\u003e\n \u003cp\u003eTable S3, Serotype prediction\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20.3077%;\"\u003e\n \u003cp\u003ePortable Document Format file (.pdf)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 30.3077%;\"\u003e\n \u003cp\u003e\u003cu\u003ehttps://doi.org/10.6084/m9.figshare.28815548.v1\u003c/u\u003e [16]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 11.5385%;\"\u003e\n \u003cp\u003eData file 4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 37.8462%;\"\u003e\n \u003cp\u003eTable S4, ResFinder summary\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20.3077%;\"\u003e\n \u003cp\u003eMS Excel file (.xlsx)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 30.3077%;\"\u003e\n \u003cp\u003e\u003cu\u003ehttps://doi.org/10.6084/m9.figshare.28829828.v1\u003c/u\u003e [17]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 11.5385%;\"\u003e\n \u003cp\u003eData file 5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 37.8462%;\"\u003e\n \u003cp\u003eTable S5, PlasmidFinder summary\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20.3077%;\"\u003e\n \u003cp\u003eMS Excel file (.xlsx)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 30.3077%;\"\u003e\n \u003cp\u003e\u003cu\u003ehttps://doi.org/10.6084/m9.figshare.28829693\u003c/u\u003e [18]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 11.5385%;\"\u003e\n \u003cp\u003eData file 6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 37.8462%;\"\u003e\n \u003cp\u003eTable S6, VFDB summary\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20.3077%;\"\u003e\n \u003cp\u003eMS Excel file (.xlsx)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 30.3077%;\"\u003e\n \u003cp\u003e\u003cu\u003ehttps://doi.org/10.6084/m9.figshare.28829870\u003c/u\u003e [20]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 11.5385%;\"\u003e\n \u003cp\u003eData set 1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 37.8462%;\"\u003e\n \u003cp\u003eRaw sequencing data of \u003cem\u003eS\u003c/em\u003e.\u003cem\u003e\u0026nbsp;enterica\u003c/em\u003e GOVDG-1, \u003cem\u003eS\u003c/em\u003e.\u003cem\u003e\u0026nbsp;enterica\u003c/em\u003e PLGS-1, \u003cem\u003eS\u003c/em\u003e.\u003cem\u003e\u0026nbsp;enterica\u003c/em\u003e PSGS-1, \u003cem\u003eS\u003c/em\u003e.\u003cem\u003e\u0026nbsp;enterica\u003c/em\u003e PSCD-1, \u003cem\u003eS\u003c/em\u003e.\u003cem\u003e\u0026nbsp;enterica\u003c/em\u003e GORGM-1, and \u003cem\u003eS\u003c/em\u003e.\u003cem\u003e\u0026nbsp;enterica\u003c/em\u003e CMCD-1\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20.3077%;\"\u003e\n \u003cp\u003eFastq file (.fastq)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 30.3077%;\"\u003e\n \u003cp\u003ePRJNA1251564 [5]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 11.5385%;\"\u003e\n \u003cp\u003eData set 2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 37.8462%;\"\u003e\n \u003cp\u003eGenome assembly of \u003cem\u003eS\u003c/em\u003e.\u003cem\u003e\u0026nbsp;enterica\u003c/em\u003e GOVDG-1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20.3077%;\"\u003e\n \u003cp\u003eFASTA file (.fasta) / GenBank file (.gbk)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 30.3077%;\"\u003e\n \u003cp\u003eJBNDEH000000000 [21]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 11.5385%;\"\u003e\n \u003cp\u003eData set 3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 37.8462%;\"\u003e\n \u003cp\u003eGenome assembly of \u003cem\u003eS\u003c/em\u003e.\u003cem\u003e\u0026nbsp;enterica\u003c/em\u003e PLGS-1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20.3077%;\"\u003e\n \u003cp\u003eFASTA file (.fasta) / GenBank file (.gbk)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 30.3077%;\"\u003e\n \u003cp\u003eJBNDEI000000000 [22]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 11.5385%;\"\u003e\n \u003cp\u003eData set 4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 37.8462%;\"\u003e\n \u003cp\u003eGenome assembly of \u003cem\u003eS\u003c/em\u003e.\u003cem\u003e\u0026nbsp;enterica\u003c/em\u003e PSGS-1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20.3077%;\"\u003e\n \u003cp\u003eFASTA file (.fasta) / GenBank file (.gbk)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 30.3077%;\"\u003e\n \u003cp\u003eJBNDEJ000000000 [23]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 11.5385%;\"\u003e\n \u003cp\u003eData set 5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 37.8462%;\"\u003e\n \u003cp\u003eGenome assembly of \u003cem\u003eS\u003c/em\u003e.\u003cem\u003e\u0026nbsp;enterica\u003c/em\u003e PSCD-1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20.3077%;\"\u003e\n \u003cp\u003eFASTA file (.fasta) / GenBank file (.gbk)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 30.3077%;\"\u003e\n \u003cp\u003eJBNDEK000000000 [24]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 11.5385%;\"\u003e\n \u003cp\u003eData set 6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 37.8462%;\"\u003e\n \u003cp\u003eGenome assembly of \u003cem\u003eS\u003c/em\u003e.\u003cem\u003e\u0026nbsp;enterica\u003c/em\u003e GORGM-1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20.3077%;\"\u003e\n \u003cp\u003eFASTA file (.fasta) / GenBank file (.gbk)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 30.3077%;\"\u003e\n \u003cp\u003eJBNDEL000000000 [25]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 11.5385%;\"\u003e\n \u003cp\u003eData set 7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 37.8462%;\"\u003e\n \u003cp\u003eGenome assembly of \u003cem\u003eS\u003c/em\u003e.\u003cem\u003e\u0026nbsp;enterica\u003c/em\u003e CMCD-1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20.3077%;\"\u003e\n \u003cp\u003eFASTA file (.fasta) / GenBank file (.gbk)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 30.3077%;\"\u003e\n \u003cp\u003eJBNDEM000000000 [26]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e"},{"header":"Limitations","content":"\u003cp\u003eThe draft nature of the genome assemblies remains fragmented, which may compromise the identification of complete plasmid structures and chromosomal rearrangements. Future studies incorporating hybrid assembly strategies and expanded sampling could enhance genomic resolution and epidemiological interpretations.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eAMR\u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;Antimicrobial Resistance\u003c/p\u003e\n\u003cp\u003eANI\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;Average Nucleotide Identity\u003c/p\u003e\n\u003cp\u003edDDH\u0026nbsp; \u0026nbsp;\u0026nbsp;Digital DNA-DNA Hybridization\u003c/p\u003e\n\u003cp\u003eNCBI\u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;National Center for Biotechnology Information\u003c/p\u003e\n\u003cp\u003eONT\u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;Oxford Nanopore Technologies\u003c/p\u003e\n\u003cp\u003eVFDB \u0026nbsp; \u0026nbsp; Virulence Factors of Pathogenic Bacteria\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003cbr\u003e\u0026nbsp;\u003c/strong\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003cbr\u003e\u0026nbsp;\u003c/strong\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe data described in this Data note can be freely and openly accessed on the GenBank of NCBI. The genome sequences of \u003cem\u003eS\u003c/em\u003e. GOVDG-1, \u003cem\u003eS\u003c/em\u003e. PLGS-1, \u003cem\u003eS\u003c/em\u003e. PSGS-1, \u003cem\u003eS\u003c/em\u003e. PSCD-1, \u003cem\u003eS\u003c/em\u003e. GORGM-1, and \u003cem\u003eS\u003c/em\u003e. CMCD-1 are available on JBNDEH000000000 [21], JBNDEI000000000 [22], JBNDEJ000000000 [23], JBNDEK000000000 [24], JBNDEL000000000 [25], and JBNDEM000000000 [26]. Associated data files are provided on Figshare [8, 15–18]. Please see table 1 and references [5, 8, 15–18, 20–26] for details and links to the data.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interests.\u003cstrong\u003e\u003cbr\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003cbr\u003e\u0026nbsp;\u003c/strong\u003eThis work was supported by the Korea Institute of Planning and Evaluation for Technology in Food, Agriculture and Forestry (IPET) through the High Value-added Food Technology Development Program, funded by the Ministry of Agriculture, Food and Rural Affairs (MAFRA) (RS-2022-IP322014).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors’ contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSu-Hyeon Kim, Investigation, Formal analysis, Writing – original draft | Ji Min Han, Formal analysis | Gyu-Sung Cho, Data curation, Formal analysis, Software | Charles M.A.P. Franz, Supervision, Resources | Mi-Kyung Park, Conceptualization, Funding acquisition, Project administration, Supervision, Writing – review and editing\u003cstrong\u003e\u003cbr\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eOh JH and Park MK Park. Recent trends in \u003cem\u003eSalmonella\u003c/em\u003e outbreaks and emerging technology for biocontrol of\u003cem\u003e Salmonella\u003c/em\u003e using phages in foods: a review. J Microbiol Biotechnol. 2017;27(12): 2075-2088.\u003c/li\u003e\n\u003cli\u003eMa\u0026nbsp;J,\u0026nbsp;Dai\u0026nbsp;J,\u0026nbsp;Cao\u0026nbsp;C,\u0026nbsp;Su\u0026nbsp;L,\u0026nbsp;Cao\u0026nbsp;M,\u0026nbsp;He\u0026nbsp;Y,\u0026nbsp;et al. Prevalence, serotype, antimicrobial susceptibility, contamination factors, and control methods of \u003cem\u003eSalmonella\u003c/em\u003e spp. in retail fresh fruits and vegetables: A systematic review and meta‐analysis. Compr Rev Food Sci Food Saf. 2024;23(4): e13407.\u003c/li\u003e\n\u003cli\u003eYang X, Wu Q, Huang J, Wu S, Zhang J, Chen L, et al., Prevalence and characterization of \u003cem\u003eSalmonella\u003c/em\u003e isolated from raw vegetables in China. Food Control. 2020;\u003cstrong\u003e109\u003c/strong\u003e: e106915.\u003c/li\u003e\n\u003cli\u003eChoe J, Kim SH, Han JM, Kim JH, Kwak MS, Jeong DW, et al. Prevalence of indigenous antibiotic-resistant \u003cem\u003eSalmonella\u003c/em\u003e isolates and their application to explore a lytic phage vB_SalS_KFSSM with an intra-broad specificity. J Microbiol. 2023;61(12): 1063-1073.\u003c/li\u003e\n\u003cli\u003eNCBI Sequence Read Archive \u003cu\u003ehttps://www.ncbi.nlm.nih.gov/sra/PRJNA1251564\u003c/u\u003e (2025).\u003c/li\u003e\n\u003cli\u003eDe Coster W and Rademakers R. NanoPack2: population-scale evaluation of long-read sequencing data. Bioinformatics. 2023;39(5): btad311.\u003c/li\u003e\n\u003cli\u003eKolmogorov M, Yuan J, Lin Y, Pevzner PA. Assembly of long, error-prone reads using repeat graphs. Nat Biotechnol. 2019;37(5): 540-546.\u003c/li\u003e\n\u003cli\u003eKim SH, Han JM, Cho GS, Franz C, Park MK. Table S1, Summary of sequencing and annotation results. Figshare. \u003cu\u003ehttps://doi.org/10.6084/m9.figshare.28815209\u003c/u\u003e (2025).\u003c/li\u003e\n\u003cli\u003eMikheenko A, Prjibelski A, Saveliev V, Antipov D, Gurevich A. Versatile genome assembly evaluation with QUAST-LG\u003cem\u003e.\u003c/em\u003e Bioinformatics. 2018;34(13):142-150.\u003c/li\u003e\n\u003cli\u003eTatusova T, DiCuccio M, Badretdin A, Chetvernin V, Nawrocki EP, Zaslavsky L, et al. NCBI prokaryotic genome annotation pipeline. Nucleic Acids Res. 2016;44(14): 6614-6624.\u003c/li\u003e\n\u003cli\u003eZhang S, Yin Y, Jones M, Zhang Z, Kaiser BD, Dinsmore BA, et al. \u003cem\u003eSalmonella \u003c/em\u003eserotype determination utilizing high-throughput genome sequencing data. J Clin Microbiol. 2015;53(5): 1685-1692.\u003c/li\u003e\n\u003cli\u003ePritchard L, Cock P, Esen \u0026Ouml;, YT. Pyani v0. 2.8: Average nucleotide identity (ANI) and related measures for whole genome comparisons. widdowquinn/pyani: v0.2.8 (v0.2.8). Zenodo. \u003cu\u003ehttps://doi.org/10.5281/zenodo.2584238\u003c/u\u003e (2019).\u003c/li\u003e\n\u003cli\u003eAuch AF, Jan MV, Klenk HP, G\u0026ouml;ker M. Digital DNA-DNA hybridization for microbial species delineation by means of genome-to-genome sequence comparison. Stand Genomic Sci. 2010;2(1): 117-134.\u003c/li\u003e\n\u003cli\u003eBharat A, Petkau A, Avery BP, Chen JC, Folster JP, Carson CA, et al. 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Table S5, PlasmidFinder summary. \u003cu\u003ehttps://doi.org/10.6084/m9.figshare.28829693\u003c/u\u003e (2025).\u003c/li\u003e\n\u003cli\u003eLiu B, Zheng D, Zhou S, Chen L, Yang J. VFDB 2022: A general classification scheme for bacterial virulence factors. Nucleic Acids Res. 2022;50(D1):912-917.\u003c/li\u003e\n\u003cli\u003eKim SH, Han JM, Cho GS, Franz C, Park MK. Table S6, VFDB summary. \u003cu\u003ehttps://doi.org/10.6084/m9.figshare.28829870\u003c/u\u003e (2025).\u003c/li\u003e\n\u003cli\u003eNCBI. https://www.ncbi.nlm.nih.gov/nuccore/JBNDEH000000000 (2025).\u003c/li\u003e\n\u003cli\u003eNCBI. https://www.ncbi.nlm.nih.gov/nuccore/JBNDEI000000000 (2025).\u003c/li\u003e\n\u003cli\u003eNCBI. https://www.ncbi.nlm.nih.gov/nuccore/JBNDEJ000000000 (2025).\u003c/li\u003e\n\u003cli\u003eNCBI. https://www.ncbi.nlm.nih.gov/nuccore/JBNDEK000000000 (2025).\u003c/li\u003e\n\u003cli\u003eNCBI. https://www.ncbi.nlm.nih.gov/nuccore/JBNDEL000000000 (2025).\u003c/li\u003e\n\u003cli\u003eNCBI. https://www.ncbi.nlm.nih.gov/nuccore/JBNDEM000000000 (2025).\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-research-notes","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"resn","sideBox":"Learn more about [BMC Research Notes](http://bmcresnotes.biomedcentral.com)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/resn/default.aspx","title":"BMC Research Notes","twitterHandle":"@BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Salmonella, antimicrobial resistance, fresh produce and agricultural environment, South Korea","lastPublishedDoi":"10.21203/rs.3.rs-6547187/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6547187/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eObjectives: \u003cbr\u003e\n \u003cem\u003eSalmonella enterica\u003c/em\u003e is a globally significant foodborne pathogen and a leading cause of gastrointestinal infections, with increasing concern over strains harboring antimicrobial resistance (AMR). This Data Note reports draft genome sequences of six \u003cem\u003eS. enterica\u003c/em\u003e subsp. \u003cem\u003eenterica\u003c/em\u003e isolates from fresh produce and agricultural environments in South Korea. The objective of this work was to provide genomic data on environmental \u003cem\u003eSalmonella\u003c/em\u003eisolates, their serovar diversity, AMR gene profiling, and genetic attributes relevant to \u003cem\u003eSalmonella\u003c/em\u003e surveillance and comparative genomics.\u003c/p\u003e\n\u003cp\u003eData description:\u003c/p\u003e\n\u003cp\u003eDraft genomes of six isolates consisted of 3 to 7 contigs with genome sizes ranging from 4.89 to 5.02 Mbp and mol% GC contents between 51.89% and 52.24%. The isolates were identified as four serovars, such as \u003cem\u003eS\u003c/em\u003e. Typhimurium, \u003cem\u003eS\u003c/em\u003e. I 4,[5],12:i:-, \u003cem\u003eS\u003c/em\u003e. Kentucky, and \u003cem\u003eS\u003c/em\u003e. Montevideo, and five MLST types. Among them, three strains representing serovars including Typhimurium,\u003cem\u003e \u003c/em\u003eI 4,[5],12:i:-, and Kentucky harbored acquired AMR genes, conferring resistance to aminoglycosides, aminopenicillins, diaminopyrimidines, sulfonamide, and tetracyclines. Our study highlights the genomic diversity and resistance potential of \u003cem\u003eS. enterica\u003c/em\u003e isolates derived from preharvest agricultural environments, providing valuable resources for future comparative analyses and AMR surveillance efforts.\u003c/p\u003e","manuscriptTitle":"Draft genome sequences of Salmonella enterica subsp. enterica isolates from fresh produce and agricultural environments in South Korea","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-05-05 05:32:36","doi":"10.21203/rs.3.rs-6547187/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-05-15T09:00:20+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-05-14T17:45:32+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"36398391589869338990497233400431490784","date":"2025-05-06T13:31:12+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-04-29T08:40:49+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-04-29T02:34:38+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-04-29T02:34:30+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Research Notes","date":"2025-04-28T10:56:50+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-research-notes","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"resn","sideBox":"Learn more about [BMC Research Notes](http://bmcresnotes.biomedcentral.com)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/resn/default.aspx","title":"BMC Research Notes","twitterHandle":"@BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"19014066-d1fa-4b73-8d92-aac0df474a61","owner":[],"postedDate":"May 5th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2025-10-20T16:00:19+00:00","versionOfRecord":{"articleIdentity":"rs-6547187","link":"https://doi.org/10.1186/s13104-025-07494-8","journal":{"identity":"bmc-research-notes","isVorOnly":false,"title":"BMC Research Notes"},"publishedOn":"2025-10-13 15:57:17","publishedOnDateReadable":"October 13th, 2025"},"versionCreatedAt":"2025-05-05 05:32:36","video":"","vorDoi":"10.1186/s13104-025-07494-8","vorDoiUrl":"https://doi.org/10.1186/s13104-025-07494-8","workflowStages":[]},"version":"v1","identity":"rs-6547187","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6547187","identity":"rs-6547187","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}