Next Article in Journal
The Role of Nitric Oxide in the Growth and Development of Schizophyllum commune Under Anaerobic Conditions
Previous Article in Journal
Hepatitis E Virus in the Role of an Emerging Food-Borne Pathogen
Previous Article in Special Issue
Host–Pathogen Interactions during Shiga Toxin-Producing Escherichia coli Adherence and Colonization in the Bovine Gut: A Comprehensive Review
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
Microorganisms
Volume 13
Issue 4
10.3390/microorganisms13040886
microorganisms-logo
Submit to this Journal Review for this Journal Propose a Special Issue
► Article Menu

Article Menu

share Share announcement Help format_quote Cite question_answer Discuss in SciProfiles
first_page
settings
Order Article Reprints
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Article

Comparative Genomic Profiles of Salmonella Typhimurium and Salmonella Dublin Bovine Isolates from the U.S. Indicate Possible Factors Associated with the Host Adaptation of Salmonella Dublin in the Region

by
Kingsley E. Bentum
1,
Emmanuel Kuufire
1,
Rejoice Nyarku
1,
Viona Osei
1,
Benjamin Adu-Addai
2,
Jonathan G. Frye
3,
Charlene R. Jackson
3,
Temesgen Samuel
1 and
Woubit Abebe
1,*
1
Center for Food Animal Health, Food Safety and Defense, Department of Pathobiology, College of Veterinary Medicine, Tuskegee University, Tuskegee, AL 36088, USA
2
Department of Biomedical Science, College of Veterinary Medicine, Tuskegee University, Tuskegee, AL 36088, USA
3
Poultry Microbiological Safety and Processing Research Unit, USDA-ARS, U.S. National Poultry Research Center, Athens, GA 30605, USA
*
Author to whom correspondence should be addressed.
Microorganisms 2025, 13(4), 886; https://doi.org/10.3390/microorganisms13040886
Submission received: 10 March 2025 / Revised: 8 April 2025 / Accepted: 10 April 2025 / Published: 12 April 2025
(This article belongs to the Special Issue Foodborne Bacteria–Host Interactions: 2nd Edition)
Browse Figures
Versions Notes

Abstract

:
Salmonella Dublin (S. Dublin) and Salmonella Typhimurium (S. Typhimurium) are commonly linked to bovine salmonellosis. S. Dublin is, however, considered a bovine-adapted serovar for primarily infecting and thriving in cattle. Using S. Typhimurium (a generalist serovar) as a benchmark, this study investigates genomic factors contributing to S. Dublin’s adaptation to cattle hosts in the U.S. A total of 1337 S. Dublin and 787 S. Typhimurium whole-genome sequences from bovine sources were analyzed with CARD (version 4.0.0), ARG-NOTT (version 6), and AMRfinderPlus (version 4.0.3) for antimicrobial resistance (AMR) genes; VFDB and AMRfinderPlus for virulence genes; AMRFinderPlus for stress genes; and Plasmidfinder for plasmids. Existing clonal groups among isolates of the two serovars were also investigated using the Hierarchical Clustering of Core Genome Multi-Locus Sequence Typing (HierCC-cgMLST) model. The results revealed minimal genomic variation among S. Dublin isolates. Comparatively, the IncX1 plasmid was somewhat exclusively identified in S. Dublin isolates and each carried an average of four plasmids (p-value < 0.05). Furthermore, S. Dublin isolates exhibited a higher prevalence of AMR genes against key antimicrobials, including aminoglycosides, beta-lactams, tetracyclines, and sulfonamides, commonly used in U.S. cattle production. Additionally, Type VI secretion system genes tssJKLM and hcp2/tssD2, essential for colonization, were found exclusively in S. Dublin isolates with over 50% of these isolates possessing genes that confer resistance to heavy metal stressors, like mercury. These findings suggest that S. Dublin’s adaptation to bovine hosts in the U.S. is supported by a conserved genetic makeup enriched with AMR genes, virulence factors, and stress-related genes, enabling it to colonize and persist in the bovine gut.

1. Introduction

Salmonella Dublin (S. Dublin) and Salmonella Typhimurium (S. Typhimurium) are among the common serovars associated with bovine salmonellosis [1]. Although the two commonly infect cattle, S. Dublin is described as a bovine-host-adapted serovar because it primarily infects and persistently thrives in the bovine host [2,3]. Calves are most susceptible to bovine salmonellosis, and infections caused by S. Dublin can result in systemic conditions such as meningoencephalitis, pneumonia, respiratory distress, hyperthermia, and diarrhea [2]. Similarly, those caused by S. Typhimurium can result in acute enteritis and exudative diarrhea [3].
A wide range of comparative genomic studies have been conducted to understand host–pathogen adaptation and the pathogenesis of Salmonella [4,5,6,7]. In the microbial world, an interplay of virulence factors, antimicrobial resistance genes, and stress response genes contributes to a pathogen’s ability to invade and thrive in its host [8,9]. For instance, to adapt to and survive in competitive host environments, pathogens utilize virulence genes to evade host defense systems and antimicrobial resistance genes to withstand antimicrobial therapies [10].
With the abundance of publicly available whole-genome sequence (WGS) data and analytical tools, it is now possible to conduct in-depth comparative analyses that provide insights beyond epidemiological evidence, extending to microbe-specific pathogenicity. One key advantage of such advanced technology is that a single next-generation sequencing (NGS) run can generate data on phylogenetics, antimicrobial resistance (AMR), and virulence profile of pathogens [11]. Furthermore, the sequence type (ST) of various Salmonella isolates can be obtained from their WGS data to aid in traceback investigations of human infections originating from food animals. For example, a recent study in China reported that S. Typhimurium ST34 from pigs and S. Typhimurium ST19 from chickens are the main serovars associated with gastro-infections in children and adults, respectively [12].
Given these benefits, major public health institutions in the U.S. now implement whole-genome sequencing of pathogens as part of routine investigations [13]. This has led to the creation of numerous pathogen sequence databases, widely accessible for research and investigation purposes [13]. Among the major public databases is the GenBank® nucleic acid sequence database of the National Center for Biotechnology Information (NCBI), which contains approximately 3.7 billion nucleotide sequences from over 557,000 species [14,15]. Some countries have pathogen-specific databases, such as the open-access Chinese Local Salmonella Genome Database (CLSG), which provides essential surveillance information about Salmonella in the country [16]. These national databases provide data for other platforms such as EnteroBase [17].
Leveraging the wealth of publicly available genomic data, this study aimed to investigate the genomic factors that may contribute to the ability of the bovine-host-adapted serovar S. Dublin to thrive in its cattle host, using S. Typhimurium, a broad-host-range serovar, as a benchmark.

2. Materials and Methods

2.1. Data Acquisition

All WGS data in this study were retrieved from Enterobase on 24 September 2024. At the time of accessing the Enterobase platform, there were a total of 523,825 curated Salmonella genomes available. Using the following search terms—serovar = Typhimurium/Dublin, country = United States, source niche = livestock, and source type = bovine—1370 S. Dublin- and 814 S. Typhimurium-assembled genomes were initially downloaded using a plug-in tab on the Enterobase platform. All genomic data available in Enterobase are automatically retrieved from Illumina short-read sequences available in various public short-read archives or those uploaded by registered users [17]. These short-read sequences are assembled using the automated and consistent QAssembly pipeline available in Enterobase. Genomes that pass quality control are finally genotyped [by Multilocus Sequence Typing (MLST), Ribosomal Multilocus Sequence Typing (rMLST), Core genome Multilocus Sequence Typing (cgMLST), and Whole genome Multilocus Sequence Typing (wgMLST)] and finally made accessible to the public together with their associated metadata [17]. The serovar definitions of the isolates were further validated with the Sistr program (https://github.com/phac-nml/sistr_cmd, accessed on 24 September 2024), and this reduced the data set to 1337 S. Dublin and 787 S. Typhimurium draft-assembled genomes, which can be accessed within the Enterobase database at (https://enterobase.warwick.ac.uk/species/senterica/search_strains?query=workspace:124148, accessed on 24 September 2024). The metadata of the isolates’ sequence type (ST), year of collection, and state of isolation are summarized in Figure 1. Details about the Enterobase QAssembly pipeline and genotyping methods can also be found at https://enterobase.readthedocs.io/en/latest/about.html, accessed on 25 September 2024.

2.2. Hierarchical Clustering of Core Genome Multilocus Sequence (HierCC-cgMLST) Analysis

Clonal groups of isolates were assigned using the Hierarchical Clustering of Core Genome Multi-Locus Sequence Typing (HierCC-cgMLST) plug-in within Enterobase, and this was accessed on 7 October 2024. For the HierCC-cgMLST, strains of serovars can differ up to a certain specified number of Core Genome Multilocus Sequence (cgMLST) alleles, which is indicated by the prefix “HC” followed by that number (e.g., HC5 will be for 5 cgMLST allelic differences) [18]. To investigate the clonal populations and the existing genetic diversities among the two serovars further, grape trees [19] were constructed using the cgMLST V2 + HierCC V1 scheme in Enterobase, which is based on a set of 3002 genes [20]. The NINJA Neighbor-Joining (NJ) algorithm [21] was employed in the construction of the grape tree for the 787 S. Typhimurium isolate genomes and 1337 S. Dublin isolate genomes.

2.3. Antimicrobial, Virulence, Stress Genes, and Plasmid Identification Analysis

The draft genome assemblies of the isolates were screened for AMR genes, virulence genes, stress genes, and plasmids using various databases. For AMR genes, three databases were used: the Comprehensive Antimicrobial Resistance Database (CARD) [22], AMRFinderPlus [23], and ARG-ANNOT [24]. For virulence genes, the VirulenceFinder [25] and the AMRFinderPlus databases were used. Stress genes were also obtained from the AMRFinderPlus results. Finally, PlasmidFinder [26] was used to identify plasmids within isolate sequences. Where more than one database was used, the independent results from the databases were aggregated to provide a single comprehensive result. Except for AMRFinderPlus (software version 4.0.3, database updated on version 2024-10-22.1), which was used in a standalone module, all other databases were updated on 24 November 2024, and used via ABRicate (version 1.0.1) (https://github.com/tseemann/abricate, accessed on 28 November 2024). For all ABRicate searches, the default Basic Local Alignment Search Tool (BLAST) threshold of 80% of minimum nucleotide identity and 80% coverage was used, as in similar studies [27].

2.4. Statistical Analysis

The statistical significance for the various comparative analyses was calculated with two-way ANOVA using GraphPad Prism (version 10.3.0). A p-value of <0.05 was deemed statistically significant.

3. Results

3.1. Grape Tree Phylogeny Based on HierCC-cgMLST

No variations were observed among isolates of the respective serovars at the Hierarchical Clustering levels 900 (HC900) and 400 (HC400) (Figure 2a–c). However, Hierarchical Clustering level 100 (HC100) showed that more variations existed among S. Typhimurium isolates than S. Dublin isolates. At this level, while 15 discrete clonal groups were observed among the S. Typhimurium isolates with the distribution of the isolates shared mainly among four major clusters, only 6 clonal groups were identified in S. Dublin isolates, with 99.4% (1329/1337) belonging to the HC100_25 cluster (Figure 2d,e).

3.2. Antimicrobial Resistance Genes

Isolates of the two serovars mainly carried AMR genes against aminoglycosides, beta-lactam, tetracycline, sulfonamides, quinolone, phenicols, and polypeptide antibiotics (Figure 3). Out of these, 89.5% (1196/1337) of S. Dublin isolates carried AMR genes against aminoglycoside, beta-lactam, quinolone, sulfonamide, phenicol, tetracycline, and polypeptide antibiotics while 48.3% (380/787) of S. Typhimurium isolates carried AMR genes against aminoglycoside, beta-lactam, quinolone, and polypeptide antibiotics (p-value = 0.0001) (Figure 3). Among aminoglycoside resistance genes, aac(6′)-Iy, aph(3″)-Ib, and aph(6)-Id were present in over 90% of S. Dublin isolates. On the other hand, except for the aac(6′)-Iaa gene carried by all S. Typhimurium isolates, low proportions of isolates belonging to the serovar carried other resistance genes (Figure 4a). Among beta-lactam resistance genes, baeR was only found in S. Typhimurium isolates. All isolates from the two serovars, however, carried the blaCRP gene. Also, over 70% of S. Dublin isolates carried the genes blaCMY-111, blaCMY-2, and blaCMY-59, but they were found in less than 20% of S. Typhimurium isolates (Figure 4a). For tetracycline resistance, the tetA and tetR genes were found in more than 90% of S. Dublin isolates but in less than 30% of S. Typhimurium isolates. For sulfonamide resistance, sul2 was the prevalent gene in S. Dublin found in 92.3% (1234/1337) of isolates. However, less than 30% of S. Typhimurium isolates carried either the sul1 or sul2 gene (Figure 4a). For resistance against quinolone and polypeptide antibiotics, all isolates from the two serovars carried the emrR and bacA genes, respectively (Figure 4a). The main gene for phenicol resistance was floR, which was found in 88% (1176/1337) and 31.1% (245/787) of S. Dublin and S. Typhimurium isolates, respectively. All AMR genes detected among the isolates in this study are presented in the Supplementary Data (AMR genes). Finally, almost 100% of isolates of the two serovars were equipped with various efflux and multidrug-resistance-related genes, which are also shown in Supplementary Data (Multidrug and Efflux genes).

3.3. Virulence Genes

The majority (70–100%) of isolates from both serovars carried genes with functions related to the Type III secretion system (T3SS), fimbriae adhesion, Type VI secretion system (T6SS), curlin formation, outer membrane protein regulatory activities, stress regulation, and iron and magnesium acquisition. However, S. Dublin isolates comparatively carried more genes related to T3SS and T6SS (Figure 5). The genes tssJ, tssK, tssL, tssM, and hcp2/tssD2, all related to the T6SS, were found in almost 100% of S. Dublin isolates but were entirely missing in all S. Typhimurium isolates (Figure 4b). On the other hand, the T6SS gene tae4 and gene clusters STM0278 and STM0279, and the T3SS genes ssek2 and iroC were found in over 90% of the S. Typhimurium isolates but were scarcely detected in the S. Dublin isolates (Figure 4b). All virulence-associated genes detected among the isolates in this study are presented in the Supplementary Data (Virulence genes).

3.4. Stress Genes

Thirty-seven (37) stress genes were identified from isolates of the two serovars, with the golT and golS genes identified in over 90% of both S. Dublin and S. Typhimurium isolates (Figure 4c). Nevertheless, more than 50% of S. Dublin isolates carried the genes merA, merB, merD, merE, merP, merR, and merT, which were found in less than 30% of S. Typhimurium isolates (Figure 4c).

3.5. Plasmids

More than 50% of the isolates from both serovars carried the plasmids IncFII(pAR0022) and IncFII(S). Additionally, over 90% of S. Dublin isolates harbored both the IncC and IncX1 plasmids. In contrast, among the S. Typhimurium isolates, the IncC plasmid was present at low levels, while the IncX1 plasmid was completely absent (Figure 4d). More plasmids were carried by S. Dublin isolates than S. Typhimurium isolates (Figure 6).

4. Discussion

This study highlights the value of utilizing publicly available genomic data for research purposes. It shows that S. Dublin possesses a conserved genomic “toolbox” that includes virulence genes for colonization, stress response genes against heavy metals commonly found in soil, and antimicrobial resistance genes targeting antibiotics frequently used in cattle production.
To begin, this study has demonstrated that the genome of the bovine-host-adapted serovar S. Dublin is highly conserved and exhibits little diversity compared to S. Typhimurium, which is described as a generalist serovar due to its broad host range [28]. The conserved genome of S. Dublin has been attributed to its unique adaptation as a bovine-host-adapted serovar [29]. On the other hand, the multiple clustering of S. Typhimurium at HC100, as also observed in other studies [30], confirms its polyphyletic nature [20] and has also been linked to its generalist characteristics [31].
According to the 2020 summary report from the U.S. Food and Drug Administration (FDA), 41% of domestic sales and the distribution of medically important antimicrobials approved for use in food-producing animals were intended for cattle [32]. This report also showed that, among these sales, 80% of cephalosporins, 57% of sulfonamides, 54% of aminoglycosides, 43% of tetracyclines, and 11% of penicillins were designated for cattle use [32]. This study highlights the multidrug-resistant (MDR) nature of S. Dublin isolates from bovine sources in the U.S., showing that, on average, these isolates carry resistance genes against aminoglycosides, beta-lactams, quinolones, sulfonamides, phenicols, tetracyclines, and polypeptide antibiotics (specifically bacitracin). Except for phenicols (specifically chloramphenicol), which have long been banned in the U.S. and many other countries for use in food-producing animals [33,34], all the aforementioned drugs are still used. However, bacitracin is not classified as medically important by the FDA, though it continues to be used due to claims of its growth-promoting effects in cattle [35]. Interestingly, this aligns with our finding that the bacA gene, which confers resistance to bacitracin [36], was among the few genes consistently present in all S. Dublin and S. Typhimurium isolates.
Further analysis showed that S. Dublin appears better equipped with antimicrobial resistance (AMR) genes to counteract drugs approved for use in U.S. cattle production. For instance, over 90% of S. Dublin isolates carried the genes aac(6′)-Iy, aph(3″)-Ib, and aph(6)-Id, which are associated with aminoglycoside resistance [37]. While S. Typhimurium isolates also carried AMR genes against aminoglycosides, their prevalence is comparatively lower, with none exceeding 30%, except for aac(6′)-Iaa, which is a cryptic non-functional gene [38]. Regarding cephalosporins, a subclass of beta-lactam antibiotics [39], the blaCRP gene was present in all isolates of both serovars. However, an interesting observation deserving further investigation was the exclusive presence of the baeR gene in S. Typhimurium isolates, which plays a role in the susceptibility of microbes to cephalosporin [40]. Over 70% of S. Dublin isolates carried the blaCMY-111, blaCMY-2, and blaCMY-59 genes, whereas they were found in less than 30% of S. Typhimurium isolates. This suggests that S. Dublin may have a greater capacity to resist cephalosporin antibiotics. Similar trends were observed with AMR genes against tetracyclines and sulfonamides. For sulfonamide resistance, the sul2 gene was the most prevalent among S. Dublin isolates and may be the preferred gene for sulfonamide resistance in S. Dublin. Although both the sul1 and the sul2 genes are reported to have the same frequency of detection among sulfonamide-resistant Gram-negative microbes [41], the sul2 gene was the most dominant gene among most sulfonamide-resistant Salmonella spp. in a study by Pavelquesi et al. [42]. A large proportion (over 90%) of S. Dublin isolates also carried genes against tetracycline resistance, which was not the case for S. Typhimurium isolates in this study. The abundance of these AMR genes in S. Dublin suggests that this serovar is more likely to resist treatment and prolong infections in its bovine host. All isolates from both serovars carried the emR gene, which confers resistance to quinolone drugs. This may be attributed to the widespread and indiscriminate use of these drugs in food animal production. However, the presence of antimicrobial resistance (AMR) genes does not necessarily indicate phenotypic resistance [43]. Further studies are therefore needed to investigate the antimicrobial susceptibilities of these isolates.
The origins of the AMR genes identified in these isolates were not investigated, due to computational limitations. However, the significantly higher number of AMR genes in S. Dublin isolates compared to S. Typhimurium could be attributed to plasmids, as these mobile genetic elements play a crucial role in the acquisition and transfer of AMR genes [44,45]. Most isolates of both serovars carried the IncFII-type and IncC plasmids, which are well known to be associated with MDR [46,47,48,49,50]. However, in addition to the fact that S. Dublin isolates carried more of these plasmids than S. Typhimurium isolates on average, a noteworthy observation was the presence of the IncX1 plasmid in over 90% of S. Dublin isolates, which were virtually missing among S. Typhimurium isolates. This interesting finding suggests that the plasmid may be important in the biology of S. Dublin. This IncX1 plasmid is noted for carrying MDR genes [51]. In addition, a study in China identified arsenic-resistant operons on this plasmid [52]. Although arsenic-resistant genes were not identified in the S. Dublin isolates in this study, over 50% carried genes of the mer operon, notable for mercury resistance [53]. S. Dublin isolates carrying mercury-resistant genes coupled with the association of the IncX1 plasmid with MDR and arsenic resistance may be a special adaptation to thrive in the gut of its host, as the grazing habit of cattle makes them prone to ingesting heavy metals such as mercury, which may be contained in soil [54]. Although this study reports the presence of these plasmids, further studies are needed to determine their functional competencies.
Almost all isolates from both serovars presented a similar profile for virulence genes with activities relating to T3SS, T6SS, magnesium and iron acquisition, curlin formation, and outer-membrane proteins. These are essential and core to Salmonella virulence [55,56,57,58,59]. However, among the subtle but significant observations in this study was that S. Dublin isolates had over 70% of the genes related to T3SS and T6SS compared to S. Typhimurium. More specifically, the tssJKLM genes, which are key components of the baseplate and membrane complex of T6SS [60], and the tssD-2/hcp2 gene, also essential for colonization [61], were found only among S. Dublin isolates. Generally, T6SS is instrumental in conferring a competitive advantage against other microbiota [62]. The presence of these genes in S. Dublin may help the serovar outcompete other microbes in colonizing the gut of its bovine host. Conversely, the sseK2 and sspH2 genes, key factors in host immune evasion [63,64], were predominantly found in S. Typhimurium and nearly absent in S. Dublin isolates. Similarly, the hcp2 gene clusters STM0278 and STM0279, located in the mid-region of the T6SS, were also missing in S. Dublin [65]. This absence warrants further investigation, as a study in Germany also reported that the sspH2 gene was missing in 78 cattle-derived S. Dublin isolates [66]. Additionally, while both iroB and iroC genes of the iro operon are important for iron uptake and oxidative stress defense [67], both were found in nearly all S. Typhimurium isolates. Only iroB was, however, detected in S. Dublin, a phenomenon that also deserves further exploration [68].
Although this study incorporated multiple databases to increase the stringency and comprehensiveness of our results, its findings are limited by the genes populating the databases used at the time of our analysis. Continued advancements in bioinformatics and the growing availability of whole-genome sequences will enable deeper investigations into Salmonella serovars’ pathogenicity and host adaptations in the future.

5. Conclusions

This study reveals that S. Dublin isolates in the U.S. have a highly conserved genomic profile, equipped with a repertoire of AMR genes against the antimicrobials commonly used in cattle production. The presence of virulence and stress-tolerance genes also enhances S. Dublin’s ability to colonize and persist within the bovine gut, contributing to its stability and persistence in cattle and posing challenges for treatment and control.

Supplementary Materials

The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/microorganisms13040886/s1, Supplementary Data.

Author Contributions

K.E.B.: Conceptualization, writing—original draft preparation, writing—review and editing, methodology, analysis, and visualization; E.K., R.N. and V.O.: writing—review and editing, data curation, visualization; J.G.F., C.R.J., T.S., B.A.-A. and W.A.: writing—review and editing, supervision; W.A. and T.S.: funding acquisition. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by grants from the National Institute of Food and Agriculture: 2021-38821-34710, 2022-67017-36982, and 2022-38821-37362.

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

All whole-genome sequences of isolates used for this study can be located within the Enterobase database using this link: https://enterobase.warwick.ac.uk/species/senterica/search_strains?query=workspace:124148 (accessed on 24 September 2024).

Acknowledgments

The authors extend their appreciation to Ruby Perry, Dean, Tuskegee University College of Veterinary Medicine, for supporting graduate research and the Center for Food Animal Health, Food Safety and Defense Laboratory. The authors also thank the entire team who facilitated the training and GPU computing support kindly provided by the Alabama Artificial Intelligence Center of Excellence (AAICE) in Auburn, Alabama.

Conflicts of Interest

The authors declare no conflicts of interest.

References

  1. Salaheen, S.; Kim, S.W.; Haley, B.J.; Van Kessel, J.A.S. Differences between the Global Transcriptomes of Salmonella enterica Serovars Dublin and Cerro Infecting Bovine Epithelial Cells. BMC Genom. 2022, 23, 498. [Google Scholar] [CrossRef] [PubMed]
  2. Velasquez-Munoz, A.; Castro-Vargas, R.; Cullens-Nobis, F.M.; Mani, R.; Abuelo, A. Review: Salmonella Dublin in Dairy Cattle. Front. Vet. Sci. 2024, 10, 1331767. [Google Scholar] [CrossRef] [PubMed]
  3. Costa, L.F.; Paixão, T.A.; Tsolis, R.M.; Bäumler, A.J.; Santos, R.L. Salmonellosis in Cattle: Advantages of Being an Experimental Model. Res. Vet. Sci. 2012, 93, 1–6. [Google Scholar] [CrossRef] [PubMed]
  4. Fenske, G.J.; Thachil, A.; McDonough, P.L.; Glaser, A.; Scaria, J.; Delaye, L. Geography Shapes the Population Genomics of Salmonella enterica Dublin. Genome Biol. Evol. 2019, 11, 2220–2231. [Google Scholar] [CrossRef]
  5. Kingsley, R.A.; Kay, S.; Connor, T.; Barquist, L.; Sait, L.; Holt, K.E.; Sivaraman, K.; Wileman, T.; Goulding, D.; Clare, S.; et al. Genome and Transcriptome Adaptation Accompanying Emergence of the Definitive Type 2 Host-Restricted Salmonella enterica Serovar Typhimurium Pathovar. MBio 2013, 5, 10–1128. [Google Scholar] [CrossRef]
  6. Tanner, J.R.; Kingsley, R.A. Evolution of Salmonella within Hosts. Trends Microbiol. 2018, 26, 986–998. [Google Scholar] [CrossRef]
  7. Tasmin, R.; Hasan, N.A.; Grim, C.J.; Grant, A.; Choi, S.Y.; Alam, M.S.; Bell, R.; Cavanaugh, C.; Balan, K.V.; Babu, U.S.; et al. Genotypic and Phenotypic Characterization of Multidrug Resistant Salmonella Typhimurium and Salmonella Kentucky Strains Recovered from Chicken Carcasses. PLoS ONE 2017, 12, e0176938. [Google Scholar] [CrossRef]
  8. Hu, L.; Brown, E.W.; Zhang, G. Diversity of Antimicrobial Resistance, Stress Resistance, and Virulence Factors of Salmonella, Shiga Toxin-Producing Escherichia coli, and Listeria monocytogenes from Produce, Spices, and Tree Nuts by Whole Genome Sequencing. Front. Sustain. Food Syst. 2023, 7, 1281005. [Google Scholar] [CrossRef]
  9. Fang, Y.; Tran, F.; Stanford, K.; Yang, X. Stress Resistance and Virulence Gene Profiles Associated with Phylogeny and Phenotypes of Escherichia coli from Cattle. J. Food Prot. 2023, 86, 100122. [Google Scholar] [CrossRef]
  10. Beceiro, A.; Tomás, M.; Bou, G. Antimicrobial Resistance and Virulence: A Successful or Deleterious Association in the Bacterial World? Clin. Microbiol. Rev. 2013, 26, 185–230. [Google Scholar] [CrossRef]
  11. Klose, C.; Scuda, N.; Ziegler, T.; Eisenberger, D.; Hanczaruk, M.; Riehm, J.M. Whole-Genome Investigation of Salmonella Dublin Considering Mountain Pastures as Reservoirs in Southern Bavaria, Germany. Microorganisms 2022, 10, 885. [Google Scholar] [CrossRef] [PubMed]
  12. Wang, Y.; Liu, Y.; Lyu, N.; Li, Z.; Ma, S.; Cao, D.; Pan, Y.; Hu, Y.; Huang, H.; Gao, G.F.; et al. The Temporal Dynamics of Antimicrobial-Resistant Salmonella enterica and Predominant Serovars in China. Natl. Sci. Rev. 2023, 10, nwac269. [Google Scholar] [CrossRef] [PubMed]
  13. Armstrong, G.L.; MacCannell, D.R.; Taylor, J.; Carleton, H.A.; Neuhaus, E.B.; Bradbury, R.S.; Posey, J.E.; Gwinn, M. Pathogen Genomics in Public Health. N. Engl. J. Med. 2019, 381, 2569–2580. [Google Scholar] [CrossRef]
  14. Agarwala, R.; Barrett, T.; Beck, J.; Benson, D.A.; Bollin, C.; Bolton, E.; Bourexis, D.; Brister, J.R.; Bryant, S.H.; Canese, K.; et al. Database Resources of the National Center for Biotechnology Information. Nucleic Acids Res. 2016, 44, D7–D19. [Google Scholar] [CrossRef]
  15. Sayers, E.W.; Cavanaugh, M.; Clark, K.; Pruitt, K.D.; Sherry, S.T.; Yankie, L.; Karsch-Mizrachi, I. GenBank 2024 Update. Nucleic Acids Res. 2024, 52, D134–D137. [Google Scholar] [CrossRef]
  16. Wang, Y.; Xu, X.; Zhu, B.; Lyu, N.; Liu, Y.; Ma, S.; Jia, S.; Wan, B.; Du, Y.; Zhang, G.; et al. Genomic Analysis of Almost 8,000 Salmonella Genomes Reveals Drivers and Landscape of Antimicrobial Resistance in China. Microbiol. Spectr. 2023, 11, e0208023. [Google Scholar] [CrossRef]
  17. Frentrup, M.; Zhou, Z.; Steglich, M.; Meier-Kolthoff, J.P.; Göker, M.; Riedel, T.; Bunk, B.; Spröer, C.; Overmann, J.; Blaschitz, M.; et al. A Publicly Accessible Database for Clostridioides Difficile Genome Sequences Supports Tracing of Transmission Chains and Epidemics. Microb. Genom. 2020, 6, mgen000410. [Google Scholar] [CrossRef] [PubMed]
  18. Bonifait, L.; Thépault, A.; Baugé, L.; Rouxel, S.; Le Gall, F.; Chemaly, M. Occurrence of Salmonella in the Cattle Production in France. Microorganisms 2021, 9, 872. [Google Scholar] [CrossRef]
  19. Zhou, Z.; Alikhan, N.F.; Sergeant, M.J.; Luhmann, N.; Vaz, C.; Francisco, A.P.; Carriço, J.A.; Achtman, M. Grapetree: Visualization of Core Genomic Relationships among 100,000 Bacterial Pathogens. Genome Res. 2018, 28, 1395–1404. [Google Scholar] [CrossRef]
  20. Achtman, M.; Zhou, Z.; Alikhan, N.F.; Tyne, W.; Parkhill, J.; Cormican, M.; Chiou, C.S.; Torpdahl, M.; Litrup, E.; Prendergast, D.M.; et al. Genomic Diversity of Salmonella enterica—The UoWUCC 10K Genomes Project. Wellcome Open Res. 2021, 5, 223. [Google Scholar] [CrossRef]
  21. Wheeler, T.J. Large-Scale Neighbor-Joining with NINJA. In Algorithms in Bioinformatics: 9th International Workshop, WABI 2009, Philadelphia, PA, USA, September 12–13, 2009. Proceedings 9; Springer: Berlin/Heidelberg, Germany, 2009; Volume 5724, pp. 375–389. [Google Scholar] [CrossRef]
  22. Alcock, B.P.; Huynh, W.; Chalil, R.; Smith, K.W.; Raphenya, A.R.; Wlodarski, M.A.; Edalatmand, A.; Petkau, A.; Syed, S.A.; Tsang, K.K.; et al. CARD 2023: Expanded Curation, Support for Machine Learning, and Resistome Prediction at the Comprehensive Antibiotic Resistance Database. Nucleic Acids Res. 2023, 51, D690–D699. [Google Scholar] [CrossRef] [PubMed]
  23. Feldgarden, M.; Brover, V.; Gonzalez-Escalona, N.; Frye, J.G.; Haendiges, J.; Haft, D.H.; Hoffmann, M.; Pettengill, J.B.; Prasad, A.B.; Tillman, G.E.; et al. AMRFinderPlus and the Reference Gene Catalog Facilitate Examination of the Genomic Links among Antimicrobial Resistance, Stress Response, and Virulence. Sci. Rep. 2021, 11, 12728. [Google Scholar] [CrossRef] [PubMed]
  24. Gupta, S.K.; Padmanabhan, B.R.; Diene, S.M.; Lopez-Rojas, R.; Kempf, M.; Landraud, L.; Rolain, J.M. ARG-Annot, a New Bioinformatic Tool to Discover Antibiotic Resistance Genes in Bacterial Genomes. Antimicrob. Agents Chemother. 2014, 58, 212–220. [Google Scholar] [CrossRef]
  25. Chen, L.; Zheng, D.; Liu, B.; Yang, J.; Jin, Q. VFDB 2016: Hierarchical and Refined Dataset for Big Data Analysis—10 Years On. Nucleic Acids Res. 2016, 44, D694–D697. [Google Scholar] [CrossRef] [PubMed]
  26. Carattoli, A.; Zankari, E.; García-Fernández, A.; Larsen, M.V.; Lund, O.; Villa, L. In Silico Detection and Typing of Plasmids Using PlasmidFinder and Plasmid Multilocus Sequence Typing. Antimicrob. Agents Chemother. 2014, 58, 3895–3903. [Google Scholar] [CrossRef]
  27. Maguire, M.; DeLappe, N.; Clarke, C.; Touhy, A.; Carlino-MacDonald, U.; Hutson, A.; Cormican, M.; Brennan, W.; Devane, G.; Morris, D.; et al. Genomic and Phylogenetic Analysis of Hypervirulent Klebsiella pneumoniae ST23 in Ireland. Microb. Genom. 2025, 11, 001373. [Google Scholar] [CrossRef]
  28. Fu, Y.; Smith, J.C.; Shariat, N.W.; M’ikanatha, N.M.; Dudley, E.G. Evidence for Common Ancestry and Microevolution of Passerine-Adapted Salmonella enterica Serovar Typhimurium in the UK and USA. Microb. Genom. 2022, 8, 000775. [Google Scholar] [CrossRef]
  29. Srednik, M.E.; Lantz, K.; Hicks, J.A.; Morningstar-Shaw, B.R.; Mackie, T.A.; Schlater, L.K. Antimicrobial Resistance and Genomic Characterization of Salmonella Dublin Isolates in Cattle from the United States. PLoS ONE 2021, 16, e0249617. [Google Scholar] [CrossRef]
  30. Carhuaricra Huaman, D.E.; Luna Espinoza, L.R.; Rodríguez Cueva, C.L.; Duran Gonzales, C.G.; Rosadio Alcántara, R.H.; Setubal, J.C.; Maturrano Hernández, L. Genomic Characterization of Salmonella Typhimurium Isolated from Guinea Pigs with Salmonellosis in Lima, Peru. Microorganisms 2022, 10, 1726. [Google Scholar] [CrossRef]
  31. Ali, M.S.; Na, S.-H.; Moon, B.-Y.; Kang, H.Y.; Kang, H.-S.; Kim, S.-J.; Kim, T.-S.; Heo, Y.-E.; Hwang, Y.-J.; Yoon, S.S.; et al. Antimicrobial Resistance Profiles and Molecular Characteristics of Extended-Spectrum β -Lactamase-Producing Salmonella enterica Serovar Typhimurium Isolates from Food Animals During 2010–2021 in South Korea. Foodborne Pathog. Dis. 2024, 21, 634–642. [Google Scholar] [CrossRef]
  32. United States Food and Drug Administration. Summary Report on Antimicrobials Sold or Distributed for Use in Food-Producing Animals. 2021. Available online: https://www.fda.gov/media/154820/download (accessed on 14 February 2025).
  33. Bren, L. Animal Health and Consumer Protection. FDA Consum. Mag. 2006, 42. Available online: https://www.fda.gov/files/about%20fda/published/Animal-Health-and-Consumer-Protection.pdf (accessed on 2 December 2024).
  34. Soares, V.M.; Pereira, J.G.; Barreto, F.; Jank, L.; Rau, R.B.; Ribeiro, C.B.D.; Dos Santos Castilhos, T.; Tomaszewski, C.A.; Hillesheim, D.R.; Mondadori, R.G.; et al. Residues of Veterinary Drugs in Animal Products Commercialized in the Border Region of Brazil, Argentina, and Uruguay. J. Food Prot. 2022, 85, 980–986. [Google Scholar] [CrossRef] [PubMed]
  35. United States Department of Agriculture. Antimicrobial Use and Stewardship on U.S. Feedlots. 2017. Available online: https://www.aphis.usda.gov/sites/default/files/amu-feedlots.pdf (accessed on 12 December 2024).
  36. Zhang, F.; Liu, F.; Sheng, X.; Liu, Q.; Cui, L.; Cao, Z.; Hu, T.; Li, D.; Dai, M. Bacitracin-Resistant Staphylococcus aureus Induced in Chicken Gut and in Vitro under Bacitracin Exposure. Microb. Pathog. 2024, 191, 106666. [Google Scholar] [CrossRef] [PubMed]
  37. Ramirez, M.S.; Tolmasky, M.E. Aminoglycoside Modifying Enzymes. Drug Resist. Updat. 2010, 13, 151–171. [Google Scholar] [CrossRef]
  38. Kagambèga, A.; McMillan, E.A.; Bouda, S.C.; Hiott, L.M.; Ramadan, H.; Soro, D.K.; Sharma, P.; Gupta, S.K.; Barro, N.; Jackson, C.R.; et al. Resistance Genes, Plasmids, Multilocus Sequence Typing (MLST), and Phenotypic Resistance of Non-Typhoidal Salmonella (NTS) Isolated from Slaughtered Chickens in Burkina Faso. Antibiotics 2022, 11, 782. [Google Scholar] [CrossRef]
  39. Lin, X.; Kück, U. Cephalosporins as Key Lead Generation Beta-Lactam Antibiotics. Appl. Microbiol. Biotechnol. 2022, 106, 8007–8020. [Google Scholar] [CrossRef]
  40. Wang, S.; You, C.; Memon, F.Q.; Zhang, G.; Sun, Y.; Si, H. BaeR Participates in Cephalosporins Susceptibility by Regulating the Expression Level of Outer Membrane Proteins in Escherichia coli. J. Biochem. 2021, 169, 101–108. [Google Scholar] [CrossRef]
  41. Pavelquesi, S.L.S.; de Oliveira Ferreira, A.C.A.; Rodrigues, A.R.M.; de Souza Silva, C.M.; Orsi, D.C.; da Silva, I.C.R. Presence of Tetracycline and Sulfonamide Resistance Genes in Salmonella spp.: Literature Review. Antibiotics 2021, 10, 1314. [Google Scholar] [CrossRef]
  42. Lunara Santos Pavelquesi, S.; Carolina Almeida de Oliveira Ferreira, A.; Fernandes Silva Rodrigues, L.; Maria de Souza Silva, C.; Cristina Rodrigues da Silva, I.; Castilho Orsi, D. Prevalence and Antimicrobial Resistance of Salmonella spp. Isolated From Chilled Chicken Meat Commercialized at Retail in Federal District, Brazil. J. Food Prot. 2023, 86, 100130. [Google Scholar] [CrossRef]
  43. Schwan, C.L.; Lomonaco, S.; Bastos, L.M.; Cook, P.W.; Maher, J.; Trinetta, V.; Bhullar, M.; Phebus, R.K.; Gragg, S.; Kastner, J.; et al. Genotypic and Phenotypic Characterization of Antimicrobial Resistance Profiles in Non-Typhoidal Salmonella enterica Strains Isolated From Cambodian Informal Markets. Front. Microbiol. 2021, 12, 711472. [Google Scholar] [CrossRef]
  44. Vrancianu, C.O.; Popa, L.I.; Bleotu, C.; Chifiriuc, M.C. Targeting Plasmids to Limit Acquisition and Transmission of Antimicrobial Resistance. Front. Microbiol. 2020, 11, 761. [Google Scholar] [CrossRef] [PubMed]
  45. Zongo, P.D.; Cabanel, N.; Royer, G.; Depardieu, F.; Hartmann, A.; Naas, T.; Glaser, P.; Rosinski-Chupin, I. An Antiplasmid System Drives Antibiotic Resistance Gene Integration in Carbapenemase-Producing Escherichia coli Lineages. Nat. Commun. 2024, 15, 4093. [Google Scholar] [CrossRef] [PubMed]
  46. Allain, M.; Morel-Journel, T.; Condamine, B.; Gibeaux, B.; Gachet, B.; Gschwind, R.; Denamur, E.; Landraud, L. IncC Plasmid Genome Rearrangements Influence the Vertical and Horizontal Transmission Tradeoff in Escherichia coli. Antimicrob. Agents Chemother. 2024, 68, e00554-24. [Google Scholar] [CrossRef] [PubMed]
  47. Feng, J.; Yin, Z.; Zhao, Q.; Zhao, Y.; Zhang, D.; Jiang, X.; Wu, W.; Chen, W.; Wang, H.; Song, Y.; et al. Genomic Characterization of Novel IncFII-Type Multidrug Resistant Plasmids P0716-KPC and P12181-KPC from Klebsiella pneumonia. Sci. Rep. 2017, 7, 5830. [Google Scholar] [CrossRef]
  48. Furlan, J.P.R.; Lopes, R.; Stehling, E.G. Multidrug Resistance IncC Plasmid Carrying BlaCMY-97 in Shiga Toxin-Producing Escherichia coli ST215-H54 of Ovine Origin. Infect. Genet. Evol. 2021, 93, 104989. [Google Scholar] [CrossRef]
  49. Grevskott, D.H.; Salvà-Serra, F.; Moore, E.R.B.; Marathe, N.P. Complete Sequence of a New Conjugative Multidrug-Resistance Encoding IncFII/IncFIA/IncFIB Plasmid Carrying NDM-6 Metallo-β-Lactamase from Pathogenic Escherichia coli Sequence Type 167 Isolated from Sewage in Norway. J. Glob. Antimicrob. Resist. 2023, 33, 291–293. [Google Scholar] [CrossRef]
  50. Muthuirulandi Sethuvel, D.P.; Anandan, S.; Devanga Ragupathi, N.K.; Gajendiran, R.; Kuroda, M.; Shibayama, K.; Veeraraghavan, B. IncFII Plasmid Carrying Antimicrobial Resistance Genes in Shigella flexneri: Vehicle for Dissemination. J. Glob. Antimicrob. Resist. 2019, 16, 215–219. [Google Scholar] [CrossRef]
  51. Zalewska, M.; Błażejewska, A.; Gawor, J.; Adamska, D.; Goryca, K.; Szeląg, M.; Kalinowski, P.; Popowska, M. The IncC and IncX1 Resistance Plasmids Present in Multi-Drug Resistant Escherichia coli Strains Isolated from Poultry Manure in Poland. Environ. Sci. Pollut. Res. 2024, 31, 47727–47741. [Google Scholar] [CrossRef]
  52. Zhang, X.Z.; Lei, C.W.; Zeng, J.X.; Chen, Y.P.; Kang, Z.Z.; Wang, Y.L.; Ye, X.L.; Zhai, X.W.; Wang, H.N. An IncX1 Plasmid Isolated from Salmonella enterica subsp. enterica Serovar Pullorum Carrying BlaTEM-1B, Sul2, Arsenic Resistant Operons. Plasmid 2018, 100, 14–21. [Google Scholar] [CrossRef]
  53. Bhat, A.; Sharma, R.; Desigan, K.; Lucas, M.M.; Mishra, A.; Bowers, R.M.; Woyke, T.; Epstein, B.; Tiffin, P.; Pueyo, J.J.; et al. Horizontal Gene Transfer of the Mer Operon Is Associated with Large Effects on the Transcriptome and Increased Tolerance to Mercury in Nitrogen-Fixing Bacteria. BMC Microbiol. 2024, 24, 247. [Google Scholar] [CrossRef]
  54. Priya, A.K.; Muruganandam, M.; Ali, S.S.; Kornaros, M. Clean-Up of Heavy Metals from Contaminated Soil by Phytoremediation: A Multidisciplinary and Eco-Friendly Approach. Toxics 2023, 11, 422. [Google Scholar] [CrossRef] [PubMed]
  55. Bao, H.; Wang, S.; Zhao, J.H.; Liu, S.L. Salmonella Secretion Systems: Differential Roles in Pathogen-Host Interactions. Microbiol. Res. 2020, 241, 126591. [Google Scholar] [CrossRef]
  56. Ferrer-Bustins, N.; Yvon, C.; Martín, B.; Leclerc, V.; Leblanc, J.C.; Corominas, L.; Sabaté, S.; Tolosa-Muñoz, E.; Chacón-Villanueva, C.; Bover-Cid, S.; et al. Genomic Insights of Salmonella Isolated from Dry Fermented Sausage Production Chains in Spain and France. Sci. Rep. 2024, 14, 11660. [Google Scholar] [CrossRef] [PubMed]
  57. Karash, S.; Jiang, T.; Kwon, Y.M. Genome-Wide Characterization of Salmonella Typhimurium Genes Required for the Fitness under Iron Restriction. BMC Genom. Data 2022, 23, 55. [Google Scholar] [CrossRef]
  58. Lou, L.; Zhang, P.; Piao, R.; Wang, Y. Salmonella Pathogenicity Island 1 (SPI-1) and Its Complex Regulatory Network. Front. Cell. Infect. Microbiol. 2019, 9, 270. [Google Scholar] [CrossRef] [PubMed]
  59. Zhou, X.; Kang, X.; Chen, J.; Song, Y.; Jia, C.; Teng, L.; Tang, Y.; Jiang, Z.; Peng, X.; Tao, X.; et al. Genome Degradation Promotes Salmonella Pathoadaptation by Remodeling Fimbriae-Mediated Proinflammatory Response. Natl. Sci. Rev. 2023, 10, 228. [Google Scholar] [CrossRef]
  60. Cherrak, Y.; Flaugnatti, N.; Durand, E.; Journet, L.; Cascales, E. Structure and Activity of the Type VI Secretion System. Microbiol. Spectr. 2019, 7, 10.1128. [Google Scholar] [CrossRef]
  61. Motta, E.V.S.; Lariviere, P.J.; Jones, K.R.; Song, Y.; Moran, N.A. Type VI Secretion Systems Promote Intraspecific Competition and Host Interactions in a Bee Gut Symbiont. Proc. Natl. Acad. Sci. USA 2024, 121, e2414882121. [Google Scholar] [CrossRef]
  62. Merciecca, T.; Bornes, S.; Nakusi, L.; Theil, S.; Rendueles, O.; Forestier, C.; Miquel, S. Role of Klebsiella pneumoniae Type VI Secretion System (T6SS) in Long-Term Gastrointestinal Colonization. Sci. Rep. 2022, 12, 16968. [Google Scholar] [CrossRef]
  63. Pillay, T.D.; Hettiarachchi, S.U.; Gan, J.; Diaz-Del-olmo, I.; Yu, X.J.; Muench, J.H.; Thurston, T.L.M.; Pearson, J.S. Speaking the Host Language: How Salmonella Effector Proteins Manipulate the Host. Microbiology 2023, 169, 001342. [Google Scholar] [CrossRef]
  64. Svahn, A.J.; Suster, C.J.E.; Chang, S.L.; Rockett, R.J.; Sim, E.M.; Cliff, O.M.; Wang, Q.; Arnott, A.; Ramsperger, M.; Sorrell, T.C.; et al. Pangenome Analysis of a Salmonella enteritidis Population Links a Major Outbreak to a Gifsy-1-Like Prophage Containing Anti-Inflammatory Gene GogB. Microbiol. Spectr. 2023, 11, e0279122. [Google Scholar] [CrossRef] [PubMed]
  65. Hodges, L.M.; Cooper, A.; Koziol, A.; Carrillo, C.D. Characterization of MLST-99 Salmonella Typhimurium and the Monophasic Variant I:4,[5],12:I:-Isolated from Canadian Atlantic Coast Shellfish. Microbiology 2024, 170, 001456. [Google Scholar] [CrossRef] [PubMed]
  66. García-Soto, S.; Linde, J.; Methner, U. Epidemiological Analysis on the Occurrence of Salmonella enterica subspecies enterica Serovar Dublin in the German Federal State Schleswig-Holstein Using Whole-Genome Sequencing. Microorganisms 2023, 11, 122. [Google Scholar] [CrossRef] [PubMed]
  67. Campioni, F.; Vilela, F.P.; Cao, G.; Kastanis, G.; dos Prazeres Rodrigues, D.; Costa, R.G.; Tiba-Casas, M.R.; Yin, L.; Allard, M.; Falcão, J.P. Whole Genome Sequencing Analyses Revealed That Salmonella enterica Serovar Dublin Strains from Brazil Belonged to Two Predominant Clades. Sci. Rep. 2022, 12, 10555. [Google Scholar] [CrossRef]
  68. Li, M.; Wang, K.; Tang, A.; Tang, A.; Chen, A.; Huang, Z. Investigation of the Genes Involved in the Outbreaks of Escherichia coli and Salmonella spp. in the United States. Antibiotics 2021, 10, 1274. [Google Scholar] [CrossRef]
Microorganisms 13 00886 g001
Figure 1. Phylogenetic tree displaying isolate metadata. The phylogenetic tree presents metadata for the isolates, including serovar, sequence type, collection year, and state of isolation. A legend for interpreting the tree is located on the left, with the number of isolates indicated in brackets. According to the legend order, the innermost track represents serovar designation, while the outermost track indicates the state of origin. S. Typhimurium isolates are shown with red branches, and S. Dublin isolates are shown with blue branches. The phylogenetic tree was plotted based on the NINJA Neighbor-Joining (NJ) algorithm in Enterobase.
Figure 1. Phylogenetic tree displaying isolate metadata. The phylogenetic tree presents metadata for the isolates, including serovar, sequence type, collection year, and state of isolation. A legend for interpreting the tree is located on the left, with the number of isolates indicated in brackets. According to the legend order, the innermost track represents serovar designation, while the outermost track indicates the state of origin. S. Typhimurium isolates are shown with red branches, and S. Dublin isolates are shown with blue branches. The phylogenetic tree was plotted based on the NINJA Neighbor-Joining (NJ) algorithm in Enterobase.
Microorganisms 13 00886 g001
Microorganisms 13 00886 g002
Figure 2. The GrapeTree of Salmonella Dublin and Salmonella Typhimurium isolates showing plots at the Hierarchical Clustering (HC) levels of HC900, HC400, and HC100. (a) A GrapeTree constructed at the HC900 level using the cgMLST V2 + HierCC V1 scheme based on a set of 3002 core genes and the Ninja Neighbor-Joining algorithm for a total of 2124 isolates comprising 787 S. Typhimurium and 1337 S. Dublin isolates, respectively. (b,c) Subtrees redrawn from (a) to show clustering results for S. Typhimurium (787 isolates) and S. Dublin (1337 isolates) at HC 400. (d,e) Subtrees redrawn from (a) to show clustering results for S. Typhimurium (787 isolates) and S. Dublin (1337 isolates) at HC100. All isolates are shown as colored nodes, and a figure legend for each plot is shown on the right side of the plot.
Figure 2. The GrapeTree of Salmonella Dublin and Salmonella Typhimurium isolates showing plots at the Hierarchical Clustering (HC) levels of HC900, HC400, and HC100. (a) A GrapeTree constructed at the HC900 level using the cgMLST V2 + HierCC V1 scheme based on a set of 3002 core genes and the Ninja Neighbor-Joining algorithm for a total of 2124 isolates comprising 787 S. Typhimurium and 1337 S. Dublin isolates, respectively. (b,c) Subtrees redrawn from (a) to show clustering results for S. Typhimurium (787 isolates) and S. Dublin (1337 isolates) at HC 400. (d,e) Subtrees redrawn from (a) to show clustering results for S. Typhimurium (787 isolates) and S. Dublin (1337 isolates) at HC100. All isolates are shown as colored nodes, and a figure legend for each plot is shown on the right side of the plot.
Microorganisms 13 00886 g002
Microorganisms 13 00886 g003
Figure 3. Antimicrobial resistance (AMR) genes against various drug classes identified among isolates of Salmonella Dublin and Salmonella Typhimurium. The colored pyramid shows the different drug classes for which AMR genes were detected in the various isolates. Each colored box represents a drug class as shown by the legend below the chart. The number of isolates within which the genes against the various antimicrobial drug classes were identified and their respective percentages are shown from center-left for S. Dublin and from center-right for S. Typhimurium (p-value < 0.05).
Figure 3. Antimicrobial resistance (AMR) genes against various drug classes identified among isolates of Salmonella Dublin and Salmonella Typhimurium. The colored pyramid shows the different drug classes for which AMR genes were detected in the various isolates. Each colored box represents a drug class as shown by the legend below the chart. The number of isolates within which the genes against the various antimicrobial drug classes were identified and their respective percentages are shown from center-left for S. Dublin and from center-right for S. Typhimurium (p-value < 0.05).
Microorganisms 13 00886 g003
Microorganisms 13 00886 g004
Figure 4. Heatmaps for genes and plasmids in the various isolates. (a) Antimicrobial genes, (b) selected virulence genes, (c) stress genes, and (d) plasmids identified in the isolates are shown in heatmaps. The scale for the heatmap is shown to the right, where black and white colors represent 100% and 0% of isolates of a given serovar, respectively.
Figure 4. Heatmaps for genes and plasmids in the various isolates. (a) Antimicrobial genes, (b) selected virulence genes, (c) stress genes, and (d) plasmids identified in the isolates are shown in heatmaps. The scale for the heatmap is shown to the right, where black and white colors represent 100% and 0% of isolates of a given serovar, respectively.
Microorganisms 13 00886 g004
Microorganisms 13 00886 g005
Figure 5. Bar chart showing the percentage of isolates with selected virulence genes. The bar chart displays the percentage of isolates carrying at least 70% of all identified genes within each functional category of virulence genes. The y-axis indicates the percentage of isolates, while the x-axis represents the different functional categories of virulence genes. Black bars represent S. Dublin isolates, and gray bars represent S. Typhimurium isolates. The legend is provided at the bottom of the figure.
Figure 5. Bar chart showing the percentage of isolates with selected virulence genes. The bar chart displays the percentage of isolates carrying at least 70% of all identified genes within each functional category of virulence genes. The y-axis indicates the percentage of isolates, while the x-axis represents the different functional categories of virulence genes. Black bars represent S. Dublin isolates, and gray bars represent S. Typhimurium isolates. The legend is provided at the bottom of the figure.
Microorganisms 13 00886 g005
Microorganisms 13 00886 g006
Figure 6. The chart illustrates the proportion of Salmonella Dublin and Salmonella Typhimurium isolates carrying at least one plasmid. The percentage of isolates from each serovar, based on the number of plasmids they carry, is indicated within the corresponding segments on the outer part of the chart. Black and gray shading represent S. Dublin and S. Typhimurium, respectively, as indicated in the legend to the right of the chart.
Figure 6. The chart illustrates the proportion of Salmonella Dublin and Salmonella Typhimurium isolates carrying at least one plasmid. The percentage of isolates from each serovar, based on the number of plasmids they carry, is indicated within the corresponding segments on the outer part of the chart. Black and gray shading represent S. Dublin and S. Typhimurium, respectively, as indicated in the legend to the right of the chart.
Microorganisms 13 00886 g006
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.

Share and Cite

MDPI and ACS Style

Bentum, K.E.; Kuufire, E.; Nyarku, R.; Osei, V.; Adu-Addai, B.; Frye, J.G.; Jackson, C.R.; Samuel, T.; Abebe, W. Comparative Genomic Profiles of Salmonella Typhimurium and Salmonella Dublin Bovine Isolates from the U.S. Indicate Possible Factors Associated with the Host Adaptation of Salmonella Dublin in the Region. Microorganisms 2025, 13, 886. https://doi.org/10.3390/microorganisms13040886

AMA Style

Bentum KE, Kuufire E, Nyarku R, Osei V, Adu-Addai B, Frye JG, Jackson CR, Samuel T, Abebe W. Comparative Genomic Profiles of Salmonella Typhimurium and Salmonella Dublin Bovine Isolates from the U.S. Indicate Possible Factors Associated with the Host Adaptation of Salmonella Dublin in the Region. Microorganisms. 2025; 13(4):886. https://doi.org/10.3390/microorganisms13040886

Chicago/Turabian Style

Bentum, Kingsley E., Emmanuel Kuufire, Rejoice Nyarku, Viona Osei, Benjamin Adu-Addai, Jonathan G. Frye, Charlene R. Jackson, Temesgen Samuel, and Woubit Abebe. 2025. "Comparative Genomic Profiles of Salmonella Typhimurium and Salmonella Dublin Bovine Isolates from the U.S. Indicate Possible Factors Associated with the Host Adaptation of Salmonella Dublin in the Region" Microorganisms 13, no. 4: 886. https://doi.org/10.3390/microorganisms13040886

APA Style

Bentum, K. E., Kuufire, E., Nyarku, R., Osei, V., Adu-Addai, B., Frye, J. G., Jackson, C. R., Samuel, T., & Abebe, W. (2025). Comparative Genomic Profiles of Salmonella Typhimurium and Salmonella Dublin Bovine Isolates from the U.S. Indicate Possible Factors Associated with the Host Adaptation of Salmonella Dublin in the Region. Microorganisms, 13(4), 886. https://doi.org/10.3390/microorganisms13040886

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop