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. 2026 Sep 27;15(5):e70408. doi: 10.1002/mbo3.70408

Genomic Detection of Multidrug‐Resistant H58 ST1 Salmonella Typhi in a South African Urban Waterway Reveals Epidemiological Links With Clinical Isolates

Sanelisiwe Thinasonke Duze 1,✉, Musa Marimani 2, Mrudula Patel 1
PMCID: PMC13617252  PMID: 42802586

ABSTRACT

Typhoid fever remains a major public health concern in South Africa. However, the role of urban waterways as environmental reservoirs and contributors to the spread of resistant Salmonella Typhi remains poorly understood. This study characterized four S. Typhi isolates recovered from the Jukskei River in Johannesburg, South Africa, using whole‐genome sequencing. Genomic DNA was extracted and sequenced on the Illumina NextSeq. 2000 platform, and assembled genomes were analyzed for sequence types (STs), AMR determinants, plasmids, virulence factors, and phylogenetic relatedness using 124 clinical Salmonella genomes for comparison. All isolates were confirmed as S. Typhi, comprising ST1 (n = 2) and ST2 (n = 2). The ST1 isolates belonged to the H58 lineage (genotype 4.3.1.1), with isolate BRSA001 further classified as a sublineage 4.3.1.1. EA1. The ST1 isolates were multidrug‐resistant, carrying the IncQ1 plasmid and resistant genes, including aph(6)‐Id, aph(3″)‐Ib, bla TEM‐1B , catA1, sul1, sul2, and dfrA7. All isolates possessed mercury resistance genes (merR, merT, merP, merC), while ST1 isolates additionally harbored qacEΔ1. Ten Salmonella Pathogenicity Islands (SPI‐1 to SPI‐10) were identified in ST1 isolates. Phylogenetic analysis revealed notable genetic diversity among the river isolates. All river isolates clustered tightly (≤ 5 allele differences) with at least two clinical isolates, indicating shared epidemiological links and possible localized transmission. This study provides the first report of multidrug‐resistant H58 S. Typhi carrying qacEΔ1 in South African surface water and highlights the Jukskei River as a potential environmental source of resistant S. Typhi, reinforcing the need for strengthened One Health genomic surveillance.

Keywords: biocide resistance, Jukskei River, multidrug resistance, Salmonella Typhi, whole genome sequencing

Genomic analysis of four S. Typhi from a South African urban river uncovered two multidrug‐resistant H58 strains carrying qacEΔ1, revealing strong phylogenetic links to clinical isolates and underscoring urban rivers as potential transmission pathways.

graphic file with name MBO3-15-e70408-g002.webp

1. Introduction

Typhoid fever is a life‐threatening systemic infection caused by a Gram‐negative bacterium, Salmonella enterica subspecies enterica serovar Typhi, abbreviated to Salmonella Typhi (Meiring et al. 2023). Clinically, it presents with gastrointestinal symptoms such as diarrhoea and abdominal cramps, along with severe febrile systemic illness requiring prompt antibiotic treatment to prevent complications (Crump et al. 2015). Globally, typhoid fever accounts for more than 10 million illnesses annually and is associated with 100,000 deaths. Without effective antimicrobial therapy, the risk of severe complications and mortality increases substantially (GBD 2017 Typhoid and Paratyphoid Collaborators 2019 Typhoid and Paratyphoid Collaborators 2019). The vast majority of typhoid cases occur in low‐ and middle‐income countries (LMICs), where inadequate sanitation systems, contaminated water, and poor hygiene practices enable sustained transmission (Liu et al. 2025; Zhang et al. 2025).

Recent multicentre surveillance studies have highlighted typhoid fever as a significant health challenge across both urban and rural settings in sub‐Saharan Africa (Carey and Steele 2019; Marks et al. 2017). In South Africa, the disease is notifiable and continues to pose an important public health concern. Although the country is considered low endemicity, outbreaks still occur, particularly among vulnerable populations residing in informal settlements or densely populated areas with limited access to safe water and adequate sanitation (Keddy et al. 2018). One of the largest documented typhoid outbreaks in South Africa occurred in Delmas, Mpumalanga, in 1993, with over 1000 reported cases. While epidemiological investigations linked the outbreak to the municipal water supply, S. Typhi was not isolated from the collected water samples (Waner et al. 1998). Currently, outbreaks and sporadic cases in South Africa are identified through active laboratory‐based surveillance conducted by the Group for Enteric, Respiratory and Meningeal Disease Surveillance in South Africa (GERMS‐SA) at the National Institute for Communicable Diseases (NICD). Through active surveillance efforts supported by whole‐genome sequencing (WGS), five confirmed typhoid fever outbreaks have been detected in the past 5 years (Smith et al. 2025; Sekwadi et al. 2024). The most recent outbreak, which occurred between January and October 2025, resulted in 147 laboratory‐confirmed cases across eight provinces, with the highest burden in Gauteng (National Institute for Communicable Diseases 2025). To date, no definitive source has been identified for any of these outbreak‐associated cases (Smith et al. 2023). This persistent lack of a definitive transmission source highlights a critical blind spot in current surveillance efforts, which are largely centered on clinical case detection, with minimal systematic monitoring of environmental reservoirs.

Environmental surveillance of wastewater and surface waters provides an opportunity to monitor pathogen circulation beyond clinically diagnosed cases. Because S. Typhi is a human‐restricted pathogen shed in feces, wastewater networks and urban rivers may serve as sensitive epidemiological indicators of community‐level transmission (Matrajt et al. 2020). Specifically, environmental surveillance can be used to track disease prevalence, identify potential transmission pathways, and evaluate the effectiveness of interventions, including vaccination campaigns and improvements in water, sanitation, and hygiene infrastructure (Matrajt et al. 2020; Uzzell et al. 2023). This approach has been successfully implemented in diverse settings, including urban river systems and informal wastewater networks in Blantyre, Malawi, and high‐burden communities in Vellore, India, where environmental monitoring detected S. Typhi circulation independent of fluctuations in clinical case reporting (Uzzell et al. 2024; Abraham et al. 2025). Comparable data from South Africa remain scarce, underscoring the need to investigate environmental waters as potential indicators of typhoid transmission. As part of a broader study examining the occurrence of Salmonella spp. in an urban river in Johannesburg, Gauteng Province, South Africa (Duze et al. 2025), four S. Typhi isolates were recovered and subsequently characterized using WGS. Therefore, this study aimed to characterize the genomic features of these environmental S. Typhi isolates and assess their genetic relatedness to clinical isolates, thereby contributing to our understanding of typhoid circulation and the potential value of environmental surveillance in South Africa.

2. Materials and Methods

2.1. Bacterial Isolates

Water samples were collected in 2023 from the Jukskei River, a major urban waterway in Johannesburg, Gauteng Province, South Africa, as part of a broader longitudinal surveillance study previously reported by Duze et al. (2025). Briefly, sampling was conducted monthly over a 12‐month period at five sites along the river, yielding a total of 60 water samples. Four S. Typhi isolates were recovered during the study period, corresponding to a recovery rate of 6.7%. All four isolates were recovered in March 2023 from two sampling sites located approximately 3.35 km apart: Site A, located within a business district (26.18147° S, 28.10237° E), and Site B, situated within a recreational park (26.16475° S, 28.13029° E). Detailed sampling procedures, site characteristics, and the sampling map have been described previously (Duze et al. 2025).

2.2. DNA Extraction

Genomic DNA was extracted from overnight cultures grown on 5% blood agar using the QIAamp DNA Mini Kit (Qiagen, GmbH, Germany). DNA quantity and purity were assessed using a NanoDrop 2000 spectrophotometer (Thermo Fisher Scientific, MA, USA). The DNA samples were sent to the Sequencing Core Facility at the NICD for sequencing.

2.3. Whole Genome Sequencing and Analysis

Libraries were prepared with Illumina DNA Prep and sequenced on an Illumina NextSeq. 2000 platform using a 2 × 150 bp paired‐end protocol at 100× coverage. Adapter sequences were automatically removed during FASTQ generation by the Illumina NextSeq instrument. Illumina paired‐end reads were analyzed using the JEKESA bioinformatics pipeline v1.0 (https://github.com/stanikae/jekesa), which incorporates multiple bioinformatics analysis tools (Kwenda et al. 2020), briefly described below with default parameters. Quality control and read filtering of raw paired‐end reads were performed using FastQC v0.11.9 (Andrews 2010) and TrimGalore v0.6.7 (Krueger 2012). Reads were screened for contaminating sequences using Kraken2 against the MiniKraken2_v2_8GB database (Wood et al. 2019; Johns Hopkins University n.d.). Species identification and closest reference detection were performed using BactInspector version 0.1.3 (Underwood 2019), and intraspecies contamination was assessed using ConFindr (Low et al. 2019). Reads were assembled using SPAdes v3.14.1 (Bankevich et al. 2012) and polished with Shovill v1.1.0 (Seemann 2019), followed by quality assessment with QUAST v5.2.0 (Gurevich et al. 2013). Annotation was performed using the NCBI Prokaryotic Genome Annotation Pipeline v6.10 (Tatusova et al. 2016). In addition, Proksee v1.2.0 (Grant et al. 2023) was used to visualize gene features, including coding sequences (CDS), tRNAs, rRNAs, and regions for clustered regularly interspaced short palindromic repeat (CRISPR).

Further genotyping was performed on Pathogen Watch (https://pathogen.watch/), which identified the haplotype 58 (H58) lineage (Argimón et al. 2021). The Centre for Genomic Epidemiology (CGE) pipeline (http://www.genomicepidemiology.org/services/) was used for Salmonella serovar prediction using SeqSero2 version 1.1.0 (http://denglab.info/SeqSero2) (Zhang et al. 2015) and SISTR version 1.1.2 (https://github.com/phac-nml/sistr_cmd) (Yoshida et al. 2016). Multilocus sequence typing (MLST) based on PubMLST typing schemes (https://pubmlst.org/) (Jolley et al. 2018), antimicrobial resistance determinants, plasmids, virulence factors, and Salmonella pathogenicity islands (SPI) were also identified using tools on the CGE platform (MLST v2.0, ResFinder v4.7.2, PlasmidFinder v2.1, and SPIFinder v2.0) with default parameters (Larsen et al. 2012; Camacho et al. 2009; Bortolaia et al. 2020; Carattoli et al. 2014; Roer et al. 2016; Hupfeld et al. 2021). Lastly, MobileElementFinder v1.0.2 was used to detect the presence of mobile genetic elements, including plasmids, insertion sequences, and transposons, in the genome sequences and their association with the antimicrobial‐resistant genes (Camacho et al. 2009; Carattoli et al. 2014; Bankevich et al. 2012; Carattoli et al. 2014).

2.4. Phylogenetic Analysis

Phylogenetic analysis was performed using the CSI Phylogeny 1.4 pipeline (https://cge.cbs.dtu.dk/services/CSIPhylogeny/) to compare the four S. Typhi genomes. A maximum‐likelihood single‐nucleotide polymorphism (SNP) tree was generated using default parameters: minimum SNP depth of 10, relative SNP depth of 10, minimum inter‐SNP distance of 10 bp, SNP quality ≥ 30, read mapping quality ≥ 25, and Z‐score ≥ 1.96 (Kaas et al. 2014). Salmonella enterica subsp. enterica serovar Typhimurium strain LT2 (accession AE006468.2) served as the reference genome. The resulting phylogenetic tree was visualized and annotated using Interactive Tree of Life (iTOL) (Letunic and Bork 2024).

In addition, raw sequencing data were submitted to EnteroBase v1.2.0 (http://enterobase.warwick.ac.uk/species/index/senterica) (Achtman et al. 2021), where they were quality‐checked and assembled. The sequence data were analyzed via multiple tools to provide information concerning Salmonella serovar, AMR determinants, MLST, and core genome MLST (cgMLST). For phylogenetic analysis, all S. Typhi from “South Africa” collected in “2023” were selected for analysis on the EnteroBase. This selection criterion was used to ensure temporal comparability, as the environmental isolates characterized in this study were recovered in 2023. The Hierarchical Clustering (HierCC) of cgMLST profiles, incorporating the “cgMLST V2 + HierCC V1” scheme, was performed, which analyzed 3002 core genes of Salmonella. The genetic relatedness of the isolates was visualized using a HierCC bubble plot, as well as with a GrapeTree‐generated minimum spanning tree (MSTree) using the “MSTree V2” algorithm (Zhou et al. 2018). The HierCC bubble plot assigns genomes to stable, nested population clusters at multiple allelic distance thresholds (HC0 to HC200+ ), thereby revealing both fine‑scale and broad‑scale genetic relatedness among isolates. On the GrapeTree‐generated MST, the clusters were detected using the steps outlined in Smith et al. (2025). Briefly, once a GrapeTree was produced, the settings/operators of the tool were set to “collapse branches” at a value of “5,” which resulted in isolates showing ≤ 5 allelic differences being collapsed together into a “cluster.” Our cluster definition was ≥ 3 isolates showing ≤ 5 allelic differences, as found by the above actions, following the cgMLST analysis and creation of a GrapeTree. We defined a cluster of isolates at a ≤ 5‐allele difference threshold to denote high genetic relatedness among isolates and identify cases likely associated with a common source (epidemiological link or transmission event).

3. Results

3.1. Genomic Features

The genomic characteristics of the four S. Typhi isolates are summarized in Table 1. The genome sizes ranged from 4.67 to 4.73 Mbp, and all isolates displayed a GC content of 52.2%, consistent with typical S. enterica genomes. The number of CDS varied slightly (4551−4606 CDS), containing between 67 and 70 tRNA genes. Ribosomal RNA (rRNA) genes ranged from five to six per genome. All isolates carried one CRISPR array, suggesting a shared capacity for adaptive immunity against foreign genetic elements (Table 1). All four isolates were confirmed as S. Typhi and classified within serogroup D1. MLST analysis assigned the isolates to sequence types ST1 (n = 2) and ST2 (n = 2). The two ST1 isolates belonged to the H58 lineage (genotype 4.3.1.1), with isolate BRSA001 further resolved as a sublineage 4.3.1.1. EA1 (genotype 4.3.1 lineage I sublineage East Africa I).

Table 1.

Genomic features of the four Salmonella enterica serovars Typhi from the Jukskei River.

Genomic features BRSA001 BRSA002 GISA00B1 GISA00B2
Genome size (bp) 4,730,647 4,687,389 4,731,182 4,672,877
GC content (%) 52% 52% 52% 52%
Genes (total) 4689 4636 4685 4683
CDS (total) 4606 4551 4603 4602
tRNAs 68 70 68 67
rRNAs 6 6 5 5
CRISPR 1 1 1 1
Sequence type 1 2 1 2
Serogroup D1 D1 D1 D1
Genotype a4.3.1.1. EA1 2.4.1 a4.3.1.1 2.4
Collection site A A B B
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a

H58 Genotype.

3.2. Antimicrobial Resistance, Virulence Factors, and Stress Response

The ST1 isolates (BRSA001 and GISA00B1) exhibited multidrug resistance genotypes harboring the aph(6)‐Id, aph(3”)‐Ib, blaTEM‐1, catA1, sul1, sul2, and dfrA7 genes, which confer resistance to aminoglycosides, β‐lactam, phenicol, sulfonamide, and trimethoprim, respectively. In addition, a point mutation (TCC → TTC) in the quinolone/triclosan resistance‐determining region in gyrA S83F was identified in the GISA00B1 isolate, as well as a stress‐response gene, qacEΔ1, in isolates BRSA001 and GISA00B1 (Figure 1).

Figure 1.

Figure 1

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A SNP‐based maximum likelihood phylogenetic tree. The tree represents the phylogenetic relationships among the different isolates based on the SNP data obtained from the S. Typhimurium str. LT2 as a reference genome. The annotations on the tips of the tree provide information about each isolate, including resistant genes, point mutations, and biocide‐resistant genes detected. The tree is presented with branch lengths to emphasize relationships.

MobileElementFinder analysis identified the IncQ1 plasmid replicon and several insertion sequences, including IS26, ISSty2, MITEEc1, and ISSen6 (Table 2). In isolate BRSA001, the IncQ1 plasmid replicon was detected on contig00061 and was associated with the resistance genes aph(6)‐Id, aph(3”)‐Ib, and sul2. The bla TEM‐1B gene was located on contig00066 together with the insertion sequence IS26.

Table 2.

Associations between mobile genetic elements, antimicrobial resistance genes, and contigs identified in the multidrug‐resistant S. Typhi isolates BRSA001 and GISA00B1.

Isolate ID MGE ARGs Contig
BRSA001 IncQ1 aph(6)‐Id, aph(3”)‐Ib and sul2 contig00061
— catA1 contig00060
— qacEΔ1, sul1 and drfA7 contig00063
IS26 bla TEM‐1B contig00066
ISSty2 — contig00003
MITEEc1 — contig00006
ISSen6 — contig00027
GISA00B1 IncQ1 and IS26 aph(6)‐Id, aph(3”)‐Ib, sul2 and bla TEM‐1B contig00052
— qacEΔ1, sul1 and drfA7 contig00057
— catA1 contig00059
ISSty2 — contig00004
MITEEc1 — contig00005
ISSen6 — contig00022
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Note: no MGE or ARG detected.

In isolate GISA00B1, IncQ1 and IS26 were detected on contig00052, where they co‐occurred with aph(6)‐Id, aph(3”)‐Ib, sul2, and bla TEM‐1B (Table 2). Notably, qacEΔ1 was co‐localized with sul1 and dfrA7 on the same contig in both isolates (BRSA001: contig00063; GISA00B1: contig00057), although no plasmids or other mobile genetic elements were identified on these contigs. Similarly, the chloramphenicol resistance gene catA1 was detected on separate contigs (BRSA001: contig00060; GISA00B1: contig00059) without association with identifiable MGE (Table 2).

The virulence profiles were uniform across all four isolates and included sinH (adhesion), iron acquisition genes (iroB and iroC), and cdtB (toxin production). In addition, the presence of SPI (SPI‐1, SPI‐2, SPI‐3, SPI‐4, SPI‐5, SPI‐6, SPI‐7, SPI‐8, SPI‐9, and SPI‐10) were observed in the two ST1 isolates (BRSA001 and GISA00B1), while the ST2 isolates (BRSA002 and GISA00B2) exhibited a comparable SPI repertoire but lacked SPI‐4. Metal stress‐associated genes merR, merT, merP, and merC, which confer mercury resistance, were consistently detected in all four isolates.

3.3. Phylogenetic Analysis

The pairwise SNP analysis indicated that the four S. Typhi isolates were genetically distinct, differing by between 50 and 382 SNPs (Figure 1). Specifically, isolates GISA00B1 and BRSA001 differed by 51 SNPs and formed a sister‐taxon cluster in the phylogenetic tree, suggesting a recent shared ancestor. Whereas isolates GISA00B2 and BRSA002 differed by 208 SNPs and clustered together, indicating recent divergence. Collectively, these results suggest that the four isolates originated from different transmission sources (Figure 1).

An additional phylogenetic analysis using a total of 128 S. Typhi isolates (human, n = 124, and Jukskei River, n = 4) was performed on the EnteroBase Database using the cgMLST V2 + HierCC V1 scheme (Figure 2). The HierCC bubble plot revealed multiple nested clusters with different levels of genetic relatedness. The 124 S. Typhi isolates from clinical specimens belong to ST1 and ST2 STs and are distributed across the HierC clusters, with the majority of the isolates concentrated within the large HC10 cluster (Figure 2A). Although the four river isolates were collected in the same month from two sampling sites located 3.35 km apart, the isolates exhibited considerable diversity and clustered into four distinct phylogenetic clusters (Figure 2A,B). Each isolate formed a tight cluster with at least two clinical isolates within the smaller inner circles of ≤ 5 allele differences, resulting in four distinct clusters of closely related S. Typhi isolates (Figure 2A). The close clustering of environmental and clinical isolates suggests a potential epidemiological association.

Figure 2.

Figure 2

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(A) Hierarchical clustering of Salmonella Typhi (n = 128) from South Africa, 2023, based on cgMLST + HierCC scheme. Each circle represents a HierCC cluster at progressively increasing allelic thresholds (HC0 to HC200 + ), with smaller inner circles corresponding to fine‑scale, closely related clusters (HC0, HC2, and HC5) and larger outer rings representing broader population groupings (HC10, HC20, HC100, and HC200+). (B) Minimum spanning tree created based on cgMLST + HierCC scheme for Salmonella Typhi (n = 128) from South Africa, 2023, by source detail. The spherical nodes represent isolates. Isolates showing ≤ 5 allelic differences were collapsed together into a single spherical node. The larger the spherical node, the more isolates it indicates. The number of segments within a spherical node is representative of the number of isolates. The numbers between adjoining nodes indicate the number of allele differences between the connecting nodes (isolates). A cluster was defined as ≥ 3 isolates showing ≤ 5 allelic differences. The gray boxes represent all ST2 isolates, with the isolates outside the gray boxes belonging to ST1.

The MSTree identified 12 clusters of S. Typhi, each comprising at least three isolates with ≤ 5 allele differences (Figure 2B). Four of these clusters contained S. Typhi isolates recovered from the Jukskei River, with cluster 3 being the largest, consisting of four clinical isolates and one environmental isolate from the river, all of which exhibited ≤ 2 allele differences (Figure 2B and Table 3). Clusters 1 and 2 were composed of ST2 S. Typhi, while Clusters 3 and 4 were mainly driven by ST1 S. Typhi H58 isolates (Figure 2B; Table 3). The clinical isolates within the four clusters were collected between February and April 2023, whereas the environmental isolates were recovered in March 2023. This temporal overlap suggests the co‐circulation of closely related S. Typhi lineages in environmental and clinical settings during the study period.

Table 3.

Small S. Typhi transmission events that may have occurred between February and April 2023 based on the cgMLST + HierC scheme using the 128 isolates from clinical and the Jukskei River.

Cluster River isolate MLST cgMLST HierCC numbera Number of isolatesb Collection monthc
1 GISA00B2 ST2 ST688335 HC2_358103 3 February
2 BRSA002 ST2 ST688338 HC2_311873 3 February
3 GISA00B1 ST1 ST688337 HC2_221022 5 April
4 BRSA001 ST1 ST688336 HC5_202 3 February
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a

HierCC—number of allele differences between the isolates in each cluster (i.e., HC2 means there are two allele differences between isolates).

b

Number of isolates in the cluster.

c

Collection month for the clinical isolates.

4. Discussion

Salmonella Typhi is a human‐restricted pathogen; therefore, its detection in the Jukskei River suggests faecal contamination of this urban waterway, likely arising from sewage discharge or poor sanitation infrastructure. In this study, four S. Typhi isolates recovered from the Jukskei River in March 2023 were characterized using WGS. These isolates belonged to two STs, ST1 and ST2, both of which represent the most commonly reported STs globally and which are frequently associated with S. Typhi in endemic settings (Stanaway et al. 2019; Díaz‐Torres et al. 2020; Fatima et al. 2023).

The ST1 S. Typhi isolates were further assigned to genotype 4.3.1, known as H58, based on SNP‐defined genotyping, with isolate BRSA001 further resolved as sublineage 4.3.1.1. EA1. The genotype 4.3.1 comprises three major lineages 4.3.1.1, 4.3.1.2, and 4.3.1.3 (Smith et al. 2023). The H58 lineage has been widely reported across sub‐Saharan Africa and Southeast Asia (Maes et al. 2020; Pragasam et al. 2020) and is the dominant genotype circulating in many parts of Southeast and South Asia, as well as East Africa. Phylogeographic analyses have demonstrated that H58 emerged in Asia before spreading internationally through multiple intercontinental transmission events, including repeated introductions into Africa, followed by sustained intra‐African transmission that facilitated its establishment across the continent (Wong et al. 2015). Consequently, this genotype is now well established across several African countries, including Tanzania, Kenya, Malawi, Zambia, Zimbabwe, and South Africa (Wong et al. 2015; Park et al. 2018; Dyson and Holt 2021).

In South Africa specifically, H58 S. Typhi has been documented in clinical isolates responsible for three major enteric fever outbreaks between 2020 and 2021 (Smith et al. 2023). However, to date, there have been no published reports of WGS‐confirmed H58 S. Typhi recovered from environmental sources such as rivers. Therefore, the detection of H58 in the Jukskei River represents a significant and novel finding, expanding current understanding of its ecological distribution and raising important questions regarding environmental reservoirs and transmission pathways. The H58 lineage of S. Typhi is of particular concern because of its strong association with multidrug resistance. In South Africa, all isolates linked to typhoid clusters in the report by Smith et al. (2025) were H58 and exhibited MDR profiles. Similarly, a study by Muturi et al. (2024) reported that S. Typhi lineage 4.3.1.2. EA3 strains isolated from a typhoid patient carried MDR determinants conferring resistance to ampicillin, sulfamethoxazole–trimethoprim, and chloramphenicol. Consistent with this pattern, the two ST1 H58 S. Typhi isolates recovered in our study were also MDR, harboring multiple resistance genes including aph(6)‐Id, aph(3”)‐Ib, bla TEM‐1B , catA1, sul1, sul2, and dfrA7. A study by Abdullah et al. (2025) reported that extensively drug‐resistant typhoid strains resistant to multiple first‐line antibiotics are linked to environmental contamination, with urban areas like Karachi demonstrating high rates of waterborne transmission. Therefore, the presence of MDR S. Typhi isolates in the Jukskei River emphasizes the role of contaminated water in spreading antibiotic resistance.

Antibiotic resistance is a significant public health threat, and Salmonella is a leading cause of antibiotic‐resistant bacterial infections in humans (Fatima et al. 2023). MDR in S. Typhi is frequently conferred by self‐transmissible plasmids carrying a suite of resistance genes, including resistance determinants for chloramphenicol (catA1 or cmlA), ampicillin (bla TEM‐1D, bla OXA‐7), and co‐trimoxazole (at least one dfrA gene and at least one sul gene) (Holt et al. 2011; McMillan et al. 2020). Notably, the two ST1 H58 S. Typhi isolates carried the IncQ plasmid. The detection of an IncQ1 plasmid in environmental S. Typhi isolates within the same contig carrying antimicrobial resistance determinants (aph(6)‐Id, aph(3”)‐Ib, sul2, and bla TEM‐1B ) is noteworthy given the established role of IncQ1 plasmids in the dissemination of resistance genes. The IncQ1 elements are known to be highly mobilizable, with a broad host range, and have been reported to disseminate AMR genes like bla TEM‐1B across diverse bacterial species, with surface waters recognized as hotspots for plasmid‐mediated horizontal gene transfer (Mina et al. 2024; Boralli et al. 2025). In our study, however, complete plasmid reconstruction and conjugation experiments were not performed; therefore, the contribution of this plasmid to horizontal gene transfer in the Jukskei River remains to be determined.

Likewise, the presence of the insertion sequence IS26 in the ST1 H58 S. Typhi isolates further underscores its likely role in mediating the mobility of ARGs. IS26 is widely recognized as a key driver of genomic plasticity, facilitating the capture, rearrangement, and mobilization of resistance determinants (Harmer and Hall 2024; Tang et al. 2024; Wang et al. 2024). In a study by Wang et al. (2024), IS26 was responsible for 63.1% of all ARG transfer events observed among plasmids. Similarly, Harmer and Hall (2024) alluded that IS26 can recruit a gene or group of genes into the mobile gene pool, supporting their continued dissemination to new locations by creating pseudo‐compound transposons that can be further mobilized by the insertion sequence.

An interesting finding in our study was the detection of the qacEΔ1 gene in the two ST1 H58 S. Typhi isolates. The qacEΔ1 gene is associated with reduced susceptibility to quaternary ammonium compounds (QACs), a widely used class of disinfectants in food processing facilities (Duze et al. 2021; Chen et al. 2023). Previous work has shown that AMR genes and disinfectant resistance determinants frequently co‐localize on the same mobile genetic elements, including plasmids, integrons, and gene cassettes (Arrieta‐Gisasola et al. 2025). Notably, in both the ST1 H58 S. Typhi isolates, qacEΔ1 was co‐localized with sul1 and dfrA7. The co‐occurrence of qacEΔ1 with AMR genes has been documented in Salmonella Indiana (Han et al. 2025). Similarly, Ali and Mohamed (2020) reported that qacEΔ1 is commonly linked to class 1 integrons in nontyphoidal Salmonella (NTS), further supporting the role of these mobile genetic elements in facilitating the concurrent dissemination of disinfectant resistance and AMR determinants. Although qacEΔ1 was co‐localized with sul1 and dfrA7 on the same contigs in both the ST1 H58 S. Typhi isolates, no plasmids, insertion sequences, or transposons were identified in association with these genes, limiting our ability to determine the mechanisms underlying their dissemination. Furthermore, phenotypic susceptibility to QAC was not assessed in this study. Consequently, the implications of the qacEΔ1 gene remain unknown and warrant further investigation.

Nevertheless, the presence of the qacEΔ1 gene is well documented in NTS, particularly in isolates associated with agricultural and food‑production environments. The qacEΔ1 gene was recently reported in clinical MDR S. Typhi isolates from pediatric patients (Riaz et al. 2025). However, no study to date has described this gene in S. Typhi isolated from urban rivers. Therefore, the detection of qacEΔ1 in our river S. Typhi isolates is novel and highlights the potential role of urban waterways in disseminating disinfectant‑ and antibiotic‑resistance determinants.

An important finding of this study was the close phylogenetic relationship between the environmental and clinical isolates. Specifically, the four S. Typhi isolates recovered from the Jukskei River formed four distinct clusters with clinical isolates, differing by ≤ 5 alleles. The observed clustering suggests a potential epidemiological association between environmental and clinical S. Typhi strains, indicating the circulation of closely related lineages in both environmental and human‐associated cases during the study period. In many LMICs, contamination of surface water and municipal supplies is a well‐established driver of typhoid transmission, often involving MDR strains (Levantesi et al. 2012; Mina et al. 2024). In contrast, identifying environmental sources during typhoid outbreaks in South Africa has remained challenging, with several investigations concluding without confirmation of a definitive source (Smith et al. 2023). Our findings, therefore, provide plausible genomic evidence supporting epidemiological links between urban waterways and human infection in this setting, further supporting the implementation of environmental surveillance as a complementary tool to enhance S. Typhi surveillance efforts.

Notably, the 2020–2023 national enteric fever surveillance data indicate that no outbreaks were notified during our sampling period. According to the Enteric Fever Surveillance Report, national case numbers in 2023 returned to baseline levels, with 141 laboratory‐confirmed cases and no cluster‐driven outbreaks reported for the year (Sekwadi et al. 2024). Therefore, the close phylogenetic clustering between river and clinical isolates suggests localized transmission events. Chronic sewage contamination of the Jukskei River likely supports the environmental persistence and recirculation of S. Typhi, creating potential exposure pathways for communities along the riverbanks, including those engaging in religious practices along the river. The small cluster sizes observed (< 5 isolates per cluster) are consistent with localized community‐level transmission rather than widespread dissemination. Although genomic relatedness cannot resolve the directionality of the transmission events, our findings highlight the need for integrated environmental and clinical surveillance. Notably, the use of genomic surveillance in South Africa has already facilitated the rapid detection of clusters indicative of emerging outbreaks. Therefore, incorporating systematic environmental monitoring, particularly of urban waterways, into the national surveillance framework will offer crucial insights into contamination sources, enhance early outbreak detection, and support more precise epidemiological investigations and public health responses.

5. Conclusion

This study provides the first evidence of the globally disseminated MDR H58 Salmonella enterica serovar Typhi lineage carrying the qacEΔ1 gene in South African surface water, with clear phylogenetic links to clinical strains. The presence of these high‐risk genotypes in an urban river underscores the potential role of polluted urban waterways as overlooked reservoirs contributing to the persistence of typhoid and antimicrobial resistance genes in urban settings. Building on national advances in genomic surveillance, our findings highlight the value of extending the current genomic surveillance to include routine environmental monitoring of high‐risk rivers to strengthen early detection of contamination events and support more targeted and timely public health interventions within a One Health framework.

Author Contributions

Sanelisiwe Thinasonke Duze: conceptualization, investigation, funding acquisition, writing – original draft, methodology, writing – review and editing, formal analysis. Musa Marimani: writing – review and editing, methodology, supervision. Mrudula Patel: conceptualization, supervision, writing – review and editing.

Ethics Statement

An ethical waiver (W‐CBP‐191004‐02) was approved by the Human Research Ethics Committee (Medical) of the University of the Witwatersrand.

Conflicts of Interest

None declared.

Acknowledgments

We would like to thank the staff of the National Health Laboratory Services at the University of the Witwatersrand, Department of Clinical Microbiology and Infectious Diseases, for their valuable contribution of expertise to this work. This project would not have been possible without the funding from the National Research Foundation (NRF) for their Thuthuka research grant (grant number 138279 for Sanelisiwe Thinasonke Duze and grant number 138302 for Musa Marimani), the Carnegie DTA research grant (RKSST23 and RKSMM21), and the Wits Chancellor's Female Academic Leadership Fellow (FALF).

Data Availability Statement

The data that support the findings of this study are openly available in NCBI at https://www.ncbi.nlm.nih.gov/bioproject/PRJNA1332730, reference number RJNA1332730. All genome sequences generated from this study have been deposited in the National Center for Biotechnology Information (NCBI) database under BioProject accession https://www.ncbi.nlm.nih.gov/bioproject/PRJNA1332730, and the sequencing data were uploaded to the public EnteroBase platform (http://enterobase.warwick.ac.uk/species/index/senterica) and are freely available to access at this platform.

References

  1. Abdullah, M. A. , Shaikh B. T., Ashraf M., and Khan S. A.. 2025. “XDR Typhoid in Pakistan: A Threat to Global Health Security and a Wake‐Up Call for Antimicrobial Stewardship.” PLoS Neglected Tropical Diseases 19, no. 5: e0013067. 10.1371/journal.pntd.0013067. [DOI] [PMC free article] [PubMed] [Google Scholar]
  2. Abraham, D. , Kathiresan L., Sasikumar M., et al. 2025. “Wastewater Surveillance for Salmonella Typhi and Its Association With Seroincidence of Enteric Fever in Vellore, India.” PLoS Neglected Tropical Diseases 19, no. 3: e0012373. 10.1371/journal.pntd.0012373. [DOI] [PMC free article] [PubMed] [Google Scholar]
  3. Achtman, M. , Zhou Z., Alikhan N.‐F., et al. 2021. “Genomic Diversity of Salmonella enterica—The UoWUCC 10K Genomes Project.” Wellcome Open Research 5: 223. 10.12688/wellcomeopenres.16291.2. [DOI] [PMC free article] [PubMed] [Google Scholar]
  4. Ali, N. M. , and Mohamed F. M.. 2020. Association of Antiseptic Resistance Gene (qacEΔ1) With Class 1 Integrons in Salmonella Isolated From Broiler Chickens. Poultry Diseases, Assiut Regional Laboratory, Animal Health Research Institute, Agricultural Research Center, Egypt.
  5. Andrews, S. 2010. “FastQC: A Quality Control Tool for High‐Throughput Sequence Data.” Babraham Bioinformatics https://www.bioinformatics.babraham.ac.uk/projects/fastqc/. [Google Scholar]
  6. Argimón, S. , Yeats C. A., Goater R. J., et al. 2021. “A Global Resource for Genomic Predictions of Antimicrobial Resistance and Surveillance of Salmonella Typhi at Pathogenwatch.” Nature Communications 12, no. 1: 2879. 10.1038/s41467-021-23091-2. [DOI] [PMC free article] [PubMed] [Google Scholar]
  7. Arrieta‐Gisasola, A. , Martínez‐Ballesteros I., Martínez‐Malaxetxebarria I., Bikandi J., and Laorden L.. 2025. “Detection of Mobile Genetic Elements Conferring Resistance to Heavy Metals in Salmonella 4,[5],12:i:‐ and Salmonella Typhimurium Serovars and Their Association With Antibiotic Resistance.” International Journal of Food Microbiology 426: 110890. 10.1016/j.ijfoodmicro.2024.110890. [DOI] [PubMed] [Google Scholar]
  8. Bankevich, A. , Nurk S., Antipov D., et al. 2012. “SPAdes: A New Genome Assembly Algorithm and Its Applications to Single‐Cell Sequencing.” Journal of Computational Biology 19, no. 5: 455–477. 10.1089/cmb.2012.0021. [DOI] [PMC free article] [PubMed] [Google Scholar]
  9. Boralli, C. M. D. S. , Paganini J. A., Meneses R. S., et al. 2025. “Dissemination of IncQ1 Plasmids Harboring NTEKPC‐IId in a Brazilian Hospital.” Microorganisms 13, no. 1: 180. 10.3390/microorganisms13010180. [DOI] [PMC free article] [PubMed] [Google Scholar]
  10. Bortolaia, V. , Kaas R. S., Ruppe E., et al. 2020. “Resfinder 4.0 for Predictions of Phenotypes From Genotypes.” Journal of Antimicrobial Chemotherapy 75: 3491–3500. [DOI] [PMC free article] [PubMed] [Google Scholar]
  11. Camacho, C. , Coulouris G., Avagyan V., et al. 2009. “BLAST+: Architecture and Applications.” BMC Bioinformatics 10, no. 1: 421. 10.1186/1471-2105-10-421. [DOI] [PMC free article] [PubMed] [Google Scholar]
  12. Carattoli, A. , Zankari E., Garcia‐Fernandez A., et al. 2014. “PlasmidFinder and pMLST: In silico Detection and Typing of Plasmids.” Antimicrobial Agents and Chemotherapy 58, no. 7: 3895–3903. 10.1128/AAC.02412-14. [DOI] [PMC free article] [PubMed] [Google Scholar]
  13. Carey, M. E. , and Steele A. D.. 2019. “The Severe Typhoid Fever in Africa Program Highlights the Need for Broad Deployment of Typhoid Conjugate Vaccines.” Clinical Infectious Diseases 69, no. Suppl 6: S413–S416. 10.1093/cid/ciz637. [DOI] [PMC free article] [PubMed] [Google Scholar]
  14. Chen, S. , Fu J., Zhao K., et al. 2023. “Class 1 Integron Carrying qacEΔ1 Gene Confers Resistance to Disinfectant and Antibiotics in Salmonella .” International Journal of Food Microbiology 404: 110319. 10.1016/j.ijfoodmicro.2023.110319. [DOI] [PubMed] [Google Scholar]
  15. Crump, J. A. , Sjölund‐Karlsson M., Gordon M. A., and Parry C. M.. 2015. “Epidemiology, Clinical Presentation, Laboratory Diagnosis, Antimicrobial Resistance, and Antimicrobial Management of Invasive Salmonella Infections.” Clinical Microbiology Reviews 28, no. 4: 901–937. 10.1128/CMR.00002-15. [DOI] [PMC free article] [PubMed] [Google Scholar]
  16. Díaz‐Torres, O. , Lugo‐Melchor O. Y., de Anda J., Gradilla‐Hernández M. S., Amézquita‐López B. A., and Meza‐Rodríguez D.. 2020. “Prevalence, Distribution, and Diversity of Salmonella Strains Isolated From a Subtropical Lake.” Frontiers in Microbiology 11: 521146. 10.3389/fmicb.2020.521146. [DOI] [PMC free article] [PubMed] [Google Scholar]
  17. Duze, S. T. , Marimani M., and Patel M.. 2021. “Tolerance of Listeria monocytogenes to Biocides Used in Food Processing Environments.” Food Microbiology 97: 103758. 10.1016/j.fm.2021.103758. [DOI] [PubMed] [Google Scholar]
  18. Duze, S. T. , Mkhize L., Marimani M., and Patel M.. 2025. “A River in Crisis: Water Quality, Microbial Burden, and Public Health Implications of a South African Urban River.” Applied and Environmental Microbiology 9, no. 11: e01566‐25. 10.1128/aem.01566-25. [DOI] [PMC free article] [PubMed] [Google Scholar]
  19. Dyson, Z. A. , and Holt K. E.. 2021. “Five Years of GenoTyphi: Updates to the Global Salmonella Typhi Genotyping Framework.” Journal of Infectious Diseases 224, no. 12 Suppl 2: S775–S780. 10.1093/infdis/jiab414. [DOI] [PMC free article] [PubMed] [Google Scholar]
  20. Fatima, S. , Ishaq Z., Irfan M., et al. 2023. “Whole‐Genome Sequencing of Multidrug‐Resistant Salmonella Typhi Clinical Strains Isolated From Balochistan, Pakistan.” Frontiers in Public Health 11: 1151805. 10.3389/fpubh.2023.1151805. [DOI] [PMC free article] [PubMed] [Google Scholar]
  21. Grant, J. R. , Enns E., Marinier E., et al. 2023. “Proksee: In‐Depth Characterization and Visualization of Bacterial Genomes.” Nucleic Acids Research 51, no. W1: W484–W492. 10.1093/nar/gkad326. [DOI] [PMC free article] [PubMed] [Google Scholar]
  22. Gurevich, A. , Saveliev V., Vyahhi N., and Tesler G.. 2013. “QUAST: Quality Assessment Tool for Genome Assemblies.” Bioinformatics 29, no. 8: 1072–1075. 10.1093/bioinformatics/btt086. [DOI] [PMC free article] [PubMed] [Google Scholar]
  23. Han, X. , Chen S., Zeng Q., et al. 2025. “Co‐Occurrence of qacEΔ1 Disinfectant Resistance Gene and ARGs Among Salmonella Indiana and Its Correlation With Resistance to Sodium Hypochlorite.” International Journal of Food Microbiology 432: 111097. 10.1016/j.ijfoodmicro.2025.111097. [DOI] [PubMed] [Google Scholar]
  24. Harmer, C. J. , and Hall R. M.. 2024. “IS26 and the IS26 Family: Versatile Resistance Gene Movers and Genome Reorganizers.” Microbiology and Molecular Biology Reviews 88, no. 2: e0011922. 10.1128/mmbr.00119-22. [DOI] [PMC free article] [PubMed] [Google Scholar]
  25. Holt, K. E. , Phan M. D., Baker S., et al. 2011. “Emergence of a Globally Dominant IncHI1 Plasmid Type Associated With Multiple Drug‐Resistant Typhoid.” PLoS Neglected Tropical Diseases 5, no. 7: e1245. 10.1371/journal.pntd.0001245. [DOI] [PMC free article] [PubMed] [Google Scholar]
  26. Hupfeld, M. , Becker N., Dalsgaard A., and Aarestrup F. M.. 2021. “Salmonella Pathogenicity Island Prediction Using Spifinder.” Microbial Genomics 7, no. 11: 000704. 10.1099/mgen.0.000704. [DOI] [Google Scholar]
  27. Johns Hopkins University . n.d. Kraken2 Database Downloads. https://ccb.jhu.edu/software/kraken2/.
  28. Jolley, K. A. , Bray J. E., and Maiden M. C. J.. 2018. “Open‐Access Bacterial Population Genomics: BIGSdb Software, the PubMLST.Org Website and Their Applications.” Wellcome Open Research 3: 124. 10.12688/wellcomeopenres.14826.1. [DOI] [PMC free article] [PubMed] [Google Scholar]
  29. Kaas, R. S. , Leekitcharoenphon P., Aarestrup F. M., and Lund O.. 2014. “Solving the Problem of Comparing Whole Bacterial Genomes Across Different Sequencing Platforms.” PLoS One 9, no. 8: e104984. 10.1371/journal.pone.0104984. [DOI] [PMC free article] [PubMed] [Google Scholar]
  30. Keddy, K. H. , Smith A. M., Sooka A., et al. 2018. “The Burden of Typhoid Fever in South Africa: The Potential Impact of Selected Interventions.” American Journal of Tropical Medicine and Hygiene 99, no. 3_Suppl: 55–63. 10.4269/ajtmh.18-0182. [DOI] [PMC free article] [PubMed] [Google Scholar]
  31. Krueger, F. 2012. Trim Galore!: A Wrapper Around Cutadapt and FastQC. Babraham Bioinformatics. https://github.com/FelixKrueger/TrimGalore. [Google Scholar]
  32. Kwenda, S. , Allam M., Khumalo Z. T. H., Mtshali S., Mnyameni F., and Ismail A.. 2020. Jekesa: An Automated Easy‐to‐Use Pipeline for Bacterial Whole Genome Typing. https://github.com/stanikae/jekesa.
  33. Larsen, M. , Cosentino S., Rasmussen S., et al. 2012. “Multilocus Sequence Typing of Total Genome Sequenced Bacteria.” Journal of Clinical Microbiology 50, no. 4: 1355–1361. 10.1128/JCM.06094-11. [DOI] [PMC free article] [PubMed] [Google Scholar]
  34. Letunic, I. , and Bork P.. 2024. “Interactive Tree of Life (iTOL) v6: Recent Updates to the Phylogenetic Tree Display and Annotation Tool.” Nucleic Acids Research 52, no. W1: W78–W82. 10.1093/nar/gkae268. [DOI] [PMC free article] [PubMed] [Google Scholar]
  35. Levantesi, C. , Bonadonna L., Briancesco R., Grohmann E., Toze S., and Tandoi V.. 2012. “Salmonella in Surface and Drinking Water: Occurrence, Survival and Relevance for Human Health.” Science of the Total Environment 438: 266–277. 10.1016/j.scitotenv.2011.12.069. [DOI] [Google Scholar]
  36. Liu, G. , Zhang X., Cao Q., Chen T., Hu B., and Shi H.. 2025. “The Global Burden of Typhoid and Paratyphoid Fever From 1990 to 2021 and the Impact on Prevention and Control.” BMC Infectious Diseases 25, no. 1: 919. 10.1186/s12879-025-11223-8. [DOI] [PMC free article] [PubMed] [Google Scholar]
  37. Low, A. J. , Koziol A. G., Manninger P. A., Blais B., and Carrillo C. D.. 2019. “ConFindr: Rapid Detection of Intraspecies and Cross‐Species Contamination in Bacterial Whole‐Genome Sequence Data.” PeerJ 7: e6995. 10.7717/peerj.6995. [DOI] [PMC free article] [PubMed] [Google Scholar]
  38. Maes, M. , Dyson Z. A., Higginson E. E., et al. 2020. “Multiple Introductions of Salmonella enterica Serovar Typhi H58 With Reduced Fluoroquinolone Susceptibility into Chile.” Emerging Infectious Diseases 26, no. 11: 2736–2740. 10.3201/eid2611.201676. [DOI] [PMC free article] [PubMed] [Google Scholar]
  39. Marks, F. , von Kalckreuth V., Aaby P., et al. 2017. “Incidence of Invasive Salmonella Disease in Sub‐Saharan Africa: A Multicentre Population‐Based Surveillance Study.” Lancet Global Health 5, no. 3: e310–e323. 10.1016/S2214-109X(17)30022-0. [DOI] [PMC free article] [PubMed] [Google Scholar]
  40. Matrajt, G. , Lillis L., and Meschke J. S.. 2020. “Review of Methods Suitable for Environmental Surveillance of Salmonella Typhi and Paratyphi.” Clinical Infectious Diseases 71, no. Suppl 2: S79–S83. 10.1093/cid/ciaa487. [DOI] [PMC free article] [PubMed] [Google Scholar]
  41. McMillan, E. A. , Jackson C. R., and Frye J. G.. 2020. “Transferable Plasmids of Salmonella enterica Associated With Antibiotic Resistance Genes.” Frontiers in Microbiology 11: 562181. 10.3389/fmicb.2020.562181. [DOI] [PMC free article] [PubMed] [Google Scholar]
  42. Meiring, J. E. , Khanam F., Basnyat B., et al. 2023. “Typhoid Fever.” Nature Reviews Disease Primers 9, no. 1: 71. 10.1038/s41572-023-00480-z. [DOI] [PubMed] [Google Scholar]
  43. Mina, S. A. , Debnath P., Hossain A. K. M. Z., Hasan M. Z., and Azad Chowdhury A. M. M.. 2024. “Screening and Identification of Multiple Antibiotic‐ Resistant Genes Containing Salmonella Typhi From Drinking Water: A Severe Public Health Concern in Bangladesh.” Heliyon 10, no. 22: e40523. 10.1016/j.heliyon.2024.e40523. [DOI] [PMC free article] [PubMed] [Google Scholar]
  44. Muturi, P. , Wachira P., Wagacha M., et al. 2024. “ Salmonella Typhi Haplotype 58 Biofilm Formation and Genetic Variation in Isolates From Typhoid Fever Patients With Gallstones in an Endemic Setting in Kenya.” Frontiers in Cellular and Infection Microbiology 14: 1468866. 10.3389/fcimb.2024.1468866. [DOI] [PMC free article] [PubMed] [Google Scholar]
  45. National Institute for Communicable Diseases . 2025. Typhoid Fever Updates. https://www.nicd.ac.za/typhoid-fever-updates/.
  46. Park, S. E. , Pham D. T., Boinett C., et al. 2018. “The Phylogeography and Incidence of Multi‐Drug‐Resistant Typhoid Fever in Sub‐Saharan Africa.” Nature Communications 9, no. 1: 5094. 10.1038/s41467-018-07370-z. [DOI] [PMC free article] [PubMed] [Google Scholar]
  47. Pragasam, A. K. , Pickard D., Wong V., et al. 2020. “Phylogenetic Analysis Indicates a Longer‐Term Presence of the Globally Distributed H58 Haplotype of Salmonella Typhi in Southern India.” Clinical Infectious Diseases 71, no. 8: 1856–1863. 10.1093/cid/ciz1112. [DOI] [PMC free article] [PubMed] [Google Scholar]
  48. Riaz, M. , Ahmad S., Sattar F., et al. 2025. “Whole‐Genome Sequencing Analysis of Drug‐Resistant Salmonella Typhi in Children.” Pathogens 14, no. 10: 967. 10.3390/pathogens14100967. [DOI] [PMC free article] [PubMed] [Google Scholar]
  49. Roer, L. , Hendriksen R. S., Leekitcharoenphon P., et al. 2016. “Is the Evolution of Salmonella enterica subsp. enterica Linked to Restriction‐Modification Systems?” mSystems 1, no. 3: e00009‐16. 10.1128/mSystems.00009-16. [DOI] [PMC free article] [PubMed] [Google Scholar]
  50. Seemann, T. 2019. Shovill: Faster SPAdes Assembly of Illumina Reads. https://github.com/tseemann/shovill.
  51. Sekwadi, P. , Ngomane H. M., Disenyeng B., et al. 2024. Enteric Fever Surveillance Report, South Africa 2020–2023. National Institute for Communicable Diseases. [Google Scholar]
  52. Sekwadi, P. , Smith A. M., Maruma W., et al. 2024. “A Prolonged Outbreak of Enteric Fever Associated With Illegal Miners in the City of Matlosana, South Africa, November 2020–September 2022.” Open Forum Infectious Diseases 11, no. 3: ofae118. 10.1093/ofid/ofae118. [DOI] [PMC free article] [PubMed] [Google Scholar]
  53. Smith, A. M. , Erasmus L. K., Tau N. P., et al. 2023. “Enteric Fever Cluster Identification in South Africa Using Genomic Surveillance of Salmonella enterica Serovar Typhi.” Microbial Genomics 9, no. 6: mgen001044. 10.1099/mgen.0.001044. [DOI] [PMC free article] [PubMed] [Google Scholar]
  54. Smith, A. M. , Sekwadi P., Ngomane H. M., et al. 2025. “Whole‐Genome Sequencing for Surveillance of Salmonella at a Public Health Institution in South Africa.” African Journal of Laboratory Medicine 14, no. 1: a2900. 10.4102/ajlm.v14i1.2900. [DOI] [PMC free article] [PubMed] [Google Scholar]
  55. Stanaway, J. D. , Reiner R. C., Blacker B. F., et al. 2019. “The Global Burden of Typhoid and Paratyphoid Fevers: A Systematic Analysis for the Global Burden of Disease Study 2017.” Lancet Infectious Diseases 19, no. 4: 369–381. 10.1016/S1473-3099(18)30685-6. [DOI] [PMC free article] [PubMed] [Google Scholar]
  56. Tang, N. , Wei D., Zeng Y., et al. 2024. “Understanding the Rapid Spread of Antimicrobial Resistance Genes Mediated by IS26.” mLife 3, no. 1: 101–109. 10.1002/mlf2.12114. [DOI] [PMC free article] [PubMed] [Google Scholar]
  57. Tatusova, T. , DiCuccio M., Badretdin A., et al. 2016. “NCBI Prokaryotic Genome Annotation Pipeline.” Nucleic Acids Research 44, no. 14: 6614–6624. 10.1093/nar/gkw569. [DOI] [PMC free article] [PubMed] [Google Scholar]
  58. Underwood, A. 2019. BactInspector: Species Identification Using Mash and RefSeq. https://pypi.org/project/BactInspectorMax/.
  59. Uzzell, C. B. , Abraham D., Rigby J., et al. 2023. “Environmental Surveillance for Salmonella Typhi and Its Association With Typhoid Fever Incidence in India and Malawi.” Journal of Infectious Diseases 229, no. 4: 979–987. 10.1093/infdis/jiad427. [DOI] [PMC free article] [PubMed] [Google Scholar]
  60. Uzzell, C. B. , Gray E., Rigby J., et al. 2024. “Environmental Surveillance for Salmonella Typhi in Rivers and Wastewater From An Informal Sewage Network in Blantyre, Malawi.” PLoS Neglected Tropical Diseases 18, no. 9: e0012518. 10.1371/journal.pntd.0012518. [DOI] [PMC free article] [PubMed] [Google Scholar]
  61. Waner, S. , Kfir R., Idema G. K., et al. 1998. “Waterborne Outbreak of Typhoid Fever in Delmas, South Africa.” Journal of Hygiene 121, no. 1: 53–57. 10.1017/S095026889800889X. [DOI] [Google Scholar]
  62. Wang, X. , Zhang H., Yu S., et al. 2024. “Inter‐Plasmid Transfer of Antibiotic Resistance Genes Accelerates Antibiotic Resistance in Bacterial Pathogens.” ISME Journal 18, no. 1: wrad032. 10.1093/ismejo/wrad032. [DOI] [PMC free article] [PubMed] [Google Scholar]
  63. Wong, V. K. , Baker S., Pickard D. J., et al. 2015. “Phylogeographical Analysis of the Dominant Multidrug‐Resistant H58 Clade of Salmonella Typhi Identifies Inter‐ and Intracontinental Transmission Events.” Nature Genetics 47, no. 6: 632–639. 10.1038/ng.3281. [DOI] [PMC free article] [PubMed] [Google Scholar]
  64. Wood, D. E. , Lu J., and Langmead B.. 2019. “Improved Metagenomic Analysis With Kraken 2.” Genome Biology 20, no. 1: 257. 10.1186/s13059-019-1891-0. [DOI] [PMC free article] [PubMed] [Google Scholar]
  65. Yoshida, C. E. , Kruczkiewicz P., Laing C. R., et al. 2016. “The Salmonella In Silico Typing Resource (SISTR): An Open Web‐Accessible Tool for Rapidly Typing and Subtyping Draft Salmonella Genome Assemblies.” PLoS One 11, no. 1: e0147101. 10.1371/journal.pone.0147101. [DOI] [PMC free article] [PubMed] [Google Scholar]
  66. Zhang, S. , Yin Y., Jones M. B., et al. 2015. “ Salmonella Serotype Determination Utilizing High‐Throughput Genome Sequencing Data.” Journal of Clinical Microbiology 53, no. 5: 1685–1692. 10.1128/JCM.00323-15. [DOI] [PMC free article] [PubMed] [Google Scholar]
  67. Zhang, Y. , Zhang Y., Chen Z., et al. 2025. “Global Burden, Subtype, Risk Factors and Etiological Analysis of Enteric Infections From 1990–2021: Population‐Based Study.” Frontiers in Cellular and Infection Microbiology 15: 1527765. 10.3389/fcimb.2025.1527765. [DOI] [PMC free article] [PubMed] [Google Scholar]
  68. Zhou, Z. , Alikhan N. F., Sergeant M. J., et al. 2018. “GrapeTree: Visualization of Core Genomic Relationships Among 100,000 Bacterial Pathogens.” Genome Research 28, no. 9: 1395–1404. 10.1101/gr.232397.117. [DOI] [PMC free article] [PubMed] [Google Scholar]

Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Data Availability Statement

The data that support the findings of this study are openly available in NCBI at https://www.ncbi.nlm.nih.gov/bioproject/PRJNA1332730, reference number RJNA1332730. All genome sequences generated from this study have been deposited in the National Center for Biotechnology Information (NCBI) database under BioProject accession https://www.ncbi.nlm.nih.gov/bioproject/PRJNA1332730, and the sequencing data were uploaded to the public EnteroBase platform (http://enterobase.warwick.ac.uk/species/index/senterica) and are freely available to access at this platform.

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