Rare serovars of non-typhoidal Salmonella enterica isolated from humans, beef cattle and abattoir environments in Nigeria

Mabel Kamweli Aworh; Pernille Nilsson; Beverly Egyir; Felicia Amoa Owusu; Rene S. Hendriksen

doi:10.1371/journal.pone.0296971

Abstract

Introduction

Salmonella is considered one of the most significant pathogens in public health since it is a bacterium that is frequently linked to food-borne illnesses in humans. Some Salmonella serovars are responsible for outbreaks that are connected to the consumption of animal products. Cattle are connected to humans through a shared environment and the food chain as a significant source of animal protein. In Nigeria, antimicrobial medications are easily accessible for use in food-producing animals. Abattoir environments are reservoirs of foodborne bacteria like non-typhoidal Salmonella enterica (NTS), that have become resistant to antibiotics used for prophylaxis or treatment in animals. This study investigated the prevalence and resistance patterns of Salmonella enterica serovars in abattoir employees, beef cattle and abattoir environments in Abuja and Lagos, Nigeria.

Methods

A total of 448 samples were collected from healthy personnel, slaughtered cattle, and abattoir environments between May and December 2020. Using Kirby-Bauer disk diffusion method, the resistance profile of NTS isolates were determined. Multidrug resistance (MDR) was considered when NTS was resistant to ≥3 antimicrobial drug classes. We performed phenotypic and genotypic characterizations of all Salmonella isolates including serotyping. Descriptive statistics were used to analyze the data.

Results

Twenty-seven (6%) NTS isolates were obtained. Prevalence of NTS was highest in abattoir environments (15.5%; 9/58), followed by cattle (4.8%;13/272) and abattoir employees (4.2%; 5/118). A high prevalence of resistance was observed for gentamicin (85.2%; 23/27) and tetracycline (77.8%; 21/27). Whole-genome sequencing of 22 NTS showed dissemination of aac(6’)-laa (22/22), qnrB19 (1/22), fosA7 (1/22), and tetA (1/22) genes. Serovar diversity of NTS varied with source. S. Anatum, a rare serovar predominated with a prevalence of 18.2% (4/22). Chromosomal point mutations showed ParC T57S substitution in 22 NTS analyzed. Among 22 NTS, 131 mobile genetic elements (MGEs) were detected including insertion sequences (56.5%) and miniature inverted repeats (43.5%). Two integrating MGEs IS6 and IS21 were observed to carry the tetA gene + Incl-1 on the same contig in NTS originating from cattle. Rare serovars namely S. Abony and S. Stormont with MDR phenotypes recovered from cattle and abattoir environments were closely related with a pairwise distance of ≤5 SNPs.

Conclusions

First report of rare serovars in Nigeria with MDR phenotypes in humans, cattle, and abattoir environments. This study demonstrates the spread of resistance in the abattoir environment possibly by MGEs and emphasizes the importance of genomic surveillance. Beef cattle may be a risk to public health because they spread a variety of rare Salmonella serovars. Therefore, encouraging hand hygiene among abattoir employees while processing beef cattle will further reduce NTS colonization in this population. This requires a One Health collaborative effort among various stakeholders in human health, animal health, and environmental health.

Citation: Aworh MK, Nilsson P, Egyir B, Owusu FA, Hendriksen RS (2024) Rare serovars of non-typhoidal Salmonella enterica isolated from humans, beef cattle and abattoir environments in Nigeria. PLoS ONE 19(1): e0296971. https://doi.org/10.1371/journal.pone.0296971

Editor: Gabriel Trueba, Universidad San Francisco de Quito, ECUADOR

Received: October 19, 2023; Accepted: December 25, 2023; Published: January 22, 2024

Copyright: © 2024 Aworh et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Data Availability: The National Center for Biotechnology Information (NCBI) has received the raw sequencing read data for this investigation and has assigned it the project accession number PRJNA804483.

Funding: This project was funded by the Fleming Fund Fellowship scheme through the Denmark Technical University according to grant No. 13534. The WGS in this project was funded by the Department of Health and Social Care’s Fleming Fund using UK aid and performed under the auspices of the SeqAfrica project. The views expressed in this publication are those of the authors and not necessarily those of the UK Department of Health and Social Care or its Management Agent, Mott MacDonald. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Competing interests: The authors have declared that no competing interests exist.

Introduction

Antimicrobial resistance (AMR) has gained global attention due to its emergence as a public health threat in recent times. AMR has been shown to cause ten million fatalities annually; if it is not controlled by the year 2050, with 40% of these deaths occurring in the human population in Africa, which is second to Asia [1]. A high level of AMR has been detected in the human population as a result of antimicrobial overuse or abuse in food and agriculture according to the World Health Organization [2]. In Nigeria, antimicrobials are easily accessible for over-the-counter purchase without a veterinarian’s prescription for preventive and therapeutic purposes, increasing abuse and misuse by livestock farmers [3]. Thus, misuse and abuse of antibiotics promotes the emergence and spread of AMR infections in both veterinary and human medicine [4]. Acquired resistance genes (ARGs) and chromosomal point mutations have been observed in both pathogenic bacteria and the internal microbiota of exposed people and food producing animals [5]. ARGs can be transmitted on to humans and zoonotic pathogens like Escherichia coli and other Salmonella spp, as well as other Gram-negative bacteria in the gut through horizontal gene transfer [6].

As a key source of animal protein, beef cattle, one of the food-producing animals, are connected to humans through a shared environment and the food chain [7]. In Nigeria, as in many emerging economies, many families rely on beef cattle rearing as a source of income as well as a major supply of animal protein, resulting in increased beef consumption [8]. Non-typhoidal Salmonella (NTS) are transmitted to humans mostly through the ingestion of contaminated raw or undercooked meat or other animal products [9, 10]. NTS is the leading cause of diarrhea, infecting 550 million people annually, including 220 million in young children under five years of age [10]. Many of the instances are fatal or life-threatening [10]. Direct contact with sick animals as well as contaminated food products, especially those of animal origin, have been linked to human salmonellosis. Salmonella spp. spread from an animal’s intestinal tract and, contaminates meat while being processed at the abattoir [11]. The emergence of AMR bacteria such as Salmonella spp in humans and animals is a global public health concern that demands prompt attention [12]. NTS are routinely detected in farm and industrial settings in a wide range of food-producing animals, including poultry, swine, and cattle [13].

Pathogenic bacteria in beef cattle and the abattoir environment, such as Salmonella species, have developed resistance to antimicrobial drugs used for prophylaxis or treatment in food animal production [14, 15]. Antimicrobial selection pressure for bacterial drug resistance is quite significant in cattle, with a comparatively high proportion of resistant bacteria in their fecal microbiota [16]. In Nigeria, human AMR trends are comparable to those seen in animal populations and the environment [17]. Furthermore the bacteria in infected people have developed resistance to antibiotics such as penicillin, tetracycline, ampicillin, nalidixic acid, chloramphenicol, and cotrimoxazole, among others, though the relationship between these resistance patterns in humans, animals, and the environment has not been proved [18]. Although a recent study in Nigeria reported ARGs, plasmids and virulence factors in NTS isolates recovered from clinical sources, food animals and environmental sources [17].

It is therefore important to establish an AMR monitoring program in Nigeria using a One Health approach to assess the burden of AMR as well as the association between AMR patterns observed in human population, beef cattle and the abattoir environment. Our hypothesis is that slaughtered beef cattle colonized with NTS could be potential sources of resistance gene transmission along the food chain or to abattoir employees exposed due to their occupation and the abattoir environments.

Thus, this study aimed to determine the prevalence and AMR patterns of NTS in human stool, beef cattle caeca samples and abattoir environments using antimicrobial susceptibility testing and whole-genome sequencing to facilitate potential public health actions.

Methods

Study design and sample collection

From May to December 2020, a cross-sectional study was conducted at one abattoir in each of Nigeria’s two largest cities namely the capital Abuja located in the Northern and dry part with 3,840,000 inhabitants and Lagos in the Southern and humid part with a population of 15,946,000 [19]. For anonymity, the abattoirs were identified as A and B. A considerable number of samples were randomly collected from abattoir employees being 18 years of age, beef cattle, and abattoir wastewater using sterile containers. A total of 118 fresh stool samples were collected from selected asymptomatic healthy abattoir employees, 272 cecal contents were collected from the ceca of slaughtered beef cattle, 58 environmental samples comprising 100 ml of abattoir wastewater as well as four meat table swabs were collected at random from different points at the selected abattoirs. The samples were transported using polystyrene boxes to the Nigeria Center for Disease Control Reference Laboratory in Gaduwa, Abuja.

Ethical considerations

The FCT Health Research Ethics Committee’s Scientific and Ethical Committee granted approval for the project (Approval Number: FHREC/2020/01/40/04-05-20). The criteria and regulations set forth by the ethics committee were followed in all the procedures. The administration of each study location was contacted for permission. Before administering the questionnaire, each eligible employee at an abattoir provided written informed consent. The gathered information was kept confidential.

Study population and sampling

A stratified sampling technique on proportional basis was used for selection of study subjects. Employees were divided into five strata based on the nature of their job: butchers, meat sellers, livestock farmers/traders, veterinarians/para-veterinarians, and others. Sampling frames for the different strata were prepared and from each frame, abattoir employees were selected based on a table of random numbers.

The beef cattle were divided into two strata based on breed (white Fulani, Sokoto gudali, Ndama and Muturu) and gender at the time of slaughter. Cattle were randomly selected to ensure only one animal was sampled per herd based on the information provided by the owner prior to slaughter of the animal in question. Animals were selected at an interval of four until the total sample size was obtained.

Data was collected through an interviewer-administered questionnaire at the time of sample collection from animals and abattoir employees. We interviewed all abattoir employees meeting the eligibility criteria. The recruitment period for this study was 23^rd June– 29^th August 2020.

Isolation and identification of Salmonella isolates

Briefly, human stool, caecum and environmental samples were pre-enriched in selenite F broth in a 1:10 sample to broth ratio at 37°C for 18–24 hours. A 10ul loop full of the enrichment broth was streaked simultaneously on Brilliant Green Agar (BGA) (Oxoid, UK) and Xylose Lysine Desoxycholate (XLD) (Oxoid, UK) media. The plates were incubated at 37°C for 24 hours [20]. Typical Salmonella colonies were confirmed by biochemical assays using commercially available kit Microbact GNB 24E (Oxoid, UK) according to the manufacturer’s instruction. Further confirmation was performed using the MALDI-TOF mass spectrometer (Bruker, Billerica, MA, USA). E. coli (ATCC 25922) was used as a negative control for this study.

Antimicrobial susceptibility testing

The Kirby Bauer disk diffusion method was applied testing the antimicrobial susceptibility of Salmonella isolates [21] and interpreted using the Clinical and Laboratory Standards Institute (CLSI) M100 32^nd Edition [22]. The isolates were tested using a panel of 14 antibiotics disks (Oxoid, Hampshire, UK) of different drug classes commonly used to treat bacterial infections in humans and animals namely ampicillin (10μg), azithromycin (15μg), cefotaxime (30μg), cefoxitin (30μg), ceftazidime (30μg), chloramphenicol (30μg), ciprofloxacin (30μg), gentamicin (10μg), imipenem (10μg), meropenem (10μg), nalidixic acid (30μg), nitrofurantoin (300μg), (tetracycline (30μg), and trimethoprim-sulfamethoxazole (1.25/23.75μg). We categorized Salmonella isolates with intermediate breakpoints as resistant because these strains are more likely to develop resistance during the treatment course. Isolates that were resistant to three or more classes of antimicrobials were considered multi-drug resistant (MDR) [23].

Whole genome sequencing of Salmonella isolates

The Noguchi Memorial Institute for Medical Research at the University of Ghana performed whole genome sequencing (WGS) of the isolates in the capacity as Regional WGS Reference Center of the UK AID Fleming Fund Regional Grant “SeqAfrica”. In brief, all Salmonella isolates (n = 27) from overnight culture were extracted and purified using the QIAamp DNA mini kit (Qiagen Inc. GmbH, Holden, Germany) according to the manufacturer’s recommendations. The DNA concentrations were quantified using the Qubit 4.0 Fluorometer Assay Kit (Thermo Fisher Scientific, MA, USA). The Nextera Flex Kit (Illumina Inc., San Diego, CA, USA) was used to prepare libraries according to the manufacturer’s instructions. Quantification of the libraries was performed using the 2100 Bioanalyzer System (Agilent) and Kapa Sybr Fast qPCR Kit. The pooled DNA sample was sequenced on an Illumina Miseq platform using a 2 × 300 paired-end approach (Illumina Inc., San Diego, CA, USA). Quality control was conducted on the raw sequencing reads to a Phred score of 30, a minimum read length of 50bp, and adaptors were trimmed using Trimmomatic (http://www.usadellab.org/cms/index.php?page=trimmomatic). Using the FastQC tool (https://www.bioinformatics.babraham.ac.uk/projects/fastq), we assessed the quality of reads. Thereafter, we used the resultant high-quality reads for de novo assembly using the Unicycler assembler v0.4.9 [24].

In silico bioinformatics analysis

ResFinder version 4.3.3 (database version 2023-04-12/ 2023-05-03) was used to detect the acquired AMR genes and chromosomal point mutations with the identity threshold and minimum length set at 90% and 60%, respectively (http://genepi.food.dtu.dk/resfinder) [25]. The MobileElementFinder of the Center for Genomic Epidemiology (CGE) (database version 2020-06-09) was used to predict the mobile genomic elements (MGEs) linked to acquired AMR genes (ARGs) and virulence factors (https://cge.food.dtu.dk/services/MobileElementFinder/) [26]. Each AMR gene was categorized as either having no linkage or located on a MGE. The MLST profiles were assigned using the EnteroBase website https://enterobase.warwick.ac.uk/species/senterica using the seven housekeeping genes (aroC, dnaN, hemD, hisD, purE, sucA, and thrA). Sequence types (STs) were assigned based on allelic variations matching with 100% identity to query database. Subsequently, the Salmonella genomes were in silico serotyped using SISTR from the EnteroBase website.

Phylogenetic analysis

Using the CSI phylogeny 1.4 tool from the CGE, the assembled genome contigs were mapped to the Salmonella reference genome (GenBank accession NZ_CP019413.1) to create a maximum likelihood phylogenetic tree (https://cge.food.dtu.dk/services/CSIPhylogeny/). The Salmonella reference genome was the best match to the collection after using the CGE K-merFinder 3.2 (database version 2022-07-11; https://cge.food.dtu.dk/services/KmerFinder/) [27]. Pairwise single-nucleotide polymorphisms (SNPs) analysis of the Salmonella core genome was used to assess the relatedness between the isolates and considered clonal related if two or more Salmonella isolates exhibited less than five distinct SNPs. The iTOL version 6 tool was applied to visualize the SNP-based phylogenetic tree (http://itol.embl.de/itol.cgi).

Data collection and analyses

Frequencies and proportions of participants’ demographics, NTS prevalence, antimicrobial susceptibility testing results, ARGs, and MGEs were computed using R version 4.2.3 (http://www.rstudio.com/). The National Center for Biotechnology Information (NCBI) has received the raw sequencing read data for this investigation and has assigned it the project accession number PRJNA804483 (S1 File).

Results

Prevalence of non-typhoidal S. enterica from different sources

Out of 448 samples collected from 118 abattoir employees, 272 beef cattle and 58 abattoir environments, 27 (6%) were positive for S. enterica. For the various sample types, the prevalence of NTS in ascending order was 4.2% (5/118), 4.8% (13/272), 15.5% (9/58) in abattoir employees, slaughtered beef cattle and abattoir environments, respectively.

Antimicrobial resistance profile of Salmonella isolates

No carbapenem resistance was observed among the 27 Salmonella isolates although a relatively high resistance level was observed for gentamicin (85.2% [23/27]) and tetracycline (77.8% [21/27]). Resistance to chloramphenicol (11.1%, [3/27]) and trimethoprim-sulfamethoxazole anti-folate combination (25.9%, [7/27]), which are the historical first-line Salmonella treatment, was however, infrequent, whereas resistance to ampicillin (40.7%, [11/27]) was mostly isolated from the abattoir environment (5/9), with only two human isolates exhibiting this characteristic (Table 1). Resistance to second-line antimicrobial agents for treating Salmonella, resistance to cephems was uncommon, with human isolates resistant to second (cefoxitin; 2/5) and third generation (cefotaxime; 1/5 and ceftazidime, 2/5) cephalosporins. In contrast, resistance to quinolones was frequent for ciprofloxacin (55.6%, [15/27]), with lower levels to nalidixic acid (22.2%, [6/27]) (Table 1).

Download:

Table 1. Antimicrobial resistance profiles of Salmonella isolates from abattoir employees, beef cattle and abattoir environments.

https://doi.org/10.1371/journal.pone.0296971.t001

Multi-drug resistance was observed in 81.5% (22/27) of Salmonella isolates, and 4/5 of the human isolates were MDR (Table 1). Co-resistance to the historical first-line drugs used in Salmonella treatment, including chloramphenicol, ampicillin, and trimethoprim-sulfamethoxazole combination, was detected in two strains: a human isolate and an abattoir environment isolate that was resistant to 7/14 antimicrobials tested.

Whole-genome sequencing (WGS) and bioinformatic analysis

Five of the genomes did not meet the quality criteria and were omitted from further genomic analysis and characterization.

In silico Salmonella serotyping and MLST

Three isolates of the 22 were not assigned any MLST hence they were excluded from the cluster analysis. The in silico serotyping and MLST of the 22 S. enterica genomes revealed a total of 12 different serovars and MLSTs from human, cattle and environmental sources including S. Anatum /ST8859 (n = 4), S. Abony /ST8856 (n = 3), S. Sinstorf /ND (n = 3), S. Concord (ST2563, ST8865) (n = 2), S. Stormont /ST9130 (n = 2), S. Tamberma /ST8860 (n = 2), S. Eastbourne /ST93, S. Give /ST524, S. Hull /ST1996, S. Leoben /ST10368, S. Muenster /ST10369, and S. Vejle /ST10370 (Table 2; Fig 1). The most prevalent serotype and MLST was S. Anatum/ ST8859 in isolates recovered from slaughtered beef cattle 18.2% (4/22) followed by S. Abony (2/22; 9.1%) and S. Sinstorf (2/22; 9.1%) co-predominated in abattoir environments. The serovar/ MLST diversity varied with source: the highest was observed in beef cattle at slaughter (7/12) followed by abattoir environments (5/12) with abattoir employees (3/12) having the least serovar diversity. S. Anatum predominated in slaughtered beef cattle with a prevalence of 18.2% (4/22). S. Abony (2/22; 9.1%) and S. Sinstorf (2/22; 9.1%) co-predominated in abattoir environments. The three abattoir employees had three different NTS serovars including S. Give, S. Vejle and S. Muenster. The S. Concord isolates originating from beef cattle at the same abattoir were both resistant to gentamicin, however, they differed by sequence type.

Download:

Fig 1. Minimum spanning tree of NTS strains used in this study.

The node colors correspond to the clustering of genetically related isolates based on the multi-locus sequence types.

https://doi.org/10.1371/journal.pone.0296971.g001

Download:

Table 2. Antimicrobial resistance profile of non-typhoidal S. enterica isolates from abattoir employees, beef cattle and abattoir environments.

https://doi.org/10.1371/journal.pone.0296971.t002

Detection of resistance genes and chromosomal point mutations

Bioinformatic analysis of NTS showed dissemination of ARGs including genes that encode quinolone resistance proteins (qnrB19 [1/22]); fosfomycin inhibitors (fosA7 [1/22], and efflux pumps (tetA [1/22]).

Among the isolates that were analyzed, chromosomal point mutations were observed. The parC T57S substitution, a known mutation in ciprofloxacin resistance was detected in all 22 S. enterica strains analyzed, but the gyrA and parE mutations in quinolone resistance-determining regions (QRDR) were not present among the isolates. All isolates with ciprofloxacin resistance phenotype which showed mutations in parC, presented single amino acid substitution of threonine (T) to serine (S) and a corresponding nucleotide change from ACC to AGC. There was a concordance between ciprofloxacin resistance phenotype and genotype among the NTS isolates.

Association of mobile genetic elements (MGEs) with AMR

Isolates from all sources showed the presence of insertion sequences (ISs) and miniature inverted repeats (MITEs). As indicated in Fig 2, MobileElementFinder predicted 131 integrating MGEs in total among 22 S. enterica isolates, of which the majority were ISs (56.5%; n = 74) followed by MITEs (43.5%; n = 57). Among all predicted ISs, 40.5% (n = 30) were IS3s, followed by IS605 (39.2%; n = 29), IS630 (12.2%; n = 9), IS110 (5.4%; n = 4), IS6 and IS21 (1.4%; n = 1 each) in descending order.

Download:

Fig 2. Predicted MGEs in non-typhoidal S. enterica isolates from all sources.

https://doi.org/10.1371/journal.pone.0296971.g002

Of 58 MITEs, 58.6% (n = 34), 27.6% (n = 16), and 13.8% (n = 8) were detected in S. enterica isolates from beef cattle, abattoir environments and abattoir employees respectively with 55.2% from abattoir A in Abuja and 44.8% from abattoir B in Lagos. Surprisingly no composite or unit transposons were detected. Two integrating MGEs IS6 and IS21 were observed to carry the tetA gene + Incl-1 on the same contig in one isolate originating from beef cattle in abattoir A. Interestingly one isolate recovered from an abattoir employee in abattoir A observed to carry a plasmid mediated quinolone resistant gene (qnrB19) was not located on any integrating MGE but rather on a plasmid (Col440I).

Clustering of Salmonella isolates based on MLST

Clustering of the S. enterica strains based on their origin and MLST revealed four distinct clusters of 11 S. enterica strains. (Table 2; Fig 1). Of those included in the tree, eight isolates were not clustered including S. Concord, S. Leoben, S. Sinstorf, S. Give, S. Vejle, S. Muenster, S. Hull, and S. Eastbourne.

Cluster 1 with S. Anatum (ST8859) comprised of four Salmonella isolates all originating from slaughtered beef cattle in Abattoir B (Lagos). Cluster 2 with S. Abony (ST8856) consisted of three Salmonella isolates originating from cattle (one strain from Abattoir A) and abattoir wastewater (two strains, one each from Abattoir A and B). Cluster 3 with S. Tamberma (ST8860) included two Salmonella isolates originating from cattle while Cluster 4 with S. Stormont (ST9130) consisted of two strains (one from cattle and the other from abattoir wastewater in Abattoir A).

Phylogenetic Single Nucleotide Polymorphism (SNP) based analysis

Many of the isolates generally formed individual lineages, however some isolates from the same source showed genetic relatedness with pairwise SNP differences of ≤5 (Table 3). For example, two pairs of NTS isolates with MDR phenotypes from beef cattle and abattoir wastewater were closely related with a pairwise distance of ≤5 SNPs. The phylogenetic SNP analysis (comprising maximum-likelihood phylogenetic tree; pairwise SNP matrix) revealed no clonal cluster shared by human and beef cattle S. enterica isolates (Fig 3). All the human isolates with different serovars were genetically diverse showing individual lineages with pairwise distance of over 27646 SNPs (S1 File). Plasmid replicons were only detected in one S. Concord isolate recovered from beef cattle and one MDR S. Muenster isolate recovered from an abattoir employee. These plasmids were observed to be harboring ARGs correlating with the observed AMR phenotypes: Incl-1 + tetA (tetracycline) and Col440I + qnrB19 (ciprofloxacin and nalidixic acid).

Download:

Fig 3. SNP-based phylogeny of non-typhoidal S. enterica isolates from abattoir employees, beef cattle, and abattoir environments in Abuja and Lagos, Nigeria.

SNP-based maximum likelihood phylogeny of S. enterica isolates visualized in iterative Tree of life tool (iTol). The tree was rooted in a reference isolate S. enterica strain with NCBI Gen bank accession.

https://doi.org/10.1371/journal.pone.0296971.g003

Download:

Table 3. Clonal relationships between non-typhoidal S. enterica isolates from different sources.

https://doi.org/10.1371/journal.pone.0296971.t003

Discussion

Many investigations around the world have found that non-typhoidal S. enterica (NTS) isolated from food animals, especially beef cattle, are frequently resistant to antimicrobials [12, 14, 17, 28]. Cattle have been implicated as a likely source in the transmission of NTS to humans via the food chain [9, 10, 13]. The hazards of people working in proximity with these food animals and the possibility of being colonized with NTS have however, not been adequately explained.

The present study investigated the prevalence of NTS in abattoir environments, slaughtered beef cattle, and abattoir employee populations. Our research demonstrated that NTS are prevalent in the abattoir environment where beef cattle are slaughtered for food, acting as a reservoir of resistant bacteria, and posing a health risk to these abattoir employees.

In this study, the prevalence of NTS was much higher than that of a recent study conducted in one of our study locations (Lagos, Nigeria) that reported a prevalence of 2.2%, 0.9% and 12.0% in isolates recovered from cattle, human stool and environmental samples, respectively [17]. Another study among poultry workers in a different location in Nigeria reported a much higher prevalence of 23.4% when compared to our study results among abattoir employees, a slightly different population [29]. The higher NTS prevalence observed in the present study may have been a result of the poor sanitary conditions of the abattoirs, especially because NTS has been reported to contaminate meat during processing at abattoirs [11]. Although a higher prevalence of 6.7% (3/45) was reported in beef in a similar study in Ghana [30] while a much lower overall prevalence of 2.8% (23/830) was reported in cattle carcass, abattoir workers and abattoir environment in Cameroon [31]. The differences in prevalence observed when compared to our study may have been because of the varying sample sizes in these studies. It is important to note that other Salmonella spp have been isolated from food animals in Africa including chickens, ducks, cattle, pigs, goats and sheep in addition to humans and the environment [32, 33].

All 27 NTS isolates in the present study were susceptible to imipenem and meropenem at phenotypic characterization, and this consistent with findings of a similar study carried out in Lagos, Nigeria [17]. The results of this study showed that the NTS isolates all together had high phenotypic resistance to aminoglycosides, tetracycline, macrolides, quinolones, nitrofurans and penicillin. Aminoglycosides, however, accounted for most of the resistance determinants detected among NTS isolates in the present study, and this is consistent with the reports of others [17, 34]. A previous study conducted in Nigeria, isolated Salmonella spp. from febrile patients in Lagos hospitals that were resistant to third generation cephalosporins with resultant bla_CTX-M and bla_CTX-M-3 genes detected by polymerase chain reaction (PCR) [35]. Although our study did not detect any of these genes, WGS has the potential to cover so many gene targets at the same time allowing for the simultaneous detection of all genetic elements in the genome, including known and novel AMR genes when compared to PCR which often targets specific resistance mechanisms [36].

Phenotypic resistance to quinolones was frequent especially for ciprofloxacin, but uncommon for nalidixic acid. A high level of resistance of NTS isolates to ciprofloxacin is consistent with the literature [17, 29, 34, 37, 38]. This is not surprising, as quinolones, which are medically important antimicrobials in human health, have been reported to be used as growth promoters in the livestock industry in Nigeria and may have contributed to the emergence of NTS resistant to ciprofloxacin [39]. Chromosomal mutations in the QRDR of parC (T57S) reported to cause clinical resistance in Salmonella species was observed in all 22 NTS isolates and this is in agreement with reports of other related studies conducted in Nigeria [17, 37, 38] and other locations [9, 34]. This is a known mutation conferring resistance in Salmonella spp isolates exhibiting low ciprofloxacin minimum inhibition concentrations as reported by other studies in recent times [40, 41].

The MDR phenotype was observed in majority of the NTS isolates, however only one isolate S. Muenster recovered from an abattoir employee had the MDR genotype with resistance determinants to quinolones (qnrB19) and fosfomycin (fosA7). A similar MDR pattern has been reported among S. Brandenburg isolates in Brazil [42].

Whole genome sequencing revealed eight rare serovars of NTS isolated from human, cattle and environmental sources including S. Abony; S. Sinstorf; S. Stormont; S. Tamberma; S. Leoben; S. Hull, S. Vejle, and S. Eastbourne. These serovars are rare based on the reports of previous surveys [43]. In addition, four common serovars, namely S. Anatum; S. Concord; S. Give; and S. Muenster were also detected. To our knowledge, except for S. Abony, S. Give, S. Muenster, and S. Eastbourne, the remaining serovars detected in this study had never been previously characterized in Nigeria, either from humans, beef cattle or the environment. According to the origin of the NTS isolates, the serovar diversity varied; the highest was found in slaughtered beef cattle, followed by abattoir environments, with abattoir employees having the lowest serovar diversity.

Interestingly, serovar S. Anatum was the dominant serovar in this study originating from beef cattle (18.2%). Our findings are consistent with a similar study conducted in Australia that reported a higher prevalence (27.9%) of S. Anatum in beef cattle ready for slaughter [44]. This serovar has also been reported to be prevalent in cattle faeces and lymph nodes [45, 46]; slaughtered pigs [47]; ground beef as well as in humans [48]. The S. Abony with MDR phenotype which was isolated from cattle and abattoir wastewater has been reported by a similar study from related sources in Nigeria further supporting our claims [17]. Although the MDR S. Abony detected in our study did not harbor any plasmid-mediated AmpC β-lactamase gene, a similar study conducted in Brazil isolated AmpC resistant S. Abony from food sources [43]. The S. Sinstorf was detected in NTS isolates from cattle, and the abattoir environment with the MDR phenotype in the present study. It is interesting to note that S. Sinstorf carrying mcr-1 gene was reported in diarrhea patients in China but also isolated from imported ducks and chickens in Egypt thus indicating the diversity of this NTS serotype [49, 50].

S. Muenster which was detected in Salmonella isolates from an abattoir employee in the present study has been previously isolated from pigs and chickens in Nigeria [37, 38, 51]; humans in Senegal [52]; imported ducks in Egypt [50] and cattle ready for slaughter at abattoirs in Australia [44]. Furthermore, serovar S. Give was detected in NTS isolates from an abattoir employee in Abuja although similar studies conducted in Nigeria reported this serotype in isolates recovered from ill humans, cattle and the environment [17, 53]. This serotype has also been detected in NTS isolates from chickens in Nigeria [51]. This is the first report of serovar S. Vejle in NTS isolates from an abattoir employee in Nigeria. A recent study conducted in Egypt detected this serovar in NTS isolates recovered from imported chickens [54].

Our results show that the S. Concord serovar was detected in isolates resistant to gentamicin and originating from slaughtered beef cattle but of different sequence type. Previously, S. Concord has been isolated from humans in Ethiopia and implicated in foodborne outbreaks. Although S. Concord in this study was not MDR, others have reported that in Ethiopia, this serotype is polyphyletic and diverse in nature spanning several lineages which are mainly MDR [55]. Other serovars with MDR phenotypes including S. Leoben and S. Eastbourne detected in NTS isolates in the current study have also been documented in slaughtered cattle and pigs [46, 47, 53]. Thus, these serovars may be emerging NTS serovars in Nigeria although not much is known about their potential to cause disease in humans. Although the present study detected S. Tamberma in NTS isolates originating from cattle, this serovar has been reported in clinical isolates resistant to tetracycline in Burkina Faso [56].

ARGs were categorized as being related to integrating MGEs if they were carried by the MGEs thus having the potential to be mobilized. Interestingly, two integrating MGEs IS6 and IS21 were observed to carry the tetA gene + Incl-1 on the same contig in one isolate originating from beef cattle in abattoir A. Tetracycline resistance genes were also carried on MGEs in Salmonella isolates originating from pigs further supporting our claims [26]. Most of the ARGs carried by integrating MGEs were mostly carried by insertion sequences and probably due to its propensity to organize into IS arrays which have been crucial for spreading ARGs among Gram-negative bacteria as reported by others [26, 57]. Surprisingly, our results did not detect the presence of composite or unit transposons in the NTS isolates. Other studies have reported that NTS strains can mediate the transfer of resistance gene through a number of transpositional mechanisms [26, 58]. Although these studies have reported fewer numbers of transposons when compared to insertion sequences in NTS isolates from other sources [26, 55].

The Col440I plasmid carrying qnrB19 was detected in one NTS isolate originating from an abattoir employee. Studies have shown that the Col440I plasmid plays an important role in the dissemination of qnrB19 especially in NTS and other members of Enterobacteriaceae [17, 37, 39]. Only one of our NTS isolates originating from beef cattle harbored the IncI-1 plasmid carrying tetA, despite the fact that they are widely distributed in Salmonella species [59], which is consistent with the results of a similar study in Cameroon [31].

Whole genome phylogenetic analysis demonstrates most of the NTS isolates were genetically diverse. The isolates clustered together in phylogenetic analysis based on their source and serotypes. Genetical diversity showing individual lineages with pairwise distance of over 27646 SNPs was observed in all the human isolates. Most of the clonal relationships observed in the current study were in NTS isolates originating from identical sources., some NTS isolates with MDR phenotypes from beef cattle and abattoir environments were however, closely related with a pairwise distance of less than 10 SNPs. Although this does not translate to direct transmission from beef cattle to abattoir environment, it is likely possible. Many studies have also documented the genetic diversity of NTS serovars as well as the possibility of clonality shared between isolates from different sources, hence supporting our claims [17, 37, 46, 51].

It is important to note that the study is limited by a small number of NTS isolates recovered from all the different sources probably because one caecal sample was evaluated per beef cattle and one stool sample per abattoir employee instead of multiple samples which enhances isolation of Salmonella species. It was also difficult to establish the risk involved for abattoir employees because of the small number of NTS isolates recovered from the samples. Furthermore, we did not have access to information on animal husbandry and antibiotic usage in the slaughtered cattle for the present study.

Conclusion

In this study, MDR NTS isolates were observed to be prevalent amongst abattoir employees, beef cattle, and abattoir environments. The highest resistance rates among MDR NTS isolates were observed to aminoglycosides, tetracycline, macrolides, quinolones, nitrofurans and penicillin which are classes of antimicrobials commonly used in veterinary practice in Nigeria. In this investigation, many rare Salmonella serovars were isolated from slaughtered cattle, emphasizing the importance of genomic surveillance, and highlighting the need for multi-sectoral collaboration to stop the transmission of bacterial illnesses from food animal products to humans. Beef cattle may be a risk to public health because they spread a variety of Salmonella serovars, many of which are uncommon and may be a source of human salmonellosis in the region. Therefore, encouraging hand hygiene among abattoir employees while processing beef cattle will further reduce NTS colonization in this population. It is also recommended that the responsible government agencies take targeted control actions against newly emerging serovars and continuously monitor and control how antimicrobials are used in food animal production. This requires a One Health collaborative effort among various stakeholders in human health, animal health, environmental health, and policy makers.

Supporting information

S1 File. This file contains the metadata for the Salmonella enterica isolates from humans, beef cattle and abattoir environment.

The metadata comprises the NCBI sequence SRR, AMR phenotype, resistance genes, mobile genetic elements, and SNP distance matrix.

https://doi.org/10.1371/journal.pone.0296971.s001

(XLSX)

Acknowledgments

The authors appreciate the UK-aid Fleming Fund Fellowship scheme for providing the training for this project. Special appreciation goes to Mott MacDonald’s team, especially Eileen Chappell and Elinam Segbefia for their excellent coordination of the project. The authors acknowledge the intellectual contributions of Prof. Iruka Okeke of University of Ibadan, Nigeria towards the success of this project. The authors acknowledge the intellectual contributions of Mr. Akinpelu Muftan, Mr. Micheal Popoola, and the bacteriology team of the National Reference Laboratory, Gaduwa, Abuja, towards the success of this research. The authors appreciate the efforts of members of the Bacteriology Department at Noguchi Memorial Institute for Medical Research, University of Ghana. Special appreciation goes to the mentors at the Denmark Technical University and Federal Ministry of Agriculture and Rural Development for their support towards the success of this project. The authors appreciate the support of Dr. Stephen Okeme, and Dr. Ayokunle Omileye as well as the management of the abattoirs where this study was conducted.

References

1. WHO. New report calls for urgent action to avert antimicrobial resistance crisis. 2019;:10–3.
2. WHO. E. coli. World Health Organization Fact Sheet. 2018. https://www.who.int/news-room/fact-sheets/detail/e-coli. Accessed 18 Jun 2019.
3. Alhaji NB, Isola TO. Antimicrobial usage by pastoralists in food animals in North-central Nigeria: The associated socio-cultural drivers for antimicrobials misuse and public health implications. One Heal. 2018;6:41–7. pmid:30533485
- View Article
- PubMed/NCBI
- Google Scholar
4. Caneschi A, Bardhi A, Barbarossa A, Zaghini A. The Use of Antibiotics and Antimicrobial Resistance in Veterinary Medicine, a Complex Phenomenon: A Narrative Review. Antibiotics. 2023;12. pmid:36978354
- View Article
- PubMed/NCBI
- Google Scholar
5. Miles TD, McLaughlin W, Brown PD. Antimicrobial resistance of Escherichia coli isolates from broiler chickens and humans. BMC Vet Res. 2006;2:7. pmid:16460561
- View Article
- PubMed/NCBI
- Google Scholar
6. Szmolka A, Nagy B. Multidrug resistant commensal Escherichia coli in animals and its impact for public health. Front Microbiol. 2013;4:258. pmid:24027562
- View Article
- PubMed/NCBI
- Google Scholar
7. Marshall BM, Levy SB. Food Animals and Antimicrobials: Impacts on Human Health. Clin Microbiol Rev. 2011;24:718–33. pmid:21976606
- View Article
- PubMed/NCBI
- Google Scholar
8. Bettencourt EMV, Tilman M, Narciso V, da Silva Carvalho ML, de Sousa Henriques PD. The livestock roles in the wellbeing of rural communities of Timor-Leste. Rev Econ e Sociol Rural. 2015;53:S063–80.
- View Article
- Google Scholar
9. Vilela FP, Rodrigues DDP, Allard MW, Falcão JP. The rare Salmonella enterica serovar Isangi: genomic characterization of the antimicrobial resistance, virulence potential and epidemiology of Brazilian strains in comparison to global isolates. J Med Microbiol. 2023;72:1–13. pmid:37462464
- View Article
- PubMed/NCBI
- Google Scholar
10. WHO. Salmonella (non-typhoidal). World Health Organization. 2018; February:1–6.
11. Costa M, Brusa V, Londero A, Galli L, Leotta GA. Molecular subtyping of Salmonella spp. strains in provincial abattoirs with no hazard analysis critical control point from Buenos Aires, Argentina. Rev Argent Microbiol. 2022;54:322–5. pmid:35644769
- View Article
- PubMed/NCBI
- Google Scholar
12. Murray CJ, Ikuta KS, Sharara F, Swetschinski L, Robles Aguilar G, Gray A, et al. Global burden of bacterial antimicrobial resistance in 2019: a systematic analysis. Lancet. 2022. pmid:35065702
- View Article
- PubMed/NCBI
- Google Scholar
13. Zadoks RN, Barker GC, Benschop J, Allan KJ, Chaters G, Cleaveland S, et al. Spread of Nontyphoidal Salmonella in the Beef Supply Chain in Northern Tanzania: Sensitivity in a Probabilistic Model Integrating Microbiological Data and Data from Stakeholder Interviews. Risk Anal. 2022;42:989–1006. pmid:34590330
- View Article
- PubMed/NCBI
- Google Scholar
14. Nair DVT, Venkitanarayanan K, Johny AK. Antibiotic-resistant Salmonella in the food supply and the potential role of antibiotic alternatives for control. Foods. 2018;7.
- View Article
- Google Scholar
15. Aworh MK, Kwaga J, Okolocha E, Mba N, Thakur S. Prevalence and risk factors for multi-drug resistant Escherichia coli among poultry workers in the Federal Capital Territory, Abuja, Nigeria. PLoS One. 2019;14:1–15. pmid:31751388
- View Article
- PubMed/NCBI
- Google Scholar
16. Akanbi BO, Mbah IP, Kerry PC. Prevalence of Escherichia coli O157:H7 on hides and faeces of ruminants at slaughter in two major abattoirs in Nigeria. Lett Appl Microbiol. 2011;53:336–40. pmid:21722147
- View Article
- PubMed/NCBI
- Google Scholar
17. Akinyemi KO, Fakorede CO, Linde J, Methner U, Wareth G. Whole genome sequencing of Salmonella enterica serovars isolated from humans, animals, and the environment in Lagos,. BMC Microbiol. 2023;23:1–17.
- View Article
- Google Scholar
18. Oloso N, Fagbo S, Garbati M, Olonitola S, Awosanya E, Aworh M, et al. Antimicrobial Resistance in Food Animals and the Environment in Nigeria: A Review. Int J Environ Res Public Health. 2018;15:1284. pmid:29914203
- View Article
- PubMed/NCBI
- Google Scholar
19. Macrotrends. Lagos, Nigeria Metro Area Population 1950–2023 Chart. 2023. https://www.macrotrends.net/cities/22007/lagos/population. Accessed 25 Sep 2023.
20. Donaldson A, Nicol M. Evaluation of Selenite F broth as an enrichment step for the isolation of salmonella and shigella in clinical faecal specimens—A retrospective study. New Zeal J Med Lab Sci. 2016;70:92–4.
- View Article
- Google Scholar
21. Hudzicki J. Kirby-Bauer Disk Diffusion Susceptibility Test Protocol. 2009.
22. CLSI. M100, Performance Standards for Antimicrobial Susceptibility Testing. 2022.
23. Magiorakos A-P, Srinivasan A, Carey RB, Carmeli Y, Falagas ME, Giske CG, et al. Multidrug-resistant, extensively drug-resistant and pandrug-resistant bacteria: an international expert proposal for interim standard definitions for acquired resistance. Clin Microbiol Infect. 2012;18:268–81. pmid:21793988
- View Article
- PubMed/NCBI
- Google Scholar
24. Wick RR, Judd LM, Gorrie CL, Holt KE. Unicycler: Resolving bacterial genome assemblies from short and long sequencing reads. PLoS Comput Biol. 2017;13:1–22. pmid:28594827
- View Article
- PubMed/NCBI
- Google Scholar
25. Bortolaia V, Kaas RS, Ruppe E, Roberts MC, Schwarz S, Cattoir V, et al. ResFinder 4.0 for predictions of phenotypes from genotypes. J Antimicrob Chemother. 2020;75:3491–500. pmid:32780112
- View Article
- PubMed/NCBI
- Google Scholar
26. Johansson MHK, Bortolaia V, Tansirichaiya S, Aarestrup FM, Roberts AP, Petersen TN. Detection of mobile genetic elements associated with antibiotic resistance in Salmonella enterica using a newly developed web tool: MobileElementFinder. J Antimicrob Chemother. 2021;76:101–9. pmid:33009809
- View Article
- PubMed/NCBI
- Google Scholar
27. Clausen PTLC, Aarestrup FM, Lund O. Rapid and precise alignment of raw reads against redundant databases with KMA. BMC Bioinformatics. 2018;19:1–8.
- View Article
- Google Scholar
28. Elbediwi M, Li Y, Paudyal N, Pan H, Li X, Xie S, et al. Global burden of colistin-resistant bacteria: Mobilized colistin resistance genes study (1980–2018). Microorganisms. 2019;7. pmid:31623244
- View Article
- PubMed/NCBI
- Google Scholar
29. Owowo EE, Umoh VJ, Okon IE. Occurrence of Typhoidal and Non-Typhoidal Salmonellae among Poultry Workers in the. Open J Med Microbiol. 2019;9:201–14.
- View Article
- Google Scholar
30. Tay MYF, Adzitey F, Sultan A, Tati M, Seow KLG. Whole-Genome Sequencing of Nontyphoidal Salmonella enterica Isolates Obtained from Various Meat Types in Ghana. Microbiol Resour Announc. 2019;8:6–9. pmid:30975795
- View Article
- PubMed/NCBI
- Google Scholar
31. Matchawe C, Machuka EM, Kyallo M, Bonny P, Nkeunen G, Njaci I, et al. Virulence Potentials of Non-Typhoidal Salmonella Isolates at the Yaounde Abattoir Using Whole-Genome Sequencing Technique. Pathogens. 2022;11:1–17.
- View Article
- Google Scholar
32. Kaonga N, Hang’ombe BM, Lupindu AM, Hoza AS. Detection of CTX-M-Type Extended Spectrum Beta-Lactamase Producing Salmonella Typhimurium in Commercial Poultry Farms in Copperbelt Province, Zambia. Ger J Vet Res. 2021;1:27–34.
- View Article
- Google Scholar
33. Ramatla T, Tawana M, Onyiche TGE, Lekota KE, Thekisoe O. One Health Perspective of Salmonella Serovars in South Africa Using Pooled Prevalence: Systematic Review and Meta-Analysis. Int J Microbiol. 2022;2022. pmid:35498396
- View Article
- PubMed/NCBI
- Google Scholar
34. Moyne A, Lawal OU, Gauthier J, Kukavica-ibrulj I, Potvin M, Goodridge L, et al. Genetic diversity of Salmonella enterica isolated over 13 years from raw California almonds and from an almond orchard. PLoS One. 2023;0291109:1–25. pmid:37676871
- View Article
- PubMed/NCBI
- Google Scholar
35. Akinyemi KO, Fakorede CO, Abegunrin RO, Ajoseh SO, Anjorin A-AA, Amisu KO, et al. Detection of invA and blaCTM-genes in Salmonella spp isolated from febrile patients in Lagos hospitals, Nigeria. Ger J Microbiol. 2021;1:1–10.
- View Article
- Google Scholar
36. Anjum MF, Zankari E, Hasman H. Molecular Methods for Detection of Antimicrobial Resistance. Microbiol Spectr. 2017;5. pmid:29219107
- View Article
- PubMed/NCBI
- Google Scholar
37. Raufu IA, Ahmed OA, Aremu A, Ameh JA, Timme RE, Hendriksen RS, et al. Occurrence, antimicrobial resistance and whole genome sequence analysis of Salmonella serovars from pig farms in Ilorin, North-central Nigeria. Int J Food Microbiol. 2021;350 December 2020:109245. pmid:34023679
- View Article
- PubMed/NCBI
- Google Scholar
38. Fagbamila OI, Ramon E, Lettini AA, Muhammad M, Longo A, Antonello K, et al. Assessing the mechanisms of multi-drug resistant non-typhoidal Salmonella (NTS) serovars isolated from layer chicken farms in Nigeria. PLoS One. 2023;0290754:1–19. pmid:37676896
- View Article
- PubMed/NCBI
- Google Scholar
39. Aworh MK, Kwaga JKP, Hendriksen RS, Okolocha EC, Harrell E, Thakur S. Quinolone-resistant Escherichia coli at the interface between humans, poultry and their shared environment- a potential public health risk. One Heal Outlook. 2023;5. pmid:36855171
- View Article
- PubMed/NCBI
- Google Scholar
40. Chang M, Zhang J, Sun Y, Li R, Lin X, Yang L, et al. Contribution of Different Mechanisms to Ciprofloxacin Resistance in Salmonella spp. Front Microbiol. 2021;12 May:663731. pmid:34025618
- View Article
- PubMed/NCBI
- Google Scholar
41. Zhang Z, Chang J, Xu X, Hu M, He S, Qin X, et al. Phylogenomic Analysis of Salmonella enterica Serovar Indiana ST17, an Emerging Multidrug-Resistant Clone in China Zengfeng. Microbiol Spectr. 2022;10:1–12.
- View Article
- Google Scholar
42. Monte DF, Lincopan N, Berman H, Cerdeira L, Keelara S, Thakur S, et al. Genomic Features of High-Priority Salmonella enterica Serovars Circulating in the Food Production. Sci Rep. 2019;11058 June:1–12.
- View Article
- Google Scholar
43. Monte DFM, Nethery MA, Barrangou R, Landgraf M, Fedorka-Cray PJ. Whole-genome sequencing analysis and CRISPR genotyping of rare antibiotic-resistant Salmonella enterica serovars isolated from food and related sources. Food Microbiol. 2021;93 July 2020:103601. pmid:32912589
- View Article
- PubMed/NCBI
- Google Scholar
44. Fegan N, Vanderlinde P, Higgs G, Desmarchelier P. Quantification and prevalence of Salmonella in beef cattle presenting at slaughter. J Appl Microbiol. 2004;97:892–8. pmid:15479403
- View Article
- PubMed/NCBI
- Google Scholar
45. Nickodem C, Arnold AN, Gehring KB, Gill JJ, Richeson JT, Samuelson KL, et al. A Longitudinal Study on the Dynamics of Salmonella enterica Prevalence and Serovar Composition in Beef Cattle Feces and Lymph Nodes and Potential Contributing Sources from the Feedlot Environment. Appl Environ Microbiol. 2023;89:1–20. pmid:37022263
- View Article
- PubMed/NCBI
- Google Scholar
46. Gutema FD, Agga GE, Abdi RD, De Zutter L, Duchateau L, Gabriël S. Prevalence and Serotype Diversity of Salmonella in Apparently Healthy Cattle: Systematic Review and. Front Vet Sci. 2019;6 April:1–11.
- View Article
- Google Scholar
47. Molla B, Berhanu A, Muckle A, Cole L, Wilkie E, Kleer J, et al. Multidrug resistance and distribution of Salmonella serovars in slaughtered pigs. J Vet Med Ser B Infect Dis Vet Public Heal. 2006;53:28–33. pmid:16460353
- View Article
- PubMed/NCBI
- Google Scholar
48. Nguyen S V., Harhay DM, Bono JL, Smith TPL, Fields PI, Dinsmore BA, et al. Complete, closed genome sequences of 10 Salmonella enterica subsp. enterica serovar Typhimurium strains isolated from human and bovine sources. Genome Announc. 2016;4:2010–1. pmid:27811097
- View Article
- PubMed/NCBI
- Google Scholar
49. Liu G, Qian H, Lv J, Tian B, Bao C, Yan H, et al. Emergence of mcr-1 -Harboring Salmonella enterica Serovar Sinstorf Type ST155 Isolated From Patients With Diarrhea in Jiangsu, China. Front Microbiol. 2021;12 September:723697. pmid:34603249
- View Article
- PubMed/NCBI
- Google Scholar
50. Badr H, Reda RM, Hagag NM, Kamel E, Elnomrosy SM, Mansour AI, et al. Multidrug-Resistant and Genetic Characterization of Extended-Spectrum Beta-Lactamase-Producing E. coli Recovered from Chickens and Humans in Egypt. Animals. 2022;12:346. pmid:35158668
- View Article
- PubMed/NCBI
- Google Scholar
51. Jibril AH, Okeke IN, Dalsgaard A, Garc V, Olsen JE. Genomic Analysis of Antimicrobial Resistance and Resistance Plasmids in Salmonella Serovars from Poultry in Nigeria. Antibiotics. 2021;10:1–21. pmid:33498344
- View Article
- PubMed/NCBI
- Google Scholar
52. Dieye Y, Hull DM, Wane AA, Harden L, Fall C, Sambe-Ba B, et al. Genomics of human and chicken Salmonella isolates in Senegal: Broilers as a source of antimicrobial resistance and potentially invasive nontyphoidal salmonellosis infections. PLoS One. 2022;17 3 March:1–18. pmid:35325007
- View Article
- PubMed/NCBI
- Google Scholar
53. Fashae K, Leekitcharoenphon P, Hendriksen RS. Phenotypic and genotypic comparison of salmonellae from diarrhoeic and healthy humans and cattle, Nigeria. Zoonoses Public Health. 2018;65:e185–95. pmid:29193894
- View Article
- PubMed/NCBI
- Google Scholar
54. Badr Heba, Roshdy Heba, Sorour Hend K., Abdelrahman Mona A.A., Erfan Ahmed M., Salem Noha, et al. Phenotypic and Genotypic Characterization of Salmonella enterica Serovars Isolated from Imported Poultry. Adv Anim Vet Sci. 2021;9:823–34.
- View Article
- Google Scholar
55. Cuypers Wim L., Meysman Pieter, Weill François-Xavier, Hendriksen Rene S., Getenet Beyene JW Satheesh Nair, Chattaway Marie A., Perez-Sepulveda Blanca M., Ceyssens Pieter-Jan, Tessa de Block, Lee WinnieW.Y., Maria Pardos de la Gandara CK. A global genomic analysis of Salmonella Concord reveals lineages with high antimicrobial resistance in Ethiopia. Nat Commun. 2023;14. pmid:37316492
- View Article
- PubMed/NCBI
- Google Scholar
56. Somda NS, Bonkoungou IJO, Sambe-Ba B, Drabo MS, Wane AA, Sawadogo-Lingani H, et al. Diversity and antimicrobial drug resistance of non-typhoid Salmonella serotypes isolated in lettuce, irrigation water and clinical samples in Burkina Faso. J Agric Food Res. 2021;5:1–8.
- View Article
- Google Scholar
57. Leekitcharoenphon P, Hans M, Johansson K, Munk P, Malorny B, Skarżyńska M, et al. Genomic evolution of antimicrobial resistance in Escherichia coli. Sci Rep. 2021;11:1–12.
- View Article
- Google Scholar
58. Zeng S, Zhuo Z, Huang Y, Luo J, Feng Y, Gong B, et al. Prevalence of Chromosomally Located bla CTX-M-55 in Salmonella Typhimurium ST34 Isolates Recovered from a Tertiary Hospital in Guangzhou, China. Microbiol Spectr. 2022;10. pmid:35616373
- View Article
- PubMed/NCBI
- Google Scholar
59. Kime L, Randall CP, Banda FI, Coll F, Wright J, Richardson J, et al. Transient Silencing of Antibiotic Resistance by Mutation Represents a Significant Potential Source of Unanticipated Therapeutic Failure. MBio. 2019;10:e01755–19. pmid:31662453
- View Article
- PubMed/NCBI
- Google Scholar

[ref1] 1. WHO. New report calls for urgent action to avert antimicrobial resistance crisis. 2019;:10–3.

[ref2] 2. WHO. E. coli. World Health Organization Fact Sheet. 2018. https://www.who.int/news-room/fact-sheets/detail/e-coli. Accessed 18 Jun 2019.

[ref3] 3. Alhaji NB, Isola TO. Antimicrobial usage by pastoralists in food animals in North-central Nigeria: The associated socio-cultural drivers for antimicrobials misuse and public health implications. One Heal. 2018;6:41–7. pmid:30533485
View Article
PubMed/NCBI
Google Scholar

[4] View Article

[5] PubMed/NCBI

[6] Google Scholar

[ref4] 4. Caneschi A, Bardhi A, Barbarossa A, Zaghini A. The Use of Antibiotics and Antimicrobial Resistance in Veterinary Medicine, a Complex Phenomenon: A Narrative Review. Antibiotics. 2023;12. pmid:36978354
View Article
PubMed/NCBI
Google Scholar

[8] View Article

[9] PubMed/NCBI

[10] Google Scholar

[ref5] 5. Miles TD, McLaughlin W, Brown PD. Antimicrobial resistance of Escherichia coli isolates from broiler chickens and humans. BMC Vet Res. 2006;2:7. pmid:16460561
View Article
PubMed/NCBI
Google Scholar

[12] View Article

[13] PubMed/NCBI

[14] Google Scholar

[ref6] 6. Szmolka A, Nagy B. Multidrug resistant commensal Escherichia coli in animals and its impact for public health. Front Microbiol. 2013;4:258. pmid:24027562
View Article
PubMed/NCBI
Google Scholar

[16] View Article

[17] PubMed/NCBI

[18] Google Scholar

[ref7] 7. Marshall BM, Levy SB. Food Animals and Antimicrobials: Impacts on Human Health. Clin Microbiol Rev. 2011;24:718–33. pmid:21976606
View Article
PubMed/NCBI
Google Scholar

[20] View Article

[21] PubMed/NCBI

[22] Google Scholar

[ref8] 8. Bettencourt EMV, Tilman M, Narciso V, da Silva Carvalho ML, de Sousa Henriques PD. The livestock roles in the wellbeing of rural communities of Timor-Leste. Rev Econ e Sociol Rural. 2015;53:S063–80.
View Article
Google Scholar

[24] View Article

[25] Google Scholar

[ref9] 9. Vilela FP, Rodrigues DDP, Allard MW, Falcão JP. The rare Salmonella enterica serovar Isangi: genomic characterization of the antimicrobial resistance, virulence potential and epidemiology of Brazilian strains in comparison to global isolates. J Med Microbiol. 2023;72:1–13. pmid:37462464
View Article
PubMed/NCBI
Google Scholar

[27] View Article

[28] PubMed/NCBI

[29] Google Scholar

[ref10] 10. WHO. Salmonella (non-typhoidal). World Health Organization. 2018; February:1–6.

[ref11] 11. Costa M, Brusa V, Londero A, Galli L, Leotta GA. Molecular subtyping of Salmonella spp. strains in provincial abattoirs with no hazard analysis critical control point from Buenos Aires, Argentina. Rev Argent Microbiol. 2022;54:322–5. pmid:35644769
View Article
PubMed/NCBI
Google Scholar

[32] View Article

[33] PubMed/NCBI

[34] Google Scholar

[ref12] 12. Murray CJ, Ikuta KS, Sharara F, Swetschinski L, Robles Aguilar G, Gray A, et al. Global burden of bacterial antimicrobial resistance in 2019: a systematic analysis. Lancet. 2022. pmid:35065702
View Article
PubMed/NCBI
Google Scholar

[36] View Article

[37] PubMed/NCBI

[38] Google Scholar

[ref13] 13. Zadoks RN, Barker GC, Benschop J, Allan KJ, Chaters G, Cleaveland S, et al. Spread of Nontyphoidal Salmonella in the Beef Supply Chain in Northern Tanzania: Sensitivity in a Probabilistic Model Integrating Microbiological Data and Data from Stakeholder Interviews. Risk Anal. 2022;42:989–1006. pmid:34590330
View Article
PubMed/NCBI
Google Scholar

[40] View Article

[41] PubMed/NCBI

[42] Google Scholar

[ref14] 14. Nair DVT, Venkitanarayanan K, Johny AK. Antibiotic-resistant Salmonella in the food supply and the potential role of antibiotic alternatives for control. Foods. 2018;7.
View Article
Google Scholar

[44] View Article

[45] Google Scholar

[ref15] 15. Aworh MK, Kwaga J, Okolocha E, Mba N, Thakur S. Prevalence and risk factors for multi-drug resistant Escherichia coli among poultry workers in the Federal Capital Territory, Abuja, Nigeria. PLoS One. 2019;14:1–15. pmid:31751388
View Article
PubMed/NCBI
Google Scholar

[47] View Article

[48] PubMed/NCBI

[49] Google Scholar

[ref16] 16. Akanbi BO, Mbah IP, Kerry PC. Prevalence of Escherichia coli O157:H7 on hides and faeces of ruminants at slaughter in two major abattoirs in Nigeria. Lett Appl Microbiol. 2011;53:336–40. pmid:21722147
View Article
PubMed/NCBI
Google Scholar

[51] View Article

[52] PubMed/NCBI

[53] Google Scholar

[ref17] 17. Akinyemi KO, Fakorede CO, Linde J, Methner U, Wareth G. Whole genome sequencing of Salmonella enterica serovars isolated from humans, animals, and the environment in Lagos,. BMC Microbiol. 2023;23:1–17.
View Article
Google Scholar

[55] View Article

[56] Google Scholar

[ref18] 18. Oloso N, Fagbo S, Garbati M, Olonitola S, Awosanya E, Aworh M, et al. Antimicrobial Resistance in Food Animals and the Environment in Nigeria: A Review. Int J Environ Res Public Health. 2018;15:1284. pmid:29914203
View Article
PubMed/NCBI
Google Scholar

[58] View Article

[59] PubMed/NCBI

[60] Google Scholar

[ref19] 19. Macrotrends. Lagos, Nigeria Metro Area Population 1950–2023 Chart. 2023. https://www.macrotrends.net/cities/22007/lagos/population. Accessed 25 Sep 2023.

[ref20] 20. Donaldson A, Nicol M. Evaluation of Selenite F broth as an enrichment step for the isolation of salmonella and shigella in clinical faecal specimens—A retrospective study. New Zeal J Med Lab Sci. 2016;70:92–4.
View Article
Google Scholar

[63] View Article

[64] Google Scholar

[ref21] 21. Hudzicki J. Kirby-Bauer Disk Diffusion Susceptibility Test Protocol. 2009.

[ref22] 22. CLSI. M100, Performance Standards for Antimicrobial Susceptibility Testing. 2022.

[ref23] 23. Magiorakos A-P, Srinivasan A, Carey RB, Carmeli Y, Falagas ME, Giske CG, et al. Multidrug-resistant, extensively drug-resistant and pandrug-resistant bacteria: an international expert proposal for interim standard definitions for acquired resistance. Clin Microbiol Infect. 2012;18:268–81. pmid:21793988
View Article
PubMed/NCBI
Google Scholar

[68] View Article

[69] PubMed/NCBI

[70] Google Scholar

[ref24] 24. Wick RR, Judd LM, Gorrie CL, Holt KE. Unicycler: Resolving bacterial genome assemblies from short and long sequencing reads. PLoS Comput Biol. 2017;13:1–22. pmid:28594827
View Article
PubMed/NCBI
Google Scholar

[72] View Article

[73] PubMed/NCBI

[74] Google Scholar

[ref25] 25. Bortolaia V, Kaas RS, Ruppe E, Roberts MC, Schwarz S, Cattoir V, et al. ResFinder 4.0 for predictions of phenotypes from genotypes. J Antimicrob Chemother. 2020;75:3491–500. pmid:32780112
View Article
PubMed/NCBI
Google Scholar

[76] View Article

[77] PubMed/NCBI

[78] Google Scholar

[ref26] 26. Johansson MHK, Bortolaia V, Tansirichaiya S, Aarestrup FM, Roberts AP, Petersen TN. Detection of mobile genetic elements associated with antibiotic resistance in Salmonella enterica using a newly developed web tool: MobileElementFinder. J Antimicrob Chemother. 2021;76:101–9. pmid:33009809
View Article
PubMed/NCBI
Google Scholar

[80] View Article

[81] PubMed/NCBI

[82] Google Scholar

[ref27] 27. Clausen PTLC, Aarestrup FM, Lund O. Rapid and precise alignment of raw reads against redundant databases with KMA. BMC Bioinformatics. 2018;19:1–8.
View Article
Google Scholar

[84] View Article

[85] Google Scholar

[ref28] 28. Elbediwi M, Li Y, Paudyal N, Pan H, Li X, Xie S, et al. Global burden of colistin-resistant bacteria: Mobilized colistin resistance genes study (1980–2018). Microorganisms. 2019;7. pmid:31623244
View Article
PubMed/NCBI
Google Scholar

[87] View Article

[88] PubMed/NCBI

[89] Google Scholar

[ref29] 29. Owowo EE, Umoh VJ, Okon IE. Occurrence of Typhoidal and Non-Typhoidal Salmonellae among Poultry Workers in the. Open J Med Microbiol. 2019;9:201–14.
View Article
Google Scholar

[91] View Article

[92] Google Scholar

[ref30] 30. Tay MYF, Adzitey F, Sultan A, Tati M, Seow KLG. Whole-Genome Sequencing of Nontyphoidal Salmonella enterica Isolates Obtained from Various Meat Types in Ghana. Microbiol Resour Announc. 2019;8:6–9. pmid:30975795
View Article
PubMed/NCBI
Google Scholar

[94] View Article

[95] PubMed/NCBI

[96] Google Scholar

[ref31] 31. Matchawe C, Machuka EM, Kyallo M, Bonny P, Nkeunen G, Njaci I, et al. Virulence Potentials of Non-Typhoidal Salmonella Isolates at the Yaounde Abattoir Using Whole-Genome Sequencing Technique. Pathogens. 2022;11:1–17.
View Article
Google Scholar

[98] View Article

[99] Google Scholar

[ref32] 32. Kaonga N, Hang’ombe BM, Lupindu AM, Hoza AS. Detection of CTX-M-Type Extended Spectrum Beta-Lactamase Producing Salmonella Typhimurium in Commercial Poultry Farms in Copperbelt Province, Zambia. Ger J Vet Res. 2021;1:27–34.
View Article
Google Scholar

[101] View Article

[102] Google Scholar

[ref33] 33. Ramatla T, Tawana M, Onyiche TGE, Lekota KE, Thekisoe O. One Health Perspective of Salmonella Serovars in South Africa Using Pooled Prevalence: Systematic Review and Meta-Analysis. Int J Microbiol. 2022;2022. pmid:35498396
View Article
PubMed/NCBI
Google Scholar

[104] View Article

[105] PubMed/NCBI

[106] Google Scholar

[ref34] 34. Moyne A, Lawal OU, Gauthier J, Kukavica-ibrulj I, Potvin M, Goodridge L, et al. Genetic diversity of Salmonella enterica isolated over 13 years from raw California almonds and from an almond orchard. PLoS One. 2023;0291109:1–25. pmid:37676871
View Article
PubMed/NCBI
Google Scholar

[108] View Article

[109] PubMed/NCBI

[110] Google Scholar

[ref35] 35. Akinyemi KO, Fakorede CO, Abegunrin RO, Ajoseh SO, Anjorin A-AA, Amisu KO, et al. Detection of invA and blaCTM-genes in Salmonella spp isolated from febrile patients in Lagos hospitals, Nigeria. Ger J Microbiol. 2021;1:1–10.
View Article
Google Scholar

[112] View Article

[113] Google Scholar

[ref36] 36. Anjum MF, Zankari E, Hasman H. Molecular Methods for Detection of Antimicrobial Resistance. Microbiol Spectr. 2017;5. pmid:29219107
View Article
PubMed/NCBI
Google Scholar

[115] View Article

[116] PubMed/NCBI

[117] Google Scholar

[ref37] 37. Raufu IA, Ahmed OA, Aremu A, Ameh JA, Timme RE, Hendriksen RS, et al. Occurrence, antimicrobial resistance and whole genome sequence analysis of Salmonella serovars from pig farms in Ilorin, North-central Nigeria. Int J Food Microbiol. 2021;350 December 2020:109245. pmid:34023679
View Article
PubMed/NCBI
Google Scholar

[119] View Article

[120] PubMed/NCBI

[121] Google Scholar

[ref38] 38. Fagbamila OI, Ramon E, Lettini AA, Muhammad M, Longo A, Antonello K, et al. Assessing the mechanisms of multi-drug resistant non-typhoidal Salmonella (NTS) serovars isolated from layer chicken farms in Nigeria. PLoS One. 2023;0290754:1–19. pmid:37676896
View Article
PubMed/NCBI
Google Scholar

[123] View Article

[124] PubMed/NCBI

[125] Google Scholar

[ref39] 39. Aworh MK, Kwaga JKP, Hendriksen RS, Okolocha EC, Harrell E, Thakur S. Quinolone-resistant Escherichia coli at the interface between humans, poultry and their shared environment- a potential public health risk. One Heal Outlook. 2023;5. pmid:36855171
View Article
PubMed/NCBI
Google Scholar

[127] View Article

[128] PubMed/NCBI

[129] Google Scholar

[ref40] 40. Chang M, Zhang J, Sun Y, Li R, Lin X, Yang L, et al. Contribution of Different Mechanisms to Ciprofloxacin Resistance in Salmonella spp. Front Microbiol. 2021;12 May:663731. pmid:34025618
View Article
PubMed/NCBI
Google Scholar

[131] View Article

[132] PubMed/NCBI

[133] Google Scholar

[ref41] 41. Zhang Z, Chang J, Xu X, Hu M, He S, Qin X, et al. Phylogenomic Analysis of Salmonella enterica Serovar Indiana ST17, an Emerging Multidrug-Resistant Clone in China Zengfeng. Microbiol Spectr. 2022;10:1–12.
View Article
Google Scholar

[135] View Article

[136] Google Scholar

[ref42] 42. Monte DF, Lincopan N, Berman H, Cerdeira L, Keelara S, Thakur S, et al. Genomic Features of High-Priority Salmonella enterica Serovars Circulating in the Food Production. Sci Rep. 2019;11058 June:1–12.
View Article
Google Scholar

[138] View Article

[139] Google Scholar

[ref43] 43. Monte DFM, Nethery MA, Barrangou R, Landgraf M, Fedorka-Cray PJ. Whole-genome sequencing analysis and CRISPR genotyping of rare antibiotic-resistant Salmonella enterica serovars isolated from food and related sources. Food Microbiol. 2021;93 July 2020:103601. pmid:32912589
View Article
PubMed/NCBI
Google Scholar

[141] View Article

[142] PubMed/NCBI

[143] Google Scholar

[ref44] 44. Fegan N, Vanderlinde P, Higgs G, Desmarchelier P. Quantification and prevalence of Salmonella in beef cattle presenting at slaughter. J Appl Microbiol. 2004;97:892–8. pmid:15479403
View Article
PubMed/NCBI
Google Scholar

[145] View Article

[146] PubMed/NCBI

[147] Google Scholar

[ref45] 45. Nickodem C, Arnold AN, Gehring KB, Gill JJ, Richeson JT, Samuelson KL, et al. A Longitudinal Study on the Dynamics of Salmonella enterica Prevalence and Serovar Composition in Beef Cattle Feces and Lymph Nodes and Potential Contributing Sources from the Feedlot Environment. Appl Environ Microbiol. 2023;89:1–20. pmid:37022263
View Article
PubMed/NCBI
Google Scholar

[149] View Article

[150] PubMed/NCBI

[151] Google Scholar

[ref46] 46. Gutema FD, Agga GE, Abdi RD, De Zutter L, Duchateau L, Gabriël S. Prevalence and Serotype Diversity of Salmonella in Apparently Healthy Cattle: Systematic Review and. Front Vet Sci. 2019;6 April:1–11.
View Article
Google Scholar

[153] View Article

[154] Google Scholar

[ref47] 47. Molla B, Berhanu A, Muckle A, Cole L, Wilkie E, Kleer J, et al. Multidrug resistance and distribution of Salmonella serovars in slaughtered pigs. J Vet Med Ser B Infect Dis Vet Public Heal. 2006;53:28–33. pmid:16460353
View Article
PubMed/NCBI
Google Scholar

[156] View Article

[157] PubMed/NCBI

[158] Google Scholar

[ref48] 48. Nguyen S V., Harhay DM, Bono JL, Smith TPL, Fields PI, Dinsmore BA, et al. Complete, closed genome sequences of 10 Salmonella enterica subsp. enterica serovar Typhimurium strains isolated from human and bovine sources. Genome Announc. 2016;4:2010–1. pmid:27811097
View Article
PubMed/NCBI
Google Scholar

[160] View Article

[161] PubMed/NCBI

[162] Google Scholar

[ref49] 49. Liu G, Qian H, Lv J, Tian B, Bao C, Yan H, et al. Emergence of mcr-1 -Harboring Salmonella enterica Serovar Sinstorf Type ST155 Isolated From Patients With Diarrhea in Jiangsu, China. Front Microbiol. 2021;12 September:723697. pmid:34603249
View Article
PubMed/NCBI
Google Scholar

[164] View Article

[165] PubMed/NCBI

[166] Google Scholar

[ref50] 50. Badr H, Reda RM, Hagag NM, Kamel E, Elnomrosy SM, Mansour AI, et al. Multidrug-Resistant and Genetic Characterization of Extended-Spectrum Beta-Lactamase-Producing E. coli Recovered from Chickens and Humans in Egypt. Animals. 2022;12:346. pmid:35158668
View Article
PubMed/NCBI
Google Scholar

[168] View Article

[169] PubMed/NCBI

[170] Google Scholar

[ref51] 51. Jibril AH, Okeke IN, Dalsgaard A, Garc V, Olsen JE. Genomic Analysis of Antimicrobial Resistance and Resistance Plasmids in Salmonella Serovars from Poultry in Nigeria. Antibiotics. 2021;10:1–21. pmid:33498344
View Article
PubMed/NCBI
Google Scholar

[172] View Article

[173] PubMed/NCBI

[174] Google Scholar

[ref52] 52. Dieye Y, Hull DM, Wane AA, Harden L, Fall C, Sambe-Ba B, et al. Genomics of human and chicken Salmonella isolates in Senegal: Broilers as a source of antimicrobial resistance and potentially invasive nontyphoidal salmonellosis infections. PLoS One. 2022;17 3 March:1–18. pmid:35325007
View Article
PubMed/NCBI
Google Scholar

[176] View Article

[177] PubMed/NCBI

[178] Google Scholar

[ref53] 53. Fashae K, Leekitcharoenphon P, Hendriksen RS. Phenotypic and genotypic comparison of salmonellae from diarrhoeic and healthy humans and cattle, Nigeria. Zoonoses Public Health. 2018;65:e185–95. pmid:29193894
View Article
PubMed/NCBI
Google Scholar

[180] View Article

[181] PubMed/NCBI

[182] Google Scholar

[ref54] 54. Badr Heba, Roshdy Heba, Sorour Hend K., Abdelrahman Mona A.A., Erfan Ahmed M., Salem Noha, et al. Phenotypic and Genotypic Characterization of Salmonella enterica Serovars Isolated from Imported Poultry. Adv Anim Vet Sci. 2021;9:823–34.
View Article
Google Scholar

[184] View Article

[185] Google Scholar

[ref55] 55. Cuypers Wim L., Meysman Pieter, Weill François-Xavier, Hendriksen Rene S., Getenet Beyene JW Satheesh Nair, Chattaway Marie A., Perez-Sepulveda Blanca M., Ceyssens Pieter-Jan, Tessa de Block, Lee WinnieW.Y., Maria Pardos de la Gandara CK. A global genomic analysis of Salmonella Concord reveals lineages with high antimicrobial resistance in Ethiopia. Nat Commun. 2023;14. pmid:37316492
View Article
PubMed/NCBI
Google Scholar

[187] View Article

[188] PubMed/NCBI

[189] Google Scholar

[ref56] 56. Somda NS, Bonkoungou IJO, Sambe-Ba B, Drabo MS, Wane AA, Sawadogo-Lingani H, et al. Diversity and antimicrobial drug resistance of non-typhoid Salmonella serotypes isolated in lettuce, irrigation water and clinical samples in Burkina Faso. J Agric Food Res. 2021;5:1–8.
View Article
Google Scholar

[191] View Article

[192] Google Scholar

[ref57] 57. Leekitcharoenphon P, Hans M, Johansson K, Munk P, Malorny B, Skarżyńska M, et al. Genomic evolution of antimicrobial resistance in Escherichia coli. Sci Rep. 2021;11:1–12.
View Article
Google Scholar

[194] View Article

[195] Google Scholar

[ref58] 58. Zeng S, Zhuo Z, Huang Y, Luo J, Feng Y, Gong B, et al. Prevalence of Chromosomally Located bla CTX-M-55 in Salmonella Typhimurium ST34 Isolates Recovered from a Tertiary Hospital in Guangzhou, China. Microbiol Spectr. 2022;10. pmid:35616373
View Article
PubMed/NCBI
Google Scholar

[197] View Article

[198] PubMed/NCBI

[199] Google Scholar

[ref59] 59. Kime L, Randall CP, Banda FI, Coll F, Wright J, Richardson J, et al. Transient Silencing of Antibiotic Resistance by Mutation Represents a Significant Potential Source of Unanticipated Therapeutic Failure. MBio. 2019;10:e01755–19. pmid:31662453
View Article
PubMed/NCBI
Google Scholar

[201] View Article

[202] PubMed/NCBI

[203] Google Scholar

Figures

Abstract

Introduction

Methods

Results

Conclusions

Introduction

Methods

Study design and sample collection

Ethical considerations

Study population and sampling

Isolation and identification of Salmonella isolates

Antimicrobial susceptibility testing

Whole genome sequencing of Salmonella isolates

In silico bioinformatics analysis

Phylogenetic analysis

Data collection and analyses

Results

Prevalence of non-typhoidal S. enterica from different sources

Antimicrobial resistance profile of Salmonella isolates

Whole-genome sequencing (WGS) and bioinformatic analysis

In silico Salmonella serotyping and MLST

Detection of resistance genes and chromosomal point mutations

Association of mobile genetic elements (MGEs) with AMR

Clustering of Salmonella isolates based on MLST

Phylogenetic Single Nucleotide Polymorphism (SNP) based analysis

Discussion

Conclusion

Supporting information

S1 File. This file contains the metadata for the Salmonella enterica isolates from humans, beef cattle and abattoir environment.

Acknowledgments

References