You are currently on the new version of our website. Access the old version .
MDPIMDPI
Search all articles
MicroorganismsMicroorganisms
Download PDF
  • Article
  • Open Access

27 January 2026

Asymptomatic Carriage and Antimicrobial Resistance of Salmonella in Humans and Poultry in Rural Burkina Faso: Phenotypic and Genotypic Profiles and Associated Risk Factors

,
,
,
,
,
,
,
,
…
1
LabESTA, École Doctorale Sciences et Technologies, Université Joseph KI-ZERBO, Ouagadougou 03 BP 7021, Burkina Faso
2
Unité de Recherche Clinique de Nanoro (URCN), Institut de Recherche en Sciences de la Santé (IRSS), Centre National de la Recherche Scientifique et Technologique (CNRST), Ouagadougou 11 BP 218, Burkina Faso
3
Bureau of Microbial Hazards, Food and Nutrition Directorate, Health Canada, Ottawa, ON K1A 0K9, Canada
4
Department of Clinical Sciences, Institute of Tropical Medicine, 2000 Antwerp, Belgium
Microorganisms2026, 14(2), 294;https://doi.org/10.3390/microorganisms14020294
This article belongs to the Section Veterinary Microbiology
Version Notes
Order Reprints
Review Reports

Abstract

Food-borne diseases affect nearly 10% of the global population annually, with Salmonella being a major cause, particularly impacting children, the elderly, and populations in low- and middle-income countries. This study aimed to assess the prevalence, serotype distribution, antibiotic resistance profiles, and genetic determinants of resistance and virulence of Salmonella enterica in humans and poultry in the Nanoro health district. A community-based cross-sectional study involving humans and poultry was conducted in the Nanoro health district. Fresh stool samples (human and poultry cloacal/cecal) were collected, transported under sterile conditions, and processed within two hours using standard bacteriological methods. Phenotypic antibiotic resistance was determined by the Kirby–Bauer disk diffusion method, and whole-genome sequencing (Illumina) identified serotypes, resistance genes, and virulence factors. Logistic regression analyzed associations between Salmonella carriage and host or environmental factors. Salmonella enterica carriage was detected in 8.7% of humans and 7.2% of poultry. Human isolates showed 24% resistance to cephalosporins, while poultry isolates showed 36.8% resistance. Resistance genes, including fosA7, qnrB19, and a cryptic aminoglycoside resistance gene, and virulence genes encoding T3SS-1 and T3SS-2, were detected in both hosts. Logistic regression indicated that residence in Sitaon and Zimidin was associated with ~70% lower odds of carriage (aOR = 0.3), while individuals aged 11–20 and 51–60 years had 2.8-fold higher odds. Carriage was also 60% higher during the rainy season. These findings suggest possible cross-transmission of Salmonella between humans and poultry and the circulation of resistant, potentially virulent strains in the community. Seasonal and age-related variations highlight environmental and behavioral influences on asymptomatic carriage. Integrated One Health surveillance and targeted hygiene interventions are essential to reduce Salmonella transmission and antimicrobial resistance in rural settings.

1. Introduction

Salmonella, a pathogenic bacterial genus, is responsible for a wide range of illnesses, including typhoid (enteric) fever and gastroenteritis. With more than 2500 known serovars, Salmonella represents a significant global public health concern, being widely distributed across human, animal, and environmental reservoirs, and increasingly associated with antimicrobial resistance at the human–animal–environment interface [1,2,3]. It has a high disease burden and can be associated with antimicrobial resistance (AMR), increasing its threat to human health [4]. Typhoidal Salmonella (TS) includes Salmonella enterica serovars Typhi and Paratyphi A, B, and C and is clinically distinct from non-typhoidal Salmonella (NTS). Non-typhoidal Salmonella, including serovars such as S. Enteritidis, S. Typhimurium and S. Gallinarum, is a major cause of foodborne gastroenteritis worldwide [5,6]. The main pathogenic Salmonella enterica strains in poultry are S. Gallinarum, which causes avian typhoid and high mortality in birds of all ages, and S. pullorum, which causes pullorum disease with very high mortality in young chicks during the first weeks of life [7]. NTS transmission to humans usually follows a ‘farm-to-fork’ pathway; some Salmonella serovars can survive in chilled meat, emphasizing proper cold chain management [1,8].
In sub-Saharan Africa, Salmonella enterica serovars Enteritidis and Typhimurium account for approximately 91% of reported African salmonellosis outbreaks [9]. The population at risk of severe outcomes or infection includes babies, young adults, and immunocompromised individuals, particularly those living with human immunodeficiency virus and/or malaria [10]. It was estimated that approximately 535,000 cases of invasive non-typhoidal salmonellosis and 77,500 deaths occurred in 2017 in the world. Of these, 422,000 cases (78.9%) and 66,500 deaths (85.9%) occurred in countries in sub-Saharan Africa [11]. The invasive form of NTS (iNTS) continues to pose a major challenge, with the 2021 Global Burden of Disease Study estimating over 500,000 global iNTS cases, the majority occurring in West Africa [12].
In both human and animal populations, chronic and asymptomatic carriers play a key role in maintaining transmission cycles and therefore complicating disease eradication efforts [13]. In poultry, Salmonella enterica often persists asymptomatically, enabling prolonged shedding into the environment and enhancing transmission among flocks, including via biofilm formation on farm surfaces [14,15]. Similarly, asymptomatic human carriers contribute to household or community spread [16,17].
The rising challenge of antimicrobial resistance (AMR) adds to the complexity of Salmonella management. Indeed effective antibiotics are now compromised by resistant strains, threatening treatment efficacy in both human and veterinary medicine [18,19,20,21].
Despite the public health importance of Salmonella, epidemiological and resistance data remain scarce in rural resource-limited settings such as Burkina Faso, where close human–animal interaction is common, and food production is frequently informal. Salmonella’s ability to colonize diverse hosts, including wildlife such as free-living turtles, further highlights its adaptability and public health challenge [22,23]. While some studies have explored avian Salmonella carriage and resistance in urban settings [22,24,25], data on rural populations remain limited. In several countries, including the United States, studies employing whole-genome sequencing and source-attribution modeling estimate that chicken alone may account for approximately one-third of sporadic human Salmonella cases, and up to ~70–75% when combining chicken with other high-confidence sources such as eggs and pork products [26]. Within the European Union, the contribution of laying hens and poultry meat/eggs to cases varies significantly between countries (e.g., approximately 42% of cases are attributed to laying hens in a multi-country European Union model) [26]. However, to date, there appears to be an absence of comparable quantitative attribution data from sub-Saharan Africa or Burkina Faso.
Prior investigations in Nanoro, a rural district of Burkina Faso, have revealed diverse Salmonella serotypes and high antibiotic resistance rates in strains from humans, animals, food, and the environment. However, the mechanisms underlying this antibiotic resistance and virulence in Salmonella enterica, including associated genetic determinants, remain poorly explored in this specific context [27]. In such settings, the management of Salmonella infection requires robust data to support surveillance, prevention, and control strategies. Therefore, this study aimed to assess the prevalence of asymptomatic Salmonella carriers among healthy humans and poultry in Nanoro, identify the Salmonella serotypes present, examine their resistance to antibiotics, and detect the related genetic markers of resistance and virulence.

2. Material and Methods

2.1. Study Site

This study was conducted in the Nanoro health district, central-west Burkina Faso, about 90 km from Ouagadougou. The district has 30 primary healthcare centers (PHCs), each covering a health catchment area. Stool samples were collected in two catchment areas: Nanoro (five villages: Nanoro, Gouroumbila, Poessi, Baziri, and Goulouré) and Nazoanga (three villages: Nazoanga, Zimidin, and Sitaon) [28], both embedded in the Clinical Research Unit of Nanoro (CRUN) Health and Demographic Surveillance System (HDSS) [29].

2.2. Study Design, Population, and Criteria for Selecting Participants; Sampling

This was a non-interventional, cross-sectional study conducted from May 2021 to May 2022 and was an ancillary embedded within the CABU pilot study (Community-level AntiBiotic use in Nanoro district Burkina Faso) [30]. Two study populations were defined: a human population and a poultry population.
The human population consisted of 1500 individuals for whom sociodemographic characteristics were collected using a standardized questionnaire (Appendix A and Appendix B). Participants were required to be healthy at the time of the survey; if ill, the visit was postponed until recovery, and all medical consultations in the preceding three months were recorded. Fresh stool samples were collected from consenting participants and stored in sterile containers.
The poultry population included birds owned by households participating in the human survey as well as poultry obtained from local vendors. A total of 528 chicken samples were collected. Household-owned poultry (500 households) were sampled using cloacal swabs collected in Amies transport medium (Deltalab. Plz. Verneda 1, Pol Ind La Llana 08191 Rubí, Barcelona, Spain). Poultry vendors were not considered a separate human study population but were included solely as focal points for poultry sampling, as they aggregate birds originating from multiple households and represent a major source of poultry supply within the community. For vendor-associated poultry, birds were sacrificed at the point of sale and cecal contents were collected in preservative-free sterile containers.
All samples were kept at 2–8 °C and transported to the laboratory within two hours of collection.

2.3. Isolation, Identification and Antibiotic Susceptibility Testing

The stool samples were analyzed in the microbiology laboratory of the Clinical Research Unit of Nanoro. Approximately 10 g of stools was inoculated into in 5 mL of selenite enrichment broth (Liofilchem® s.r.l Via Scozia, 64026 Roseto degli Abruzzi (TE), Italy). After incubation for 18–24 h at 35 ± 2 °C, 10 µL of the enrichment broth was plated on CHROMagarTM Salmonella (CHROMagar 29 Av. George Sand, 93210 Saint-Denis, France) for the detection of suspected Salmonella colonies. The plated agar was incubated 18–24 h at 35 ± 2 °C. Poultry cecum and rectal samples were initially pre-enriched in 5 mL of peptone water and then incubated for 18–24 h at 35 ± 2 °C. Subsequently, 1 mL was transferred into 5 mL of selenite broth for targeted enrichment at 41 °C for 48 h. Following this, 10 µL of the enriched broth was plated onto CHROMagar™ Salmonella and then incubated for 18–24 h at 35 ± 2 °C. Presumptive Salmonella, identified by purple colonies, was then isolated and identified using the API 20E system. Our strains were stored in STGG (skim milk–Tryptone–Glucose–Glycerol) broth at −80 °C for further analysis and as copies. Human stool samples were directly enriched in selenite broth, as stools generally contain a higher background level of enteric bacteria and Salmonella, and selenite enrichment is well established for selective recovery from human fecal samples. In contrast, poultry cecal and cloacal samples were first pre-enriched in non-selective buffered peptone water to allow recovery of stressed or low-abundance Salmonella cells potentially affected by environmental conditions or slaughter procedures. This was followed by selective enrichment in selenite broth, in accordance with standard bacteriological protocols for animal samples.
Antibiotic susceptibility testing (AST) was performed using the disc diffusion method according to the Clinical and Laboratory Standard Institute (CLSI) to assess bacterial resistance and the results were interpreted using the CLSI guidelines, version 2023 [31].

2.4. DNA Extraction and Illumina Sequencing

Cells were retrieved from storage at −80 °C and plated onto tryptic soya agar (TSA). Plates were incubated at 35 °C overnight and 1 µL loopful of cells was collected in 0.4 mL of 1x Zymo DNA/RNA Shield (Cedarlane, Burlington, VT, Canada) and held at 4 °C. DNA extractions were performed using the Zymo Quick-DNA HMW MagBead kit (Cedarlane) according to manufacturer protocols (RNAseA treatment without enzymatic lysis). Illumina libraries were constructed using the NexteraXT DNA Library Preparation Kit and Nextera DNA UD Indexes according to the manufacturer’s instructions (Illumina Inc., San Diego, CA, USA). Paired-end Illumina sequencing was performed on a MiSeq instrument (v3 chemistry, 2 × 300 bp) according to manufacturer instructions (Illumina Inc.).

2.5. Bioinformatic Analysis

2.5.1. Read Processing, De Novo Genome Assembly, and Taxonomic Assignment

Illumina reads were processed using BBMap v38.26 (sourceforge.net/projects/bbmap) to remove adapters and filter low-quality reads (BBDuk), and correct errors (Tadpole). De novo assemblies were generated using SKESA v2.4.0 [32] and error corrected using Pilon v1.23 [33]. Assemblies were analyzed with QUAST v5.0.2 (github.com/ablab/quast) and taxonomically assigned using Mash v2.2.1 [34] and the highest RefSeq (v93, ncbi.nlm.nih.gov/refseq) identities of >0.8 (excluding hits containing the words phage or plasmid). Analysis tools were used with default settings unless noted.

2.5.2. In Silico Typing, Antimicrobial Resistance, and Virulence Factor Identification

Multi-locus sequence typing was performed using MLST (v2.23.0, github.com/tseemann/mlst) and Enterobase database (v2023-11-20) [35,36] using the --scheme senterica_achtman_2 and default settings. SISTR (v1.1.3) [37] was used for serovar prediction using the --qc option and default settings. Antimicrobial resistance genes were identified using the assembled genomes and ResFinder (v4.3.1) [38]. ResFinder was run using the -acquired option, minimum coverage length, and percent identity settings of 60 and 90 percent, respectively (-l 0.6 and -t 0.9), and the ResFinder database (v2023-05-25). Pathogenicity islands were identified using SPIFinder (v2.0) [39] and SPIFinder database (v2020-12-04, bitbucket.org/genomicepidemiology/spifinder_db) using minimum coverage length and threshold settings of 60 and 90 percent, respectively (-l 0.6 and -t 0.9). Virulence genes were identified using ABRicate (v1.0.1, github.com/tseemann/abricate) and the Virulence Factor Database (v2023-11-04) [40] using default settings. All parameters have been summarized in Supplementary Table S1.

2.6. Statistical Analysis

Statistical analyses were performed using STATA software version 17, 2021. Quality control checks were performed prior to the statistical analysis. Outliers from measurement or data entry errors were corrected, and those likely to affect the analyses were excluded. Categorical variables were summarized using counts and percentages, whereas continuous variables were summarized using medians and interquartile ranges (IQR), when appropriate. Comparisons between the outcome variable and continuous variables were performed using the Wilcoxon–Mann–Whitney test, while Pearson’s chi-square or Fisher’s exact tests were applied for categorical variables, as appropriate. A binary logistic regression analysis was conducted to identify determinants of salmonellosis. Odds ratios (ORs) and their 95% confidence intervals (95% CI) were reported. Multivariable models were built using a stepwise elimination procedure, including variables with a nominal p-value < 0.20 in the univariate analysis. A p-value < 0.05 was considered statistically significant. Missing values were handled using mode imputation after comparing several approaches, including the creation of a missing category and multiple imputations. This method was applied for the multivariate analysis to preserve sample size and maintain consistency of the estimates.

2.7. Ethics

The study protocol was approved by the national ethics committee of Burkina Faso with approval number 2021-03-052 at deliberations on 10 March 2021 and the Institute of Tropical Medicine Institutional Review Board ref. 1397/20. Written informed consent was obtained from all study participants aged 18 years and above. For participants aged 14 to <18 years, oral assent was obtained in addition to written informed consent from their parents or caregivers. For participants younger than 14 years, written informed consent was obtained directly from their parents or caregivers.

3. Results

3.1. Sociodemographic Characteristics by Salmonella enterica Carriage in Humans

A total of 1500 individuals or households were enrolled in the study, of which 1393 were included in the final analysis of the human population; the remaining participants were excluded due to refusal of consent or missing data. Among the poultry samples, 500 out of 528 were collected from households participating in the human survey, while the remaining 28 originated from poultry vendors. Due to small numbers of events in certain categories, the findings should be interpreted with caution. The study population was predominantly young, with a median age of 13 years with 39 (IQR; 5.0–44.0). Asymptomatic Salmonella carriers tended to be slightly older, with a median age of 15 years. Although the median age among asymptomatic carriers was higher, this difference was not statistically significant (p = 0.480). In contrast, the distribution of Salmonella carriage across age groups showed a statistically significant variation. Geographical distribution varied considerably across study sites, with participants from Nanoro representing the largest proportion of the study population. Detailed demographic characteristics are summarized in Table 1.
Table 1. Sociodemographic and environmental characteristics distribution by Salmonella enterica carriage in humans.

3.2. Prevalence of Asymptomatic Carriage and Distribution of Salmonella Serotypes

Out of the 1393 people enrolled in this study, 121 were identified as Salmonella carriers, resulting in an asymptomatic carriage prevalence of 8.7% (121/1393). Among participants owning poultry, fecal samples revealed that 7.2% (38/528) of the poultry was Salmonella positive. The distribution of Salmonella prevalence differed significantly between villages (p < 0.001). Nanoro Center recorded the highest proportion of Salmonella-positive individuals, accounting for 42.5% (51/121) of all asymptomatic carriers, whereas Poessi showed the highest Salmonella carriage rate among poultry (15.8%, 6/38). In contrast, the villages of Zimidin and Sitaon had the lowest prevalence rates, 4.2% (5/121) and 2.5% (3/121), respectively (p = 0.036 and p = 0.050). Most Salmonella isolates were detected between July and December, corresponding to the transition between the rainy and dry seasons. Table 2 shows the prevalence and distribution of Salmonella isolates from asymptomatic carriers and their poultry in the Nanoro health district.
Table 2. Isolated serotypes and serovar distribution in human and poultry.

3.3. Frequent Sequence Type (ST)

Most isolates, whether from asymptomatic humans or poultry, remain untyped (-). Among the typed isolates, ST516 is the only sequence type found in both populations, while the other types are specific to each source (ST513 and ST1737 in humans, ST3031 in poultry). However, each type represents only a small fraction of the total, highlighting high genetic diversity and the absence of a clearly dominant type.

3.4. Antibiotics Resistances and Virulence Genes

Several antibiotics were used for susceptibility testing against Salmonella strains. There was a high prevalence of cephalosporin resistance in humans and poultry (24.0% and 36.8% respectively). Multidrug-resistant strains (isolates that show non-susceptibility acquired to at least one agent in three or more different classes of antimicrobial drug) were detected only in humans, at 1.7%. Details are given in Table 3. Poultry is a reservoir of resistance to antibiotics for human use, since resistant strains can be found there.
Table 3. Antibiotic resistance profiles distribution in human and poultry.
This study enabled us to highlight genotypic resistance to certain antibiotics commonly used to treat salmonellosis, as well as to those not used at all for these strains. For example, we identified a cryptic resistance gene to aminoglycosides, fosfomycin, and quinolones shown in Table 4. Human and poultry isolates carried fosfomycin resistance genes when this antibiotic is not used for salmonellosis treatment. We have also identified the presence of virulence genes in Salmonella isolates from both humans and poultry, notably aac(6’)-Iaa (cryptic aminoglycoside resistance gene) and SPI1 to 5 (Table 4).
Table 4. Prevalence of resistance and virulence genes in Salmonella enterica in human and poultry.

3.5. PointFinder Results

PointFinder analysis revealed point mutations in genes associated with quinolone resistance. A well-characterized resistance-associated substitution in gyrA was detected and was consistent with phenotypic resistance to quinolones and fluoroquinolones.
In addition, the parC T57S substitution was frequently identified among the isolates; however, this mutation was not systematically associated with phenotypic resistance to quinolones. The genes found are summarized in Supplementary Table S2.

3.6. Associated Risks Factors of Salmonella Carriage

Overall, the findings indicate that Salmonella carriage is influenced by a combination of geographic, demographic, and seasonal factors. In the multivariate analysis of the human population, significant differences in asymptomatic Salmonella carriage were observed between villages. Specifically, individuals from Sitaon and Zimidin exhibited a markedly lower risk of carriage (adjusted odds ratio [aOR] 0.3; 95% CI [0.1–0.9]), with p-values of 0.050 and 0.036, respectively, compared with individuals from other villages. In contrast, participants from Nanoro showed a slightly higher risk (aOR 1.5 [0.7–3.5]), although this association did not reach statistical significance. Individuals aged 11–20 years and 51–60 years had an increased likelihood of Salmonella carriage (aOR 2.8 [1.0–8.9]). Children aged 0–10 years and adults aged 61–70 years also showed a higher risk of carriage (≈60%), but these associations were not statistically significant. Seasonal variation (p-value = 0.008) further influenced carriage risk. The rainy season was associated with a 60% increase in the likelihood of carriage among humans (aOR 1.6 [1.0–2.7]), highlighting the potential impact of environmental factors on transmission. Among the poultry population, the month of May was associated with a substantial increase in Salmonella carriage (aOR 5.3), suggesting temporal patterns in bacterial prevalence within animal reservoirs. A detailed summary of all significant determinants is presented in Table 5.
Table 5. Risks factor associated with Salmonella carriage.

4. Discussion

This study provides comprehensive insights into Salmonella carriage in rural Burkina Faso in humans and poultry in their immediate environment, highlighting substantial geographic and demographic variability. The prevalence of Salmonella was 8.7% in humans and 7.2% in poultry, higher than reports from Ghana, Togo, and Nigeria (0.8–7.5%) [41] and comparable to some regions in Kenya (0.4–13.1%) [42] and Mexico (11.4% in children under five) [43]. These variations likely reflect differences in environmental conditions, socio-economic factors, urbanization, poultry production systems, hygiene practices, and methodological approaches [6,44,45,46]. Within our study, carriage was higher in the 6–64 age group, possibly due to increased occupational and food-related exposure and a more mature gut microbiota, whereas children under five also showed notable carriage relative to previous studies in Kenya [42].
In poultry, prevalence in our study was lower than in previous reports in Burkina Faso, which ranged from 9.9% to 63.5% in urban centers [47,48,49], and lower than in neighboring West African countries (29–47%) [50,51,52]. Differences with developed countries were also notable, with lower or comparable prevalence reported in Europe (8.6–22.7%) [53,54] and North America (6.5–26.9%) [55,56], whereas Asia showed highly variable rates (18–40%) depending on local food safety controls [57]. These findings suggest that Salmonella circulation is influenced by poultry production systems, biosecurity measures, density of animal and human populations, slaughtering practices, and local hygiene conditions. Seasonal variations, particularly during the rainy season, may further promote bacterial survival and environmental dissemination [58,59].
Phenotypic antimicrobial resistance (AMR) was higher in human isolates than in poultry, with resistance observed against cephalosporins, particularly ceftazidime, alongside evidence of multidrug resistance (MDR). Corresponding resistance genes, including bla_TEM, tetA, and sul1, were detected in both humans and poultry, although discrepancies between phenotype and genotype likely reflect gene expression variability or regulatory mechanisms [52,60,61,62,63,64]. Resistance to fosfomycin, not used clinically for salmonellosis, was also identified, highlighting the potential for emerging MDR and the need for rational antimicrobial use in livestock [65,66,67]. These findings have clinical and public health implications, as asymptomatic carriers can act as reservoirs for resistant strains, facilitating transmission through food and environmental pathways.
Genomic analyses revealed a core virulome, consisting of virulence genes broadly conserved across Salmonella isolates, including pathogenicity islands SPI-1 to SPI-5, SPI-9, SPI-13, SPI-14, and fimbriae/pili genes [68,69]. The presence of these genes in asymptomatic carriers underscores the role of host factors and gene regulation in determining infection outcomes, beyond the acquisition of novel virulence determinants. The co-occurrence of resistance and virulence genes further emphasizes the risk of genetic exchange and the potential for zoonotic transmission, reinforcing the need for integrated interventions [70,71]. Mutations in gyrA identified in this study are well-recognized determinants of quinolone and fluoroquinolone resistance in Salmonella, due to alterations in the quinolone resistance-determining region (QRDR) of DNA gyrase, with common substitutions at positions S83 and D87 reported worldwide [72,73]. In contrast, although frequently detected, the parC T57S substitution was not consistently associated with phenotypic resistance, supporting recent reports suggesting a limited or unclear role in quinolone resistance [74]. These findings highlight the importance of integrating genomic and phenotypic data for accurate interpretation of quinolone resistance mechanisms.
Demographic and environmental factors significantly influenced carriage in humans. Residents of Sitaon and Zimidin had a lower risk (aOR = 0.3; p < 0.05), while individuals aged 11–20 and 51–60 years exhibited higher risk. No significant association with sex was observed. These patterns suggest that local hygiene practices, environmental exposure, and age-related behaviors contribute to colonization. In poultry, although associations were not statistically significant, a trend toward increased carriage during the rainy season supports the hypothesis of environmental and zoonotic influences. Collectively, these findings highlight the importance of integrated One Health approaches that combine human, animal, and environmental surveillance to limit transmission within communities [6,46,75]
This study has several limitations. Its cross-sectional design prevents establishing causal relationships, and testing a single stool sample per participant may underestimate actual carriage. Finally, some confidence intervals were wide, reflecting low precision of the estimates due to small numbers of events in certain categories. Therefore, these results should be interpreted with caution. Despite these limitations, the study provides valuable baseline data on Salmonella prevalence, resistance, and virulence in both humans and poultry. Future research should employ longitudinal designs, larger and concomitant sample sizes, and extended genomic analyses to better understand the dynamics of resistance gene dissemination and zoonotic transmission. Strengthened surveillance, targeted interventions, improved poultry biosecurity, and judicious antimicrobial use remain essential to limit the spread of resistant and virulent Salmonella strains, particularly in high-risk rural areas.

5. Conclusions

This study demonstrates that asymptomatic carriage of Salmonella occurs in both humans and poultry in Burkina Faso, with a notable prevalence of antibiotic-resistant and multidrug-resistant strains. Resistance genes, particularly those conferring resistance to cephalosporin, fosfomycin, and others, were frequently detected. The presence of asymptomatic carriers represents a significant public health concern, as humans and animals can act as reservoirs for transmission, potentially leading to outbreaks and impacting food safety and local economies. These findings highlight the need for integrated surveillance across human, poultry, and environmental samples, alongside targeted hygiene education in high-prevalence communities and antibiotic stewardship in both human and veterinary sectors. Implementing such measures is crucial to mitigate the risk of Salmonella transmission, curb the spread of multidrug-resistant strains, and protect public health in Burkina Faso.

Supplementary Materials

The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/microorganisms14020294/s1, Table S1: Summary of bioinformatics analyses, tools, and parameters; Table S2: Point mutations associated with quinolone resistance detected by PointFinder.

Author Contributions

I.K. conceptualized the study and developed the methodology. The investigation, including experiments and data collection, was performed by I.K., A.P.V., W.A.L.T., Z.G., M.P. and E.W.T. Data analysis and formal analysis were conducted by G.S., M.R., K.W. and A.F., with data curation performed by G.S. The original draft of the manuscript was written by I.K., and all authors contributed to reviewing and editing the manuscript. Supervision and funding acquisition were provided by C.M.T., S.Y., P.L., S.T., D.V., N.B., H.T., H.G.O., M.A.B.v.d.S. and J.J. All authors have read and agreed to the published version of the manuscript.

Funding

The study was funded by the Swedish International Development Cooperation Agency (SIDA) through the European Joint Programme Initiative on AMR (grant JPIAMR2021-053) and the Belgian Development Cooperation through ITM-CRUN Framework Agreement 5.

Institutional Review Board Statement

The study protocol was approved by the national ethics committee of Burkina Faso with approval number 2021-03-052 deliberated on 10 March 2021 and the Institute of Tropical Medicine Institutional Review Board ref. 1397/20.

Data Availability Statement

All data (BioSamples, reads, and genome assemblies) for this study are available via NCBI under BioProject PRJNA1400071.

Acknowledgments

We acknowledge all study participants for their willingness to participate in this study. We are also thankful to the Nanoro Health District authorities and all the research staff of the Clinical Research Unit of Nanoro for being part of the success of this study.

Conflicts of Interest

The authors declare no conflicts of interest.

Appendix A. Blse-Salmonella Survey Questionnaire

1.
Do you have antibiotics at home? (Check all medications available to the participant at home at the time of the survey to ensure that they are antibiotics or not)
  • Yes
  • No
2.
If yes, specify the name.............
3.
Have you used antibiotics in the last three (03) months? (Present to the participants the medications you have so that they can show the medications they have used)
  • Yes
  • No
  • Don’t know
4.
If so, were these leftover antibiotics used to treat ongoing episodes of illness, or antibiotics purchased to treat each episode in their own right?
  • Use of remaining antibiotics
  • Antibiotics purchased to treat each episode in its own right
  • Don’t know
5.
Have you visited any formal health care providers (ASBC, CSPS, CM, CMA, CHR, CHNU, Private Clinic) here or elsewhere in the last three (03) months?
  • Yes
  • No
  • Don’t know
6.
Have you visited informal health care providers (medicine vendors at the market, medicine vendors in shops and kiosks, street medicine vendors) here or elsewhere in the last three (03) months?
  • Yes
  • No
  • Don’t know
7.
Have you purchased antibiotics directly without prior consultation with a formal health worker (nurse and/or doctor) to treat one or more episodes of illness in the last three (03) months?
  • Yes
  • No
  • Don’t know
8.
Have you been hospitalized in a formal health center (CSPS, CM, CMA, CHR, CHNU, Private Clinic) here or elsewhere in the last three (03) months?
  • Yes
  • No
  • Don’t know
9.
What is your source of drinking water
  • Taps/Fountain taps
  • Drilling
  • Well built (protected from external contamination)
  • Undeveloped wells
  • Surface water (backwaters, dams, rivers, etc.)
  • Other (to be specified): .....................................
10.
Do you have livestock?
  • Yes
  • No
11.
If so, are they kept inside or outside the dealership?
  • Inside the concession
  • Outside the concession
12.
How often have you consumed raw food (lettuce, cabbage, carrot, fresh unpasteurized cow’s milk) in the last three (03) months? |__|___
13.
Do you wash hands with soap before every meal?
  • Yes
  • No

Appendix B. Collection of Additional Data Asymptomatic Ebl-Salmonella Carriage

Questionnaire addressed to the head of household
Village: .....................................Collection Date :...../...../......
 Collection time: ......:......
ID Ménage: |___|___|___|___|___| - |_V_| 
  • Have poultry in your household
    |___| Yes |___| No
  • If so, how much head do you have approximately: ____________________
  • If so, do you treat them when they are a cause of illness?
    |___| Yes |___| No
  • If so, what do you use to treat them?
    Antibiotic? |___| Yes |___| No
    Another product to treat poultry? |___| Yes |___| No
    If which: ____________________
  • Can we take feces from any of them if you have them?
    |___| Yes |___| No
  • feces sampling done?
    |___| Yes |___| No
  • If the sample is not taken, state the reason: ____________________
Initial and Signature Agent:...................

References

  1. Teklemariam, A.D.; Al-Hindi, R.R.; Albiheyri, R.S.; Alharbi, M.G.; Alghamdi, M.A.; Filimban, A.A.R.; Al Mutiri, A.S.; Al-Alyani, A.M.; Alseghayer, M.S.; Almaneea, A.M.; et al. Human Salmonellosis: A Continuous Global Threat in the Farm-to-Fork Food Safety Continuum. Foods 2023, 12, 1756. [Google Scholar] [CrossRef]
  2. Fatima, M.; Kausar, T.; Aqib, A.I.; Shoaib, M.; Ataya, F.S.; Daoud, M.S. Molecular Epidemiology and Resistant Gene Mapping of Bacterial Isolates from Houbara bustards. Curr. Microbiol. 2025, 82, 571. [Google Scholar] [CrossRef]
  3. Mahmood, S.; Rasool, M.H.; Khurshid, M.; Aslam, B. Genetic outlook of Colistin resistant Salmonella enterica Serovar Typhimurium recovered from Poultry-Environment Interface: A One Health Standpoint. Pak. Vet. J. 2025, 45, 246–256. [Google Scholar] [CrossRef]
  4. Nazir, J.; Manzoor, T.; Saleem, A.; Gani, U.; Bhat, S.S.; Khan, S.; Haq, Z.; Jha, P.; Ahmad, S.M. Combatting Salmonella: A focus on antimicrobial resistance and the need for effective vaccination. BMC Infect. Dis. 2025, 25, 84. [Google Scholar] [CrossRef] [PubMed]
  5. Durrani, R.H.; Sheikh, A.A.; Humza, M.; Ashraf, S.; Kokab, A.; Mahmood, T.; Khan, M.U.Z. Evaluation of Antibiotic Resistance Profile and Multiple Antibiotic Resistance Index in Avian Adapted Salmonella enterica serovar Gallinarum Isolates. Pak. Vet. J. 2024, 44, 1322–1326. [Google Scholar] [CrossRef]
  6. Crump, J.A.; Sjölund-Karlsson, M.; Gordon, M.A.; Parry, C.M. Epidemiology, clinical presentation, laboratory diagnosis, antimicrobial resistance, and antimicrobial management of invasive Salmonella infections. Clin. Microbiol. Rev. 2015, 28, 901–937. [Google Scholar] [CrossRef] [PubMed]
  7. Julianingsih, D.; Martinez, Z.A.; Tabashsum, Z.; Tung, C.-W.; Aditya, A.; Kapadia, S. Identification of Salmonella enterica biovars Gallinarum and Pullorum and their antibiotic resistance pattern in integrated. Access Microbiol. 2024, 6, 000775-v6. [Google Scholar] [CrossRef]
  8. Sarma, D.K.; Boro, P.K.; Hussain, P.; Gogoi, P. Low Temperature-Survivability Behavior of Salmonella Enterica Subsp. Enterica Serovar Typhimurium and Salmonella Enterica Subsp. Enterica Serovar Enteritidis in a Minced Beef Meat Model as an Evaluation of the Cold Chain’s Preserving-Effectiveness. Int. J. Vet. Sci. 2023, 5, 231–233. [Google Scholar]
  9. Uche, I.V.; MacLennan, C.A.; Saul, A. A Systematic Review of the Incidence, Risk Factors and Case Fatality Rates of Invasive Nontyphoidal Salmonella (iNTS) Disease in Africa (1966 to 2014). PLoS Negl. Trop. Dis. 2017, 11, e0005118. [Google Scholar] [CrossRef]
  10. Majowicz, S.E.; Musto, J.; Scallan, E.; Angulo, F.J.; Kirk, M.; O’Brien, S.J.; Jones, T.F.; Fazil, A.; Hoekstra, R.M. The global burden of nontyphoidal salmonella gastroenteritis. Clin. Infect. Dis. 2010, 50, 882–889. [Google Scholar] [CrossRef]
  11. Crump, J.A.; Nyirenda, T.S.; Kalonji, L.M.; Phoba, M.F.; Tack, B.; Platts-Mills, J.A.; Gordon, M.A.; Kariuki, S.M. Nontyphoidal Salmonella Invasive Disease: Challenges and Solutions. Open Forum Infect. Dis. 2023, 10, S32–S37. [Google Scholar] [CrossRef] [PubMed]
  12. He, Y.; Jia, Q.; Cai, K.; Xu, S.; Li, H.; Xie, Q. The global, regional, and national burden of Invasive Non-typhoidal Salmonella (iNTS): An analysis from the Global Burden of Disease Study 1990–2021. PLoS Negl. Trop. Dis. 2025, 19, e0012960. [Google Scholar] [CrossRef]
  13. Stanaway, J.D.; Reiner, R.C.; Blacker, B.F.; Goldberg, E.M.; Khalil, I.A.; Troeger, C.E.; Andrews, J.R.; Bhutta, Z.A.; Crump, J.A.; Im, J. The global burden of typhoid and paratyphoid fevers: A systematic analysis for the Global Burden of Disease Study 2017. Lancet Infect. Dis. 2019, 19, 369–381. [Google Scholar] [CrossRef] [PubMed]
  14. Menanteau, P.; Kempf, F.; Trotereau, J.; Virlogeux-Payant, I.; Gitton, E.; Dalifard, J.; Gabriel, I.; Rychlik, I.; Velge, P. Role of systemic infection, cross contaminations and super-shedders in Salmonella carrier state in chicken. Environ. Microbiol. 2018, 20, 3246–3260. [Google Scholar] [CrossRef] [PubMed]
  15. Laban, S.E.; Arafa, A.A.; Ibrahim, E.S.; Fe, E.; Khalefa, H.S. Dry Biofilm Formation, Mono-and Dual-attachment, on Plastic and Galvanized Surfaces by Salmonella typhimurium and Staphylococcus aureus Isolated from Poultry House. Int. J. Vet. Sci. 2025, 14, 25–31. [Google Scholar] [CrossRef]
  16. Kering, K.; Njaanake, K.; Wairimu, C.; Mureithi, M.; Kebenei, C.; Odityo, G.; Mugo, M.; Kavai, S.M.; Mbae, C.; Weber, K.; et al. Shedding of nontyphoidal Salmonella by asymptomatic convalescing children under 5 years as a risk factor for invasive disease in Mukuru informal settlement in Nairobi, Kenya. J. Clin. Microbiol. 2024, 62, e0075024. [Google Scholar] [CrossRef]
  17. Srinivasan, M.; Sindhu, K.N.; Giri, S.; Kumar, N.; Mohan, V.R.; Grassly, N.C.; Kang, G. Salmonella Typhi Shedding and Household Transmission by Children with Blood Culture-Confirmed Typhoid Fever in Vellore, South India. J. Infect. Dis. 2021, 224, S593–S600. [Google Scholar] [CrossRef]
  18. Gut, A.M.; Vasiljevic, T.; Yeager, T.; Donkor, O.N. Salmonella infection—Prevention and treatment by antibiotics and probiotic yeasts: A review. Microbiology 2018, 164, 1327–1344. [Google Scholar] [CrossRef]
  19. Wen, S.C.H.; Best, E.; Nourse, C. Non-typhoidal Salmonella infections in children: Review of literature and recommendations for management. J. Paediatr. Child Health 2017, 53, 936–941. [Google Scholar] [CrossRef]
  20. Khadka, P.; Thapaliya, J.; Thapa, S. Susceptibility pattern of Salmonella enterica against commonly prescribed antibiotics, to febrile-pediatric cases, in low-income countries. BMC Pediatr. 2021, 21, 38. [Google Scholar] [CrossRef]
  21. Gunn, J.S.; Marshall, J.M.; Baker, S.; Dongol, S.; Charles, R.C.; Ryan, E.T. Salmonella chronic carriage: Epidemiology, diagnosis, and gallbladder persistence. Trends Microbiol. 2014, 22, 648–655. [Google Scholar] [CrossRef]
  22. Kagambèga, A.; Bouda, S.C.; Bako, E.; Cissé, H.; Barro, N.; Haukka, K. Diversity and antimicrobial resistance of Salmonella strains isolated from different sources in Burkina Faso. Afr. J. Microbiol. Res. 2017, 11, 1495–1504. [Google Scholar] [CrossRef]
  23. Phuket, N.R.N.; Buahom, J.; Senaphan, K.; Sukon, P.; Sringam, S.; Angkititrakul, S.; Sringam, P. Serovars and Antimicrobial Resistance of Salmonella Isolated from Free-living Turtles in the Turtle Village, Northeastern Thailand: One Health Perspective. Int. J. Vet. Sci. 2025, 14, 212–218. [Google Scholar] [CrossRef]
  24. Bouda, S.C.; Kagambèga, A.; Bonifait, L.; Bako, E.; Cisse, H.; Ibrahim, H.B.; Le Gall, F.; Wereme-N’Diaye, A.; Traore, S.A.; Chemaly, M.; et al. Serotypes and Multiresistant Salmonella sp. from Chicken Eggs and Laying Hens in Burkina Faso. Int. J. Sci. 2019, 8, 19–25. [Google Scholar] [CrossRef]
  25. Kagambèga, A.; Lienemann, T.; Aulu, L.; Traoré, A.S.; Barro, N.; Siitonen, A.; Haukka, K. Prevalence and characterization of Salmonella enterica from the feces of cattle, poultry, swine and hedgehogs in Burkina Faso and their comparison to human Salmonella isolates. BMC Microbiol. 2013, 13, 253. [Google Scholar] [CrossRef]
  26. Rose, E.B.; Steele, M.K.; Tolar, B.; Pettengill, J.; Batz, M.; Bazaco, M.; Tameru, B.; Cui, Z.; Lindsey, R.L.; Simmons, M.; et al. Attribution of Salmonella enterica to Food Sources by Using Whole-Genome Sequencing Data. Emerg. Infect. Dis. 2025, 31, 783–790. [Google Scholar] [CrossRef]
  27. Traore, K.A.; Aboubacar-Paraiso, A.R.; Bouda, S.C.; Ouoba, J.B.; Kagambèga, A.; Roques, P.; Barro, N. Characteristics of Nontyphoid Salmonella Isolated from Human, Environmental, Animal, and Food Samples in Burkina Faso: A Systematic Review and Meta-Analysis. Antibiotics 2024, 13, 556. [Google Scholar] [CrossRef]
  28. Valia, D.; Kouanda, J.S.; Ingelbeen, B.; Derra, K.; Kaboré, B.; Kiemdé, F.; Rouamba, T.; Rouamba, E.; Hien, F.S.; Campbell, L.; et al. Healthcare seeking outside healthcare facilities and antibiotic dispensing patterns in rural Burkina Faso: A mixed methods study. Trop. Med. Int. Health 2023, 28, 391–400. [Google Scholar] [CrossRef]
  29. Derra, K.; Rouamba, E.; Kazienga, A.; Ouedraogo, S.; Tahita, M.C.; Sorgho, H.; Valea, I.; Tinto, H. Profile: Nanoro health and demographic surveillance system. Int. J. Epidemiol. 2012, 41, 1293–1301. [Google Scholar] [CrossRef]
  30. van der Sande, M.; Ingelbeen, B.; Meudec, M.; van Kleef, E.; Campbell, L.; Wouters, E.; Marien, J.; van Vredendaal, R.; Leirs, H.; Valia, D.; et al. Evaluating the effect of a behavioural intervention bundle on antibiotic use, quality of care, and household transmission of resistant Enterobacteriaceae in intervention versus control clusters in rural Burkina Faso and DR Congo (CABU-EICO). Trials 2024, 25, 91. [Google Scholar] [CrossRef]
  31. CLSI M100; Performance Standards for Antimicrobial Susceptibility Testing. Clinical and Laboratory Standards Institute: Wayne, PN, USA, 2023.
  32. Souvorov, A.; Agarwala, R.; Lipman, D.J. SKESA: Strategic k-mer extension for scrupulous assemblies. Genome Biol. 2018, 19, 153. [Google Scholar] [CrossRef]
  33. Walker, B.J.; Abeel, T.; Shea, T.; Priest, M.; Abouelliel, A.; Sakthikumar, S.; Cuomo, C.A.; Zeng, Q.; Wortman, J.; Young, S.K.; et al. Pilon: An integrated tool for comprehensive microbial variant detection and genome assembly improvement. PLoS ONE 2014, 9, e112963. [Google Scholar] [CrossRef]
  34. Ondov, B.D.; Treangen, T.J.; Melsted, P.; Mallonee, A.B.; Bergman, N.H.; Koren, S.; Phillippy, A.M. Mash: Fast genome and metagenome distance estimation using MinHash. Genome Biol. 2016, 17, 132. [Google Scholar] [CrossRef]
  35. Zhou, Z.; Charlesworth, J.; Achtman, M. HierCC: A multi-level clustering scheme for population assignments based on core genome MLST. Bioinformatics 2021, 37, 3645–3646. [Google Scholar] [CrossRef]
  36. Zhou, Z.; Alikhan, N.F.; Mohamed, K.; Fan, Y.; Achtman, M. The EnteroBase user’s guide, with case studies on Salmonella transmissions, Yersinia pestis phylogeny, and Escherichia core genomic diversity. Genome Res. 2020, 30, 138–152. [Google Scholar] [CrossRef]
  37. Yoshida, C.E.; Kruczkiewicz, P.; Laing, C.R.; Lingohr, E.J.; Gannon, V.P.J.; Nash, J.H.E.; Taboada, E.N. The salmonella in silico typing resource (SISTR): An open web-accessible tool for rapidly typing and subtyping draft salmonella genome assemblies. PLoS ONE 2016, 11, e0147101. [Google Scholar] [CrossRef]
  38. Bortolaia, V.; Kaas, R.S.; Ruppe, E.; Roberts, M.C.; Schwarz, S.; Cattoir, V.; Philippon, A.; Allesoe, R.L.; Rebelo, A.R.; Florensa, A.F.; et al. ResFinder 4.0 for predictions of phenotypes from genotypes. J. Antimicrob. Chemother. 2020, 75, 3491–3500. [Google Scholar] [CrossRef] [PubMed]
  39. Roer, L.; Hendriksen, R.S.; Leekitcharoenphon, P.; Lukjancenko, O.; Kaas, S.; Hasman, H. Is the evolution of Salmonella enterica subsp. enterica linked to restriction-modification systems? mSystems 2016, 1, e00009-16. [Google Scholar] [CrossRef] [PubMed]
  40. Chen, L.; Zheng, D.; Liu, B.; Yang, J.; Jin, Q. VFDB 2016: Hierarchical and refined dataset for big data analysis—10 years on. Nucleic Acids Res. 2016, 44, D694–D697. [Google Scholar] [CrossRef] [PubMed]
  41. Im, J.; Nichols, C.; Bjerregaard-Andersen, M.; Sow, A.G.; Løfberg, S.; Tall, A.; Pak, G.D.; Aaby, P.; Baker, S.; Clemens, J.D.; et al. Prevalence of Salmonella Excretion in Stool: A Community Survey in 2 Sites, Guinea-Bissau and Senegal. Clin. Infect. Dis. 2016, 62, s50–s55. [Google Scholar] [CrossRef]
  42. Muthumbi, E.M.; Mwanzu, A.; Mbae, C.; Bigogo, G.; Karani, A.; Mwarumba, S.; Verani, J.R.; Kariuki, S.; Scott, J.A.G. The epidemiology of fecal carriage of nontyphoidal Salmonella among healthy children and adults in three sites in Kenya. PLoS Negl. Trop. Dis. 2023, 17, e0011716. [Google Scholar] [CrossRef]
  43. Zaidi, M.B.; McDermott, P.F.; Fedorka-Cray, P.; Leon, V.; Canche, C.; Hubert, S.K.; Abbott, J.; León, M.; Zhao, S.; Headrick, M.; et al. Nontyphoidal Salmonella from human clinical cases, asymptomatic children, and raw retail meats in Yucatan, Mexico. Clin. Infect. Dis. 2006, 42, 21–28. [Google Scholar] [CrossRef]
  44. Kirk, M.D.; Pires, S.M.; Black, R.E.; Caipo, M.; Crump, J.A.; Devleesschauwer, B.; Döpfer, D.; Fazil, A.; Fischer-Walker, C.L.; Hald, T.; et al. World Health Organization Estimates of the Global and Regional Disease Burden of 22 Foodborne Bacterial, Protozoal, and Viral Diseases, 2010: A Data Synthesis. PLoS Med. 2015, 12, e1001921. [Google Scholar] [CrossRef]
  45. Kraft, A.L.; Wells, J.E.; Frye, J.G.; Ibekwe, A.M.; Durso, L.M.; Hiott, L.; East, C.; McConn, B.R.; Franklin, A.M.; Boczek, L.A.; et al. A comparison of methods to detect low levels of Salmonella enterica in surface waters to support antimicrobial resistance surveillance efforts performed in multiple laboratories. Sci. Total Environ. 2023, 905, 167189. [Google Scholar] [CrossRef]
  46. Milani, C.; Bottacini, F.; Duranti, S.; Casey, E.; Mahony, J.; Belzer, C.; Arboleya Montes, S.E.; Lugli, G.A.; Rodriguez, J.M.; Bode, L.; et al. The First Microbial Colonizers of the Human Gut: Composition, Activities, and Health Implications of the Infant Gut Microbiota. Microbiol. Mol. Biol. Rev. 2017, 81, 10-1128. [Google Scholar] [CrossRef] [PubMed]
  47. Bénao, S.L.C.S.; Métuor, D.A.; Ouattara, A.K.; Tiemtoré, R.Y.W.; Ouédraogo, N.; Ouédraogo, B.; Badini, R.O.; Bambara, L.E.B.; Sougué, S.; Simporé, J. Présence de souches d’ Escherichia coli et de Salmonella spp. multirésistantes aux antibiotiques dans des fèces de poulet destinées à la consommation dans les espaces ouverts de Ouagadougou, au Burkina Faso. Open J. Appl. Sci. 2024, 14, 881–892. [Google Scholar] [CrossRef]
  48. Bénao, S.L.C.S.; Dabiré, A.M.; Tiemtoré, R.Y.W.; Sorgho, P.A.; Joël, G.G.; Simporé, J. Prevalence of Escherichia coli and Salmonella spp Strains Isolated from Chicken Feces and Their Resistance to Antibiotics by Cefotaximase (CTX-M) Enzyme Production. Biochem. Mol. Biol. 2023, 8, 5–11. [Google Scholar] [CrossRef]
  49. Caroline Bouda, S.; Kagambèga, A.; Bonifait, L.; Le Gall, F.; Bawa Ibrahim, H.; Bako, E.; Serge Bagre, T.; Zongo, C.; Wereme N’diaye, A.; Alfred Traore, S.; et al. Prevalence and Antimicrobial Resistance of Salmonella enterica Isolated from Chicken and Guinea Fowl in Burkina Faso. Int. J. Microbiol. Biotechnol. 2019, 4, 64. [Google Scholar] [CrossRef]
  50. Fagbamila, I.O.; Barco, L.; Mancin, M.; Kwaga, J.; Ngulukun, S.S.; Zavagnin, P.; Lettini, A.A.; Lorenzetto, M.; Abdu, P.A.; Kabir, J.; et al. Salmonella serovars and their distribution in Nigerian commercial chicken layer farms. PLoS ONE 2017, 12, e0173097. [Google Scholar] [CrossRef] [PubMed]
  51. Mokgophi, T.M.; Gcebe, N.; Fasina, F.; Jambwa, K.; Adesiyun, A.A. Prevalence, Serovars, and factors associated with salmonella contamination of chicken carcasses sold in outlets of the informal market in Gauteng Province, South Africa. J. Food Prot. 2021, 84, 991–999. [Google Scholar] [CrossRef] [PubMed]
  52. Andoh, L.A.; Dalsgaard, A.; Obiri-Danso, K.; Newman, M.J.; Barco, L.; Olsen, J.E. Prevalence and antimicrobial resistance of Salmonella serovars isolated from poultry in Ghana. Epidemiol. Infect. 2016, 144, 3288–3299. [Google Scholar] [CrossRef]
  53. Le Bouquin, S.; Allain, V.; Rouxel, S.; Petetin, I.; Picherot, M.; Michel, V.; Chemaly, M. Prevalence and risk factors for Salmonella spp. contamination in French broiler-chicken flocks at the end of the rearing period. Prev. Vet. Med. 2010, 97, 245–251. [Google Scholar] [CrossRef]
  54. Álvarez-Fernández, E.; Alonso-Calleja, C.; García-Fernández, C.; Capita, R. Prevalence and antimicrobial resistance of Salmonella serotypes isolated from poultry in Spain: Comparison between 1993 and 2006. Int. J. Food Microbiol. 2012, 153, 281–287. [Google Scholar] [CrossRef] [PubMed]
  55. Siceloff, A.T.; Waltman, D.; Shariat, N.W. Regional Salmonella Differences in United States Broiler Production from 2016 to 2020 and the Contribution of Multiserovar Populations to Salmonella Surveillance. Appl. Environ. Microbiol. 2022, 88, e0020422. [Google Scholar] [CrossRef] [PubMed]
  56. Public Health Agency of Canada. FoodNet Canada. 2022. Available online: https://www.canada.ca/en/public-health/services/surveillance/foodnet-canada/publications.html#a3 (accessed on 29 November 2025).
  57. Tan, S.J.; Nordin, S. Salmonella spp. in Chicken: Prevalence, Antimicrobial Resistance, and Detection Methods. Microbiol. Res. 2022, 13, 691–705. [Google Scholar] [CrossRef]
  58. Gal-Mor, O. Persistent infection and long-term carriage of typhoidal and nontyphoidal salmonellae. Clin. Microbiol. Rev. 2019, 32, e00088-18. [Google Scholar] [CrossRef] [PubMed]
  59. Chen, J.; Huang, L.; An, H.; Wang, Z. One Health approach probes zoonotic non-typhoidal Salmonella infections in China: A systematic review and meta-analysis. J. Glob. Health 2024, 14, 04256. [Google Scholar] [CrossRef]
  60. Ahmet, N.A.; Bissoume, S.B.; Abdoulaye, S.; Amadou, D.; Khota, F.N.; Aziz, W.A.; Raymond, B.; Roughyatou, K.; Iyane, S.A.; Amy, G.S. Phenotypic and genotypic characterization of Salmonella isolated from Asymptomatic Carriers in the Suburb of Dakar. J. Trop. Dis. 2020, 8, 346. [Google Scholar]
  61. Liu, C.; Yao, K.; Ren, D.; Xiao, Y. Prevalence and characterization of Salmonella from meat in slaughterhouses in Hangzhou, China. Int. J. Food Microbiol. 2022, 371, 109649. [Google Scholar] [CrossRef]
  62. Chen, Z.; Bai, J.; Zhang, X.; Wang, S.; Chen, K.; Lin, Q.; Xu, C.; Qu, X.; Zhang, H.; Liao, M.; et al. Highly prevalent multidrug resistance and QRDR mutations in Salmonella isolated from chicken, pork and duck meat in Southern China, 2018–2019. Int. J. Food Microbiol. 2021, 340, 109055. [Google Scholar] [CrossRef]
  63. Yin, X.; Dudley, E.G.; Pinto, C.N.; M’ikanatha, N.M. Fluoroquinolone sales in food animals and quinolone resistance in non-typhoidal Salmonella from retail meats: United States, 2009–2018. J. Glob. Antimicrob. Resist. 2022, 29, 163–167. [Google Scholar] [CrossRef]
  64. Liu, Y.; Jiang, J.; Ed-Dra, A.; Li, X.; Peng, X.; Xia, L.; Guo, Q.; Yao, G.; Yue, M. Prevalence and genomic investigation of Salmonella isolates recovered from animal food-chain in Xinjiang, China. Food Res. Int. 2021, 142, 110198. [Google Scholar] [CrossRef]
  65. Laxminarayan, R.; Van Boeckel, T.; Teillant, A. The Economic Costs of Withdrawing Antimicrobial Growth Promoters from the Livestock Sector. J. For. 2015, 32, 209–213. [Google Scholar]
  66. Zhukovskyi, M.; Nedosekov, V. Economic Analysis of Animal Diseases; Food and Agriculture Organization of the United Nations: Rome, Italy, 2022; ISBN 9789251091661. [Google Scholar]
  67. Van Boeckel, T.P.; Pires, J.; Silvester, R.; Zhao, C.; Song, J.; Criscuolo, N.G.; Gilbert, M.; Bonhoeffer, S.; Laxminarayan, R. Global trends in antimicrobial resistance in animals in low- And middle-income countries. Science 2019, 365, eaaw1944. [Google Scholar] [CrossRef] [PubMed]
  68. Medvedev, K.E.; Zhang, J.; Schaeffer, R.D.; Kinch, L.N.; Cong, Q.; Grishin, N.V. Structure classification of the proteins from Salmonella enterica pangenome revealed novel potential pathogenicity islands. Sci. Rep. 2024, 14, 12260. [Google Scholar] [CrossRef] [PubMed]
  69. Wang, M.; Qazi, I.H.; Wang, L.; Zhou, G.; Han, H. Salmonella virulence and immune escape. Microorganisms 2020, 8, 407. [Google Scholar] [CrossRef]
  70. Nagpala, M.J.M.; Mora, J.F.B.; Pavon, R.D.N.; Rivera, W.L. Genomic characterization of antimicrobial-resistant Salmonella enterica in chicken meat from wet markets in Metro Manila, Philippines. Front. Microbiol. 2025, 16, 1496685. [Google Scholar] [CrossRef]
  71. Algammal, A.M.; El-Tarabili, R.M.; Abd El-Ghany, W.A.; Almanzalawi, E.A.; Alqahtani, T.M.; Ghabban, H.; Al-otaibi, A.S.; Alatfeehy, N.M.; Abosleima, N.M.; Hetta, H.F.; et al. Resistance profiles, virulence and antimicrobial resistance genes of XDR S. Enteritidis and S. Typhimurium. AMB Express 2023, 13, 110. [Google Scholar] [CrossRef]
  72. Shaheen, A.; Tariq, A.; Iqbal, M.; Mirza, O.; Haque, A.; Walz, T.; Rahman, M. Mutational Diversity in the Quinolone Resistance-Determining Regions of Type-II Topoisomerases of Salmonella Serovars. Antibiotics 2021, 10, 1455. [Google Scholar] [CrossRef] [PubMed]
  73. Wang, J.; Sang, Z.; Zhao, Y.; Wang, Q.; Hu, X.; Ni, T.; Liu, C.; Liao, H.; Zhang, H.; Xia, X. The molecular mechanisms of quinolone resistance in Salmonella: A review. Arch. Microbiol. 2025, 207, 230. [Google Scholar] [CrossRef]
  74. Barrientos-Villegas, S.; García-Álvarez, M.I.; Vidal, J.L.; Gómez-Osorio, L.M.; López-Osorio, S.; Chaparro-Gutiérrez, J.J. Systematic review: Genotypic and phenotypic resistance of fluoroquinolone-resistant Salmonella in livestock in South America (2020–2024). Front. Vet. Sci. 2025, 12, 1614486. [Google Scholar] [CrossRef] [PubMed]
  75. Melaku, A.; Id, B.; Alemie, Y.; Gizachew, M.; Yousef, A.E.; Dessalegn, B.; Bitew, A.B.; Alemu, A.; Gobena, W.; Christian, K.; et al. Serovars, virulence factors, and antimicrobial resistance profile of non-typhoidal Salmonella in the human-dairy interface in Northwest Ethiopia: A one health approach. PLoS Negl. Trop. Dis. 2024, 18, e0012646. [Google Scholar] [CrossRef]
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.

Article Metrics

Citations

Article Access Statistics

Journal Statistics
Article metric data becomes available approximately 24 hours after publication online.
Download PDF
Changes in the Rhizospheric Microbiota of Pepitilla Maize in Response to Drought: Functional and Taxonomic Analysis
Previous
Crohn’s Disease in Malaysia: Could Application of the Precautionary Principle Reduce Future Incidence?
Next