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Article

Detection of Antimicrobial Resistance, Pathogenicity, and Virulence Potentials of Non-Typhoidal Salmonella Isolates at the Yaounde Abattoir Using Whole-Genome Sequencing Technique

by
Chelea Matchawe
1,*,
Eunice M. Machuka
2,
Martina Kyallo
2,
Patrice Bonny
1,
Gerard Nkeunen
3,
Isaac Njaci
2,
Seraphine Nkie Esemu
4,
Dedan Githae
2,
John Juma
2,
Bawe M. Nfor
5,
Bonglaisin J. Nsawir
1,
Marco Galeotti
6,
Edi Piasentier
6,
Lucy M. Ndip
4 and
Roger Pelle
2,*
1
Institute of Medical Research and Medicinal Plants Studies, Yaounde P.O. Box 6163, Cameroon
2
Bioscience Eastern and Central Africa-International Livestock Research Institute (BecA-ILRI) Hub, Nairobi P.O. Box 30709, Kenya
3
Department of Biochemistry, University of Dschang, Dschang P.O. Box 96, Cameroon
4
Department of Microbiology and Parasitology, University of Buea, Buea P.O. Box 63, Cameroon
5
Department of Rangeland, Animal Nutrition and Livestock Infrastructures, Sub Department of Animal Nutrition, Ministry of Livestock, Fisheries and Animal Industries (MINEPIA), Yaounde P.O. Box 930, Cameroon
6
Department of Agricultural, Food, Environment and Animal Sciences, University of Udine, 33100 Udine, Italy
*
Authors to whom correspondence should be addressed.
Pathogens 2022, 11(5), 502; https://doi.org/10.3390/pathogens11050502
Submission received: 21 February 2022 / Revised: 5 April 2022 / Accepted: 6 April 2022 / Published: 23 April 2022
(This article belongs to the Section Bacterial Pathogens)
Review Reports Versions Notes

Abstract

:
One of the crucial public health problems today is the emerging and re-emerging of multidrug-resistant (MDR) bacteria coupled with a decline in the development of new antimicrobials. Non-typhoidal Salmonella (NTS) is classified among the MDR pathogens of international concern. To predict their MDR potentials, 23 assembled genomes of NTS from live cattle (n = 1), beef carcass (n = 19), butchers’ hands (n = 1) and beef processing environments (n = 2) isolated from 830 wet swabs at the Yaounde abattoir between December 2014 and November 2015 were explored using whole-genome sequencing. Phenotypically, while 22% (n = 5) of Salmonella isolates were streptomycin-resistant, 13% (n = 3) were MDR. Genotypically, all the Salmonella isolates possessed high MDR potentials against several classes of antibiotics including critically important drugs (carbapenems, third-generation cephalosporin and fluoroquinolone). Moreover, >31% of NTS exhibited resistance potentials to polymyxin, considered as the last resort drug. Additionally, ≤80% of isolates harbored “silent resistant genes” as a potential reservoir of drug resistance. Our isolates showed a high degree of pathogenicity and possessed key virulence factors to establish infection even in humans. Whole-genome sequencing unveiled both broader antimicrobial resistance (AMR) profiles and inference of pathogen characteristics. This study calls for the prudent use of antibiotics and constant monitoring of AMR of NTS.

1. Introduction

Today’s world is experiencing an antibiotic resistance pandemic due to growing bacterial resistance to a broad range of drugs in animals and clinical settings [1,2,3]. Microbial multidrug resistance (MDR) frustrates efforts for infection control resulting in a considerable increase in morbidity and mortality worldwide [1,2].
MDR has also been reported in non-typhoidal Salmonella (NTS). For instance, while 16% of NTS isolates exhibited resistance to at least one essential antibiotic, as high as 2% of them were resistant to at least three different classes of antibiotics in the US [2]. In Europe, 23% of NTS isolated from calf carcasses were MDR [3]. In Europe, 23% of NTS isolated from calf carcasses were MDR [3]. In Africa, there is an emergence of an invasive non-typhoidal Salmonella (iNTS) lineage with increased multidrug resistance (MDR) potential playing a considerable role in outbreaks, thereby threatening the global market and tourism [4,5]. Moreover, close to 80% of all the reported cases in 2017 occurred in sub-Saharan Africa alone, affecting mainly children under five, adolescents and active young people under fifty [5]. Therefore, NTS seems to be a significant cause of loss of economic activity in Africa [6]. In Cameroon, despite the absence of official statistics on foodborne diseases in general, sporadic cases of invasive salmonellosis mistaken either for malaria or typhoid fever have been recently reported [7,8,9].
Current knowledge of the type of Salmonella serovars and their antibiotic resistance profile is essential to inform policy and guide treatment strategies for appropriate therapy and the development of new antimicrobials [1,2]; thus, the WHO recommendation for national surveillance of antimicrobial resistance (AMR) in Salmonella [1]. Given their zoonotic nature, there is a need for an integrative ‘One Health’ approach for the surveillance of AMR among humans and animal Salmonella isolates [10]. Healthy or asymptomatic live animals such as cattle may carry Salmonella, thereby representing an important risk factor for beef carcass contamination during processing at the abattoirs such as the Yaoundé abattoir [11,12].
The Yaoundé abattoir where more than 6000 animals are slaughtered every week, is one of the major slaughterhouses in Cameroon that has the capacity to supply meat to three regions (centre, west and south) of Cameroon and neighboring countries (Equatorial Guinea and Gabon). Following the WHO recommendation and given the use of antibiotics for disease prevention or animal growth promotion, it seems crucial to monitor the antimicrobial resistance profile of Salmonella isolates at the Yaounde abattoir using molecular techniques such as whole-genome sequencing (WGS).
Unlike traditional antimicrobial susceptibility testing, whole-genome sequencing (WGS) can give information on the presence of MDR genes [13] and pathogenicity and virulence factors in Salmonella organisms. This study was aimed at predicting the MDR, pathogenicity, and virulence potentials of Salmonella isolated at the Yaounde abattoir using WGS.

2. Results

2.1. Phenotypic Antibiotic Resistance Profile of Salmonella Isolates

Twenty-three genomes of 38 identified Salmonella isolates were successfully sequenced. Only 19 sequenced genomes were thoroughly exploitable for the required bioinformatics analyses. The genomic profile of NTS isolates and their GenBank accession numbers are outlined in Table S1.
Isolates 8ev, 20de, 22sa, 34de, 60sa, and evjul were resistant to streptomycin, whereas between 18 and 20 isolates were highly susceptible to ampicillin, chloramphenicol, and tetracycline (Table 1 and Table S2). Interestingly, isolates 8ev, 22sa, and 34de were MDR.

2.2. Genotypic Antibiotic Resistance Profile of Salmonella Isolates

The distribution of specific antibiotic resistance genes among the Salmonella isolates is summarized in Table 2. The streptomycin-resistance genes, aadA, aadA1, and aadA2 were, respectively, present in 15.8%, 26.3%, and 21% of Salmonella isolates. Moreover, chloramphenicol-resistance was found in 26.3%, 15.8%, 10.5, 10.5% of the isolates carrying cat, cat1, cat2, and cat3 genes, separately while 10.5% of isolates harbored cmlA1, cml5, and cml6 genes, respectively. The tetracycline-resistance genes, tetA, tetB, tetC and tetR were present in 5.26% 26.3%, 26.3%, 31.6% and 84.2% of isolates, respectively. Ampicillin-resistance genes TEM-1 and TEM-163 were identified in 15.8% and 21% of NTS isolates, respectively. However, close to 80% of Salmonella isolates harbored at least one false-negative result (Table 2 and Table 3).

2.3. Matching AST with Genotypic Resistance Potentials of Salmonella Isolates

The true positive and true negatives cases represented perfect matching between their phenotypic and genotypic antibiotic resistance (Table 3). The sensitivity was highest (62.5%) for resistance against streptomycin and chloramphenicol, and lowest for ampicillin (16.7%), respectively. Nevertheless, despite a general low sensitivity (averagely 45.3%), the specificity was 100% for all the antibiotics with the sum of sensitivity + specificity being ≥1.5 for all the tested Salmonella in the present study. The positive predictive value was 100% for all the tested antibiotics while the negative predictive values varied between 6.2% (for ampicillin) and 81.2% (for ampicillin).
Generally, results indicate that whole-genome sequencing predicted four times the antimicrobial resistance for NST than the traditional susceptibility testing (OR = 4.0, p = 0.46).

2.4. Multidrug Resistance Potentials of Salmonella Isolates

Eighteen genes (in purple) involved in the efflux, transport, and reduced permeability of antimicrobials were identified in Salmonella isolates (Table 4). Except for phenicol, the gene TolC was reported only in the phenotypic MDR isolates 8ev, 22sa, and 34de. The gene golS was detected in all the isolates. Interestingly, the MdsABC (multidrug transporter) complex was also present alongside gene golS in isolates that exhibited resistance potential against phenicols. The E. coli soxS and soxR genes were detected in 78.94 to 100% of Salmonella isolates.
Furthermore, the gene mdtk that promotes resistance solely against fluoroquinolone was present in 100% Salmonella isolates. The AcraB regulator gene sdiA was detected in all the isolates. Furthermore, sulfonamide-resistance genes sul1 and sul2 and the gene CTX-M-14 that regulates resistance against third-generation cephalosporin were present in 15.8 and 5.26% of Salmonella isolates, respectively. Then, isolates 8ev, 22sa and 34de harbored cephalosporin-resistance genes OXA-1, OXA-2, and OXA-7. Additionally, the fluoroquinolone-resistance gene qnrB1 was reported in isolates 8ev, 20de, 34de, and 60sa. The gene macA that mediates efflux of macrolide and secretion of enterotoxin ST11 was detected in 52.63% of NTS isolates. Moreover, 47.36% of Salmonella isolates hosted the gene marA, which exports antibiotics and disinfectants out of bacteria.

2.5. Polymorphism on PmrAB System Inducing Polymyxin-Resistance Potential in Salmonella Isolates

Mutations of the PmrAB system were detected in more than 31% of isolates (Table 5).

2.6. Pathogenicity and Virulence Factors

Our isolates had a mean probability of 94% to cause diseases in humans, though no significant difference (p > 0.05) was found among different Salmonella serovars in pathogenicity (Table 6). Moreover, their proteomes matched with a broad range of pathogenic bacterial families (466–787). Serovars Enteritidis and Poona were predicted greatest (p = 0.95) and least (p = 0.93) human pathogens, respectively.
Of the 11 identified SPIs, only C63PI was present in all the isolates (Table 7). The function of each virulence factor and each effector protein is summarized in Tables S3 and S4, respectively. Other determinants including effector proteins, adhesion factors, virulence plasmids, and toxins were detailed in Table S5a,b. About 82% of Salmonella isolates possessed SipABCD, SopBDE, SopE2, EnvEF, InvAE, Sii E, IagB, SptP, OrgB, PhoP, MisL, ShdA, and rtn, as well as cell entry-facilitating factors; between 56% and 61% of expressed adhesion factors including FimA, FimC, FimZ, HilD, ecpD1, CsgD, FliZ, FliT; also, between 56% and 69%, NTS expressed SopABDE, SpiC, SpvB, SseBCD, PipB2, SptP, SopE2, Spa family as response regulators. Additionally, while 95% of NTS carried H-NS, between 21% and 47% expressed heat shock and stress proteins, specifically CuSA, ScsC, and bacteriocin exporter. Four types of plasmids (IncF, IncI, Col (PHAD28), and IncH) and four bacterial toxins (Flavodoxin, Shiga-like toxin A, entericidin A, and Thioredoxin 1) were detected in some Salmonella isolates.

3. Discussion

Despite a relatively moderate resistance shown to streptomycin (21.7%), the majority of Salmonella strains isolated at the Yaounde abattoir were sensitive to tetracycline, chloramphenicol, and ampicillin (Table 1). These results corroborate the findings of previous studies that showed common resistance against streptomycin among Salmonella [14].
Findings from the present study indicate that tetracycline, chloramphenicol, and ampicillin are still effective antibiotics in Cameroon contrary to the situation in many countries where these drugs are no longer appropriate for the treatment of invasive salmonellosis. Notwithstanding, the presence of 13% of MDR Salmonella isolates constitutes a serious health concern. Similarly, MDR was recently reported to be observed among Salmonella isolated in an Ethiopian abattoir on similar drugs [15]. In the present work, MDR salmonellae were isolated from beef at different processing steps underlining a significant food safety hazard. The development and spread of AMR among NTS are specifically crucial when found in environmental settings such as the abattoir. Such an environment may be a source of cross-contamination between meat products and meat handlers to become a threat to public health.
Furthermore, the detection of the respective resistance genes to streptomycin, chloramphenicol, tetracycline, and ampicillin in the Salmonella isolates confirms their phenotypic antimicrobial resistance status described in Table 1. However, false-negative cases or “silent genes” were reported for each of the tested antibiotics. They represent genes that were previously detected in the susceptible isolates but failed to be expressed phenotypically. Previous studies attributed this mismatch between the phenotypic and genotypic resistance profile of bacteria to the fact that such genes were in “silent mode” in vitro [16,17,18,19]. The resistance phenotype depends on the mode and level of expression of the resistance gene that could be influenced by growth or environmental factors [16]. However, the reasons for their no phenotypic expression are not yet fully elucidated based on previous studies [16,17,18,19]. The absence or impairment of the promoter sequence or the presence of negative regulators might have downregulated their expression in vitro. Alternatively, the expression of these genes might have occurred without the expression of the gene products. Therefore, many factors exist to control the expression of the resistance phenotypes. However, if by any mechanism these genes are switched onto “activated mode”, the host bacteria will modify their phenotypic resistance status [20]. Consequently, “silent genes” can be regarded as a reservoir of antimicrobial resistance for major foodborne bacteria via horizontal gene transfer or other mechanisms [17]. This hypothesis supports previous works that considered “silent genes” as a significant potential threat to the therapeutic efficacy of antibiotics [17,19].
Furthermore, the non-expression of the “silent genes” in the susceptible phenotypes underscores a lack of definite genotype–phenotype correlation testing indicating a lack of clear genotype–phenotype correlation (OR = 4.0; p = 0.46). However, the value of greater than 1.5 of the sum of sensitivity and specificity reflects the usefulness of the WGS prediction (Table 3). The high PPV predicts a high prevalence of antimicrobial resistance among NTS isolates at the Yaounde abattoir. Similarly, the absence of a conclusive correlation between the phenotypic and genotypic characteristics was also reported among non-typhoidal Salmonella isolated from wildlife [13]. These findings clearly show that antibiotic susceptibility testing (AST) does not provide any information about the underlying genes responsible for the resistance. This suggests that WGS could be used as a complementary tool to AST to extract additional information such as the presence of silent resistance genes.
In addition, WGS is also useful in predicting the multiple drug resistance of NTS. Based on their mode of action, the eighteen MDR-promoting genes detected in this study are grouped into: (i) those that export drugs out of the bacterial cells (sdiA, mdsA, mdsB, mdsC, E. coli soxS, E. coli soxR, acrB, acrD, golS, marA, patA and ramR); (ii) those that reduce membrane permeability to drugs (E. coli soxR, E. coli soxS, and marA); and (iii) those that alter antibiotic target configuration (bacA, E. coli soxR, E. coli soxS, and ramA) [17]. Nevertheless, the mechanism of action of some genes, such as E. coli sox R, E. coli soxS, and ramA overlap (Data not shown).
All the genes involved in drug efflux are part of the resistance nodulation cell division (RND) efflux systems and effectively perform their duty in synergy with TolC [19]. Moreover, there were also multi-efflux pumps such as mdsA, mdsB, mdsC, acrA, acrB, and mdtk, which generally work in synergy either with transcriptional activators (E. coli soxS and E. coli soxR, ramA, marA) or promotors such as TolC and golS [20]. The presence of TolC only in MDR isolates justifies the critical role it plays in synergy with RND efflux systems in exporting a range of antimicrobials. The golS gene promotes the MdsABC complex to disseminate resistance against a variety of drugs and toxins and confers virulence and pathogenicity potentials to Salmonella [21]; thus, the presence of the MdsABC complex in all the isolates that had golS. The gene sdiA, a regulator for AcraB, a multi-drug resistance pump [22] detected in 100% of the isolates is a powerful promoter of resistance against a vast arsenal of antibiotics (data not shown).
The presence of mdtK and qnrB1 in our isolates is extremely crucial because they could synergistically offer Salmonella an advantage to develop resistance via a plasmid-mediated or efflux pump mechanism against fluoroquinolone [13,23]. However, transcriptional activators, particularly ramR, soxS and marA could equally induce resistance against fluoroquinolone via overexpression of acrAB-TolC efflux pump in Salmonella [24]. The detection of OXA-1, OXA-2, OXA-7, CTX-M-14, and qnrB in some isolates is critical because they, respectively, confer resistance against third-generation cephalosporin and fluoroquinolone, all considered as the WHOs highest priority drugs [25,26]. Particularly, OXA-1 and OXA-2 genes exhibit broad-spectrum cephalosporin-hydrolyzing and carbapenem-hydrolyzing activities, respectively [27,28]. Furthermore, the resistance potential to polymyxin is crucial because of its consideration as the last resort drug.
Polymyxin is a bactericidal polypeptide, which disrupts lipid A subunit of the LPS outer membrane of Gram-negative bacteria causing cell lysis and their eventual irreversible death. One of the key resistance mechanisms to polymyxin adapted by Salmonella resides in the modification of lipid A, via mutations on the PmrA/PmrB system causing overexpression of LPS-modified genes [28]. The resistance potential of Salmonella isolates to polymyxin in this study unveils both clinical and veterinary importance [29]. Not only did the NTS isolates in this study exhibit resistance potentials to several antibiotics, they equally demonstrated high pathogenicity ability to cause diseases in humans.
In fact, their belonging to large pathogenic families confirms the zoonotic status of NTS and their ability to exhibit broad-host adaptation. It is not surprising that serovar Enteritidis scored the highest probability to cause disease in humans. Previously, Salmonella Enteritidis has been the most prevalent world foodborne pathogen after S. Typhimurium based on its involvement in disease outbreaks [30]. Curiously, one out of two (50%) Enteritidis isolates in the current study belonged to sequence type ST-11, already incriminated as the major cause of African NTS [30]. Despite its exceptional MDR potential, serovar Poona was the least human pathogen. Its relatively low pathogenicity may reflect its low virulence power. Indeed, NTS disposes an arsenal of virulence factors to cause damage to their hosts.
The Salmonella pathogenicity islands described in Table 7 represent powerful virulence weapons during Salmonella infections. The ubiquity of C63PI may explain its role in Salmonella survival during iron uptake, thus its conservation among Salmonella species [31,32]. Out of the 11 identified SPIs, SPI-1, SPI-2, SPI-3, SPI-4, and SPI-5 play a more critical role during Salmonella pathogenesis [33]. While SPI-1 is mainly involved in the initiating stage of infection, SPI-2 is required for systemic infection by easing the replication of intracellular bacteria within SCV [34]. Additionally, SPI-3 is required for survival in macrophages including in a low-magnesium environment; SPI-4 is needed for intramacrophage survival, toxin secretion, and apoptosis [35]. Furthermore, Salmonella uses SPI-5 to induce a pro-inflammatory immune response sometimes resulting in diarrhea [36]. However, the functions of SPI-1, SPI-2, SPI-3, SPI-4, and SPI-5 could also overlap [37]. Conversely, SPI-8, SPI-9, SPI-13, and SPI-14 are associated with the regulation of the expression of other SPI genes or associated effector proteins [33] (Table S4).
The observed variation in the SPI profile among NTS in this study unveils their differential degree of virulence. Each of the SPIs works in synergy with an arsenal of genes that code for different specialized effector proteins. These effectors are either secreted or translocated into the host cells to enable Salmonella to manipulate the host’s key cellular functions such as signal transduction, membrane trafficking, and immune responses. In occurrence virulence factors, notably SipA, SipC, SopB, SopD, SopE, SopE2, SipB, EnvE, EnvF, InvA, InvE, SipD, Sii E, IagB, SptP, OrgB, PhoP, MisL, ShdA, and rtn detected in roughly 82% of Salmonella isolates appear necessary to facilitate their entry into host cells to establish infection (Table S5a). Interestingly, all the effectors described in the Salmonella entry process are SPI-1 encoded [38,39]. Putative fimbriae usher (FimA, FimC, FimZ, HilD), pilin chaperon (ecpD1, CsgD), and flagellin (proteins FliZ, FliT) collectively called adhesion factors are additional virulence determinants to ensure successful colonization and persistence of Salmonella in the hosts [32]. Moreover, the expression of “SipA and SipC” and “SopE and SopE2” required, respectively, for invasion and internalization efficiency [38,39] reinforced the pathogenicity potentials of our isolates. The expression of SopB, SopE, SopA, SopD, SpiC, SpvB, SseB, SseC, SseD, PipB2, SptP, SopE2, and antigen presentation proteins, especially SpaK, SpaN, SpaS, and SpaR was sufficient for the maturation and trafficking process of SCV, replication within SCV and the monitoring of the host immune responses. Likewise, virulence determinants such as heat shock protein, stress protein, CuSA, ScsC, and putative ABC-type bacteriocin exporter could enable isolates to successfully control competing bacteria during infection [37,40,41].
Though HilA, the leading regulator of SPI-1 [38] was not detected in the isolates, between 59% and 68% of NTS isolates possessed PhoP, HilD, and FliZ, three important transcriptional regulators; also, 95% carried H-NS, the master silencer of horizontal transfer genes [37,42] to regulate the secretion or translocation of effector proteins during each step of Salmonella infection [43]. Otherwise, virulence plasmids were not widely distributed among NTS in this study (Table S5b). IncF plasmid known as a crucial virulence plasmid [19] was carried by Salmonella isolates that harbored the gene qnrB. This observation corroborates a previous study that seemed to establish a certain relationship between harboring IncF plasmid and fluoroquinolone-resistance potential [44]. Despite the wide distribution of IncI1 plasmids in Salmonella, [18,45] only 23% of our NTS isolates harbored them. The restricted presence of Col (PHAD28) plasmid to MDR Poona isolates seems to underline a certain correlation between harboring this plasmid and the MDR potential of the host [18]. Equally, the exclusive presence of IncH plasmid seems to correlate well with the detection of genes cat, qnr, strAB, TEM-1, and tet in isolate 34de as previously reported [18]. Remarkably, all the aforementioned plasmids are mobilizable plasmids that are known to promote multiple drug resistance in bacteria [18].

4. Conclusions

Although the fact that the samples analyzed were limited to a single abattoir, this study has revealed the public health importance of Salmonella isolates at the Yaounde abattoir. Despite a relatively moderate phenotypic resistance to streptomycin, genotypically, Salmonella isolates possessed high MDR potentials against several classes of antibiotics notably third-generation cephalosporin and fluoroquinolone. More than 31% of the isolates exhibited resistance potential to polymyxin, considered a critically important drug. Additionally, close to 80% of NTS isolates harbored “silent genes” which could act as a reservoir of resistance to foodborne bacteria. The role of plasmids should be integrated into the antibiotic surveillance program. Salmonella isolates also exhibited a high degree of pathogenicity and virulence to establish infection in their hosts including humans. The presence of virulence determinants is crucial in Salmonella pathogenicity and in understanding the epidemiological knowledge about the potential severity of infections and to mitigate potential outbreaks of Salmonellosis. The combined effect of high pathogenicity and MDR potentials of NTS at the Yaounde abattoir highlights the need for improvement in food safety practices and the need for antibiotic stewardship in livestock production systems in Cameroon. Given the fact that there is a lack of reliable data on the NTS AMR prevalence in Cameroon, this study suggests that further studies are needed to elucidate the epidemiology of the antibiotic resistance among Salmonella across all the Cameroonian slaughterhouses including characterizing the genes involved and the plasmids that carry them.

5. Materials and Methods

5.1. Bacterial Isolates

This study was a cross-sectional study. Prior to slaughter, five cattle were randomly chosen per week for every sampling session following the Meat Industry Guide describing sampling frequency for red meat carcasses [46]. Moreover, five butchers (one-fifth of the butchers at duty) were recruited for every sampling session. Wet swabs (n = 830) from live cattle (n = 145), beef carcasses (n = 435), butchers’ hands (n = 145) and the meat contact surfaces (n = 105), were aseptically collected between December 2014 and November 2015 at the Yaounde abattoir to isolate non-typhoidal Salmonella (NTS) following ISO 6579 [47]. Finally, 23 NTS were isolated and were distributed as follows: live cattle (n = 1), beef carcasses (n = 19), processing environments (n = 2), and butchers’ hands (n = 1) All Salmonella isolates were confirmed using API-20 E kit (BioMérieux, France) and a qualitative real-time PCR assay [48].

5.2. Antibiotic Susceptibility Tests

Salmonella concentration (1.5 × 108 CFU/mL) and those of controls (Escherichia coli ATCC 25922 and Staphylococcus aureus ATCC 43300) were spread onto the surface of Mueller–Hinton agar to which the antibiotic disks were placed and incubated for 18 to 24 h. The diameter of the zones of inhibition around each antibiotic disk was measured with a ruler and recorded to the nearest millimeter and isolates were classified as resistant, susceptible, or intermediate [49]. The antibiotics used were ampicillin (AMP) 10 μg, chloramphenicol (C) 30 μg, tetracycline (TE) 30 μg, and streptomycin (STR) 25 μg.

5.3. DNA Extraction

Total DNA was extracted from Salmonella overnight culture using Quick-DNA™ Miniprep Plus Kit (Zymo Research, Irvine, CA, USA) following the manufacturer’s instructions. The purified DNA was quantified using a NanoDrop 2000c spectrophotometer (ThermoFisher Scientific, Santa Clara, CA, USA) and stored at −20 °C until use.

5.4. Library Preparation and Sequencing

Paired-end libraries were constructed with 0.2 ng/µL of purified DNA using the Nextera XT DNA Library Prep Kit as recommended by the manufacturer (Illumina, San Diego, CA, USA) and were quantified using a Qubit fluorometer (ThermoFisher Scientific, USA). WGS was performed on Illumina NextSeq platform using NextSeq 500/550 high-output kit v2 (300 cycles) at Murdoch University (Australia) and on Illumina Miseq platform using pair-ended MiSeq reagent v3 kit (2 × 201 bp) at the BecA-ILRI Hub (Nairobi, Kenya) following the manufacturers’ guidelines.

5.5. Resistance Genes, Pathogenicity, and Virulence Factors

The qualities of the raw sequences were checked with FASTQC and trimmed using Trimmomatic 2.6 at Q score below 20. The trimmed data were assembled using SPAdes version 3.11, and genomes were annotated with the NCBI Prokaryotic Genome Annotation Pipeline [50]. The antibiotic resistance genes (ARG) in the assembled genomes were identified by BLAST search against the reference ARG sequences from ResFinder 3.0 [23] and CARD 2017 [51] with ≥95% gene identity and 60% sequence length of the resistance gene. The pathogenicity of NTS was predicted using PathogenFinder 1.1 [52] with the threshold for minimum % identity at 95% and minimum % coverage at 60%. The Salmonella pathogenicity islands were detected using SPIFinder 1.0 [33] at ≥95% gene identity and 60% sequence length cut off. Acquired virulence genes were detected by uploading the raw reads into VirulenceFinder 2.0 [53] while virulence plasmid was assessed using PlasmidFinder 2.1 [53] with the threshold for % identity at minimum % coverage at 60%.

5.6. Statistical Analysis

Statistical significance for all tests was set at the level of p ≤ 0.05, using the methods of Duncan [54], and descriptive statistics were calculated for all variables as appropriate using IBM SPSS 20 [55]. Sensitivity and specificity were calculated to determine the relationship between phenotypic and genotypic profiles of NTS following the method described by Genders et al. [56]. The odds ratio was determined using online MedCalc Version 20.027.

Supplementary Materials

The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/pathogens11050502/s1. The GenBank accession numbers of the Salmonella genomes are in Tables S1, S2, S4, S5a and S5b. The function of each SPI is summarized in Table S3.

Author Contributions

Conceptualization: C.M., L.M.N., M.G., E.P., R.P.; data curation: C.M.; formal analysis: C.M., I.N., B.J.N., S.N.E.; funding acquisition: C.M.; investigation: C.M.; methodology: C.M., E.M.M., M.K., I.N., D.G., J.J., P.B., G.N.; project administration: R.P.; Software: C.M., I.N., J.J., D.G., P.B.; resources: M.G., E.P., E.M.M., M.K., B.M.N.; supervision: L.M.N., E.P., R.P.; validation: C.M.; visualization: C.M.; writing—original draft: C.M.; writing—review & editing: C.M., P.B., G.N., S.N.E., B.M.N., B.J.N., M.K., E.M.M., M.G., R.P. All authors have read and agreed to the published version of the manuscript.

Funding

This work was funded by the Biosciences eastern and central Africa-International Livestock Research Institute (BecA-ILRI) Hub, by the Australian Agency for international Development (AusAID) through a partnership between Australia’s commonwealth Scientific and Industrial Research Organization (CSIRO) and the BecA-ILRI Hub; and by Syngenta Foundation for Sustainable Agriculture (SFSA); the Bill & Melinda Gates Foundation (BMGF); and the Swedish Ministry of Foreign Affairs through the Swedish International Development Agency (Sida), through Africa Biosciences Challenge Fund (ABCF) program (Ref07/RF/2017/4688).

Institutional Review Board Statement

The study was conducted according to the guidelines of the Declaration of Helsinki, and approved by the Cameroon Bioethics Initiative Review and Consultation Committee (Ref CBI/406/ERCC/CAMBIN).

Informed Consent Statement

Informed consent was obtained from the butchers and abattoir personnel.

Data Availability Statement

The data that support the findings of this study are available from the corresponding author upon reasonable request.

Acknowledgments

We thank the University of Udine, Italy for providing the Qiagen extraction kit and Real-Time PCR reagents. We also thank Mary Barton and Darren Trott (University of Adelaide, Australia), Sam Abraham and Mark O’Dea (Murdoch University, Australia), for sequencing facilities. Finally, we are grateful to Joyce and Jean Baka for the Bioinformatics support.

Conflicts of Interest

The authors declare no conflict of interest.

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Table
Table 1. Antimicrobial sensitivity of non-typhoidal Salmonella isolated at the Yaounde abattoir.
Table 1. Antimicrobial sensitivity of non-typhoidal Salmonella isolated at the Yaounde abattoir.
Sample CodeSalmonella IsolateTetracyclineChloramphenicolStreptomycinAmpicillin
34evWernigerode−−−−
8evPoona++++
35deaWilhelmsburg−−−−
35debWilhelmsburg−−−−
22saPoona−+++
31evaWilhelmsburg−−−−
31evbWilhelmsburg−−−−
32evaWilhelmsburg−−−−
32evbWernigerode−−−−
86evInfantis−−+I
100evWernigerode−−−−
36evWilhelmsburg−−−−
98seWernigerode−−−−
108evKibusi−−−−
20deEnteritidis−−+−
34dePoona++++
60saEnteritidis−−I−
88saInfantis−−−−
88sabInfantis−−−−
133saEnteritidis−−−I
103boWilhelmsburg−−−−
EVJULMbandaka− +−
DEF1Not sequenced−−−−
NB: + indicates resistance to the test antibiotic; I = intermediate isolates; − susceptible isolates; code and serovar of Salmonella strain written in red, represent the multidrug-resistant (MDR) strains.
Table
Table 2. Distribution of resistance genes against specific antibiotic across Salmonella isolates in the study.
Table 2. Distribution of resistance genes against specific antibiotic across Salmonella isolates in the study.
AntibioticsResistance Genes8ev20de22sa31eva31evb32eva32evb34de34ev35dea35deb36ev60sa88sa88sab98se100ev103bo108ev
PoonaEnteritidisPoonaWilhelmsburgWilhelmsburgWilhelmsburgwernigerodePoonaWilhelmsburgWilhelmsburgWilhelmsburgWilhelmsburgEnteritidisInfantisInfantiswernigerodewernigerodeWilhelmsburgKibusi
STRaadA−+−+−−−−−+−−−−−−−−−
aadA1+++−−−−+−−−−+−−−−−−
aadA2+−−+−−−+−−−−−+−−−−−
CHLCat−++−−−+−−−−−−−−−−−+
catI++−−−−−+−−−−−−−−−−−
CatA2&3−−−+−−−−−−−−+−−−−−−
cmlA1,5,6++−−−−−+−−−−−−−−−−−
TEtet(A)+−−−−−−−−−−−−−−−−−−
tet(B), (C)−−++−−++−−−−−+−−−−−
tet(G)+−++−−++−−−−−+−−−−−
tetR++−+++−+++−++++++++
AMPTEM-1++−−−−−−−−−−+−−−−−−
TEM-163++−−−−−+−−−−−+−−−−−
+, detected presence of resistance gene; − , resistance gene not detected; +, false negative; AMP = Ampicillin; CHL = Chloramphenicol; STR = Streptomycin; TE = Tetracycline.
Table
Table 3. Matching antibiotic susceptibility test with genotypic resistance potentials of Salmonella isolates.
Table 3. Matching antibiotic susceptibility test with genotypic resistance potentials of Salmonella isolates.
AntibioticsNumber of Test ResultsSensitivity (%)Specificity (%)Negative PV (%)Positive PV (%)
Resistant Phenotype Sensitive Phenotype
Genotype
(TP)		Genotype
(FP)		Genotype
(FN)		Genotype
(TN)		Odds Ratiop Value
STR503114.00.4662.510078.6100
AMP303135010081.2100
CHL3051162.510068.7100
TE3015116.71006.2100
Key: Sensitivity = TP/(TP + FN) × 100; Specificity = TN/(FP + TN) × 100; PPV = TP/(TP + FP) × 100; NPV = TN/(FN + TN) × 100; TP = True positive; FP = False positive; TN = True negative; FN = False negative; PPV = Positive predictive value; NPV = Negative predictive value.
Table
Table 4. Distribution of multidrug resistance genes among Salmonella serotypes.
Table 4. Distribution of multidrug resistance genes among Salmonella serotypes.
Class of AntibioticResistance Gene8ev20de22sa31eva31evb32eva32evb34de34ev35dea35deb36ev60sa88sa88sab98se100ev103bo108ev
PoonaEnteritidisPoonaWilhelmsburgWilhelmsburgWilhelmsburgWernigerodePoonaWilhelmsburgWilhelmsburgWilhelmsburgWilhelmsburgEnteritidisInfantisInfantisWernigerodeWernigerodeWilhelmsburgKibusi
AminoglycosidesacrD+−−−−−−−−−−−−−−−−−−
bacA+−−−−−−+−−−−−−−−−−−
baeR+−−+−−+++++++−−++++
cpxA+++−−−++++++++++−−−
PhenicolacrB−−−−−−−+−−−−−−−−−−−
golS+++++++++++++++++++
mdsABC+++++++++++++++++++
sdiA+++++++++++++++++++
E.coli soxR&S+++++++++++++++++++
ramR−−−−−−−−−−−−−−−−+−−
TolC+++++++++++++++++++
TetracyclineacrB−−+−−−−−−−−−−−−−−−−
acrD+−−+−−−+−−−−−−−−+−−
golS−−−−−−−+−−−−−−−−−−−
mdfA−−−−−−−+−−−−−−−−−−−
ramA−−−−−−−−−−−−−−−−+−−
sdiA+++++++++++++++++++
E.col soxR&S+−+−−−+++++++++++++
β-lactamacrB−−+−−−−+−−−−−−−−−−−
CMY-9−−−−−−−+−−−−−−−−−−−
CTX-M-14+−−−−−−+−−−−−−−−−−−
DHA-1−−−−−−−+−−−−−−−−−−−
FOX-1+++++++++++++++++++
golS+++−−−++−−−+−−−−−−−
marA−+−++−+−−−−+−−++−++
OXA-1+−+−−−−+−−−−−−−−−−−
OXA-2+−−−−−−+−−−−−−−−−−−
OXA-7−−−−−−−+−−−−−−−−−−−
ramR−−−−−−−−−−−−−−−−+−−
E.coli soxR&S+−−+++++ +++++−++++
FluoroquinoloneacrB−−−−−−−+−−−−−−−−−−−
acrF−−−−−−−−−−−−−−−−−−−
CRP−−−−−−−+++++++−+++−
E.coli soxR&S+++++++++++++++++++
emrB+−−−−−−+−−−−−−−+++
mdtK+++++++++++++++++++
patA−−+−−−−−−−−−−−−−−−−
qnrB1++ −−−−+−−−−+−−−−−−
ramR−−−−−−−+−−−−−−−−−−−
sdiA+++++++++++++++++++
TolC+−+−−−−+−−−−−−−−−−−
SulfaSul1++−−−−−−−−−−+−−−−−−
Sul2+−−−−−−−−−−−−−−−−−−
Macro
lide		TolC+−+−−−−+−−−−−−−−−−−
CRP-7++−−−−−+++++++−+++−
macA++++−+++−−−+−−+−+−−
PMBmgrB++++++++−+−+++−++++
+ = Presence multiple resistance gene; − = no multiple resistance gene detected; Sulfa = Sulfonamide; PMB = Polymyxin B; Genes in purple = multiple resistance genes; genes in red = genes coding resistance against antibiotics high concern by WHO; Genes in black = represent genes that code resistance against not more than one class of antibiotics.
Table
Table 5. Polymorphism in Salmonella serovars with potential resistance to polymyxins.
Table 5. Polymorphism in Salmonella serovars with potential resistance to polymyxins.
Isolate CodePosition of Mutation on pmrA & pmrBNucleotide ChangeAmino Acid Change
8evpmrB
pmrB p.M15TATG → ACTM → T
pmrB p.G73SGGC → AGCG → S
pmrB p.V74IGTA → ATAV → I
pmrA
pmrA p.T89SACC → AGCT → S
22sapmrB
pmrB p.M15TATG → ACTM → T
pmrB p.G73SGC → AGCG → S
pmrB p.A111TGCG → ACGA→ T
pmrA
pmrA p.T89SACC → AGCT → S
31evapmrB
pmrB p.M15TATG→ACTM → T
pmrB p.G73SGGC → AGCG → S
pmrB p.V74IGTA → ATAV →I
pmrB p.A111TGCG →ACGA → T
pmrA p.T89SACC → AGCT → S
32evapmrB
pmrB p.I18LATT→CTTI → L
34depmrB
pmrB p.M15TATG → ACTM → T
pmrB p.G73SGGC → AGCG → S
pmrB p.V74IGTA → ATAV → I
pmrB p.A111TGCG → ACGA → T
pmrB p.L352MCTG → ATGL → M
pmrA
pmrA p.T89SACC → AGCT → S
35deapmrB
pmrB p.V126GGTC → GGCV → G
pmrB p.S127ATCG → GCGS → A
pmrB p.I129LATC → CTCI → L
pmrB p.V133DGTT → GATV → D
pmrB p.L136RTTG → AGGL → R
pmrB p.T139PACG → CCGT → P
Table
Table 6. Prediction of Salmonella isolates as human pathogens.
Table 6. Prediction of Salmonella isolates as human pathogens.
SerovarsPHPathogenProteome Coverage (%)Matched PFNon PF
Poona0.93 a10.14665
Enteritidis0.95 a18.27872
Wilhelmsburg0.94 a15.716913
Wernigerode0.94 a156613
Infantis0.94 a17.457463
Kibusi0.94 a187783
Key: PHPathogen = predicted as human pathogen; PF = pathogenic family; NPF = Non pathogenic family. Values with the same superscript letters are not significantly different (p > 0.05).
Table
Table 7. Virulence factors among Salmonella serovars.
Table 7. Virulence factors among Salmonella serovars.
Salmonella Pathogenicity Island (SPI)Function Category8ev20de22sa31eva31evb32eva32evb34de34ev35dea35deb36ev60sa88sa88sab98se100ev103bo108ev
Gene LocusPoonaEnteritidisPoonaWilhelmsburgWilhelmsburgWilhelmsburgWernigerodePoonaWilhelmsburgWilhelmsburgWilhelmsburgWilhelmsburgEnteritidisInfantisInfantisWernigerodeWernigerodeWilhelmsburgKibusi
SPI-15−−−−−−−−−+−+−−−+−−−
SPI-214−−−−−−−+−−++++++−−−
SPI-315−−−+−+−−−++−−+++−−−
SPI-316−−−++−+−+−−+++−−+−−
SPI-315−−−−−−−−−+−−+−−−−−−
SPI-417−−−−−−−−−−++−−+−−−−
SPI-518−+−−−−−−−+−−++−−−+−
SPI-821−−−++++−+−++−−−−−−−
SPI-922−−−−−−−−−−−−++−−−−−
SPI-139+++−−−−+−−−−++−−−++
SPI-1310+++−−−−+−−−−++−−−++
SPI-1311+++−−−−+−−−−++−−−++
SPI-1412+++−−−−+−−−−++−−−++
SPI-1413+−+−−−−+−+−−++−−−++
C63PI1++−++++++++++++++++
+ = detected presence of virulence factor; − = Virulence factor not detected.
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Matchawe, C.; Machuka, E.M.; Kyallo, M.; Bonny, P.; Nkeunen, G.; Njaci, I.; Esemu, S.N.; Githae, D.; Juma, J.; Nfor, B.M.; et al. Detection of Antimicrobial Resistance, Pathogenicity, and Virulence Potentials of Non-Typhoidal Salmonella Isolates at the Yaounde Abattoir Using Whole-Genome Sequencing Technique. Pathogens 2022, 11, 502. https://doi.org/10.3390/pathogens11050502

AMA Style

Matchawe C, Machuka EM, Kyallo M, Bonny P, Nkeunen G, Njaci I, Esemu SN, Githae D, Juma J, Nfor BM, et al. Detection of Antimicrobial Resistance, Pathogenicity, and Virulence Potentials of Non-Typhoidal Salmonella Isolates at the Yaounde Abattoir Using Whole-Genome Sequencing Technique. Pathogens. 2022; 11(5):502. https://doi.org/10.3390/pathogens11050502

Chicago/Turabian Style

Matchawe, Chelea, Eunice M. Machuka, Martina Kyallo, Patrice Bonny, Gerard Nkeunen, Isaac Njaci, Seraphine Nkie Esemu, Dedan Githae, John Juma, Bawe M. Nfor, and et al. 2022. "Detection of Antimicrobial Resistance, Pathogenicity, and Virulence Potentials of Non-Typhoidal Salmonella Isolates at the Yaounde Abattoir Using Whole-Genome Sequencing Technique" Pathogens 11, no. 5: 502. https://doi.org/10.3390/pathogens11050502

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