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Antimicrobial-Resistant Listeria monocytogenes in Ready-to-Eat Foods: Implications for Food Safety and Risk Assessment

Adeoye John Kayode
1,2,* and
Anthony Ifeanyi Okoh
Applied and Environmental Microbiology Research Group (AEMREG), Department of Biochemistry and Microbiology, University of Fort Hare, Private Bag X1314, Alice 5700, South Africa
SAMRC Microbial Water Quality Monitoring Center, University of Fort Hare, Private Bag X1314, Alice 5700, South Africa
Department of Environmental Health Sciences, College of Medical and Health Sciences, University of Sharjah, Sharjah P.O. Box 27272, United Arab Emirates
Author to whom correspondence should be addressed.
Foods 2023, 12(6), 1346;
Submission received: 8 February 2023 / Revised: 16 March 2023 / Accepted: 17 March 2023 / Published: 22 March 2023
(This article belongs to the Section Food Quality and Safety)


Antimicrobial resistance is an existential threat to the health sector, with far-reaching consequences in managing microbial infections. In this study, one hundred and ninety-four Listeria monocytogenes isolates were profiled for susceptibility using disc diffusion techniques. Possible foodborne listeriosis risk associated with ready-to-eat (RTE) foods (RTEF) and the risk of empirical treatment (EMPT) of L. monocytogenes infections, using multiple antimicrobial resistance indices (MARI) and antimicrobial resistance indices (ARI), respectively, were investigated. Twelve European Committee on Antimicrobial Susceptibility Testing (EUCAST) prescribed/recommended antimicrobials (EPAS) for the treatment of listeriosis and ten non-prescribed antimicrobials (non-PAS)] were evaluated. Antimicrobial resistance > 50% against PAs including sulfamethoxazole (61.86%), trimethoprim (56.19%), amoxicillin (42.27%), penicillin (41.24%), and erythromycin (40.21%) was observed. Resistance > 50% against non-PAS, including oxytetracycline (60.89%), cefotetan (59.28%), ceftriaxone (53.09%), and streptomycin (40.21%) was also observed. About 55.67% and 65.46% of the isolates had MARI scores ranging from 0.25–0.92 and 0.30–0.70 for EPAs and non-PAs, respectively. There was a significant difference (p < 0.01) between the MARI scores of the isolates for EPAs and non-PAs (means of 0.27 ± 0.21 and 0.31 ± 0.14, respectively). MARI/ARI scores above the Krumperman permissible threshold (>0.2) suggested a high risk/level of antimicrobial-resistant L. monocytogenes. The MARI risks of the non-success of empirical treatment (EMPT) attributed to EPAs and non-PAs were generally high (55.67% and 65.463%, respectively) due to the antimicrobial resistance of the isolates. MARI-based estimated success and non-success of EMPT if EUCAST-prescribed antimicrobials were administered for the treatment of listeriosis were 44.329% and 55.67%, respectively. The EMPT if non-prescribed antimicrobials were administered for the treatment of listeriosis was 34.53% and 65.46%, respectively. This indicates a potentially high risk with PAs and non-PAs for the treatment of L. monocytogenes infection. Furthermore, ARI scores ≤ 0.2 for EPAs were observed in polony, potato chips, muffins, and assorted sandwiches, whereas ARI scores for non-PAs were >0.2 across all the RTE food types. The ARI-based estimate identified potential risks associated with some RTE foods, including fried fish, red Vienna sausage, Russian sausage, fruit salad, bread, meat pies, fried chicken, cupcakes, and vetkoek. This investigation identified a high risk of EMPT due to the presence of antimicrobial-resistant L. monocytogenes in RTE foods, which could result in severe health consequences.

1. Introduction

Antimicrobials are substances that kill or inhibit microbial growth and could be synthetic, semi-synthetic, or derived originally from natural sources. They are used specifically to prevent or treat bacterial infections. Antimicrobial discovery is a breakthrough in modern medicine. However, this success might be obliterated by the exponential increase of antimicrobial resistance among pathogenic microorganisms with a far-reaching impact on human health [1,2,3]. Antimicrobial resistance impairs the capacity of the human immune system to fight infections. It contributes to complications in at-risk patients having chronic health conditions like arthritis, asthma, rheumatoid, and diabetes. Patients undergoing chemotherapy, dialysis, joint replacement, and surgery are also vulnerable. Furthermore, the emergence of new superbugs, treatment failures, high mortality, and morbidity rates are major effects of antimicrobial resistance. It has also been established that antimicrobial-resistant pathogens will increase the probability of the occurrence of a serious health issue twofold and triple the chances of death compared to the non-resistant ones [4,5] if not accorded adequate attention.
Apart from the health impact highlighted earlier, antimicrobial resistance has serious economic consequences. For instance, the financial implication of antimicrobial resistance is extremely high due to an increase in drug usage and hospital admissions, and this differs in each country. The Centre for Disease Control (CDC) estimated a yearly cost of 20 billion USD in the United States for healthcare and 35 billion USD in productivity loss [4,5]. In 2015, Alessandro and colleagues estimated the cases of infection with some antimicrobial-resistant bacteria (AMB) in the EU to be 671,689 [6]. The estimated burden directly associated with drug-resistant infections globally was 1.27 million deaths in 2019 [7]. Furthermore, de Kraker et al. estimated that death due to the challenge of antimicrobial resistance will exceed 10 million and a cumulative cost of 1 trillion USD to global productivity per year by 2050 if the global response to antibiotic resistance is not put on global alert [8,9]. Hence, the WHO has declared antimicrobial resistance a global problem [10]. In the food sector, antimicrobial resistance has potentially led to greater food safety concerns, a reduction in food production, economic losses to farm households, and reduced food security.
Antimicrobial misuse has a significant impact on resistance selection in bacteria and the emergence of resistance is most pronounced within the hospital environmental ecological niches and the community. In a survey of 155 students, Davey et al. observed that a reduction in excessive antimicrobial prescriptions is associated with a decline in Clostridium difficile colonization and infections or infection with cephalosporin, aminoglycoside-resistant Gram-negative bacteria, vancomycin-resistant Enterococcus faecalis, and methicillin-resistant Staphylococcus aureus (MRSA). Interventions targeted at increasing effective prescription also improved clinical outcomes [11,12]; as such, there is a close connection between antimicrobial misuse/abuse and resistance development. The excessive use of antimicrobials in aquaculture, food animal production, and crop culture has contributed immensely to the challenge of antimicrobial resistance. Antimicrobials are administered not only for prophylactics and metaphylaxis but also as growth promoters to boost food animal production for the teaming human population [1,13].
In the food chain, antimicrobial-resistant bacteria represent a potential global public health threat. This is because the ecosystem of the food production chain is ecologically composed of various niches, where there is a co-existence of numerous bacteria, and many antimicrobials are used. Vegetables, food animals, and fishes are known to be reservoirs of antimicrobial-resistant bacteria. Along the food chain, humans encounter resistant bacteria when contaminated foods or food products (e.g., meat, eggs, dairy products) are consumed or through direct contact with infected animals or biological fluids from animals.
There have been reports describing the presence of large numbers of AMB and ARGs in various foods like bulk milk, ready-to-eat foods, and cooked meats at different stages of production [1,14,15,16]. Reports of clonally related AMB (including L. monocytogenes) and ARGs from foods have also been identified in human populations with no history of occupational exposures. These provide evidence of transmission of AMB due to food consumption or handling [17,18]. Given the health consequence associated with AMB in foods, our present study evaluated the antimicrobial susceptibility of L. monocytogenes from RTE foods. Based on the result obtained, the associated risk (listeriosis) with RTE foods (RTEF) and the risk of empirical treatment (EMPT) of L. monocytogenes infections when prescribed and non-prescribed antimicrobials are used for the management of listeriosis were evaluated. This was achieved using the data generated from multiple antimicrobial resistance indices (MARI) and antimicrobial resistance indices (ARI), respectively. This report provided a background to the antibiotic sensitivity of Listeria monocytogenes from RTE foods in the Eastern Cape Province, South Africa (ECPSA), as an indicator for the evaluation of RTEF and EMPT.

2. Methodology

2.1. Study Area

This study was carried out to estimate the possible risks associated with antimicrobial-resistant L. monocytogenes from RTE foods and the risk of empirical treatment at three Municipality Districts (Sarah Baartman, Chris Hani, and Amathole) of the Eastern Cape Province, South Africa. These municipalities occupy 33.57° S, 25.36° E, 31.8743° S, 26.7968° E, and 32.5842° S, 27.3616° E of the geographical coordinates on the map, respectively.

2.1.1. Sample Collection

Two hundred and thirty-nine (239) ready-to-eat food samples of thirteen different food types popularly consumed by many South Africans were randomly selected at the sampling points. The food samples included polony (soft-textured sausage made of beef and pork enclosed in a hued red or orange skin), Russian sausage or kolbasa (made from ground meat—poultry or pork, beef, along with spices, flavorings, and salt) wrapped in a special casing, fruit salad (blends of fruits in a sweet sauce made from juice and honey), potato chips, Vienna sausage (soft, meaty, red-skinned, fine textured sausage produced from mechanically deboned chicken, pork, spices, salt, vegetable protein and other ingredients), fried fish, vetkoek/fat cake/amagwinya (South African fried dough that is fluffy inside and crispy outside and often stuffed with savory or sweet fillings), meat pie, bread, fried chicken, assorted sandwiches (made from lemon rind, cream cheese, bread slices, lettuce, sausages, and tomato) muffins and cupcakes were obtained in supermarkets/grocery stores at different points in towns and cities within the municipalities chosen for this study. The samples were collected between February and September 2019. Samples were collected aseptically from the sampling points and wrapped in labeled sterile plastic bags to avoid cross-contamination. They were within 6 h conveyed in iced insulated boxes to the laboratory for analysis.

2.1.2. Enumeration of Presumptive Listeria in RTE Food Samples

Presumptive aerobic plate count was carried out according to the standard methods of the International Organization for Standardization (EN ISO 11290-2:2017. Twenty-five grams of each sample was aseptically stomached in 225 mL of Buffered Peptone Water (BPW, Oxoid Ltd., Basingstoke, Hampshire, UK). The samples were serially diluted in three replicates of tenfold dilutions and 0.5 mL of the dilutions were plated on Listeria Chromogenic agar, as previously described [19].

2.2. Detection of L. monocytogenes

L. monocytogenes detection in ready-to-eat food samples was undertaken employing the guidelines of EN ISO 11290-1:2017. A 25 g of each aseptically stomached sample was pre-enriched and plated on selective media using the procedure previously described [19]. Blue colonies surrounded by halos were subcultured on Tryptone Soy Agar to purify the isolates. The purified cultures of the presumptive isolates were preserved at –80 °C in Tryptone Soy Broth with 25% glycerol.

2.2.1. DNA Extraction

Genomic DNA isolation was carried out by the boiling method before the molecular confirmation of the isolates described previously [20,21]. Presumptive isolates previously preserved in 25% glycerol stock were grown in 5 mL Tryptone Soy Broth (CM0129 Oxoid Ltd., Basingstoke, Hampshire, UK) at 37 °C for 18 h to resuscitate the isolates. The broths were transferred into 2 mL Eppendorf tubes and centrifuged at 16,000 rpm for 5 min using a mini-spin micro-centrifuge. The supernatants were removed after centrifugation, the pellets were washed in normal saline, and 300 μL nuclease-free water was added to the pellet and allowed to boil in a heating block (TECHNE Digital Dri-Block DB-3D, London, UK) at 100 °C for 10 min. After boiling, the samples were removed from the heating block and left to cool for 10 min on ice. The samples were centrifuged at 16,000 rpm for 5 min to remove the cell debris. The DNA template (supernatant) was transferred into a clean Eppendorf tube and kept at 20 °C for further analysis.

2.2.2. Molecular Characterization of L. monocytogenes Isolates

Presumptive isolates were screened for the Listeria genus using the 370 base pairs (370 bp) section of the 16S rRNA prs gene. Amplification of this segment was made using the primer sets F-GCTGAAGAGATTGCGAAAGAAG and R-CAAAGAAACCTTGGATTTGCGG as described previously [22]. Polymerase chain reaction (PCR) was carried out in a thermal cycler (BIO-RAD T100) using the cycling condition 94 °C: 5 min; 33 cycles (94 °C: 45 s; 56 °C: 30 s, 72 °C: 1 min, 72 °C: 5 min). The iap (invasion-associated protein) gene targeting L. monocytogenes at 131 base pair (bp) using primer set: F-ACAAGCTGCACCTGTTGCAG and R-TGACAGCGTGTGTAGTAGCA was amplified by PCR using the above-mentioned thermal cycler. All PCR reactions were prepared in a final volume (25 µL) containing 12.5 µL master mix (One taq Quick Load 2 × Master mix; BioLabs Inc., Hitchin, UK), 1 µL of prs/iap primers, 0.5 µL buffer, and MgCl2, and 6.5 µL of sterile nuclease-free water. The PCR cycling condition [94 °C: 5 min; 35 cycles (94 °C: 35 s; 52 °C: 30 s, 72 °C: 1 min; 72 °C: 10 min)] was optimized using positive controls to validate the procedure. The gel electrophoresis system (ADVANCE Mupid™-One, Takara, Japan) was used to separate the PCR products in agarose and detected 131 bp with Alliance 4.7 UV trans-illuminator (Alliance XD-79.WL/26MX, Paris, France). Referenced strains of L. monocytogenes (ATCC 19118 and ATCC 7644) were used as positive controls, and nuclease-free water was used as the negative control. The PCR products were sequenced using a Sanger sequencer to further verify the isolates’ identity. Some of the sequences (OL694843, OL694844, OL694845) were submitted to NCBI GenBank.

2.3. Antimicrobial Susceptibility Testing (AST)

Testing for the antimicrobial susceptibility of L. monocytogenes isolates adopted the Kirby Bauer disc diffusion method in conformity with the standard procedure described by the European Committee on Antimicrobial Susceptibility Testing (EUCAST) [23]. The test organisms were tested for susceptibility against 22 different antimicrobials (Table S1a) belonging to the β-lactams, aminoglycosides, carbapenems, cephalosporin, glycopeptides, macrolides, fluoroquinolones, sulfonamides, tetracyclines, phenicol, phosphonic acid derivative, and colistin sulphate. The antimicrobials were classified into two distinct groups [12 EUCAST-prescribed antimicrobials (EPAs) for the treatment of listeriosis infections and 10 first-line antimicrobials for the empirical treatment of infections caused by pathogenic microorganisms (non-prescribed antibiotics for treatment of microbial infections)]. The referenced strains of L. monocytogenes (ATCC 7644 and ATCC 19118) served as the positive control. The isolates’ susceptibility was categorized as susceptible (S), resistant (R) or intermediate (I) to each of the antimicrobials, in line with the result obtained from the susceptibility testing using standard reference documents (Table S1a) [23].

2.3.1. Computation of Resistance Quotient (RQs) of L. monocytogenes Isolates

The frequency of antimicrobial resistance phenotypes of the RTE food isolates was calculated for each antimicrobial across all the RTE food samples [24].
Resistant   quotient = No .   of   antimicrobial   resistant   isolates   from   a   particular   food   sample   Total   no .   of   isolates   from   the   sample × 100  

2.3.2. Antimicrobial Resistance Phenotyping, Multiple Antimicrobial Resistance Indexing of Isolates and Risk Evaluation

Twelve EUCAST-prescribed antimicrobials (EPAs) for the treatment of listeriosis infections and ten first-line antimicrobials for the empirical treatment of microbial infections (non-prescribed antibiotics for treatment of listeriosis, non-PAs) were investigated for Multiple Antimicrobial Resistance Indices (MARI) and Antimicrobial Resistance Index (ARI) scores. The Multiple Antimicrobial Resistance Phenotypes (MARPs) of L. monocytogenes in respect of EPAs and non-PAs were computed for each of the isolates that showed resistance against three or more antimicrobials and indexed for MARI values [25]. The MARI scores were calculated as follows:
MARI   index = no .   of   antibiotics   to   which   isolate   was   resistant no .   of   antibiotics   to   which   isolate   was   exposed
The MARI of isolates above the Krumperman threshold (>0.2) indicated exposure of the isolates to high antimicrobial selection pressure in the region.
In addition, the Antimicrobial Resistance Index (ARI) was computed for each of the RTE food samples as described by [25]. Thus, ARI for PAs and non-PAs was computed.
ARI = a b
where a = aggregate antibiotic resistance score of all isolates from a sample; b = number of antimicrobial resistance score of all isolates from a sample
ARI > 0.2 suggested a high level of antimicrobial-resistant L. monocytogenes associated with a particular RTE food.
The frequency of resistance, the number of antimicrobials against which the isolates are resistant, and the pattern of multiple antimicrobial resistance were described.
The possible risks associated with RTE food (RTEF) and the risk of empirical treatment (EMPT) were investigated by comparing the MARI and ARI scores of the EPAs and non-Pas, respectively, using the statistical model described in a previous report [24]. The risks were computed and interpreted based on the outlined assumptions construed around the arbitrary Krumperman value [25]:
The MARI (risk) with EPAS for treatment of L. monocytogenes infection is often lower (MARIEPAs ≤ 0.2, when PAs are used for treatment) as against when non-PAs are used for the treatment of infections (MARInon-PAs > 0.2). The arbitrary MARI or ARI threshold (0.2) was adopted to identify/differentiate high and low risks [25].
When isolates are susceptible to EPAs for therapy, EMPT = 0; if otherwise, EMPT > 0. The EMPT of listeriosis infection is illustrated thus: MARIEMPT = MARIEPAs + MARInon-PAs (Where either of MARIEPAs + MARInon-PAs = 0, depending on the selected group of antimicrobials for empirical treatment).
The ARI for each RTE food sample is ≤0.2 when EPAs are assessed, only if there is no antimicrobial resistance selection pressure (ARIEPAs ≤ 0.2), whereas ARInon-PAs > 0.2 (when non-PAs are assessed) in any event of the presence or absence of antimicrobial resistance selection pressure. Consequently, the RTEF of a particular RTE food based on the profiling of antimicrobial resistance is defined as ARIRTEF = ARIEPAs + ARInon-PAs (where either ARIEPAs + ARInon-PAs = 0, depending on the group selected for AST).

2.4. Data Analysis

The data obtained were processed for descriptive analysis. The resistance quotients (RQs) across all the RTE foods were computed using Microsoft Excel Sheet, Microsoft Corporation 365, Bellevue, WA, USA. (Retrieved from, accessed on 28 October 2021). Hierarchical clustering of the antibiotic susceptibility test results was achieved by K-mean and visualized by “ComplexHeatmap” OriginPro 2023 (version 10.0) statistical software, Northampton, MA, USA. The differences between MARI and ARI of the two groups of antimicrobials were subjected to Wilcoxon signed-rank test for comparison. Values were considered statistically significant at p < 0.01 and p < 0.05.

3. Results

3.1. Occurrence of L. monocytogenes in RTE Foods

The presumptive counts observed from the lowest to the highest ranged between 1.0 × 103–2.7 × 106 CFU/g. Higher presumptive counts were recorded from meat pie, fried fish, sliced polony, cupcakes, Russian sausages, bread, and potato chips. This indicates a higher risk of L. monocytogenes in these foods compared with other ones in this study. RTE foods that had 0 CFU/g and less than 10 CFU/g presumptive counts were more in number compared with those with 10–100 and >100 CFU/g (Table 1). One hundred and ninety-four L. monocytogenes isolates were detected in the ready-to-eat food samples as follows: 20 from polony; 23 from sliced polony; 30 from fruit salad; 16 from chips; 21 from fried fish; 4 from Vienna sausages; 14 from Russian sausages; 11 from bread; 2 from fried chicken; 22 from meat pie; 10 from cupcakes; 12 from muffins, and 9 from assorted sandwiches Table 1. Figure 1 shows the gel electrophoresis image of confirmed L. monocytogenes.

3.2. Antibiotic Susceptibility and Cluster Analysis of L. monocytogenes Isolates

L. monocytogenes (194) isolates were profiled for susceptibility to 22 antimicrobials (12 EPAS and 10 non-PAS). Figures S1–S3 described the phenotypic antimicrobial pattern of susceptibility of each of the L. monocytogenes isolates to both EPAS and non-PAS. This pattern observed reflects the resistance attributes as it revealed the efficacy of the antimicrobials towards each isolate. Susceptibility to EPAs (>50%) was observed and ranged from 57.73 (amoxicillin) to 94.85 (ampicillin-sulbactam), except for erythromycin (27.32) and sulfamethoxazole (34.02%), that had low susceptibility rates. Susceptibility > 50% to non-PAS ranged from 59.28% (vancomycin) to 81.96% (fosfomycin) except for cefotetan, ceftriaxone, and oxytetracycline with 31.44, 29.90, and 29.38%, respectively. However, resistance > 50% against EPAs, including sulfamethoxazole (61.86%), trimethoprim (56.19%), amoxicillin (42.27%), penicillin (41.24%), and erythromycin (40.21%) were observed. Furthermore, resistance > 50% against non-PAS, including oxytetracycline (60.89%), cefotetan (59.28%), ceftriaxone (53.09%), and streptomycin (40.21%) were observed (Table S1b).

3.2.1. Prevalence of Antimicrobial-Resistant L. monocytogenes and Computation of Resistance Quotient (RQs) of Isolates

The distribution of antimicrobial-resistant L. monocytogenes across RTE food samples is provided in Table 2. Phenotypic resistance of L. monocytogenes against various antibiotics in each RTE food sample ranged between 1 to 22. Table 2 describes the RQs of the isolates to various antimicrobials ranging from 3.33 to 100%. Higher RQs of antimicrobial against EPAs, including penicillin, amoxicillin, ertapenem, trimethoprim, sulfamethoxazole, and non-PAs, including streptomycin, ceftriaxone, cefotetan, oxytetracycline were observed in RTE foods such as red Vienna, cupcake, Russian sausage, fruit salad, bread, fried fish, fried chicken, potato chips, pies, and muffins. Lower RQs were recorded for EPAs, including ampicillin, ampicillin–sulbactam, doripenem, imipenem, clarithromycin, and trimethoprim–sulfamethoxazole. Lower RQs were also observed for non-PAs including gentamicin, amikacin, ciprofloxacin, chloramphenicol, and fosfomycin across the RTE food samples. A significant (p < 0.01) relationship in the distribution of phenotypically resistant L. monocytogenes isolates and all RTE foods was observed.

3.2.2. Multiple Antimicrobial Resistance Phenotypes and Index (MARPs and MARI) of L. monocytogenes

The MARPs patterns and MARI are provided in Table 3. Ready-to-eat food L. monocytogenes displayed 65 patterns of MARPs for EPAs ranging from 3 to 11 antimicrobials, while 63 MARPs patterns were observed for non-PAs ranging from 3 to 7 antimicrobials. The P/AML/W/RL (n = 12) MARP occurred most for PAs, while the MARPs that occurred once were the most predominant. The CRO/CCT/OT phenotype (n = 21) occurred most among the MAR phenotypes observed. For non-PAs, the MARPs that occurred once were the most predominant. Twenty-seven (n = 27, 13.92%) of the isolates were not resistant against any of the EPAs, 34 (17.53%) showed resistance against one of the EPAs, 24 (12.37%) showed resistance against at least 2 of the EPAs. In comparison, 108 (55.67%) exhibited multiple resistance phenotypes against EPAs. Also, 24 (12.37%) of the isolates were resistant against one non-PAs, 41 (21.13%) showed resistance against two non-PAs, and 127 (65.46%) showed multiple antibiotic resistance against non-PAs. The phenotypic resistance patterns against both EPAs and non-PAs and the prevalence of L. monocytogenes in each RTE food are provided in Table 3.

3.3. Evaluation of the RTEF and the EMPT Entrenched on the MAR and ARI of L. monocytogenes Isolates

EMPT from the Comparison of MARI of L. monocytogenes

The comparative assessment of MARI of EPAs and non-PAs antimicrobials is presented in Table 3. The difference between the MARI scores of the isolates for EPAs and non-PAs with a means of 0.27 ± 0.21 (median = 0.33, mode = 0.80) and 0.31 ± 0.14 (mean = 0.30, mode = 0.30), respectively, were significant (p < 0.01). Table S2 revealed that 86 (44.329%) and 108 (55.67%) of L. monocytogenes isolates had MARI of 0–0.17 and 0.25–0.92 for EPAs, respectively. In addition, 67 (34.53%) and 127 (65.463%) of the isolates had MARI ranging between 0–0.2 and 0.3–70 for non-Pas, respectively. The MARI of the RTE food isolates for EPAs ranged between 0.25 and 0.92 for EPAs and non-PAs, ranging between 0.30 and 0.70, and are greater than the permissible (0.2) benchmark. This indicates high-risk contamination of the RTE foods. Notably, the responses of the isolates to EPAs and non-PAs varied considerably. While some isolates had zero MARI scores for EPAs, they had MARI > 0.2 for non-PAs.
In summary, the success of EMPT (MARI ≤ 0.2) and non-success of EMPT (MARI > 0.2) of L. monocytogenes infections due to RTE food L. monocytogenes isolates for EPAs represent 44.329 and 55.67%, respectively. In like manner, the success of EMPT (MARI ≤ 0.2) and non-success of EMPT (MARI > 0.2) of L. monocytogenes infections due to RTE food isolates for non-PAs represent 34.53 and 65.463%, respectively. In any case, risk varied with individual antibiotics and isolates.

3.4. RTEF from the Comparison of MARI of L. monocytogenes Isolates

RTEF from the Comparison of ARI across the Ready-to-Eat Foods

The comparative ARI scores across all ready-to-eat foods tested are provided in Figure 2. The differences in ARI across the ready-to-eat foods for EPAs (ARI average 3.58) and non-PAs (ARI average 4.02) were not significant (p > 0.01). The ARI scores ≤ 0.2 for EPAs were observed in polony, potato chips, muffins, and assorted sandwiches, whereas ARI scores for non-PAs were >0.2 across all the RTE food types except red Vienna (ARI = 0.2). The ARI-based estimation identified potential risks associated with some RTE foods such as Russian sausage, fruit salad, fried fish, red Vienna sausage, bread, meat pies, vetkoek, fried chicken, and cupcakes.

4. Discussion

This study evaluated the antimicrobial susceptibility of L. monocytogenes against antimicrobial agents currently in use for managing listeriosis and the potential possible risks of antimicrobial resistance. Useful information/insight from the data obtained could guide relevant authorities in decision-making and preparedness to mitigate public health emergencies. The detection of L. monocytogenes in the RTE food samples we analyzed could suggest unhygienic practices/exposure to contamination from humans/surfaces of processing facilities and cross-contamination after processing; this is also attributable to the resilience of L. monocytogenes contributing to the high prevalence observed in RTE foods.
Foodborne infection (listeriosis) caused by L. monocytogenes is often acquired when foods contaminated with the vegetative cells of the pathogen are consumed. It is one of the major infections affecting food safety, causing human illness worldwide with significant public health and economic impact [26]. In this study, the aerobic plate count revealed that <100 CFU/g was observed in 96.77% of the food samples processed. This conforms with the 100 CFU/g permissible limit of L. monocytogenes in foods in the EU. However, 55.23% met the zero-tolerance adopted in the USA, whereas 44.77% of the foods failed the zero-tolerance permissible limit. The actual infective dose of L. monocytogenes widely accepted is not yet documented. Nonetheless, a previous European Food Safety Authority study stated that over 90% of listeriosis is attributable to ingesting foods with more than 2000 CFU/g, and 33% is attributable to the proliferation of L. monocytogenes in foods at the storage or consumption phase [27]. Foods including meat pie, fried fish, sliced polony, cupcakes, Russian sausages, bread, and potato chips had higher counts compared with others. This indicates a higher risk of L. monocytogenes in the RTE foods considering that L. monocytogenes has the capacity to proliferate in foods in storage, even at refrigeration temperatures. The detection of L. monocytogenes in RTE foods in our study agrees with the reports from previous studies on processed foods, including fish products, meats, and delicatessens in Poland [28]; pate, cheese, shellfish, and sausages in Chile [29].
According to the WHO, foodborne infection attributed to L. monocytogenes is caused by multi-drug resistant strains [30]. This is because antimicrobial-resistant pathogens have different strategies they employ to defeat the efficacy of antimicrobial drugs, including reduced protein synthesis, resistance to the inhibition of nucleotide synthesis, cell membrane disruption, transport-based mechanisms by protecting the ribosomal binding site of tetracycline via RNA binding proteins and fluoroquinolones resistance due to topoisomerase IV genes and DNA gyrase mutations [30,31]. Several reports revealing the emergence of antimicrobial-resistant foodborne pathogens, especially L. monocytogenes in the food chain, have been published [32,33,34,35]. The existence of antimicrobial-resistant bacteria in food may lead to difficulty treating foodborne infections in humans. Their presence in food can also facilitate the transfer of resistant genes to other microorganisms through the food chain [36,37,38]. In our study, the phenotypic resistance observed against streptomycin, sulfamethoxazole, trimethoprim, cefotetan, oxytetracyclines vancomycin, and ceftriaxone was similar to previous reports on antibiotic resistance of L. monocytogenes from food tested against several antimicrobials [34,35,39,40]. The high level of resistance observed against these antimicrobials could suggest a gradual decline in their efficacy for the treatment of listeriosis [39] due to drug misuse or the residual impact of antimicrobials in the environment. This could also be attributed to the resistance acquired during adaptation to environmental stresses like heat, desiccation, and biological stress due to microbial antagonism that could induce cross-protection responses, giving rise to cells with increased resistance. Furthermore, the development of resistance to stress, such as oxidants, irradiation, and elevated pressure in food production processes, could also occur [41,42]. A previous study reported that exposure to cold, salt stress, and pH increased L. monocytogenes resistance against different antimicrobials [43]. Notably, the high resistance observed against certain non-prescribed antimicrobials (non-PAs) could describe the intrinsic resistance of L. monocytogenes against such antimicrobials. The RQs values recorded for certain antimicrobials across the RTE food matrixes could likely indicate the pressure of antimicrobial selection in the region.
The comparative assessment of MARI of EUCAST prescribed antimicrobial and non-prescribed antimicrobial suggests the underlying risk which may be involved in the treatment of listeriosis cases with the antimicrobial groups. In essence, the estimated success of empirical treatment (EMPT) of listeriosis infection in our study indicated that when treatment of foodborne listeriosis is required in this study area, clinicians that select the prescribed antimicrobials for EMPT, after diagnosis was properly made have a 44.329% chance of preventing fatal treatment outcomes and 55.67% non-success compared with 34.53% successful and 65.463% non-successful chance of preventing fatal outcomes for selecting non-PAs for empirical treatment. Although, treatment of listeriosis is challenging, primarily because the highest percent of affected patients is usually immunocompromised due to comorbidity or immune impairment related to aging or weak immunity as in the case of infants. In this regard, empirical treatment using a drug active against L. monocytogenes was advised because they are usually found to be associated with reduced mortality [44]. Late or incorrect diagnoses and wrongly prescribed antimicrobials for treatment could also lead to a high mortality rate [44,45]. There have been non-consistent reports of success and non-success of empirical treatment of listeriosis infection. Bateman et al. reported the success of empirical treatment of listeriosis patients treated with amoxicillin, ceftriaxone, ampicillin, acyclovir, rifampicin, isoniazid, dexamethasone, methylprednisolone, trimethoprim/sulfamethoxazole, and metronidazole [46]. However, reports of non-success of empirical treatment involving antimicrobials, including telithromycin, moxifloxacin, methylprednisolone, acyclovir, ceftriaxone, dexamethasone, ampicillin, gentamycin, chloramphenicol, and trimethoprim/sulfamethoxazole were documented [47,48]. Furthermore, a combination of azithromycin and ceftriaxone, imipenem, and ampicillin, cotrimoxazole, and ampicillin for empirical treatment of listeriosis infection was unsuccessful in South Africa [47]. Some of the antibiotics employed in treatment here were not prescribed by the CLSI/EUCAST [23,49]. However, an ideal active antimicrobial against L. monocytogenes must be able to penetrate the host cell and bind to intracellular target tightly” [50,51,52]. Some scholars attempted to investigate the suitability of certain antimicrobials for alleviating listeriosis infection, but they all arrived at divergent opinions. Among active antimicrobials against L. monocytogenes, ampicillin, amoxicillin, and penicillin are the most commonly used and supported by expert guidelines/opinions [53]. Some scholars also put forward that a synergistic combination of aminopenicillin (ampicillin and amoxicillin) and gentamicin as the reference treatment of L. monocytogenes infection would be appropriate to alleviate listeriosis [51,54]. A previous randomized study described by de Gans found that dexamethasone did not worsen outcomes when administered in patients with non-pneumococcal meningitis [50]. However, another study revealed a harmful effect of dexamethasone adjunct in neurolisteriosis in a subset of patients and advocated the use of cotrimoxazole, gentamicin, and beta-lactam over other antimicrobials [45]. Furthermore, the strength and limitations of the therapeutic use of cotrimoxazole [45,55,56], quinolones [57], levofloxacin [58,59], linezolid [60,61], meropenem [62,63,64], rifampin [65], vancomycin [66] were documented. The challenge of non-consistent outcomes as regards successes and non-successes of antimicrobials used for empirical treatment could be a result of a lack of adequate clinical trials and non-existing evidence-based medical management of listeriosis cases [45]. Although, this scenario is also dependent on the high rate of resistance acquisition/ARGs among L. monocytogenes, which largely influence the efficacy of antibiotics against the pathogen.
ARI observed was not a good indicator for the evaluation of EMPT for both EPAs and non-PAs, as the group means were >0.2. ARI for EPAs was <0.2 in RTE foods including potato chips, polony, assorted sandwiches, and muffins, while ARI for non-PAs (ARI = 0.2) was observed for red Vienna. Higher ARI in other RTE foods could be an indication of antimicrobial selection pressures and high-risk contamination of foods within the study area.
Of note, the approach of comparing MARI/ARI between EPAs and non-PAs against L. monocytogenes calls for caution when the Krumperman’s threshold (0.2) is applied [25] to establish low/high-risk contamination of L. monocytogenes in RTE foods. Most importantly, the MAR/AR Indices of intrinsically resistant microbes against certain antimicrobials are usually ≥0.2. Most isolates that have MARI < 0.1 to EPAs in this study were observed with a value ≥ 0.2 for non-PAs (Table S4).
Summarily, this study revealed the antimicrobial resistance profiles and the potential health risks due to antimicrobial resistance. High resistance (>50%) against amoxicillin, penicillin, ertapenem, erythromycin, sulfamethoxazole, cefotetan, ceftriaxone, trimethoprim, streptomycin, oxytetracyclines, and vancomycin was observed. The resistance against antimicrobials among L. monocytogenes indicates the possible health risk that could arise from the consumption of such foods, especially among immunocompromised persons. MARI evaluation disclosed a high risk of EMPT of listeriosis in EPAs and non-PAs. The chance of successful EMPT is generally low but a little higher for PAs. ARI based on EPAs revealed potential risk across all RTE foods except polony, potato chips, muffins, and assorted sandwiches, while the AR Index for non-PAs in all RTE foods was above the Krumperman value except for red Vienna alone. We, therefore, suggest a more intensified campaign against antimicrobial misuse and prioritizing the search for novel antimicrobial agents that can serve as an alternative option for the treatment of listeriosis. Lastly, newly updated clinical trials for evidence-based medical management could improve the success of empirical treatments of listeriosis, to address the challenge of non-consistent outcomes regarding successes and non-successes of antimicrobials for the empirical treatment of foodborne listeriosis are required.

Supplementary Materials

The following supporting information can be downloaded at:, Figure S1: Heatmap cluster analysis of L. monocytogenes isolates from ready-to-eat foods. The Column and row clusters grouped isolates and antimicrobials according to the response/susceptibility. Figure S2. Heatmap cluster analysis of L. monocytogenes isolates from ready-to-eat foods. The Column and row clusters grouped isolates and antimicrobials according to the response/susceptibility. Figure S3. Heatmap cluster analysis of L. monocytogenes isolates from ready-to-eat foods. The Column and row clusters grouped isolates and antimicrobials according to the response/susceptibility. Table S1a: Antibiotic breakpoints for the description of the antibiotic susceptibility testing for L. monocytogenes. Table S1b: Description of antibiotic susceptibility profile of L. monocytogenes isolates (n = 194) recovered from RTE food. Table S2: Multiple/Antibiotic resistance index of L. monocytogenes to EUCAST recommended and non-recommended antibiotics.

Author Contributions

Conceptualization, A.J.K. and A.I.O.; methodology, A.J.K. and A.I.O.; software, A.J.K.; validation, A.J.K.; formal analysis, A.J.K.; investigation, A.I.O.; resources, A.I.O.; data curation, A.J.K.; writing—original draft, A.J.K.; writing—review & editing, A.I.O. All authors have read and agreed to the published version of the manuscript.


This research was funded by The World Academy of Sciences, the National Research Foundation of South Africa (grant number 110811 and 130765), and the South African Medical Research Council (SAMRC/UFH/P790). Opinions and conclusions of investigations in this article should not be automatically credited to NRF-TWAS or SAMRC but to the authors.

Data Availability Statement

Data is contained within the article or Supplementary Material.

Conflicts of Interest

The authors declare no conflict of interest.


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Figure 1. A representation of the electrophoresis gel image of the PCR products by the simplex PCR showing the gene fragment (131 bp) for the confirmation of L. monocytogenes. Lane DL: 100 bp DNA ladder, lane 1: +ve control, lane 2: −ve control (Listeria monocytogenes ATCC 19118), lane 3–7 positive L. monocytogenes isolates.
Figure 1. A representation of the electrophoresis gel image of the PCR products by the simplex PCR showing the gene fragment (131 bp) for the confirmation of L. monocytogenes. Lane DL: 100 bp DNA ladder, lane 1: +ve control, lane 2: −ve control (Listeria monocytogenes ATCC 19118), lane 3–7 positive L. monocytogenes isolates.
Foods 12 01346 g001
Figure 2. The comparative antimicrobial resistance index (ARI) scores across all ready-to-eat foods tested were represented. ARI scores across non-PAs > 0.2 across all the RTE foods except red Vienna (ARI = 0.2). ARI < 0.2 for PAs for polony, potato chips, muffins, and assorted sandwiches. Spol—sliced polony, Pol—polony, Fs—fruit salad, Ff—fried fish, Ch—chips, Rs—Russian sausage, Bd—bread, Rv—red Vienna, Fc—fried chicken, Cc—cupcakes, Mp—meat pie, As—assorted sandwiches, Mu—muffins.
Figure 2. The comparative antimicrobial resistance index (ARI) scores across all ready-to-eat foods tested were represented. ARI scores across non-PAs > 0.2 across all the RTE foods except red Vienna (ARI = 0.2). ARI < 0.2 for PAs for polony, potato chips, muffins, and assorted sandwiches. Spol—sliced polony, Pol—polony, Fs—fruit salad, Ff—fried fish, Ch—chips, Rs—Russian sausage, Bd—bread, Rv—red Vienna, Fc—fried chicken, Cc—cupcakes, Mp—meat pie, As—assorted sandwiches, Mu—muffins.
Foods 12 01346 g002
Table 1. Distribution of L. monocytogenes in ready-to-eat foods and presumptive aerobic plate count.
Table 1. Distribution of L. monocytogenes in ready-to-eat foods and presumptive aerobic plate count.
Type of SamplesSamples
Presumptive Counts of Listeria in RTE Food Samples (cfu/g)RTE Foods Positive for L. monocytogenes (%)L. monocytogenes
in RTE Foods (%)
Fruit salad200371010/20 (50)30 (15.46)
Fried fish (snoek)21233138 (38.10)21 (10.82)
Sliced polony21355813 (61.90)23 (11.85)
Polony19019910 (52.63)20 (10.30)
Russian sausage14236311 (78.57)14 (7.21)
Bread21033156 (28.57)11 (5.67)
Chips210191110 (47.62)16 (8.24
Cupcakes21117129 (42.86)10 (5.15)
Vienna sausages801344 (50)4 (2.06)
Meat pie210291011 (52.38)22 (11.34)
Fried chicken501132 (40)2 (1.03)
Assorted sandwiches1401586 (42.86)9 (4.63)
Muffins1210657 (58.33)12 (6.18)
Total (%)2399 (3.77)25 (10.46)73 (30.54)132 (55.23)107 (44.77)194 (100)
Table 2. Antibiotic RQs (%) of L. monocytogenes in RTE food samples.
Table 2. Antibiotic RQs (%) of L. monocytogenes in RTE food samples.
Pol—polony, Spol—sliced polony, FS—fruit salad, Ch—potato chips, FF—fried fish, RS—Russian sausage, RV—red Vienna, Bd—bread, FC—fried chicken, Vk—vetkoek, Ps—pie, Cc—cupcakes, Mu—muffins, AS—assorted sandwiches. Antimicrobials: penicillin G (P), ampicillin (AMP), ampicillin–sulbactam (SAM), amoxicillin (AML), gentamicin (CN), amikacin (AK), streptomycin (S), doripenem (DOR), ertapenem (ETP), imipenem (IPM), ceftriaxone (CRO), cefotetan (CTT), vancomycin (VA), erythromycin (E), clarithromycin CLA, ciprofloxacin (CIP), trimethoprim (W), sulfamethoxazole (RL), trimethoprim–sulfamethoxazole (TS), oxytetacyclin (OT), chloramphenicol (C), fosfomycin (FOS). Regions highlighted in green represents RQs > 50% while red represents RQs ≥ 70%.
Table 3. Comparison of the multiple antibiotic resistance phenotypes of L. monocytogenes to EUCAST prescribed and non-prescribed antibiotics.
Table 3. Comparison of the multiple antibiotic resistance phenotypes of L. monocytogenes to EUCAST prescribed and non-prescribed antibiotics.
MARPs (Prescribed Antibiotics)No of AntibioticsNo ObservedMARIMARPs (Non-Prescribed)No of AntibioticsNo ObservedMARI
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Kayode, A.J.; Okoh, A.I. Antimicrobial-Resistant Listeria monocytogenes in Ready-to-Eat Foods: Implications for Food Safety and Risk Assessment. Foods 2023, 12, 1346.

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Kayode AJ, Okoh AI. Antimicrobial-Resistant Listeria monocytogenes in Ready-to-Eat Foods: Implications for Food Safety and Risk Assessment. Foods. 2023; 12(6):1346.

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Kayode, Adeoye John, and Anthony Ifeanyi Okoh. 2023. "Antimicrobial-Resistant Listeria monocytogenes in Ready-to-Eat Foods: Implications for Food Safety and Risk Assessment" Foods 12, no. 6: 1346.

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