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Article

Antimicrobial Susceptibility Profiles of Escherichia coli Isolates from Clinical Cases of Ducks in Hungary Between 2022 and 2023

by
Ádám Kerek
1,2,*,
Ábel Szabó
1 and
Ákos Jerzsele
1,2
1
Department of Pharmacology and Toxicology, University of Veterinary Medicine, István utca 2, HU-1078 Budapest, Hungary
2
National Laboratory of Infectious Animal Diseases, Antimicrobial Resistance, Veterinary Public Health and Food Chain Safety, University of Veterinary Medicine, István utca 2, HU-1078 Budapest, Hungary
*
Author to whom correspondence should be addressed.
Antibiotics 2025, 14(5), 491; https://doi.org/10.3390/antibiotics14050491
Submission received: 29 March 2025 / Revised: 27 April 2025 / Accepted: 9 May 2025 / Published: 10 May 2025
(This article belongs to the Section Antibiotics in Animal Health)
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Abstract

:
Background: Antimicrobial resistance (AMR) poses a growing threat to veterinary medicine and food safety. This study examines Escherichia coli antibiotic resistance patterns in ducks, focusing on multidrug-resistant (MDR) strains. Understanding resistance patterns and predicting MDR occurrence are critical for effective intervention strategies. Methods: E. coli isolates were collected from duck samples across multiple regions. Descriptive statistics and resistance frequency analyses were conducted. A decision tree classifier and a neural network were trained to predict MDR status. Cross-resistance relationships were visualized using graph-based models, and Monte Carlo simulations estimated MDR prevalence variations. Results: Monte Carlo simulations estimated an average MDR prevalence of 79.6% (95% CI: 73.1–86.1%). Key predictors in MDR classification models were enrofloxacin, neomycin, amoxicillin, and florfenicol. Strong cross-resistance associations were detected between neomycin and spectinomycin, as well as amoxicillin and doxycycline. Conclusions: The high prevalence of MDR strains underscores the urgent need to revise antibiotic usage guidelines in veterinary settings. The effectiveness of predictive models suggests that machine learning tools can aid in the early detection of MDR, contributing to the optimization of treatment strategies and the mitigation of resistance spread. The alarming MDR prevalence in E. coli isolates from ducks reinforces the importance of targeted surveillance and antimicrobial stewardship. Predictive models, including decision trees and neural networks, provide valuable insights into resistance trends, while Monte Carlo simulations further validate these findings, emphasizing the need for proactive antimicrobial management.

1. Introduction

The discovery of antibiotics revolutionized 20th-century medicine, enabling the effective treatment of the previously fatal infectious bacterial diseases. However, the widespread and often indiscriminate use of antimicrobial agents has accelerated the emergence of resistant bacterial strains, making AMR one of the most pressing issues in healthcare systems today [1]. Antimicrobial resistance (AMR) has emerged as one of the most critical global health challenges and has been recognized by the World Health Organization (WHO) as one of the top ten threats to public health [2]. It is increasingly referred to as a “silent pandemic” [3], due to its deadly but largely unseen effects.
Birds play a crucial role in disseminating and maintaining antimicrobial resistance [4]. In waterfowl farming, AMR presents both a health crisis and a serious economic burden, as antibiotic-resistant bacterial infections contribute to high mortality rates and production losses. Combined with widespread use of antibiotics, conditions in intensive duck farming—such as overcrowding, inadequate hygiene, and environmental stress [5]—facilitate the rapid spread of bacterial infections, increasing the likelihood of resistant strains emerging and persisting [6]. The presence of multidrug-resistant Escherichia coli is widespread in the duck industry [7]. Responsible antibiotic use must be accompanied by strict biosecurity measures [8] and pharmacological assessments before antibiotic administration [9].
The increasing prevalence of antibiotic resistance underscores the need to explore alternative approaches to reduce or replace antibiotic use. Potential alternatives include pre- and probiotics [10,11], medium-chain fatty acids [12], plant extracts [13,14,15,16,17,18], antimicrobial peptides [19], and various metal compounds [20]. These strategies are particularly important for the poultry sector [21], which, after the swine industry, is the second-largest consumer of antibiotics in animal agriculture [22].
E. coli is a Gram-negative, rod-shaped bacterium and one of the most prevalent facultative anaerobic bacteria commonly found as part of the normal gut microbiota in mammals and birds. However, it is considered a major foodborne pathogen, posing significant risks to agricultural productivity [23], animal welfare, and human health [24,25]. Certain pathotypes can cause severe infections [26], including urinary tract infections, bacteremia, diarrhea, and neonatal meningitis in humans [27,28]. The bacterium is widely used across various industries for enzyme production and serves as an indicator of fecal contamination in food safety assessments [29]. Thus, E. coli surveillance is mandatory in livestock and retail meat samples within the European Union [30,31].
Gastrointestinal pathogenic E. coli strains (e.g., enteropathogenic E. coli (EPEC), enterohemorrhagic E. coli (EHEC), can be transmitted through contaminated food or water, leading to severe diarrheal diseases [32,33,34]. Diarrheal diseases remain a significant global health burden, particularly in developing countries, where they not only have the highest incidence rates but also rank among the leading causes of mortality.
Avian pathogenic E. coli (APEC), a major subgroup of extraintestinal pathogenic E. coli (ExPEC), is responsible for severe respiratory and systemic diseases in poultry, leading to significant economic losses. In poultry health management, respiratory diseases are considered the most economically significant due to their substantial impact on industry losses [35]. APEC causes extraintestinal infections known as colibacillosis, leading to respiratory diseases and cellulitis in poultry [36,37,38]. Colibacillosis in poultry manifests in multiple clinical forms, including airsacculitis, pneumonia, septicemia, pericarditis, perihepatitis, omphalitis, and cellulitis. These conditions result in high mortality, growth retardation, increased medication costs, and carcass condemnation at slaughterhouses, significantly affecting animal welfare and farm profitability [39].
The diarrheagenic E. coli (DEC) pathotype is often associated with enteroaggregative E. coli (EAEC), a key agent in enteric infections [33,40]. Enteric E. coli strains primarily cause watery or bloody diarrhea [41,42].
This study investigates the antimicrobial resistance profiles of E. coli isolates obtained from clinical cases and deceased ducks in Hungary between 2022 and 2023. Minimum inhibitory concentration (MIC) determination and phenotypic resistance testing were conducted to assess resistance patterns. Our findings contribute to a deeper understanding of AMR trends and support the development of targeted biosecurity strategies.

2. Results

A total of 108 E. coli isolates from clinical cases were subjected to phenotypic antimicrobial susceptibility testing, with MIC determination. The majority of isolates originated from Hungary’s Dél-Alföld region (91.7%; n = 99), followed by the Dél-Dunántúl region (3.7%; n = 4) and the Észak-Alföld region (3.7%; n = 4), with a single isolate (0.7%) originating from the Közép-Magyarország region.
Most isolates (Figure 1) were recovered from samples taken from bone marrow (n = 76) or liver (n = 19).
Based on clinical breakpoints, we determined the proportion of isolates classified as susceptible, intermediate, or resistant to each tested antimicrobial agent (Figure 2). The majority of isolates (88.9%) were resistant to neomycin, while concerning levels of resistance were also observed for florfenicol (58.3%), colistin (38.9%), and enrofloxacin (35.2%). The lowest resistance rate was detected for imipenem (3.7%), although 13.9% of isolates exhibited reduced susceptibility.
Correlation analysis was performed to identify associations between resistance patterns among antibiotics based on clinical breakpoint-derived resistance data (Figure 3). Strong positive correlations were observed between ceftriaxone and colistin (0.53), amoxicillin and doxycycline (0.38), and enrofloxacin and amoxicillin-clavulanic acid (0.35). A notable negative correlation was found between imipenem and neomycin (−0.24).
We assessed the prevalence of multidrug-resistant (MDR) strains, finding that 79.6% (n = 86) of isolates were MDR, defined as resistant to at least three tested antibiotics. Extensively drug-resistant (XDR) strains accounted for 28.7% (n = 31), while no pan-drug-resistant (PDR) isolates were identified.
The MIC frequency distributions for each antimicrobial agent are summarized in Table 1. For agents lacking established clinical breakpoints, MIC distributions are presented in Supplementary Table S1. Based on clinical breakpoints, approximately half of the tested population remained susceptible to amoxicillin, amoxicillin-clavulanic acid, ceftriaxone, imipenem, spectinomycin, doxycycline, enrofloxacin, and colistin. However, 90% of isolates exhibited resistance to at least one tested antibiotic.
When applying the epidemiological cut-off values (ECOFF) defined by the European Committee on Antimicrobial Susceptibility Testing (EUCAST), approximately half of the tested population was classified as wild-type for doxycycline and colistin. The proportion of non-wild-type strains for each antimicrobial is presented in Supplementary Figure S1.
Detailed MIC values and additional isolate-specific information are available in the Supplementary Materials.
Principal components analysis (PCA) was performed based on resistance patterns (Figure 4). Three major clusters were identified. Isolates in Cluster 1 (purple) exhibited high resistance to neomycin (86%) and potentiated sulfonamides (78%). Cluster 2 (green) was characterized by high resistance to neomycin (98%), amoxicillin (80%), and enrofloxacin (75%). Cluster 3 (yellow) consisted of isolates resistant to imipenem.
Network analysis was conducted using graph-based models (Figure 5), which revealed that resistance to neomycin and spectinomycin frequently co-occurred. Additionally, the resistance to doxycycline, amoxicillin, and florfenicol was commonly observed together. Isolates resistant to imipenem formed a distinct subgroup. The strongest association with other antibiotics was found for potentiated sulfonamides.
A predictive model was subsequently developed (Figure 6) to classify MDR strains. Potentiated sulfonamides were selected as the primary feature, as network analysis indicated the strongest correlation with other antibiotics. The model achieved 100% accuracy (precision, recall, and F1-score) in classifying MDR strains. The most influential branching points in the model were antibiotics that strongly determined MDR status. This suggests that resistance to potentiated sulfonamides significantly influenced whether isolates were also resistant to florfenicol, doxycycline, and enrofloxacin or to florfenicol, neomycin, and amoxicillin.
To further estimate MDR prevalence under different antibiotic usage scenarios, we performed stochastic modeling using a Monte Carlo simulation (Figure 7). This approach enables the prediction of potential MDR prevalence rates through random sampling across thousands of iterations, illustrating the probability of an increase or decrease in MDR frequency. The simulation estimated an average MDR prevalence of 79.6%, with the mean and median coinciding, indicating a symmetrical distribution. The standard deviation was 3.86%, with MDR prevalence typically ranging between 75% and 84%. The 95% confidence interval for MDR prevalence was 73.1–86.1%.
Finally, we compared our findings with human resistance data (Figure 8). Resistance patterns were highly similar for aminopenicillins in both human and animal isolates. However, resistance to cephalosporins was higher in veterinary isolates (22.2%) compared to human clinical isolates (13.5%). In contrast, resistance to aminoglycosides was significantly more prevalent in veterinary isolates.

3. Discussion

A total of 108 E. coli isolates from clinical cases with fatal outcomes were subjected to antimicrobial susceptibility testing over a one-year period (2022–2023). The majority of isolates (91.7%) originated from the Dél-Alföld region of Hungary, reflecting the geographical concentration of the waterfowl industry in this area. This regional concentration is largely influenced by historical factors, such as the longstanding tradition of intensive waterfowl farming, and environmental conditions, including the availability of extensive wetland habitats. Additionally, specific farming practices characterized by high stocking densities and regionally variable antibiotic usage policies may contribute to the observed AMR patterns. These findings emphasize the importance of considering not only geographical but also management-related factors when investigating antimicrobial resistance trends.
E. coli is widely used as an indicator species in AMR studies [30,43,44] and plays a particularly important role in waterfowl farming, where antibiotic use is significant. Our findings revealed that over 70% of the isolates were resistant to neomycin, while resistance to florfenicol exceeded 50%.
Resistance levels varied significantly compared to previously published data. For instance, the amoxicillin resistance rate in our study was 46.3%, whereas Afayibo et al. (2022) reported 84% [45]. These discrepancies may stem from differences in geographical factors, antibiotic usage practices, and testing methodologies. The strong positive correlation between amoxicillin and doxycycline resistance (r = 0.38) suggests that these antibiotics are frequently used together or sequentially, potentially promoting cross-resistance. Although differences in authorization status exist among countries, both amoxicillin and doxycycline remain approved for veterinary use in Hungary, under regulated conditions ensuring food safety.
Similarly, the ceftriaxone resistance rate in our study was 22.2%, while previous studies reported 29% [45] and 11.4% resistance [46]. However, Varga et al. [47] and Jeong et al. [48] did not detect resistant strains. It is important to note that the use of ceftriaxone is currently prohibited in veterinary medicine in the European Union, and resistance patterns observed likely reflect past exposures or environmental contamination. The strong correlation between ceftriaxone and colistin resistance (r = 0.53) suggests that resistance to these antibiotics may be co-selected through the presence of linked resistance genes on mobile genetic elements, rather than through direct antibiotic exposure, considering that colistin does not efficiently cross the intestinal barrier [49]. Resistance to third-generation cephalosporins, such as ceftriaxone, is typically associated with the production of extended-spectrum beta-lactamases (ESBLs). Standard testing methods, including the Jarlier et al. (1988) procedure [50] and the double disk synergy test, are recommended to confirm ESBL production. However, in this study, resistance was assessed based on MIC values without specific ESBL confirmatory testing, which represents a limitation of the present work.
Resistance to amoxicillin-clavulanic acid was 18.5% in our isolates, whereas Yassin et al. [46], Varga et al. [47], and Jeong et al. [48] did not detect resistant strains. This may be attributed to the presence of clavulanic acid, which can maintain antibiotic efficacy in some E. coli strains. However, given the high prevalence of β-lactamase-producing E. coli, it is also possible that resistance mechanisms rendering this combination ineffective have already emerged in certain populations [51].
Resistance to imipenem, a carbapenem antibiotic, was detected in 3.7% of the isolates we tested, while other studies reported no resistant strains [45]. The negative correlation between imipenem and neomycin resistance (r = −0.24) suggests that these antibiotics exert selective pressure in different ways and do not exhibit clear cross-resistance. These variations likely reflect differences in antibiotic use policies and regional variations in veterinary administration of carbapenems.
Network analysis highlighted the complexity of AMR patterns. Strong associations were observed between neomycin and spectinomycin resistance, as well as between amoxicillin and doxycycline resistance. This suggests that cross-resistance between aminoglycosides and tetracyclines is widespread, which has significant implications for antibiotic stewardship. Decision tree models and neural network predictions confirmed that enrofloxacin, neomycin, and amoxicillin-clavulanic acid were the most significant predictors of MDR status. The predictive models demonstrated that machine learning tools can effectively identify MDR strains, contributing to better management of antimicrobial resistance.
Doxycycline resistance was detected in 38% of duck isolates, whereas Cen et al. reported a much higher rate of 95.4% [52]. This discrepancy suggests that doxycycline usage varies significantly across geographical regions, and the spectrum of antimicrobial agents used in duck farming differs between countries.
Florfenicol resistance was observed in 58.3% of isolates, which is consistent with the 62% resistance rate reported by Afayibo et al. [45]. This suggests that florfenicol resistance may be widespread in E. coli strains isolated from ducks, likely as a consequence of extensive or prolonged usage.
For enrofloxacin, we recorded a resistance rate of 35.2%, while Afayibo et al. reported 100% in 2022 [45], and Jeong et al. found 58.6% in 2021 [48]. In 2017, Yassin et al., however, did not detect any resistant strains [46]. The wide variability in enrofloxacin resistance highlights differences in fluoroquinolone use across regions and emphasizes the role of national regulatory policies in shaping resistance trends.
Colistin resistance was detected in 38.9% of isolates, whereas Jeong et al. [48]. found no resistant strains in ducks. Moreover, antibiotic usage, including colistin, may differ substantially between farms and regions due to several factors. Local farming practices are often shaped by the information accessible to farmers and their level of education. Additionally, the availability of specific antibiotics is frequently influenced by factors such as cost and regulatory constraints. Furthermore, husbandry practices significantly impact bird health, the spread of infections, and the necessity for specific antibiotics. Collectively, these factors determine the extent of antimicrobial exposure faced by E. coli strains, thereby influencing their potential to develop resistance.
For potentiated sulfonamides, we observed a resistance rate of 38.0%, compared to 97.7% reported by Yassin et al. [46], 51.7% by Jeong et al. [48], and only 16.7% by Varga et al. [47]. These substantial variations likely reflect differences in sulfonamide use across regions and industries, leading to significant fluctuations in resistance patterns.
Monte Carlo simulation results estimated an average MDR prevalence of 79.6%, with a 95% confidence interval ranging from 73.1% to 86.1%. These findings further validate the high prevalence of MDR strains and underscore the need for continuous AMR surveillance. The observed high prevalence of MDR isolates highlights a significant risk for treatment failures in veterinary practice and raises concerns regarding the potential transmission of resistant strains to humans through the food chain. Cross-resistance correlations among antibiotics may be explained by the co-selection of resistance genes located on mobile genetic elements, such as plasmids and integrons. Although the Monte Carlo simulations provide valuable statistical estimates, the practical implications include the urgent necessity for stricter antibiotic stewardship programs, regular resistance monitoring, and the implementation of targeted biosecurity measures in poultry production systems.
AMR represents a major global public health threat, jeopardizing the ability to combat bacterial infections, as demonstrated by the emergence of multidrug-resistant “superbugs” [53,54]. Our findings support the importance of the WHO’s One Health approach, which emphasizes the interconnected health of animals, humans, and the environment [55,56]. The extensive use of antibiotics in intensive duck farming not only affects livestock but also has environmental and indirect human health implications [57,58]. The widespread application of antimicrobial agents contributes to the dissemination of resistant bacteria [59,60], posing a significant public health risk [61,62]. Veterinarians play a crucial role in monitoring antibiotic use [63,64], ensuring responsible administration, and minimizing the emergence of resistant strains [65,66].
The zoonotic potential of antimicrobial-resistant E. coli strains originating from ducks cannot be overlooked. Although direct evidence of transmission is limited, the contamination of meat products, environmental runoff, and occupational exposure represent possible pathways for the transfer of resistant bacteria from waterfowl to humans. These risks underline the importance of comprehensive surveillance programs across the food production chain [67,68].
Overall, our study highlights the need for a multidisciplinary approach to combat antimicrobial resistance, integrating epidemiological surveillance, machine learning-based prediction models, and responsible antibiotic usage strategies. In Hungary, veterinary antimicrobial usage is regulated by national laws aligned with EU directives, emphasizing prescription-only access and the restriction of critically important antibiotics. However, differences in implementation, enforcement intensity, and farming practices compared to other European countries may influence local AMR trends. For instance, Denmark and the Netherlands have more stringent reduction programs, leading to lower antimicrobial usage rates [68].
The high prevalence of MDR and XDR strains emphasizes the urgency of revising antibiotic policies and exploring alternative therapeutic options. A combination of strategies is necessary to mitigate AMR development in duck farming. These include implementing stricter regulations on antibiotic usage, promoting vaccination programs to reduce infection pressure, improving farm biosecurity measures such as controlled access, sanitation protocols, and all-in/all-out production systems, and enhancing farmer education on responsible antibiotic stewardship. Investment in microbiological monitoring and alternative therapies, such as probiotics and bacteriophage applications, could also play a role in reducing antimicrobial dependency.

4. Materials and Methods

4.1. Origin of Bacterial Strains and Human Resistance Data

The E. coli isolates analyzed in this study were collected between 2022 and 2023 and were isolated by experts at the National Reference Laboratory of the National Food Chain Safety Office in Hungary (NÉBIH) from deceased and diagnostically necropsied ducks; following the guidelines of ISO 16649-2:2001 for the enumeration and isolation of coliform bacteria [69]. The necropsies were performed by poultry health specialists, while the bacterial isolations were carried out by laboratory technicians. The E. coli isolates were initially isolated using Coliform agar (Biolab Zrt., Budapest, Hungary) following the ISO 16649-2:2001 protocol. Presumptive identification was based on colony morphology, and isolates were received as pure cultures for further analysis. The isolates were received as pure cultures and stored in Microbank™ cryopreservation vials (Pro-Lab Diagnostics, Richmond Hill, Canada) containing a cryoprotective bead and a proprietary cryopreservative medium at −80 °C. A total of 108 E. coli isolates were recovered from ducks, while 58,168 human isolates were analyzed for comparison.
Human antimicrobial resistance data were provided by the National Public Health Center of Hungary. Human resistance data were based on ampicillin resistance, while in veterinary cases, amoxicillin was considered. For third-generation cephalosporins, ceftriaxone resistance was compared between human and animal isolates. Resistance data for aminoglycosides were aggregated for gentamicin, tobramycin, and amikacin, with additional specific data available for neomycin. Similarly, fluoroquinolone resistance was analyzed collectively, while enrofloxacin resistance was examined separately for veterinary isolates. It is important to note that ceftriaxone is not authorized for routine veterinary use. In veterinary medicine, third-generation cephalosporins such as ceftiofur are commonly used, particularly in cattle. For the detection of ESBL-producing strains, cefotaxime or ceftazidime is typically employed as a screening agent in both human and veterinary microbiological practices. The human resistance data, including both national and region-specific statistics, were provided in an Excel file with the approval of the Chief Medical Officer of Hungary. The dataset contained resistance prevalence expressed as percentage values.
For E. coli isolates from ducks, information was available on the organ of origin (liver, bone marrow, lungs, brain chamber, articulatio, pericardium) and the geographic location of sample collection. Based on the collection sites, the isolates were categorized into one of Hungary’s seven administrative regions.

4.2. Minimum Inhibitory Concentration (MIC) Determination

Phenotypic resistance expression was assessed by determining MICs following the guidelines of the Clinical Laboratory Standards Institute (CLSI) [70]. Breakpoints were established according to CLSI recommendations [71], and results were compared against the ECOFFs defined by the EUCAST [72]. The literature references for MIC determination were consulted, including those for amoxicillin–clavulanate [73], neomycin [74], spectinomycin [73], and colistin [75].
Bacterial strains stored at −80 °C were suspended in 3 mL of cation-adjusted Mueller–Hinton broth (CAMHB) and incubated at 37 °C for 18–24 h before testing. MIC testing was conducted using 96-well microtiter plates (VWR International, LLC, Debrecen, Hungary). Except for the first column, all wells were filled with 90 µL of CAMHB. Stock solutions of tested antimicrobial agents (Merck KGaA, Darmstadt, Germany) at 1024 µg/mL were prepared following CLSI guidelines [71].
Amoxicillin and amoxicillin-clavulanate were dissolved at a 2:1 ratio in phosphate-buffered solution (pH 7.2, 0.01 mol/L), while imipenem was dissolved in phosphate buffer at pH 6.0 (0.1 mol/L). Doxycycline, neomycin, tilozin, and vancomycin were dissolved in distilled water. For potentiated sulfonamides (trimethoprim and sulfamethoxazole at a 1:19 ratio), sulfamethoxazole was dissolved in hot water with a few drops of 2.5 mol/L NaOH, while trimethoprim was dissolved in distilled water containing 0.05 mol/L HCl. Enrofloxacin was prepared using distilled water with a few drops of 1 mol/L NaOH, while florfenicol was dissolved in distilled water with 95% ethanol.
A 1:1 dilution of the 512 µg/mL antimicrobial solutions was prepared in CAMB, and 180 µL was added to the first column of the microtiter plates, followed by two-fold serial dilutions. After the 10th column, excess 90 µL was discarded to ensure uniform volume. Bacterial suspensions were adjusted to 0.5 McFarland standard using a nephelometer (ThermoFisher Scientific, Budapest, Hungary) and inoculated into the microtiter plates from the 11th column backward, at a 10 µL/well concentration [70].
MIC readings were obtained using the Sensititre™ SWIN™ automatic MIC reader (ThermoFisher Scientific, Budapest, Hungary) and analyzed with VIZION system software v3.4 (ThermoFisher Scientific, Budapest, Hungary, 2024). The reference quality control strain was E. coli (ATCC 25922).

4.3. Statistical Analysis

Statistical analyses were performed using R (v4.2.2) in the RStudio (2023.09.1) environment [76]. The normality of data distribution was tested using the Shapiro–Wilk test. Non-normally distributed data were analyzed using non-parametric tests. Differences in resistance levels between groups were assessed using the Kruskal–Wallis test [77], which does not assume normal distribution and is well-suited for comparing multiple group medians. Post hoc analyses were conducted using the Mann–Whitney U test [78], and t-tests, with pairwise comparisons corrected using the Bonferroni correction [79], to control for inflated p-values due to multiple testing. It should be noted that Bonferroni correction can increase the likelihood of Type II errors, potentially reducing the ability to detect true differences.
Correlation analysis of antimicrobial resistance among antibiotics was performed using a heatmap analysis, employing the corrplot (v0.92) and pheatmap (v1.0.12) packages.
For cluster analysis, hierarchical clustering was conducted using the factoextra (v1.0.7) package for visualization, cluster (v2.1.4) for agglomerative hierarchical clustering, and dendextend (v1.16.0) for dendrogram visualization.
Network analysis was performed to examine cross-resistance patterns among antibiotics. Graphs were constructed and analyzed using igraph (v1.3.5), while ggraph (v2.1.0) was used for visualization.
For MDR strain prediction, a decision tree model was developed using rpart (v4.1.16). Model performance was evaluated using the caret (v6.0.93) package, and the decision tree was visualized with rpart.plot (v3.1.2).
Monte Carlo simulations were conducted to estimate MDR prevalence under different antibiotic usage scenarios. Bootstrap sampling with 10,000 iterations was performed using the boot (v1.3.28) package, while data aggregation and statistical calculations were carried out using dplyr (v1.1.0). Simulation result distributions were visualized using ggplot2 (v3.4.0).
All analyses were performed using open-source R VERSION 4.2.2 packages, ensuring full reproducibility of the study.

5. Conclusions

The spread of antibiotic-resistant bacterial strains poses both veterinary and public health challenges, necessitating the development of new antimicrobial agents and alternative therapeutic approaches. Our findings underscore the growing significance of AMR in waterfowl farming, particularly given the high prevalence of MDR and XDR E. coli strains. The patterns of resistance and network analysis results reveal strong cross-resistance relationships among certain antibiotics, which are likely to be influenced by antibiotic usage practices, although this requires further investigation to be conclusively demonstrated.
The application of predictive models, particularly decision trees and neural networks, proved to be effective tools for identifying MDR strains, enhancing our understanding of AMR dynamics and aiding in their prediction. However, it is important to note that while statistical correlations identified by predictive models may provide valuable insights, they do not necessarily imply direct biological causation. Experimental validation is needed to confirm the biological relevance of the observed associations. Additionally, Monte Carlo simulations provided further insights into the potential occurrence of MDR strains, reinforcing the necessity of continuous epidemiological monitoring.
Overall, these findings emphasize the importance of a multidisciplinary approach in combating antimicrobial resistance, integrating ongoing surveillance, data-driven decision-making, and responsible antibiotic stewardship. Future research should focus on gaining a more detailed mechanistic understanding of cross-resistance development and its clinical implications.

Supplementary Materials

The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/antibiotics14050491/s1. Figure S1: Proportion of non-wild-type strains per antimicrobial agent based on the epidemiological cut-off values (ECOFF) defined by the European Committee on Antimicrobial Susceptibility Testing (EUCAST). Table S1: Frequency distribution table of minimum inhibitory concentrations (MICs) for Escherichia coli isolates (n = 108) from ducks, tested against antibiotics without established clinical breakpoints. The upper row represents the frequency values, while the lower row indicates the corresponding percentage.

Author Contributions

Conceptualization, Á.K. and Á.J.; methodology, Á.K.; software, Á.K.; validation, Á.J.; formal analysis, Á.S.; investigation, Á.J.; resources, Á.K.; data curation, Á.S.; writing—original draft preparation, Á.S.; writing—review and editing, Á.K.; visualization, Á.S.; supervision, Á.J.; project administration, Á.S.; funding acquisition, Á.J. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by Project No. RRF-2.3.1-21-2022-00001 was implemented with the support provided by the Recovery and Resilience Facility (RRF), financed under the National Recovery Fund budget estimate, RRF-2.3.1-21 funding scheme.

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

The data presented in this study are available from the corresponding author upon reasonable request.

Acknowledgments

We would like to thank Katalin Balogh and Tamásné Pénzes Imre for the preparation of the laboratory work. We extend our thanks to Ákos Thuma for the isolation of the strains.

Conflicts of Interest

The authors declare no conflicts of interest.

Abbreviations

The following abbreviations are used in this manuscript:
AMRAntimicrobial resistance
CAMHBCation-adjusted Mueller Hinton Broth
CLSIClinical Laboratory Standards Institute
ECOFFEpidemiological cut-off values
ESBLExtended-spectrum beta-lactamases
EUCASTEuropean Committee on Antimicrobial Susceptibility Testing
MDRMultidrug-resistant
MICMinimum inhibitory concentration
WHOWorld Health Organization
XDRExtensively drug-resistant

References

  1. Akram, F.; Imtiaz, M.; Haq, I. ul Emergent Crisis of Antibiotic Resistance: A Silent Pandemic Threat to 21st Century. Microb. Pathog. 2023, 174, 105923. [Google Scholar] [CrossRef] [PubMed]
  2. Bhargav, A.; Gupta, S.; Seth, S.; James, S.; Fatima, F.; Chaurasia, P.; Ramachandran, S. Knowledgebase of Potential Multifaceted Solutions to Antimicrobial Resistance. Comput. Biol. Chem. 2022, 101, 107772. [Google Scholar] [CrossRef]
  3. Zhou, N.; Cheng, Z.; Zhang, X.; Lv, C.; Guo, C.; Liu, H.; Dong, K.; Zhang, Y.; Liu, C.; Chang, Y.-F.; et al. Global Antimicrobial Resistance: A System-Wide Comprehensive Investigation Using the Global One Health Index. Infect. Dis. Poverty 2022, 11, 92. [Google Scholar] [CrossRef] [PubMed]
  4. Benmazouz, I.; Kövér, L.; Kardos, G. The Rise of Antimicrobial Resistance in Wild Birds: Potential AMR Sources and Wild Birds as AMR Reservoirs and Disseminators: Literature Review. Magy. Állatorvosok Lapja 2024, 146, 91–105. [Google Scholar] [CrossRef]
  5. Nhung, N.T.; Chansiripornchai, N.; Carrique-Mas, J.J. Antimicrobial Resistance in Bacterial Poultry Pathogens: A Review. Front. Vet. Sci. 2017, 4, 126. [Google Scholar] [CrossRef]
  6. Eid, H.M.; Algammal, A.M.; Elfeil, W.K.; Youssef, F.M.; Harb, S.M.; Abd-Allah, E.M. Prevalence, Molecular Typing, and Antimicrobial Resistance of Bacterial Pathogens Isolated from Ducks. Vet. World 2019, 12, 677–683. [Google Scholar] [CrossRef]
  7. Zhang, S.; Chen, S.; Abbas, M.; Wang, M.; Jia, R.; Chen, S.; Liu, M.; Zhu, D.; Zhao, X.; Wu, Y.; et al. High Incidence of Multi-Drug Resistance and Heterogeneity of Mobile Genetic Elements in Escherichia coli Isolates from Diseased Ducks in Sichuan Province of China. Ecotoxicol. Environ. Saf. 2021, 222, 112475. [Google Scholar] [CrossRef] [PubMed]
  8. Farkas, M.; Könyves, L.; Csorba, S.; Farkas, Z.; Józwiák, Á.; Süth, M.; Kovács, L. Biosecurity Situation of Large-Scale Poultry Farms in Hungary According to the Databases of National Food Chain Safety Office Centre for Disease Control and Biosecurity Audit System of Poultry Product Board of Hungary in the Period of 2021–2022. Magy. Állatorvosok Lapja 2024, 146, 723–742. [Google Scholar] [CrossRef]
  9. Mag, P.; Németh, K.; Somogyi, Z.; Jerzsele, Á. Antibacterial therapy based on pharmacokinetic/ pharmacodynamic models in small animal medicine-1. Literature review. Magy. Állatorvosok Lapja 2023, 145, 419–438. [Google Scholar] [CrossRef]
  10. Kovács, L.; Hejel, P.; Farkas, M.; László, L. Könyves László Study Report on the Effect of a Litter Treatment Product Containing Bacillus licheniformis and Zeolite in Male Fattening Turkey Flock. Magy. Állatorvosok Lapja 2024, 146, 291–305. [Google Scholar] [CrossRef]
  11. Such, N.; Molnár, A.; Pál, L.; Farkas, V.; Menyhárt, L.; Husvéth, F.; Dublecz, K. The Effect of Pre- and Probiotic Treatment on the Gumboro-Titer Values of Broilers. Magy. Állatorvosok Lapja 2021, 143, 119–127. [Google Scholar]
  12. Hetényi, N.; Bersényi, A.; Hullár, I. Physiological Effects of Medium-Chain Fatty Acids and Triglycerides, and Their Potential Use in Poultry and Swine Nutrition: A Literature Review. Magy. Állatorvosok Lapja 2024, 146, 651–659. [Google Scholar] [CrossRef]
  13. Jerzsele, Á.; Somogyi, Z.; Szalai, M.; Kovács, D. Effects of Fermented Wheat Germ Extract on Artificial Salmonella Typhimurium Infection in Broiler Chickens. Magy. Állatorvosok Lapja 2020, 142, 77–85. [Google Scholar]
  14. Kerek, Á.; Csanády, P.; Jerzsele, Á. Antibacterial Efficiency of Propolis—Part 1. Magy. Állatorvosok Lapja 2022, 144, 285–298. [Google Scholar]
  15. Kerek, Á.; Csanády, P.; Jerzsele, Á. Antiprotozoal and Antifungal Efficiency of Propolis—Part 2. Magy. Állatorvosok Lapja 2022, 144, 691–704. [Google Scholar]
  16. Kovács, L.; Nagy, D.; Könyves, L.; Jerzsele, Á.; Kerek, Á. Antimicrobial Properties of Essential Oils–Animal Health Aspects. Magy. Állatorvosok Lapja 2023, 145, 497–510. [Google Scholar] [CrossRef]
  17. Olasz, Á.; Jerzsele, Á.; Balta, L.; Dobra, P.F.; Kerek, Á. In Vivo Efficacy of Different Extracts of Propolis in Broiler Salmonellosis. Magy. Állatorvosok Lapja 2023, 145, 461–475. [Google Scholar] [CrossRef]
  18. Petrilla, J.; Mátis, G.; Molnár, A.; Jerzsele, Á.; Pál, L.; Gálfi, P.; Neogrády, Z.; Dublecz, K. In Vitro Investigation of the Antibacterial Efficacy of Butyrate on Various Campylobacter jejuni Strains. MÁL 2021, 143, 57–64. [Google Scholar]
  19. Sebők, C.; Márton, R.A.; Meckei, M.; Neogrády, Z.; Mátis, G. Antimicrobial Peptides as New Tools to Combat Infectious Diseases. Magy. Állatorvosok Lapja 2024, 146, 181–191. [Google Scholar] [CrossRef]
  20. Jócsák, G.; Schilling-Tóth, B.; Bartha, T.; Tóth, I.; Ondrašovičová, S.; Kiss, D.S. Metal Nanoparticles-Immersion in the “tiny” World of Medicine. Magy. Állatorvosok Lapja 2025, 147, 115–127. [Google Scholar] [CrossRef]
  21. Essősy, M.; Fodor, I.; Ihnáth, Z.; Karancsi, Z.; Kovács, D.; Szalai, K.V.; Szentmiklósi, D.; Jerzsele, Á. The Possibilities of Antibiotic-Free Broiler-Hen Fattening, with Special Reference to the Use of Pre- and Probiotics. Magy. Állatorvosok Lapja 2020, 142, 397–407. [Google Scholar]
  22. Kovács, D.; Palkovicsné Pézsa, N.; Farkas, O.; Jerzsele, Á. Usage of Antibiotic Alternatives in Pig Farming: Literature Review. Magy. Állatorvosok Lapja 2021, 143, 281–282. [Google Scholar]
  23. Bintsis, T. Foodborne Pathogens. AIMS Microbiol. 2017, 3, 529–563. [Google Scholar] [CrossRef]
  24. Zhang, S.; Chen, S.; Rehman, M.U.; Yang, H.; Yang, Z.; Wang, M.; Jia, R.; Chen, S.; Liu, M.; Zhu, D.; et al. Distribution and Association of Antimicrobial Resistance and Virulence Traits in Escherichia coli Isolates from Healthy Waterfowls in Hainan, China. Ecotoxicol. Environ. Saf. 2021, 220, 112317. [Google Scholar] [CrossRef] [PubMed]
  25. Johnson, J.R.; Delavari, P.; O’Bryan, T.T.; Smith, K.E.; Tatini, S. Contamination of Retail Foods, Particularly Turkey, from Community Markets (Minnesota, 1999-2000) with Antimicrobial-Resistant and Extraintestinal Pathogenic Escherichia coli. Foodborne Pathog. Dis. 2005, 2, 38–49. [Google Scholar] [CrossRef] [PubMed]
  26. Arbab, S.; Ullah, H.; Wang, W.; Zhang, J. Antimicrobial Drug Resistance against Escherichia coli and Its Harmful Effect on Animal Health. Vet. Med. Sci. 2022, 8, 1780–1786. [Google Scholar] [CrossRef]
  27. Palaniappan, R.U.M.; Zhang, Y.; Chiu, D.; Torres, A.; DebRoy, C.; Whittam, T.S.; Chang, Y.-F. Differentiation of Escherichia coli Pathotypes by Oligonucleotide Spotted Array. J. Clin. Microbiol. 2006, 44, 1495–1501. [Google Scholar] [CrossRef] [PubMed]
  28. Tivendale, K.A.; Logue, C.M.; Kariyawasam, S.; Jordan, D.; Hussein, A.; Li, G.; Wannemuehler, Y.; Nolan, L.K. Avian-Pathogenic Escherichia coli Strains Are Similar to Neonatal Meningitis E. coli Strains and Are Able to Cause Meningitis in the Rat Model of Human Disease. Infect. Immun. 2010, 78, 3412–3419. [Google Scholar] [CrossRef]
  29. Jang, J.; Hur, H.-G.; Sadowsky, M.J.; Byappanahalli, M.N.; Yan, T.; Ishii, S. Environmental Escherichia coli: Ecology and Public Health Implications—A Review. J. Appl. Microbiol. 2017, 123, 570–581. [Google Scholar] [CrossRef]
  30. Anjum, M.F.; Schmitt, H.; Börjesson, S.; Berendonk, T.U.; Donner, E.; Stehling, E.G.; Boerlin, P.; Topp, E.; Jardine, C.; Li, X.; et al. The Potential of Using E. coli as an Indicator for the Surveillance of Antimicrobial Resistance (AMR) in the Environment. Curr. Opin. Microbiol. 2021, 64, 152–158. [Google Scholar] [CrossRef]
  31. Schrijver, R.; Stijntjes, M.; Rodríguez-Baño, J.; Tacconelli, E.; Rajendran, N.B.; Voss, A. Review of Antimicrobial Resistance Surveillance Programmes in Livestock and Meat in EU with Focus on Humans. Clin. Microbiol. Infect. 2018, 24, 577–590. [Google Scholar] [CrossRef] [PubMed]
  32. Franiek, N.; Orth, D.; Grif, K.; Ewers, C.; Wieler, L.H.; Thalhammer, J.G.; Wuerzner, R. ESBL-producing E. coli and EHEC in dogs and cats in the Tyrol as possible source of human infection. Berl. Munch. Tierarztl. Wochenschr. 2012, 125, 469–475. [Google Scholar] [CrossRef] [PubMed]
  33. Adorján, A.; Makrai, L.; Könyves, L.; Tóth, I. Enteropathogenic Escherichia coli (EPEC): Short Literature Summary. Magy. Állatorvosok Lapja 2021, 143, 429–438. [Google Scholar]
  34. da Silva, G.J.; Mendonça, N. Association between Antimicrobial Resistance and Virulence in Escherichia coli. Virulence 2012, 3, 18–28. [Google Scholar] [CrossRef]
  35. Dziva, F.; Stevens, M.P. Colibacillosis in Poultry: Unravelling the Molecular Basis of Virulence of Avian Pathogenic Escherichia coli in Their Natural Hosts. Avian Pathol. 2008, 37, 355–366. [Google Scholar] [CrossRef]
  36. Soares, B.D.; de Brito, K.C.T.; Grassotti, T.T.; Filho, H.C.K.; de Camargo, T.C.L.; Carvalho, D.; Dorneles, I.C.; Otutumi, L.K.; Cavalli, L.S.; de Brito, B.G. Respiratory Microbiota of Healthy Broilers Can Act as Reservoirs for Multidrug-Resistant Escherichia coli. Comp. Immunol. Microbiol. Infect. Dis. 2021, 79, 101700. [Google Scholar] [CrossRef]
  37. de Oliveira, A.L.; Newman, D.M.; Sato, Y.; Noel, A.; Rauk, B.; Nolan, L.K.; Barbieri, N.L.; Logue, C.M. Characterization of Avian Pathogenic Escherichia coli (APEC) Associated With Turkey Cellulitis in Iowa. Front. Vet. Sci. 2020, 7, 380. [Google Scholar] [CrossRef] [PubMed]
  38. Ewers, C.; Janßen, T.; Wieler, L.H. Avian pathogenic Escherichia coli (APEC). Berl. Munch. Tierarztl. Wochenschr. 2003, 116, 381–395. [Google Scholar]
  39. Watts, A.; Wigley, P. Avian Pathogenic Escherichia coli: An Overview of Infection Biology, Antimicrobial Resistance and Vaccination. Antibiotics 2024, 13, 809. [Google Scholar] [CrossRef]
  40. Kong, H.; Hong, X.; Li, X. Current Perspectivesin Pathogenesis and Antimicrobial Resistance of Enteroaggregative Escherichia coli. Microb. Pathog. 2015, 85, 44–49. [Google Scholar] [CrossRef]
  41. Allocati, N.; Masulli, M.; Alexeyev, M.F.; Di Ilio, C. Escherichia coli in Europe: An Overview. Int. J. Environ. Res. Public Health 2013, 10, 6235–6254. [Google Scholar] [CrossRef] [PubMed]
  42. Day, M.; Doumith, M.; Jenkins, C.; Dallman, T.J.; Hopkins, K.L.; Elson, R.; Godbole, G.; Woodford, N. Antimicrobial Resistance in Shiga Toxin-Producing Escherichia coli Serogroups O157 and O26 Isolated from Human Cases of Diarrhoeal Disease in England, 2015. J. Antimicrob. Chemother. 2017, 72, 145–152. [Google Scholar] [CrossRef]
  43. Lalak, A.; Wasyl, D.; Zając, M.; Skarżyńska, M.; Hoszowski, A.; Samcik, I.; Woźniakowski, G.; Szulowski, K. Mechanisms of Cephalosporin Resistance in Indicator Escherichia coli Isolated from Food Animals. Vet. Microbiol. 2016, 194, 69–73. [Google Scholar] [CrossRef]
  44. Aerts, M.; Battisti, A.; Hendriksen, R.; Kempf, I.; Teale, C.; Tenhagen, B.-A.; Veldman, K.; Wasyl, D.; Guerra, B.; Liebana, E.; et al. Technical Specifications on Harmonised Monitoring of Antimicrobial Resistance in Zoonotic and Indicator Bacteria from Food-Producing Animals and Food. EFSA J. 2019, 17, 5709. [Google Scholar] [CrossRef]
  45. Afayibo, D.J.A.; Zhu, H.; Zhang, B.; Yao, L.; Abdelgawad, H.A.; Tian, M.; Qi, J.; Liu, Y.; Wang, S. Isolation, Molecular Characterization, and Antibiotic Resistance of Avian Pathogenic Escherichia coli in Eastern China. Vet. Sci. 2022, 9, 319. [Google Scholar] [CrossRef]
  46. Yassin, A.K.; Gong, J.; Kelly, P.; Lu, G.; Guardabassi, L.; Wei, L.; Han, X.; Qiu, H.; Price, S.; Cheng, D.; et al. Antimicrobial Resistance in Clinical Escherichia coli Isolates from Poultry and Livestock, China. PLoS ONE 2017, 12, e0185326. [Google Scholar] [CrossRef] [PubMed]
  47. Varga, C.; Guerin, M.T.; Brash, M.L.; Slavic, D.; Boerlin, P.; Susta, L. Antimicrobial Resistance in Fecal Escherichia coli and Salmonella enterica Isolates: A Two-Year Prospective Study of Small Poultry Flocks in Ontario, Canada. BMC Vet. Res. 2019, 15, 464. [Google Scholar] [CrossRef]
  48. Jeong, J.; Lee, J.-Y.; Kang, M.-S.; Lee, H.-J.; Kang, S.-I.; Lee, O.-M.; Kwon, Y.-K.; Kim, J.-H. Comparative Characteristics and Zoonotic Potential of Avian Pathogenic Escherichia coli (APEC) Isolates from Chicken and Duck in South Korea. Microorganisms 2021, 9, 946. [Google Scholar] [CrossRef]
  49. Liu, Y.-Y.; Wang, Y.; Walsh, T.R.; Yi, L.-X.; Zhang, R.; Spencer, J.; Doi, Y.; Tian, G.; Dong, B.; Huang, X.; et al. Emergence of Plasmid-Mediated Colistin Resistance Mechanism MCR-1 in Animals and Human Beings in China: A Microbiological and Molecular Biological Study. Lancet Infect. Dis. 2016, 16, 161–168. [Google Scholar] [CrossRef]
  50. Jarlier, V.; Nicolas, M.H.; Fournier, G.; Philippon, A. Extended Broad-Spectrum Beta-Lactamases Conferring Transferable Resistance to Newer Beta-Lactam Agents in Enterobacteriaceae: Hospital Prevalence and Susceptibility Patterns. Rev. Infect. Dis. 1988, 10, 867–878. [Google Scholar] [CrossRef]
  51. Aliyu, A.B.; Saleha, A.A.; Jalila, A.; Zunita, Z. Risk Factors and Spatial Distribution of Extended Spectrum Beta-Lactamase-Producing-Escherichia coli at Retail Poultry Meat Markets in Malaysia: A Cross-Sectional Study. BMC Public Health 2016, 16, 699. [Google Scholar] [CrossRef]
  52. Cen, D.-J.; Sun, R.-Y.; Mai, J.-L.; Jiang, Y.-W.; Wang, D.; Guo, W.-Y.; Jiang, Q.; Zhang, H.; Zhang, J.-F.; Zhang, R.-M.; et al. Occurrence and Transmission of BlaNDM-Carrying Enterobacteriaceae from Geese and the Surrounding Environment on a Commercial Goose Farm. Appl. Environ. Microbiol. 2021, 87, e00087-21. [Google Scholar] [CrossRef]
  53. Mitra, S.; Sultana, S.A.; Prova, S.R.; Uddin, T.M.; Islam, F.; Das, R.; Nainu, F.; Sartini, S.; Chidambaram, K.; Alhumaydhi, F.A.; et al. Investigating Forthcoming Strategies to Tackle Deadly Superbugs: Current Status and Future Vision. Expert. Rev. Anti Infect. Ther. 2022, 20, 1309–1332. [Google Scholar] [CrossRef]
  54. Munguia, J.; Nizet, V. Pharmacological Targeting of the Host-Pathogen Interaction: Alternatives to Classical Antibiotics to Combat Drug-Resistant Superbugs. Trends Pharmacol. Sci. 2017, 38, 473–488. [Google Scholar] [CrossRef] [PubMed]
  55. McEwen, S.A.; Collignon, P.J. Antimicrobial Resistance: A One Health Perspective. Microbiol. Spectr. 2018, 6, 521–547. [Google Scholar] [CrossRef] [PubMed]
  56. Collineau, L.; Bourély, C.; Rousset, L.; Berger-Carbonne, A.; Ploy, M.-C.; Pulcini, C.; Colomb-Cotinat, M. Towards One Health Surveillance of Antibiotic Resistance: Characterisation and Mapping of Existing Programmes in Humans, Animals, Food and the Environment in France, 2021. Euro Surveill. 2023, 28, 2200804. [Google Scholar] [CrossRef] [PubMed]
  57. Coculescu, B.-I. Antimicrobial Resistance Induced by Genetic Changes. J. Med. Life 2009, 2, 114–123. [Google Scholar]
  58. Pulingam, T.; Parumasivam, T.; Gazzali, A.M.; Sulaiman, A.M.; Chee, J.Y.; Lakshmanan, M.; Chin, C.F.; Sudesh, K. Antimicrobial Resistance: Prevalence, Economic Burden, Mechanisms of Resistance and Strategies to Overcome. Eur. J. Pharm. Sci. 2022, 170, 106103. [Google Scholar] [CrossRef]
  59. Whittaker, A.; Do, T.T.; Davis, M.D.M.; Barr, J. AMR Survivors? Chronic Living with Antimicrobial Resistant Infections. Glob. Public Health 2023, 18, 2217445. [Google Scholar] [CrossRef]
  60. Ferri, M.; Ranucci, E.; Romagnoli, P.; Giaccone, V. Antimicrobial Resistance: A Global Emerging Threat to Public Health Systems. Crit. Rev. Food Sci. Nutr. 2017, 57, 2857–2876. [Google Scholar] [CrossRef]
  61. Hossain, A.; Habibullah-Al-Mamun, M.; Nagano, I.; Masunaga, S.; Kitazawa, D.; Matsuda, H. Antibiotics, Antibiotic-Resistant Bacteria, and Resistance Genes in Aquaculture: Risks, Current Concern, and Future Thinking. Environ. Sci. Pollut. Res. Int. 2022, 29, 11054–11075. [Google Scholar] [CrossRef] [PubMed]
  62. Bortolaia, V.; Espinosa-Gongora, C.; Guardabassi, L. Human Health Risks Associated with Antimicrobial-Resistant Enterococci and Staphylococcus aureus on Poultry Meat. Clin. Microbiol. Infect. 2016, 22, 130–140. [Google Scholar] [CrossRef]
  63. Sun, W.; Wang, D.; Yan, S.; Xue, Y. Characterization of Escherichia coli Strains Isolated from Geese by Detection of Integron-Mediated Antimicrobial Resistance. J. Glob. Antimicrob. Resist. 2022, 31, 10–14. [Google Scholar] [CrossRef] [PubMed]
  64. de Jong, A.; Garch, F.E.; Simjee, S.; Moyaert, H.; Rose, M.; Youala, M.; Siegwart, E. VetPath Study Group Monitoring of Antimicrobial Susceptibility of Udder Pathogens Recovered from Cases of Clinical Mastitis in Dairy Cows across Europe: VetPath Results. Vet. Microbiol. 2018, 213, 73–81. [Google Scholar] [CrossRef] [PubMed]
  65. Tang, B.; Elbediwi, M.; Nambiar, R.B.; Yang, H.; Lin, J.; Yue, M. Genomic Characterization of Antimicrobial-Resistant Salmonella enterica in Duck, Chicken, and Pig Farms and Retail Markets in Eastern China. Microbiol. Spectr. 2022, 10, e0125722. [Google Scholar] [CrossRef]
  66. Wang, X.-R.; Lian, X.-L.; Su, T.-T.; Long, T.-F.; Li, M.-Y.; Feng, X.-Y.; Sun, R.-Y.; Cui, Z.-H.; Tang, T.; Xia, J.; et al. Duck Wastes as a Potential Reservoir of Novel Antibiotic Resistance Genes. Sci. Total Environ. 2021, 771, 144828. [Google Scholar] [CrossRef]
  67. Mulchandani, R.; Wang, Y.; Gilbert, M.; Van Boeckel, T.P. Global Trends in Antimicrobial Use in Food-Producing Animals: 2020 to 2030. PLoS Glob. Public Health 2023, 3, e0001305. [Google Scholar] [CrossRef]
  68. Munk, P.; Yang, D.; Röder, T.; Maier, L.; Petersen, T.N.; Duarte, A.S.R.; Clausen, P.T.L.C.; Brinch, C.; Van Gompel, L.; Luiken, R.; et al. The European Livestock Resistome. mSystems 2024, 9, e0132823. [Google Scholar] [CrossRef]
  69. ISO 16649-2:2001. Available online: https://www.iso.org/standard/29824.html (accessed on 27 April 2025).
  70. Clinical and Laboratory Standards Institute CLSI. Methods for Dilution Antimicrobial Susceptibility Tests for Bacteria That Grow Aerobically, 11th ed.; Clinical and Laboratory Standards Institute: Wayne, PA, USA, 2018; Volume CLSI standards M07. [Google Scholar]
  71. Brian, V.L. VET01SEd5 | Performance Standards for Antimicrobial Disk and Dilution Susceptibility Tests for Bacteria Isolated From Animals, 5th Edition. Available online: https://clsi.org/standards/products/veterinary-medicine/documents/vet01s/ (accessed on 8 May 2022).
  72. EUCAST: MIC and Zone Distributions and ECOFFs. Available online: https://www.eucast.org/mic_distributions_and_ecoffs/ (accessed on 1 May 2022).
  73. Boulianne, M.; Arsenault, J.; Daignault, D.; Archambault, M.; Letellier, A.; Dutil, L. Drug Use and Antimicrobial Resistance among Escherichia coli and Enterococcus spp. Isolates from Chicken and Turkey Flocks Slaughtered in Quebec, Canada. Can. J. Vet. Res. 2016, 80, 49–59. [Google Scholar]
  74. Lima-Filho, J.V.; Martins, L.V.; de Oliveira Nascimento, D.C.; Ventura, R.F.; Batista, J.E.C.; Silva, A.F.B.; Ralph, M.T.; Vaz, R.V.; Rabello, C.B.-V.; da Silva, I.d.M.M.; et al. Zoonotic Potential of Multidrug-Resistant Extraintestinal Pathogenic Escherichia coli Obtained from Healthy Poultry Carcasses in Salvador, Brazil. Braz. J. Infect. Dis. 2013, 17, 54–61. [Google Scholar] [CrossRef]
  75. Hesp, A.; van Schaik, G.; Wiegel, J.; Heuvelink, A.; Mevius, D.; Veldman, K. Antimicrobial Resistance Monitoring in Commensal and Clinical Escherichia coli from Broiler Chickens: Differences and Similarities. Prev. Vet. Med. 2022, 204, 105663. [Google Scholar] [CrossRef] [PubMed]
  76. R Core Team. R: A Language and Environment for Statistical Computing; R Foundation for Statistical Computing: Vienna, Austria, 2020. [Google Scholar]
  77. Kruskal, W.H.; Wallis, W.A. Use of Ranks in One-Criterion Variance Analysis. J. Am. Stat. Assoc. 1952, 47, 583–621. [Google Scholar] [CrossRef]
  78. Fay, M.P.; Proschan, M.A. Wilcoxon-Mann-Whitney or t-Test? On Assumptions for Hypothesis Tests and Multiple Interpretations of Decision Rules. Stat. Surv. 2010, 4, 1–39. [Google Scholar] [CrossRef] [PubMed]
  79. Dunn, O.J. Multiple Comparisons among Means. J. Am. Stat. Assoc. 1961, 56, 52–64. [Google Scholar] [CrossRef]
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Figure 1. Organ distribution of Escherichia coli isolates (n = 108) and their relative proportions.
Figure 1. Organ distribution of Escherichia coli isolates (n = 108) and their relative proportions.
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Figure 2. Phenotypic antimicrobial susceptibility profile of Escherichia coli isolates (n = 108) from ducks, tested against clinically relevant antimicrobials in veterinary and public health contexts.
Figure 2. Phenotypic antimicrobial susceptibility profile of Escherichia coli isolates (n = 108) from ducks, tested against clinically relevant antimicrobials in veterinary and public health contexts.
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Figure 3. Correlation analysis of antimicrobial resistance among Escherichia coli isolates, visualized as a heatmap for each antimicrobial.
Figure 3. Correlation analysis of antimicrobial resistance among Escherichia coli isolates, visualized as a heatmap for each antimicrobial.
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Figure 4. Principal components analysis (PCA) based on resistance patterns identified three major clusters. Isolates in Cluster 1 are marked in purple, Cluster 2 in green, and Cluster 3 in yellow.
Figure 4. Principal components analysis (PCA) based on resistance patterns identified three major clusters. Isolates in Cluster 1 are marked in purple, Cluster 2 in green, and Cluster 3 in yellow.
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Figure 5. Network analysis of resistance patterns using graph-based models. Imipenem-resistant isolates formed a distinct group. ACA—amoxicillin clavulanic acid; CRX—ceftriaxone; DOX—doxycycline; COL—colistin; PSA—potentiated sulphonamide; SPE—spectinomycin; NEO—neomycin; FLO—florfenicol; AMX—amoxicillin; ENR—enrofloxacin; IMI—imipenem.
Figure 5. Network analysis of resistance patterns using graph-based models. Imipenem-resistant isolates formed a distinct group. ACA—amoxicillin clavulanic acid; CRX—ceftriaxone; DOX—doxycycline; COL—colistin; PSA—potentiated sulphonamide; SPE—spectinomycin; NEO—neomycin; FLO—florfenicol; AMX—amoxicillin; ENR—enrofloxacin; IMI—imipenem.
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Figure 6. Decision tree model for predicting MDR strain occurrence. Potentiated sulfonamide resistance was selected as the starting point due to its strong association with other antimicrobials.
Figure 6. Decision tree model for predicting MDR strain occurrence. Potentiated sulfonamide resistance was selected as the starting point due to its strong association with other antimicrobials.
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Figure 7. Monte Carlo simulation-based stochastic modeling to predict MDR strain prevalence.
Figure 7. Monte Carlo simulation-based stochastic modeling to predict MDR strain prevalence.
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Figure 8. Comparison of Escherichia coli resistance rates in duck isolates with human resistance data provided by the National Public Health and Pharmaceutical Center.
Figure 8. Comparison of Escherichia coli resistance rates in duck isolates with human resistance data provided by the National Public Health and Pharmaceutical Center.
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Table 1. The frequency distribution table of minimum inhibitory concentrations (MICs) for Escherichia coli isolates (n = 108) from ducks, tested against antibiotics with established clinical breakpoints. The upper row represents the frequency values, while the lower row indicates the corresponding percentage. Vertical red lines denote the clinical breakpoints, while vertical green lines represent the epidemiological cut-off values (ECOFF) defined by the European Committee on Antimicrobial Susceptibility Testing (EUCAST).
Table 1. The frequency distribution table of minimum inhibitory concentrations (MICs) for Escherichia coli isolates (n = 108) from ducks, tested against antibiotics with established clinical breakpoints. The upper row represents the frequency values, while the lower row indicates the corresponding percentage. Vertical red lines denote the clinical breakpoints, while vertical green lines represent the epidemiological cut-off values (ECOFF) defined by the European Committee on Antimicrobial Susceptibility Testing (EUCAST).
AntibioticsBreakpoint0.0010.0020.0040.0080.0160.0310.0630.1250.250.512481632641282565121024MIC50MIC90ECOFF 3
(µg/mL)
Amoxicillin32 504222252273558165128
4.6%0.0%3.7%20.4%20.4%4.6%1.9%25.0%2.8%4.6%4.6%7.4%
Amoxicillin-clavulanic acid 132 1000517292520710111 8648
0.9%0.0%0.0%0.0%4.6%0.9%6.5%26.9%23.1%18.5%6.5%9.3%0.9%0.9%0.9%
Ceftriaxone4 52328784630238333110.06320.125
4.6%21.3%25.9%6.5%7.4%3.7%5.6%2.8%0.0%1.9%2.8%7.4%2.8%2.8%2.8%0.9%0.9%
Colistin2 3262816115121160214100.55122
2.8%1.9%5.6%25.9%14.8%10.2%4.6%0.9%1.9%0.9%14.8%0.0%1.9%0.9%3.7%9.3%
Doxycycline16 126331312101876 4648
0.9%1.9%5.6%30.6%12.0%11.1%9.3%16.7%6.5%5.6%
Enrofloxacin2 3226732273424142801 0.5320.125
2.8%20.4%5.6%6.5%2.8%20.4%6.5%2.8%3.7%1.9%3.7%13.0%1.9%7.4%0.0%0.9%
Florfenicol16 21033341331021 1612816
1.9%9.3%30.6%31.5%12.0%2.8%9.3%1.9%0.9%
Imipenem4 1014821262815121 0.5160.5
0.9%0.0%0.9%3.7%7.4%19.4%24.1%25.9%13.9%0.9%1.9%0.9%
Neomycin32 12939435162641288
0.9%1.9%8.3%36.1%39.8%4.6%0.9%5.6%1.9%
Potentiated sulphonamide 24 414211212422195131610240.5
3.7%13.0%19.4%11.1%11.1%3.7%1.9%1.9%17.6%4.6%12.0%
Spectinomycin128 100236984036412864
0.9%0.0%0.0%21.3%63.9%7.4%3.7%0.0%2.8%
1 ratio 2:1; 2 trimethoprim and sulfamethoxazole in a 19:1 ratio, 3 Epidemiological cut-off values (ECOFF) defined by the European Committee on Antimicrobial Susceptibility Testing (EUCAST).
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MDPI and ACS Style

Kerek, Á.; Szabó, Á.; Jerzsele, Á. Antimicrobial Susceptibility Profiles of Escherichia coli Isolates from Clinical Cases of Ducks in Hungary Between 2022 and 2023. Antibiotics 2025, 14, 491. https://doi.org/10.3390/antibiotics14050491

AMA Style

Kerek Á, Szabó Á, Jerzsele Á. Antimicrobial Susceptibility Profiles of Escherichia coli Isolates from Clinical Cases of Ducks in Hungary Between 2022 and 2023. Antibiotics. 2025; 14(5):491. https://doi.org/10.3390/antibiotics14050491

Chicago/Turabian Style

Kerek, Ádám, Ábel Szabó, and Ákos Jerzsele. 2025. "Antimicrobial Susceptibility Profiles of Escherichia coli Isolates from Clinical Cases of Ducks in Hungary Between 2022 and 2023" Antibiotics 14, no. 5: 491. https://doi.org/10.3390/antibiotics14050491

APA Style

Kerek, Á., Szabó, Á., & Jerzsele, Á. (2025). Antimicrobial Susceptibility Profiles of Escherichia coli Isolates from Clinical Cases of Ducks in Hungary Between 2022 and 2023. Antibiotics, 14(5), 491. https://doi.org/10.3390/antibiotics14050491

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