Nikolaos Tzimotoudis¹, Antonia Mataragka²*, Alexandros Mavrommatis³, Chrisovalantis Malesios⁴, Vasiliki Anastasiadi², Dimitrios Galanis², Dorina Timofte⁵, Flavia Zendri⁵, Helena C de Carvalho Ferreira⁶, Erwin Wauters⁶, Eleni Tsiplakou³, George Zervas³ and John Ikonomopoulos ²

¹Hellenic Army Biological Research Center/Laboratory of Microbiology, Athens, Greece
²Department of Animal Science, Laboratory of Anatomy and Physiology of Farm Animals, School of Animal Biosciences, Agricultural University of Athens, Greece
³Department of Animal Science, Laboratory of Nutritional Physiology and Feeding, School of Animal Biosciences, Agricultural University of Athens, Greece
⁴Department of Agricultural Economics and Rural Development, Agricultural University of Athens, Greece
⁵Department of Veterinary Anatomy, Physiology and Pathology, Institute of Infection, Veterinary and Ecological Sciences, University of Liverpool, Neston, United Kingdom
⁶Flanders Research Institute for Agriculture, Fisheries and Food, Social Sciences Unit, Belgium

*Corresponding Author: Antonia Mataragka, Department of Animal Science, Laboratory of Anatomy and Physiology of Farm Animals, School of Animal Biosciences, Agricultural University of Athens, Greece.

Received: October 20, 2022; Published: October 29, 2022

Abstract

The aim of this study was to assess the spread of antimicrobial resistance (AMR) in sheep farms in Greece and identify potential indicators to improve field surveillance.

Ninety-four samples of milk, drinking water, animal feed, bedding, and faeces were collected from 5 dairy sheep farms. The samples were processed for isolation of Staphylococcus aureus and Escherichia coli, and the assessment of AMR using conventional microbiology and the polymerase chain reaction.

Positivity to Escherichia coli and Staphylococcus aureus was 38.3% and 16%, respectively (36 and 15 of 94). Detection of Escherichia coli in animal feeds was significantly higher compared to the other types of test samples, and the presence of Staphylococcus aureus AMR in the certain types of samples increased probability of its detection in milk by 3.25 times. Investigation for associations between sample positivity with the use of antibiotics indicated that the higher the amount of antibiotics, the higher the proportion of Escherichia coli non-susceptible to at least two antimicrobial categories (AMR+), detected in milk.

Escherichia coli isolates were significantly more likely to be AMR+ when the latter pathogen was resistant to ampicillin.

The results indicate that AMR is a common problem in the sampled farms, which is associated with high occurrence of mastitis and poor antibiotic stewardship for its treatment. Animal feeds and milk collected from the bulk milk tank proved suitable for the assessment of AMR, as did detection of ampicillin-resistant Escherichia coli. Isolation of AMR Staphylococcus aureus in animal feeds emerged as a promising indicator for monitoring mastitis.

Keywords: Antimicrobial Resistance; Staphylococcus aureus; Escherichia coli, Antibiotics; Dairy Sheep; Multidrug Resistance

References

1. World Health Organization, 2020. “Antimicrobial resistance” (2021).
2. Food and Agriculture Organization, 2016. “Drivers, dynamics and epidemiology of antimicrobial resistance in animal production” (2021).
3. Okonko IO., et al. “Incidence of Multi-Drug Resistant (MDR) organisms in some poultry feeds sold in Calabar Metropolis, Nigeria”. Electronic Journal of Environmental, Agricultural and Food Chemistry3 (2010): 514-532.
4. Mahami T., et al. “Microbial food safety risk to humans associated with poultry feed: the role of irradiation”. International Journal of Food Science (2019): 6915736.
5. Apata DF. “Antibiotic Resistance in Poultry”. International Journal of Poultry Science4 (2009): 404-408.
6. Burow E., et al. “Oral antimicrobials increase antimicrobial resistance in porcine coli-a systematic review”. Preventive Veterinary Medicine 113.4 (2014): 364-375.
7. Sekyere JO. “Antibiotic types and handling practices in disease management among pig farms in Ashanti Region, Ghana”. Journal of Veterinary Medicine (2014): 531952.
8. Ma F., et al. “Use of antimicrobials in food animals and impact of transmission of antimicrobial resistance on humans”. Biosafety and Health 1 (2021): 32-38.
9. ISO 9308-1:2014, 2014. Water Quality - Enumeration of Escherichia coli and Coliform Bacteria - Part 1: Membrane Filtration Method for waters with low bacterial background flora. Geneva: International Standards Organization (2014).
10. Markey B., et al. “Clinical Veterinary Microbiology”. 2^nd, Elsevier, Chapter 17, 239-244.
11. Brakstad OG., et al. “Detection of Staphylococcus aureus by polymerase chain reaction amplification of the nuc gene”. Journal of Clinical Microbiology 7 (1992): 1654-1660.
12. Abdulmawjood A., et al. “Two methods for construction of internal amplification controls for the detection of Escherichia coli O157 by polymerase chain reaction”. Molecular and Cellular Probes 16 (2002): 335-339.
13. Madden T. “The BLAST Sequence Analysis Tool”. In: McEntyre, J., Ostell, J., editors. The NCBI Handbook Internet. Bethesda (MD): National Center for Biotechnology Information (US); 2002-. Chapter 16 (2002).
14. The European Committee on Antimicrobial Susceptibility Testing. Breakpoint tables for interpretation of MICs and zone diameters. Version 11.0 (2021).
15. Vannuffel P., et al. “Specific detection of Methicillin-Resistant Staphylococcus species by multiplex PCR”. Journal of Clinical Microbiology11 (1995): 2864-2867.
16. Francois P., et al. “Rapid detection of Methicillin-Resistant Staphylococcus aureus directly from sterile or nonsterile clinical samples by a new molecular assay”. Journal of Clinical Microbiology1 (2003): 254-260.
17. Magiorakos AP., et al. “Multidrug resistant, extensively drug-resistant and pandrug-resistant bacteria: an international expert proposal for interim standard definitions for acquired resistance”. Clinical Microbiology and Infection 3 (2012): 268-281.
18. Agresti A. “Categorical Data Analysis”. Wiley, 2^nd John Wiley and Sons, New York, USA (2002).
19. R Development Core Team. R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria (2008).
20. Davies R and Wales A. “Antimicrobial Resistance on Farms: A Review Including Biosecurity and the Potential Role of Disinfectants in Resistance Selection”. Comprehensive Reviews in Food Science and Food Safety 3 (2019): 753-774.
21. Van Cuong N., et al. “Antimicrobial consumption in medicated feeds in vietnamese pig and poultry production”. EcoHealth 13 (2016): 490-498.
22. Amir M., et al. “Impact of unhygienic conditions during slaughtering and processing on spread of antibiotic resistant Escherichia coli from poultry”. Microbiology Research2 (2017): 7330.
23. Zhao L., et al. “Residues of veterinary antibiotics in manures from feedlot livestock in eight provinces of China”. Science of The Total Environment 5 (2010): 1069-1075.
24. Wang H., et al. “Occurrence of veterinary antibiotics in swine manure from large-scale feedlots in Zhejiang Province, China”. Bulletin of Environmental Contamination and Toxicology 98 (2017): 472-477.

Citation

Citation: Antonia Mataragka., et al. “Antimicrobial Drug Resistance in Sheep Farms in Greece: Assessment of the Current Situation and Investigation Towards Improving Surveillance and Control".Acta Scientific Veterinary Sciences 4.11 (2022): 173-186.

Copyright

Copyright: © 2022 Antonia Mataragka., et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.