Objective: The current investigation was designed to point out the prevalence of multidrug-resistant Streptococcus spp. causing acute clinical mastitis and their pattern of antibiotic resistance in dairy cows. Materials and methods: Milk was sampled from 128 dairy cows with 191 infected quarters during the period from August 2017 to December 2018. Bacterial species were isolated from the milk samples and identified based on colony morphology and biochemical tests. Multiplex PCR was done for confirmatory detection of the Streptococcus spp. isolates. Results: The chief isolation percentages, from the sampled milk, were Escherichia coli (26%), then Staphylococcus aureus (23%), and Streptococcus dysagalactiae (23%), then Streptococcus agalactiae (20.1%), and finally coagulase-negative Staphylococci (7.7%). In confirmed PCR streptococci isolates, the antibiotic resistance genes have been detected, including macrolides antibiotic resistance genes (ermB and mefA genes), lincosamides antibiotic resistance genes (linB gene), and tetracycline resistance genes (tetM and tetO genes). Age, parity number, cleaning of bedding materials, cleaning of milking facilities, and utensils and udder cleaning practice were significant risk factors for multidrug-resistant streptococcal mastitis in dairy cows. Conclusion: The results of this study explored the phenotypic and genotypic traits of Streptococcus spp. which constitute a usual cause of acute clinical mastitis in dairy cows. The ermB, mefA, tetM, and tetO antibiotic-resistant genes were identified in streptococci isolates from dairy cows' milk with acute clinical mastitis, indicating a public health hazard. Thus, veterinary clinical breakpoints are needed to improve surveillance data, improve the hygiene regimen on the farms, and promote the wise use of antimicrobials.
CITATION STYLE
Saed, H. A. E. M. R., & Ibrahim, H. M. M. (2020). Antimicrobial profile of multidrug-resistant Streptococcus spp. isolated from dairy cows with clinical mastitis. Journal of Advanced Veterinary and Animal Research, 7(2), 186–197. https://doi.org/10.5455/JAVAR.2020.G409
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