Antimicrobial resistance (AMR) in , the causative agent of Enzootic Pneumonia in swine, poses a significant challenge to the swine industry. This review focuses on the genetic foundations of AMR in , highlighting the complexity of resistance mechanisms, including mutations, horizontal gene transfer, and adaptive evolutionary processes. Techniques such as Whole Genome Sequencing (WGS) and multiple-locus variable number tandem repeats analysis (MLVA) have provided insights into the genetic diversity and resistance mechanisms of . The study underscores the role of selective pressures from antimicrobial use in driving genomic variations that enhance resistance. Additionally, bioinformatic tools utilizing machine learning algorithms, such as CARD and PATRIC, can predict resistance traits, with PATRIC predicting 7 to 12 AMR genes and CARD predicting 0 to 3 AMR genes in 24 whole genome sequences available on NCBI. The review advocates for a multidisciplinary approach integrating genomic, phenotypic, and bioinformatics data to combat AMR effectively. It also elaborates on the need for refining genotyping methods, enhancing resistance prediction accuracy, and developing standardized antimicrobial susceptibility testing procedures specific to as a fastidious microorganism. By leveraging contemporary genomic technologies and bioinformatics resources, the scientific community can better manage AMR in , ultimately safeguarding animal health and agricultural productivity. This comprehensive understanding of AMR mechanisms will be beneficial in the adaptation of more effective treatment and management strategies for Enzootic Pneumonia in swine.
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http://www.ncbi.nlm.nih.gov/pmc/articles/PMC11598952 | PMC |
http://dx.doi.org/10.3390/vetsci11110542 | DOI Listing |
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