Unravelling Antimicrobial Resistance in Mycoplasma hyopneumoniae : Genetic Mechanisms and Future Directions.

Simple Summary: Antimicrobial resistance (AMR) in bacteria is a critical issue threatening both human and animal health. This paper focuses on Mycoplasma hyopneumoniae, a bacterium causing lung disease in pigs, leading to significant economic losses in the swine industry worldwide. The problem is that this bacterium has developed resistance to many antibiotics, making treatment difficult. The study aims to understand the genetic basis of AMR by analyzing the reported genome of Mycoplasma hyopneumoniae strains using advanced techniques like whole genome sequencing. Key findings indicate that genetic mutations in certain genes are responsible for this resistance. This review paper suggests a multidisciplinary approach combining genetic, phenotypic, and bioinformatics data is essential in combating ever-increasing AMR in Mycoplasma hyopneumoniae. These insights could lead to better treatment strategies, ultimately benefiting the swine industry by improving animal health and reducing economic losses. Understanding and managing AMR in Mycoplasma hyopneumoniae is crucial for developing more effective antimicrobial agents and securing sustainable food production, which has a direct impact on society by ensuring food security and animal welfare. Antimicrobial resistance (AMR) in Mycoplasma hyopneumoniae, 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 M. hyopneumoniae, 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 M. hyopneumoniae. 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 M. hyopneumoniae as a fastidious microorganism. By leveraging contemporary genomic technologies and bioinformatics resources, the scientific community can better manage AMR in M. hyopneumoniae, 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. [ABSTRACT FROM AUTHOR]