Multi-Omics Approaches to Resolve Antimicrobial Resistance

Antimicrobial resistance (AMR) has emerged as a significant global concern for public health, and a deeper understanding of the mechanisms and molecular features of AMR is required to overcome this challenge. The development of whole genome sequencing and other large-scale, high-throughput molecular techniques to profile DNA, RNA, proteins, and metabolites allows for the acquisition of different layers of multiple “omics” data from a single sample. The integration of “omics” data generated through genomics, metagenomics, transcriptomics, proteomics, and metabolomics has provided insight into the complex molecular mechanisms underlying AMR as well as helped identify novel drug targets. Additionally, the application of advanced machine learning algorithms toward the development of predictive models has enabled researchers to apply precision medicine strategies to combat AMR effectively, using a multi-omics approach. In this review, various applications and examples of multi-omics data integration are described, and the rapidly expanding arsenal of bioinformatics tools used in the integration and analysis of multi-omics data in pursuit of treatment strategies for AMR is discussed. Finally, future directions and limitations of this strategy are addressed, providing a comprehensive perspective on the application of multi-omics integration in addressing AMR.