Antimicrobial resistance is an escalating global threat that undermines the efficacy of modern antibiotics and places a substantial economic burden on healthcare systems—costing Europe alone over EUR 11.7 billion each year due to rising medical expenses and productivity losses. While genomics and transcriptomics have significantly advanced our understanding of the genetic foundations of resistance, they often fail to capture the dynamic, real-time adaptations that enable bacterial survival. Proteomics, particularly mass spectrometry-based strategies, bridges this gap by uncovering the functional protein-level changes that drive resistance, persistence, and tolerance under antibiotic pressure. In this review, we examine how proteomic approaches provide new insights into resistance mechanisms across various antibiotic classes, with a particular focus on β-lactams, aminoglycosides, and fluoroquinolones, highlighting clinically relevant pathogens, especially members of the ESKAPE group. Finally, we examine future directions, including the integration of proteomics with other omic technologies and the growing role of artificial intelligence in resistance prediction, paving the way for more predictive, personalized, and effective solutions to combat antimicrobial resistance.
Aita et al. (Sun,) studied this question.