A window into resistance: The MEGA-plate as a model of evolutionary dynamics
Abstract
OBJECTIVES: The Microbial Evolution and Growth Arena (MEGA-plate) is a spatially structured experimental system that enables direct visualization of microbial adaptation under antimicrobial pressure. Unlike traditional well-mixed models, the MEGA-plate captures spatial dynamics and evolutionary bottlenecks in real time, offering unique insights into resistance development. METHODS: Here, we review advances in the application of the MEGA-plate across diverse contexts, integrating findings from bacterial, fungal, and veterinary domains. We also consider the MEGA-plate alongside established experimental evolution systems (e.g., serial passaging, continuous culture, and feedback-controlled selection devices). RESULTS: Since its introduction, the MEGA-plate has been applied across diverse contexts, from education and evolutionary theory to clinically relevant investigations of antibiotic resistance. Published studies show that the platform can reveal mutational trajectories, multidrug co-selection, and cross-resistance to critical antibiotic classes such as β-lactams and fluoroquinolones, as well as tolerance phenomena in fungal pathogens. CONCLUSIONS: These advances have important implications for antimicrobial stewardship, resistance surveillance, and therapeutic strategy. By integrating findings across bacterial, fungal, and veterinary domains, the MEGA-plate highlights translational potential as a tool to anticipate resistance pathways and inform clinical microbiology. Importantly, positioning the MEGA-plate alongside established experimental evolution systems clarifies where spatial drug landscapes provide unique mechanistic value and where alternative approaches remain essential for fastidious organisms and community-level questions.
Fields of Study & Subjects
Evolution and Genetic Dynamics
Antibiotic Resistance in Bacteria
Antibiotic Use and Resistance
Publication details
- Journal / source
- International Journal of Antimicrobial Agents
- Publisher
- Elsevier BV
- Volume
- 67
- Issue
- 5
- Pages
- 107751–107751
- Publication date
- 20 February 2026
- Language
- EN