Diffusion-driven enhancement of the antibiotic resistance selection window

Area of Science:

• Microbiology
• Evolutionary Biology
• Mathematical Modeling

Background:

• Antimicrobial resistance (AMR) is a global health crisis, yet resistance evolution is poorly understood under heterogeneous conditions.
• Current models often assume well-mixed environments, neglecting spatial heterogeneity and antibiotic gradients in human tissues.
• Spatial heterogeneity creates dynamic selective pressures influencing bacterial adaptation to antibiotics.

Purpose of the Study:

• To investigate the impact of spatial heterogeneity and connectivity on the population dynamics of antibiotic-susceptible and resistant bacteria.
• To model the spread of drug resistance in a network of interconnected micro-environments.

Main Methods:

• Mathematical modeling and computer simulations were employed to study bacterial population dynamics.
• Experiments utilized 3D-printed devices to control antibiotic diffusion rates between compartments.
• Fluorescently labeled *Escherichia coli* strains were used to quantify spatio-temporal distribution of resistant and susceptible cells.

Main Results:

• Highly connected environments facilitate increased diffusion of antibiotic molecules.
• Enhanced diffusion enables resistant bacterial phenotypes to colonize a larger spatial area.
• Experimental validation confirmed theoretical predictions regarding the role of connectivity in resistance spread.

Conclusions:

• Spatial connectivity significantly influences the dynamics of antibiotic resistance evolution.
• Understanding environmental connectivity is crucial for predicting and controlling the spread of antimicrobial resistance.
• This research provides a framework for studying resistance in spatially structured microbial communities.