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Distinct sub-MIC kill kinetics of Cu and Ag in Escherichia coli

Rosenberg, M.; Umerov, S.; Tear, C. M.; Ivask, A.

2025-10-29 microbiology
10.1101/2025.08.27.672559 bioRxiv
Show abstract

Copper and silver are well-known and widely used antimicrobial metals that are often considered to employ diverse overlapping biocidal mechanisms of action and induce corresponding bacterial defense responses. Exposure to antimicrobial metals at concentrations below the minimal inhibitory concentration (sub-MIC) are widespread in natural, clinical and built environments and can shape evolutionary trajectories of the affected microbes that could lead to antimicrobial tolerance and/or resistance. By analyzing growth and kill kinetics of Escherichia coli under copper or silver exposure we observed that sub-MIC copper concentrations resulted in a lasting dose-dependent slowing of exponential growth with reduced yield while silver seemed to cause dose-dependent growth delay without substantially affecting exponential growth or yield. Time-kill experiments revealed minimal loss of viability in early copper exposure while in case of silver a rapid dose-dependent transient killing followed by normal exponential regrowth of the survivors was observed, underlying the seemingly dose-dependently extended lag phase durations. Distinguishing conditions that select for antimicrobial resistance (sustained growth) versus tolerance (survival without growth) is essential for antimicrobial stewardship as acquiring tolerance is considered a steppingstone towards developing resistance. Our results suggest that short-term survival of the initial killing by silver is sufficient for selective advantage while maintaining energy-intensive enhanced growth in the presence of copper is needed to gain competitive benefit over the general population. The findings highlight new and known challenges in antimicrobial characterization and risk assessment of metal-based formulations by using wide-spread non-kinetic endpoint assays such as MIC.

Published in Microbiology Spectrum (predicted rank #1) · training set

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