Quantifying treatment-emergent persisters reveals substantial drug-induced persistence along a tolerance spectrum
Year: 2026
Type: preprint
Abstract: Antibiotics are known to induce new persister cells during treatment, yet the inability to distinguish and quantify pre-existing versus drug-induced persisters has long underestimated the extent of treatment-emergent persistence and obscured how antibiotics and genetic factors shape persistence. Here, we develop a quantitative framework integrating kinetic modeling with a novel serial-dilution time-kill (SDTK) strategy to resolve persister population dynamics and accurately quantify both persister types. We find that bactericidal antibiotics dynamically generate a substantial number of persisters that are heterogeneous and distributed along a persistence spectrum. Across antibiotics, we uncover pronounced differences in rates of persister induction and elimination, with ampicillin inducing persisters at the highest rate and kanamycin at the lowest. Depending on the dilution history, drug-induced persisters can dominate the persister pool. Our framework enables identification of drug-dependent pre-existing persister fractions and genetic determinants that differentially regulate pre-existing and/or drug-induced persistence. Using systematic sequential-drug treatments, we resolve the nested hierarchical structure among persister subsets, demonstrating that kanamycin persisters form the most tolerant subset, embedded within ciprofloxacin persisters that in turn are nested within the broader ampicillin persister subpopulation. Together, we propose a Drug-Induced Persistence-Spectrum (DIPS) model in which antibiotics differentially induce and select persister subsets along a tolerance continuum. These findings reframe persistence as stress-induced, treatment-responsive phenotypic heterogeneity and provide a unifying model with broad implications for drug tolerance and therapeutic failure in bacteria, yeasts, and cancers.
Source: Research Square
Institution: Dartmouth College
Language: English
Subfield: Genetics
Domain: Life Sciences
Open Access status: green
OpenAlex ID: W7135092401