Etheridge, K. K. | bioRxiv Science

bioRxiv2026

Antibiotic exposure dynamically generates a substantial number of heterogeneous persisters along a spectrum of tolerance

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 obscured how antibiotics and genes shape persistence. Here, we develop a quantitative framework integrating kinetic modeling with serial-dilution time-kill (SDTK) assays to resolve persister population dynamics and accurately quantify both persister types. We show that antibiotic exposure dynamically generates 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 dilution history, drug-induced persisters can dominate the persister pool. Our framework enables identification of genetic determinants specific to pre-existing and/or drug-induced persistence and reveals drug-dependent pre-existing persister fractions. Systematic sequential-drug treatments demonstrate 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 for persister subsets along a tolerance continuum.

microbiology↗

bioRxiv2024

Growth-coupled continuous directed evolution by MutaT7 enables efficient and automated enzyme engineering

Traditional directed evolution is limited by labor-intensive iterative steps and low-throughput selection and screening. To address these challenges, we developed a growth-coupled continuous directed evolution (GCCDE) approach, enabling automated and efficient enzyme engineering. By linking enzyme activity to bacterial growth and utilizing the MutaT7 system, GCCDE combines in vivo mutagenesis and high-throughput selection of superior enzyme variants in a single process. To validate this approach, we evolved the thermostable enzyme CelB to enhance {beta}-galactosidase activity at lower temperatures while maintaining thermal stability. CelB activity was coupled to the growth of E. coli, allowing variants with improved activity to metabolize lactose more efficiently and promote faster bacterial growth in a minimal medium. Using a continuous culture system, we achieved automated mutagenesis and real-time selection of over 109 variants per culture. Integrating in vitro and in vivo mutagenesis further increased genetic diversity, yielding CelB variants with significantly enhanced low-temperature activity compared to the wild type while preserving thermostability. DNA sequencing identified key mutations likely responsible for improved substrate binding and catalytic turnover. This GCCDE approach is broadly applicable for optimizing diverse enzymes, demonstrating the potential of automated continuous evolution for industrial and research applications. IMPORTANCEEnzyme engineering aims to develop enzymes with improved or novel traits, but traditional methods are slow and require repetitive manual steps. This study presents a faster, automated protein engineering approach. We utilized an in vivo mutagenesis technique, MutaT7 tools, to induce mutations in living bacteria and established a direct link between enzyme activity and bacterial growth. A continuous culture setup was used to enable growth-coupled high-throughput selection of better-performing variants. Bacteria with improved enzymes grew faster, selecting superior variants without manual intervention. Using this method, we engineered CelB with better performance at lower temperatures while maintaining high-temperature stability. The approach is adaptable to many enzymes. It offers a faster and more efficient solution for enzyme engineering. This system enables high-throughput mutagenesis and selection simultaneously, showing the power of automated continuous evolution to advance enzyme engineering.

bioengineering↗