Cryptic Evolution of Heteroresistance as Adaptation to Treatment Interruptions

The evolution of antibiotic resistance is traditionally understood as a selective sweep to fixation, yielding easily detectable, population-wide resistance. Many clinical isolates, however, exhibit a subtle phenotype in which resistance remains hidden within a susceptible majority despite a clonal genetic background: a phenomenon clinically recognized as heteroresistance (HR). Treatment failure driven by HR has been widely reported across bacterial and fungal infections and in cancer therapy. To understand when and how HR evolves, and why it is selected over classical population-wide resistance, we conducted de novo evolution experiments starting from susceptible Escherichia coli and analyzed the genetic changes and fitness effects in the evolved strains. Prolonged gaps in antibiotic exposure are required for HR to evolve, implicating treatment interruptions as a key driver. HR emerges rapidly and reproducibly with minimal antibiotic use, yet its emergence is not readily detected by routine susceptibility testing. Unlike classical resistance, an evolved HR population partitions at the single-cell level into multiple phenotypes with distinct growth-resistance trade-offs. Their relative abundance shifts dynamically with antibiotic exposure, enabling robust population survival while avoiding the constitutive fitness burden associated with classical resistance. Despite this phenotypic flexibility, stable single mutations including a missense substitution and a short in-frame deletion are sufficient to generate HR, indicating a low evolutionary barrier. Additionally, we found that clinical isolates exhibit genetic and fitness signatures resembling those of our lab-evolved strains, suggesting that clinical HR emerges through the selective mechanism uncovered in our experiments. Together, our results establish HR as a readily evolvable adaptive strategy under treatment interruptions that leverages phenotypic flexibility to maintain resistance at minimal fitness cost, providing mechanistic insight into its emergence and prevalence.