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Summary: The innate immune system is critical for infection survival. Drosophila melanogaster is a key model for understanding the evolution and dynamics of innate immunity. Current toolsets for fly infection studies are limited in throughput and, because of their destructive nature, cannot generate longitudinal measurements in individual animals. We report a bioluminescent imaging strategy enabling non-invasive characterization of pathogen load. By using Escherichia coli expressing the ilux operon, we demonstrate that photon flux from autobioluminescent bacteria can be used to monitor pathogen loads in individual, living flies. Because animal sacrifice is not necessary to estimate pathogen load, stochastic responses to infection can be characterized in individuals over time. The high temporal resolution of bioluminescence imaging enables visualization of the dynamics of microbial clearance on the hours time-scale. This non-invasive imaging strategy provides a simple and scalable platform to observe changes in pathogen load in vivo over time.

Abstract Dose-response models (DRMs) are used to predict the probability of microbial infection when a person is exposed to a given number of pathogens. In this study, we propose a new DRM for Staphylococcus aureus (SA), which causes skin and soft-tissue infections. The current approach to SA dose-response is only partially mechanistic and assumes that individual bacteria do not interact with each other. Our proposed two-compartment (2C) model assumes that bacteria that have not adjusted to the host environment decay. After adjusting to the host, they exhibit logistic/cooperative growth, eventually causing disease. The transition between the adjusted and un-adjusted states is a stochastic process, which the 2C DRM explicitly models to predict response probabilities. By fitting the 2C model to SA pathogenesis data, we show that cooperation between individual SA bacteria is sufficient (and, within the scope of the 2C model, necessary) to characterize the dose-response. This is a departure from the classical single-hit theory of dose-response, where complete independence is assumed between individual pathogens. From a quantitative microbial risk assessment standpoint, the mechanistic basis of the 2C DRM enables transparent modeling of dose-response of antibiotic-resistant SA that has not been possible before. It also enables the modeling of scenarios having multiple/non-instantaneous exposures, with minimal assumptions.