Rickettsia Aglow: A Fluorescence Assay and Machine Learning Model to Identify Inhibitors of Intracellular Infection.

Abstract

Rickettsia is a genus of Gram-negative bacteria that has for centuries caused large-scale morbidity and mortality. In recent years, the resurgence of rickettsial diseases as a major cause of pyrexias of unknown origin, bioterrorism concerns, vector movement, and concerns over drug resistance is driving a need to identify novel treatments for these obligate intracellular bacteria. Utilizing an uvGFP plasmid reporter, we developed a screen for identifying anti-rickettsial small molecule inhibitors using Rickettsia canadensis as a model organism. The screening data were utilized to train a Bayesian model to predict growth inhibition in this assay. This two-pronged methodology identified anti-rickettsial compounds, including duartin and JSF-3204 as highly specific, efficacious, and noncytotoxic compounds. Both molecules exhibited in vitro growth inhibition of R. prowazekii, the causative agent of epidemic typhus. These small molecules and the workflow, featuring a high-throughput phenotypic screen for growth inhibitors of intracellular Rickettsia spp. and machine learning models for the prediction of growth inhibition of an obligate intracellular Gram-negative bacterium, should prove useful in the search for new therapeutic strategies to treat infections from Rickettsia spp. and other obligate intracellular bacteria.