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Rickettsia Aglow: A Fluorescence Assay and Machine Learning Model to Identify Inhibitors of Intracellular Infection
Alexander Lemenze1, Nisha Mittal2, Alexander L Perryman2
1Department of Medicine, and the Ruy V. Lourenco Center for the Study of Emerging and Reemerging Pathogens, Rutgers University - New Jersey Medical School, Medical Sciences Building, 185 South Orange Avenue, Newark, New Jersey 07103, United States.
Researchers identified new treatments for Rickettsia bacteria, which cause serious diseases. A novel screening method and machine learning pinpointed effective compounds like duartin and JSF-3204 against these difficult-to-treat intracellular pathogens.
Area of Science:
- Microbiology
- Infectious Diseases
- Drug Discovery
Background:
- Rickettsia bacteria cause significant global morbidity and mortality.
- Emerging concerns include drug resistance and bioterrorism potential.
- Novel treatments are urgently needed for these obligate intracellular pathogens.
Purpose of the Study:
- To develop and implement a high-throughput screening method for identifying novel anti-rickettsial compounds.
- To utilize machine learning to predict bacterial growth inhibition.
- To discover specific, efficacious, and non-cytotoxic small molecules against Rickettsia species.
Main Methods:
- Development of a uvGFP plasmid reporter assay for Rickettsia canadensis.
- Implementation of a high-throughput phenotypic screen for small molecule inhibitors.
- Training a Bayesian model using screening data to predict growth inhibition.
- In vitro testing of identified compounds against Rickettsia prowazekii.
Main Results:
- Identification of duartin and JSF-3204 as potent anti-rickettsial compounds.
- Demonstration of high specificity, efficacy, and low cytotoxicity for these compounds.
- Confirmation of in vitro growth inhibition of Rickettsia prowazekii by duartin and JSF-3204.
Conclusions:
- The developed screening workflow and machine learning models are effective for discovering inhibitors of intracellular Rickettsia.
- Duartin and JSF-3204 represent promising therapeutic leads for rickettsial infections.
- This methodology can be applied to find treatments for other obligate intracellular bacterial infections.
