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  • Title: Development of a Machine Learning-Based Cysticidal Assay and Identification of an Amebicidal and Cysticidal Marine Microbial Metabolite against Acanthamoeba.
    Author: Shing B, Balen M, Fenical W, Debnath A.
    Journal: Microbiol Spectr; 2022 Jun 29; 10(3):e0007722. PubMed ID: 35467370.
    Abstract:
    Traditional cysticidal assays for Acanthamoeba species revolve around treating cysts with compounds and manually observing the culture for evidence of excystation. This method is time-consuming, labor-intensive, and low throughput. We adapted and trained a YOLOv3 machine learning, object detection neural network to recognize Acanthamoeba castellanii trophozoites and cysts in microscopy images to develop an automated cysticidal assay. This trained neural network was used to count trophozoites in wells treated with compounds of interest to determine if a compound treatment was cysticidal. We validated this new assay with known cysticidal and noncysticidal compounds. In addition, we undertook a large-scale bioluminescence-based screen of 9,286 structurally unique marine microbial metabolite fractions against the trophozoites of A. castellanii and identified 29 trophocidal hits. These hits were then subjected to this machine learning-based automated cysticidal assay. One marine microbial metabolite fraction was identified as both trophocidal and cysticidal. IMPORTANCE The free-living Acanthamoeba can exist as a trophozoite or cyst and both stages can cause painful blinding keratitis. Infection recurrence occurs in approximately 10% of cases due to the lack of efficient drugs that can kill both trophozoites and cysts. Therefore, the discovery of therapeutics that are effective against both stages is a critical unmet need to avert blindness. Current efforts to identify new anti-Acanthamoeba compounds rely primarily upon assays that target the trophozoite stage of the parasite. We adapted and trained a machine learning, object detection neural network to recognize Acanthamoeba trophozoites and cysts in microscopy images. Our machine learning-based cysticidal assay improved throughput, demonstrated high specificity, and had an exquisite ability to identify noncysticidal compounds. We combined this cysticidal assay with our bioluminescence-based trophocidal assay to screen about 9,000 structurally unique marine microbial metabolites against A. castellanii. Our screen identified a marine metabolite that was both trophocidal and cysticidal.
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