Identification of putative coral pathogens in endangered Caribbean staghorn coral using machine learning

Abstract

Coral diseases contribute to the rapid decline in coral reefs worldwide, and yet coral bacterial pathogens have proved difficult to identify because 16S rRNA gene surveys typically identify tens to hundreds of disease-associate bacteria as putative pathogens. An example is white band disease (WBD), which has killed up to 95% of the now-endangered Caribbean Acropora corals since 1979, yet the pathogen is still unknown. The 16S rRNA gene surveys have identified hundreds of WBD-associated bacterial amplicon sequencing variants (ASVs) from at least nine bacterial families with little consensus across studies. We conducted a multi-year, multi-site 16S rRNA gene sequencing comparison of 269 healthy and 143 WBD-infected Acropora cervicornis and used machine learning modelling to accurately predict disease outcomes and identify the top ASVs contributing to disease. Our ensemble ML models accurately predicted disease with greater than 97% accuracy and identified 19 disease-associated ASVs and five healthy-associated ASVs that were consistently differentially abundant across sampling periods. Using a tank-based transmission experiment, we tested whether the 19 disease-associated ASVs met the assumption of a pathogen and identified two pathogenic candidate ASVs—ASV25 Cysteiniphilum litorale and ASV8 Vibrio sp. to target for future isolation, cultivation, and confirmation of Henle-Koch's postulate via transmission assays.