Abstract Sorghum Anthracnose and Black Sigatoka of bananas are problematic fungal diseases worldwide, with a particularly devastating impact on small-holder farmers in Sub-Saharan Africa. We screened a total of 1,227 bacterial isolates for antifungal activity against these pathogens using detached-leaf methods and identified 72 isolates with robust activity against one or both of these pathogens. These bacterial isolates represent a diverse set of five phyla, 14 genera and 22 species, including taxa for which this is the first observation of fungal disease suppression. We identified biosynthetic gene clusters associated with activity against each pathogen. Through a machine learning workflow we discovered additional active isolates, including an isolate from a genus that had not been included in previous screening or model training. Machine-learning improved the discovery rate of our screen by 3-fold. This work highlights the wealth of biocontrol mechanisms available in the microbial world for management of fungal pathogens, generates opportunities for future characterization of novel fungicidal mechanisms, and provides a set of genomic features and models for discovering additional bacterial isolates with activity against these two pathogens.

Genomics-accelerated discovery of diverse fungicidal bacteria