Members of the genus Pseudomonas are metabolically versatile and capable of adapting to a wide variety of environments. Stress physiology of Pseudomonas strains has been extensively studied because of their biotechnological potential in agriculture as well as their medical importance with regards to pathogenicity and antibiotic resistance. This versatility and scientific relevance led to a substantial amount of information regarding the stress response of a diverse set of species such as Pseudomonas chlororaphis, P. fluorescens, P. putida, P. aeruginosa, and P. syringae. In this review, environmental and industrial stressors including desiccation, heat, and cold stress, are cataloged along with their corresponding mechanisms of survival in Pseudomonas. Mechanisms of survival are grouped by the type of inducing stress with a focus on adaptations such as synthesis of protective substances, biofilm formation, entering a non-culturable state, enlisting chaperones, transcription and translation regulation, and altering membrane composition. The strategies Pseudomonas strains utilize for survival can be leveraged during the development of beneficial strains to increase viability and product efficacy.
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.
Microorganisms with antimicrobial activity have been used to successfully control various plant pathogens. The discovery of organisms with protective activity depends on empirical screenings to assess microbial activity against pathogens of interest. Machine learning can accelerate the discovery process by making screening and thus, discovery, more efficient. We developed a novel machine-learning workflow to identify genomic features associated with fungicidal activity of bacteria, and leveraged those genomic features to discover additional bacteria with the desired activity. We applied our workflow to discover solutions to two problematic fungal diseases: Sorghum Anthracnose and Black Sigatoka of bananas. These diseases are problematic 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 taxonomically-diverse isolates with robust activity against one or both of these pathogens. We identified biosynthetic gene clusters associated with activity against each pathogen. Machine-learning improved the discovery rate of our screen by 3-fold, and led to the discovery of a taxonomic group in which fungicidal activity has never been reported. 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. Finally, our workflow generalizes to any discovery effort where genomic information is available to guide candidate selection.
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