Biocontrol of Soil-Borne Pathogens of Solanum lycopersicum L. and Daucus carota L. by Plant Growth-Promoting Actinomycetes: In Vitro and In Planta Antagonistic Activity.

Biotic stress caused by pathogenic microorganisms leads to damage in crops. Tomato and carrot are among the most important vegetables cultivated worldwide. These plants are attacked by several pathogens, affecting their growth and productivity. Fourteen plant growth-promoting actinomycetes (PGPA) were screened for their in vitro biocontrol activity against Solanum lycopersicum and Daucus carota microbial phytopathogens. Their antifungal activity was evaluated against Fusarium oxysporum f. sp. radicis-lycopersici (FORL) and Rhizoctonia solani (RHS). Antibacterial activity was evaluated against Pseudomonas syringae, Pseudomonas corrugata, Pseudomonas syringae pv. actinidiae, and Pectobacterium carotovorum subsp. carotovorum. Strains that showed good in vitro results were further investigated in vitro (cell-free supernatants activity, scanning electron microscope observations of fungal inhibition). The consortium of the most active PGPA was then utilized as biocontrol agents in planta experiments on S. lycopersicum and D. carota. The Streptomyces albidoflavus H12 and Nocardiopsis aegyptica H14 strains showed the best in vitro biocontrol activities. The diffusible and volatile compounds and cell-free supernatants of these strains showed both antifungal (in vitro inhibition up to 85%, hyphal desegregation and fungicidal properties) and antibacterial activity (in vitro inhibition >25 mm and bactericidal properties). Their consortium was also able to counteract the infection symptoms of microbial phytopathogens during in planta experiments, improving plant status. The results obtained highlight the efficacy of the selected actinomycetes strains as biocontrol agents of S. lycopersicum and D. carota.

Valorization and optimization of agro-industrial orange waste for the production of enzyme by halophilic Streptomyces sp.

This study underlines the biotechnical valorization of the accumulated and unusable remains of agro-industrial orange fruit peel waste to produce α-amylase under submerged conditions by Streptomyces sp. KP314280 (20r). The response surface methodology based on central composite design (RSM-CCD) and artificial neural network coupled with a genetic algorithm (ANN-GA) were used to model and optimize the conditions for the α-amylase production. Four independent variables were evaluated for α-amylase activity including substrate concentration, inoculum size, sodium chloride powder (NaCl), and pH. A ten-fold cross-validation indicated that the ANN has a greater ability than the RSM to predict the α-amylase activity (R2ANN = 0.884 and R2RSM = 0.725). The analysis of variance indicated that the aforementioned four factors significantly affected the α-amylase activity. Additionally, the α-amylase production experiments were conducted according to the optimal conditions generated by the GA. The results indicated that the amylase yield increased by 4-fold. Moreover, the α-amylase production (12.19 U/mL) in the optimized medium was compatible with the predicted conditions outlined by the ANN-GA model (12.62 U/mL). As such, the ANN and GA combination is optimizable for α-amylase production and exhibits an accurate prediction which provides an alternative to other biological applications.