Exploration of Algerian ecosystems for the selection of Actinobacteria belonging to the genus Streptomyces developing potentialities of PGPR and antagonists of wheat phytopathogens

Abstract

This work aims to isolate Actinobacteria strains with a growth promotion ability and the biocontrol potential of Fusarium culmorum, the wheat root rot-causing fungi. The exploration of terrestrial and aquatic Algerian ecosystems, i.e. the wheat rhizosphere in the Tiffeche region (Souk-Ahras) and the aquatic sediments of Lake Oubeira (El Taref), respectively, resulted in 102 native isolates. Therefore, 37 have morphological and cultural characteristics similar to the genus Streptomyces. These isolates were screened for their plant growth-promoting traits. These activities consist of the production of hydrogen cyanide (HCN), ammonia (NH3), and indole-3- acetic acid (IAA), as well as in vitro antagonism against F. culmorum. Among the auxin family, IAA constitutes a crucial phytohormone regulating specific tropic responses of plants and functions as a chemical signal between host plants and their symbionts. IAA derived from Actinobacteria grown on agricultural waste represents a more economical alternative than its synthetic homologous. Rhizospheric isolate AW 22 was positive for HCN and NH3 production, growth inhibition of F. culmorum with an index of 67.320±8.99% and high IAA content of 23.999±1.126 μg. mL-1 in standard growth conditions on yeast-tryptone broth (YTB) amended with 0.2% (w/v) L-Tryptophan. Thus, the AW22 isolate was selected for a polyphasic chemotaxonomic characterization and the optimization of the production process of this phytohormone. Molecular and phylogenetic analysis identified isolate AW22 as Streptomyces rubrogriseus, and its sequence was deposited in Genbank under accession ID OP176004. Analysis of the putative IAA produced by S. rubrogriseus AW22 on YTB using thin-layer chromatography (TLC) and (HPLC) revealed Rf values equal to 0.69 and a retention time of 3.711 min, equivalent to the authentic IAA. Artificial intelligence-based approaches (i.e. Behnken design from response surface methodology (BBD-RSM) with artificial neural networks (ANNs) coupled with the genetic algorithm (GA)) were employed to bioengineer in vitro and silico a suitable medium for maximum IAA bioproduction. According to the Box Behnken Design matrix, data were based on empirical studies involving the inoculation of AW22 in various cultural conditions and low-cost feedstocks notably, the spent coffee grounds (SCGs). Four input variables comprising L-Trp (X1), incubation T° (X2), initial pH (X3) and SCG concentration (X4) were screened via Plackett-Burman design (PBD) and served as BBD and ANN-GA inputs. The IAA yield constituted the output variable (Y in µg. mL-1). Upon training the model, the optimal conditions suggested by the ANN-GA model were X1= 0.6%, X2= 25.8°C, X3 = 9, X4=30%). An R2 of 99.98%, adding to an MSE of 1.86x10-5 at 129 epochs, postulated higher reliability of the ANN-GA approach in predicting responses, compared with BBD-RSM modeling exhibiting an R2 of 76,28%. Using the process parameters generated by ANN-GA AW 22 achieved a maximum IAA yield of 188.290±0.38 µg. mL-1. This optimization resulted in a 4.55-fold and 4.46- fold increase in IAA secretion after eight days of incubation, corresponding to ANN-GA and BBD-RSM models, respectively. These results confirm the validity of both models in maximizing IAA yield from the multifunctional S. rubrogriseus AW22 isolated for the first time in Algeria.