Modeling-based optimization approaches for the development of Anti- Agrobacterium tumefaciens activity using Streptomyces sp TN71

Optimized carotenoid production and antioxidant capacity of Gordonia hongkongensis

The current emphasis within the cosmetic market on sustainable ingredients has heightened the exploration of new sources for natural, active components. Actinomycetota, recognized for producing pigments with bioactive potential, offer promising functional cosmetic ingredients. This study aimed to optimize pigment and antioxidant metabolite production from the Gordonia hongkongensis strain EUFUS-Z928 by implementing the Plackett-Burman experimental design and response surface methodology. Extracts derived from this strain exhibited no cytotoxic activity against human primary dermal fibroblast (HDFa, ATCC® PCS-201-012™, Primary Dermal Fibroblast; Normal, Human, Adult). Eight variables, including inoculum concentration, carbon and nitrogen source concentration, NaCl concentration, pH, incubation time, temperature, and stirring speed, were analyzed using the Plackett-Burman experimental design. Subsequently, factors significantly influencing pigment and antioxidant metabolite production, such as temperature, inoculum concentration, and agitation speed, were further optimized using response surface methodology and Box-Behnken design. The results demonstrated a substantial increase in absorbance (from 0.091 to 0.32), DPPH radical scavenging capacity (from 27.60% to 84.61%), and ABTS radical scavenging capacity (from 17.39% to 79.77%) compared to responses obtained in the isolation medium. The validation of the mathematical model accuracy exceeded 90% for all cases. Furthermore, liquid chromatography coupled with mass spectrometry (LC-MS) facilitated the identification of compounds potentially responsible for enhanced pigment production and antioxidant capacity in extracts derived from G. hongkongensis. Specifically, six carotenoids, red-orange pigments with inherent antioxidant capacity, were identified as the main enhanced compounds. This comprehensive approach effectively optimized the culture conditions and medium of a G. hongkongensis strain, resulting in enhanced carotenoid production and antioxidant capacity. Beyond identifying bioactive compounds and their potential cosmetic applications, this study offers insights into the broader industrial applicability of these extracts. It underscores the potential of G. hongkongensis and hints at the future utilization of other untapped sources of rare actinomycetes within the industry.

Identification of potent anti-Candida metabolites produced by the soft coral associated Streptomyces sp. HC14 using chemoinformatics

Candida albicans is the most common pathogen responsible for both spontaneous and recurrent candidiasis. The available treatment of Candida infections has several adverse effects, and the development of new drugs is critical. The current study looked at the synthesis of anti-Candida metabolites by Streptomyces sp. HC14 recovered from a soft coral. Using the Plackett Burman design, the medium composition was formulated to maximize production. Using GC-MS, the compounds have been identified, and a cheminformatics approach has been used to identify the potential source of activity. The compounds that showed high potential for activity were identified as pyrrolo[1,2-a]pyrazine-1,4-dione, hexahydro-3-(phenylmethyl)-3 and di-n-octyl based on their docking score against the cytochrome monooxygenase (CYP51) enzyme in Candida albicans. As a result of their discovery, fewer molecules need to be chemically synthesized, and fermentation optimization maximizes their synthesis, providing a strong foundation for the development of novel anti-Candida albicans agents.

Purification and characterization of seven bioactive compounds from the newly isolated Streptomyces cavourensis TN638 strain via solid-state fermentation

The strain TN638 was isolated from Tunisian soil contaminated with industrial wastewater and selected for its potent antimicrobial activity against the tested Gram positive bacteria: Staphylococcus aureus (S. aureus) ATCC 6538 and Listeria monocytogenes (L. monocytogenes) ATCCC 19117, and Gram negative bacteria: Agrobacterium tumefaciens (A. tumefaciens) ATCC 23308 and Salmonella typhimurium (S. typhimurium) ATCC 14028 and fungi: Candida albicans (C. albicans) ATCC 10231, Rhizoctonia solani (R. solani) ATCC 58938 and Fusarium sp. Solide-state fermentation (SSF) dry crude extract of the TN638 strain presents a strong inhibitory activity notably against the phytopathogenic microorganism A. tumefaciens ATCC 23308 and the two pathogenic bacteria S. aureus ATCC 6538 and L. monocytogenes ATCCC 19117 with a zone of inhibition of 48, 34 and 34 mm respectively. According to the morphological characteristic, the complete 16S rRNA gene nucleotide sequence determination [1492 bp deposited in National Center of Biotechnology Information (NCBI) database under the accession no. LN854629.1; https://www.ncbi.nlm.nih.gov/nuccore/LN854629.1/], and the phylogenetic analysis, we can deduce that our isolate is an actinomycete bacterium belonging to the genus Streptomyces and the most closely related strain was Streptomyces cavourensis (S. cavourensis) NRRL 2740T (99.9%). We propose the assignment of our strain as Streptomyces cavourensis (S. cavourensis) TN638 strain. Work-up and purification of the strain extract using different chromatographic techniques afforded seven bio-compounds namely: Cyclo-(Leu-Pro) (1), Cyclo-(Val-Pro) (2), Cyclo-(Phe-Pro) (3), nonactin (4), monactin (5), dinactin (6) and trinactin (7). The chemical structures of compounds 1-7 were confirmed by nuclear magnetic resonance (NMR) 1D and 2D spectroscopy, mass spectrometry, and comparison with literature data. The three purified diketopiperazine (DKP) derivatives (1-3), demonstrated significant antibacterial activity against A. tumefaciens ATCC 23308 and S. typhimurium ATCC 14028. The four pure macrotetrolides (4-7), exhibited strong inhibitory effect against all tested Gram positive and Gram negative bacteria notably against A. tumefaciens ATCC 23308 and S. typhimurium ATCC 14028 with a minimum inhibitory concentration (MIC) around 8 μg/mL quite similar to that of ampicillin. Thus, we propose the use of the (SSF) active extract of the S. cavourensis TN638 strain as safe biological product to control disease caused by plant pathogen A. tumefaciens. Also, the purified active molecules produced by this strain could be used in pharmaceutical field.

Multiresponse Optimization of Pomegranate Peel Extraction by Statistical versus Artificial Intelligence: Predictive Approach for Foodborne Bacterial Pathogen Inactivation

Pomegranate (Punica granatum L.) peel is a potential source of polyphenols known for their activity against foodborne pathogen bacteria. In this study, the effects of pomegranate peel extraction time (10–60 min), agitation speed (120–180 rpm), and solvent/solid ratio (10–30) on phytochemical content and antibacterial activity were determined. Response surface methodology (RSM) and artificial neural network (ANN) methods were used, respectively, for multiresponse optimization and predictive modelling. Compared with the original conditions, the total phenolic content (TPC), the total flavonoid content (TFC), and the total anthocyanin content (TAC) increased by 56.22, 63.47, and 64.6%, respectively. Defined by minimal inhibitory concentration (MIC), the maximum of antibacterial activity was higher than that from preoptimized conditions. With an extraction time of 11 min, an agitation speed 125 rpm, and a solvent/solid ratio of 12, anti-S. aureus activity remarkably decreased from 1.56 to 0.171 mg/mL. Model comparisons through the coefficient of determination (R²) and mean square error (MSE) showed that ANN models were better than the RSM model in predicting the photochemical content and antibacterial activity. To explore the mode of action of the pomegranate peel extract (PPE) at optimal conditions against S. aureus and S. enterica, Chapman and Xylose Lysine Deoxycholate broth media were artificially contaminated at 10⁴ CFU/mL. By using statistical approach, linear (ANOVA), and general (ANCOVA) models, PPE was demonstrated to control the two dominant foodborne pathogens by suppressing bacterial growth.

Statistical versus artificial intelligence -based modeling for the optimization of antifungal activity against Fusarium oxysporum using Streptomyces sp. strain TN71

A Streptomyces sp. strain TN71 was isolated from Tunisian Saharan soil and selected for its antimicrobial activity against phytopathogenic fungi. In an attempt to increase its anti-Fusarium oxysporum activity, GYM+S (glucose, yeast extract, malt extract and starch) culture medium was selected out of five different production media. Plackett-Burman design (PBD) was used to select yeast extract, malt extract and calcium carbonate (CaCO₃) as parameters having significant effects on antifungal activity, and a Box-Behnken design was applied for further optimization. The analysis revealed that the optimum concentrations for the anti-F. oxysporum activity of the tested variables were yeast extract 5.03g/L, malt extract 8.05g/L and CaCO₃ 4.51g/L. Artificial Neural Networks (ANNs): the Multilayer perceptron (MLP) and the Radial basis function (RBF) were created to predict the anti-F. oxysporum activity. The comparison between experimental and predicted outputs from ANN and Response Surface Methodology (RSM) were studied. The ANN model presents an improvement of 14.73%. To our knowledge, this is the first work reporting the statistical versus artificial intelligence -based modeling for the optimization of bioactive molecules against mycotoxigenic and phytopathogenic fungi.