Strain improvement, artificial intelligence optimization, and sensitivity analysis of asparaginase-mediated acrylamide reduction in sweet potato chips.

In recent times, L-asparaginase has emerged as a potential anti-carcinogen through hydrolysis of L-asparagine in the blood for anti-leukemic application, and in carbohydrate-based foods, for acrylamide reduction applications. In this study, Aspergillus sydowii strain UCCM 00124 produced an L-asparaginase with a baseline acrylamide reduction potential of 64.5% in sweet potato chips. Plasma mutagenesis at atmospheric pressure and room temperature (ARTP) was employed to improve L-asparaginase production while artificial neural network embedded with genetic algorithm (ANN-GA) and global sensitivity analysis were used to identify and optimize process conditions for improved acrylamide reduction in sweet potato chips. The ARTP mutagenesis generated a valine-deficient mutant, Val-Asp-S-180-L with 2.5-fold L-asparaginase improvement. The ANN-GA hybrid evolutionary intelligence significantly improved process efficiency to 98.18% under optimized conditions set as 118.6 °C, 726.37 g/L asparagine content, 9.92 µg/mL L-asparaginase, 4.54% NaCl, and soaking time of 15 h without significant changes in sensory properties. The sensitivity index revealed initial asparagine content as the most sensitive parameter to the bioprocess. The enzyme demonstrated significant thermo-stability with Arrhenius deactivation rate constant, Kd, of 0.00562 min-1 and half-life, t1/2, of 123.35 min at 338 K. These conditions are recommended for sustainable healthier, and safer sweet potato chips processing in the food industry. Supplementary Information: The online version contains supplementary material available at 10.1007/s13197-023-05757-5.

Stochastic modeling and meta-heuristic multivariate optimization of bioprocess conditions for co-valorization of feather and waste frying oil toward prodigiosin production.

Serratia marcescens strain UCCM 00009 produced a mixture of gelatinase and keratinase to facilitate feather degradation but concomitant production of prodigiosin could make waste feather valorization biotechnologically more attractive. This article describes prodigiosin fermentation through co-valorization of waste feather and waste frying peanut oil by S. marcescens UCCM 00009 for anticancer, antioxidant, and esthetic applications. The stochastic conditions for waste feather degradation (WFD), modeled by multi-objective particle swarm-embedded-neural network optimization (ANN-PSO), revealed a gelatinase/keratinase ratio of 1.71 for optimal prodigiosin production and WFD. Luedeking-Piret kinetics revealed a non-exclusive, non-growth-associated prodigiosin yield of 9.66 g/L from the degradation of 88.55% waste feather within 96 h. The polyethylene glycol (PEG) 6000/Na+ citrate aqueous two-phase system-purified serratiopeptidase demonstrated gelatinolytic and keratinolytic activities that were stable for 240 h at 55 °C and pH 9.0. In vitro evaluations revealed that the prodigiosin inhibited methicillin-resistant Staphylococcus aureus at IC50 of 4.95 µg/mL, the plant-pathogen, Sclerotinia sclerotiorum, at IC50 of 2.58 µg/mL, breast carcinoma at IC50 of 0.60 µg/mL and 2,2-diphenyl-1-picryl-hydrazyl hydrate (DPPH) free-radical at IC50 of 96.63 µg/mL). The pigment also demonstrated commendable textile dyeing potential of fiber and cotton fabrics. The technology promises cost-effective prodigiosin development through sustainable waste feather-waste frying oil co-management.

A novel strain of Stenotrophomonas acidaminiphila produces thermostable alkaline peptidase on agro-industrial wastes: process optimization, kinetic modeling and scale-up.

Bacterial alkaline peptidases, especially from Bacillus species, occupy the frontline in global enzyme market, albeit with poor production economics. Here, we report the deployment of response surface methodology approximations to optimize fermentation parameters for enhanced yield of alkaline peptidase by the non-Bacillus bacterium; Stenotrophomonas acidaminiphila. Shake flask production under optimized conditions was scaled up in a 5-L bench-scale bioreactor. Logistic and modified Gompertz models revealed significant fits for biomass formation, total protein, and substrate consumption models. Maximum specific growth rate (µmax = 0.362 h-1) of the bacterium in the optimized medium did not differ significantly from those in Luria-Bertani and trypticase soy broths. The aqueous two-phase system-purified 45.7 kDa alkaline protease retained 83% activity which improved with increasing sodium dodecyl sulfate concentration thus highlighting potential laundry application. Maximum enzyme activity occurred at 75ºC and pH 10.5 but was inhibited by 5 mM phenyl-methyl-sulfonyl fluoride suggesting a serine-protease nature.

Bioprocess Optimization of Nutritional Parameters for Enhanced Anti-leukemic L-Asparaginase Production by Aspergillus candidus UCCM 00117: A Sequential Statistical Approach.

Sequential optimization of bioprocess nutritional conditions for production of glutaminase-near-free L-asparaginase by Aspergillus candidus UCCM 00117 was conducted under shake flask laboratory conditions. Catalytic and anti-cancer activities of the poly-peptide were evaluated using standard in vitro biochemical methods. Medium nutrients were selected by one-factor-at-a-time (OFAT) approach while Plackett-Burman design (PBD) screened potential factors for optimization. Path of steepest ascent (PSA) and response surface methodology (RSM) of a Min-Run-Res V fractional factorial of a central composite rotatable design (CCRD) were employed to optimize factor levels towards improved enzyme activity. A multi-objective approach using desirability function generated through predictor importance and weighted coefficient methodology was adopted for optimization. The approach set optimum bioprocess conditions as 49.55 g/L molasses, 64.98% corn steep liquor, 44.23 g/L asparagine, 1.73 g/L potassium, 0.055 g/L manganese and 0.043 g/L chromium (III) ions, at a composite desirability of 0.943 and an L-asparaginase activity of 5216.95U. The Sephadex-200 partially-purified polypeptide had a specific activity of 476.84 U/mg; 0.087U glutaminase activity, 36.46% yield and 20-fold protein purification. Anti-cancer activity potentials of the catalytic poly-peptide were dose-dependent with IC50 (µg/mL): 4.063 (HL-60), 13.75 (HCT-116), 15.83 (HeLa), 11.68 (MCF-7), 7.61 (HepG-2). The therapeutic enzyme exhibited 15-fold more cytotoxicity to myeloid leukemia cell line than to normal (HEK 238 T) cell. Optimum temperature and pH for activity were within physiological range. However, significant interactions between exposure time and levels of each of temperature and pH made interpretations of residual enzyme activities difficult. The manganese-dependent L-asparaginase from Aspergillu s candidus UCCM 00117 is recommended for further anticancer drug investigations.

Statistical and Artificial Neural Network Approaches to Modeling and Optimization of Fermentation Conditions for Production of a Surface/Bioactive Glyco-lipo-peptide.

A freshwater alkaliphilic strain of Pseudomonas aeruginosa, grown on waste frying oil-basal medium, produced a surface-active metabolite identified as glycolipopeptide. Bioprocess conditions namely temperature, pH, agitation and duration were comparatively modeled using statistical and artificial neural network (ANN) methods to predict and optimize product yield using the matrix of a central composite rotatable design (CCRD). Response surface methodology (RSM) was the statistical approach while a feed-forward neural network, trained with Levenberg-Marquardt back-propagation algorithm, was the neural network method. Glycolipopeptide model was predicted by a significant (P < 0.001, R 2 of 0.9923) quadratic function of the RSM with a mean squared error (MSE) of 3.6661. The neural network model, on the other hand, returned an R 2 value of 0.9964 with an MSE of 1.7844. From all error metrics considered, ANN glycolipopeptide model significantly (P < 0.01) outperformed RSM counterpart in predictive modeling capability. Optimization of factor levels for maximum glycolipopeptide concentration produced bioprocess conditions of 32 °C for temperature, 7.6 for pH, agitation speed of 130 rpm and a fermentation time of 66 h, at a combined desirability function of 0.872. The glycosylated lipid-tailed peptide demonstrated significant anti-bacterial activity (MIC = 8.125 µg/mL) against Proteus vulgaris, dose-dependent anti-biofilm activities against Escherichia coli (83%) and Candida dubliniensis (90%) in 24 h and an equally dose-dependent cytotoxic activity against human breast (MCF-7: IC50 = 65.12 µg/mL) and cervical (HeLa: IC50 = 16.44 µg/mL) cancer cell lines. The glycolipopeptide compound is recommended for further studies and trials for application in human cancer therapy.