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.

Intracellular-to-extracellular localization switch of acidic lipase in Enterobacter cloacae through multi-objective medium optimization: aqueous two-phase purification and activity kinetics

As physiological impairments that require replacement therapy continue to increase, so also does the need for improved production of acidic lipase from new microbial sources. Enterobacter cloacae strain UCCM 00116 produced a novel acidic lipase in kernel oil-processing waste-basal broth with 0.023:1 extracellular: intracellular localization ratio. This research re-directed enzyme localization to the extracellular milieu to reduce recovery cost using multi-objective response surface optimization of medium parameters. Results revealed a 1:0.32 extracellular:intracellular lipase ratio. Product formation kinetics, modeled by the Luedeking-Piret function, showed a significant switch from a completely growth-associated intracellular production to a predominantly non-growth-associated extracellular localization through medium optimization. Aqueous two-phase system purification conditions extracted 95.22% lipase with 72.36 purity, a V_max of 370.37 µmolmin^-1, and a K_m of 0.63 mmol. Enzyme activity was enhanced by K⁺ and Ca²⁺ ions, stable in many organic solvents, except acetone, and had pH and temperature optima at 2.5-3.5 and 50 °C, respectively.

Overproduction of a thermo-stable halo-alkaline protease on agro-waste-based optimized medium through alternate combinatorial random mutagenesis of Stenotrophomonas acidaminiphila

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Alternate combinatorial random mutagenesis selected a protease high-yielding mutant.

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Medium optimization led to 25.55-fold raise in specific protease yield in bioreactor.

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20% PEG-1500/Na+ 15% citrate recovered 74% activity yield with 52.55 purity.

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Activity was retained at elevated physicochemical levels but inhibited by PMSF.

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Keratinolytic and blood-stain removal activities confer industrial potential on protease.

A strain of Stenotrophomonas acidaminiphila, isolated from fermenting bean-processing wastewater, produced alkaline protease in pretreated cassava waste-stream, but with low yield. Strain improvement by alternate combinatorial random mutagenesis and bioprocess optimization using comparative statistical and neural network methods enhanced yield by 17.8-fold in mutant kGy-04-UV-25. Kinetics of production by selected mutant modeled by logistic and modified Gompertz functions revealed higher specific growth rate in mutant than in the parent strain, likewise volumetric and specific productivities. Purification by PEG/Na⁺ citrate aqueous two-phase system recovered 73.87% yield and 52.55-fold of protease. Its activity was stable at 5–35% NaCl, 45–75°C, and was significantly enhanced by 1–15 mM sodium dodecyl sulfate (SDS). The protease was inhibited by low concentrations of phenyl-methyl-sulfonyl fluoride but was activated by 1–5 mM Mn²⁺ suggesting a manganese-dependent serine‑protease. The 45.7 kDa thermo-halo-stable alkaline protease demonstrated keratinolytic and blood-stain removal potentials showing prospects in textile and detergent industries, respectively.

Intracellular-to-extracellular localization switch of acidic lipase in Enterobacter cloacae: evaluation of production kinetics and enantioselective esterification potential for pharmaceutical applications

Downstream processing is a significant part of a production process and accounts for 50-90% of the production cost of biotechnological products. Post-fermentation localization of a microbial metabolite contributes significantly to the recovery cost of the product. Enterobacter cloacae produced naturally, acidic lipase with a 0.023:1 extracellular localization ratio. This research aimed to re-direct the localization of lipase to the extracellular milieu to reduce recovery costs using multi-objective response surface optimization (MO-RSM). The approach resulted in a 1:0.32 extracellular: intracellular lipase ratio, with product formation kinetics of Luedeking-Piret function showing a significant switch from a completely growth-associated intracellular production to a predominantly non-growth-associated extracellular localization. The enzyme was purified by an aqueous two-phase system which extracted 95.22% lipase with 72.36 purity. Characterization of the enzyme showed a molecular weight of 55.7 kDa, kcat of 68.59 s^-1, and a K_m of 0.63 mmol. Lipase activity occurred optimally at pH 2.5-3.5 and 50 °C, and was stable in most organic solvents tested. The acidic lipase demonstrated pH-dependent enantioselective esterification in resolving (R, S)-ibuprofen (E = 14, pH 4.5) and (R, S)-Naproxen (E = 13, pH 2.5), with an enantioselective preference for (S)-enantiomer in both drugs thus underpinning its potential for pharmaceutical applications.

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 (µ_max = 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.

Kinetic modeling and quasi-economic analysis of fermentative glycolipopeptide biosurfactant production in a medium co-optimized by statistical and neural network approaches

This study presents the kinetics of production of a glycolipopeptide biosurfactant in a medium previously co-optimized by response surface and neural network methods to gain some insight into its volumetric and specific productivities for possible scale-up towards industrial production. Significant kinetic parameters including maximum specific growth rate, µmax, specific substrate consumption rate, qs and specific biosurfactant yield, Yp/x were determined from logistic model parameters after comparison with other kinetic models. Results showed that bio-catalytic rates of lipase and urease reached exponential values within the first 12 h of fermentation leading to high specific rates of substrate consumption and bacterial growth. Volumetric biosurfactant production reached significantly high levels during prolonged stationary growth and specific urease activity. This suggests that glycolipopeptide biosynthesis may proceed through stationary phase transpeptidation of the glycolipid base. A high cross-correlation coefficient of 0.950 confirmed that substrate consumption and glycolipopeptide production occurred contemporaneously during the 66-h fermentation. The maximum biosurfactant concentration of 132.52 g/L, µmax of 0.292 h^-1, qp of 1.674 g/gDCW/h, rp of 2.008 g/(Lh) and Yp/x of 4.413 g/g predicted by the selected logistic model and a unit cost of €0.57/g glycolipopeptide in the optimized medium may lead to technical and economic benefits.

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, R² 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 R² 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.