Solid state fermentation of mixed substrate for l-asparaginase production using tray and in-house designed rotary bioreactor

Improvement of L-asparaginase, an Anticancer Agent of Aspergillus arenarioides EAN603 in Submerged Fermentation Using a Radial Basis Function Neural Network with a Specific Genetic Algorithm (RBFNN-GA)

The present study aimed to optimize the production of L-asparaginase from Aspergillus arenarioides EAN603 in submerged fermentation using a radial basis function neural network with a specific genetic algorithm (RBFNN-GA) and response surface methodology (RSM). Independent factors used included temperature (x1), pH (x2), incubation time (x3), and soybean concentration (x4). The coefficient of the predicted model using the Box–Behnken design (BBD) was R2 = 0.9079 (p < 0.05); however, the lack of fit was significant indicating that independent factors are not fitted with the quadratic model. These results were confirmed during the optimization process, which revealed that the standard error (SE) of the predicted model was 11.65 while the coefficient was 0.9799, at which 145.35 and 124.54 IU mL−1 of the actual and predicted enzyme production was recorded at 34 °C, pH 8.5, after 7 days and with 10 g L−1 of organic soybean powder concentrations. Compared to the RBFNN-GA, the results revealed that the investigated factors had benefits and effects on L-asparaginase, with a correlation coefficient of R = 0.935484, and can classify 91.666667% of the test data samples with a better degree of precision; the actual values are higher than the predicted values for the L-asparaginase data.

Characterization, Anti-proliferative Activity, and Bench-Scale Production of Novel pH-Stable and Thermotolerant L-Asparaginase from Bacillus licheniformis PPD37

Bacterial L-asparaginase (LA) is a chemotherapeutic drug that has remained mainstay of cancer treatment for several decades. LA has been extensively used worldwide for the treatment of acute lymphoblastic leukemia (ALL). A halotolerant bacterial strain Bacillus licheniformis sp. isolated from marine environment was used for LA production. The enzyme produced was subjected to purification and physico-chemical characterisation. Purified LA was thermotolerant and demonstrated more than 90% enzyme activity after 1 h of incubation at 80 °C. LA has also proved to be resistant against pH gradient and retained activity at pH ranging from 3.0 to 10. The enzyme also had high salinity tolerance with 90% LA activity at 10% NaCl concentration. Detergents like Triton X-100 and Tween-80 were observed to inhibit LA activity while more than 70% catalytic activity was maintained in the presence of metals. Electrophoretic analysis revealed that LA is a heterodimer (~ 63 and ~ 65 kDa) and has molecular mass of around 130 kDa in native form. The kinetic parameters of LA were tested with LA having low K_m value of 1.518 µM and V_max value of 6.94 µM/min/mL. Purified LA has also exhibited noteworthy antiproliferative activity against cancer cell lines-HeLa, SiHa, A549, and SH-SY-5Y. In addition, bench-scale LA production was conducted in a 5-L bioreactor using moringa leaves as cost-effective substrate.

L-asparaginase production in solid-state fermentation using Aspergillus niger: process modeling by artificial neural network approach

L-asparaginase has proven itself as a potential anti-cancer drug and in the mitigation of acrylamide formation in the food industry. In the present investigation, a novel utilization of niger (Guizotia abyssinica) de-oiled cake as the sole source for the cost-effective production of L-asparaginase was evaluated and compared with different agro-substrates in solid-state fermentation. The substrate provided a favorable C/N content for the L-asparaginase production as evident from the chemical composition (CHNS analysis) of the substrate. The influential process parameters viz; autoclaving time, moisture content, temperature and pH were optimized and modeled using machine-learning based artificial neural network (ANN) and statistical-based response surface methodology (RSM). The maximum enzyme activity of 34.65 ± 2.18 IU/gds was observed at 30.3 min of autoclaving time, 62% moisture content, 30 °C temperature and 6.2 pH in 96 h. A 1.36 fold improvement in enzyme activity was observed on utilizing optimized parameters. In comparison with RSM, the ANN model showed superior prediction with a low mean squared error of 0.072, low root mean squared error of 0.268 and 0.99 value of regression coefficient. The present study demonstrates the novel utilization of inexpensive and readily available agro-industrial waste for the development of cost-effective L-asparaginase production process.

Potential of Anti-Cancer Activity of Secondary Metabolic Products from Marine Fungi

The promising feature of the fungi from the marine environment as a source for anticancer agents belongs to the fungal ability to produce several compounds and enzymes which contribute effectively against the cancer cells growth. L-asparaginase acts by degrading the asparagine which is the main substance of cancer cells. Moreover, the compounds produced during the secondary metabolic process acts by changing the cell morphology and DNA fragmentation leading to apoptosis of the cancer cells. The current review has analyed the available information on the anticancer activity of the fungi based on the data extracted from the Scopus database. The systematic and bibliometric analysis revealed many of the properties available for the fungi to be the best candidate as a source of anticancer drugs. Doxorubicin, actinomycin, and flavonoids are among the primary chemical drug used for cancer treatment. In comparison, the most anticancer compounds producing fungi are Aspergillus niger, A. fumigatus A. oryzae, A. flavus, A. versicolor, A. terreus, Penicillium citrinum, P. chrysogenum, and P. polonicum and have been used for investigating the anticancer activity against the uterine cervix, pancreatic cancer, ovary, breast, colon, and colorectal cancer.

Engineering interventions in enzyme production: Lab to industrial scale

Commercial enzyme production has gained popularity due to its extensive applications in traditional and modern industrial sectors. Rigorous research activities are being conducted worldwide to make the enzyme production system more efficient, cost-effective and hence, sustainable. To overcome the lacunae in earlier enzyme production methods, new engineering interventions are being introduced to meet the growing demand for industrial enzymes. This review focuses initially on the current global scenario of the enzyme market followed by a discussion on different bioreactor design approaches. The use of novel membrane based, airlift and reciprocating plate bioreactors along with the emergence of micro-reactors have also been discussed. Further, the review covers different modelling and optimization strategies for the enzyme production process including advanced techniques like neural networks, adaptive neuro-fuzzy inference systems and genetic algorithms. Finally, the required thrust areas in the enzyme production sector have been highlighted with directions for future research.

Impact of nanoparticle inclusion on bioethanol production process kinetic and inhibitor profile

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NiO nanoparticle (NP) inclusion enhanced bioethanol production up to 59.96 %.

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Band energy gap impact NP catalytic performance in bioethanol production.

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NiO nanoparticle biocatalyst improved bioethanol productivity by 145 %.

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Modified Gompertz model was used to describe ethanol production with NP inclusion.

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Metallic NiO nanoparticles significantly reduced acetic acid concentration by 110 %.

This study examines the effects of nanoparticle inclusion in instantaneous saccharification and fermentation (NIISF) of waste potato peels. The effect of nanoparticle inclusion on the fermentation process was investigated at different stages which were: pre-treatment, liquefaction, saccharification and fermentation. Inclusion of NiO NPs at the pre-treatment stage gave a 1.60-fold increase and 2.10-fold reduction in bioethanol and acetic acid concentration respectively. Kinetic data on the bioethanol production fit the modified Gompertz model (R ² > 0.98). The lowest production lag time (t _L) of 1.56 h, and highest potential bioethanol concentration (P _m) of 32 g/L were achieved with NiO NPs inclusion at different process stages; the liquefaction stage and the pre-treatment phase, respectively. Elevated bioethanol yield, coupled with substantial reduction in process inhibitors in the NIISF processes, demonstrated the significance of point of nanobiocatalysts inclusion for the scale-up development of bioethanol production from potato peels.