Mathematical modeling for bioprocess optimization of a protein drug, uricase, production by Aspergillus welwitschiae strain 1-4.

Microbial uricase is effective protein drug used to treat hyperuricemia and its complications, including chronic gout, also in prophylaxis and treatment of tumor lysis and organ transplants hyperuricemia. Uricase is commonly used as diagnostic reagent in clinical analysis for quantification of uric acid in blood and other biological fluids. Also, it can be used as an additive in formulations of hair coloring agents. A newly isolated strain, Aspergillus sp. 1-4, was able to produce extracellular uricase on a medium containing uric acid as inducer. Phylogenetic analysis based on ITS region sequence analysis and phenotypic characteristics showed that Aspergillus sp. strain 1-4 is closely related to Aspergillus welwitschiae and its nucleotide sequence was deposited in the GenBank database and assigned sequence accession number MG323529. Statistical screening using Plackett-Burman design with 20 runs was applied to screen fifteen factors for their significance on uricase production by Aspergillus welwitschiae. Results of statistical analysis indicated that incubation time has the most significant positive effect on uricase production followed by yeast extract and inoculum size with the highest effect values of 13.48, 5.26 and 4.75; respectively. The interaction effects and optimal levels of these factors were evaluated using central composite design. The maximum uricase production was achieved at incubation time (5 days), yeast extract (2 g/L) and inoculum size (4 mL/50 mL medium) are the optimum levels for maximum uricase production (60.03 U/mL). After optimization, uricase production increased by 3.02-folds as compared with that obtained from the unoptimized medium (19.87 U/mL).

Identification of newly isolated Talaromyces pinophilus and statistical optimization of ß-glucosidase production under solid-state fermentation.

Fungi able to degrade agriculture wastes were isolated from different soil samples, rice straw, and compost; these isolates were screened for their ability to produce ß-glucosidase. The most active fungal isolate was identified as Talaromyces pinophilus strain EMOO 13-3. The Plackett-Burman design is used for identifying the significant variables that influence ß-glucosidase production under solid-state fermentation. Fifteen variables were examined for their significances on the production of ß-glucosidase in 20 experimental runs. Among the variables screened, moisture content, Tween 80, and (NH4)2SO4 had significant effects on ß-glucosidase production with confidence levels above 90% (p < 0.1). The optimal levels of these variables were further optimized using Box-Behnken statical design. As a result, the maximal ß-glucosidase activity is 3648.519 U g(-1), which is achieved at the following fermentation conditions: substrate amount 0.5 (g/250 mL flask), NaNO3 0.5 (%), KH2PO4 0.3 (%), KCl 0.02 (%), MgSO4 · 7H2O 0.01 (%), CaCl2 0.01 (%), yeast extract 0.07 (%), FeSO4 · 7H2O 0.0002 (%), Tween 80 0.02 (%), (NH4)2SO4 0.3 (%), pH 6.5, temperature 25°C, moisture content 1 (mL/g dry substrate), inoculum size 0.5 (mL/g dry substrate), and incubation period 5 days.

Optimization of ß-glucosidase production by Aspergillus terreus strain EMOO 6-4 using response surface methodology under solid-state fermentation.

Forty-two morphologically different fungal strains were isolated from different soil samples and agricultural wastes and screened for ß-glucosidase activity under solid-state fermentation. Eight species were chosen as the most active ß-glucosidase producers and were subjected to primary morphological identification. ß-Glucosidase was highly produced by Aspergillus terreus, which showed the highest activity, and was subjected to full identification using scanning electron microscopy and molecular identification. Initial screening of different variables affecting ß-glucosidase production was performed using Plackett-Burman design and the variables with statistically significant effects were identified. The optimal levels of the most significant variables with positive effect and the effect of their mutual interactions on ß-glucosidase production were determined using Box-Behnken design. Fifteen variables including temperature, pH, incubation time, inoculum size, moisture content, substrate concentration, NaNO3, KH2PO4, MgSO4 · 7H2O, KCl, CaCl2, yeast extract, FeSO4 · 7H2O, Tween 80, and (NH4)2SO4 were screened in 20 experimental runs. Among the 15 variables, NaNO3, KH2PO4 and Tween 80 were found as the most significant factors with positive effect on ß-glucosidase production. The Box-Behnken design was used for further optimization of these selected factors for better ß-glucosidase production. The maximum ß-glucosidase production was 4457.162 U g(-1).