Evaluation of Various Factors Affecting Bioconversion of l-Tyrosine to l-DOPA by Yeast Yarrowia lipolytica-NCIM 3450 Using Response Surface Methodology

Abstract

Abstract3,4-Dihydroxy l-phenylalanine (l-DOPA) is considered a potent drug for the treatment of Parkinson disease. Physical and nutritional parameters where optimized by using Yarrowia lipolytica-NCIM 3450 to accomplished the highest production of l-DOPA. Screenings of critical components were completed by using a Plackett–Burman design, while further optimization was carried out using the Box–Behnken design. The optimized factor levels predicted by the model were pH 6.1, 1.659 g L−1 yeast extract, 1.491 g L−1l-tyrosine and 0.0290 g L−1 CuSO4. The predicted yield of l-DOPA with these levels was 1.319 g L−1, while actual yield obtained was 1.273 g L−1. The statistical analysis revealed that model is significant with F value 19.55 and R2 value 0.9514. This process resulted in a 3.594-fold increase in the yield of l-DOPA. l-DOPA was confirmed by HPTLC and HPLC analysis. Thus, Yarrowia lipolytica-NCIM 3450 has potential to be a new source for the production of l-DOPA.Graphical AbstractElectronic supplementary materialThe online version of this article (doi:10.1007/s13659-014-0017-3) contains supplementary material, which is available to authorized users.

Enhancement of antioxidative and antimicrobial activities of immature pear (Pyrus pyrifolia cv. Niitaka) fruits by fermentation with Leuconostoc mesenteroides

“…The CE-OOH was analyzed at 235 nm using UV detector (SPD-10A; Shimadzu). The concentration of CE-OOH was determined from a standard curve of cholesteryl linoleate hydroperoxide.Determination of tyrosinase inhibitory activity Tyrosinase inhibitory activity was measured according to the modified method of Gurme et al(20). The MeOH extracts of UIPF and FIPF (100 μL, 75 mg fresh wt.…”

Yarrowia lipolytica Strains and Their Biotechnological Applications: How Natural Biodiversity and Metabolic Engineering Could Contribute to Cell Factories Improvement

“…In addition to their interest for the design of metabolic engineering strategies, GEMs can also be applied to predicting the metabolic responses of yeast cells to environmental conditions such as industrial-scale production, as demonstrated at the University of Graz (Austria) during the optimization of a Y. lipolytica bioprocess for lipid production [ 313 ]. Some statistical modeling tools, such as response surface methodology, are also now currently applied to bioprocess optimization, from wild-type as well as for GM strains [ 315 , 316 , 317 , 318 ]. As seen above in Section 2.3.3 , a lot of research is performed on remodelling the hydrolytic secretome of Y. lipolytica in order to allow the use of plant biomass as a renewable and cheap carbon source, an approach aiming towards sustainable development and circular bioeconomy.…”

Yarrowia lipolytica strains and their biotechnological applications: how natural biodiversity and metabolic engineering could contribute to cell factories improvement

“…Thanks to its specific characteristics (naturally secreted emulsifiers, protrusions and cell surface hydrophobicitycf. Section 2.2.5) allowing an efficient uptake of these substrates from organic solvents, GM strains expressing P450s and their reductases have been applied to the oxidation of hardly soluble hydrophobic steroids in a two-liquid biphasic system [275]. Employing such a biphasic system allowed a more efficient bioconversion compared to an aqueous system while considerably simplifying the whole process [275].…”

“…Section 2.2.5) allowing an efficient uptake of these substrates from organic solvents, GM strains expressing P450s and their reductases have been applied to the oxidation of hardly soluble hydrophobic steroids in a two-liquid biphasic system [275]. Employing such a biphasic system allowed a more efficient bioconversion compared to an aqueous system while considerably simplifying the whole process [275]. Beside their interest for the design of metabolic engineering strategies, GEMs can also be applied to predicting the metabolic responses of yeast cells to environmental conditions such as industrial-scale production, as demonstrated at the University of Graz (Austria) during the optimization of a Y. lipolytica bioprocess for lipid production [276].…”