Multi response optimization for enhanced xylitol production by Debaryomyces nepalensis in bioreactor

Xylitol biosynthesis enhancement by Candida tropicalis via medium, process parameter optimization, and co-substrate supplementation

The present study examines the impact of nitrogen sources (yeast extract, ammonium sulfate peptone, ammonium nitrate, urea, and sodium nitrate), salt solution (0.5 g/L MgSO4, 0.5 g/L KH2PO4, 0.3 g/L CaCl2), trace elements solution (0.1 g/L CuSO4, 0.1 g/L FeSO4, 0.02 g/L MnCl2, 0.02 g/L ZnSO4), operational parameters (temperature, aeration, agitation, initial pH and xylose concentration) and co- substrate supplementation (glucose, fructose, maltose, sucrose, and glycerol) on xylitol biosynthesis by Candida tropicalis ATCC 13803 using synthetic xylose. The significant medium components were identified using the Plackett Burman design followed by central composite designs to obtain the optimal concentration for the critical medium components in shaker flasks. Subsequently, the effect of operational parameters was examined using the One Factor At a Time method, followed by the impact of five co-substrates on xylitol biosynthesis in a 1 L bioreactor. The optimal media components and process parameters are as follows: peptone: 12.68 g/L, yeast extract: 6.62 g/L, salt solution (0.5 g/L MgSO4, 0.5 g/L KH2PO4, and 0.3 g/L CaCl2): 1.23 X (0.62 g/L, 0.62 g/L, and 0.37 g/L respectively), temperature: 30 °C, pH: 6, agitation: 400 rpm, aeration: 1 vvm, and xylose: 50 g/L. Optimization studies resulted in xylitol yield and productivity of 0.71 ± 0.004 g/g and 1.48 ± 0.018 g/L/h, respectively. Glycerol supplementation (2 g/L) further improved xylitol yield (0.83 ± 0.009 g/g) and productivity (1.87 ± 0.020 g/L/h) by 1.66 and 3.12 folds, respectively, higher than the unoptimized conditions thus exhibiting the potential of C. tropicalis ATCC 13803 being used for commercial xylitol production.

Strategy to reduce acetic acid in sugarcane bagasse hemicellulose hydrolysate concomitantly with xylitol production by the promising yeast Cyberlindnera xylosilytica in a bioreactor

The yeast Cyberlindnera xylosilytica UFMG-CM-Y309 has been identified as a promising new xylitol producer from sugarcane bagasse hemicellulosic hydrolysate (SCHH). However, SCHH pretreatment process generates byproducts, which are toxic to cell metabolism, including furans, phenolic compounds, and carboxylic acids, such as acetic acid, typically released at high concentrations. This research aims to reduce acetic acid in sugarcane hemicellulose hydrolysate concomitantly with xylitol production by yeast strain Cy. xylosilytica UFMG-CM-Y309 in a bioreactor by strategically evaluating the influence of volumetric oxygen transfer coefficient (k_La) (21 and 35 h^-1). Experiments were conducted on a bench bioreactor (2 L volumetric capacity) at different initial k_La values (21 and 35 h^-1). SCHH medium was supplemented with rice bran extract (10 g L^-1) and yeast extract (1 g L^-1). Cy. xylosilytica showed high xylitol production performance (19.56 g L^-1), xylitol yield (0.56 g g^-1) and, maximum xylitol-specific production rate (μp_máx 0.20 g_xylitol·g^-1 h^-1) at k_La value of 21 h^-1, concomitantly slowing the rate of acetic acid consumption. A faster acetic acid consumption (100%) by Cy. xylosilytica was observed at k_La of 35 h^-1, concomitantly with an increase in maximum cellular growth (14.60 g L^-1) and reduction in maximum xylitol production (14.56 g L^-1 and Y_p/s 0.34 g g^-1). This study contributes to pioneering research regarding this yeast performance in bioreactors, emphasizing culture medium detoxification and xylitol production.

Combining Xylose Reductase from Spathaspora arborariae with Xylitol Dehydrogenase from Spathaspora passalidarum to Promote Xylose Consumption and Fermentation into Xylitol by Saccharomyces cerevisiae

In recent years, many novel xylose-fermenting yeasts belonging to the new genus Spathaspora have been isolated from the gut of wood-feeding insects and/or wood-decaying substrates. We have cloned and expressed, in Saccharomyces cerevisiae, a Spathaspora arborariae xylose reductase gene (SaXYL1) that accepts both NADH and NADPH as co-substrates, as well as a Spathaspora passalidarum NADPH-dependent xylose reductase (SpXYL1.1 gene) and the SpXYL2.2 gene encoding for a NAD+-dependent xylitol dehydrogenase. These enzymes were co-expressed in a S. cerevisiae strain over-expressing the native XKS1 gene encoding xylulokinase, as well as being deleted in the alkaline phosphatase encoded by the PHO13 gene. The S. cerevisiae strains expressing the Spathaspora enzymes consumed xylose, and xylitol was the major fermentation product. Higher specific growth rates, xylose consumption and xylitol volumetric productivities were obtained by the co-expression of the SaXYL1 and SpXYL2.2 genes, when compared with the co-expression of the NADPH-dependent SpXYL1.1 xylose reductase. During glucose-xylose co-fermentation by the strain with co-expression of the SaXYL1 and SpXYL2.2 genes, both ethanol and xylitol were produced efficiently. Our results open up the possibility of using the advantageous Saccharomyces yeasts for xylitol production, a commodity with wide commercial applications in pharmaceuticals, nutraceuticals, food and beverage industries.

Biovalorisation of crude glycerol and xylose into xylitol by oleaginous yeast Yarrowia lipolytica

Background

Xylitol is a commercially important chemical with multiple applications in the food and pharmaceutical industries. According to the US Department of Energy, xylitol is one of the top twelve platform chemicals that can be produced from biomass. The chemical method for xylitol synthesis is however, expensive and energy intensive. In contrast, the biological route using microbial cell factories offers a potential cost-effective alternative process. The bioprocess occurs under ambient conditions and makes use of biocatalysts and biomass which can be sourced from renewable carbon originating from a variety of cheap waste feedstocks.

Result

In this study, biotransformation of xylose to xylitol was investigated using Yarrowia lipolytica, an oleaginous yeast which was firstly grown on a glycerol/glucose for screening of co-substrate, followed by media optimisation in shake flask, scale up in bioreactor and downstream processing of xylitol. A two-step medium optimization was employed using central composite design and artificial neural network coupled with genetic algorithm. The yeast amassed a concentration of 53.2 g/L xylitol using pure glycerol (PG) and xylose with a bioconversion yield of 0.97 g/g. Similar results were obtained when PG was substituted with crude glycerol (CG) from the biodiesel industry (titer: 50.5 g/L; yield: 0.92 g/g). Even when xylose from sugarcane bagasse hydrolysate was used as opposed to pure xylose, a xylitol yield of 0.54 g/g was achieved. Xylitol was successfully crystallized from PG/xylose and CG/xylose fermentation broths with a recovery of 39.5 and 35.3%, respectively.

Conclusion

To the best of the author’s knowledge, this study demonstrates for the first time the potential of using Y. lipolytica as a microbial cell factory for xylitol synthesis from inexpensive feedstocks. The results obtained are competitive with other xylitol producing organisms.

Different methodologies for sustainability of optimization techniques used in submerged and solid state fermentation

Optimization techniques are considered as a part of nature's way of adjusting to the changes happening around it. There are different factors that establish the optimum working condition or the production of any value-added product. A model is accepted for a particular process after its sustainability has been verified on a statistical and analytical level. Optimization techniques can be divided into categories as statistical, nature inspired and artificial neural network each with its own benefits and usage in particular cases. A brief introduction about subcategories of different techniques that are available and their computational effectivity will be discussed. The main focus of the study revolves around the applicability of these techniques to any particular operation such as submerged fermentation (SmF) and solid state fermentation (SSF), their ability to produce secondary metabolites and the usefulness in the laboratory and industrial level. Primary studies to determine the enzyme activity of different microorganisms such as bacteria, fungi and yeast will also be discussed. l-Asparaginase, the most commonly used drugs in the treatment of acute lymphoblastic leukemia (ALL) shall be considered as an example, a short discussion on models used in the production by the processes of SmF and SSF will be discussed to understand the optimization techniques that are being dealt. It is expected that this discussion would help in determining the proper technique that can be used in running any optimization process for different purposes, and would help in making these processes less time-consuming with better output.

Modeling and simulation of xylitol production in bioreactor by Debaryomyces nepalensis NCYC 3413 using unstructured and artificial neural network models

This study examines the use of unstructured kinetic model and artificial neural networks as predictive tools for xylitol production by Debaryomyces nepalensis NCYC 3413 in bioreactor. An unstructured kinetic model was proposed in order to assess the influence of pH (4, 5 and 6), temperature (25°C, 30°C and 35°C) and volumetric oxygen transfer coefficient kLa (0.14h(-1), 0.28h(-1) and 0.56h(-1)) on growth and xylitol production. A feed-forward back-propagation artificial neural network (ANN) has been developed to investigate the effect of process condition on xylitol production. ANN configuration of 6-10-3 layers was selected and trained with 339 experimental data points from bioreactor studies. Results showed that simulation and prediction accuracy of ANN was apparently higher when compared to unstructured mechanistic model under varying operational conditions. ANN was found to be an efficient data-driven tool to predict the optimal harvest time in xylitol production.