Solid-State Fermentation of Chestnut Shells and Effect of Explanatory Variables in Predictive Saccharification Models.

In this study, chestnut shells (CNS), a recalcitrant and low-value agro-industrial waste obtained during the peeling of Castanea sativa fruits, were subjected to solid-state fermentation by six white-rot fungal strains (Irpex lacteus, Ganoderma resinaceum, Phlebia rufa, Bjerkandera adusta and two Trametes isolates). After being fermented, CNS was subjected to hydrolysis by a commercial enzymatic mix to evaluate the effect of fermentation in saccharification yield. After 48 h hydrolysis with 10 CMCase U mL−1 enzymatic mix, CNS fermented with both Trametes strains was recorded with higher saccharification yield (around 253 mg g−1 fermented CNS), representing 25% w/w increase in reducing sugars as compared to non-fermented controls. To clarify the relationships and general mechanisms of fungal fermentation and its impacts on substrate saccharification, the effects of some independent or explanatory variables in the production of reducing sugars were estimated by general predictive saccharification models. The variables considered were lignocellulolytic activities in fungal fermentation, CNS hydrolysis time, and concentration of enzymatic hydrolysis mix. Multiple linear regression analysis revealed a very high significant effect (p < 0.0001) of fungal laccase and xylanase activities in the saccharification models, thus proving the key potential of these enzymes in CNS solid-state fermentation.

A Kinetic Process to Determine the Interaction Type Between Two Compounds, One of Which Is a Reaction Product, Using Alkaline Phosphatase Inhibition as a Case Study.

This study describes the development of a new methodology based on a new integrated equation which allows the determination of the kinetic parameters for two mutually non-exclusive inhibitors when one of which is produced during the time-course reaction. Alkaline phosphatase simultaneously inhibited by phosphate and urea is used to illustrate this methodology, including the evaluation of interaction effects between them. Data analyses were carried out using two integrated velocity equations: exclusive linear mixed inhibition (EMI) and non-exclusive linear mixed inhibition (NEMI). Kinetic parameters are estimated using non-linear regression and results show that (i) the interaction between enzyme and the inhibitors urea and phosphate exhibit a mutually non-exclusive behavior; (ii) more specifically, these inhibitors are non-exclusive only in free enzyme (E) species; (iii) the inhibitors also show an interaction with enzyme classified as facilitation; (iv) phosphate is a competitive inhibitor and urea a mixed inhibitor; (v) the inhibition constant for phosphate is much lower than that determined for urea. In addition, a functional Excel Spreadsheet which can be adapted to any kinetic study is also included as a supplement.

Enzyme inhibition studies by integrated Michaelis-Menten equation considering simultaneous presence of two inhibitors when one of them is a reaction product.

To determine initial velocities of enzyme catalyzed reactions without theoretical errors it is necessary to consider the use of the integrated Michaelis-Menten equation. When the reaction product is an inhibitor, this approach is particularly important. Nevertheless, kinetic studies usually involved the evaluation of other inhibitors beyond the reaction product. The occurrence of these situations emphasizes the importance of extending the integrated Michaelis-Menten equation, assuming the simultaneous presence of more than one inhibitor because reaction product is always present. This methodology is illustrated with the reaction catalyzed by alkaline phosphatase inhibited by phosphate (reaction product, inhibitor 1) and urea (inhibitor 2). The approach is explained in a step by step manner using an Excel spreadsheet (available as a template in Appendix). Curve fitting by nonlinear regression was performed with the Solver add-in (Microsoft Office Excel). Discrimination of the kinetic models was carried out based on Akaike information criterion. This work presents a methodology that can be used to develop an automated process, to discriminate in real time the inhibition type and kinetic constants as data (product vs. time) are achieved by the spectrophotometer.

Influence of ligninolytic enzymes on straw saccharification during fungal pretreatment.

Solid state and submerged fermentations in the presence of white-rot basidiomycetes (Bjerkandera adusta, Fomes fomentarius, Ganoderma resinaceum, Irpex lacteus, Phanerochaete chrysosporium, Trametes versicolor and basidiomycete Euc-1) and the litter-decomposing basidiomycete Lepista nuda were evaluated as a pretreatment to increase enzymatic saccharification of wheat straw. Enzymatic hydrolysis of holocellulose after solid state pretreatment showed a significant (P<0.05) increase of saccharification process for T. versicolor, Euc-1, G. resinaceum and I. lacteus, being T. versicolor (strain Tv2) the best one with a sugar yield increase of 91% compared with untreated straw. In submerged medium the pretreatment with I. lacteus, Euc-1 and P. chrysosporium enhanced saccharification but at a lesser extent. Covariance analysis was used to evaluate the relationships between ligninolytic enzymes (lignin peroxidase, manganese-dependent peroxidase and laccase) and saccharification increase. Results showed that only the presence of lignin peroxidase during pretreatment can lead to a significant (P<0.05) increase in the saccharification yield.