Effect of increasing oxygen partial pressure on Saccharomyces cerevisiae growth and antioxidant and enzyme productions.

This study investigated the impact of oxygen partial pressure on yeast growth. Saccharomyces cerevisiae cells were exposed to various hyperbaric air conditions from 1 bar to 9 bar absolute pressure (A). Batch cultures were grown under continuous airflow in a 750 mL (500 mL culture) bioreactor and monitored through growth rate and specific yields of ethanol and glycerol. In addition, the concentrations of antioxidant metabolites glutathione (reduced state, GSH and oxidized state, GSSG) and the activity of antioxidative enzymes superoxide dismutases (SOD) and catalases (CAT) were monitored. The results demonstrated that the different oxygen partial pressures significantly impacted the key growth parameters monitored. Compared with atmospheric pressure, under 2 to 5 bar (A), yeast cells showed higher growth rates (µ = 0.32 ± 0.01 h-1) and higher catalase (CAT) concentrations (214 ± 5 mU/g). GSH/GSSG ratio (6.36 ± 0.37) maintained until 6 bar (A) and total SOD (240 ± 5 mU/g) level significantly increased compared with 2 bar (A) until 7 bar (A). Under 6 to 9 bar (A), cell growth was inhibited, and a pressure of 9 bar (A) led to excessive GSSG accumulation (GSH/GSSG = 0.31 ± 0.06). The inhibition of t-SOD (160 ± 3 mU/g) and CAT (62.73 ± 0.2 mU/g) was observed under 9 bar (A). A reference experiment (8 bar (A) N2 + 1 bar (A) air) confirmed that the observed behaviors were entirely due to O2. In addition to their utility in biotechnological process design, these results showed that growth impairment was solely due to oxidative stress induced by excessive oxygen pressure. KEY POINTS: • Yeast cells were grown in batch mode under 1 to 9 bar (A) air pressures and up to 5 bar (A) promoted then hindered growth. • The GSH/GSSG ratio was stable up to 5 bar (A) then GSSG accumulated to excess. • Complementary investigations of the activity of SOD and CAT validated growth limitations due to oxidative stress.

Improvement of Sinapine Extraction from Mustard Seed Meal by Application of Emerging Technologies.

Sinapine is a phenolic compound found in mustard (Brassica juncea) seed meal. It has numerous beneficial properties such as antitumor, neuroprotective, antioxidant, and hepatoprotective effects, making its extraction relevant. In this study, the extraction of sinapine was investigated using three methods: (i) from a mustard seed meal defatted by a supercritical CO2 (SC-CO2) pretreatment, (ii) by the implementation of high-voltage electrical discharges (HVEDs), (iii) and by the use of ultrasound. The use of SC-CO2 pretreatment resulted in a dual effect on the valorization of mustard seed meal, acting as a green solvent for oil recovery and increasing the yield of extracted sinapine by 24.4% compared to the control. The combination of ultrasound and SC-CO2 pretreatment further increased the yield of sinapine by 32%. The optimal conditions for ultrasound-assisted extraction, determined through a response surface methodology, are a temperature of 75 °C, 70% ethanol, and 100% ultrasound amplitude, resulting in a sinapine yield of 6.90 ± 0.03 mg/g dry matter. In contrast, the application of HVEDs in the extraction process was not optimized, as it led to the degradation of sinapine even at low-energy inputs.

A validated Distributed Activation Energy Model (DAEM) to predict the chemical degradation of biomass as a function of hydrothermal treatment conditions.

This study proposes a DAEM (Distributed Activation Energy Model) approach to predict the chemical alterations of lignocellulosic biomass as a function of hydrothermal treatment conditions. The model is first tuned by an original device allowing the sample shrinkage to be continuously assessed during hydrothermal treatment in saturated water vapor up to 190 °C. The shrinkage dynamic is supplied in the DAEM model as an indicator of the degree of biomass conversion. A set of chemical analyses was performed at selected residence times and treatment temperatures to correlate this degree of conversion with the resulting chemical molecules. A set of functions was then derived from this database to correlate the degree of conversion with the components concentrations. Finally, a validation database was built with different combinations of temperature levels and residence times. The model was proved to be predictive on this new dataset.