Paradigm shift in xylose isomerase usage: a novel scenario with distinct applications.

Isomerases are enzymes that induce physical changes in a molecule without affecting the original molecular formula. Among this class of enzymes, xylose isomerases (XIs) are the most studied to date, partly due to their extensive application in industrial processes to produce high-fructose corn sirups. In recent years, the need for sustainable initiatives has triggered efforts to improve the biobased economy through the use of renewable raw materials. In this context, D-xylose usage is crucial as it is the second-most abundant sugar in nature. The application of XIs in biotransforming xylose, enabling downstream metabolism in several microorganisms, is a smart strategy for ensuring a low-carbon footprint and producing several value-added biochemicals with broad industrial applications such as in the food, cosmetics, pharmaceutical, and polymer industries. Considering recent advancements that have expanded the range of applications of XIs, this review provides a comprehensive and concise overview of XIs, from their primary sources to the biochemical and structural features that influence their mechanisms of action. This comprehensive review may help address the challenges involved in XI applications in different industries and facilitate the exploitation of xylose bioprocesses.

Metabolic fluxes-oriented control of bioreactors: a novel approach to tune micro-aeration and substrate feeding in fermentations.

BACKGROUND: Fine-tuning the aeration for cultivations when oxygen-limited conditions are demanded (such as the production of vaccines, isobutanol, 2-3 butanediol, acetone, and bioethanol) is still a challenge in the area of bioreactor automation and advanced control. In this work, an innovative control strategy based on metabolic fluxes was implemented and evaluated in a case study: micro-aerated ethanol fermentation. RESULTS: The experiments were carried out in fed-batch mode, using commercial Saccharomyces cerevisiae, defined medium, and glucose as carbon source. Simulations of a genome-scale metabolic model for Saccharomyces cerevisiae were used to identify the range of oxygen and substrate fluxes that would maximize ethanol fluxes. Oxygen supply and feed flow rate were manipulated to control oxygen and substrate fluxes, as well as the respiratory quotient (RQ). The performance of the controlled cultivation was compared to two other fermentation strategies: a conventional "Brazilian fuel-ethanol plant" fermentation and a strictly anaerobic fermentation (with ultra-pure nitrogen used as the inlet gas). The cultivation carried out under the proposed control strategy showed the best average volumetric ethanol productivity (7.0 g L-1 h-1), with a final ethanol concentration of 87 g L-1 and yield of 0.46 gethanol g substrate -1 . The other fermentation strategies showed lower yields (close to 0.40 gethanol g substrate -1 ) and ethanol productivity around 4.0 g L-1 h-1. CONCLUSION: The control system based on fluxes was successfully implemented. The proposed approach could also be adapted to control several bioprocesses that require restrict aeration.

High-throughput strategies for penicillin G acylase production in rE. coli fed-batch cultivations.

BACKGROUND: Penicillin G acylase (PGA) is used industrially to catalyze the hydrolysis of penicillin G to obtain 6-aminopenicillanic acid. In Escherichia coli, the most-studied microorganism for PGA production, this enzyme accumulates in the periplasmic cell space, and temperature plays an important role in the correct synthesis of its subunits. RESULTS: This work investigates the influence of medium composition, cultivation strategy, and temperature on PGA production by recombinant E. coli cells. Shake flask cultures carried out using induction temperatures ranging from 18 to 28°C revealed that the specific enzyme activity achieved at 20°C (3000 IU gDCW-1) was 6-fold higher than the value obtained at 28°C. Auto-induction and high cell density fed-batch bioreactor cultures were performed using the selected induction temperature, with both defined and complex media, and IPTG and lactose as inducers. Final biomass concentrations of 100 and 120 gDCW L-1, and maximum enzyme productivities of 7800 and 5556 IU L-1 h-1, were achieved for high cell density cultures using complex and defined media, respectively. CONCLUSIONS: To the best of our knowledge, the volumetric enzyme activity and productivity values achieved using the complex medium are the highest ever reported for PGA production using E. coli. Overall PGA recovery yields of 64 and 72% after purification were achieved for crude extracts obtained from cells cultivated in defined and complex media, respectively. The complex medium was the most cost-effective for PGA production, and could be used in both high cell density and straightforward auto-induction protocols.

Non-conventional induction strategies for production of subunit swine erysipelas vaccine antigen in rE. coli fed-batch cultures.

In spite of the large number of reports on fed-batch cultivation of E. coli, alternative cultivation/induction strategies remain to be more deeply exploited. Among these strategies, it could be mentioned the use of complex media with combination of different carbon sources, novel induction procedures and feed flow rate control matching the actual cell growth rate. Here, four different carbon source combinations (glucose, glycerol, glucose + glycerol and auto-induction) in batch media formulation were compared. A balanced combination of glucose and glycerol in a complex medium formulation led to: fast growth in the batch-phase; reduced plasmid instability by preventing early expression leakage; and protein volumetric productivity of 0.40 g.L(-1).h(-1). Alternative induction strategies were also investigated. A mixture of lactose and glycerol as supplementary medium fully induced a high biomass population, reaching a good balance between specific protein production (0.148 gprot.gDCW (-1)) and volumetric productivity (0.32 g.L(-1).h(-1)). The auto-induction protocol showed excellent results on specific protein production (0.158 gprot.gDCW (-1)) in simple batch cultivations. An automated feed control based on the on-line estimated growth rate was implemented, which allowed cells to grow at higher rates than those generally used to avoid metabolic overflow, without leading to acetate accumulation. Some of the protocols described here may provide a useful alternative to standard cultivation and recombinant protein production processes, depending on the performance index that is expected to be optimized. The protocols using glycerol as carbon source and induction by lactose feeding, or glycerol plus glucose in batch medium and induction by lactose pulse led to rSpaA production in the range of 6 g.L(-1), in short fed-batch processes (16 to 20 h) with low accumulation of undesired side metabolites.

Cloning, auto-induction expression, and purification of rSpaA swine erysipelas antigen.

This work reports the cloning, expression, and purification of a 42-kDa fragment of the SpaA protein from Erysipelothrix rhusiopathiae, the main antigenic candidate for a subunit vaccine against swine erysipelas. The use of an auto-induction protocol to improve heterologous protein expression in recombinant Escherichia coli cultures was also investigated. The cellular growth pattern and metabolite formation were evaluated under different induction conditions. The His-tagged protein was over-expressed as inclusion bodies, and was purified by a single chromatography step under denaturing conditions. Auto-induction conditions were shown to be an excellent process strategy, leading to a high level of rSpaA expression (about 25 % of total cellular protein content) in a short period of time.

Intensification of high cell-density cultivations of rE. coli for production of S. pneumoniae antigenic surface protein, PspA3, using model-based adaptive control.

This work proposes an innovative methodology to control high density fed-batch cultures of E. coli, based on measurements of the concentration of dissolved oxygen and on estimations of the cellular specific growth rate (µ), of the yield of biomass/limiting substrate (Y (xs)) and of the maintenance coefficient (m). The underlying idea is to allow cells to grow according to their metabolic capacity, without the constraints inherent to pre-set growth rates. Cellular concentration was assessed on-line through a capacitance probe. Three configurations of the control system were compared: (1) pre-set value for the three control parameters; (2) continuously updating µ; (3) updating µ, Y (xs) and m. Implementation of an efficient noise filter for the signal of the capacitance probe was essential for a good performance of the control system. The third control strategy, within the framework of an adaptive model-based control, led to the best results, with biomass productivity reaching 9.2 g(DCW)/L/h.

Robust artificial intelligence tool for automatic start-up of the supplementary medium feeding in recombinant E. coli cultivations.

One of the most important events in fed-batch fermentations is the definition of the moment to start the feeding. This paper presents a methodology for a rational selection of the architecture of an artificial intelligence (AI) system, based on a neural network committee (NNC), which identifies the end of the batch phase. The AI system was successfully used during high cell density cultivations of recombinant Escherichia coli. The AI algorithm was validated for different systems, expressing three antigens to be used in human and animal vaccines: fragments of surface proteins of Streptococcus pneumoniae (PspA), clades 1 and 3, and of Erysipelothrix rhusiopathiae (SpaA). Standard feed-forward neural networks (NNs), with a single hidden layer, were the basis for the NNC. The NN architecture with best performance had the following inputs: stirrer speed, inlet air, and oxygen flow rates, carbon dioxide evolution rate, and CO2 molar fraction in the exhaust gas.

Robust artificial intelligence tool for automatic start-up of the supplementary medium feeding in recombinant E. colicultivations

One of the most important events in fed-batch fermentations is the definition of the moment to start the feeding. This paper presents a methodology for a rational selection of the architecture of an artificial intelligence (AI)system, based on a neural network committee (NNC),which identifies the end of the batch phase. The AI systemwas successfully used during high cell density cultivations of recombinant Escherichia coli. The AI algorithm wasvalidated for different systems, expressing three antigens to be used in human and animal vaccines: fragments of surface proteins of Streptococcus pneumoniae (PspA), clades 1 and 3, and of Erysipelothrix rhusiopathiae (SpaA). Standard feed-forward neural networks (NNs), with a single hidden layer, were the basis for the NNC. The NN architecture with best performance had the following inputs: stirrer speed, inlet air, and oxygen flow rates, carbon dioxide evolution rate, and CO2 molar fraction in the exhaust gas.