Fed-batch Bioreactor Research Articles

Growth of Methylobacterium organophilum in Methanol for the Simultaneous Production of Single-Cell Protein and Metabolites of Interest.

This study aims to monitor the growth of the methylotrophic bacteria Methylobacterium organophilum in a culture medium with methanol as a carbon source and to verify the production of unicellular proteins and other biomolecules, such as carotenoids, exopolysaccharides and polyhydroxyalkanoates, making them more attractive as animal feed. Bacterial growth was studied in shake flasks using different carbon/nitrogen (C:N) ratios to determine their best ratio for achieving the highest volumetric productivity of cells and substrate consumption rate. This optimal parameter was further used in a fed-batch operating bioreactor system to define the kinetic profile of cell growth. Methanol consumption was measured by HPLC analysis and the extracted pigments were analyzed by liquid chromatography/mass spectrometry. Chemical composition and rheological properties of the produced exopolysaccharides were also determined. The best experimental parameters were verified using an initial methanol concentration of 7 g/L in the culture medium. The same initial substrate concentration was used in the fed-batch operation and after 60 h of cultivation 5 g/L of biomass were obtained. The accumulation of carotenoids associated with cell growth was monitored, reaching a concentration of 1.6 mg/L at the end of the process. These pigments were then analyzed and characterized as a set of xanthophylls (oxidized carotenoids). In addition, two other product types were identified during the fed-batch operation: exopolysaccharides, which reached a concentration of 8.9 g/L at the end of the cultivation, and an intracellular granular structure that was detected by transmission electron microscopy (TEM), suggesting the accumulation of polyhydroxyalkanoate (PHA), most likely polyhydroxybutyrate. Methylobacterium organophilum demonstrated a unique ability to produce compounds of commercial interest. The distinct metabolic diversity of this bacterium makes room for its use in biorefineries.

Metabolic Engineering of Corynebacterium glutamicum for Sustainable Production of the Aromatic Dicarboxylic Acid Dipicolinic Acid.

Dipicolinic acid (DPA) is an aromatic dicarboxylic acid that mediates heat-stability and is easily biodegradable and non-toxic. Currently, the production of DPA is fossil-based, but bioproduction of DPA may help to replace fossil-based plastics as it can be used for the production of polyesters or polyamides. Moreover, it serves as a stabilizer for peroxides or organic materials. The antioxidative, antimicrobial and antifungal effects of DPA make it interesting for pharmaceutical applications. In nature, DPA is essential for sporulation of Bacillus and Clostridium species, and its biosynthesis shares the first three reactions with the L-lysine pathway. Corynebacterium glutamicum is a major host for the fermentative production of amino acids, including the million-ton per year production of L-lysine. This study revealed that DPA reduced the growth rate of C. glutamicum to half-maximal at about 1.6 g·L−1. The first de novo production of DPA by C. glutamicum was established by overexpression of dipicolinate synthase genes from Paenibacillus sonchi genomovar riograndensis SBR5 in a C. glutamicum L-lysine producer strain. Upon systems metabolic engineering, DPA production to 2.5 g·L−1 in shake-flask and 1.5 g·L−1 in fed-batch bioreactor cultivations was shown. Moreover, DPA production from the alternative carbon substrates arabinose, xylose, glycerol, and starch was established. Finally, expression of the codon-harmonized phosphite dehydrogenase gene from P. stutzeri enabled phosphite-dependent non-sterile DPA production.

Population balance modelling captures host cell protein dynamics in CHO cell cultures.

Monoclonal antibodies (mAbs) have been extensively studied for their wide therapeutic and research applications. Increases in mAb titre has been achieved mainly by cell culture media/feed improvement and cell line engineering to increase cell density and specific mAb productivity. However, this improvement has shifted the bottleneck to downstream purification steps. The higher accumulation of the main cell-derived impurities, host cell proteins (HCPs), in the supernatant can negatively affect product integrity and immunogenicity in addition to increasing the cost of capture and polishing steps. Mathematical modelling of bioprocess dynamics is a valuable tool to improve industrial production at fast rate and low cost. Herein, a single stage volume-based population balance model (PBM) has been built to capture Chinese hamster ovary (CHO) cell behaviour in fed-batch bioreactors. Using cell volume as the internal variable, the model captures the dynamics of mAb and HCP accumulation extracellularly under physiological and mild hypothermic culture conditions. Model-based analysis and orthogonal measurements of lactate dehydrogenase activity and double-stranded DNA concentration in the supernatant show that a significant proportion of HCPs found in the extracellular matrix is secreted by viable cells. The PBM then served as a platform for generating operating strategies that optimise antibody titre and increase cost-efficiency while minimising impurity levels.

Engineering redox sensors into CHO cells enables near-real-time quantification of intracellular redox in bioprocesses.

The production of biologics that treat complex diseases, such as cancer, autoimmune, and infectious disease, requires careful monitoring and control of cell cultures. While bioprocess optimizations have dramatically improved production yields, a lack of analytical tools has made it challenging to identify accompanying intracellular improvements. Intracellular redox can diminish the growth and productivity of biologics-producing cells and adversely impact product quality profiles yet characterizing redox is challenging due to its complex and highly transient nature. In this study, we integrated a fluorescent thiol-based redox biosensor to monitor intracellular redox in one bisAb- and two monoclonal antibody-producing clonal cell lines in a 14-day fed-batch bioreactor. We characterized biosensor functionality using three fluorescence measurement techniques and determined sensor oxidation correlates with the intracellular ratio of reduced (GSH)and oxidized glutathione (GSSG), an important cellular antioxidant. Our fed-batch bioreactor studies showed that sensor expression minimally affected bioprocess outcomes, including growth, productivity, product quality attributes, or intracellular redox attributes, including mitochondrial reactive oxygen species and total cellular GSH levels in all cell lines tested. Biosensor measurements taken throughout the culture revealed that the intracellular environment in these cell lines became more reduced throughout the culture, with the exception of a high pH condition which became more oxidized. Our results demonstrate the potential of using biosensors to monitor intracellular changes in near-real-time with minimal process effects, thus potentially improving future bioprocess optimizations.

Design of a Robust sliding mode controller for bioreactor cultures in overflow metabolism via an interdisciplinary approach

AbstractMicroorganism culture is highly complex due to the different metabolic pathways, which are very complex. A metabolic phenomenon called overflow is a challenge to overcome in automatic control tasks of microorganism cultures. In this study, a nonlinear algorithm by sliding modes (sliding mode nonlinear control, SMNC) is proposed for the robust regulation of a fed-batch bioreactor in the presence of parametric and system perturbations. A control design is obtained using Lyapunov functions by techniques to propose a control law such that it is robust, only the output signals (biomass and volume) are used, and the reaction rates do not have to be wholly known. Therefore, a novel and simple controller capable of solving the above problems is obtained.

Application of (Bio-) Chemical Engineering Concepts and Rules in Bioinformatics. Review of a CCM-Based Modular and Hybrid Kinetic Model Used to Simulate and Optimize a Bioreactor with Genetically Modified Cells

This work briefly reviews the main concepts and principles used to solve difficult biochemical engineering, or bioengineering problems, by using extended structured cell kinetic models (including nano-scale state variables) linked in hybrid models to the biological reactor dynamic models (including macro-scale state variables). Exemplification is made for the case of the numerical methodology used by [Maria, 2021] to derive a structured hybrid modular kinetic model able to simulate the dynamics of a Fed-batch bioreactor (FBR), at both cell and bulk-phase species levels. The approached FBR includes a genetically modified E. coli culture used for the tryptophan (TRP) production. Eventually this complex hybrid dynamic model was used by [Maria and Renea, 2021] to derived the FBR optimal operating policy leading to the TRP production maximization.

Process Engineering of Biopharmaceutical Production in Moss Bioreactors via Model-Based Description and Evaluation of Phytohormone Impact.

The moss Physcomitrella is an interesting production host for recombinant biopharmaceuticals. Here we produced MFHR1, a synthetic complement regulator which has been proposed for the treatment of diseases associated to the complement system as part of human innate immunity. We studied the impact of different operation modes for the production process in 5 L stirred-tank photobioreactors. The total amount of recombinant protein was doubled by using fed-batch or batch compared to semi-continuous operation, although the maximum specific productivity (mg MFHR1/g FW) increased just by 35%. We proposed an unstructured kinetic model which fits accurately with the experimental data in batch and semi-continuous operation under autotrophic conditions with 2% CO2 enrichment. The model is able to predict recombinant protein production, nitrate uptake and biomass growth, which is useful for process control and optimization. We investigated strategies to further increase MFHR1 production. While mixotrophic and heterotrophic conditions decreased the MFHR1-specific productivity compared to autotrophic conditions, addition of the phytohormone auxin (NAA, 10 µM) to the medium enhanced it by 470% in shaken flasks and up to 230% and 260%, in batch and fed-batch bioreactors, respectively. Supporting this finding, the auxin-synthesis inhibitor L-kynurenine (100 µM) decreased MFHR1 production significantly by 110% and 580% at day 7 and 18, respectively. Expression analysis revealed that the MFHR1 transgene, driven by the Physcomitrella actin5 (PpAct5) promoter, was upregulated 16 h after NAA addition and remained enhanced over the whole process, whereas the auxin-responsive gene PpIAA1A was upregulated within the first 2 hours, indicating that the effect of auxin on PpAct5 promoter-driven expression is indirect. Furthermore, the day of NAA supplementation was crucial, leading to an up to 8-fold increase of MFHR1-specific productivity (0.82 mg MFHR1/g fresh weight, 150 mg accumulated over 7 days) compared to the productivity reported previously. Our findings are likely to be applicable to other plant-based expression systems to increase biopharmaceutical production and yields.

LC-HRMS-based targeted metabolomics for high-throughput and quantitative analysis of 21 growth inhibition-related metabolites in Chinese hamster ovary cell fed-batch cultures.

Chinese hamster ovary (CHO) cells have been widely used in the biopharmaceutical industry for production of therapeutic proteins. CHO cells in fed-batch cultures produce various amino acid-derived intermediate metabolites. These small molecule metabolic byproducts have proven to be critical to cell growth, culture performance, and, more interestingly, antibody drug productivity. Herein, we developed an LC-HRMS-based targeted metabolomics approach for comprehensive quantification of total 21 growth inhibition-related metabolites generated from 14 different amino acids in CHO cell fed-batch cultures. High throughput derivatization procedures, matrix-matched calibration curves, stable isotope-labeled internal standards, and accurate mass full MS scan were utilized to achieve our goal for a wide range of metabolite screening as well as validity and reliability of metabolite quantification. We further present a novel analytical strategy for extending the assay's dynamic range by utilizing naturally occurring isotope M + 1 ion as a quantification analog in the circumstances where the principal M ion is beyond its calibration range. The integrated method was qualified for selectivity, sensitivity, linearity, accuracy, precision, isotope analysis, and other analytical aspects to demonstrate assay robustness. We then applied this metabolomics approach to characterize metabolites of interest in a CHO cell-based monoclonal antibody (mAb) production process with fed-batch bioreactor culture mode. Absolute quantification combined with multivariate statistical analysis illustrated that our target analytes derived from amino acids, especially from branched-chain amino acids, closely correlated with cell viability and significantly differentiated cellular stages in production process.

Production of l-arabinonic acid from l-arabinose by the acetic acid bacterium Gluconobacter oxydans

l-Arabinonic acid is a five carbon sugar acid valuable for various potential applications. Based on previous observations that Gluconobacter oxydans 621H oxidizes l-arabinose in the periplasm by the membrane-bound glucose dehydrogenase GdhM yielding l-arabinonic acid, we tested its ability for production. In fed-batch shake flasks without pH control, 17.4 g/L l-arabinonic acid and 5.7 g/L l-arabino-1,4-lactone were obtained. A large fraction of l-arabinose remained unused since acidification of the medium prevented further substrate oxidation. In pH 6-controlled fed-batch bioreactors, up to 120 g/L l-arabinonic acid and 13 g/L l-arabino-1,4-lactone could be obtained with strain 621H (144 h). Constitutive overexpression of gdhM did not increase product titers and resulted in reduced biomass formation, yet the gdhM overexpression strain showed higher biomass-specific production (169 g/L/gcdw) compared to 621H (131 g/L/gcdw). The high combined product titer (133 g/L, 814 mM) makes G. oxydans a very promising host for high-level production of l-arabinonic acid.

Selection of initial culture medium in fed-batch bioreactor culture of Rhodiola sachalinensis cells

In fed-batch culture, numerous factors, such as initial culture conditions and feeding strategies, affect the culture efficiency. Among the factors, the effect of initial culture medium is rarely investigated. In this work, Rhodiola sachalinensis cells were cultured in the fed-batch bioreactor system and the effects of volume, medium strength, and sucrose concentration of initial culture medium on biomass and accumulation of salidroside, polysaccharides, flavonoids, and phenolics were investigated. The results showed that an initial medium volume of 3 L significantly (p < 0.05) increased biomass and the four bioactive compound contents. The maximum biomass and the highest contents of different bioactive compounds were determined at various MS medium strengths. Therefore, analytic hierarchy process (AHP) - technique for order preference by similarity to ideal solution (TOPSIS) was implemented and half-strength MS medium was selected. Furthermore, the effect of sucrose concentration was examined and 30 g/L sucrose in the initial medium was optimal, at which concentration, 39.45 mg/g DW of salidroside, 531.25 mg/g DW of total polysaccharides, 3.89 mg/g DW of total flavonoids, and 10.84 mg/g DW of total phenolics were produced. The findings of the present study provided a reference for further establishing the fed-batch culture system of R. sachalinensis cells.

Enzyme-constrained models predict the dynamics of Saccharomyces cerevisiae growth in continuous, batch and fed-batch bioreactors.

SummaryGenome‐scale, constraint‐based models (GEM) and their derivatives are commonly used to model and gain insights into microbial metabolism. Often, however, their accuracy and predictive power are limited and enable only approximate designs. To improve their usefulness for strain and bioprocess design, we studied here their capacity to accurately predict metabolic changes in response to operational conditions in a bioreactor, as well as intracellular, active reactions. We used flux balance analysis (FBA) and dynamic FBA (dFBA) to predict growth dynamics of the model organism Saccharomyces cerevisiae under different industrially relevant conditions. We compared simulations with the latest developed GEM for this organism (Yeast8) and its enzyme‐constrained version (ecYeast8) herein described with experimental data and found that ecYeast8 outperforms Yeast8 in all the simulations. EcYeast8 was able to predict well‐known traits of yeast metabolism including the onset of the Crabtree effect, the order of substrate consumption during mixed carbon cultivation and production of a target metabolite. We showed how the combination of ecGEM and dFBA links reactor operation and genetic modifications to flux predictions, enabling the prediction of yields and productivities of different strains and (dynamic) production processes. Additionally, we present flux sampling as a tool to analyse flux predictions of ecGEM, of major importance for strain design applications. We showed that constraining protein availability substantially improves accuracy of the description of the metabolic state of the cell under dynamic conditions. This therefore enables more realistic and faithful designs of industrially relevant cell‐based processes and, thus, the usefulness of such models.

Combinatorial pathway engineering of Bacillus subtilis for production of structurally defined and homogeneous chitooligosaccharides

Chitooligosaccharides (COSs) have a widespread range of biological functions and an incredible potential for various pharmaceutical and agricultural applications. Although several physical, chemical, and biological techniques have been reported for COSs production, it is still a challenge to obtain structurally defined COSs with defined polymerization (DP) and acetylation patterns, which hampers the specific characterization and application of COSs. Herein, we achieved the de novo production of structurally defined COSs using combinatorial pathway engineering in Bacillus subtilis. Specifically, the COSs synthase NodC from Azorhizobium caulinodans was overexpressed in B. subtilis, leading to 30 ± 0.86 mg/L of chitin oligosaccharides (CTOSs), the homo-oligomers of N-acetylglucosamine (GlcNAc) with a well-defined DP lower than 6. Then introduction of a GlcNAc synthesis module to promote the supply of the sugar acceptor GlcNAc, reduced CTOSs production, which suggested that the activity of COSs synthase NodC and the supply of sugar donor UDP-GlcNAc may be the limiting steps for CTOSs synthesis. Therefore, 6 exogenous COSs synthase candidates were examined, and the nodCM from Mesorhizobium loti yielded the highest CTOSs titer of 560 ± 16 mg/L. Finally, both the de novo pathway and the salvage pathway of UDP-GlcNAc were engineered to further promote the biosynthesis of CTOSs. The titer of CTOSs in 3-L fed-batch bioreactor reached 4.82 ± 0.11 g/L (85.6% CTOS5, 7.5% CTOS4, 5.3% CTOS3 and 1.6% CTOS2), which was the highest ever reported. This is the first report proving the feasibility of the de novo production of structurally defined CTOSs by synthetic biology, and provides a good starting point for further engineering to achieve the commercial production.

Model Identification and Model Predictive Control of Biopharmaceutical and Biomedical Systems

This paper demonstrates the use of model predictive control (MPC) formulations for uncertain time-varying biopharmaceutical and biomedical systems implemented using measured data without prior knowledge of an accurate model. Furthermore, we demonstrate how prior knowledge can be incorporated in the identification of the model either through constraints or as regularization of the system identification procedure. We demonstrate the use of system identification to develop a model of the fed-batch Chinese hamster ovary mammalian cell bioreactor process and the implementation of model-based control to maximize therapeutic product yields. We also use a time-varying nonlinear biomedical system to demonstrate improvements due to incorporating prior information in the learning of the models and reidentification of the models when prediction accuracy deteriorates. We propose a new partial least squares algorithm that incorporates regularization from prior knowledge and can handle missing data in the independent covariates. Simulation case studies involving a biopharmaceutical production process and automated drug delivery demonstrate the capabilities of the proposed techniques.

Analysis of control models based on dFBA for fed-batch bioreactors solved by interior-point methods

Model-based control of bioprocesses based on dynamic flux balance analysis (dFBA) is an interesting strategy due to the possibility of accounting for wider ranges of cellular behaviour and operation conditions than the one based on unstructured models. This type of control models are bi-level optimization problems, since dFBA comprises of differential equations and a linear programming (LP) model. They can be solved with a nonlinear programming (NLP) solver by replacing the LP model with its first-order optimality conditions (KKT conditions) and discretizing the differential equations. When following this approach, it is important to carefully design the optimization problem considering properties of the selected solver. In this work, we show that we can formulate and solve model-based control models with dFBA for fed-batch bioprocesses by using the KKT conditions of the LP model, relaxing the inequality-multiplier complementarities that arise from these conditions and employing a line search interior point solver. We demonstrate this process on a case study that seeks to the maximize growth of Escherichia coli on glucose and discuss fundamental steps that one should carefully evaluate, such as constraint dependence, handling of complementarities and model initialization.

Tryptophan Production Maximization in a Fed-Batch Bioreactor with Modified E. coli Cells, by Optimizing Its Operating Policy Based on an Extended Structured Cell Kinetic Model.

Hybrid kinetic models, linking structured cell metabolic processes to the dynamics of macroscopic variables of the bioreactor, are more and more used in engineering evaluations to derive more precise predictions of the process dynamics under variable operating conditions. Depending on the cell model complexity, such a math tool can be used to evaluate the metabolic fluxes in relation to the bioreactor operating conditions, thus suggesting ways to genetically modify the microorganism for certain purposes. Even if development of such an extended dynamic model requires more experimental and computational efforts, its use is advantageous. The approached probative example refers to a model simulating the dynamics of nanoscale variables from several pathways of the central carbon metabolism (CCM) of Escherichia coli cells, linked to the macroscopic state variables of a fed-batch bioreactor (FBR) used for the tryptophan (TRP) production. The used E. coli strain was modified to replace the PTS system for glucose (GLC) uptake with a more efficient one. The study presents multiple elements of novelty: (i) the experimentally validated modular model itself, and (ii) its efficiency in computationally deriving an optimal operation policy of the FBR.

Investigation of the robustness of Cupriavidus necator engineered strains during fed-batch cultures

It is of major interest to ensure stable and performant microbial bioprocesses, therefore maintaining high strain robustness is one of the major future challenges in industrial microbiology. Strain robustness can be defined as the persistence of genotypic and/or phenotypic traits in a system. In this work, robustness of an engineered strain is defined as plasmid expression stability, cultivability, membrane integrity and macroscopic cell behavior and was assessed in response to implementations of sugar feeding strategies (pulses and continuous) and two plasmid stabilization systems (kanamycin resistance and Post-Segregational Killing hok/sok). Fed-batch bioreactor cultures, relevant mode to reach high cell densities and higher cell generation number, were implemented to investigate the robustness of C. necator engineered strains. Host cells bore a recombinant plasmid encoding for a plasmid expression level monitoring system, based on eGFP fluorescence quantified by flow cytometry. We first showed that well-controlled continuous feeding in comparison to a pulse-based feeding allowed a better carbon use for protein synthesis (avoiding organic acid excretion), a lower heterogeneity of the plasmid expression and a lower cell permeabilization. Moreover, the plasmid stabilization system Post-Segregational Killing hok/sok, an autonomous system independent on external addition of compounds, showed the best ability to maintain plasmid expression level stability insuring a greater population homogeneity in the culture. Therefore, in the case of engineered C. necator, the PSK system hok/sok appears to be a relevant and an efficient alternative to antibiotic resistance system for selection pressure, especially, in the case of bioprocess development for economic and environmental reasons.

Metabolic trends of Chinese hamster ovary cells in biopharmaceutical production under batch and fed-batch conditions.

Extensive knowledge of Chinese hamster ovary (CHO) cell metabolism is required to improve process productivity and culture performance in biopharmaceutical manufacturing. However, CHO cells show a dynamic metabolism during culturing in batch and fed-batch bioreactors. CHO cell metabolism is generally described as taking place in three stages: exponential growth phase, stationary phase, and death phase. This review aims to summarize the trends of central metabolism for CHO cells during each stage. Additional insights into how culture conditions are related to phase transitions and force metabolic rewiring are provided. Understanding of CHO cell metabolism lends itself to improving culture qualities by, for example, identifying sources of toxic byproducts and pathways for cellular engineering. In summary, this review describes the changes in CHO cell central metabolism over the course of the culture.

Multi-objective optimization of monoclonal antibody production in bioreactor

The increasing demand for monoclonal antibodies (mAbs) based therapeutics stipulates their efficient production. mAbs are predominantly produced in a fed-batch bioreactor with temperature and media composition as key control variables. In this paper, we have developed a mathematical model incorporating the effect of temperature, biomass, glucose, protein, and lactate concentration on mAb productivity. The model parameter values are estimated by minimizing the normalized error function using the particle swarm optimization (PSO) algorithm. The model is further used to find the optimum temperature and reactor input-output flow rate strategy for maximum mAb production in minimum reactor run time for a traditional sequential combination of the batch, fed-batch, and perfusion modes of operation. The non-dominated sorting genetic algorithm (NSGA-II) has been successfully implemented for bi-objective optimization, which estimated a 5% increase in mAb production with respect to the experimentally produced mAb following combined mode of operation, and the same reactor run time and nutrients’ consumption using the optimized operating conditions. Continuous production is becoming increasingly popular for the intensification of biopharmaceutical processes. Therefore, the simulation is further extended to continuous mAb production, and multiple flow strategies are compared to maximize daily mAb production while minimizing nutrients’ requirement, wastage, and reactor volume.

Biodegradation of waste cooking oil and simultaneous production of rhamnolipid biosurfactant by Pseudomonas aeruginosa P7815 in batch and fed-batch bioreactor.

Biosurfactants are non-toxic, surface-active biomolecules capable of reducing surface tension (ST) and emulsifying interface at a comparably lower concentration than commercial surfactants. Yet, poor yield, costlier substrates, and complex cultivation processes limit their commercial applications. This study focuses on producing biosurfactants by Pseudomonas aeruginosa P7815 in batch and fed-batch bioreactor systems using waste cooking oil (WCO) as the sole carbon source. The batch study showed a 92% of WCO biodegradation ability of P. aeruginosa producing 11g L-1 of biosurfactant. To enhance this biosurfactant production, a fed-batch oil feeding strategy was opted to extend the stationary phase of the bacterium and minimize the effects of substrate deprivation. An enhanced biosurfactant production of 16g L-1 (i.e. 1.5 times of batch study) was achieved at a feed rate of 5.7g L-1d-1 with almost 94% of WCO biodegradation activity. The biosurfactant was characterized as rhamnolipid using Fourier transform infrared spectroscopy (FTIR), and its interfacial characterization showed ST reduction to 29 ± 1 mN m-1 and effective emulsification stability at pH value of 4, temperature up to 40°C and salinity up to 40g L-1. The biosurfactant exhibited antibacterial activity with minimum inhibitory concentration (MIC) values of 100µgmL-1 and 150µgmL-1 for pathogenic E. hirae and E. coli, respectively. These findings suggest that biodegradation of WCO by P. aeruginosa in a fed-batch cultivation strategy is a potential alternative for the economical production of biosurfactants, which can be further explored for biomedical, cosmetics, and oil washing/recovery applications.

Polyhydroxyalkanoates production from syngas fermentation effluents: Effect of nitrogen availability

Syngas fermentation is a promising technology for bioalcohols production even though some produced volatile fatty acids (VFA) may remain unconsumed in the effluent. The present research explored the production of polyhydroxyalkanoates (PHA) from the remaining VFA of the syngas fermentation effluents in fed-batch bioreactors so that bioalcohols and biopolymers could be obtained in a combined way. In order to perform these PHA accumulation tests, two different syngas fermentation effluents, composed mainly of a mixture of VFA and alcohols, were used as substrate. Those effluents were characterized by different nitrogen availabilities, one being N-rich and the other N-limited. A mixed microbial culture (MMC) with a maximum PHA storage ability of 53.6% was used as inoculum. The microorganisms of the MMC mainly consumed the VFA rather than the alcohols, allowing the latter to accumulate as end product, besides PHA. However, when the N-rich effluent was used as substrate, the consumption rate of alcohols was 20 times higher (0.040 Cmmol-Alcohol Cmmol-X-1 h-1) compared to the N-limited effluent (0.002 Cmmol-Alcohol Cmmol-X-1 h-1). Despite not observing large differences in the maximum amount of accumulated PHA (40.5 – 41.5%), there was a decrease in the PHA content after reaching its maximum peak when the N-rich effluent was used as substrate. This trend was not observed with the N-limited effluent, in which the maximum PHA peak exactly matched with the end of the experiment.