Clostridium butyricum maximizes growth while minimizing enzyme usage and ATP production: metabolic flux distribution of a strain cultured in glycerol

Kinetics-based development of two-stage continuous fermentation of 1,3-propanediol from crude glycerol by Clostridium butyricum

BACKGROUND

Glycerol, as a by-product, mainly derives from the conversion of many crops to biodiesel, ethanol, and fatty ester. Its bioconversion to 1,3-propanediol (1,3-PDO) is an environmentally friendly method. Continuous fermentation has many striking merits over fed-batch and batch fermentation, such as high product concentration with easy feeding operation, long-term high productivity without frequent seed culture, and energy-intensive sterilization. However, it is usually difficult to harvest high product concentrations.

RESULTS

In this study, a three-stage continuous fermentation was firstly designed to produce 1,3-PDO from crude glycerol by Clostridium butyricum, in which the first stage fermentation was responsible for providing the excellent cells in a robust growth state, the second stage focused on promoting 1,3-PDO production, and the third stage aimed to further boost the 1,3-PDO concentration and reduce the residual glycerol concentration as much as possible. Through the three-stage continuous fermentation, 80.05 g/L 1,3-PDO as the maximum concentration was produced while maintaining residual glycerol of 5.87 g/L, achieving a yield of 0.48 g/g and a productivity of 3.67 g/(L·h). Based on the 14 sets of experimental data from the first stage, a kinetic model was developed to describe the intricate relationships among the concentrations of 1,3-PDO, substrate, biomass, and butyrate. Subsequently, this kinetic model was used to optimize and predict the highest 1,3-PDO productivity of 11.26 g/(L·h) in the first stage fermentation, while the glycerol feeding concentration and dilution rate were determined to be 92 g/L and 0.341 h-1, separately. Additionally, to achieve a target 1,3-PDO production of 80 g/L without the third stage fermentation, the predicted minimum volume ratio of the second fermenter to the first one was 11.9. The kinetics-based two-stage continuous fermentation was experimentally verified well with the predicted results.

CONCLUSION

A novel three-stage continuous fermentation and a kinetic model were reported. Then a simpler two-stage continuous fermentation was developed based on the optimization of the kinetic model. This kinetics-based development of two-stage continuous fermentation could achieve high-level production of 1,3-PDO. Meanwhile, it provides a reference for other bio-chemicals production by applying kinetics to optimize multi-stage continuous fermentation.

Dynamic flux balance analysis of 1,3-propanediol production by clostridium butyricum fermentation

To study the relationship between the yield of 1,3-propanediol (1,3-PDO) and the flux change of the Clostridium butyricum metabolic pathway, an optimized calculation method based on dynamic flux balance analysis was used by combining genome-scale flux balance analysis with a kinetic model. A more comprehensive and extensive metabolic pathway was obtained by optimization calculations. The primary extended branches include: the dihydroxyacetone node, which enters the pentose phosphate pathway; the α-oxoglutarate node, which has synthetic metabolic pathways for glutamic acid and amino acids; and the serine and homocysteine nodes, which produce cystathionine before homocysteine enters the methionine cycle pathway. According to the expanded metabolic network, the flux distribution of key nodes in the metabolic pathway and the relationship between the flux distribution ratio of nodes and the yield of 1,3-PDO were analyzed. At the dihydroxyacetone node, the flux of dihydroxyacetone converted to dihydroxyacetone phosphate was positively correlated with the yield of 1,3-PDO. As an important intermediate product, the flux change in the metabolic pathway of α-oxoglutarate reacting with amino acids to produce glutamic acid is positively correlated with the yield. When pyruvate was used as the central node to convert into lactic acid and α-oxoglutarate, the proportion of branch flux was negatively correlated with the yield of 1,3-PDO. These studies provide a theoretical basis for the optimization and further study of the metabolic pathway of C. butyricum.

A Holistic Approach from Systems Biology Reveals the Direct Influence of the Quorum-Sensing Phenomenon on Pseudomonas aeruginosa Metabolism to Pyoverdine Biosynthesis

Computational modeling and simulation of biological systems have become valuable tools for understanding and predicting cellular performance and phenotype generation. This work aimed to construct, model, and dynamically simulate the virulence factor pyoverdine (PVD) biosynthesis in Pseudomonas aeruginosa through a systemic approach, considering that the metabolic pathway of PVD synthesis is regulated by the quorum-sensing (QS) phenomenon. The methodology comprised three main stages: (i) Construction, modeling, and validation of the QS gene regulatory network that controls PVD synthesis in P. aeruginosa strain PAO1; (ii) construction, curating, and modeling of the metabolic network of P. aeruginosa using the flux balance analysis (FBA) approach; (iii) integration and modeling of these two networks into an integrative model using the dynamic flux balance analysis (DFBA) approximation, followed, finally, by an in vitro validation of the integrated model for PVD synthesis in P. aeruginosa as a function of QS signaling. The QS gene network, constructed using the standard System Biology Markup Language, comprised 114 chemical species and 103 reactions and was modeled as a deterministic system following the kinetic based on mass action law. This model showed that the higher the bacterial growth, the higher the extracellular concentration of QS signal molecules, thus emulating the natural behavior of P. aeruginosa PAO1. The P. aeruginosa metabolic network model was constructed based on the iMO1056 model, the P. aeruginosa PAO1 strain genomic annotation, and the metabolic pathway of PVD synthesis. The metabolic network model included the PVD synthesis, transport, exchange reactions, and the QS signal molecules. This metabolic network model was curated and then modeled under the FBA approximation, using biomass maximization as the objective function (optimization problem, a term borrowed from the engineering field). Next, chemical reactions shared by both network models were chosen to combine them into an integrative model. To this end, the fluxes of these reactions, obtained from the QS network model, were fixed in the metabolic network model as constraints of the optimization problem using the DFBA approximation. Finally, simulations of the integrative model (CCBM1146, comprising 1123 reactions and 880 metabolites) were run using the DFBA approximation to get (i) the flux profile for each reaction, (ii) the bacterial growth profile, (iii) the biomass profile, and (iv) the concentration profiles of metabolites of interest such as glucose, PVD, and QS signal molecules. The CCBM1146 model showed that the QS phenomenon directly influences the P. aeruginosa metabolism to PVD biosynthesis as a function of the change in QS signal intensity. The CCBM1146 model made it possible to characterize and explain the complex and emergent behavior generated by the interactions between the two networks, which would have been impossible to do by studying each system's individual components or scales separately. This work is the first in silico report of an integrative model comprising the QS gene regulatory network and the metabolic network of P. aeruginosa.

Developing Clostridia as Cell Factories for Short- and Medium-Chain Ester Production

Short- and medium-chain volatile esters with flavors and fruity fragrances, such as ethyl acetate, butyl acetate, and butyl butyrate, are usually value-added in brewing, food, and pharmacy. The esters can be naturally produced by some microorganisms. As ester-forming reactions are increasingly deeply understood, it is possible to produce esters in non-natural but more potential hosts. Clostridia are a group of important industrial microorganisms since they can produce a variety of volatile organic acids and alcohols with high titers, especially butanol and butyric acid through the CoA-dependent carbon chain elongation pathway. This implies sufficient supplies of acyl-CoA, organic acids, and alcohols in cells, which are precursors for ester production. Besides, some Clostridia could utilize lignocellulosic biomass, industrial off-gas, or crude glycerol to produce other branched or straight-chain alcohols and acids. Therefore, Clostridia offer great potential to be engineered to produce short- and medium-chain volatile esters. In the review, the efforts to produce esters from Clostridia via in vitro lipase-mediated catalysis and in vivo alcohol acyltransferase (AAT)-mediated reaction are comprehensively revisited. Besides, the advantageous characteristics of several Clostridia and clostridial consortia for bio-ester production and the driving force of synthetic biology to clostridial chassis development are also discussed. It is believed that synthetic biotechnology should enable the future development of more effective Clostridia for ester production.

Towards continuous industrial bioprocessing with solventogenic and acetogenic clostridia: challenges, progress and perspectives

The sustainable production of solvents from above ground carbon is highly desired. Several clostridia naturally produce solvents and use a variety of renewable and waste-derived substrates such as lignocellulosic biomass and gas mixtures containing H2/CO2 or CO. To enable economically viable production of solvents and biofuels such as ethanol and butanol, the high productivity of continuous bioprocesses is needed. While the first industrial-scale gas fermentation facility operates continuously, the acetone-butanol-ethanol (ABE) fermentation is traditionally operated in batch mode. This review highlights the benefits of continuous bioprocessing for solvent production and underlines the progress made towards its establishment. Based on metabolic capabilities of solvent producing clostridia, we discuss recent advances in systems-level understanding and genome engineering. On the process side, we focus on innovative fermentation methods and integrated product recovery to overcome the limitations of the classical one-stage chemostat and give an overview of the current industrial bioproduction of solvents.

Development of a Genome-Scale Metabolic Model of Clostridium thermocellum and Its Applications for Integration of Multi-Omics Datasets and Computational Strain Design

Solving environmental and social challenges such as climate change requires a shift from our current non-renewable manufacturing model to a sustainable bioeconomy. To lower carbon emissions in the production of fuels and chemicals, plant biomass feedstocks can replace petroleum using microorganisms as biocatalysts. The anaerobic thermophile Clostridium thermocellum is a promising bacterium for bioconversion due to its capability to efficiently degrade lignocellulosic biomass. However, the complex metabolism of C. thermocellum is not fully understood, hindering metabolic engineering to achieve high titers, rates, and yields of targeted molecules. In this study, we developed an updated genome-scale metabolic model of C. thermocellum that accounts for recent metabolic findings, has improved prediction accuracy, and is standard-conformant to ensure easy reproducibility. We illustrated two applications of the developed model. We first formulated a multi-omics integration protocol and used it to understand redox metabolism and potential bottlenecks in biofuel (e.g., ethanol) production in C. thermocellum. Second, we used the metabolic model to design modular cells for efficient production of alcohols and esters with broad applications as flavors, fragrances, solvents, and fuels. The proposed designs not only feature intuitive push-and-pull metabolic engineering strategies, but also present novel manipulations around important central metabolic branch-points. We anticipate the developed genome-scale metabolic model will provide a useful tool for system analysis of C. thermocellum metabolism to fundamentally understand its physiology and guide metabolic engineering strategies to rapidly generate modular production strains for effective biosynthesis of biofuels and biochemicals from lignocellulosic biomass.

Clostridium butyricum population balance model: Predicting dynamic metabolic flux distributions using an objective function related to extracellular glycerol content

BACKGROUND

Extensive experimentation has been conducted to increment 1,3-propanediol (PDO) production using Clostridium butyricum cultures in glycerol, but computational predictions are limited. Previously, we reconstructed the genome-scale metabolic (GSM) model iCbu641, the first such model of a PDO-producing Clostridium strain, which was validated at steady state using flux balance analysis (FBA). However, the prediction ability of FBA is limited for batch and fed-batch cultures, which are the most often employed industrial processes.

RESULTS

We used the iCbu641 GSM model to develop a dynamic flux balance analysis (DFBA) approach to predict the PDO production of the Colombian strain Clostridium sp IBUN 158B. First, we compared the predictions of the dynamic optimization approach (DOA), static optimization approach (SOA), and direct approach (DA). We found no differences between approaches, but the DOA simulation duration was nearly 5000 times that of the SOA and DA simulations. Experimental results at glycerol limitation and glycerol excess allowed for validating dynamic predictions of growth, glycerol consumption, and PDO formation. These results indicated a 4.4% error in PDO prediction and therefore validated the previously proposed objective functions. We performed two global sensitivity analyses, finding that the kinetic input parameters of glycerol uptake flux had the most significant effect on PDO predictions. The other input parameters evaluated during global sensitivity analysis were biomass composition (precursors and macromolecules), death constants, and the kinetic parameters of acetic acid secretion flux. These last input parameters, all obtained from other Clostridium butyricum cultures, were used to develop a population balance model (PBM). Finally, we simulated fed-batch cultures, predicting a final PDO production near to 66 g/L, almost three times the PDO predicted in the best batch culture.

CONCLUSIONS

We developed and validated a dynamic approach to predict PDO production using the iCbu641 GSM model and the previously proposed objective functions. This validated approach was used to propose a population model and then an increment in predictions of PDO production through fed-batch cultures. Therefore, this dynamic model could predict different scenarios, including its integration into downstream processes to predict technical-economic feasibilities and reducing the time and costs associated with experimentation.

Advances in metabolic modeling of oleaginous microalgae

Production of biofuels and bioenergy precursors by phototrophic microorganisms, such as microalgae and cyanobacteria, is a promising alternative to conventional fuels obtained from non-renewable resources. Several species of microalgae have been investigated as potential candidates for the production of biofuels, for the most part due to their exceptional metabolic capability to accumulate large quantities of lipids. Constraint-based modeling, a systems biology approach that accurately predicts the metabolic phenotype of phototrophs, has been deployed to identify suitable culture conditions as well as to explore genetic enhancement strategies for bioproduction. Core metabolic models were employed to gain insight into the central carbon metabolism in photosynthetic microorganisms. More recently, comprehensive genome-scale models, including organelle-specific information at high resolution, have been developed to gain new insight into the metabolism of phototrophic cell factories. Here, we review the current state of the art of constraint-based modeling and computational method development and discuss how advanced models led to increased prediction accuracy and thus improved lipid production in microalgae.

Development of a Chlamydomonas reinhardtii metabolic network dynamic model to describe distinct phenotypes occurring at different CO2 levels

The increase in atmospheric CO2 due to anthropogenic activities is generating climate change, which has resulted in a subsequent rise in global temperatures with severe environmental impacts. Biological mitigation has been considered as an alternative for environmental remediation and reduction of greenhouse gases in the atmosphere. In fact, the use of easily adapted photosynthetic organisms able to fix CO2 with low-cost operation is revealing its high potential for industry. Among those organism, the algae Chlamydomonas reinhardtii have gain special attention as a model organism for studying CO2 fixation, biomass accumulation and bioenergy production upon exposure to several environmental conditions. In the present study, we studied the Chlamydomonas response to different CO2 levels by comparing metabolomics and transcriptomics data with the predicted results from our new-improved genomic-scale metabolic model. For this, we used in silico methods at steady dynamic state varying the levels of CO2. Our main goal was to improve our capacity for predicting metabolic routes involved in biomass accumulation. The improved genomic-scale metabolic model presented in this study was shown to be phenotypically accurate, predictive, and a significant improvement over previously reported models. Our model consists of 3726 reactions and 2436 metabolites, and lacks any thermodynamically infeasible cycles. It was shown to be highly sensitive to environmental changes under both steady-state and dynamic conditions. As additional constraints, our dynamic model involved kinetic parameters associated with substrate consumption at different growth conditions (i.e., low CO2-heterotrophic and high CO2-mixotrophic). Our results suggest that cells growing at high CO2 (i.e., photoautotrophic and mixotrophic conditions) have an increased capability for biomass production. In addition, we have observed that ATP production also seems to be an important limiting factor for growth under the conditions tested. Our experimental data (metabolomics and transcriptomics) and the results predicted by our model clearly suggest a differential behavior between low CO2-heterotrophic and high CO2-mixotrophic growth conditions. The data presented in the current study contributes to better dissect the biological response of C. reinhardtii, as a dynamic entity, to environmental and genetic changes. These findings are of great interest given the biotechnological potential of this microalga for CO2 fixation, biomass accumulation, and bioenergy production.

Metabolic pairing of aerobic and anaerobic production in a one-pot batch cultivation

BACKGROUND

The versatility of microbial metabolic pathways enables their utilization in vast number of applications. However, the electron and carbon recovery rates, essentially constrained by limitations of cell energetics, are often too low in terms of process feasibility. Cocultivation of divergent microbial species in a single process broadens the metabolic landscape, and thus, the possibilities for more complete carbon and energy utilization.

RESULTS

In this study, we integrated the metabolisms of two bacteria, an obligate anaerobe Clostridium butyricum and an obligate aerobe Acinetobacter baylyi ADP1. In the process, a glucose-negative mutant of A. baylyi ADP1 first deoxidized the culture allowing C. butyricum to grow and produce hydrogen from glucose. In the next phase, ADP1 produced long chain alkyl esters (wax esters) utilizing the by-products of C. butyricum, namely acetate and butyrate. The coculture produced 24.5 ± 0.8 mmol/l hydrogen (1.7 ± 0.1 mol/mol glucose) and 28 mg/l wax esters (10.8 mg/g glucose).

CONCLUSIONS

The cocultivation of strictly anaerobic and aerobic bacteria allowed the production of both hydrogen gas and long-chain alkyl esters in a simple one-pot batch process. The study demonstrates the potential of 'metabolic pairing' using designed microbial consortia for more optimal electron and carbon recovery.