Peter C. St. John

Succinic acid production from lignocellulosic hydrolysate by Basfia succiniciproducens

The production of chemicals alongside fuels will be essential to enhance the feasibility of lignocellulosic biorefineries. Succinic acid (SA), a naturally occurring C4-diacid, is a primary intermediate of the tricarboxylic acid cycle and a promising building block chemical that has received significant industrial attention. Basfia succiniciproducens is a relatively unexplored SA-producing bacterium with advantageous features such as broad substrate utilization, genetic tractability, and facultative anaerobic metabolism. Here B. succiniciproducens is evaluated in high xylose-content hydrolysates from corn stover and different synthetic media in batch fermentation. SA titers in hydrolysate at an initial sugar concentration of 60g/L reached up to 30g/L, with metabolic yields of 0.69g/g, and an overall productivity of 0.43g/L/h. These results demonstrate that B. succiniciproducens may be an attractive platform organism for bio-SA production from biomass hydrolysates.

Efficient estimation of the maximum metabolic productivity of batch systems

BackgroundProduction of chemicals from engineered organisms in a batch culture involves an inherent trade-off between productivity, yield, and titer. Existing strategies for strain design typically focus on designing mutations that achieve the highest yield possible while maintaining growth viability. While these methods are computationally tractable, an optimum productivity could be achieved by a dynamic strategy in which the intracellular division of resources is permitted to change with time. New methods for the design and implementation of dynamic microbial processes, both computational and experimental, have therefore been explored to maximize productivity. However, solving for the optimal metabolic behavior under the assumption that all fluxes in the cell are free to vary is a challenging numerical task. Previous studies have therefore typically focused on simpler strategies that are more feasible to implement in practice, such as the time-dependent control of a single flux or control variable.ResultsThis work presents an efficient method for the calculation of a maximum theoretical productivity of a batch culture system using a dynamic optimization framework. The proposed method follows traditional assumptions of dynamic flux balance analysis: first, that internal metabolite fluxes are governed by a pseudo-steady state, and secondly that external metabolite fluxes are dynamically bounded. The optimization is achieved via collocation on finite elements, and accounts explicitly for an arbitrary number of flux changes. The method can be further extended to calculate the complete Pareto surface of productivity as a function of yield. We apply this method to succinate production in two engineered microbial hosts, Escherichia coli and Actinobacillus succinogenes, and demonstrate that maximum productivities can be more than doubled under dynamic control regimes.ConclusionsThe maximum theoretical yield is a measure that is well established in the metabolic engineering literature and whose use helps guide strain and pathway selection. We present a robust, efficient method to calculate the maximum theoretical productivity: a metric that will similarly help guide and evaluate the development of dynamic microbial bioconversions. Our results demonstrate that nearly optimal yields and productivities can be achieved with only two discrete flux stages, indicating that near-theoretical productivities might be achievable in practice.

Metabolic engineering of Actinobacillus succinogenes provides insights into succinic acid biosynthesis

ABSTRACT Actinobacillus succinogenes, a Gram-negative facultative anaerobe, exhibits the native capacity to convert pentose and hexose sugars to succinic acid (SA) with high yield as a tricarboxylic acid (TCA) cycle intermediate. In addition, A. succinogenes is capnophilic, incorporating CO2 into SA, making this organism an ideal candidate host for conversion of lignocellulosic sugars and CO2 to an emerging commodity bioproduct sourced from renewable feedstocks. In this work, we report the development of facile metabolic engineering capabilities in A. succinogenes, enabling examination of SA flux determinants via knockout of the primary competing pathways—namely, acetate and formate production—and overexpression of the key enzymes in the reductive branch of the TCA cycle leading to SA. Batch fermentation experiments with the wild-type and engineered strains using pentose-rich sugar streams demonstrate that the overexpression of the SA biosynthetic machinery (in particular, the enzyme malate dehydrogenase) enhances flux to SA. Additionally, removal of competitive carbon pathways leads to higher-purity SA but also triggers the generation of by-products not previously described from this organism (e.g., lactic acid). The resultant engineered strains also lend insight into energetic and redox balance and elucidate mechanisms governing organic acid biosynthesis in this important natural SA-producing microbe. IMPORTANCE Succinic acid production from lignocellulosic residues is a potential route for enhancing the economic feasibility of modern biorefineries. Here, we employ facile genetic tools to systematically manipulate competing acid production pathways and overexpress the succinic acid-producing machinery in Actinobacillus succinogenes. Furthermore, the resulting strains are evaluated via fermentation on relevant pentose-rich sugar streams representative of those from corn stover. Overall, this work demonstrates genetic modifications that can lead to succinic acid production improvements and identifies key flux determinants and new bottlenecks and energetic needs when removing by-product pathways in A. succinogenes metabolism.

Prediction of reaction knockouts to maximize succinate production by Actinobacillus succinogenes

Succinate is a precursor of multiple commodity chemicals and bio-based succinate production is an active area of industrial bioengineering research. One of the most important microbial strains for bio-based production of succinate is the capnophilic gram-negative bacterium Actinobacillus succinogenes, which naturally produces succinate by a mixed-acid fermentative pathway. To engineer A. succinogenes to improve succinate yields during mixed acid fermentation, it is important to have a detailed understanding of the metabolic flux distribution in A. succinogenes when grown in suitable media. To this end, we have developed a detailed stoichiometric model of the A. succinogenes central metabolism that includes the biosynthetic pathways for the main components of biomass—namely glycogen, amino acids, DNA, RNA, lipids and UDP-N-Acetyl-α-D-glucosamine. We have validated our model by comparing model predictions generated via flux balance analysis with experimental results on mixed acid fermentation. Moreover, we have used the model to predict single and double reaction knockouts to maximize succinate production while maintaining growth viability. According to our model, succinate production can be maximized by knocking out either of the reactions catalyzed by the PTA (phosphate acetyltransferase) and ACK (acetyl kinase) enzymes, whereas the double knockouts of PEPCK (phosphoenolpyruvate carboxykinase) and PTA or PEPCK and ACK enzymes are the most effective in increasing succinate production.

Bayesian inference of metabolic kinetics from genome-scale multiomics data

Modern biological tools generate a wealth of data on metabolite and protein concentrations that can be used to help inform new strain designs. However, integrating these data sources to generate predictions of steady-state metabolism typically requires a kinetic description of the enzymatic reactions that occur within a cell. Parameterizing these kinetic models from biological data can be computationally difficult, especially as the amount of data increases. Robust methods must also be able to quantify the uncertainty in model parameters as a function of the available data, which can be particularly computationally intensive. The field of Bayesian inference offers a wide range of methods for estimating distributions in parameter uncertainty. However, these techniques are poorly suited to kinetic metabolic modeling due to the complex kinetic rate laws typically employed and the resulting dynamic system that must be solved. In this paper, we employ linear-logarithmic kinetics to simplify the calculation of steady-state flux distributions and enable efficient sampling and variational inference methods. We demonstrate that detailed information on the posterior distribution of kinetic model parameters can be obtained efficiently at a variety of different problem scales, including large-scale kinetic models trained on multiomics datasets. These results allow modern Bayesian machine learning tools to be leveraged in understanding biological data and developing new, efficient strain designs.