Metabolic modelling in the development of cell factories by synthetic biology

Applications of the Whole-Cell System in the Efficient Biosynthesis of Heme

Heme has a variety of functions, from electronic reactions to binding gases, which makes it useful in medical treatments, dietary supplements, and food processing. In recent years, whole-cell system-based heme biosynthesis methods have been continuously explored and optimized as an alternative to the low-yield, lasting, and adverse ecological environment of chemical synthesis methods. This method relies on two biosynthetic pathways of microbial precursor 5-aminolevulinic acid (C4, C5) and three known downstream biosynthetic pathways of heme. This paper reviews the genetic and metabolic engineering strategies for heme production in recent years by optimizing culture conditions and techniques from different microorganisms. Specifically, we summarized and analyzed the possibility of using biosensors to explore new strategies for the biosynthesis of heme from the perspective of synthetic biology, providing a new direction for future exploration.

Machine learning-assisted medium optimization revealed the discriminated strategies for improved production of the foreign and native metabolites

The composition of medium components is crucial for achieving the best performance of synthetic construction in genetically engineered cells. Which and how medium components determine the performance, e.g., productivity, remain poorly investigated. To address the questions, a comparative survey with two genetically engineered Escherichia coli strains was performed. As a case study, the strains carried the synthetic pathways for producing the aromatic compounds of 4-aminophenylalanine (4APhe) or tyrosine (Tyr), common in the upstream but differentiated in the downstream metabolism. Bacterial growth and compound production were examined in hundreds of medium combinations that comprised 48 pure chemicals. The resultant data sets linking the medium composition to bacterial growth and production were subjected to machine learning for improved production. Intriguingly, the primary medium components determining the production of 4PheA and Tyr were differentiated, which were the initial resource (glucose) of the synthetic pathway and the inducer (IPTG) of the synthetic construction, respectively. Fine-tuning of the primary component significantly increased the yields of 4APhe and Tyr, indicating that a single component could be crucial for the performance of synthetic construction. Transcriptome analysis observed the local and global changes in gene expression for improved production of 4APhe and Tyr, respectively, revealing divergent metabolic strategies for producing the foreign and native metabolites. The study demonstrated that ML-assisted medium optimization could provide a novel point of view on how to make the synthetic construction meet the designed working principle and achieve the expected biological function.

Insights on the Advancements of In Silico Metabolic Studies of Succinic Acid Producing Microorganisms: A Review with Emphasis on Actinobacillus succinogenes

Succinic acid (SA) is one of the top candidate value-added chemicals that can be produced from biomass via microbial fermentation. A considerable number of cell factories have been proposed in the past two decades as native as well as non-native SA producers. Actinobacillus succinogenes is among the best and earliest known natural SA producers. However, its industrial application has not yet been realized due to various underlying challenges. Previous studies revealed that the optimization of environmental conditions alone could not entirely resolve these critical problems. On the other hand, microbial in silico metabolic modeling approaches have lately been the center of attention and have been applied for the efficient production of valuable commodities including SA. Then again, literature survey results indicated the absence of up-to-date reviews assessing this issue, specifically concerning SA production. Hence, this review was designed to discuss accomplishments and future perspectives of in silico studies on the metabolic capabilities of SA producers. Herein, research progress on SA and A. succinogenes, pathways involved in SA production, metabolic models of SA-producing microorganisms, and status, limitations and prospects on in silico studies of A. succinogenes were elaborated. All in all, this review is believed to provide insights to understand the current scenario and to develop efficient mathematical models for designing robust SA-producing microbial strains.

In silico gene knockout prediction using a hybrid of Bat algorithm and minimization of metabolic adjustment

Microorganisms commonly produce many high-demand industrial products like fuels, food, vitamins, and other chemicals. Microbial strains are the strains of microorganisms, which can be optimized to improve their technological properties through metabolic engineering. Metabolic engineering is the process of overcoming cellular regulation in order to achieve a desired product or to generate a new product that the host cells do not usually need to produce. The prediction of genetic manipulations such as gene knockout is part of metabolic engineering. Gene knockout can be used to optimize the microbial strains, such as to maximize the production rate of chemicals of interest. Metabolic and genetic engineering is important in producing the chemicals of interest as, without them, the product yields of many microorganisms are normally low. As a result, the aim of this paper is to propose a combination of the Bat algorithm and the minimization of metabolic adjustment (BATMOMA) to predict which genes to knock out in order to increase the succinate and lactate production rates in Escherichia coli (E. coli).

Error propagation in constraint-based modeling of Chinese hamster ovary cells

Chinese hamster ovary (CHO) cells are the most popular mammalian cell factories for the production of glycosylated biopharmaceuticals. To further increase titer and productivity and ensure product quality, rational system-level engineering strategies based on constraint-based metabolic modeling, such as flux balance analysis (FBA), have gained strong interest. However, the quality of FBA predictions depends on the accuracy of the experimental input data, especially on the exchange rates of extracellular metabolites. Yet, it is not standard practice to devote sufficient attention to the accurate determination of these rates. In this work, we investigated to what degree the sampling frequency during a batch culture and the measurement errors of metabolite concentrations influence the accuracy of the calculated exchange rates and further, how this error then propagates into FBA predictions of growth rates. We determined that accurate measurements of essential amino acids with low uptake rates are crucial for the accuracy of FBA predictions, followed by a sufficient number of analyzed time points. We observed that the measured difference in growth rates of two cell lines can only be reliably predicted when both high measurement accuracy and sampling frequency are ensured.

Metabolic modeling and response surface analysis of an Escherichia coli strain engineered for shikimic acid production

Background

Classic metabolic engineering strategies often induce significant flux imbalances to microbial metabolism, causing undesirable outcomes such as suboptimal conversion of substrates to products. Several mathematical frameworks have been developed to understand the physiological and metabolic state of production strains and to identify genetic modification targets for improved bioproduct formation. In this work, a modeling approach was applied to describe the physiological behavior and the metabolic fluxes of a shikimic acid overproducing Escherichia coli strain lacking the major glucose transport system, grown on complex media.

Results

The obtained flux distributions indicate the presence of high fluxes through the pentose phosphate and Entner-Doudoroff pathways, which could limit the availability of erythrose-4-phosphate for shikimic acid production even with high flux redirection through the pentose phosphate pathway. In addition, highly active glyoxylate shunt fluxes and a pyruvate/acetate cycle are indicators of overflow glycolytic metabolism in the tested conditions. The analysis of the combined physiological and flux response surfaces, enabled zone allocation for different physiological outputs within variant substrate conditions. This information was then used for an improved fed-batch process designed to preserve the metabolic conditions that were found to enhance shikimic acid productivity. This resulted in a 40% increase in the shikimic acid titer (60 g/L) and 70% increase in volumetric productivity (2.45 gSA/L*h), while preserving yields, compared to the batch process.

Conclusions

The combination of dynamic metabolic modeling and experimental parameter response surfaces was a successful approach to understand and predict the behavior of a shikimic acid producing strain under variable substrate concentrations. Response surfaces were useful for allocating different physiological behavior zones with different preferential product outcomes. Both model sets provided information that could be applied to enhance shikimic acid production on an engineered shikimic acid overproducing Escherichia coli strain.

Electronic supplementary material

The online version of this article (10.1186/s12918-018-0632-4) contains supplementary material, which is available to authorized users.

Toward Genome-Based Metabolic Engineering in Bacteria

Prokaryotes modified stably on the genome are of great importance for production of fine and commodity chemicals. Traditional methods for genome engineering have long suffered from imprecision and low efficiencies, making construction of suitable high-producer strains laborious. Here, we review the recent advances in discovery and refinement of molecular precision engineering tools for genome-based metabolic engineering in bacteria for chemical production, with focus on the λ-Red recombineering and the clustered regularly interspaced short palindromic repeats/Cas9 nuclease systems. In conjunction, they enable the integration of in vitro-synthesized DNA segments into specified locations on the chromosome and allow for enrichment of rare mutants by elimination of unmodified wild-type cells. Combination with concurrently developing improvements in important accessory technologies such as DNA synthesis, high-throughput screening methods, regulatory element design, and metabolic pathway optimization tools has resulted in novel efficient microbial producer strains and given access to new metabolic products. These new tools have made and will likely continue to make a big impact on the bioengineering strategies that transform the chemical industry.

Designing overall stoichiometric conversions and intervening metabolic reactions

Existing computational tools for de novo metabolic pathway assembly, either based on mixed integer linear programming techniques or graph-search applications, generally only find linear pathways connecting the source to the target metabolite. The overall stoichiometry of conversion along with alternate co-reactant (or co-product) combinations is not part of the pathway design. Therefore, global carbon and energy efficiency is in essence fixed with no opportunities to identify more efficient routes for recycling carbon flux closer to the thermodynamic limit. Here, we introduce a two-stage computational procedure that both identifies the optimum overall stoichiometry (i.e., optStoic) and selects for (non-)native reactions (i.e., minRxn/minFlux) that maximize carbon, energy or price efficiency while satisfying thermodynamic feasibility requirements. Implementation for recent pathway design studies identified non-intuitive designs with improved efficiencies. Specifically, multiple alternatives for non-oxidative glycolysis are generated and non-intuitive ways of co-utilizing carbon dioxide with methanol are revealed for the production of C₂₊ metabolites with higher carbon efficiency.

Shikimic Acid Production in Escherichia coli: From Classical Metabolic Engineering Strategies to Omics Applied to Improve Its Production

Shikimic acid (SA) is an intermediate of the SA pathway that is present in bacteria and plants. SA has gained great interest because it is a precursor in the synthesis of the drug oseltamivir phosphate (OSF), an efficient inhibitor of the neuraminidase enzyme of diverse seasonal influenza viruses, the avian influenza virus H5N1, and the human influenza virus H1N1. For the purposes of OSF production, SA is extracted from the pods of Chinese star anise plants (Illicium spp.), yielding up to 17% of SA (dry basis content). The high demand for OSF necessary to manage a major influenza outbreak is not adequately met by industrial production using SA from plants sources. As the SA pathway is present in the model bacteria Escherichia coli, several "intuitive" metabolically engineered strains have been applied for its successful overproduction by biotechnological processes, resulting in strains producing up to 71 g/L of SA, with high conversion yields of up to 0.42 (mol SA/mol Glc), in both batch and fed-batch cultures using complex fermentation broths, including glucose as a carbon source and yeast extract. Global transcriptomic analyses have been performed in SA-producing strains, resulting in the identification of possible key target genes for the design of a rational strain improvement strategy. Because possible target genes are involved in the transport, catabolism, and interconversion of different carbon sources and metabolic intermediates outside the central carbon metabolism and SA pathways, as genes involved in diverse cellular stress responses, the development of rational cellular strain improvement strategies based on omics data constitutes a challenging task to improve SA production in currently overproducing engineered strains. In this review, we discuss the main metabolic engineering strategies that have been applied for the development of efficient SA-producing strains, as the perspective of omics analysis has focused on further strain improvement for the production of this valuable aromatic intermediate.

Succinate Overproduction: A Case Study of Computational Strain Design Using a Comprehensive Escherichia coli Kinetic Model

Computational strain-design prediction accuracy has been the focus for many recent efforts through the selective integration of kinetic information into metabolic models. In general, kinetic model prediction quality is determined by the range and scope of genetic and/or environmental perturbations used during parameterization. In this effort, we apply the k-OptForce procedure on a kinetic model of E. coli core metabolism constructed using the Ensemble Modeling (EM) method and parameterized using multiple mutant strains data under aerobic respiration with glucose as the carbon source. Minimal interventions are identified that improve succinate yield under both aerobic and anaerobic conditions to test the fidelity of model predictions under both genetic and environmental perturbations. Under aerobic condition, k-OptForce identifies interventions that match existing experimental strategies while pointing at a number of unexplored flux re-directions such as routing glyoxylate flux through the glycerate metabolism to improve succinate yield. Many of the identified interventions rely on the kinetic descriptions that would not be discoverable by a purely stoichiometric description. In contrast, under fermentative (anaerobic) condition, k-OptForce fails to identify key interventions including up-regulation of anaplerotic reactions and elimination of competitive fermentative products. This is due to the fact that the pathways activated under anaerobic condition were not properly parameterized as only aerobic flux data were used in the model construction. This study shed light on the importance of condition-specific model parameterization and provides insight on how to augment kinetic models so as to correctly respond to multiple environmental perturbations.

Escherichia coli redox mutants as microbial cell factories for the synthesis of reduced biochemicals

Bioprocesses conducted under conditions with restricted O₂ supply are increasingly exploited for the synthesis of reduced biochemicals using different biocatalysts. The model facultative aerobe Escherichia coli, the microbial cell factory par excellence, has elaborate sensing and signal transduction mechanisms that respond to the availability of electron acceptors and alternative carbon sources in the surrounding environment. In particular, the ArcBA and CreBC two-component signal transduction systems are largely responsible for the metabolic regulation of redox control in response to O₂ availability and carbon source utilization, respectively. Significant advances in the understanding of the biochemical, genetic, and physiological duties of these regulatory systems have been achieved in recent years. This situation allowed to rationally-design novel engineering approaches that ensure optimal carbon and energy flows within central metabolism, as well as to manipulate redox homeostasis, in order to optimize the production of industrially-relevant metabolites. In particular, metabolic flux analysis provided new clues to understand the metabolic regulation mediated by the ArcBA and CreBC systems. Genetic manipulation of these regulators proved useful for designing microbial cells factories tailored for the synthesis of reduced biochemicals with added value, such as poly(3-hydroxybutyrate), under conditions with restricted O₂ supply. This network-wide strategy is in contrast with traditional metabolic engineering approaches, that entail direct modification of the pathway(s) at stake, and opens new avenues for the targeted modulation of central catabolic pathways at the transcriptional level.