Systems Metabolic Engineering: The Creation of Microbial Cell Factories by Rational Metabolic Design and Evolution

In silico Design for Systems-Based Metabolic Engineering for the Bioconversion of Valuable Compounds From Industrial By-Products

Selecting appropriate metabolic engineering targets to build efficient cell factories maximizing the bioconversion of industrial by-products to valuable compounds taking into account time restrictions is a significant challenge in industrial biotechnology. Microbial metabolism engineering following a rational design has been widely studied. However, it is a cost-, time-, and laborious-intensive process because of the cell network complexity; thus, it is important to use tools that allow predicting gene deletions. An in silico experiment was performed to model and understand the metabolic engineering effects on the cell factory considering a second complexity level by transcriptomics data integration. In this study, a systems-based metabolic engineering target prediction was used to increase glycerol bioconversion to succinic acid based on Escherichia coli. Transcriptomics analysis suggests insights on how to increase cell glycerol utilization to further design efficient cell factories. Three E. coli models were used: a core model, a second model based on the integration of transcriptomics data obtained from growth in an optimized culture media, and a third one obtained after integration of transcriptomics data from adaptive laboratory evolution (ALE) experiments. A total of 2,402 strains were obtained with fumarase and pyruvate dehydrogenase being frequently predicted for all the models, suggesting these reactions as essential to increase succinic acid production. Finally, based on using flux balance analysis (FBA) results for all the mutants predicted, a machine learning method was developed to predict new mutants as well as to propose optimal metabolic engineering targets and mutants based on the measurement of the importance of each knockout's (feature's) contribution. Glycerol has become an interesting carbon source for industrial processes due to biodiesel business growth since it has shown promising results in terms of biomass/substrate yields. The combination of transcriptome, systems metabolic modeling, and machine learning analyses revealed the versatility of computational models to predict key metabolic engineering targets in a less cost-, time-, and laborious-intensive process. These data provide a platform to improve the prediction of metabolic engineering targets to design efficient cell factories. Our results may also work as a guide and platform for the selection/engineering of microorganisms for the production of interesting chemical compounds.

Engineering a Bifunctional Phr60-Rap60-Spo0A Quorum-Sensing Molecular Switch for Dynamic Fine-Tuning of Menaquinone-7 Synthesis in Bacillus subtilis

Quorum sensing (QS)-based dynamic regulation has been widely used as basic tool for fine-tuning gene expression in response to cell density changes without adding expensive inducers. However, most reported QS systems primarily relied on down-regulation rather than up-regulation of gene expression, significantly limiting its potential as a molecular switch to control metabolic flux. To solve this challenge, we developed a bifunctional and modular Phr60-Rap60-Spo0A QS system, based on two native promoters, P_abrB (down-regulation by Spo0A-P) and P_spoiiA (up-regulation by Spo0A-P). We constructed a library of promoters with different capacities to implement down-regulation and up-regulation by changing the location, number, and sequences of the binding sites for Spo0A-P. The QS system can dynamically balance the relationship between efficient synthesis of the target product and cell growth. Finally, we validated the usefulness of this strategy by dynamic control of menaquinone-7 (MK-7) synthesis in Bacillus subtilis 168, a model Gram-positive bacterium, with the bifunctional Phr60-Rap60-Spo0A quorum sensing system. Our dynamic pathway regulation led to a 40-fold improvement of MK-7 production from 9 to 360 mg/L in shake flasks and 200 mg/L in 15-L bioreactor. Taken together, our bilayer QS system has been successfully integrated with biocatalytic functions to achieve dynamic pathway regulation in B. subtilis 168, which may be extended for use in other microbes to fine-tune gene expression and improve metabolites production.

Towards creating an extended metabolic model (EMM) for E. coli using enzyme promiscuity prediction and metabolomics data

BACKGROUND

Metabolic models are indispensable in guiding cellular engineering and in advancing our understanding of systems biology. As not all enzymatic activities are fully known and/or annotated, metabolic models remain incomplete, resulting in suboptimal computational analysis and leading to unexpected experimental results. We posit that one major source of unaccounted metabolism is promiscuous enzymatic activity. It is now well-accepted that most, if not all, enzymes are promiscuous-i.e., they transform substrates other than their primary substrate. However, there have been no systematic analyses of genome-scale metabolic models to predict putative reactions and/or metabolites that arise from enzyme promiscuity.

RESULTS

Our workflow utilizes PROXIMAL-a tool that uses reactant-product transformation patterns from the KEGG database-to predict putative structural modifications due to promiscuous enzymes. Using iML1515 as a model system, we first utilized a computational workflow, referred to as Extended Metabolite Model Annotation (EMMA), to predict promiscuous reactions catalyzed, and metabolites produced, by natively encoded enzymes in Escherichia coli. We predict hundreds of new metabolites that can be used to augment iML1515. We then validated our method by comparing predicted metabolites with the Escherichia coli Metabolome Database (ECMDB).

CONCLUSIONS

We utilized EMMA to augment the iML1515 metabolic model to more fully reflect cellular metabolic activity. This workflow uses enzyme promiscuity as basis to predict hundreds of reactions and metabolites that may exist in E. coli but may have not been documented in iML1515 or other databases. We provide detailed analysis of 23 predicted reactions and 16 associated metabolites. Interestingly, nine of these metabolites, which are in ECMDB, have not previously been documented in any other E. coli databases. Four of the predicted reactions provide putative transformations parallel to those already in iML1515. We suggest adding predicted metabolites and reactions to iML1515 to create an extended metabolic model (EMM) for E. coli.

A design-build-test cycle using modeling and experiments reveals interdependencies between upper glycolysis and xylose uptake in recombinant S. cerevisiae and improves predictive capabilities of large-scale kinetic models

BACKGROUND

Recent advancements in omics measurement technologies have led to an ever-increasing amount of available experimental data that necessitate systems-oriented methodologies for efficient and systematic integration of data into consistent large-scale kinetic models. These models can help us to uncover new insights into cellular physiology and also to assist in the rational design of bioreactor or fermentation processes. Optimization and Risk Analysis of Complex Living Entities (ORACLE) framework for the construction of large-scale kinetic models can be used as guidance for formulating alternative metabolic engineering strategies.

RESULTS

We used ORACLE in a metabolic engineering problem: improvement of the xylose uptake rate during mixed glucose-xylose consumption in a recombinant Saccharomyces cerevisiae strain. Using the data from bioreactor fermentations, we characterized network flux and concentration profiles representing possible physiological states of the analyzed strain. We then identified enzymes that could lead to improved flux through xylose transporters (XTR). For some of the identified enzymes, including hexokinase (HXK), we could not deduce if their control over XTR was positive or negative. We thus performed a follow-up experiment, and we found out that HXK2 deletion improves xylose uptake rate. The data from the performed experiments were then used to prune the kinetic models, and the predictions of the pruned population of kinetic models were in agreement with the experimental data collected on the HXK2-deficient S. cerevisiae strain.

CONCLUSIONS

We present a design-build-test cycle composed of modeling efforts and experiments with a glucose-xylose co-utilizing recombinant S. cerevisiae and its HXK2-deficient mutant that allowed us to uncover interdependencies between upper glycolysis and xylose uptake pathway. Through this cycle, we also obtained kinetic models with improved prediction capabilities. The present study demonstrates the potential of integrated "modeling and experiments" systems biology approaches that can be applied for diverse applications ranging from biotechnology to drug discovery.

Exometabolomics Assisted Design and Validation of Synthetic Obligate Mutualism

Synthetic microbial ecology has the potential to enhance the productivity and resiliency of biotechnology processes compared to approaches using single isolates. Engineering microbial consortia is challenging; however, one approach that has attracted significant attention is the creation of synthetic obligate mutualism using auxotrophic mutants that depend on each other for exchange or cross-feeding of metabolites. Here, we describe the integration of mutant library fitness profiling with mass spectrometry based exometabolomics as a method for constructing synthetic mutualism based on cross-feeding. Two industrially important species lacking known ecological interactions, Zymomonas mobilis and Escherichia coli, were selected as the test species. Amino acid exometabolites identified in the spent medium of Z. mobilis were used to select three corresponding E. coli auxotrophs (proA, pheA and IlvA), as potential E. coli counterparts for the coculture. A pooled mutant fitness assay with a Z. mobilis transposon mutant library was used to identify mutants with improved growth in the presence of E. coli. An auxotroph mutant in a gene (ZMO0748) with sequence similarity to cysteine synthase A (cysK), was selected as the Z. mobilis counterpart for the coculture. Exometabolomic analysis of spent E. coli medium identified glutathione related metabolites as potentially available for rescue of the Z. mobilis cysteine synthase mutant. Three sets of cocultures between the Z. mobilis auxotroph and each of the three E. coli auxotrophs were monitored by optical density for growth and analyzed by flow cytometry to confirm high cell counts for each species. Taken together, our methods provide a technological framework for creating synthetic mutualisms combining existing screening based methods and exometabolomics for both the selection of obligate mutualism partners and elucidation of metabolites involved in auxotroph rescue.

Combining chemoinformatics with bioinformatics: in silico prediction of bacterial flavor-forming pathways by a chemical systems biology approach "reverse pathway engineering"

The incompleteness of genome-scale metabolic models is a major bottleneck for systems biology approaches, which are based on large numbers of metabolites as identified and quantified by metabolomics. Many of the revealed secondary metabolites and/or their derivatives, such as flavor compounds, are non-essential in metabolism, and many of their synthesis pathways are unknown. In this study, we describe a novel approach, Reverse Pathway Engineering (RPE), which combines chemoinformatics and bioinformatics analyses, to predict the “missing links” between compounds of interest and their possible metabolic precursors by providing plausible chemical and/or enzymatic reactions. We demonstrate the added-value of the approach by using flavor-forming pathways in lactic acid bacteria (LAB) as an example. Established metabolic routes leading to the formation of flavor compounds from leucine were successfully replicated. Novel reactions involved in flavor formation, i.e. the conversion of alpha-hydroxy-isocaproate to 3-methylbutanoic acid and the synthesis of dimethyl sulfide, as well as the involved enzymes were successfully predicted. These new insights into the flavor-formation mechanisms in LAB can have a significant impact on improving the control of aroma formation in fermented food products. Since the input reaction databases and compounds are highly flexible, the RPE approach can be easily extended to a broad spectrum of applications, amongst others health/disease biomarker discovery as well as synthetic biology.

In silico aided metabolic engineering of Klebsiella oxytoca and fermentation optimization for enhanced 2,3-butanediol production

Klebsiella oxytoca naturally produces a large amount of 2,3-butanediol (2,3-BD), a promising bulk chemical with wide industrial applications, along with various byproducts. In this study, the in silico gene knockout simulation of K. oxytoca was carried out for 2,3-BD overproduction by inhibiting the formation of byproducts. The knockouts of ldhA and pflB genes were targeted with the criteria of maximization of 2,3-BD production and minimization of byproducts formation. The constructed K. oxytoca ΔldhA ΔpflB strain showed higher 2,3-BD yields and higher final concentrations than those obtained from the wild-type and ΔldhA strains. However, the simultaneous deletion of both genes caused about a 50 % reduction in 2,3-BD productivity compared with K. oxytoca ΔldhA strain. Based on previous studies and in silico investigation that the agitation speed during 2,3-BD fermentation strongly affected cell growth and 2,3-BD synthesis, the effect of agitation speed on 2,3-BD production was investigated from 150 to 450 rpm in 5-L bioreactors containing 3-L culture media. The highest 2,3-BD productivity (2.7 g/L/h) was obtained at 450 rpm in batch fermentation. Considering the inhibition of acetoin for 2,3-BD production, fed-batch fermentations were performed using K. oxytoca ΔldhA ΔpflB strain to enhance 2,3-BD production. Altering the agitation speed from 450 to 350 rpm at nearly 10 g/L of acetoin during the fed-batch fermentation allowed for the production of 113 g/L 2,3-BD, with a yield of 0.45 g/g, and for the production of 2.1 g/L/h of 2,3-BD.

Genome-scale reconstruction and in silico analysis of Klebsiella oxytoca for 2,3-butanediol production

Background

Klebsiella oxytoca, a Gram-negative, rod-shaped, and facultative anaerobic bacterium, is one of the most promising 2,3-butanediol (2,3-BD) producers. In order to improve the metabolic performance of K. oxytoca as an efficient biofactory, it is necessary to assess its metabolic characteristics with a system-wide scope, and to optimize the metabolic pathways at a systems level. Provision of the complete genome sequence of K. oxytoca enabled the construction of genome-scale metabolic model of K. oxytoca and its in silico analyses.

Results

The genome-scale metabolic model of K. oxytoca was constructed using the annotated genome with biochemical and physiological information. The stoichiometric model, KoxGSC1457, is composed of 1,457 reactions and 1,099 metabolites. The model was further refined by applying biomass composition equations and comparing in silico results with experimental data based on constraints-based flux analyses. Then, the model was applied to in silico analyses to understand the properties of K. oxytoca and also to improve its capabilities for 2,3-BD production according to genetic and environmental perturbations. Firstly, in silico analysis, which tested the effect of augmenting the metabolic flux pool of 2,3-BD precursors, elucidated that increasing the pyruvate pool is primarily important for 2,3-BD synthesis. Secondly, we performed in silico single gene knockout simulation for 2,3-BD overproduction, and investigated the changes of the in silico flux solution space of a ldhA gene knockout mutant in comparison with that of the wild-type strain. Finally, the KoxGSC1457 model was used to optimize the oxygen levels during fermentation for 2,3-BD production.

Conclusions

The genome-scale metabolic model, KoxGSC1457, constructed in this study successfully investigated metabolic characteristics of K. oxytoca at systems level. The KoxGSC1457 model could be employed as an useful tool to analyze its metabolic capabilities, to predict its physiological responses according to environmental and genetic perturbations, and to design metabolic engineering strategies to improve its metabolic performance.