13C metabolic flux analysis-guided metabolic engineering of Escherichia coli for improved acetol production from glycerol

Segmentation and evaluation of pathway module efficiency: Quantitative approach to monitor and overcome evolving bottlenecks in xylose to ethanol pathway

Engineering microbes that can efficiently ferment xylose to ethanol is critical to the development of renewable fuels from lignocellulosic biomass. To accelerate the strain optimization process, a method termed Segmentation and Evaluation of Pathway Module Efficiency (SEPME) was developed to enable rapid and iterative identification and removal of metabolic bottlenecks. Using SEPME, the overall pathway was segmented into two modules: the upstream xylose assimilation pathway and the downstream pentose phosphate pathway, glycolysis, and fermentation. The efficiencies of both modules were then quantified to identify the rate controlling module, followed by analyses of control coefficients, reaction rates, and byproduct concentrations to narrow down targets within the module. SEPME analysis revealed that as the strain was engineered with increasing xylose-to-ethanol yields, the bottlenecks shifted within a module and across the two modules. Guided by SEPME, these bottlenecks were removed one by one, and a strain approaching the theoretical ethanol yield was obtained.

Omics data for sampling thermodynamically feasible kinetic models

Kinetic models are key to understanding and predicting the dynamic behaviour of metabolic systems. Traditional models require kinetic parameters which are not always available and are often estimated in vitro. Ensemble models overcome this challenge by sampling thermodynamically feasible models around a measured reference point. However, it is unclear if the convenient distributions used to generate the ensemble produce a natural distribution of model parameters and hence if the model predictions are reasonable. In this paper, we produced a detailed kinetic model for the central carbon metabolism of Escherichia coli. The model consists of 82 reactions (including 13 reactions with allosteric regulation) and 79 metabolites. To sample the model, we used metabolomic and fluxomic data from a single steady-state time point for E. coli K-12 MG1655 growing on glucose minimal M9 medium (average sampling time for 1000 models: 11.21 ± 0.14 min). Afterwards, in order to examine whether our sampled models are biologically sound, we calculated Km, Vmax and kcat for the reactions and compared them to previously published values. Finally, we used metabolic control analysis to identify enzymes with high control over the fluxes in the central carbon metabolism. Our analyses demonstrate that our platform samples thermodynamically feasible kinetic models, which are in agreement with previously published experimental results and can be used to investigate metabolic control patterns within cells. This renders it a valuable tool for the study of cellular metabolism and the design of metabolic pathways.

Metabolic flux analysis: a comprehensive review on sample preparation, analytical techniques, data analysis, computational modelling, and main application areas

Metabolic flux analysis (MFA) quantitatively describes cellular fluxes to understand metabolic phenotypes and functional behaviour after environmental and/or genetic perturbations. In the last decade, the application of stable isotopes became extremely important to determine and integrate in vivo measurements of metabolic reactions in systems biology. 13C-MFA is one of the most informative methods used to study central metabolism of biological systems. This review aims to outline the current experimental procedure adopted in 13C-MFA, starting from the preparation of cell cultures and labelled tracers to the quenching and extraction of metabolites and their subsequent analysis performed with very powerful software. Here, the limitations and advantages of nuclear magnetic resonance spectroscopy and mass spectrometry techniques used in carbon labelled experiments are elucidated by reviewing the most recent published papers. Furthermore, we summarise the most successful approaches used for computational modelling in flux analysis and the main application areas with a particular focus in metabolic engineering.

Linear programming based gene expression model (LPM-GEM) predicts the carbon source for Bacillus subtilis

BACKGROUND

Elucidating cellular metabolism led to many breakthroughs in biotechnology, synthetic biology, and health sciences. To date, deriving metabolic fluxes by ¹³C tracer experiments is the most prominent approach for studying metabolic fluxes quantitatively, often with high accuracy and precision. However, the technique has a high demand for experimental resources. Alternatively, flux balance analysis (FBA) has been employed to estimate metabolic fluxes without labeling experiments. It is less informative but can benefit from the low costs and low experimental efforts and gain flux estimates in experimentally difficult conditions. Methods to integrate relevant experimental data have been emerged to improve FBA flux estimations. Data from transcription profiling is often selected since it is easy to generate at the genome scale, typically embedded by a discretization of differential and non-differential expressed genes coding for the respective enzymes.

RESULT

We established the novel method Linear Programming based Gene Expression Model (LPM-GEM). LPM-GEM linearly embeds gene expression into FBA constraints. We implemented three strategies to reduce thermodynamically infeasible loops, which is a necessary prerequisite for such an omics-based model building. As a case study, we built a model of B. subtilis grown in eight different carbon sources. We obtained good flux predictions based on the respective transcription profiles when validating with ¹³C tracer based metabolic flux data of the same conditions. We could well predict the specific carbon sources. When testing the model on another, unseen dataset that was not used during training, good prediction performance was also observed. Furthermore, LPM-GEM outperformed a well-established model building methods.

CONCLUSION

Employing LPM-GEM integrates gene expression data efficiently. The method supports gene expression-based FBA models and can be applied as an alternative to estimate metabolic fluxes when tracer experiments are inappropriate.

Boosting the acetol production in methanotrophic biocatalyst Methylomonas sp. DH-1 by the coupling activity of heteroexpressed novel protein PmoD with endogenous particulate methane monooxygenase

BACKGROUND

Methylacidiphilum sp. IT6 has been validated its C3 substrate assimilation pathway via acetol as a key intermediate using the PmoCAB3, a homolog of the particulate methane monooxygenase (pMMO). From the transcriptomic data, the contribution of PmoD of strain IT6 in acetone oxidation was questioned. Methylomonas sp. DH-1, a type I methanotroph containing pmo operon without the existence of its pmoD, has been deployed as a biocatalyst for the gas-to-liquid bioconversion of methane and propane to methanol and acetone. Thus, Methylomonas sp. DH-1 is a suitable host for investigation. The PmoD-expressed Methylomonas sp. DH-1 can also be deployed for acetol production, a well-known intermediate for various industrial applications. Microbial production of acetol is a sustainable approach attracted attention so far.

RESULTS

In this study, bioinformatics analyses elucidated that novel protein PmoD is a C-terminal transmembrane-helix membrane with the proposed function as a transport protein. Furthermore, the whole-cell biocatalyst was constructed in Methylomonas sp. DH-1 by co-expression the PmoD of Methylacidiphilum sp. IT6 with the endogenous pMMO to enable acetone oxidation. Under optimal conditions, the maximum accumulation, and specific productivity of acetol were 18.291 mM (1.35 g/L) and 0.317 mmol/g cell/h, respectively. The results showed the first coupling activity of pMMO with a heterologous protein PmoD, validated the involvement of PmoD in acetone oxidation, and demonstrated an unprecedented production of acetol from acetone in type I methanotrophic biocatalyst. From the data achieved in batch cultivation conditions, an assimilation pathway of acetone via acetol as the key intermediate was also proposed.

CONCLUSION

Using bioinformatics tools, the protein PmoD has been elucidated as the membrane protein with the proposed function as a transport protein. Furthermore, results from the assays of PmoD-heteroexpressed Methylomonas sp. DH-1 as a whole-cell biocatalyst validated the coupling activity of PmoD with pMMO to convert acetone to acetol, which also unlocks the potential of this recombinant biocatalyst for acetol production. The proposed acetone-assimilated pathway in the recombinant Methylomonas sp. DH-1, once validated, can extend the metabolic flexibility of Methylomonas sp. DH-1.

Organic waste conversion through anaerobic digestion: A critical insight into the metabolic pathways and microbial interactions

Anaerobic digestion is a promising method for energy recovery through conversion of organic waste to biogas and other industrial valuables. However, to tap the full potential of anaerobic digestion, deciphering the microbial metabolic pathway activities and their underlying bioenergetics is required. In addition, the behavior of organisms in consortia along with the analytical abilities to kinetically measure their metabolic interactions will allow rational optimization of the process. This review aims to explore the metabolic bottlenecks of the microbial communities adopting latest advances of profiling and ¹³C tracer-based analysis using state of the art analytical platforms (GC, GC-MS, LC-MS, NMR). The review summarizes the phases of anaerobic digestion, the role of microbial communities, key process parameters of significance, syntrophic microbial interactions and the bottlenecks that are critical for optimal bioenergetics and enhanced production of valuables. Considerations into the designing of efficient synthetic microbial communities as well as the latest advances in capturing their metabolic cross talk will be highlighted. The review further explores how the presence of additives and inhibiting factors affect the metabolic pathways. The critical insight into the reaction mechanism covered in this review may be helpful to optimize and upgrade the anaerobic digestion system.

13C-metabolic flux analysis in glycerol-assimilating strains of Saccharomyces cerevisiae

Glycerol is an attractive raw material for the production of useful chemicals using microbial cells. We previously identified metabolic engineering targets for the improvement of glycerol assimilation ability in Saccharomyces cerevisiae based on adaptive laboratory evolution (ALE) and transcriptome analysis of the evolved cells. We also successfully improved glycerol assimilation ability by the disruption of the RIM15 gene encoding a Greatwall protein kinase together with overexpression of the STL1 gene encoding the glycerol/H⁺ symporter. To understand glycerol assimilation metabolism in the evolved glycerol-assimilating strains and STL1-overexpressing RIM15 disruptant, we performed metabolic flux analysis using ¹³C-labeled glycerol. Significant differences in metabolic flux distributions between the strains obtained from the culture after 35 and 85 generations in ALE were not found, indicating that metabolic flux changes might occur in the early phase of ALE (i.e., before 35 generations at least). Similarly, metabolic flux distribution was not significantly changed by RIM15 gene disruption. However, fluxes for the lower part of glycolysis and the TCA cycle were larger and, as a result, flux for the pentose phosphate pathway was smaller in the STL1-overexpressing RIM15 disruptant than in the strain obtained from the culture after 85 generations in ALE. It could be effective to increase flux for the pentose phosphate pathway to improve the glycerol assimilation ability in S. cerevisiae.

A kinetic model of the central carbon metabolism for acrylic acid production in Escherichia coli

Acrylic acid is a value-added chemical used in industry to produce diapers, coatings, paints, and adhesives, among many others. Due to its economic importance, there is currently a need for new and sustainable ways to synthesise it. Recently, the focus has been laid in the use of Escherichia coli to express the full bio-based pathway using 3-hydroxypropionate as an intermediary through three distinct pathways (glycerol, malonyl-CoA, and β-alanine). Hence, the goals of this work were to use COPASI software to assess which of the three pathways has a higher potential for industrial-scale production, from either glucose or glycerol, and identify potential targets to improve the biosynthetic pathways yields. When compared to the available literature, the models developed during this work successfully predict the production of 3-hydroxypropionate, using glycerol as carbon source in the glycerol pathway, and using glucose as a carbon source in the malonyl-CoA and β-alanine pathways. Finally, this work allowed to identify four potential over-expression targets (glycerol-3-phosphate dehydrogenase (G3pD), acetyl-CoA carboxylase (AccC), aspartate aminotransferase (AspAT), and aspartate carboxylase (AspC)) that should, theoretically, result in higher AA yields.

Engineering cofactor metabolism for improved protein and glucoamylase production in Aspergillus niger

Background

Nicotinamide adenine dinucleotide phosphate (NADPH) is an important cofactor ensuring intracellular redox balance, anabolism and cell growth in all living systems. Our recent multi-omics analyses of glucoamylase (GlaA) biosynthesis in the filamentous fungal cell factory Aspergillus niger indicated that low availability of NADPH might be a limiting factor for GlaA overproduction.

Results

We thus employed the Design-Build-Test-Learn cycle for metabolic engineering to identify and prioritize effective cofactor engineering strategies for GlaA overproduction. Based on available metabolomics and ¹³C metabolic flux analysis data, we individually overexpressed seven predicted genes encoding NADPH generation enzymes under the control of the Tet-on gene switch in two A. niger recipient strains, one carrying a single and one carrying seven glaA gene copies, respectively, to test their individual effects on GlaA and total protein overproduction. Both strains were selected to understand if a strong pull towards glaA biosynthesis (seven gene copies) mandates a higher NADPH supply compared to the native condition (one gene copy). Detailed analysis of all 14 strains cultivated in shake flask cultures uncovered that overexpression of the gsdA gene (glucose 6-phosphate dehydrogenase), gndA gene (6-phosphogluconate dehydrogenase) and maeA gene (NADP-dependent malic enzyme) supported GlaA production on a subtle (10%) but significant level in the background strain carrying seven glaA gene copies. We thus performed maltose-limited chemostat cultures combining metabolome analysis for these three isolates to characterize metabolic-level fluctuations caused by cofactor engineering. In these cultures, overexpression of either the gndA or maeA gene increased the intracellular NADPH pool by 45% and 66%, and the yield of GlaA by 65% and 30%, respectively. In contrast, overexpression of the gsdA gene had a negative effect on both total protein and glucoamylase production.

Conclusions

This data suggests for the first time that increased NADPH availability can indeed underpin protein and especially GlaA production in strains where a strong pull towards GlaA biosynthesis exists. This data also indicates that the highest impact on GlaA production can be engineered on a genetic level by increasing the flux through the pentose phosphate pathway (gndA gene) followed by engineering the flux through the reverse TCA cycle (maeA gene). We thus propose that NADPH cofactor engineering is indeed a valid strategy for metabolic engineering of A. niger to improve GlaA production, a strategy which is certainly also applicable to the rational design of other microbial cell factories.

ScalaFlux: A scalable approach to quantify fluxes in metabolic subnetworks

13C-metabolic flux analysis (13C-MFA) allows metabolic fluxes to be quantified in living organisms and is a major tool in biotechnology and systems biology. Current 13C-MFA approaches model label propagation starting from the extracellular 13C-labeled nutrient(s), which limits their applicability to the analysis of pathways close to this metabolic entry point. Here, we propose a new approach to quantify fluxes through any metabolic subnetwork of interest by modeling label propagation directly from the metabolic precursor(s) of this subnetwork. The flux calculations are thus purely based on information from within the subnetwork of interest, and no additional knowledge about the surrounding network (such as atom transitions in upstream reactions or the labeling of the extracellular nutrient) is required. This approach, termed ScalaFlux for SCALAble metabolic FLUX analysis, can be scaled up from individual reactions to pathways to sets of pathways. ScalaFlux has several benefits compared with current 13C-MFA approaches: greater network coverage, lower data requirements, independence from cell physiology, robustness to gaps in data and network information, better computational efficiency, applicability to rich media, and enhanced flux identifiability. We validated ScalaFlux using a theoretical network and simulated data. We also used the approach to quantify fluxes through the prenyl pyrophosphate pathway of Saccharomyces cerevisiae mutants engineered to produce phytoene, using a dataset for which fluxes could not be calculated using existing approaches. A broad range of metabolic systems can be targeted with minimal cost and effort, making ScalaFlux a valuable tool for the analysis of metabolic fluxes.

Systems and synthetic biology tools for advanced bioproduction hosts

The genomic revolution ushered in an era of discovery and characterization of enzymes from novel organisms that fueled engineering of microbes to produce commodity and high-value compounds. Over the past decade advances in synthetic biology tools in recent years contributed to significant progress in metabolic engineering efforts to produce both biofuels and bioproducts resulting in several such related items being brought to market. These successes represent a burgeoning bio-economy; however, significant resources and time are still necessary to progress a system from proof-of-concept to market. In order to fully realize this potential, methods that examine biological systems in a comprehensive, systematic and high-throughput manner are essential. Recent success in synthetic biology has coincided with the development of systems biology and analytical approaches that kept pace and scaled with technology development. Here, we review a selection of systems biology methods and their use in synthetic biology approaches for microbial biotechnology platforms.