From reconstruction to C4 metabolic engineering: A case study for overproduction of polyhydroxybutyrate in bioenergy grasses

LooplessFluxSampler: an efficient toolbox for sampling the loopless flux solution space of metabolic models

BACKGROUND

Uniform random sampling of mass-balanced flux solutions offers an unbiased appraisal of the capabilities of metabolic networks. Unfortunately, it is impossible to avoid thermodynamically infeasible loops in flux samples when using convex samplers on large metabolic models. Current strategies for randomly sampling the non-convex loopless flux space display limited efficiency and lack theoretical guarantees.

RESULTS

Here, we present LooplessFluxSampler, an efficient algorithm for exploring the loopless mass-balanced flux solution space of metabolic models, based on an Adaptive Directions Sampling on a Box (ADSB) algorithm. ADSB is rooted in the general Adaptive Direction Sampling (ADS) framework, specifically the Parallel ADS, for which theoretical convergence and irreducibility results are available for sampling from arbitrary distributions. By sampling directions that adapt to the target distribution, ADSB traverses more efficiently the sample space achieving faster mixing than other methods. Importantly, the presented algorithm is guaranteed to target the uniform distribution over convex regions, and it provably converges on the latter distribution over more general (non-convex) regions provided the sample can have full support.

CONCLUSIONS

LooplessFluxSampler enables scalable statistical inference of the loopless mass-balanced solution space of large metabolic models. Grounded in a theoretically sound framework, this toolbox provides not only efficient but also reliable results for exploring the properties of the almost surely non-convex loopless flux space. Finally, LooplessFluxSampler includes a Markov Chain diagnostics suite for assessing the quality of the final sample and the performance of the algorithm.

The topology of genome-scale metabolic reconstructions unravels independent modules and high network flexibility

The topology of metabolic networks is recognisably modular with modules weakly connected apart from sharing a pool of currency metabolites. Here, we defined modules as sets of reversible reactions isolated from the rest of metabolism by irreversible reactions except for the exchange of currency metabolites. Our approach identifies topologically independent modules under specific conditions associated with different metabolic functions. As case studies, the E.coli iJO1366 and Human Recon 2.2 genome-scale metabolic models were split in 103 and 321 modules respectively, displaying significant correlation patterns in expression data. Finally, we addressed a fundamental question about the metabolic flexibility conferred by reversible reactions: “Of all Directed Topologies (DTs) defined by fixing directions to all reversible reactions, how many are capable of carrying flux through all reactions?”. Enumeration of the DTs for iJO1366 model was performed using an efficient depth-first search algorithm, rejecting infeasible DTs based on mass-imbalanced and loopy flux patterns. We found the direction of 79% of reversible reactions must be defined before all directions in the network can be fixed, granting a high degree of flexibility.

Genome-scale metabolic reconstructions represent all biochemical reactions that an organism can accomplish. These reconstructions are complex and often difficult to study in great detail. A way to overcome this limitation is to focus on specific pathways or subsystems. We present a novel method to identify metabolic modules based on the network topology. The method relies on reaction directions and ignores currency metabolites, which artificially connect distant metabolic reactions. In this way, topologically independent modules are built, where inputs and outputs are controlled by irreversible reactions. The method is automatic and unbiased, and, the result is a set of condition specific modules with defined metabolic functions. As a proof-of-concept we generated biologically relevant modules for the E.coli and Human genome-scale metabolic reconstructions supported by transcriptomic data. Finally, we applied the novel approach to study the network flexibility conferred by reversible reactions. In the case of the E. coli model, we found that the direction of 79% of structurally reversible reactions (those not directionally constrained by surrounding irreversible reactions) must be fixed to determine all the reaction directions in the network. Therefore, reversible reactions operate practically independent of each other.

Elucidation of Triacylglycerol Overproduction in the C4 Bioenergy Crop Sorghum bicolor by Constraint-Based Analysis

Upregulation of triacylglycerols (TAGs) in vegetative plant tissues such as leaves has the potential to drastically increase the energy density and biomass yield of bioenergy crops. In this context, constraint-based analysis has the promise to improve metabolic engineering strategies. Here we present a core metabolism model for the C4 biomass crop Sorghum bicolor (iTJC1414) along with a minimal model for photosynthetic CO2 assimilation, sucrose and TAG biosynthesis in C3 plants. Extending iTJC1414 to a four-cell diel model we simulate C4 photosynthesis in mature leaves with the principal photo-assimilatory product being replaced by TAG produced at different levels. Independent of specific pathways and per unit carbon assimilated, energy content and biosynthetic demands in reducing equivalents are about 1.3 to 1.4 times higher for TAG than for sucrose. For plant generic pathways, ATP- and NADPH-demands per CO2 assimilated are higher by 1.3- and 1.5-fold, respectively. If the photosynthetic supply in ATP and NADPH in iTJC1414 is adjusted to be balanced for sucrose as the sole photo-assimilatory product, overproduction of TAG is predicted to cause a substantial surplus in photosynthetic ATP. This means that if TAG synthesis was the sole photo-assimilatory process, there could be an energy imbalance that might impede the process. Adjusting iTJC1414 to a photo-assimilatory rate that approximates field conditions, we predict possible daily rates of TAG accumulation, dependent on varying ratios of carbon partitioning between exported assimilates and accumulated oil droplets (TAG, oleosin) and in dependence of activation of futile cycles of TAG synthesis and degradation. We find that, based on the capacity of leaves for photosynthetic synthesis of exported assimilates, mature leaves should be able to reach a 20% level of TAG per dry weight within one month if only 5% of the photosynthetic net assimilation can be allocated into oil droplets. From this we conclude that high TAG levels should be achievable if TAG synthesis is induced only during a final phase of the plant life cycle.

Metabolic Modeling of Wine Fermentation at Genome Scale

Wine fermentation is an ancient biotechnological process mediated by different microorganisms such as yeast and bacteria. Understanding of the metabolic and physiological phenomena taking place during this process can be now attained at a genome scale with the help of metabolic models. In this chapter, we present a detailed protocol for modeling wine fermentation using genome-scale metabolic models. In particular, we illustrate how metabolic fluxes can be computed, optimized and interpreted, for both yeast and bacteria under winemaking conditions. We also show how nutritional requirements can be determined and simulated using these models in relevant test cases. This chapter introduces fundamental concepts and practical steps for applying flux balance analysis in wine fermentation, and as such, it is intended for a broad microbiology audience as well as for practitioners in the metabolic modeling field.

Reconfiguring Plant Metabolism for Biodegradable Plastic Production

For decades, plants have been the subject of genetic engineering to synthesize novel, value-added compounds. Polyhydroxyalkanoates (PHAs), a large class of biodegradable biopolymers naturally synthesized in eubacteria, are among the novel products that have been introduced to make use of plant acetyl-CoA metabolic pathways. It was hoped that renewable PHA production would help address environmental issues associated with the accumulation of nondegradable plastic wastes. However, after three decades of effort synthesizing PHAs, and in particular the simplest form polyhydroxybutyrate (PHB), and seeking to improve their production in plants, it has proven very difficult to reach a commercially profitable rate in a normally growing plant. This seems to be due to the growth defects associated with PHA production and accumulation in plant cells. Here, we review major breakthroughs that have been made in plant-based PHA synthesis using traditional genetic engineering approaches and discuss challenges that have been encountered. Then, from the point of view of plant synthetic biology, we provide perspectives on reprograming plant acetyl-CoA pathways for PHA production, with the goal of maximizing PHA yield while minimizing growth inhibition. Specifically, we suggest genetic elements that can be considered in genetic circuit design, approaches for nuclear genome and plastome modification, and the use of multiomics and mathematical modeling in understanding and restructuring plant metabolic pathways.

A mass and charge balanced metabolic model of Setaria viridis revealed mechanisms of proton balancing in C4 plants

Background

C4 photosynthesis is a key domain of plant research with outcomes ranging from crop quality improvement, biofuel production and efficient use of water and nutrients. A metabolic network model of C4 “lab organism” Setaria viridis with extensive gene-reaction associations can accelerate target identification for desired metabolic manipulations and thereafter in vivo validation. Moreover, metabolic reconstructions have also been shown to be a significant tool to investigate fundamental metabolic traits.

Results

A mass and charge balance genome-scale metabolic model of Setaria viridis was constructed, which was tested to be able to produce all major biomass components in phototrophic and heterotrophic conditions. Our model predicted an important role of the utilization of NH\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$_{4}^{+}$\end{document}4+ and NO\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$_{3}^{-}$\end{document}3− ratio in balancing charges in plants. A multi-tissue extension of the model representing C4 photosynthesis was able to utilize NADP-ME subtype of C4 carbon fixation for the production of lignocellulosic biomass in stem, providing a tool for identifying gene associations for cellulose, hemi-cellulose and lignin biosynthesis that could be potential target for improved lignocellulosic biomass production. Besides metabolic engineering, our modeling results uncovered a previously unrecognized role of the 3-PGA/triosephosphate shuttle in proton balancing.

Conclusions

A mass and charge balance model of Setaria viridis, a model C4 plant, provides the possibility of system-level investigation to identify metabolic characteristics based on stoichiometric constraints. This study demonstrated the use of metabolic modeling in identifying genes associated with the synthesis of particular biomass components, and elucidating new role of previously known metabolic processes.

Electronic supplementary material

The online version of this article (10.1186/s12859-019-2941-z) contains supplementary material, which is available to authorized users.