Flux analysis in plant metabolic networks: increasing throughput and coverage

Metabolic engineering in woody plants: challenges, advances, and opportunities

Woody plant species represent an invaluable reserve of biochemical diversity to which metabolic engineering can be applied to satisfy the need for commodity and specialty chemicals, pharmaceuticals, and renewable energy. Woody plants are particularly promising for this application due to their low input needs, high biomass, and immeasurable ecosystem services. However, existing challenges have hindered their widespread adoption in metabolic engineering efforts, such as long generation times, large and highly heterozygous genomes, and difficulties in transformation and regeneration. Recent advances in omics approaches, systems biology modeling, and plant transformation and regeneration methods provide effective approaches in overcoming these outstanding challenges. Promises brought by developments in this space are steadily opening the door to widespread metabolic engineering of woody plants to meet the global need for a wide range of sustainably sourced chemicals and materials.

Towards model-driven characterization and manipulation of plant lipid metabolism

Plant lipids have versatile applications and provide essential fatty acids in human diet. Therefore, there has been a growing interest to better characterize the genetic basis, regulatory networks, and metabolic pathways that shape lipid quantity and composition. Addressing these issues is challenging due to context-specificity of lipid metabolism integrating environmental, developmental, and tissue-specific cues. Here we systematically review the known metabolic pathways and regulatory interactions that modulate the levels of storage lipids in oilseeds. We argue that the current understanding of lipid metabolism provides the basis for its study in the context of genome-wide plant metabolic networks with the help of approaches from constraint-based modeling and metabolic flux analysis. The focus is on providing a comprehensive summary of the state-of-the-art of modeling plant lipid metabolic pathways, which we then contrast with the existing modeling efforts in yeast and microalgae. We then point out the gaps in knowledge of lipid metabolism, and enumerate the recent advances of using genome-wide association and quantitative trait loci mapping studies to unravel the genetic regulations of lipid metabolism. Finally, we offer a perspective on how advances in the constraint-based modeling framework can propel further characterization of plant lipid metabolism and its rational manipulation.

Enzymes as Parts in Need of Replacement - and How to Extend Their Working Life

Enzymes catalyze reactions in vivo at different rates and each enzyme molecule has a lifetime limit before it is degraded and replaced to enable catalysis to continue. Considering these rates together as a unitless ratio of catalytic cycles until replacement (CCR) provides a new quantitative tool to assess the replacement schedule of and energy investment into enzymes as they relate to function. Here, we outline the challenges of determining CCRs and new approaches to overcome them and then assess the CCRs of selected enzymes in bacteria and plants to reveal a range of seven orders of magnitude for this ratio. Modifying CCRs in plants holds promise to lower cellular costs, to tailor enzymes for particular environments, and to breed enzyme improvements for crop productivity.

Using metabolome data for mathematical modeling of plant metabolic systems

Plant metabolism is characterized by a wide diversity of metabolites, with systems far more complicated than those of microorganisms. Mathematical modeling is useful for understanding dynamic behaviors of plant metabolic systems for metabolic engineering. Time-series metabolome data has great potential for estimating kinetic model parameters to construct a genome-wide metabolic network model. However, data obtained by current metabolomics techniques does not meet the requirement for constructing accurate models. In this article, we highlight novel strategies and algorithms to handle the underlying difficulties and construct dynamic in vivo models for large-scale plant metabolic systems. The coarse but efficient modeling enables the prediction of unknown mechanisms regulating plant metabolism.

Engineering central metabolism - a grand challenge for plant biologists

The goal of increasing crop productivity and nutrient-use efficiency is being addressed by a number of ambitious research projects seeking to re-engineer photosynthetic biochemistry. Many of these projects will require the engineering of substantial changes in fluxes of central metabolism. However, as has been amply demonstrated in simpler systems such as microbes, central metabolism is extremely difficult to rationally engineer. This is because of multiple layers of regulation that operate to maintain metabolic steady state and because of the highly connected nature of central metabolism. In this review we discuss new approaches for metabolic engineering that have the potential to address these problems and dramatically improve the success with which we can rationally engineer central metabolism in plants. In particular, we advocate the adoption of an iterative 'design-build-test-learn' cycle using fast-to-transform model plants as test beds. This approach can be realised by coupling new molecular tools to incorporate multiple transgenes in nuclear and plastid genomes with computational modelling to design the engineering strategy and to understand the metabolic phenotype of the engineered organism. We also envisage that mutagenesis could be used to fine-tune the balance between the endogenous metabolic network and the introduced enzymes. Finally, we emphasise the importance of considering the plant as a whole system and not isolated organs: the greatest increase in crop productivity will be achieved if both source and sink metabolism are engineered.

Metabolic network modeling with model organisms

Flux balance analysis (FBA) with genome-scale metabolic network models (GSMNM) allows systems level predictions of metabolism in a variety of organisms. Different types of predictions with different accuracy levels can be made depending on the applied experimental constraints ranging from measurement of exchange fluxes to the integration of gene expression data. Metabolic network modeling with model organisms has pioneered method development in this field. In addition, model organism GSMNMs are useful for basic understanding of metabolism, and in the case of animal models, for the study of metabolic human diseases. Here, we discuss GSMNMs of most highly used model organisms with the emphasis on recent reconstructions.

In silico metabolic network analysis of Arabidopsis leaves

Background

During the last decades, we face an increasing interest in superior plants to supply growing demands for human and animal nutrition and for the developing bio-based economy. Presently, our limited understanding of their metabolism and its regulation hampers the targeted development of desired plant phenotypes. In this regard, systems biology, in particular the integration of metabolic and regulatory networks, is promising to broaden our knowledge and to further explore the biotechnological potential of plants.

Results

The thale cress Arabidopsis thaliana provides an ideal model to understand plant primary metabolism. To obtain insight into its functional properties, we constructed a large-scale metabolic network of the leaf of A. thaliana. It represented 511 reactions with spatial separation into compartments. Systematic analysis of this network, utilizing elementary flux modes, investigates metabolic capabilities of the plant and predicts relevant properties on the systems level: optimum pathway use for maximum growth and flux re-arrangement in response to environmental perturbation. Our computational model indicates that the A. thaliana leaf operates near its theoretical optimum flux state in the light, however, only in a narrow range of photon usage. The simulations further demonstrate that the natural day-night shift requires substantial re-arrangement of pathway flux between compartments: 89 reactions, involving redox and energy metabolism, substantially change the extent of flux, whereas 19 reactions even invert flux direction. The optimum set of anabolic pathways differs between day and night and is partly shifted between compartments. The integration with experimental transcriptome data pinpoints selected transcriptional changes that mediate the diurnal adaptation of the plant and superimpose the flux response.

Conclusions

The successful application of predictive modelling in Arabidopsis thaliana can bring systems-biological interpretation of plant systems forward. Using the gained knowledge, metabolic engineering strategies to engage plants as biotechnological factories can be developed.

Electronic supplementary material

The online version of this article (doi:10.1186/s12918-016-0347-3) contains supplementary material, which is available to authorized users.

Metabolic flux analyses of Pseudomonas aeruginosa cystic fibrosis isolates

Pseudomonas aeruginosa is a metabolically versatile wide-ranging opportunistic pathogen. In humans P. aeruginosa causes infections of the skin, urinary tract, blood, and the lungs of Cystic Fibrosis patients. In addition, P. aeruginosa's broad environmental distribution, relatedness to biotechnologically useful species, and ability to form biofilms have made it the focus of considerable interest. We used ¹³C metabolic flux analysis (MFA) and flux balance analysis to understand energy and redox production and consumption and to explore the metabolic phenotypes of one reference strain and five strains isolated from the lungs of cystic fibrosis patients. Our results highlight the importance of the oxidative pentose phosphate and Entner-Doudoroff pathways in P. aeruginosa growth. Among clinical strains we report two divergent metabolic strategies and identify changes between genetically related strains that have emerged during a chronic infection of the same patient. MFA revealed that the magnitude of fluxes through the glyoxylate cycle correlates with growth rates.

Can stable isotope mass spectrometry replace ‎radiolabelled approaches in metabolic studies?

Metabolic pathways and the key regulatory points thereof can be deduced using isotopically labelled substrates. One prerequisite is the accurate measurement of the labeling pattern of targeted metabolites. The subsequent estimation of metabolic fluxes following incubation in radiolabelled substrates has been extensively used. Radiolabelling is a sensitive approach and allows determination of total label uptake since the total radiolabel content is easy to detect. However, the incubation of cells, tissues or the whole plant in a stable isotope enriched environment and the use of either mass spectrometry or nuclear magnetic resonance techniques to determine label incorporation within specific metabolites offers the possibility to readily obtain metabolic information with higher resolution. It additionally also offers an important complement to other post-genomic strategies such as metabolite profiling providing insights into the regulation of the metabolic network and thus allowing a more thorough description of plant cellular function. Thus, although safety concerns mean that stable isotope feeding is generally preferred, the techniques are in truth highly complementary and application of both approaches in tandem currently probably provides the best route towards a comprehensive understanding of plant cellular metabolism.

Research using Mesenchymal Stem/Stromal Cells: quality metric towards developing a reference material

Mesenchymal stem/stromal cells (MSCs) have been extensively investigated for their regenerative, immune-modulatory, and wound healing properties. While the laboratory studies have suggested that MSC's have a unique potential for modulating the etiopathology of multiple diseases, the results from clinical trials have not been encouraging or reproducible. One of the explanations for such variability is explained by the "art" of isolating and propagating MSCs. Therefore, establishing more than minimal criteria to define MSC would help understand best protocols to isolate, propagate and deliver MSCs. Developing a calibration standard, a database and a set of functional tests would be a better quality metric for MSCs. In this review, we discuss the importance of selecting a standard, issues associated with coming up with such a standard and how these issues can be mitigated.

Omics data input for metabolic modeling

Recent advancements in high-throughput large-scale analytical methods to sequence genomes of organisms, and to quantify gene expression, proteins, lipids and metabolites have changed the paradigm of metabolic modeling. The cost of data generation and analysis has decreased significantly, which has allowed exponential increase in the amount of omics data being generated for an organism in a very short time. Compared to progress made in microbial metabolic modeling, plant metabolic modeling still remains limited due to its complex genomes and compartmentalization of metabolic reactions. Herein, we review and discuss different omics-datasets with potential application in the functional genomics. In particular, this review focuses on the application of omics-datasets towards construction and reconstruction of plant metabolic models.

Elucidating temporal resource allocation and diurnal dynamics in phototrophic metabolism using conditional FBA

The computational analysis of phototrophic growth using constraint-based optimization requires to go beyond current time-invariant implementations of flux-balance analysis (FBA). Phototrophic organisms, such as cyanobacteria, rely on harvesting the sun’s energy for the conversion of atmospheric CO2 into organic carbon, hence their metabolism follows a strongly diurnal lifestyle. We describe the growth of cyanobacteria in a periodic environment using a new method called conditional FBA. Our approach enables us to incorporate the temporal organization and conditional dependencies into a constraint-based description of phototrophic metabolism. Specifically, we take into account that cellular processes require resources that are themselves products of metabolism. Phototrophic growth can therefore be formulated as a time-dependent linear optimization problem, such that optimal growth requires a differential allocation of resources during different times of the day. Conditional FBA then allows us to simulate phototrophic growth of an average cell in an environment with varying light intensity, resulting in dynamic time-courses for all involved reaction fluxes, as well as changes in biomass composition over a diurnal cycle. Our results are in good agreement with several known facts about the temporal organization of phototrophic growth and have implications for further analysis of resource allocation problems in phototrophic metabolism.

Parallel labeling experiments for pathway elucidation and (13)C metabolic flux analysis

Metabolic pathway models provide the foundation for quantitative studies of cellular physiology through the measurement of intracellular metabolic fluxes. For model organisms metabolic models are well established, with many manually curated genome-scale model reconstructions, gene knockout studies and stable-isotope tracing studies. However, for non-model organisms a similar level of knowledge is often lacking. Compartmentation of cellular metabolism in eukaryotic systems also presents significant challenges for quantitative (13)C-metabolic flux analysis ((13)C-MFA). Recently, innovative (13)C-MFA approaches have been developed based on parallel labeling experiments, the use of multiple isotopic tracers and integrated data analysis, that allow more rigorous validation of pathway models and improved quantification of metabolic fluxes. Applications of these approaches open new research directions in metabolic engineering, biotechnology and medicine.

Fluxes through plant metabolic networks: measurements, predictions, insights and challenges

Although the flows of material through metabolic networks are central to cell function, they are not easy to measure other than at the level of inputs and outputs. This is particularly true in plant cells, where the network spans multiple subcellular compartments and where the network may function either heterotrophically or photoautotrophically. For many years, kinetic modelling of pathways provided the only method for describing the operation of fragments of the network. However, more recently, it has become possible to map the fluxes in central carbon metabolism using the stable isotope labelling techniques of metabolic flux analysis (MFA), and to predict intracellular fluxes using constraints-based modelling procedures such as flux balance analysis (FBA). These approaches were originally developed for the analysis of microbial metabolism, but over the last decade, they have been adapted for the more demanding analysis of plant metabolic networks. Here, the principal features of MFA and FBA as applied to plants are outlined, followed by a discussion of the insights that have been gained into plant metabolic networks through the application of these time-consuming and non-trivial methods. The discussion focuses on how a system-wide view of plant metabolism has increased our understanding of network structure, metabolic perturbations and the provision of reducing power and energy for cell function. Current methodological challenges that limit the scope of plant MFA are discussed and particular emphasis is placed on the importance of developing methods for cell-specific MFA.

Methods and advances in metabolic flux analysis: a mini-review

Metabolic flux analysis (MFA) is one of the pillars of metabolic engineering. Over the past three decades, it has been widely used to quantify intracellular metabolic fluxes in both native (wild type) and engineered biological systems. Through MFA, changes in metabolic pathway fluxes are quantified that result from genetic and/or environmental interventions. This information, in turn, provides insights into the regulation of metabolic pathways and may suggest new targets for further metabolic engineering of the strains. In this mini-review, we discuss and classify the various methods of MFA that have been developed, which include stoichiometric MFA, (13)C metabolic flux analysis, isotopic non-stationary (13)C metabolic flux analysis, dynamic metabolic flux analysis, and (13)C dynamic metabolic flux analysis. For each method, we discuss key advantages and limitations and conclude by highlighting important recent advances in flux analysis approaches.

Multi-scale engineering of plant cell cultures for promotion of specialized metabolism

To establish plant culture systems for product synthesis, a multi-scale engineering approach is necessary. At the intracellular level, the influx of 'omics' data has necessitated development of new methods to properly annotate and establish useful metabolic models that can be applied to elucidate unknown steps in specialized metabolite biosynthesis, define effective metabolic engineering strategies and increase enzyme diversity available for synthetic biology platforms. On an intercellular level, the presence of aggregates in culture leads to distinct metabolic sub-populations. Recent advances in flow cytometric analyses and mass spectrometry imaging allow for resolution of metabolites on the single cell level, providing an increased understanding of culture heterogeneity. Finally, extracellular engineering can be used to enhance culture performance through media manipulation, co-culture with bacteria, the use of exogenous elicitors or modulation of shear stress.

Integrated network analysis and effective tools in plant systems biology

One of the ultimate goals in plant systems biology is to elucidate the genotype-phenotype relationship in plant cellular systems. Integrated network analysis that combines omics data with mathematical models has received particular attention. Here we focus on the latest cutting-edge computational advances that facilitate their combination. We highlight (1) network visualization tools, (2) pathway analyses, (3) genome-scale metabolic reconstruction, and (4) the integration of high-throughput experimental data and mathematical models. Multi-omics data that contain the genome, transcriptome, proteome, and metabolome and mathematical models are expected to integrate and expand our knowledge of complex plant metabolisms.