Plant genome-scale modeling and implementation

Construction and application of the genome-scale metabolic model of Streptomyces radiopugnans

Geosmin is one of the most common earthy-musty odor compounds, which is mainly produced by Streptomyces. Streptomyces radiopugnans was screened in radiation-polluted soil, which has the potential to overproduce geosmin. However, due to the complex cellular metabolism and regulation mechanism, the phenotypes of S. radiopugnans were hard to investigate. A genome-scale metabolic model of S. radiopugnans named iZDZ767 was constructed. Model iZDZ767 involved 1,411 reactions, 1,399 metabolites, and 767 genes; its gene coverage was 14.1%. Model iZDZ767 could grow on 23 carbon sources and five nitrogen sources, which achieved 82.1% and 83.3% prediction accuracy, respectively. For the essential gene prediction, the accuracy was 97.6%. According to the simulation of model iZDZ767, D-glucose and urea were the best for geosmin fermentation. The culture condition optimization experiments proved that with D-glucose as the carbon source and urea as the nitrogen source (4 g/L), geosmin production could reach 581.6 ng/L. Using the OptForce algorithm, 29 genes were identified as the targets of metabolic engineering modification. With the help of model iZDZ767, the phenotypes of S. radiopugnans could be well resolved. The key targets for geosmin overproduction could also be identified efficiently.

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

The compartmentalization of C₄ plants increases photosynthetic efficiency, while constraining how material and energy must flow in leaf tissues. To capture this metabolic phenomenon, a generic plant metabolic reconstruction was replicated into four connected spatiotemporal compartments, namely bundle sheath (B) and mesophyll (M) across the day and night cycle. The C₄ leaf model was used to explore how amenable polyhydroxybutyrate (PHB) production is with these four compartments working cooperatively. A strategic pattern of metabolite conversion and exchange emerged from a systems-level network that has very few constraints imposed; mainly the sequential two-step carbon capture in mesophyll, then bundle sheath and photosynthesis during the day only. The building of starch reserves during the day and their mobilization during the night connects day and night metabolism. Flux simulations revealed that PHB production did not require rerouting of metabolic pathways beyond what is already utilised for growth. PHB yield was sensitive to photoassimilation capacity, availability of carbon reserves, ATP maintenance, relative photosynthetic activity of B and M, and type of metabolites exchanged in the plasmodesmata, but not sensitive towards compartmentalization. Hence, the compartmentalization issues currently encountered are likely to be kinetic or thermodynamic limitations rather than stoichiometric.

Plant genome-scale reconstruction: from single cell to multi-tissue modelling and omics analyses

In this review, we present the latest developments in plant systems biology with particular emphasis on plant genome-scale reconstructions and multi-omics analyses. Understanding multicellular metabolism is far from trivial and 'omics' data are difficult to interpret in the absence of a systems framework. 'Omics' data appropriately integrated with genome-scale reconstructions and modelling facilitates our understanding of how individual components interact and influence overall cell, tissue or organisms function. Here we present examples of how plant metabolic reconstructions and modelling are used as a systems-based framework for improving our understanding of the plant metabolic processes in single cells and multiple tissues.

Unraveling the early molecular and physiological mechanisms involved in response to phenanthrene exposure

BACKGROUND

Higher plants have to cope with increasing concentrations of pollutants of both natural and anthropogenic origin. Given their capacity to concentrate and metabolize various compounds including pollutants, plants can be used to treat environmental problems - a process called phytoremediation. However, the molecular mechanisms underlying the stabilization, the extraction, the accumulation and partial or complete degradation of pollutants by plants remain poorly understood.

RESULTS

Here, we determined the molecular events involved in the early plant response to phenanthrene, used as a model of polycyclic aromatic hydrocarbons. A transcriptomic and a metabolic analysis strongly suggest that energy availability is the crucial limiting factor leading to high and rapid transcriptional reprogramming that can ultimately lead to death. We show that the accumulation of phenanthrene in leaves inhibits electron transfer and photosynthesis within a few minutes, probably disrupting energy transformation.

CONCLUSION

This kinetic analysis improved the resolution of the transcriptome in the initial plant response to phenanthrene, identifying genes that are involved in primary processes set up to sense and detoxify this pollutant but also in molecular mechanisms used by the plant to cope with such harmful stress. The identification of first events involved in plant response to phenanthrene is a key step in the selection of candidates for further functional characterization, with the prospect of engineering efficient ecological detoxification systems for polycyclic aromatic hydrocarbons.

Metabolic Reconstruction of Setaria italica: A Systems Biology Approach for Integrating Tissue-Specific Omics and Pathway Analysis of Bioenergy Grasses

The urgent need for major gains in industrial crops productivity and in biofuel production from bioenergy grasses have reinforced attention on understanding C4 photosynthesis. Systems biology studies of C4 model plants may reveal important features of C4 metabolism. Here we chose foxtail millet (Setaria italica), as a C4 model plant and developed protocols to perform systems biology studies. As part of the systems approach, we have developed and used a genome-scale metabolic reconstruction in combination with the use of multi-omics technologies to gain more insights into the metabolism of S. italica. mRNA, protein, and metabolite abundances, were measured in mature and immature stem/leaf phytomers, and the multi-omics data were integrated into the metabolic reconstruction framework to capture key metabolic features in different developmental stages of the plant. RNA-Seq reads were mapped to the S. italica resulting for 83% coverage of the protein coding genes of S. italica. Besides revealing similarities and differences in central metabolism of mature and immature tissues, transcriptome analysis indicates significant gene expression of two malic enzyme isoforms (NADP- ME and NAD-ME). Although much greater expression levels of NADP-ME genes are observed and confirmed by the correspondent protein abundances in the samples, the expression of multiple genes combined to the significant abundance of metabolites that participates in C4 metabolism of NAD-ME and NADP-ME subtypes suggest that S. italica may use mixed decarboxylation modes of C4 photosynthetic pathways under different plant developmental stages. The overall analysis also indicates different levels of regulation in mature and immature tissues in carbon fixation, glycolysis, TCA cycle, amino acids, fatty acids, lignin, and cellulose syntheses. Altogether, the multi-omics analysis reveals different biological entities and their interrelation and regulation over plant development. With this study, we demonstrated that this systems approach is powerful enough to complement the functional metabolic annotation of bioenergy grasses.

A multi-tissue genome-scale metabolic modeling framework for the analysis of whole plant systems

Genome scale metabolic modeling has traditionally been used to explore metabolism of individual cells or tissues. In higher organisms, the metabolism of individual tissues and organs is coordinated for the overall growth and well-being of the organism. Understanding the dependencies and rationale for multicellular metabolism is far from trivial. Here, we have advanced the use of AraGEM (a genome-scale reconstruction of Arabidopsis metabolism) in a multi-tissue context to understand how plants grow utilizing their leaf, stem and root systems across the day-night (diurnal) cycle. Six tissue compartments were created, each with their own distinct set of metabolic capabilities, and hence a reliance on other compartments for support. We used the multi-tissue framework to explore differences in the "division-of-labor" between the sources and sink tissues in response to: (a) the energy demand for the translocation of C and N species in between tissues; and (b) the use of two distinct nitrogen sources (NO(-) 3 or NH(+) 4). The "division-of-labor" between compartments was investigated using a minimum energy (photon) objective function. Random sampling of the solution space was used to explore the flux distributions under different scenarios as well as to identify highly coupled reaction sets in different tissues and organelles. Efficient identification of these sets was achieved by casting this problem as a maximum clique enumeration problem. The framework also enabled assessing the impact of energetic constraints in resource (redox and ATP) allocation between leaf, stem, and root tissues required for efficient carbon and nitrogen assimilation, including the diurnal cycle constraint forcing the plant to set aside resources during the day and defer metabolic processes that are more efficiently performed at night. This study is a first step toward autonomous modeling of whole plant metabolism.