Effects of varying nitrogen sources on amino acid synthesis costs in Arabidopsis thaliana under different light and carbon-source conditions

Dynamic interactions between SPX proteins, the ubiquitination machinery, and signalling molecules for stress adaptation at a whole-plant level

The plant macronutrient phosphorus is a scarce resource and plant-available phosphate is limiting in most soil types. Generally, a gene regulatory module called the phosphate starvation response (PSR) enables efficient phosphate acquisition by roots and translocation to other organs. Plants growing on moderate to nutrient-rich soils need to co-ordinate availability of different nutrients and repress the highly efficient PSR to adjust phosphate acquisition to the availability of other macro- and micronutrients, and in particular nitrogen. PSR repression is mediated by a small family of single SYG1/Pho81/XPR1 (SPX) domain proteins. The SPX domain binds higher order inositol pyrophosphates that signal cellular phosphorus status and modulate SPX protein interaction with PHOSPHATE STARVATION RESPONSE1 (PHR1), the central transcriptional regulator of PSR. Sequestration by SPX repressors restricts PHR1 access to PSR gene promoters. Here we focus on SPX4 that primarily acts in shoots and sequesters many transcription factors other than PHR1 in the cytosol to control processes beyond the classical PSR, such as nitrate, auxin, and jasmonic acid signalling. Unlike SPX1 and SPX2, SPX4 is subject to proteasomal degradation not only by singular E3 ligases, but also by SCF-CRL complexes. Emerging models for these different layers of control and their consequences for plant acclimation to the environment will be discussed.

Electrofermentation increases concentration of poly γ-glutamic acid in Bacillus subtilis biofilms

Fluctuations in redox conditions in bioprocesses can alter the end-products, reduce their concentration, and lengthen the process time. Electrofermentation enables rapid metabolic modulation of biosynthesis and allows control of redox imbalances in biofilm-based fermentation processes. In this study, electrofermentation is used to boost the production of the bacterial biopolymer poly-γ-glutamic acid (γ-PGA) from Bacillus subtilis ATCC 6051. When compared to control experiments (3.3 ± 0.99 g L-1 ), the application of an electrode potential E = 0.4 V versus Ag/AgCl results in a more than two-fold increase in the production of γ-PGA (9.13 ± 1.4 g L-1 ). Using an engineered B. subtilis strain, in which γ-PGA production is driven by isopropyl β-d-1-thiogalactopyranoside, electrofermentation improves polymer concentrations from 15.4 ± 1.5 to 23.1 ± 1.6 versus g L-1 . These results confirm that electrofermentation conditions can be adopted to increase the concentration of γ-PGA and perhaps other extracellular biopolymers in industrial strains.

The diversity of substrates for plant respiration and how to optimize their use

Plant respiration is a foundational biological process with the potential to be optimized to improve crop yield. To understand and manipulate the outputs of respiration, the inputs of respiration-respiratory substrates-need to be probed in detail. Mitochondria house substrate catabolic pathways and respiratory machinery, so transport into and out of these organelles plays an important role in committing substrates to respiration. The large number of mitochondrial carriers and catabolic pathways that remain unidentified hinder this process and lead to confusion about the identity of direct and indirect respiratory substrates in plants. The sources and usage of respiratory substrates vary and are increasing found to be highly regulated based on cellular processes and environmental factors. This review covers the use of direct respiratory substrates following transport through mitochondrial carriers and catabolism under normal and stressed conditions. We suggest the introduction of enzymes not currently found in plant mitochondria to enable serine and acetate to be direct respiratory substrates in plants. We also compare respiratory substrates by assessing energetic yields, availability in cells, and their full or partial oxidation during cell catabolism. This information can assist in decisions to use synthetic biology approaches to alter the range of respiratory substrates in plants. As a result, respiration could be optimized by introducing, improving, or controlling specific mitochondrial transporters and mitochondrial catabolic pathways.

Respiratory energy demands and scope for demand expansion and destruction

Nonphotosynthetic plant metabolic processes are powered by respiratory energy, a limited resource that metabolic engineers—like plants themselves—must manage prudently.

Toward mechanistic modeling and rational engineering of plant respiration

Plant respiration not only provides energy to support all cellular processes, including biomass production, but also plays a major role in the global carbon cycle. Therefore, modulation of plant respiration can be used to both increase the plant yield and mitigate the effects of global climate change. Mechanistic modeling of plant respiration at sufficient biochemical detail can provide key insights for rational engineering of this process. Yet, despite its importance, plant respiration has attracted considerably less modeling effort in comparison to photosynthesis. In this update review, we highlight the advances made in modeling of plant respiration, emphasizing the gradual but important change from phenomenological to models based on first principles. We also provide a detailed account of the existing resources that can contribute to resolving the challenges in modeling plant respiration. These resources point at tangible improvements in the representation of cellular processes that contribute to CO2 evolution and consideration of kinetic properties of underlying enzymes to facilitate mechanistic modeling. The update review emphasizes the need to couple biochemical models of respiration with models of acclimation and adaptation of respiration for their effective usage in guiding breeding efforts and improving terrestrial biosphere models tailored to future climate scenarios.

An automated workflow that generates atom mappings for large-scale metabolic models and its application to Arabidopsis thaliana

Quantification of reaction fluxes of metabolic networks can help us understand how the integration of different metabolic pathways determines cellular functions. Yet, intracellular fluxes cannot be measured directly but are estimated with metabolic flux analysis (MFA), which relies on the patterns of isotope labeling of metabolites in the network. The application of MFA also requires a stoichiometric model with atom mappings that are currently not available for the majority of large-scale metabolic network models, particularly of plants. While automated approaches such as the Reaction Decoder Toolkit (RDT) can produce atom mappings for individual reactions, tracing the flow of individual atoms of the entire reactions across a metabolic model remains challenging. Here we establish an automated workflow to obtain reliable atom mappings for large-scale metabolic models by refining the outcome of RDT, and apply the workflow to metabolic models of Arabidopsis thaliana. We demonstrate the accuracy of RDT through a comparative analysis with atom mappings from a large database of biochemical reactions, MetaCyc. We further show the utility of our automated workflow by simulating ¹⁵ N isotope enrichment and identifying nitrogen (N)-containing metabolites which show enrichment patterns that are informative for flux estimation in future ¹⁵ N-MFA studies of A. thaliana. The automated workflow established in this study can be readily expanded to other species for which metabolic models have been established and the resulting atom mappings will facilitate MFA and graph-theoretic structural analyses with large-scale metabolic networks.

Differential Metabolic Responses of Lettuce Grown in Soil, Substrate and Hydroponic Cultivation Systems under NH4+/NO3- Application

Nitrogen (N) is an essential element for plant growth and development. The application of a balanced and optimal amount of N is required for sustainable plant yield. For this, different N sources and forms are used, that including ammonium (NH4+) and nitrate (NO3-). These are the main sources for N uptake by plants where NH4+/NO3- ratios have a significant effect on the biomass, quality and metabolites composition of lettuce grown in soil, substrate and hydroponic cultivation systems. A limited supply of N resulted in the reduction in the biomass, quality and overall yield of lettuce. Additionally, different types of metabolites were produced with varying concentrations of N sources and can be used as metabolic markers to improve the N use efficiency. To investigate the differential metabolic activity, we planted lettuce with different NH4+/NO3- ratios (100:0, 75:25, 50:50, 25:75 and 0:100%) and a control (no additional N applied) in soil, substrate and hydroponic cultivation systems. The results revealed that the 25% NH4+/75% NO3- ratio increased the relative chlorophyll contents as well as the biomass of lettuce in all cultivation systems. However, lettuce grown in the hydroponic cultivation system showed the best results. The concentration of essential amino acids including alanine, valine, leucine, lysine, proline and serine increased in soil and hydroponically grown lettuce treated with the 25% NH4+/75% NO3- ratio. The taste and quality-related compounds in lettuce showed maximum relative abundance with the 25% NH4+/75% NO3- ratio, except ascorbate (grown in soil) and lactupicrin (grown in substrate), which showed maximum relative abundance in the 50% NH4+/50% NO3- ratio and control treatments, respectively. Moreover, 1-O-caffeoylglucose, 1,3-dicaffeoylquinic acid, aesculetin and quercetin-3-galactoside were increased by the application of the 100% NH4+/0% NO3- ratio in soil-grown lettuce. The 25% NH4+/75% NO3- ratio was more suitable in the hydroponic cultivation system to obtain increased lettuce biomass. The metabolic profiling of lettuce showed different behaviors when applying different NH4+/NO3- ratios. Therefore, the majority of the parameters were largely influenced by the 25% NH4+/75% NO3- ratio, which resulted in the hyper-accumulation of health-promoting compounds in lettuce. In conclusion, the optimal N applications improve the quality of lettuce grown in soil, substrate and hydroponic cultivation systems which ultimately boost the nutritional value of lettuce.

A multi-organ metabolic model of tomato predicts plant responses to nutritional and genetic perturbations

Predicting and understanding plant responses to perturbations require integrating the interactions between nutritional sources, genes, cell metabolism, and physiology in the same model. This can be achieved using metabolic modeling calibrated by experimental data. In this study, we developed a multi-organ metabolic model of a tomato (Solanum lycopersicum) plant during vegetative growth, named Virtual Young TOmato Plant (VYTOP) that combines genome-scale metabolic models of leaf, stem and root and integrates experimental data acquired from metabolomics and high-throughput phenotyping of tomato plants. It is composed of 6,689 reactions and 6,326 metabolites. We validated VYTOP predictions on five independent use cases. The model correctly predicted that glutamine is the main organic nutrient of xylem sap. The model estimated quantitatively how stem photosynthetic contribution impacts exchanges between the different organs. The model was also able to predict how nitrogen limitation affects vegetative growth and the metabolic behavior of transgenic tomato lines with altered expression of core metabolic enzymes. The integration of different components, such as a metabolic model, physiological constraints, and experimental data, generates a powerful predictive tool to study plant behavior, which will be useful for several other applications, such as plant metabolic engineering or plant nutrition.

Relative flux trade-offs and optimization of metabolic network functionalities

Trade-offs between traits are present across different levels of biological systems and ultimately reflect constraints imposed by physicochemical laws and the structure of underlying biochemical networks. Yet, mechanistic explanation of how trade-offs between molecular traits arise and how they relate to optimization of fitness-related traits remains elusive. Here, we introduce the concept of relative flux trade-offs and propose a constraint-based approach, termed FluTOr, to identify metabolic reactions whose fluxes are in relative trade-off with respect to an optimized fitness-related cellular task, like growth. We then employed FluTOr to identify relative flux trade-offs in the genome-scale metabolic networks of Escherichia coli, Saccharomyces cerevisiae, and Arabidopsis thaliana. For the metabolic models of E. coli and S. cerevisiae we showed that: (i) the identified relative flux trade-offs depend on the carbon source used and that (ii) reactions that participated in relative trade-offs in both species were implicated in cofactor biosynthesis. In contrast to the two microorganisms, the relative flux trade-offs for the metabolic model of A. thaliana did not depend on the available nitrogen sources, reflecting the differences in the underlying metabolic network as well as the considered environments. Lastly, the established connection between relative flux trade-offs allowed us to identify overexpression targets that can be used to optimize fitness-related traits. Altogether, our computational approach and findings demonstrate how relative flux trade-offs can shape optimization of metabolic tasks, important in biotechnological applications.

High NH4+/NO3− Ratio Inhibits the Growth and Nitrogen Uptake of Chinese Kale at the Late Growth Stage by Ammonia Toxicity

The aim of this study was to determine the effects of various NH4+/NO3− ratios in a nutrient solution on the growth and nitrogen uptake of Chinese kale under hydroponic conditions. The four NH4+/NO3− ratios in the nutrient solution were CK (0/100), T1 (10/90), T2 (25/75), and T3 (50/50). An appropriate NH4+/NO3− ratio (10/90, 25/75) promoted the growth of Chinese kale. T2 produced the highest fresh and dry weight among treatments, and all indices of seedling root growth were the highest under T2. A high NH4+/NO3− ratio (50/50) promoted the growth of Chinese kale seedlings at the early stage but inhibited growth at the late growth stage. At harvest, the nutrient solution showed acidity. The pH value was the lowest in T3, whereas NH4+ and NH4+/NO3− ratios were the highest, which caused ammonium toxicity. Total N accumulation and N use efficiency were the highest in T2, and total N accumulation was the lowest in T3. Principal component analysis showed that T2 considerably promoted growth and N absorption of Chinese kale, whereas T3 had a remarkable effect on the pH value. These findings suggest that an appropriate increase in NH4+ promotes the growth and nutrient uptake of Chinese kale by maintaining the pH value and NH4+/NO3− ratios of the nutrient solution, whereas excessive addition of NH4+ may induce rhizosphere acidification and ammonia toxicity, inhibiting plant growth.

Appropriate NH4 +/NO3 - Ratio Triggers Plant Growth and Nutrient Uptake of Flowering Chinese Cabbage by Optimizing the pH Value of Nutrient Solution

Compared with sole nitrogen (N), the nutrition mixture of ammonium (NH₄ ⁺) and nitrate (NO₃ ^-) is known to better improve crop yield and quality. However, the mechanism underlying this improvement remains unclear. In the present study, we analyzed the changes in nutrient solution composition, content of different N forms in plant tissues and exudates, and expression of plasma membrane (PM) H⁺-ATPase genes (HAs) under different NH₄ ⁺/NO₃ ^- ratios (0/100, 10/90, 25/75, 50/50 as control, T1, T2, and T3) in flowering Chinese cabbage. We observed that compared with the control, T1 and T2 increased the economical yield of flowering Chinese cabbage by 1.26- and 1.54-fold, respectively, whereas T3 significantly reduced plant yield. Compared with the control, T1-T3 significantly reduced the NO₃ ^- content and increased the NH₄ ⁺, amino acid, and soluble protein contents of flowering Chinese cabbage to varying extents. T2 significantly increased the N use efficiency (NUE), whereas T3 significantly decreased it to only being 70.25% of that of the control. Owing to the difference in N absorption and utilization among seedlings, the pH value of the nutrient solution differed under different NH₄ ⁺/NO₃ ^- ratios. At harvest, the pH value of T2 was 5.8; in the control and T1, it was approximately 8.0, and in T3 it was only 3.6. We speculated that appropriate NH₄ ⁺/NO₃ ^- ratios may improve N absorption and assimilation and thus promote the growth of flowering Chinese cabbage, owing to the suitable pH value. On the contrary, addition of excessive NH₄ ⁺ may induce rhizosphere acidification and ammonia toxicity, causing plant growth inhibition. We further analyzed the transcription of PM H⁺-ATPase genes (HAs). HA1 and HA7 transcription in roots was significantly down-regulated by the addition of the mixture of NH₄ ⁺ and NO₃ ^-, whereas the transcription of HA2, HA9 in roots and HA7, HA8, and HA10 in leaves was sharply up-regulated by the addition of the mixture; the transcription of HA3 was mainly enhanced by the highest ratio of NH₄ ⁺/NO₃ ^-. Our results provide valuable information about the effects of treatments with different NH₄ ⁺/NO₃ ^- ratios on plant growth and N uptake and utilization.

Environment-coupled models of leaf metabolism

The plant leaf is the main site of photosynthesis. This process converts light energy and inorganic nutrients into chemical energy and organic building blocks for the biosynthesis and maintenance of cellular components and to support the growth of the rest of the plant. The leaf is also the site of gas–water exchange and due to its large surface, it is particularly vulnerable to pathogen attacks. Therefore, the leaf's performance and metabolic modes are inherently determined by its interaction with the environment. Mathematical models of plant metabolism have been successfully applied to study various aspects of photosynthesis, carbon and nitrogen assimilation and metabolism, aided suggesting metabolic intervention strategies for optimized leaf performance, and gave us insights into evolutionary drivers of plant metabolism in various environments. With the increasing pressure to improve agricultural performance in current and future climates, these models have become important tools to improve our understanding of plant–environment interactions and to propel plant breeders efforts. This overview article reviews applications of large-scale metabolic models of leaf metabolism to study plant–environment interactions by means of flux-balance analysis. The presented studies are organized in two ways — by the way the environment interactions are modelled — via external constraints or data-integration and by the studied environmental interactions — abiotic or biotic.

The Role of a Glucosinolate-Derived Nitrile in Plant Immune Responses

Glucosinolates are defense-related secondary metabolites found in Brassicaceae. When Brassicaceae come under attack, glucosinolates are hydrolyzed into different forms of glucosinolate hydrolysis products (GHPs). Among the GHPs, isothiocyanates are the most comprehensively characterized defensive compounds, whereas the functional study of nitriles, another group of GHP, is still limited. Therefore, this study investigates whether 3-butenenitrile (3BN), a nitrile, can trigger the signaling pathways involved in the regulation of defense responses in Arabidopsis thaliana against biotic stresses. Briefly, the methodology is divided into three stages, (i) evaluate the physiological and biochemical effects of exogenous 3BN treatment on Arabidopsis, (ii) determine the metabolites involved in 3BN-mediated defense responses in Arabidopsis, and (iii) assess whether a 3BN treatment can enhance the disease tolerance of Arabidopsis against necrotrophic pathogens. As a result, a 2.5 mM 3BN treatment caused lesion formation in Arabidopsis Columbia (Col-0) plants, a process found to be modulated by nitric oxide (NO). Metabolite profiling revealed an increased production of soluble sugars, Krebs cycle associated carboxylic acids and amino acids in Arabidopsis upon a 2.5 mM 3BN treatment, presumably via NO action. Primary metabolites such as sugars and amino acids are known to be crucial components in modulating plant defense responses. Furthermore, exposure to 2.0 mM 3BN treatment began to increase the production of salicylic acid (SA) and jasmonic acid (JA) phytohormones in Arabidopsis Col-0 plants in the absence of lesion formation. The production of SA and JA in nitrate reductase loss-of function mutant (nia1nia2) plants was also induced by the 3BN treatments, with a greater induction for JA. The SA concentration in nia1nia2 plants was lower than in Col-0 plants, confirming the previously reported role of NO in controlling SA production in Arabidopsis. A 2.0 mM 3BN treatment prior to pathogen assays effectively alleviated the leaf lesion symptom of Arabidopsis Col-0 plants caused by Pectobacterium carotovorum ssp. carotovorum and Botrytis cinerea and reduced the pathogen growth on leaves. The findings of this study demonstrate that 3BN can elicit defense response pathways in Arabidopsis, which potentially involves a coordinated crosstalk between NO and phytohormone signaling.

Genotypic Variation in the Root and Shoot Metabolite Profiles of Wheat (Triticum aestivum L.) Indicate Sustained, Preferential Carbon Allocation as a Potential Mechanism in Phosphorus Efficiency

Changes in the levels of plant metabolites in response to nutrient deficiency is indicative of how plants utilize scarce resources. In this study, changes in the metabolite profile of roots and shoots of wheat genotypes differing in phosphorus use efficiency (PUE) was investigated. Under low P supply and at 28 days after sowing (DAS), the wheat breeding line, RAC875 (P efficient) produced 42% more shoot biomass than the wheat variety, and Wyalkatchem (P inefficient). Significant changes in the metabolite profile in leaves and roots were observed under low P supply and significant genotypic variation was evident. Under low P supply, an increase in raffinose and 1-kestose was evident in roots of both wheat genotypes, with RAC875 accumulating more when compared to Wyalkatchem. There was no significant increase in raffinose and 1-kestose in leaves when plants were grown under P deficiency. P deficiency had no significant impact on the levels of sucrose, maltose, glucose and fructose in both genotypes, and while phosphorylated sugars (glucose-6-P and fructose-6-P) remained unchanged in RAC875, in Wyalkatchem, glucose-6-P significantly decreased in roots, and fructose-6-P significantly decreased in both leaves and roots. Glycerol-3-P decreased twofold in roots of both wheat genotypes in response to low P. In roots, RAC875 exhibited significantly lower levels of fumarate, malate, maleate and itaconate than Wyalkatchem, while low P enhanced organic acid exudation in RAC875 but not in Wyalkatchem. RAC875 showed greater accumulation of aspartate, glutamine and β-alanine in leaves than Wyalkatchem under low P supply. Greater accumulation of raffinose and 1-kestose in roots and aspartate, glutamine and β-alanine in leaves appears to be associated with enhanced PUE in RAC875. Glucose-6-P and fructose-6-P are important for glycolysis, thus maintaining these metabolites would enable RAC875 to maintain carbohydrate metabolism and shoot biomass under P deficiency. The work presented here provides evidence that differences in metabolite profiles can be observed between wheat varieties that differ in PUE and key metabolic pathways are maintained in the efficient genotype to ensure carbon supply under P deficiency.

The impact of genome evolution on the allotetraploid Nicotiana rustica - an intriguing story of enhanced alkaloid production

BACKGROUND

Nicotiana rustica (Aztec tobacco), like common tobacco (Nicotiana tabacum), is an allotetraploid formed through a recent hybridization event; however, it originated from completely different progenitor species. Here, we report the comparative genome analysis of wild type N. rustica (5 Gb; 2n = 4x = 48) with its three putative diploid progenitors (2.3-3 Gb; 2n = 2x =24), Nicotiana undulata, Nicotiana paniculata and Nicotiana knightiana.

RESULTS

In total, 41% of N. rustica genome originated from the paternal donor (N. undulata), while 59% originated from the maternal donor (N. paniculata/N. knightiana). Chloroplast genome and gene analyses indicated that N. knightiana is more closely related to N. rustica than N. paniculata. Gene clustering revealed 14,623 ortholog groups common to other Nicotiana species and 207 unique to N. rustica. Genome sequence analysis indicated that N. knightiana is more closely related to N. rustica than N. paniculata, and that the higher nicotine content of N. rustica leaves is the result of the progenitor genomes combination and of a more active transport of nicotine to the shoot.

CONCLUSIONS

The availability of four new Nicotiana genome sequences provide insights into how speciation impacts plant metabolism, and in particular alkaloid transport and accumulation, and will contribute to better understanding the evolution of Nicotiana species.

Metabolic Patterns in Spirodela polyrhiza Revealed by 15N Stable Isotope Labeling of Amino Acids in Photoautotrophic, Heterotrophic, and Mixotrophic Growth Conditions

In this study we describe a [¹⁵N] stable isotopic labeling study of amino acids in Spirodela polyrhiza (common duckweed) grown under three different light and carbon input conditions which represent unique potential metabolic modes. Plants were grown with a light cycle, either with supplemental sucrose (mixotrophic) or without supplemental sucrose (photoautotrophic) and in the dark with supplemental sucrose (heterotrophic). Labeling patterns, pool sizes (both metabolically active and inactive), and kinetics/turnover rates were estimated for 17 of the proteinogenic amino acids. Estimation of these parameters followed several overall trends. First, most amino acids showed plateaus in labeling patterns of <100% [¹⁵N]-labeling, indicating the possibility of a large proportion of amino acids residing in metabolically inactive metabolite pools. Second, total pool sizes appear largest in the dark (heterotrophic) condition, whereas active pool sizes appeared to be largest in the light with sucrose (mixotrophic) growth condition. In contrast turnover measurements based on pool size were highest overall in the light with sucrose experiment, with the exception of leucine/isoleucine, lysine, and arginine, which all showed higher turnover in the dark. K-means clustering analysis also revealed more rapid turnover in the light treatments with many amino acids clustering in lower-turnover groups. Emerging insights from other research were also supported, such as the prevalence of alternate pathways for serine metabolism in non-photosynthetic cells. These data provide extensive novel information on amino acid pool size and kinetics in S. polyrhiza and can serve as groundwork for future metabolic studies.

iReMet-flux: constraint-based approach for integrating relative metabolite levels into a stoichiometric metabolic models

MOTIVATION

Understanding the rerouting of metabolic reaction fluxes upon perturbations has the potential to link changes in molecular state of a cellular system to alteration of growth. Yet, differential flux profiling on a genome-scale level remains one of the biggest challenges in systems biology. This is particularly relevant in plants, for which fluxes in autotrophic growth necessitate time-consuming instationary labeling experiments and costly computations, feasible for small-scale networks.

RESULTS

Here we present a computationally and experimentally facile approach, termed iReMet-Flux, which integrates relative metabolomics data in a metabolic model to predict differential fluxes at a genome-scale level. Our approach and its variants complement the flux estimation methods based on radioactive tracer labeling. We employ iReMet-Flux with publically available metabolic profiles to predict reactions and pathways with altered fluxes in photo-autotrophically grown Arabidopsis and four photorespiratory mutants undergoing high-to-low CO2 acclimation. We also provide predictions about reactions and pathways which are most strongly regulated in the investigated experiments. The robustness and variability analyses, tailored to the formulation of iReMet-Flux, demonstrate that the findings provide biologically relevant information that is validated with external measurements of net CO2 exchange and biomass production. Therefore, iReMet-Flux paves the wave for mechanistic dissection of the interplay between pathways of primary and secondary metabolisms at a genome-scale.

AVAILABILITY AND IMPLEMENTATION

The source code is available from the authors upon request.

CONTACT

nikoloski@mpimp-golm.mpg.de

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Flux Balance Analysis of Plant Metabolism: The Effect of Biomass Composition and Model Structure on Model Predictions

The biomass composition represented in constraint-based metabolic models is a key component for predicting cellular metabolism using flux balance analysis (FBA). Despite major advances in analytical technologies, it is often challenging to obtain a detailed composition of all major biomass components experimentally. Studies examining the influence of the biomass composition on the predictions of metabolic models have so far mostly been done on models of microorganisms. Little is known about the impact of varying biomass composition on flux prediction in FBA models of plants, whose metabolism is very versatile and complex because of the presence of multiple subcellular compartments. Also, the published metabolic models of plants differ in size and complexity. In this study, we examined the sensitivity of the predicted fluxes of plant metabolic models to biomass composition and model structure. These questions were addressed by evaluating the sensitivity of predictions of growth rates and central carbon metabolic fluxes to varying biomass compositions in three different genome-/large-scale metabolic models of Arabidopsis thaliana. Our results showed that fluxes through the central carbon metabolism were robust to changes in biomass composition. Nevertheless, comparisons between the predictions from three models using identical modeling constraints and objective function showed that model predictions were sensitive to the structure of the models, highlighting large discrepancies between the published models.

Mathematical models of plant metabolism

Among various modeling approaches in plant metabolic research, applications of Constraint-Based modeling are fast increasing in recent years, apparently driven by current advances in genomics and genome sequencing. Constraint-Based modeling, the functional analysis of metabolic networks at the whole cell or genome scale, is more difficult to apply to plants than to microbes. Here we discuss recent developments in Constraint-Based modeling in plants with focus on issues of model reconstruction and flux prediction. Another topic is the emerging application of integration of Constraint-Based modeling with omics data to increase predictive power. Furthermore, advances in experimental measurements of cellular fluxes by (13)C-Metabolic Flux Analysis are highlighted, including instationary (13)C-MFA used to probe autotrophic metabolism in photosynthetic tissue in the light.

Plant sulfur and Big Data

Sulfur is an essential mineral nutrient for plants, therefore, the pathways of its uptake and assimilation have been extensively studied. Great progress has been made in elucidation of the individual genes and enzymes and their regulation. Sulfur assimilation has been intensively investigated by -omics technologies and has been target of several genome wide genetic approaches. This brought a significant step in our understanding of the regulation of the pathway and its integration in cellular metabolism. However, the large amount of information derived from other experiments not directly targeting sulfur has also brought new and exciting insights into processes affecting sulfur homeostasis. In this review we will integrate the findings of the targeted experiments with those that brought unintentional progress in sulfur research, and will discuss how to synthesize the large amount of information available in various repositories into a meaningful dissection of the regulation of a specific metabolic pathway. We then speculate how this might be used to further advance knowledge on control of sulfur metabolism and what are the main questions to be answered.

Amino Acid Catabolism in Plants

Amino acids have various prominent functions in plants. Besides their usage during protein biosynthesis, they also represent building blocks for several other biosynthesis pathways and play pivotal roles during signaling processes as well as in plant stress response. In general, pool sizes of the 20 amino acids differ strongly and change dynamically depending on the developmental and physiological state of the plant cell. Besides amino acid biosynthesis, which has already been investigated in great detail, the catabolism of amino acids is of central importance for adjusting their pool sizes but so far has drawn much less attention. The degradation of amino acids can also contribute substantially to the energy state of plant cells under certain physiological conditions, e.g. carbon starvation. In this review, we discuss the biological role of amino acid catabolism and summarize current knowledge on amino acid degradation pathways and their regulation in the context of plant cell physiology.

Seasonal Variations of Metabolome and Tyrosinase Inhibitory Activity of Lespedeza maximowiczii during Growth Periods

Lespedeza species are useful for pasture and energy crops as well as medical plants. We determined the metabolites discriminated from the each growth period (3, 4, 6, 8, 15, and 18 months) after germination in leaves and stems of Lespedeza maximowizii by a metabolomics technique. Specifically, levels of sugars and luteolin-dominated derivatives were significantly elevated in samples harvested in November. This may be related to the cold tolerance mechanism against the low temperatures of the winter season. The concentrations of secondary metabolites, isoflavones and flavanones, as well as tyrosinase inhibitory activity were the highest in the 6 month samples, which were harvested in September, during the fall season. The tyrosinase inhibitory activity in leaves was higher than that in stems irrespective of the growth period. This study suggests that mass spectrometry-based metabolite profiling could be used as a tool to examine quantitative or qualitative metabolite changes related to seasonal variations and to understand the correlation between activity and metabolites.