Unraveling the Light-Specific Metabolic and Regulatory Signatures of Rice through Combined in Silico Modeling and Multiomics Analysis

Elucidating the impact of in vitro cultivation on Nicotiana tabacum metabolism through combined in silico modeling and multiomics analysis

The systematical characterization and understanding of the metabolic behaviors are the basis of the efficient plant metabolic engineering and synthetic biology. Genome-scale metabolic networks (GSMNs) are indispensable tools for the comprehensive characterization of overall metabolic profile. Here we first constructed a GSMN of tobacco, which is one of the most widely used plant chassis, and then combined the tobacco GSMN and multiomics analysis to systematically elucidate the impact of in-vitro cultivation on the tobacco metabolic network. In-vitro cultivation is a widely used technique for plant cultivation, not only in the field of basic research but also for the rapid propagation of valuable horticultural and pharmaceutical plants. However, the systemic effects of in-vitro cultivation on overall plant metabolism could easily be overlooked and are still poorly understood. We found that in-vitro tobacco showed slower growth, less biomass and suppressed photosynthesis than soil-grown tobacco. Many changes of metabolites and metabolic pathways between in-vitro and soil-grown tobacco plants were identified, which notably revealed a significant increase of the amino acids content under in-vitro condition. The in silico investigation showed that in-vitro tobacco downregulated photosynthesis and primary carbon metabolism, while significantly upregulated the GS/GOGAT cycle, as well as producing more energy and less NADH/NADPH to acclimate in-vitro growth demands. Altogether, the combination of experimental and in silico analyses offers an unprecedented view of tobacco metabolism, with valuable insights into the impact of in-vitro cultivation, enabling more efficient utilization of in-vitro techniques for plant propagation and metabolic engineering.

Identification of gene function based on models capturing natural variability of Arabidopsis thaliana lipid metabolism

Lipids play fundamental roles in regulating agronomically important traits. Advances in plant lipid metabolism have until recently largely been based on reductionist approaches, although modulation of its components can have system-wide effects. However, existing models of plant lipid metabolism provide lumped representations, hindering detailed study of component modulation. Here, we present the Plant Lipid Module (PLM) which provides a mechanistic description of lipid metabolism in the Arabidopsis thaliana rosette. We demonstrate that the PLM can be readily integrated in models of A. thaliana Col-0 metabolism, yielding accurate predictions (83%) of single lethal knock-outs and 75% concordance between measured transcript and predicted flux changes under extended darkness. Genome-wide associations with fluxes obtained by integrating the PLM in diel condition- and accession-specific models identify up to 65 candidate genes modulating A. thaliana lipid metabolism. Using mutant lines, we validate up to 40% of the candidates, paving the way for identification of metabolic gene function based on models capturing natural variability in metabolism.

The Role of Blue and Red Light in the Orchestration of Secondary Metabolites, Nutrient Transport and Plant Quality

Light is a fundamental environmental parameter for plant growth and development because it provides an energy source for carbon fixation during photosynthesis and regulates many other physiological processes through its signaling. In indoor horticultural cultivation systems, sole-source light-emitting diodes (LEDs) have shown great potential for optimizing growth and producing high-quality products. Light is also a regulator of flowering, acting on phytochromes and inducing or inhibiting photoperiodic plants. Plants respond to light quality through several light receptors that can absorb light at different wavelengths. This review summarizes recent progress in our understanding of the role of blue and red light in the modulation of important plant quality traits, nutrient absorption and assimilation, as well as secondary metabolites, and includes the dynamic signaling networks that are orchestrated by blue and red wavelengths with a focus on transcriptional and metabolic reprogramming, plant productivity, and the nutritional quality of products. Moreover, it highlights future lines of research that should increase our knowledge to develop tailored light recipes to shape the plant characteristics and the nutritional and nutraceutical value of horticultural products.

Antioxidant Green Factories: Toward Sustainable Production of Vitamin E in Plant In Vitro Cultures

Vitamin E is a dietary supplement synthesized only by photosynthetic organisms and, hence, is an essential vitamin for human well-being. Because of the ever-increasing demand for natural vitamin E and limitations in existing synthesis modes, attempts to improve its yield using plant in vitro cultures have gained traction in recent years. With inflating industrial production costs, integrative approaches to conventional bioprocess optimization is the need of the hour for multifold vitamin E productivity enhancement. In this review, we briefly discuss the structure, isomers, and important metabolic routes of biosynthesis for vitamin E in plants. We then emphasize its vital role in human health and its industrial applications and highlight the market demand and supply. We illustrate the advantages of in vitro plant cell/tissue culture cultivation as an alternative to current commercial production platforms for natural vitamin E. We touch upon the conventional vitamin E metabolic pathway engineering strategies, such as single/multigene overexpression and chloroplast engineering. We highlight the recent progress in plant systems biology to rationally identify metabolic bottlenecks and knockout targets in the vitamin E biosynthetic pathway. We then discuss bioprocess optimization strategies for sustainable vitamin E production, including media/process optimization, precursor/elicitor addition, and scale-up to bioreactors. We culminate the review with a short discussion on kinetic modeling to predict vitamin E production in plant cell cultures and suggestions on sustainable green extraction methods of vitamin E for reduced environmental impact. This review will be of interest to a wider research fraternity, including those from industry and academia working in the field of plant cell biology, plant biotechnology, and bioprocess engineering for phytochemical enhancement.

Effects of light quality, photoperiod, CO2 concentration, and air temperature on chlorogenic acid and rutin accumulation in young lettuce plants

Environmental stimuli modulate plant metabolite accumulation, facilitating adaptation to stressful conditions. In this study, the effects of blue and red light, photoperiod, CO2 concentration, and air temperature on the chlorogenic acid (CGA) and rutin contents of lettuce (Lactuca sativa L.) were evaluated. Under continuous blue light and a high CO2 concentration (1000 ppm), the CGA level increased. The increased expression of phenylalanine ammonia-lyase (PAL) and activity of its product were correlated with high expression of cinnamate 4-hydroxylase (C4H) and coumarate 3-hydroxylase (C3H). Furthermore, changes in PAL activity altered the CGA content in lettuce exposed to the three environmental factors, blue light, continuous lighting and high CO2 concentration. In addition, the expression levels of genes related to flavonoid biosynthesis increased in accordance with the promotion of CGA accumulation by the environmental factors. Under continuous blue light, 400 ppm CO2 promoted rutin accumulation to a greater degree compared to 1000 ppm CO2, by downregulating DFR expression. Low air temperature induced CGA accumulation in lettuce grown under continuous blue light and 1000 ppm CO2. Therefore, light quality, photoperiod, CO2 concentration, and air temperature exert synergistic effects on the CGA and rutin contents of lettuce by modulating activity in the corresponding biosynthesis pathways.

Between Light and Shading: Morphological, Biochemical, and Metabolomics Insights Into the Influence of Blue Photoselective Shading on Vegetable Seedlings

High nursery densities reduce the seedling quality due to the competition for light. High light intensity, shading, and blue light depletion activate morphophysiological and metabolomic responses in plants, resulting in size modification to gain an advantage over neighboring plants. Our research aimed to unravel the effects of light intensity and quality on nursery seedlings at the morphological and biochemical levels. To this aim, the effect of black shading and blue photoselective shading nets were investigated in terms of morphometric, ionomic, and untargeted metabolomics signatures in Cucurbita pepo L., Citrullus lanatus L., Solanum lycopersicum L., and Solanum melongena L. seedlings. Plant height, diameter, sturdiness index, leaf area, specific leaf area, shoot/root ratio, and mineral content (by ion chromatography-IC) were evaluated. In C. pepo L and C. lanatus L., the blue net reduced the shoot/root and chlorophyll a/b ratios and increased stem diameter and total chlorophyll content. The black net increased plant height, stem diameter, and sturdiness index in Solanum lycopersicum L. and Solanum melongena L. At the same time, unshading conditions reduced leaf area, specific leaf area, shoot/root ratio, and total chlorophyll content. The blue net improved the sturdiness index and quality of C. pepo L. and C. lanatus L. Such impact on morphological parameters induced by the different shading conditions was corroborated by a significant modulation at the metabolomics level. Untargeted metabolomic phytochemical signatures of the selected plants, and the subsequent multivariate analysis coupled to pathway analysis, allowed highlighting a broad and diverse biochemical modulation. Metabolomics revealed that both primary and secondary metabolism were largely affected by the different shading conditions, regardless of the species considered. A common pattern arose to point at the activation of plant energy metabolism and lipid biosynthesis, together with a generalized down accumulation of several secondary metabolites, particularly phenylpropanoids. Our findings indicate an intriguing scientific interest in the effects of selective shading and its application to other species and different phenological stages.

Toward Multiplexed Optogenetic Circuits

Owing to its ubiquity and easy availability in nature, light has been widely employed to control complex cellular behaviors. Light-sensitive proteins are the foundation to such diverse and multilevel adaptive regulations in a large range of organisms. Due to their remarkable properties and potential applications in engineered systems, exploration and engineering of natural light-sensitive proteins have significantly contributed to expand optogenetic toolboxes with tailor-made performances in synthetic genetic circuits. Progressively, more complex systems have been designed in which multiple photoreceptors, each sensing its dedicated wavelength, are combined to simultaneously coordinate cellular responses in a single cell. In this review, we highlight recent works and challenges on multiplexed optogenetic circuits in natural and engineered systems for a dynamic regulation breakthrough in biotechnological applications.

Transcriptomic analyses of cacao flavonoids produced in photobioreactors

BACKGROUND

Theobroma cacao is a major source of flavonoids such as catechins and their monomers proanthocyanidins (PAs), widely studied for their potential benefits in cardiovascular diseases. Light has been shown to promote plant secondary metabolite production in vitro. In this study, cacao cells cultured in 7.5 L stirred tank photobioreactors (STPs) were exposed to a change of white to blue LED lights for 28 days (d).

RESULTS

Transcriptomic analyses were performed in three time points comparing changing expression patterns, after cell exposure to white light (d0-VS-d14), after a shift from white to blue light (d14-VS-d15), and after an extended period of blue light for the following 15 days (d15-VS-d28). Under white light, there was enrichment in metabolic pathways associated with cell growth (carbon, glycolysis, and amino acid biosynthesis) accompanied by a significant increase in the PAs content. In the shift to blue light, further increase in PAs content was observed concomitantly with the significant expression of TWO-COMPONENT RESPONSE REGULATOR genes involved in the early stress responses via circadian clock and hormone pathways. Under blue light exposure, we observed a depletion of PAs content associated with ROS-mediated stress pathways.

CONCLUSIONS

Light effects on large-scale cell cultures in photobioreactors are complex and pleiotropic; however, we have been able to identify key regulatory players upstream cacao flavonoid biosynthesis in STPs, including TWO-COMPONENT SYSTEM and ROS-signaling genes. The crosstalk between flavonoid biosynthesis and regulatory networks led to understand the dynamics of flavonoid production and degradation in response to light-driven ROS signals. This can be used to optimize the time, and the yield of in vitro targeted metabolites in large-scale culture systems.

Characterization of effects of genetic variants via genome-scale metabolic modelling

Genome-scale metabolic networks for model plants and crops in combination with approaches from the constraint-based modelling framework have been used to predict metabolic traits and design metabolic engineering strategies for their manipulation. With the advances in technologies to generate large-scale genotyping data from natural diversity panels and other populations, genome-wide association and genomic selection have emerged as statistical approaches to determine genetic variants associated with and predictive of traits. Here, we review recent advances in constraint-based approaches that integrate genetic variants in genome-scale metabolic models to characterize their effects on reaction fluxes. Since some of these approaches have been applied in organisms other than plants, we provide a critical assessment of their applicability particularly in crops. In addition, we further dissect the inferred effects of genetic variants with respect to reaction rate constants, abundances of enzymes, and concentrations of metabolites, as main determinants of reaction fluxes and relate them with their combined effects on complex traits, like growth. Through this systematic review, we also provide a roadmap for future research to increase the predictive power of statistical approaches by coupling them with mechanistic models of metabolism.

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.

Deciphering rice metabolic flux reprograming under salinity stress via in silico metabolic modeling

Rice is one of the most economically important commodities globally. However, rice plants are salt susceptible species in which high salinity can significantly constrain its productivity. Several physiological parameters in adaptation to salt stress have been observed, though changes in metabolic aspects remain to be elucidated. In this study, rice metabolic activities of salt-stressed flag leaf were systematically characterized. Transcriptomics and metabolomics data were combined to identify disturbed pathways, altered metabolites and metabolic hotspots within the rice metabolic network under salt stress condition. Besides, the feasible flux solutions in different context-specific metabolic networks were estimated and compared. Our findings highlighted metabolic reprogramming in primary metabolic pathways, cellular respiration, antioxidant biosynthetic pathways, and phytohormone biosynthetic pathways. Photosynthesis and hexose utilization were among the major disturbed pathways in the stressed flag leaf. Notably, the increased flux distribution of the photorespiratory pathway could contribute to cellular redox control. Predicted flux statuses in several pathways were consistent with the results from transcriptomics, end-point metabolomics, and physiological studies. Our study illustrated that the contextualized genome-scale model together with multi-omics analysis is a powerful approach to unravel the metabolic responses of rice to salinity stress.

Exploration of the Effects of Different Blue LED Light Intensities on Flavonoid and Lipid Metabolism in Tea Plants via Transcriptomics and Metabolomics

Blue light extensively regulates multiple physiological processes and secondary metabolism of plants. Although blue light quantity (fluence rate) is important for plant life, few studies have focused on the effects of different blue light intensity on plant secondary metabolism regulation, including tea plants. Here, we performed transcriptomic and metabolomic analyses of young tea shoots (one bud and two leaves) under three levels of supplemental blue light, including low-intensity blue light (LBL, 50 μmol m^–2 s^–1), medium-intensity blue light (MBL, 100 μmol m^–2 s^–1), and high-intensity blue light (HBL, 200 μmol m^–2 s^–1). The total number of differentially expressed genes (DEGs) in LBL, MBL and HBL was 1, 7 and 1097, respectively, indicating that high-intensity blue light comprehensively affects the transcription of tea plants. These DEGs were primarily annotated to the pathways of photosynthesis, lipid metabolism and flavonoid synthesis. In addition, the most abundant transcription factor (TF) families in DEGs were bHLH and MYB, which have been shown to be widely involved in the regulation of plant flavonoids. The significantly changed metabolites that we detected contained 15 lipids and 6 flavonoid components. Further weighted gene co-expression network analysis (WGCNA) indicated that CsMYB (TEA001045) may be a hub gene for the regulation of lipid and flavonoid metabolism by blue light. Our results may help to establish a foundation for future research investigating the regulation of woody plants by blue light.

Integrating molecular markers into metabolic models improves genomic selection for Arabidopsis growth

The current trends of crop yield improvements are not expected to meet the projected rise in demand. Genomic selection uses molecular markers and machine learning to identify superior genotypes with improved traits, such as growth. Plant growth directly depends on rates of metabolic reactions which transform nutrients into the building blocks of biomass. Here, we predict growth of Arabidopsis thaliana accessions by employing genomic prediction of reaction rates estimated from accession-specific metabolic models. We demonstrate that, comparing to classical genomic selection on the available data sets for 67 accessions, our approach improves the prediction accuracy for growth within and across nitrogen environments by 32.6% and 51.4%, respectively, and from optimal nitrogen to low carbon environment by 50.4%. Therefore, integration of molecular markers into metabolic models offers an approach to predict traits directly related to metabolism, and its usefulness in breeding can be examined by gathering matching datasets in crops.

An increase in genomic selection (GS) accuracy can accelerate genetic gain by shortening the breeding cycles. Here, the authors introduce a network-based GS method that uses metabolic models and improves the prediction accuracy of Arabidopsis growth within and across environments.

Red and blue light differentially impact retrograde signalling and photoprotection in rice

Chloroplast-to-nucleus retrograde signalling (RS) is known to impact plant growth and development. In Arabidopsis, we and others have shown that RS affects seedling establishment by inhibiting deetiolation. In the presence of lincomycin, a chloroplast protein synthesis inhibitor that triggers RS, Arabidopsis light-grown seedlings display partial skotomorphogenesis with undeveloped plastids and closed cotyledons. By contrast, RS in monocotyledonous has been much less studied. Here, we show that emerging rice seedlings exposed to lincomycin do not accumulate chlorophyll but otherwise remain remarkably unaffected. However, by using high red (R) and blue (B) monochromatic lights in combination with lincomycin, we have uncovered a RS inhibition of length and a reduction in the B light-induced declination of the second leaf. Furthermore, we present data showing that seedlings grown in high B and R light display different non-photochemical quenching capacity. Our findings support the view that excess B and R light impact seedling photomorphogenesis differently to photoprotect and optimize the response to high-light stress. This article is part of the theme issue 'Retrograde signalling from endosymbiotic organelles'.

Genome-scale metabolic reconstruction and in silico analysis of the rice leaf blight pathogen, Xanthomonas oryzae

Xanthomonas oryzae pv. oryzae (Xoo) is a vascular pathogen that causes leaf blight in rice, leading to severe yield losses. Since the usage of chemical control methods has not been very promising for the future disease management, it is of high importance to systematically gain new insights about Xoo virulence and pathogenesis, and devise effective strategies to combat the rice disease. To do this, we reconstructed a genome-scale metabolic model of Xoo (iXOO673) and validated the model predictions using culture experiments. Comparison of the metabolic architecture of Xoo and other plant pathogens indicated that the Entner-Doudoroff pathway is a more common feature in these bacteria than previously thought, while suggesting some of the unique virulence mechanisms related to Xoo metabolism. Subsequent constraint-based flux analysis allowed us to show that Xoo modulates fluxes through gluconeogenesis, glycogen biosynthesis, and degradation pathways, thereby exacerbating the leaf blight in rice exposed to nitrogenous fertilizers, which is remarkably consistent with published experimental literature. Moreover, model-based interrogation of transcriptomic data revealed the metabolic components under the diffusible signal factor regulon that are crucial for virulence and survival in Xoo. Finally, we identified promising antibacterial targets for the control of leaf blight in rice by using gene essentiality analysis.

Comparative Analysis of Phenolic Compound Profiles, Antioxidant Capacities, and Expressions of Phenolic Biosynthesis-Related Genes in Soybean Microgreens Grown under Different Light Spectra

Light-emitting diode (LED) based light sources, which can selectively and quantitatively provide different spectra, have been frequently applied to manipulate plant growth and development. In this study, the effects of different LED light spectra on the growth, phenolic compounds profile, antioxidant capacity, and transcriptional changes in genes regulating phenolic biosynthesis in soybean microgreens were investigated. The results showed that light illumination decreased the seedling length and yield but increased phenolic compound content. Blue light and ultraviolet-A (UV-A) induced significant increases in total phenolic and total flavonoid content, as compared with the white light control. Sixty-six phenolic compounds were identified in the soybean samples, of which isoflavone, phenolic acid, and flavonol were the main components. Ten phenolic compounds obtained from the orthogonal partial least-squares discriminant analysis (OPLS-DA) were reflecting the effect of light spectra. The antioxidant capacity was consistent with the phenolic metabolite levels, which showed higher levels under blue light and UV-A compared with the control. The highest transcript levels of phenolic biosynthesis-related genes were observed under blue light and UV-A. The transcript levels of GmCHI, GmFLS, and GmIOMT were also upregulated under far-red and red light. Taken together, our findings suggested that the application of LED light could pave a green and effective way to produce phenolic compound-enriched soybean microgreens with high nutritional quality, which could stimulate further investigations for improving plant nutritional value and should have a wide impact on maintaining human health.

Leaf Energy Balance Requires Mitochondrial Respiration and Export of Chloroplast NADPH in the Light

Key aspects of leaf mitochondrial metabolism in the light remain unresolved. For example, there is debate about the relative importance of exporting reducing equivalents from mitochondria for the peroxisomal steps of photorespiration versus oxidation of NADH to generate ATP by oxidative phosphorylation. Here, we address this and explore energetic coupling between organelles in the light using a diel flux balance analysis model. The model included more than 600 reactions of central metabolism with full stoichiometric accounting of energy production and consumption. Different scenarios of energy availability (light intensity) and demand (source leaf versus a growing leaf) were considered, and the model was constrained by the nonlinear relationship between light and CO2 assimilation rate. The analysis demonstrated that the chloroplast can theoretically generate sufficient ATP to satisfy the energy requirements of the rest of the cell in addition to its own. However, this requires unrealistic high light use efficiency and, in practice, the availability of chloroplast-derived ATP is limited by chloroplast energy dissipation systems, such as nonphotochemical quenching, and the capacity of the chloroplast ATP export shuttles. Given these limitations, substantial mitochondrial ATP synthesis is required to fulfill cytosolic ATP requirements, with only minimal, or zero, export of mitochondrial reducing equivalents. The analysis also revealed the importance of exporting reducing equivalents from chloroplasts to sustain photorespiration. Hence, the chloroplast malate valve and triose phosphate-3-phosphoglycerate shuttle are predicted to have important metabolic roles, in addition to their more commonly discussed contribution to the avoidance of photooxidative stress.

Computational Approaches to Design and Test Plant Synthetic Metabolic Pathways

Successfully designed and implemented plant-specific synthetic metabolic pathways hold promise to increase crop yield and nutritional value. Advances in synthetic biology have already demonstrated the capacity to design artificial biological pathways whose behavior can be predicted and controlled in microbial systems. However, the transfer of these advances to model plants and crops faces the lack of characterization of plant cellular pathways and increased complexity due to compartmentalization and multicellularity. Modern computational developments provide the means to test the feasibility of plant synthetic metabolic pathways despite gaps in the accumulated knowledge of plant metabolism. Here, we provide a succinct systematic review of optimization-based and retrobiosynthesis approaches that can be used to design and in silico test synthetic metabolic pathways in large-scale plant context-specific metabolic models. In addition, by surveying the existing case studies, we highlight the challenges that these approaches face when applied to plants. Emphasis is placed on understanding the effect that metabolic designs can have on native metabolism, particularly with respect to metabolite concentrations and thermodynamics of biochemical reactions. In addition, we discuss the computational developments that may help to transform the identified challenges into opportunities for plant synthetic biology.

Analysis of Key Genes Involved in Potato Anthocyanin Biosynthesis Based on Genomics and Transcriptomics Data

The accumulation of secondary metabolites, such as anthocyanins, in cells plays an important role in colored plants. The synthesis and accumulation of anthocyanins are regulated by multiple genes, of which the R2R3-MYB transcription factor gene family plays an important role. Based on the genomic data in the Potato Genome Sequencing Consortium database (PGSC) and the transcriptome data in the SRA, this study used potato as a model plant to comprehensively analyze the plant anthocyanin accumulation process. The results indicated that the most critical step in the synthesis of potato anthocyanins was the formation of p-coumaroyl-CoA to enter the flavonoid biosynthetic pathway. The up-regulated expression of the CHS gene and the down-regulated expression of HCT significantly promoted this process. At the same time, the anthocyanins in the potato were gradually synthesized during the process from leaf transport to tubers. New transcripts of stAN1 and PAL were cloned and named stAN1-like and PAL-like, respectively, but the functions of these two new transcripts still need further study. In addition, the sequence characteristics of amino acids in the R2-MYB and R3-MYB domains of potato were preliminarily identified. The aims of this study are to identify the crucial major genes that affect anthocyanin biosynthesis through multi-omics joint analysis and to transform quantitative traits into quality traits, which provides a basis and reference for the regulation of plant anthocyanin biosynthesis. Simultaneously, this study provides the basis for improving the anthocyanin content in potato tubers and the cultivation of new potato varieties with high anthocyanin content.

Vitamin D5 in Arabidopsis thaliana

Vitamin D₃ is a secosterol hormone critical for bone growth and calcium homeostasis, produced in vertebrate skin by photolytic conversion of the cholesterol biosynthetic intermediate provitamin D₃. Insufficient levels of vitamin D₃ especially in the case of low solar UV-B irradiation is often compensated by an intake of a dietary source of vitamin D₃ of animal origin. Small amounts of vitamin D₃ were described in a few plant species and considered as a peculiar feature of their phytochemical diversity. In this report we show the presence of vitamin D₅ in the model plant Arabidopsis thaliana. This plant secosterol is a UV-B mediated derivative of provitamin D₅, the precursor of sitosterol. The present work will allow a further survey of vitamin D distribution in plant species.

Crop metabolomics: from diagnostics to assisted breeding

BACKGROUND

Until recently, plant metabolomics have provided a deep understanding on the metabolic regulation in individual plants as experimental units. The application of these techniques to agricultural systems subjected to more complex interactions is a step towards the implementation of translational metabolomics in crop breeding.

AIM OF REVIEW

We present here a review paper discussing advances in the knowledge reached in the last years derived from the application of metabolomic techniques that evolved from biomarker discovery to improve crop yield and quality.

KEY SCIENTIFIC CONCEPTS OF REVIEW

Translational metabolomics applied to crop breeding programs.

Genome and evolution of the shade-requiring medicinal herb Panax ginseng

Panax ginseng C. A. Meyer, reputed as the king of medicinal herbs, has slow growth, long generation time, low seed production and complicated genome structure that hamper its study. Here, we unveil the genomic architecture of tetraploid P. ginseng by de novo genome assembly, representing 2.98 Gbp with 59 352 annotated genes. Resequencing data indicated that diploid Panax species diverged in association with global warming in Southern Asia, and two North American species evolved via two intercontinental migrations. Two whole genome duplications (WGD) occurred in the family Araliaceae (including Panax) after divergence with the Apiaceae, the more recent one contributing to the ability of P. ginseng to overwinter, enabling it to spread broadly through the Northern Hemisphere. Functional and evolutionary analyses suggest that production of pharmacologically important dammarane-type ginsenosides originated in Panax and are produced largely in shoot tissues and transported to roots; that newly evolved P. ginseng fatty acid desaturases increase freezing tolerance; and that unprecedented retention of chlorophyll a/b binding protein genes enables efficient photosynthesis under low light. A genome-scale metabolic network provides a holistic view of Panax ginsenoside biosynthesis. This study provides valuable resources for improving medicinal values of ginseng either through genomics-assisted breeding or metabolic engineering.

Metabolite-Centric Reporter Pathway and Tripartite Network Analysis of Arabidopsis Under Cold Stress

The study of plant resistance to cold stress and the metabolic processes underlying its molecular mechanisms benefit crop improvement programs. Here we investigate the effects of cold stress on the metabolic pathways of Arabidopsis when directly inferred at system level from transcriptome data. A metabolite-centric reporter pathway analysis approach enabled the computation of metabolites associated with transcripts at four time points of cold treatment. Tripartite networks of gene-metabolite-pathway connectivity outlined the response of metabolites and pathways to cold stress. Our metabolome-independent analysis revealed stress-associated metabolites in pathway routes of the cold stress response, including amino acid, carbohydrate, lipid, hormone, energy, photosynthesis, and signaling pathways. Cold stress first triggered the mobilization of energy from glycolysis and ethanol degradation to enhance TCA cycle activity via acetyl-CoA. Interestingly, tripartite networks lacked power law behavior and scale free connectivity, favoring modularity. Network rewiring explicitly involved energetics, signal, carbon and redox metabolisms and membrane remodeling.

Marker-trait association for low-light intensity tolerance in rice genotypes from Eastern India

Light intensity is a crucial environmental factor that affects photosynthesis and ultimately, grain yield in rice. However, no gene or marker directly associated with improved performance under low-light intensity under field conditions has been identified till date. With an aim of identifying genes and markers associated with improved performance (measured in terms of better yields) under low-light intensity, an integrated field screening, in silico and wet lab validation analysis was performed. Field-based screening of a diverse set of 110 genotypes led to the identification of two physiological and three morphological parameters critical for low-light tolerance in rice. In silico analysis using information available in public databases led to the identification of a set of 90 potential candidate genes which were narrowed to thirteen genic targets for possible marker-trait association. Marker-trait association on the panel of 48 diverse rice genotypes varying in their response to low-light intensity led to the identification of six markers [HvSSR02-44 (biological yield), HvSSR02-52 (spikelet fertility), HvSSR02-54 (grain yield), HvSSR06-56 (spikelet fertility), HvSSR06-69 (spikelet fertility; biological yield), HvSSR09-45 (spikelet fertility)] lying on chromosomes 2, 6 and 9 showing significant association (R² > 0.1) for traits like grain yield/plant, biological yield and spikelet fertility under low light. Eight rice genes [including member of BBX (B-box) family] lying within 10 kb distance of these identified markers already reported for their role in response to stress or change in plant architecture in rice were also identified. The eight rice genotypes, five traits, eight genes and six markers identified in the current study will help in devising strategies to increase yield under low light intensity and pave way for future application in marker-assisted breeding.

Transcriptomic and metabolic flux analyses reveal shift of metabolic patterns during rice grain development

BACKGROUND

Rice (Oryza sativa) is one of the most important grain crops, which serves as food source for nearly half of the world population. The study of rice development process as well as related strategies for production has made significant progress. However, the comprehensive study on development of different rice tissues at both transcriptomic and metabolic flux level across different stages was lacked.

RESULTS

In this study, we performed RNA-Seq and characterized the expression profiles of differentiated tissues from Oryza sativa Zhonghua 11, including leaves, sheath, stamen, pistil, lemma and palea of the booting stage, and embryo, endosperm, lemma and palea of the mature grain stage. By integrating this set of transcriptome data of different rice tissues at different stages with a genome-scale rice metabolic model, we generated tissue-specific models and investigated the shift of metabolic patterns, and the discrepancy between transcriptomic and metabolic level. We found although the flux patterns are not very similar with the gene expression pattern, the tissues at booting stage and mature grain stage can be separately clustered by primary metabolism at either level. While the gene expression and flux distribution of secondary metabolism is more diverse across tissues and stages. The critical rate-limiting reactions and pathways were also identified. In addition, we compared the patterns of the same tissue at different stages and the different tissues at same stage. There are more altered pathways at gene expression level than metabolic level, which indicate the metabolism is more robust to reflect the phenotype, and might largely because of the complex post-transcriptional modification.

CONCLUSIONS

The tissue-specific models revealed more detail metabolic pattern shift among different tissues and stages, which is of great significance to uncover mechanism of rice grain development and further improve production and quality of rice.

Reconstruction of Oryza sativa indica Genome Scale Metabolic Model and Its Responses to Varying RuBisCO Activity, Light Intensity, and Enzymatic Cost Conditions

To combat decrease in rice productivity under different stresses, an understanding of rice metabolism is needed. Though there are different genome scale metabolic models (GSMs) of Oryza sativa japonica, no GSM with gene-protein-reaction association exist for Oryza sativa indica. Here, we report a GSM, OSI1136 of O.s. indica, which includes 3602 genes and 1136 metabolic reactions and transporters distributed across the cytosol, mitochondrion, peroxisome, and chloroplast compartments. Flux balance analysis of the model showed that for varying RuBisCO activity (Vc/Vo) (i) the activity of the chloroplastic malate valve increases to transport reducing equivalents out of the chloroplast under increased photorespiratory conditions and (ii) glyceraldehyde-3-phosphate dehydrogenase and phosphoglycerate kinase can act as source of cytosolic ATP under decreased photorespiration. Under increasing light conditions we observed metabolic flexibility, involving photorespiration, chloroplastic triose phosphate and the dicarboxylate transporters of the chloroplast and mitochondrion for redox and ATP exchanges across the intracellular compartments. Simulations under different enzymatic cost conditions revealed (i) participation of peroxisomal glutathione-ascorbate cycle in photorespiratory H2O2 metabolism (ii) different modes of the chloroplastic triose phosphate transporters and malate valve, and (iii) two possible modes of chloroplastic Glu-Gln transporter which were related with the activity of chloroplastic and cytosolic isoforms of glutamine synthetase. Altogether, our results provide new insights into plant metabolism.

Metabolomics and Integrative Omics for the Development of Thai Traditional Medicine

In recent years, interest in studies of traditional medicine in Asian and African countries has gradually increased due to its potential to complement modern medicine. In this review, we provide an overview of Thai traditional medicine (TTM) current development, and ongoing research activities of TTM related to metabolomics. This review will also focus on three important elements of systems biology analysis of TTM including analytical techniques, statistical approaches and bioinformatics tools for handling and analyzing untargeted metabolomics data. The main objective of this data analysis is to gain a comprehensive understanding of the system wide effects that TTM has on individuals. Furthermore, potential applications of metabolomics and systems medicine in TTM will also be discussed.

Eigenvector metabolite analysis reveals dietary effects on the association among metabolite correlation patterns, gene expression, and phenotypes

INTRODUCTION

'Multi-omics' datasets obtained from an organism of interest reared under different environmental treatments are increasingly common. Identifying the links among metabolites and transcripts can help to elucidate our understanding of the impact of environment at different levels within the organism. However, many methods for characterizing physiological connections cannot address unidentified metabolites.

OBJECTIVES

Here, we use Eigenvector Metabolite Analysis (EvMA) to examine links between metabolomic, transcriptomic, and phenotypic variation data and to assess the impact of environmental factors on these associations. Unlike other methods, EvMA can be used to analyze datasets that include unidentified metabolites and unannotated transcripts.

METHODS

To demonstrate the utility of EvMA, we analyzed metabolomic, transcriptomic, and phenotypic datasets produced from 20 Drosophila melanogaster genotypes reared on four dietary treatments. We used a hierarchical distance-based method to cluster the metabolites. The links between metabolite clusters, gene expression, and overt phenotypes were characterized using the eigenmetabolite (first principal component) of each cluster.

RESULTS

EvMA recovered chemically related groups of metabolites within the clusters. Using the eigenmetabolite, we identified genes and phenotypes that significantly correlated with each cluster. EvMA identifies new connections between the phenotypes, metabolites, and gene transcripts. Conclusion EvMA provides a simple method to identify correlations between metabolites, gene expression, and phenotypes, which can allow us to partition multivariate datasets into meaningful biological modules and identify under-studied metabolites and unannotated gene transcripts that may be central to important biological processes. This can be used to inform our understanding of the effect of environmental mechanisms underlying physiological states of interest.

Metabolomic Responses of Arabidopsis Suspension Cells to Bicarbonate under Light and Dark Conditions

Global CO₂ level presently recorded at 400 ppm is expected to reach 550 ppm in 2050, an increment likely to impact plant growth and productivity. Using targeted LC-MS and GC-MS platforms we quantified 229 and 29 metabolites, respectively in a time-course study to reveal short-term responses to different concentrations (1, 3, and 10 mM) of bicarbonate (HCO₃⁻) under light and dark conditions. Results indicate that HCO₃⁻ treatment responsive metabolomic changes depend on the HCO₃⁻ concentration, time of treatment, and light/dark. Interestingly, 3 mM HCO₃⁻ concentration treatment induced more significantly changed metabolites than either lower or higher concentrations used. Flavonoid biosynthesis and glutathione metabolism were common to both light and dark-mediated responses in addition to showing concentration-dependent changes. Our metabolomics results provide insights into short-term plant cellular responses to elevated HCO₃⁻ concentrations as a result of ambient increases in CO₂ under light and dark.

Circadian Oscillation of the Lettuce Transcriptome under Constant Light and Light-Dark Conditions

Although, the circadian clock is a universal biological system in plants and it orchestrates important role of plant production such as photosynthesis, floral induction and growth, there are few such studies on cultivated species. Lettuce is one major cultivated species for both open culture and plant factories and there is little information concerning its circadian clock system. In addition, most of the relevant genes have not been identified. In this study, we detected circadian oscillation in the lettuce transcriptome using time-course RNA sequencing (RNA-Seq) data. Constant light (LL) and light-dark (LD) conditions were used to detect circadian oscillation because the circadian clock has some basic properties: one is self-sustaining oscillation under constant light and another is entrainment to environmental cycles such as light and temperature. In the results, 215 contigs were detected as common oscillating contigs under both LL and LD conditions. The 215 common oscillating contigs included clock gene-like contigs CCA1 (CIRCADIAN CLOCK ASSOCIATED 1)-like, TOC1 (TIMING OF CAB EXPRESSION 1)-like and LHY (LATE ELONGATED HYPOCOTYL)-like, and their expression patterns were similar to those of Arabidopsis. Functional enrichment analysis by GO (gene ontology) Slim and GO Fat showed that the GO terms of response to light stimulus, response to stress, photosynthesis and circadian rhythms were enriched in the 215 common oscillating contigs and these terms were actually regulated by circadian clocks in plants. The 215 common oscillating contigs can be used to evaluate whether the gene expression pattern related to photosynthesis and optical response performs normally in lettuce.

Light-specific transcriptional regulation of the accumulation of carotenoids and phenolic compounds in rice leaves

Carotenoids and phenolic compounds are important subgroups of secondary metabolites having an array of functional roles in the growth and development of plants. They are also major sources for health and pharmaceutical benefits, and industrially relevant biochemicals. The control of the biosynthesis of these compounds depends mainly on the quality and quantity of different light sources. Thus, to unravel their light-specific transcriptional regulation in rice leaves, we performed promoter analysis of genes upregulated in response to blue and red lights. The analysis results suggested a crosstalk between different phytohormones and the involvement of key transcription factors such as bHLH, bZIP, MYB, WRKY, ZnF and ERF [jasmonic acid inducible], in the regulation of higher accumulation of carotenoids and phenolic compounds upon blue light. Overall, the current analysis could improve our understanding of the light-specific regulatory mechanism involved in the biosynthesis of secondary metabolites via possible critical links between different TFs in rice leaves.

Modeling Rice Metabolism: From Elucidating Environmental Effects on Cellular Phenotype to Guiding Crop Improvement

Crop productivity is severely limited by various biotic and abiotic stresses. Thus, it is highly needed to understand the underlying mechanisms of environmental stress response and tolerance in plants, which could be addressed by systems biology approach. To this end, high-throughput omics profiling and in silico modeling can be considered to explore the environmental effects on phenotypic states and metabolic behaviors of rice crops at the systems level. Especially, the advent of constraint-based metabolic reconstruction and analysis paves a way to characterize the plant cellular physiology under various stresses by combining the mathematical network models with multi-omics data. Rice metabolic networks have been reconstructed since 2013 and currently six such networks are available, where five are at genome-scale. Since their publication, these models have been utilized to systematically elucidate the rice abiotic stress responses and identify agronomic traits for crop improvement. In this review, we summarize the current status of the existing rice metabolic networks and models with their applications. Furthermore, we also highlight future directions of rice modeling studies, particularly stressing how these models can be used to contextualize the affluent multi-omics data that are readily available in the public domain. Overall, we envisage a number of studies in the future, exploiting the available metabolic models to enhance the yield and quality of rice and other food crops.

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.