Identifying essential genes/reactions of the rice photorespiration by in silico model-based analysis

Systems-based rice improvement approaches for sustainable food and nutritional security

An integrated research approach to ensure sustainable rice yield increase of a crop grown by 25% of the world's farmers in 10% of cropland is essential for global food security. Rice, being a global staple crop, feeds about 56% of the world population and sustains 40% of the world's poor. At ~ $200 billion, it also accounts for 13% of the annual crop value. With hunger and malnutrition rampant among the poor, rice research for development is unique in global food and nutrition security. A systems-based, sustainable increase in rice quantity and quality is imperative for environmental and biodiversity benefits. Upstream 'discovery' through biotechnology, midstream 'development' through breeding and agronomy, downstream 'dissemination and deployment' must be 'demand-driven' for 'distinct socio-economic transformational impacts'. Local agro-ecology and livelihood nexus must drive the research agenda for targeted benefits. This necessitates sustained long-term investments by government, non-government and private sectors to secure the future food, nutrition, environment, prosperity and equity status.

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.

Transcriptome integrated metabolic modeling of carbon assimilation underlying storage root development in cassava

The existing genome-scale metabolic model of carbon metabolism in cassava storage roots, rMeCBM, has proven particularly resourceful in exploring the metabolic basis for the phenotypic differences between high and low-yield cassava cultivars. However, experimental validation of predicted metabolic fluxes by carbon labeling is quite challenging. Here, we incorporated gene expression data of developing storage roots into the basic flux-balance model to minimize infeasible metabolic fluxes, denoted as rMeCBMx, thereby improving the plausibility of the simulation and predictive power. Three different conceptual algorithms, GIMME, E-Flux, and HPCOF were evaluated. The rMeCBMx-HPCOF model outperformed others in predicting carbon fluxes in the metabolism of storage roots and, in particular, was highly consistent with transcriptome of high-yield cultivars. The flux prediction was improved through the oxidative pentose phosphate pathway in cytosol, as has been reported in various studies on root metabolism, but hardly captured by simple FBA models. Moreover, the presence of fluxes through cytosolic glycolysis and alanine biosynthesis pathways were predicted with high consistency with gene expression levels. This study sheds light on the importance of prediction power in the modeling of complex plant metabolism. Integration of multi-omics data would further help mitigate the ill-posed problem of constraint-based modeling, allowing more realistic simulation.

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.

SSER: Species specific essential reactions database

BACKGROUND

Essential reactions are vital components of cellular networks. They are the foundations of synthetic biology and are potential candidate targets for antimetabolic drug design. Especially if a single reaction is catalyzed by multiple enzymes, then inhibiting the reaction would be a better option than targeting the enzymes or the corresponding enzyme-encoding gene. The existing databases such as BRENDA, BiGG, KEGG, Bio-models, Biosilico, and many others offer useful and comprehensive information on biochemical reactions. But none of these databases especially focus on essential reactions. Therefore, building a centralized repository for this class of reactions would be of great value.

DESCRIPTION

Here, we present a species-specific essential reactions database (SSER). The current version comprises essential biochemical and transport reactions of twenty-six organisms which are identified via flux balance analysis (FBA) combined with manual curation on experimentally validated metabolic network models. Quantitative data on the number of essential reactions, number of the essential reactions associated with their respective enzyme-encoding genes and shared essential reactions across organisms are the main contents of the database.

CONCLUSION

SSER would be a prime source to obtain essential reactions data and related gene and metabolite information and it can significantly facilitate the metabolic network models reconstruction and analysis, and drug target discovery studies. Users can browse, search, compare and download the essential reactions of organisms of their interest through the website http://cefg.uestc.edu.cn/sser .

Photorespiratory Bypasses Lead to Increased Growth in Arabidopsis thaliana: Are Predictions Consistent with Experimental Evidence?

Arguably, the biggest challenge of modern plant systems biology lies in predicting the performance of plant species, and crops in particular, upon different intracellular and external perturbations. Recently, an increased growth of Arabidopsis thaliana plants was achieved by introducing two different photorespiratory bypasses via metabolic engineering. Here, we investigate the extent to which these findings match the predictions from constraint-based modeling. To determine the effect of the employed metabolic network model on the predictions, we perform a comparative analysis involving three state-of-the-art metabolic reconstructions of A. thaliana. In addition, we investigate three scenarios with respect to experimental findings on the ratios of the carboxylation and oxygenation reactions of Ribulose-1,5-bisphosphate carboxylase/oxygenase (RuBisCO). We demonstrate that the condition-dependent growth phenotypes of one of the engineered bypasses can be qualitatively reproduced by each reconstruction, particularly upon considering the additional constraints with respect to the ratio of fluxes for the RuBisCO reactions. Moreover, our results lend support for the hypothesis of a reduced photorespiration in the engineered plants, and indicate that specific changes in CO₂ exchange as well as in the proxies for co-factor turnover are associated with the predicted growth increase in the engineered plants. We discuss our findings with respect to the structure of the used models, the modeling approaches taken, and the available experimental evidence. Our study sets the ground for investigating other strategies for increase of plant biomass by insertion of synthetic reactions.

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.

A genome-scale metabolic network reconstruction of tomato (Solanum lycopersicum L.) and its application to photorespiratory metabolism

Tomato (Solanum lycopersicum L.) has been studied extensively due to its high economic value in the market, and high content in health-promoting antioxidant compounds. Tomato is also considered as an excellent model organism for studying the development and metabolism of fleshy fruits. However, the growth, yield and fruit quality of tomatoes can be affected by drought stress, a common abiotic stress for tomato. To investigate the potential metabolic response of tomato plants to drought, we reconstructed iHY3410, a genome-scale metabolic model of tomato leaf, and used this metabolic network to simulate tomato leaf metabolism. The resulting model includes 3410 genes and 2143 biochemical and transport reactions distributed across five intracellular organelles including cytosol, plastid, mitochondrion, peroxisome and vacuole. The model successfully described the known metabolic behaviour of tomato leaf under heterotrophic and phototrophic conditions. The in silico investigation of the metabolic characteristics for photorespiration and other relevant metabolic processes under drought stress suggested that: (i) the flux distributions through the mevalonate (MVA) pathway under drought were distinct from that under normal conditions; and (ii) the changes in fluxes through core metabolic pathways with varying flux ratio of RubisCO carboxylase to oxygenase may contribute to the adaptive stress response of plants. In addition, we improved on previous studies of reaction essentiality analysis for leaf metabolism by including potential alternative routes for compensating reaction knockouts. Altogether, the genome-scale model provides a sound framework for investigating tomato metabolism and gives valuable insights into the functional consequences of abiotic stresses.

Primary metabolism plays a central role in moulding silicon-inducible brown spot resistance in rice

Over recent decades, a multitude of studies have shown the ability of silicon (Si) to protect various plants against a range of microbial pathogens exhibiting different lifestyles and infection strategies. Despite this relative wealth of knowledge, an understanding of the action mechanism of Si is still in its infancy, which hinders its widespread application for agricultural purposes. In an attempt to further elucidate the molecular underpinnings of Si-induced disease resistance, we studied the transcriptome of control and Si-treated rice plants infected with the necrotrophic brown spot fungus Cochliobolus miyabeanus. Analysis of brown spot-infected control plants suggested that C. miyabeanus represses plant photosynthetic processes and nitrate reduction in order to trigger premature senescence and cause disease. In Si-treated plants, however, these pathogen-induced metabolic alterations are strongly impaired, suggesting that Si alleviates stress imposed by the pathogen. Interestingly, Si also significantly increased photorespiration rates in brown spot-infected plants. Although photorespiration is often considered as a wasteful process, recent studies have indicated that this metabolic bypass also enhances resistance during abiotic stress and pathogen attack by protecting the plant's photosynthetic machinery. In view of these findings, our results favour a scenario in which Si enhances brown spot resistance by counteracting C. miyabeanus-induced senescence and cell death via increased photorespiration. Moreover, our results shed light onto the mechanistic basis of Si-induced disease control and support the view that, in addition to activating plant immune responses, Si can also reduce disease severity by interfering with pathogen virulence strategies.

The molecular cloning and clarification of a photorespiratory mutant, oscdm1, using enhancer trapping

Enhancer trap systems have been demonstrated to increase the effectiveness of gene identification in rice. In this study, a chlorophyll-deficient mutant, named oscdm1, was screened and characterized in detail from a T-DNA enhancer-tagged population. The oscdm1 plants were different from other chlorophyll-deficient mutants; they produced chlorotic leaves at the third leaf stage, which gradually died with further growth of the plants. However, the oscdm1 plants were able to survive exposure to elevated CO₂ levels, similar to photorespiratory mutants. An analysis of the T-DNA flanking sequence in the oscdm1 plants showed that the T-DNA was inserted into the promoter region of a serine hydroxymethyltransferase (SHMT) gene. OsSHMT1 is a key enzyme that is ubiquitous in nature and structurally conserved across kingdoms. The enzyme is responsible for the interconversion of serine and glycine and is essential for cellular one-carbon metabolism. Full-length OsSHMT1 complemented the oscdm1 phenotype, and the downregulation of OsSHMT1 in wild-type plants by RNA interference (RNAi) produced plants that mimicked the oscdm1 phenotype. GUS assays and quantitative PCR revealed the preferential expression of OsSHMT1 in young leaves. TEM revealed serious damage to the thylakoid membrane in oscdm1 chloroplasts. The oscdm1 plants showed more extensive damage than wild type using an IMAGING-PAM fluorometer, especially under high light intensities. OsSHMT1-GFP localized exclusively to mitochondria. Further analysis revealed that the H₂O₂ content in the oscdm1 plants was twice that in wild type at the fourth leaf stage. This suggests that the thylakoid membrane damage observed in the oscdm1 plants was caused by excessive H₂O₂. Interestingly, OsSHMT1-overexpressing plants exhibited increased photosynthetic efficiency and improved plant productivity. These results lay the foundation for further study of the OsSHMT1 gene and will help illuminate the functional role of OsSHMT1 in photorespiration in rice.

Structural comparison, substrate specificity, and inhibitor binding of AGPase small subunit from monocot and dicot: present insight and future potential

ADP-glucose pyrophosphorylase (AGPase) is the first rate limiting enzyme of starch biosynthesis pathway and has been exploited as the target for greater starch yield in several plants. The structure-function analysis and substrate binding specificity of AGPase have provided enormous potential for understanding the role of specific amino acid or motifs responsible for allosteric regulation and catalytic mechanisms, which facilitate the engineering of AGPases. We report the three-dimensional structure, substrate, and inhibitor binding specificity of AGPase small subunit from different monocot and dicot crop plants. Both monocot and dicot subunits were found to exploit similar interactions with the substrate and inhibitor molecule as in the case of their closest homologue potato tuber AGPase small subunit. Comparative sequence and structural analysis followed by molecular docking and electrostatic surface potential analysis reveal that rearrangements of secondary structure elements, substrate, and inhibitor binding residues are strongly conserved and follow common folding pattern and orientation within monocot and dicot displaying a similar mode of allosteric regulation and catalytic mechanism. The results from this study along with site-directed mutagenesis complemented by molecular dynamics simulation will shed more light on increasing the starch content of crop plants to ensure the food security worldwide.