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

Analysis of drought and heat stress response genes in rice using co-expression network and differentially expressed gene analyses

Studies on Oryza sativa (rice) are crucial for improving agricultural productivity and ensuring global sustenance security, especially considering the increasing drought and heat stress caused by extreme climate change. Currently, the genes and mechanisms underlying drought and heat resistance in rice are not fully understood, and the scope for enhancing the development of new strains remains considerable. To accurately identify the key genes related to drought and heat stress responses in rice, multiple datasets from the Gene Expression Omnibus (GEO) database were integrated in this study. A co-expression network was constructed using a Weighted Correlation Network Analysis (WGCNA) algorithm. We further distinguished the core network and intersected it with differentially expressed genes and multiple expression datasets for screening. Differences in gene expression levels were verified using quantitative real-time polymerase chain reaction (PCR). OsDjC53, MBF1C, BAG6, HSP23.2, and HSP21.9 were found to be associated with the heat stress response, and it is also possible that UGT83A1 and OsCPn60a1, although not directly related, are affected by drought stress. This study offers significant insights into the molecular mechanisms underlying stress responses in rice, which could promote the development of stress-tolerant rice breeds.

Over-Expression of Dehydroascorbate Reductase Improves Salt Tolerance, Environmental Adaptability and Productivity in Oryza sativa

Abiotic stress induces reactive oxygen species (ROS) generation in plants, and high ROS levels can cause partial or severe oxidative damage to cellular components that regulate the redox status. Here, we developed salt-tolerant transgenic rice plants that overexpressed the dehydroascorbate reductase gene (OsDHAR1) under the control of a stress-inducible sweet potato promoter (SWPA2). OsDHAR1-expressing transgenic plants exhibited improved environmental adaptability compared to wild-type plants, owing to enhanced ascorbate levels, redox homeostasis, photosynthetic ability, and membrane stability through cross-activation of ascorbate-glutathione cycle enzymes under paddy-field conditions, which enhanced various agronomic traits, including root development, panicle number, spikelet number per panicle, and total grain yield. dhar2-knockdown plants were susceptible to salt stress, and owing to poor seed maturation, exhibited reduced biomass (root growth) and grain yield under paddy field conditions. Microarray revealed that transgenic plants highly expressed genes associated with cell growth, plant growth, leaf senescence, root development, ROS and heavy metal detoxification systems, lipid metabolism, isoflavone and ascorbate recycling, and photosynthesis. We identified the genetic source of functional genomics‒based molecular breeding in crop plants and provided new insights into the physiological processes underlying environmental adaptability, which will enable improvement of stress tolerance and crop species productivity in response to climate change.

Crops for Carbon Farming

Agricultural cropping systems and pasture comprise one third of the world's arable land and have the potential to draw down a considerable amount of atmospheric CO2 for storage as soil organic carbon (SOC) and improving the soil carbon budget. An improved soil carbon budget serves the dual purpose of promoting soil health, which supports crop productivity, and constituting a pool from which carbon can be converted to recalcitrant forms for long-term storage as a mitigation measure for global warming. In this perspective, we propose the design of crop ideotypes with the dual functionality of being highly productive for the purposes of food, feed, and fuel, while at the same time being able to facilitate higher contribution to soil carbon and improve the below ground ecology. We advocate a holistic approach of the integrated plant-microbe-soil system and suggest that significant improvements in soil carbon storage can be achieved by a three-pronged approach: (1) design plants with an increased root strength to further allocation of carbon belowground; (2) balance the increase in belowground carbon allocation with increased source strength for enhanced photosynthesis and biomass accumulation; and (3) design soil microbial consortia for increased rhizosphere sink strength and plant growth-promoting (PGP) properties.

Omics and CRISPR-Cas9 Approaches for Molecular Insight, Functional Gene Analysis, and Stress Tolerance Development in Crops

Plants are regularly exposed to biotic and abiotic stresses that adversely affect agricultural production. Omics has gained momentum in the last two decades, fueled by statistical methodologies, computational capabilities, mass spectrometry, nucleic-acid sequencing, and peptide-sequencing platforms. Functional genomics—especially metabolomics, transcriptomics, and proteomics—have contributed substantially to plant molecular responses to stress. Recent progress in reverse and forward genetics approaches have mediated high-throughput techniques for identifying stress-related genes. Furthermore, web-based genetic databases have mediated bioinformatics techniques for detecting families of stress-tolerant genes. Gene ontology (GO) databases provide information on the gene product’s functional features and help with the computational estimation of gene function. Functional omics data from multiple platforms are useful for positional cloning. Stress-tolerant plants have been engineered using stress response genes, regulatory networks, and pathways. The genome-editing tool, CRISPR-Cas9, reveals the functional features of several parts of the plant genome. Current developments in CRISPR, such as de novo meristem induction genome-engineering in dicots and temperature-tolerant LbCas12a/CRISPR, enable greater DNA insertion precision. This review discusses functional omics for molecular insight and CRISPR-Cas9-based validation of gene function in crop plants. Omics and CRISPR-Cas9 are expected to garner knowledge on molecular systems and gene function and stress-tolerant crop production.

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.

Integrated transcriptomic and metabolomic analyses of glutamine metabolism genes unveil key players in Oryza sativa (L.) to ameliorate the unique and combined abiotic stress tolerance

Plants can be considered to biosynthesize the specialized metabolites to adapt to various environmental stressors mainly on abiotic stresses (AbS). Among specialized metabolites, glutamine (Gln) is an essential plant metabolite to achieve sustainable plant growth, yield and food security. In this pilot study, swe employed computational metabolomics genome wide association survey (cmGWAS) of Gln metabolite profiling in Oryza sativa, targeting at the identification of abiotic stress responsible (AbSR) - Gln metabolite producing genes (GlnMPG). Identified 5 AbSR-GlnMPG alter the metabolite levels and play a predominant role in delineating the physiological significance of rice. These genes were systematically analysed for their biological features via OryzaCyc. Spatio-temporal and plant hormonal expression pattern of AbSR-GlnMPG was analysed and their differential expression profiling were noted in 48 different tissues and hormones, respectively. Furthermore, comparative ideogram of these genes revealed the chromosomal synteny with C4 grass genomes. Molecular crosstalks of these proteins, unravelled the various metabolic interaction. The systems expression profiling of AbSR-GlnMPG will lead to unravel the metabolite signaling and putative responses in multiple AbS. On the whole, this holistic study provides deeper insights on biomolecular features of AbSR-GlnMPG, which could be analysed further to decipher their functional metabolisms in AbS dynamism.

Multiscale computational models can guide experimentation and targeted measurements for crop improvement

Computational models of plants have identified gaps in our understanding of biological systems, and have revealed ways to optimize cellular processes or organ-level architecture to increase productivity. Thus, computational models are learning tools that help direct experimentation and measurements. Models are simplifications of complex systems, and often simulate specific processes at single scales (e.g. temporal, spatial, organizational, etc.). Consequently, single-scale models are unable to capture the critical cross-scale interactions that result in emergent properties of the system. In this perspective article, we contend that to accurately predict how a plant will respond in an untested environment, it is necessary to integrate mathematical models across biological scales. Computationally mimicking the flow of biological information from the genome to the phenome is an important step in discovering new experimental strategies to improve crops. A key challenge is to connect models across biological, temporal and computational (e.g. CPU versus GPU) scales, and then to visualize and interpret integrated model outputs. We address this challenge by describing the efforts of the international Crops in silico consortium.

Global analysis of threonine metabolism genes unravel key players in rice to improve the abiotic stress tolerance

The diversity in plant metabolites with improved phytonutrients is essential to achieve global food security and sustainable crop yield. Our study using computational metabolomics genome wide association study (cmGWAS) reports on a comprehensive profiling of threonine (Thr) metabolite in rice. Sixteen abiotic stress responsive (AbSR) – Thr metabolite producing genes (ThrMPG), modulate metabolite levels and play a significant role determining both physiological and nutritional importance of rice. These AbSR-ThrMPG were computationally analysed for their protein properties using OryzaCyc through plant metabolic network analyser. A total of 1373 and 1028 SNPs were involved in complex traits and genomic variations. Comparative mapping of AbSR-ThrMPG revealed the chromosomal colinearity with C4 grass species. Further, computational expression pattern of these genes predicted a differential expression profiling in diverse developmental tissues. Protein interaction of protein coding gene sequences revealed that the abiotic stresses (AbS) are multigenic in nature. In silico expression of AbSR-ThrMPG determined the putative involvement in response to individual AbS. This is the first comprehensive genome wide study reporting on AbSR –ThrMPG analysis in rice. The results of this study provide a pivotal resource for further functional investigation of these key genes in the vital areas of manipulating AbS signaling in rice improvement.

A Versatile Vector Toolkit for Functional Analysis of Rice Genes

BACKGROUND

Rice (Oryza sativa) is the main food for half of the world's population, and is considered the model for molecular biology studies of monocotyledon species. Although the rice genome was completely sequenced about 15 years ago, the function of most rice genes is still unknown.

RESULTS

In this study, we developed a vector toolkit that contains 42 vectors for transient expression studies in rice protoplasts and stable expression analysis in transgenic rice. These vectors have been successfully used to study protein subcellular localization, protein-protein interaction, gene overexpression, and the CRISPR/Cas9-mediated gene editing. A novel feature of these vectors is that they contain a universal multiple cloning site, which enables more than 99% of the rice coding sequences to be conveniently transferred between vectors.

CONCLUSIONS

The versatile vectors represent a highly efficient and high-throughput toolkit for functional analysis of rice genes.

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.

SIZ1-mediated SUMOylation during phosphate homeostasis in plants: Looking beyond the tip of the iceberg

Availability of phosphate (Pi) is often limited in rhizospheres in different agroclimatic zones and adversely affects growth and development of plants. To circumvent this impasse, there is an urgent need and global consensus to develop Pi use efficient crops. To achieve this goal, it is essential to identify the molecular entities that exert regulatory influences on the sensing and signaling cascade governing Pi homeostasis. SIZ1 encodes a small ubiquitin-like modifier (SUMO E3) ligase, and plays a pivotal role in the post-translational SUMOylation of proteins. In this review, we discuss the reverse genetics approach conventionally used for providing circumstantial evidence towards the regulatory influences of SIZ1 on several morphophysiological and molecular traits that govern Pi homeostasis in taxonomically diverse Arabidopsis thaliana (Arabidopsis) and Oryza sativa (rice) model species. However, the efforts have been rather modest in identifying SUMO protein targets that play key roles in the maintenance of Pi homeostasis in these model plants contrary to the plethora of them now known in lower organisms and animals. Therefore, to predict the SIZ1-mediated SUMOylome involved in Pi homeostasis, the state-of-the-art high-throughput technologies often used for animals thus provide an attractive paradigm towards achieving the long-term goal of developing Pi use efficient crops.

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

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

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.