Unraveling the light-specific metabolic and regulatory signatures of rice through combined in silico modeling and multi-omics analysis

Abstract

Light quality is an important signaling component upon which plants orchestrate various morphological processes, including seed germination and seedling photomorphogenesis. However, it is still unclear how plants, especially food crops, sense various light qualities and modulate their cellular growth and other developmental processes. Therefore, in this work, we initially profiled the transcripts of a model crop, rice (Oryza sativa), under four different light treatments (blue, green, red, and white) as well as in the dark. Concurrently, we reconstructed a fully compartmentalized genome-scale metabolic model of rice cells, iOS2164, containing 2,164 unique genes, 2,283 reactions, and 1,999 metabolites. We then combined the model with transcriptome profiles to elucidate the light-specific transcriptional signatures of rice metabolism. Clearly, light signals mediated rice gene expressions, differentially regulating numerous metabolic pathways: photosynthesis and secondary metabolism were upregulated in blue light, whereas reserve carbohydrates degradation was pronounced in the dark. The topological analysis of gene expression data with the rice genome-scale metabolic model further uncovered that phytohormones, such as abscisate, ethylene, gibberellin, and jasmonate, are the key biomarkers of light-mediated regulation, and subsequent analysis of the associated genes' promoter regions identified several light-specific transcription factors. Finally, the transcriptional control of rice metabolism by red and blue light signals was assessed by integrating the transcriptome and metabolome data with constraint-based modeling. The biological insights gained from this integrative systems biology approach offer several potential applications, such as improving the agronomic traits of food crops and designing light-specific synthetic gene circuits in microbial and mammalian systems.Light is the primary energy source as well as a key signaling element for plant growth and development. Although both light quantity (fluence) and quality (wavelength) are important for plant life, the latter is a crucial environmental indicator for plants to modulate their growth and morphological processes, such as seed germination, stem elongation, phototropism, circadian rhythms, and flowering induction (Neff et al., 2000). Since the discovery of red light (R)-stimulated seed germination in lettuce (Lactuca sativa;Borthwick et al., 1952), several studies have been focused on investigating the effect of individual light quality on plant growth and development. Earlier works used the classical genetic and molecular approaches, such as the use of light signaling-deficient mutants, measurement of enzyme activities, and enzyme/metabolite levels of certain pathway(s). However, it is required to comprehensively interrogate plant metabolism, because the transitions of light quality are likely to affect the plant physiology by modulating several metabolic effectors

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

“…1). [4][5][6] The promoter analysis results also suggest the role of bZIP TF as a positive regulator, and bHLH TFs particularly PIFs as negative regulators of chlorophyll and carotenoid accumulation. 7 Among the bZIP TFs, HY5 is a strong antagonist of PIF and functions as an activator of genes for chlorophyll and carotenoid accumulation upon blue light signaling.…”

“…Although the biosynthesis of these compounds has been extensively studied, the information on the light-driven regulatory mechanism is relatively limited. In our recently published paper, 4 we have already discussed that in rice leaves, the highest and lowest accumulation of carotenoids and total phenolic in rice leaves, under blue light and dark conditions, respectively, following the order of blue>white>green>red>dark (carotenoids) and blue>whi-te>red>green>dark (total phenolics). We also found a very divergent gene expression pattern in leaves grown in blue and red lights, which was consistent with the observed phenotypes.…”

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

“…To date, two rice GSMNs (Dharmawardhana et al, 2013; Liu et al, 2013) and three rice genome-scale metabolic models (GEMs) (Poolman et al, 2013; Seaver et al, 2014; Lakshmanan et al, 2015) have been reconstructed based on multiple data sources. Here, we make a distinction between GSMNs and GEMs, similar to the definitions used in Lewis et al (2012), where a GSMN comprises of all known metabolic reactions in an organism while a GEM is computable derivative of a metabolic network which can be analyzed using structural metabolic modeling and constraint-based modeling techniques such as FBA.…”

“…We tested the rice model from PlantSEED and found that the model has no stoichiometric inconsistency, but energy conservation is violated presumably due to the lack of manual curation in reaction reversibility. To the best of our knowledge, the most recent rice GEM is i OS2164 (Lakshmanan et al, 2015) reconstructed based on multiple databases including RiceCyc, PlantCyc, KEGG, and TransportDB (Ren et al, 2007). A distinct feature of this model is the detailed definition of all possible electron transport reactions in the mitochondrion, the plastid and the thylakoid including the light-driven photophosphorylation reactions in a wavelength specific manner, which allowed for the modeling of photosynthetic metabolism under different light sources.…”

“…The model was manually curated for elemental balance, reaction reversibility and directions, assignment of reactions into seven subcellular compartments, gap-filling and gene-protein-reaction mappings. Energy is not conserved when considering the reaction stoichiometries, reversibility and directions of this model, but additional constraints were applied at the stage of model simulations to prevent spontaneous ATP generation (Lakshmanan et al, 2015). Here, it should be noted that while all GEMs represent rice metabolism, still the number of genes and reactions accounted vary drastically.…”