unraveling the light-speciﬁc metabolic and regulatory ... · well as in the dark. concurrently,...

Unraveling the Light-Specific Metabolic and RegulatorySignatures of Rice through Combined in SilicoModeling and Multiomics Analysis1[OPEN]

Meiyappan Lakshmanan2, Sun-Hyung Lim2, Bijayalaxmi Mohanty, Jae Kwang Kim, Sun-Hwa Ha*, andDong-Yup Lee*

Department of Chemical and Biomolecular Engineering, National University of Singapore, Singapore 117576(M.L., B.M., D.-Y.L.); Bioprocessing Technology Institute, Agency for Science, Technology and Research,Singapore 138668 (M.L., D.-Y.L.); Metabolic Engineering Division, National Academy of Agricultural Science,Rural Development Administration, Jeonju 560–500, Republic of Korea (S.-H.L.); Division of Life Sciences,College of Life Sciences and Bioengineering, Incheon National University, Incheon 406–772, Republic of Korea(J.K.K.); and Department of Genetic Engineering and Graduate School of Biotechnology, College of LifeSciences, Kyung Hee University, Yongin 446–701, Republic of Korea (S.-H.H.)

ORCID IDs: 0000-0003-2356-3458 (M.L.); 0000-0002-8226-2367 (B.M.); 0000-0003-0901-708X (D.-Y.L.).

Light quality is an important signaling component upon which plants orchestrate various morphological processes, includingseed germination and seedling photomorphogenesis. However, it is still unclear how plants, especially food crops, sense variouslight qualities and modulate their cellular growth and other developmental processes. Therefore, in this work, we initiallyprofiled the transcripts of a model crop, rice (Oryza sativa), under four different light treatments (blue, green, red, and white) aswell 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 transcriptomeprofiles to elucidate the light-specific transcriptional signatures of rice metabolism. Clearly, light signals mediated rice geneexpressions, differentially regulating numerous metabolic pathways: photosynthesis and secondary metabolism were up-regulated in blue light, whereas reserve carbohydrates degradation was pronounced in the dark. The topological analysis ofgene 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 theassociated genes’ promoter regions identified several light-specific transcription factors. Finally, the transcriptional control ofrice metabolism by red and blue light signals was assessed by integrating the transcriptome and metabolome data withconstraint-based modeling. The biological insights gained from this integrative systems biology approach offer several potentialapplications, such as improving the agronomic traits of food crops and designing light-specific synthetic gene circuits in microbialand mammalian systems.

Light is the primary energy source as well as a keysignaling element for plant growth and development.Although both light quantity (fluence) and quality(wavelength) are important for plant life, the latter is acrucial environmental indicator for plants to modulatetheir growth andmorphological processes, such as seedgermination, stem elongation, phototropism, circadianrhythms, and flowering induction (Neff et al., 2000).Since the discovery of red light (R)-stimulated seedgermination in lettuce (Lactuca sativa; Borthwick et al.,1952), several studies have been focused on investi-gating the effect of individual light quality on plantgrowth and development. Earlier works used the clas-sical genetic and molecular approaches, such as the useof light signaling-deficient mutants, measurement ofenzyme activities, and enzyme/metabolite levels ofcertain pathway(s). However, it is required to com-prehensively interrogate plant metabolism, because thetransitions of light quality are likely to affect the plantphysiology by modulating several metabolic effectors

1 This work was supported by the National University of Singa-pore Biomedical Research Council of the Agency for Science, Tech-nology and Research and the Next-Generation BioGreen 21Program of the Rural Development Administration, Republic ofKorea (Systems and Synthetic Agrobiotech Center; grant no.PJ01109405).

2 These authors contributed equally to the article.* Address correspondence to [email protected] and cheld@nus.

edu.sg.The author responsible for distribution of materials integral to the

findings presented in this article in accordance with the policy de-scribed in the Instructions for Authors (www.plantphysiol.org) is:Dong-Yup Lee ([email protected]).

M.L. performed the in silico modeling and analysis and wrote thearticle; S.-H.L. performed all of the experiments, analyzed the data,and wrote the article; B.M. performed the TF analysis and revised thearticle; J.K.K. measured the phenolic contents and analyzed the data;S.-H.H. coordinated all of the experiments, contributed to the dataanalysis, and revised the article; D.-Y.L. generated the research plan,coordinated the project, and revised the article.

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http://orcid.org/0000-0003-2356-3458

http://orcid.org/0000-0002-8226-2367

http://orcid.org/0000-0003-0901-708X

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and signaling cascades at almost all levels of cellularhierarchy. Accordingly, plant biologists in the moderngenomic era have adopted the omics technologies, suchas transcriptomics (Ma et al., 2001; Tepperman et al.,2001; Wang et al., 2001), proteomics (Kim et al., 2006b),and metabolomics (Jung et al., 2013), thus enabling thehigh-throughput analysis of light signaling mecha-nisms in themodel eudicot plant Arabidopsis (Arabidopsisthaliana). Of them, the genome-wide transcript profilinghas been most widely exploited, highlighting severalnoticeable traits, including the massive reprogrammingof gene expression between photomorphogenesis andskotomorphogenesis, coordinated transcriptional regu-lation among several cellular pathways in certain lightcolors (Ma et al., 2001), the critical role of the R receptorphytochrome A (phyA) for the cell growth under lights(Tepperman et al., 2001), and the constitutivelyphotomorgenesis1-mediated blue light (B) regulation ofseedling development (Wang et al., 2001).Although the light transduction mechanisms have

been largely characterized by using the model plantArabidopsis in general, relatively little is known aboutthe monocot food crops, such as rice (Oryza sativa),wheat (Triticum aestivum), and maize (Zea mays). In thisregard, the increasing availability of rice genome in-formation (International Rice Genome SequencingProject, 2005) has enabled a few genome-wide studies,and studies have already explored the disparities be-tween dicots and monocots in terms of light percep-tion and photomorphogenesis. Among them, the mostremarkable difference is the number of available pho-toreceptor genes, especially phy genes, and their indi-vidual functional roles: Arabidopsis has five genes,phyA to phyE, whereas rice has only three, phyA to phyC(Takano et al., 2009). Moreover, the comparative tran-scriptome analyses of Arabidopsis and rice under lightand/or dark (D) treatments have revealed a few moredistinct characteristics: (1) gene expression patterns aremore conserved between the two in photomorpho-genesis but not in skotomorphogenesis (Jiao et al.,2005), and (2) the leaf growth in Arabidopsis is moretightly controlled by circadian rhythms, even undercontinuous light treatment, than in rice (Poiré et al.,2010). Collectively, these differences stress the impor-tance of understanding the light-mediated signalingmechanisms in rice, so that the knowledge gained fromsuch studies can be used to manipulate the crop agro-nomic traits for its improvement. To this end, herein,we use a systems biology approach, where the abun-dantly available omics data sets can be examined com-bined with predictive computational models.Concurrent to the high-throughput omics analysis,

the advances in genomic technologies have also accel-erated the development of large-scale computationalmodels and related simulation methods for analyzingthe cellular behavior (Lee et al., 2005). Constraints-based in silico metabolic modeling and analysis is oneof the well-established techniques to elucidate thephysiological behavior and metabolic states of an or-ganism upon various environmental/genetic changes,

because it systematically captures the genotype-phenotype relationships from the genome annotation,biochemical, and cell physiological data (Lewis et al.,2012; Lakshmanan et al., 2014). Moreover, these modelscan contextualize multiple omics data through severalintegrative analyses, as such providing unique biolog-ical insights at the systems level (Hyduke et al., 2013).Several constraints-based models have been devel-oped at the genome scale for a wide range of microbesand mammals (Kim et al., 2012b), including humans(Duarte et al., 2007), and a few plants, such as Arabi-dopsis (Poolman et al., 2009; Saha et al., 2011; Mintz-Oron et al., 2012; Chung et al., 2013), maize (Saha et al.,2011; Simons et al., 2014), and rice (Poolman et al.,2013). Among these, the human genome-scale meta-bolic model (GEM) has been integrated with tran-scriptome and proteome data to characterize thetranscriptional regulatory mechanisms andmetabolicphenotypes of various diseases, which could not bedeciphered from either of them alone (Zelezniaket al., 2010; Hu et al., 2013; Mardinoglu et al., 2014). Inplants, the transcriptome data were successfully in-tegrated with the Arabidopsis GEM to understand itsmetabolic acclimation under different light and/ortemperature conditions (Töpfer et al., 2013, 2014).Similarly, here, we combined the GEM of rice cellswith multiple omics data to uncover the heterogene-ity in light-mediated transcriptional regulation of cel-lular metabolism across various colors.

The overall approach of this study is illustratedin Figure 1. We first profiled the transcripts of riceplants grown under different light-emitting diode(LED) illumination and in D. In parallel, we recon-structed a completely curated, fully compartmental-ized GEM of rice cells, allowing us to integrate thegene expression data onto the metabolic networktopology for the systematic characterization of light-specific transcriptional responses at several levels ofcellular hierarchy: systems, pathways, and individ-ual reactions and metabolites. Using the biomarkersidentified from such an integrative study, we theninvestigated the putative cis-acting regulatory ele-ments for the potential transcription factors (TFs) thatfunction light specifically. Finally, the metabolomedata were also used in conjunction with the tran-scriptome and genome-scale in silico modeling forelucidating the metabolic diversity between red andblue colors.

RESULTS

Unraveling the Heterogeneity of Light-MediatedMetabolic Phenotypes and Transcriptional Regulationin Rice

To compare the effects of different light quality onplant growth and cellular metabolism, we grew riceplants in LED chambers under five conditions: B (450 nm),green light (G; 530 nm), R (660 nm), white light (W;mixture of B, G, and R), and D (no light treatment;

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Fig. 1). In each light treatment, the photosyntheticphoton flux (PPF) at the top of plants was maintainedat 94 mmol m22 s21. Here, it should be noted that thisphoton fluence rate was distributed differently in all ofthe three color light treatments, such that the B, R, andG chambers receive 100% B, R, and G, respectively. TheW chamber was designed to receive equal amounts ofall three colored lights (“Materials andMethods”; Fig. 1).

Expectedly, the plants grown under different lightsshowed diverse phenotypes: shorter plant with widerbreadth and larger angle of leaf blades in B, pale yellow

plant with longer coleoptile length and very low freshweight in D, and comparatively similar structuresamong W, G, and R (Fig. 2A; Supplemental Fig. S1;Supplemental Data Set S1). Such light-specific pheno-typic diversity of plants, especially in B and D, can becharacterized with relevant intracellular secondarymetabolites, which are largely present in leaves. Thus,we quantified terpenoids and phenolic compounds,resulting in the highest amount in B and the lowest in D,following the orders B . W . G . R .. D and B .W . R . G .. D, respectively (“Materials and

Figure 1. Schematic illustration of systematic framework combining the in silico modeling and omics data analysis. Rice plantswere grown in five different light treatments, and the metabolome and transcriptome were profiled. Concurrently, the rice GEMwas reconstructed based on genome annotation, biochemical data, and literature sources. The model and omics data were thensystematically combined to identify the key light-specific regulatory and metabolic signatures. PCA, Principal componentanalysis; PLS-DA, partial least squares-discriminant analysis.

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Methods”; Fig. 2B). These consistent results con-firmed that the increased photosynthesis in B mayenhance terpenoids synthesis, leading to wider leafblades, through the transcriptional regulation ofcorresponding biosynthetic genes with expressionlevels that are possibly modulated by particular lightcolors (Ma et al., 2001; Jiao et al., 2005). Therefore, toanalyze the light-mediated transcriptional regulatorypatterns, the gene expression profiling was con-ducted with a genome-wide 39Tiling Microarray(Roche NimbleGen, Inc.) designed using the 27,448genes deposited at the International Rice GenomeSequencing Project, Rice Annotation Project database(http://rapdb.dna.affrc.go.jp/). Among the 27,448transcripts, 20,507 genes are based on the full com-plementary DNA (cDNA)/EST supports in RAPdatabase, and the remaining 6,941 genes have thepartial cDNA/EST sequences. The scanned micro-array hybridization signals were digitized and ana-lyzed byNimblescan (RocheNimbleGen, Inc.). Finally,the data were manually inspected and normalized tominimize the experimental variations and eliminatenoise before being further processed (“Materials andMethods”).

Reconstruction of a Fully Compartmentalized GEM of RiceCells and Mapping of Transcriptome Data

We expanded our previous model of rice centralmetabolism (Lakshmanan et al., 2013) into a completelycurated, fully compartmentalized GEM, which can beintegratedwith the gene expression data to pinpoint theregulatory metabolic hotspots from the topologicalpoint of view (Zelezniak et al., 2010; Mardinoglu et al.,2014). Basically, the model reconstruction involvesthree key steps: (1) compilation of metabolic genes andrelated reactions from rice genome annotation, bio-chemical databases, and other literature sources; (2)manual curation of metabolic reactions by verifyingelemental balances and reaction directionalities, de-veloping gene-protein reaction (GPR) mappings, andassigning proper subcellular compartments; and (3)dead-end identification andmanual network gap fillingbased on literature sources (for detailed procedures, see“Materials andMethods” and Supplemental Figure S2).The final GEM of rice, iOS2164, accounts for 2,164unique genes, 2,283 reactions, and 1,999 metaboliteslocalized across seven intracellular compartments: cy-tosol, plastid, mitochondrion, peroxisome, endoplas-mic reticulum, vacuole, and thylakoid (Fig. 3). Adetailed list of the iOS2164 metabolic network con-taining the various genes, reactions, and metabolitescan be obtained from Supplemental Data Set S2; and also,it is available as a Systems BiologyMarkup Language file(level 2, version 1; http://sbml.org/; Supplemental DataSet S4). It should be noted that the presence of GPR iniOS2164 allowed us to map the transcriptome data,where the expression levels of 1,915 metabolic genes,pertaining to 1,659 reactions, could be associatedwith themodel for further analysis.

During the reconstruction, significant efforts weremade to develop iOS2164 as the most comprehensiverice metabolic model. Clearly, it is more extensive thanthe previous one (Poolman et al., 2013) in terms ofmetabolic pathways coverage, appropriate subcellularlocalization of reactions, detailed accounting of electrontransport metabolism in both plastid and mitochon-drion, enhanced network connectivity, and a low per-centage of blocked reactions (for thorough comparisons,see Supplemental Fig. S3 and Supplemental Data Set S1).Noticeably, iOS2164 is the first plant model, to ourknowledge, to account for all of the possible electrontransport reactions in mitochondrion, plastid, and thy-lakoid, including the light-driven photophosphorylationreactions, in a wavelength-specific manner. Photosyn-thesis at various wavelengths in the visible spectrumwas modeled by adopting the approach proposed in theChlamydomonas reinhardtiimodel, iRC1080 (Chang et al.,2011). Because the photon-absorbing metabolic reac-tions, such as PSI and PSII, in iRC1080 only consider theR and B range, we have amended these reactions tocover all wavelengths in the visible spectra based on theabsorptivity data (for detailed procedures, see “Mate-rials and Methods”). It should be noted that, in total, 35

Figure 2. Light-regulated morphological diversity of rice plants. A,Image of rice plants grown under continuous illumination of diverselights (B, G, R, and W) and in D. B, Carotenoid and total phenoliccontents in each light treatment and D. GAE, Gallic acid equivalent.




http://rapdb.dna.affrc.go.jp/



http://sbml.org/






Figure 3. Network characteristics and phenotype predictions of iOS2164. A, Compartmentalized network diagram of iOS2164,where different pathways are highlighted using different colors and the inset table summarizes the model properties. B, Reactiondistribution across various pathways in iOS2164. C, Wavelength-specific model predictions compared against the experimentallyobtained photosynthetic action spectra. D, Comparison between in silico and experimental growth of germinating seed cells. The


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reactions denoting the wavelength-specific spectral de-composition were included into iOS2164.After the rice GEM reconstruction, we performed

several tests to validate its predictive ability. First, themodel was evaluated for simulating 46 known meta-bolic functions, including the biosynthesis and degra-dation of amino acids and secondary metabolites, suchas terpenoids and phenolic compounds. Second, theability of iOS2164 to simulate the photosynthetic actionspectra (rate of O2 evolution from photosynthesis) at allpossible wavelength in the visible spectra (Bowsheret al., 2008) was tested and observed to be fairly cor-related (R2 = 0.6641; Fig. 3C). Third, the model predic-tions of a nonphotosynthetic cell were validated usingthe previously published batch culture data of rice cellsgrown on Suc and Glc under aerobic and anaerobicconditions (Fig. 3D; Lakshmanan et al., 2013).

Evaluating the Role of Alternative Electron Flow Pathwaysduring Photosynthesis and Cell Growth Using iOS2164

Photosynthetic organisms use various electron flowpathways for generating the redox power from lightenergy in terms of ATP and NADPH. The linear elec-tron flow (LEF) pathway, where the electrons flow fromPSII to PSI through Cytochrome b6f complex and fer-redoxin NADP+-oxidoreductase, is the most exten-sively used pathway (Allen, 2002). Additionally, plantsalso possess other alternative electron flow (AEF) path-ways, such as the cyclic electronflow (CEF) andMehler’sreaction for generating the required redox power (Fig.4A). Although the physiological roles of AEF pathwaysare not clear, they are suggested to work alongside LEF(Allen, 2003). Therefore, to evaluate the possible con-tributions of AEF to photosynthesis and cell growth,here, we systematically performed in silico flux simu-lation using iOS2164.Initially, we examined the phototropic growth of rice

leaf at various light and CO2 uptake rates by maxi-mizing the corresponding biomass equation. Thesesimulations revealed that all three electron flow path-ways carried noticeable fluxeswhile achievingmaximalgrowth rates, suggesting that the AEF is possibly op-erational at all times (data not shown). Subsequently,the contribution of individual electron flow pathwaysto photosynthesis and cellular growth was computedby constraining each AEF pathway at zero in a se-quential manner. Interestingly, the resultant photo-synthesis and cellular growth at high light intensitieswere highly dependent on AEF pathways. From Figure4C, it could be observed that the ratio of PSI to PSII

increases drastically at high light intensities and lowcarbon uptake, thus highlighting the flexibility of AEFpathways, especially CEF. Under such conditions,plants increasingly use the CEF around PSI instead ofusing LEF to dissipate the surplus energy; any excessNADPH produced from LEF beyond a threshold can-not be used in the Calvin cycle because of low CO2uptake. Plants without CEF are still viable but grow atsuboptimal growth rates (Fig. 4D), confirming that theCEF has indeed contributed to maximal growth in thewild type. Noticeably, these mutants maintained anequal ratio of PSI to PSII, because they cannot recyclethe excess redox power with PSI alone (Fig. 4E). Incontrast to the CEF mutants, plants that lack Mehler’sreaction grow at growth rates very close to those of thewild type (#0.02%), suggesting that the contribution ofthis AEF to photophosphorylation is negligible com-pared with that of CEF (Fig. 4F). Finally, we also eval-uated the growth rates and PSI to PSII ratio of doublemutants (i.e. plants that are devoid of both CEF andMehler’s reaction) and observed the results to be verysimilar to those of CEF mutants (Fig. 4, H and I).

Global Analysis of Metabolic Gene Expression

Global expression patterns of metabolic genes acrossvarious light treatments were examined by two differ-ent methods: (1) plotting the log2 expression values ofany two conditions pairwise and (2) principal compo-nent analysis. Overall, the relative differences betweenany two conditions were consistently identified by bothmethods (Fig. 5A). Not surprisingly, the expressionpattern of D is markedly different from all other lighttreatments. Among light conditions, B and R showedthe most divergent expression patterns (R2 = 0.9167),whereas G and W had the lowest differences in geneexpression (R2 = 0.9883), which is consistent with theobserved phenotypes (Fig. 2A). A closer examination oflog2 expression plots (Fig. 5A) has further highlightedthe specific patterns of up- or down-regulation betweenany two conditions. For example, in R-W comparison,most of the genes are up-regulated in W, whereas inB-R, genes are mostly down-regulated in R. Interest-ingly, a similar clustering result was observed in theprincipal component analysis of metabolome data fromidentical experiments (Jung et al., 2013). Thus, themetabolic gene expression and metabolome profilesare potentially correlated to each other.

We also analyzed the overall gene expression patternof the entire microarray, including nonmetabolic genes,leading to similar results (Supplemental Fig. S4).

Figure 3. (Continued.)photosynthetic action spectra under individual light colors were simulated by maximizing the leaf biomass while constraining the cor-responding wavelength’s photon uptake at 100 mmol g21 dry cell weight (DCW) h21, and the resulting trend is compared with the trendreported earlier (Bowsher et al., 2008). Cellular growth of nonphotosynthetic cells was simulated using the previously published batchculture data of rice cells grown on Suc and Glc under aerobic and anaerobic conditions (Lakshmanan et al., 2013). A high-resolutionnetwork diagram is also available in Supplemental Data S5. GPR, Gene-protein-reaction; TCA, tricarboxylic acid; tRNA, transfer RNA.







However, the difference between W and G expressionpatterns is slightly higher when considering all genes(R2 = 0.9719 compared with R2 = 0.9883), presumablyrevealing that most of the transcriptional changes be-tween W and G may occur in nonmetabolic genes.

Expression Changes of Individual Metabolic Pathways

Subsequent to the global analysis of metabolic genes,we examined the up- and down-regulation of individ-ual metabolic pathways in G, B, R, and D treatmentsrelative to W. The significance of such expressionchanges in metabolic pathways was calculated usingWilcoxon signed rank test adjusted for multiple hy-pothesis testing (“Materials and Methods”). The resultsare presented in Figure 5B, where the color intensitydenotes the statistical significance of differentialexpression.

The individual pathway analysis unraveled the up-regulation of several plastidic pathways, such as pho-tosynthesis; terpenoids biosynthesis; starch and Sucmetabolism; Calvin cycle; Phe, Tyr, and Trpmetabolism;GAs metabolism, and abscisate (ABA) biosynthesis in B(Fig. 5B). It should be noted that the up-regulation ofseveral secondary metabolite biosynthetic pathways isconsistentwith our observation of higher terpenoids andphenolic compounds in B (Fig. 2B). Moreover, the up-

and down-regulation of ABA and ethylene biosynthesis,respectively, in B clearly explains the short plant phe-notype: it is known that the accumulation of ABA willreduce the biosynthesis of plant growth stimulator,ethylene, thus inhibiting stem growth (Hoffmann-Benning and Kende, 1992). In R treatment, severalpathways, including photosynthesis and Calvin cy-cle, showed significant down-regulation comparedwith W. Again, these observations are highly con-sistent with earlier experiments, which showed thatthese pathways are up-regulated in R supplementedwith B rather than R only (Goins et al., 1997). Unlike Rand B, which showed a clear up- or down-regulation ofmetabolic pathways, a mixed pattern was found inD. Expectedly, photosynthesis is significantly down-regulated, whereas the amino acid metabolism, fattyacid degradation, starch and Suc metabolism, and ox-idative phosphorylation were up-regulated in D, sug-gesting that, in the absence of photosynthesis, plantsmay degrade the available storage carbon to surviveunder such stressful conditions (Kunz et al., 2010).

We also quantified the light-specific functional en-richment of various biological processes using DAVID(Huang et al., 2007) on the basis of Gene Ontology (GO;BP:GO) terms. This complementary approach identi-fied carbohydrate catabolism (GO:0016311, GO:0044036,GO:0006022,GO:0006026,GO:0006030, andGO:0006032)

Figure 4. Contributions of various electron flow pathways to photophosphorylation. A, Schematic illustration of plastidic/thylakoidal membrane electron transport chains included in iOS2164, showing the Z scheme, cyclic flow, andMehler’s reaction.B, Growth rate of thewild type. C, PSI to PSII ratio of thewild type. D,Growth rate of CEFmutant. E, PSI to PSII ratio of CEFmutant.F, Growth rate of MEHLER mutant. G, PSI to PSII ratio of MEHLERmutant. H, Growth rate of the double mutant. I, PSI to PSII ratioof the double mutant. The PSI to PSII ratio was calculated by dividing the PSI flux by the PSII flux. ATPS, ATP synthase; C/bf6,cytochrome b6f complex; Fd, ferredoxin, NDH, NAD(P)H dehydrogenase; pc, plastocyanin; pq, plastoquinone; PTOX, plastidterminal oxidase.


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from the up-regulated genes in D and isoprenoid bio-synthesis (GO:0008299) in B (Supplemental Fig. S5)as unique biological processes under the relevant lightcolors as consistently observed by the individualpathway analysis. Interestingly, the cellular processes,

such as transcription (GO:0006350) and its regulation(GO:0006355), were also enriched in the down-regulatedgenes of B, suggesting that most of the phenotypicchanges in B could be mainly attributed to the differ-ences in transcriptional processes.

Figure 5. Light-mediated transcriptional regulatory patterns of rice identified using iOS2164. A, Global analysis of metabolicgene expression under various light treatments. The scatter plots show the pairwise comparison of average log2 gene expressionvalues between various light treatments, whereas the inset shows the principal component analysis of transcriptome data in eachcondition. B, Expression levels of individual metabolic pathways. C, Differential expression of individual reactions. D, Enrich-ment of the top 20 reporter metabolites in up- and down-regulated genes. The up- and down-regulation in each condition wasidentified using W as reference. The color intensity in the heat map and network diagram indicates the significance of up- ordown-regulation and not fold change. For visualization purposes, the negative or positive log10 of the P value is presented. Formetabolite abbreviations and a complete list of reporter metabolites, see Supplemental Datas S2 and S3, respectively. TCA,Tricarboxylic acid; tRNA, transfer RNA.







Expression Changes of Individual Reactions andReporter Metabolites

Next, we explored the expression patterns at individ-ual reaction and metabolite levels. The differential ex-pression of each reaction under various light treatments

was first analyzed in a pairwise manner using modifiedt statistic adjusted for multiple hypotheses testing(“Materials and Methods”). Considering W as the refer-ence,most of the reactions inB are up-regulated,whereasD and R have a large number of reactions down-regulated as similarly identified by the global analysis

Table I. List of TFs with total enrichment scores of target motifs among the up- and down-regulated genesneighboring reporter metabolites in B versus W, R versus W, and D versus W treatments

TFs

Total Enrichment Score of Motifsa

Up-Regulated Genes Down-Regulated Genes

B R D B R D

Myeloblastosis (MYB) 925 1,629 604 1,233 1,053 876R2R3 268 800 234 550 400 453MYB1 71 121 67MYB2 86 68MYB15 86 61MYB5, MYB30 86MYB80 91 68

Basic Leucine Zipper (bZIP) 1,037 401 269 175 403 397TGA 337 57 196 117 197 222Group-A (Abscisic Acid Response

Element [ABRE]-type)700 344 73 58 206 175

Protein homologous to G/HBF/G-box andH-box Binding Factor

75

G-Box Binding Factor1 (GBF1), GBF2 75 61Common Plant Regulatory

Factor5 (CPR5), CPRF6, CPRF775

LONG HYPOCOTYL5 (HY5) 131Tobacco (Nicotiana tabacum) Basic

Leucine Zipper Transcription Activator75

Dc3 Promoter Binding Factor-1(DPBF-1) and DPBF-2

75

Abscisic Acid-Insensitive3 (ABI3) 152 86 53 108Ethylene Responsive Factor (ERF) 231 557 203 191 249 172ERF (jasmonate responsive) 175 128 203 133 197 172

Basic Helix-Loop-Helix (bHLH) 225 229 126 292 116 50Phytochrome Interacting

Factor (PIF1), PIF3, PIF4, PIF775

WRKY 56 62Zinc Finger (ZnF) 200 228 67 154Zinc Finger Catharanthus Transcription

Factor1 (ZCT1), ZCT2, ZCT356

Multiprotein Bridging Factor1 63 214 74 56Auxin Response Factor (ARF) 69 228 191 92 162DNA-Binding with One Finger 169 228 217 168FUSCA3, Leafy Cotyledon2 (ABA) 75 172 53 108DNA-Binding Protein Phosphatase1 369 186 141 166 122 205Type-B Arabidopsis Response Regulators 56 71 121 68 125WUSCHEL-Related Homeobox 86 53BRASSINOSTEROID INSENSITIVE1-ETHYLMETHANESULFONATE-SUPPRESSOR1

75

Rice Nuclear Factors 105 58Barley B Recombinant 119Cytosine- and Adenine-Rlch Region from DNase I 81 75Cys protease 71Ethylene-Insensitive3-Like 86GC-Rich Binding Protein1, Specificity Protein1 100Heat Stress Factor 100

aTotal target motif enrichment score is the sum of the percentage of occurrences of all motifs belonging to the sameTF family in the up- and down-regulated genes of all comparisons (i.e. B versus W, R versus W, and D versus W).The individual motif enrichment scores on each of these comparisons can be found in Supplemental Data S3.


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(Fig. 5C). The complete list of reactions, which are dif-ferentially expressed in all of the comparisons (i.e. BversusW, R versusW, G versusW, andD versusW), areprovided in Supplemental Data Set S3.From the list of the reactionswith themost significant

transcriptional changes, we then performed the re-porter metabolite analysis (Patil and Nielsen, 2005) tofind the metabolic hotspots in the rice network thatshare a common transcriptional regulatory mechanism(“Materials and Methods”). The top 20 statisticallyenriched reporter metabolites in each of the lighttreatments are provided in Figure 5D (for reportermetabolite enrichment scores of all relevant metabo-lites, see Supplemental Data Set S3). It should be notedthat there is no reporter metabolite in G versus Wcomparison because of very similar expression patterns(R2 = 0.9883), leading to the small physiological differ-ence. Overall, the top-ranked reporter metabolites aremostly from terpenoids biosynthesis, GA metabolism,indole-3-acetic acid (IAA) biosynthesis, ABA biosyn-thesis, amino acid metabolism, and Suc and starchmetabolism. Specifically, reporter metabolites weremainly associated with phytohormones, such as GAs,ethylene, and ABA, in the B versus W comparison (Fig.5D). Thus, they can be suggested as the key biomarkersfor shorter plant phenotype. Similarly, R versus Wcomparison identified reporter metabolites along ter-penoids, IAA, and GAs pathways, where most of thegenes are grossly down-regulated, clearly indicating adecrease in the phytohormone availability in R (Joneset al., 1991). However, ethylene and its precursor,1-aminocyclopropane-1-carboxylate, were asso-ciated with up-regulated genes in R, explaining theethylene-mediated hypocotyl elongation under W or Rbut not B. Supplemental Figure S6 provides the net-work visualization of top-ranked reporter metabo-lites and the expression patterns of neighboring genes.The overlapping and unique top-ranked reporter me-

tabolites (P value , 0.05) among different light treat-mentswere then shortlisted to delineate the common anddistinct biomarkers of transcriptional regulation, respec-tively. From this comparison, we identified 32 metabo-lites as mutual indicators in all three conditions (i.e. B, R,andD; SupplementalData Set S3). Notably,most of thesemetabolites are from the IAA biosynthesis, jasmonatebiosynthesis, and GAs metabolism. Indeed, these phy-tohormones have a global role in plant metabolism andrespond specifically to diverse environmental stimuli.Interestingly, unlike other comparisons, D versus Wexhibited a large number of reporter metabolites fromfatty acid metabolism and carbohydrate metabolism,confirming that these pathways are functionally active inthe absence of photosynthesis.

Motif Analysis of Differentially ExpressedMetabolic Genes

After the identification of reporter metabolites ineach light treatment, we further analyzed the promoter

regions of their neighboring up- and down-regulatedgenes to link them with the regulatory pathways thatmodulate their gene expression. Here, unlike the con-ventional approach, which considers the whole set ofdifferentially expressed genes, the genes that orches-trate the coordinated changes in the metabolism wereused to identify the plausible TF binding sequencemotifs (Zelezniak et al., 2010). This analysis allowedus to disclose a number of light-specific TFs that

Figure 6. Metabolic and regulatory signatures of rice in B and R. A,Comparison of intracellular metabolite levels in R and B. Metaboliteswith a significant change (P , 0.05) are highlighted above the dottedline. B, The rice central metabolic map showing the differences inmetabolic fluxes obtained through random sampling after the applica-tion of constraints based on transcriptome data. The calculated differ-ences between scaled fluxes where multipliedwith a large integer value(i.e. 106) for visualization purposes. The fold changes (FCs) in bothmetabolite levels and flux differences are calculated using R as a ref-erence. For metabolite and reaction abbreviations, see SupplementalData S2.











differentially regulate the plant phytohormone levelsthrough the corresponding biosynthetic gene expressionand thus, the plant phenotype under B, R, and D con-ditions (Table I).

Notably, the promoter analysis confirmed the B re-sponsiveness of a key bZIP protein,HY5,which is knownto respond positively to B through a specific interactionwith the G-boxmotif and subsequently, mediate the lightcontrol of gene expressions (Chattopadhyay et al., 1998).In addition, many other group G-type bZIP TFs, such asGBF1, GBF2, CPRF5, CPRF6, andCPRF7, were identifiedas B specific, in good agreement with earlier reports(Schindler et al., 1992; Kircher et al., 1998). Variousunique TFs were also established to be either B or Rspecific putatively. For example, the significant enrich-ment of motifs associated with R2R3 MYB TFs, MYB1,MYB2, MYB5, MYB15, and MYB30, in the up-regulatedgenes of R versus W (Table I) indicates that these TFs arepossibly reactive to R. Interestingly, among them, MYB1has been shown to have a general light-specific response(Feldbrügge et al., 1997). Similarly, ZnF TFs, such asZCT1, ZCT2, and ZCT3, andWRKY TFs are determinedto be B specific (Table I). In total, we identified 62 and 65TFs to be R and B responsive, respectively. The validity ofour promoter analysis was further evaluated by com-paring the resultant TF list with existing literature evi-dence, revealing that 9 and 22 TFs are either directly orindirectly associated with the R and B, respectively (forcomparison results, see Supplemental Data Set S3).

Wavelength-Specific Constraint-Based Modeling Unravelsthe Metabolic Signatures of B and R Colors andComplements the Metabolomics Data

Among various colors, because B and R mediate themost divergent transcriptional responses, leading tomarked differences in plant phenotype, we comparedthe heterogeneity in their cellular metabolisms throughan integrative analysis of metabolomics and constraint-based modeling. For this purpose, we first evaluatedthe differences in metabolite levels between two con-ditions using the previously published metabolomedata (Jung et al., 2013), thereby identifying 18 of the 43measured (P value , 0.05). It can be observed fromFigure 6A that all of the amino acids and fatty acids arepresent at larger quantities in B, whereas the sugars,such as Glc, Suc, and Fru, are abundant in R.

As highlighted earlier, iOS2164 includes the light-driven photophosphorylation reactions in a wavelength-specific manner. Thus, we can analyze the differentialutilization of various metabolic pathways under B and Rcolor regimes. To do so, we sampled the solution space ofiOS2164 using the artificial centering hit and ran MonteCarlo sampling (Schellenberger and Palsson, 2009) byconstraining the same amount of metabolically activephoton fluxes through spectral decomposition reactions,corresponding to the B and R conditions. The differencesin the scaled flux values between the two conditionswerethen estimated based on the range of the possible steady-

state flux values, which were determined by randomsampling (“Materials and Methods”). Such sampling re-sults clearly indicated that rice cells commonly use thesame metabolic pathways for fixing CO2 and generatingthe biomass precursors (i.e. the Calvin cycle, glycolysis,and tricarboxylic acid cycle, where all of the reactionswere up-regulated in B because of its higher ability togenerate ATP and/or NADPH compared with R;Supplemental Fig. S7). These observations were furthersubstantiated by simulating the phototrophic growth ofrice leaves over a range of photon uptake fluxes. The re-sultant constraints-based flux analysis simulations re-vealed that, for a particular photon influx, the amount ofATP and NADPH generated by photophosphorylationand ferredoxin-NADP reductase is always higher in Bthan R because of the high selectivity of photosystems tothe former color (for details, see Supplemental Data SetS1; Supplemental Fig. S8). Here, it should be noted that,although such results agree reasonably well with themetabolome data, which showed most of the metabolitesto be present at abundant quantities in B, we found sugarmolecules to bemostly down-regulated, in contrast to thesimulation results. Therefore, to analyze whether theintegration of transcriptome data onto GEM improvesthe model prediction, we further constrained the in-ternal reaction fluxes using the gene expression valuesusing the E-Flux approach (Colijn et al., 2009). Suchadditional constraints clearly differentiated the al-lowable ranges of 122 reactions, including ribulosebisphosphate carboxylase/oxygenase, xylanase, Xylisomerase, vacuolar ATP synthase, and pyruvatephosphate dikinase, which have flux values that weredifferent by a ratio of at least 61.5 (for a list of reac-tions that have significant differences in flux ranges,see Supplemental Data Set S2). Overall, the uniquesampling results confirmed that most of the primarymetabolic pathways, including the Calvin cycle, gly-colysis, tricarboxylic acid cycle, amino acid biosyn-thesis, and fatty acid biosynthesis, carry higherfluxes in Bthan in R, thereby enabling the enhanced biosynthesisof proteins and lipids (Fig. 6B). The transcriptome-constrained sampling analysis also highlighted that thefluxes within starch and Suc metabolism and cell wallsynthetic pathways are slightly down-regulated in B,agreeing well with the metabolome data, which showedthe carbohydrate levels to be higher in R. Collectively,transcriptome-constrained flux sampling and metab-olome data indicate that B has a slightly higher carbonfixing ability alongwith a highly active catabolism,wherethefixed carbohydrates are rapidlymetabolized into fattyacids, amino acids, and other secondary metabolites.Plants grown in R, however, have relatively higher car-bohydrate contents because of less internal catabolic flux.

DISCUSSION

To date, several studies have focused on light signalperception and subsequentmodulation of various plantmorphological processes, including large-scale transcript


Lakshmanan et al.









profiling experiments in Arabidopsis (Ma et al., 2001)and rice (Jiao et al., 2005). Although these studiesidentified light as a key signaling component that, in-deed, regulates the gene expression and rearrangescellular metabolism globally, a comprehensive inter-rogation of which pathways/enzymes/metabolites/TFs respond specifically to a certain light quality andhow they modulate the plant growth and develop-ment processes was largely missing. Thus, to eluci-date the underlying mechanisms and functions of riceplants grown under various light treatments, we de-veloped the integrative analysis framework, where thecondition-specific omics data and invariant rice met-abolic network are systematically combined to derivemeaningful hypotheses.This study clearly revealed that each wavelength in

light spectra orchestrates various plant morphologicalprocesses: the plant phenotype and corresponding geneexpression under various monochromatic light colorsare significantly different (Figs. 2A and 5A). Particu-larly, the rice plants grown under B have the mostunique phenotype of all of the other light colors (i.e.shorter plants with wider leaf blades). The functionalanalysis of individual metabolic pathways allowed usto explain that the up-regulation of ABA metabolismand down-regulation of ethylene biosynthesis by Bsuppress the stem elongation (Fig. 5B). Here, it shouldbe noted that, although the biosynthesis of GA, a pos-itive regulator of hypocotyl elongation, was also up-regulated in B, it may not control the stem growthcompletely but rather, just modulate it in the presenceof ethylene as reported earlier (Vandenbussche et al.,2007). Additionally, B signals also induce a higherproduction of terpenoids and phenolic compoundsthan other light qualities, suggesting that most of thefixed carbon is converted into secondary metabolites(Fig. 2B). Comparatively, a gross down-regulation ofseveral pathways, including photosynthesis, was ob-served in R, confirming that the intracellular metabo-lism in W or B is more active than in R as identified bythe earlier study, which showed that the photosyntheticrates and leaf nitrogen content are higher in rice plantsgrown under B + R than under R alone (Goins et al.,1997). Such findings were further substantiated by theintegrative analysis of transcriptome and metabolomedata with genome-scale modeling, which revealed thatB, in general, has higher nutrient uptake and catabolicfluxes because of its efficient photosynthesis metabo-lism and thus, could synthesize more amino acids andfatty acids. Based on these observations, we hypothe-size that the relative amounts of B and R in sunlightcould be an important signaling factor for plants tomodulate the transcriptional control between two of itscontending tasks: enhancing the metabolic efficiencyand increasing the plant size (Kleessen et al., 2014).Moreover, we speculate that, because the sunlight hasmaximum B at noon rather than dawn or dusk, plantsmay use it as a vital signaling component to over-express the photosynthesis and primary metabolismgenes by down-regulating the expression of growth-

related genes to optimally use the available resources(de Montaigu et al., 2010).

Intrigued by such precise control of plant gene ex-pression by individual light colors, we also analyzedthe promoter regions of B- and R-regulated genes todiscover the underlying transcriptional mechanismsthat possibly regulate them under these conditions.Remarkably, the integration of the cis-element enrich-ment (Table I) with TF expression data (Table II) sug-gests that most of the B-specific metabolic genes arelikely to be regulated by transcriptional modules in-volving ERF, WRKY, MYB, bHLH, ZnF, and bZIP TFs.Among these TFs, we consistently identified some ofthe well-known B-specific bZIP TFs, such as HY5,CPRF5, CPRF6, CPRF7, GBF1, and GBF2 (Jiao et al.,2007), controlling various plant developmental pro-cesses, such as photosynthetic machinery assembly,photopigment production, and chloroplast develop-ment, for enhanced photosynthesis (Toledo-Ortiz et al.,2014). The high enrichment of ABRE-like motifs asso-ciated with bZIP and ABI3 TFs under B prompts us tospeculate a critical role of ABA in the expression oflight-harvesting chlorophyll a/b-binding (LHCB) pro-teins (Liu et al., 2013) in addition to the inhibition of

Table II. List of up-regulated TF genes with fold change among Bversus W treatment

TF Family and Locus Identification (Annotation)a Fold Increase

ERFOs03g0150200 (ethylene-responsive

element binding factor 5 homolog)37.09

Os09g0457900 (A-class organ identitygene APETALA2 [AP2] domaincontaining protein RAP2.6 [fragment]Ethylene-Response AP2/ERF Factor)

3.17

Os04g0257500 (ethylene-responsiveelement binding protein)

2.46

Os06g0592500 (ethylene-responsivetranscriptional coactivator)

2.03

WRKYOs02g0462800 (WRKY TF 42 [TF WRKY02]) 10.69

MYBOs02g0685200 (Myb, DNA-binding

domain containing protein)2.76

Os02g0706400 (Myb, DNA-bindingdomain containing protein)

1.55

bHLHOs03g0741100 (basic helix-loop-helix

dimerization region bHLHdomain-containing protein)

1.89

Os01g0286100 (basic helix-loop-helixdimerization region bHLHdomain-containing protein)

1.70

ZnFOs05g0195200 (Zn finger,

C-x8-C-x5-C-x3-H-typedomain-containing protein)

2.29

bZIPOs12g0156200 (DNA-binding

factor of bZIP class)1.63

aInformation based on RAP database (http://rapdb.dna.affrc.go.jp/).




http://rapdb.dna.affrc.go.jp/


hypocotyl elongation by down-regulating ethylenebiosynthesis as discussed earlier. Similarly, the identi-fication of B-responsive ZnF and WRKY TFs in con-junction with significantly higher terpenoid contents inB than in other colors allowed us to hypothesize thatthese TFs could play a pivotal role in B-mediated sec-ondary metabolite synthesis (Suttipanta et al., 2011).However, the high enrichment of putative EthyleneResponsive Element cis-elements among the up-regulated genes under R (Table I) and up-regulationof corresponding ERF (Os06g0592500: ethylene-responsive transcriptional coactivator) TF suggeststhat the R-specific ethylene signaling is possibly im-portant for the elongation of hypocotyl. Taken together,these identifications indicate positive regulation of thehypocotyl elongation by the phytohormones, such asauxin, ethylene, and GAs, in a combined manner underR rather than under B, which is suppressed throughABA- and ethylene-mediated signaling pathways in thelatter (Fig. 7).

This motif analysis identified various light-specificTFs, which are likely to be activated upon a particularlight exposure. However, some of them could also beregulated in a negative manner by certain light colors.One such example is the detection of cis-elements cor-responding to PIF1, PIF3, PIF4, and PIF7 TFs among theup-regulated genes under B. PIFs are a class of TFs thatis constitutively expressed and positively induces sev-eral skotomorphogenesis and certain photomorpho-genesis processes. Upon exposure to R, the PIFs aredegraded by direct interaction with phys, and thus,skotomorphogenesis effects are repressed (Jiao et al.,2007). In this regard, we have possibly detected the PIFTFs among the up-regulated genes of B caused by theabsence R rather than the induction of PIF caused by B;thus, we are awaiting experimental validation to con-firm the hypothesized light specificity of the TFs.

Generally, plant growth and development are mod-ulated upon the light quality (wavelength), quantity(intensity), and photoperiod (duration of treatment) in

Figure 7. The proposed transcrip-tional regulation model of rice un-der B and R. The lines represent thefindings from our study, which areconsistent with those of previousstudies, and the dotted lines are thehypothetical links proposed in thisstudy, which need to be validatedexperimentally. In B, the biosyn-thesis of ethylene, a growth stimu-lator, is down-regulated by ABA,thus preventing stem growth. ABAalso positively stimulates LHCBexpression in B. Moreover, a highmetabolic activity is observed inseveral pathways, including terpe-noids and phenolics biosynthesis inB. Under R, the up-regulation ofethylene biosynthesis, GA metabo-lism, and cell wall metabolismpositively promotes plant growthand cell expansion comparedwith B.


Lakshmanan et al.



an integrative way. Of these factors, this study mostlyanswers the question of how light quality at high in-tensities and long photoperiods adjusts plant morpho-logical processes through cellular metabolism. It wasaddressed by integrating the transcriptome and meta-bolome data of rice plants with the GEM, thereby deci-phering the key light-specific transcriptional regulatorysignatures of the rice metabolic network. Our systematicapproach rendered it possible to derive a hypotheticalmodel explaining the light-sensing mechanisms and thetranscriptional regulation of relevant metabolic path-ways in R and B, linking to the observed phenotypes(Fig. 7). The insights gained from such analysis couldpossibly help design efficient closed growth systems, suchas greenhouses or vertical indoor farming (Bergstrandand Hultin, 2014; Dong et al., 2014), for cultivating cropplants (i.e. rice and wheat) and improve their agronomictraits by modulating the light-controlled metabolicpathways, which was successfully shown in tomato(Solanum lycopersicum; Liu et al., 2004). Similarly, theunraveling of WRKY and ZnF TFs-mediated secondarymetabolites synthesis provides us with plausible ma-nipulation targets to enhance the desired metabolite ac-cumulation, thus increasing the nutritional capacity ofcrop plants (Luo et al., 2008). Moreover, the potential B-,R- and common light-specific TFs identified in this studyoffer us unprecedented opportunities in the emergingfield of optogenetics; several light-sensitive syntheticgene circuits could be designed using such light-specificTFs for the control of gene expression in microbial andmammalian cells. The use of light-specific TFs in syn-thetic circuits, particularly in mammalian systems, is apreferred choice for a myriad of reasons: precise, spa-tiotemporally controllable, and no potential pharmaco-logic side effects (Bacchus et al., 2013). Taken together,this study represents a significant step toward thecharacterization of light-specific transcriptional regula-tory andmetabolic landscapes in plants by resorting to asystems biology approach. In the future, we believe thatthe suggested integrative framework can be leveraged toelucidate the molecular mechanisms of diverse plantphenotypes from multiple environments.

MATERIALS AND METHODS

Plant Materials and Growth Conditions

Rice (Oryza sativa japonica ‘Ilmi’) was used in this study. Seeds were surfacesterilized as previously reported (Jung et al., 2013) and germinated onMurashigeand Skoog agar plates with 2% (w/v) Suc. Rice seedlings was cultivated for 7 d inan LED Chamber System (SJ I&C Co. Ltd.) under a single LED light for a 16-hphotoperiod per day at a temperature of 25°C6 2°C. The rice plants were grownin LED chambers with 3-W LED devices (Osram) under five different conditions:B (450 nm), G (530 nm), R (660 nm), W (mixture of B, G, and R), and D (no lighttreatment). The PPF at the top of plants was 94mmol m22 s21 in all light, such thatthe photon fluence rate was distributed differently in the four conditions: the Bconditionwith 100%B, the R conditionwith 100%R, theG conditionwith 100%G,and the W condition with equal amounts each of R, G, and B.

For the measurements of several parts of seedlings, rice seedling were cul-tivated and removed after 7 d in the same condition as mentioned above, andtheir images were taken. Lengths and widths of seedling parts were measuredusing ImageJ (http://rsb.info.nih.gov/ij/).

RNA Isolation and Labeling of Probes

Total RNAwas extracted from the aerial parts of rice plants using theQiagenRNeasy Plant Mini Prep Kit. For the synthesis of double-strand cDNAs,RevertAid H Minus First-Strand cDNA Synthesis Kit (Fermentas) was used.Briefly, 1 mL of oligo dT primer (100 mM) and 11 mL (10 mg) of total RNA werecombined and denatured at 70°C for 5 min and then renatured by cooling themixture in ice. First-strand DNA was synthesized by adding 4 mL of 53 First-Strand Buffer, 1 mL of RiboLock RNase Inhibitor, 2 mL of 10 mM deoxynucle-otide triphosphate mix, and 1 mL of RevertAid H Minus M-MuLV ReverseTranase Enzyme and incubating at 42°C for 1 h. The reaction was stopped byheating at 70°C for 10min. To synthesize the second strand, 66.7 mL of nuclease-free water, 5 mL of 103 reaction buffer for DNA Polymerase I (Fermentas), 5 mLof 103 T4DNA Ligase Buffer (Takara), 3 mL of 10 unitsmL21 DNA Polymersase I(Fermentas), 0.2 mL of 5 units mL21 RNase H (Fermentas), and 0.1 mL of 350units mL21 T4 DNA Ligase (Takara) were added to the first-strand reactionmixture, and the reaction was proceeded at 15°C for 2 h. The double-strandedcDNA mixture was purified using the MinElute Reaction Cleanup Kit(QIAGEN). For the synthesis of Cy3-labeled target DNA fragments, 1 mg ofdouble-strand cDNA was mixed with 30 mL (1 optical density) of Cy3-9merprimers (Sigma-Aldrich) and denatured by heating at 98°C for 10 min. Thereaction was further carried out by adding 10 mL of 503 dNTP mix (10 mM),8 mL of deionized water, and 2 mL of Klenow fragment (50 units mL21; Takara)and incubating at 37°C for 2 h. Finally, DNAwas precipitated by centrifugationat 12,000g after adding 11.5 mL of 5 M NaCl and 110 mL of isopropanol. Pre-cipitated samples were rehydrated with 13 mL of water.

Microarray Hybridization, Washing, and Scanning

Tenmilligrams ofDNAwasused formicroarrayhybridization. The collectedsamples were mixed with 19.5 mL of 23 hybridization buffer (RocheNimbleGen, Inc.) and finalized to 39 mL with deionized water. Hybridizationwas performed with anMAUI Chamber (Biomicro) at 42°C for 16 to 18 h. Afterthe hybridization, the microarray was washed three times and dried in a cen-trifuge for 1 min at 500g. Hybridized microarray slides were scanned using theGenePix Scanner 4000B (Axon) preset with a 5-mm resolution for Cy3 signal.Scanned signals were then digitized and analyzed by Nimblescan (RocheNimbleGen, Inc.). The grid was aligned to the image with a chip design file,NDF file. The alignment was checked by ensuring that the grid’s corners areoverlaid on the images corners. This was further checked by uniformity scoresin the program. The analysis was performed in a two-part process. Pair report(.pair) files were generated, in which sequence, probe, and signal intensity in-formation for Cy3 channel were collected. Data-based background subtractionusing a local background estimator was performed to improve fold changeestimates on arrays with high-background signal. The data were normalizedand processed with cubic spline normalization using quantiles to adjust signalvariations between chips (Workman et al., 2002). A probe-level summarizationby Robust Multi-Chip Analysis using a median polish algorithm implementedin Nimblescan was used to produce call files to improve the sensitivity andreproducibility of microarray results (Irizarry et al., 2003).

Measurement of Carotenoid Contents

Carotenoids were extracted and measured using HPLC as described pre-viously (Kim et al., 2012a). Briefly, the carotenoids were extracted from ricesamples (0.12 g) by adding 3mL of ethanol containing 0.1% (w/v) ascorbic acid,vortex mixing for 20 s, and placing in a water bath at 85°C for 5 min. The ca-rotenoid extract was saponified with potassium hydroxide (120 mL at 80%[w/v]) in awater bath at 85°C for 10min. After saponification, the sampleswereimmediately placed on ice, and cold deionized water (1.5 mL) was added. Toseparate the layers, carotenoids were extracted twice with hexane (1.5 mL) bycentrifugation at 1,200g. Aliquots of the extracts were dried under a stream ofnitrogen and again dissolved in 50:50 (v/v) dichloromethane:methanol beforeHPLC analysis. The carotenoids were then separated in a C30 YMC Column(2503 4.6 mm, 3mm; YMCCo.) by anAgilent 1100 HPLC Instrument equippedwith a photodiode array detector. Chromatograms were generated at 450 nm.Solvent A consisted of methanol:water (92:8, v/v) with 10 mM ammonium ac-etate; solvent B consisted of 100% methyl tert-butyl ether. Gradient elutionwas performed at 1 mL min21 under the following conditions: 0 min, 90% Aand 10% B; 20 min, 83% A and 17% B; 29 min, 75% A and 25% B; 35 min, 30%A and 70% B; 40 min, 30% A and 70% B; 42 min, 25% A and 75% B; 45 min, 90%A and 10% B; and 55 min, 90% A and 10% B. Carotenoid standards were




http://rsb.info.nih.gov/ij/


purchased from CaroteNature. Calibration curves were drawn for quantifica-tion by plotting four concentrations of the carotenoid standards.

Measurement of Total Phenolic Contents

The phenols in rice leaf were extracted by the ultrasound-assisted method(Kim et al., 2006a). The powdered samples (0.1 g)were extracted twice with 80%(v/v)methanol (2mL) bywater-based sonication for 20min filledwith nitrogengas to provide an oxygen-free environment. Supernatants were collected bycentrifuging at 13,000g for 20 min at 4°C and diluted to a final volume of 4 mLwith distilled water. The crude extracts were filtered using 0.45 mm of poly-tetrafluoroethylene. The total phenolic contents of rice leaf extracts were de-termined by spectrophotometric method using Folin-Ciocalteu’s phenolreagent (Singleton and Rossi, 1965) with some minor modifications. Dilutedextracts (0.2 mL) were mixedwith 2.6 mL of distilled water, and a reagent blankusing 2.8mL of distilled water was prepared. Folin-Ciocalteu’s reagent (0.2 mL)was added to the mixtures. After 6 min, 2 mL of 7% Na2CO3 solution wasadded, and the mixtures were allowed to stand for 1 h at 23°C. The absorbancewas read against the prepared blank at 750 nm. Total phenolic concentrationswere expressed as micrograms of gallic acid equivalents milligram21 rice leaf.

Metabolic Network Reconstruction

The GEM of rice ‘Nipponbare’ was reconstructed by expanding the previ-ously published central model (Lakshmanan et al., 2013) using the genomesequence (International Rice Genome Sequencing Project, 2005) and the infor-mation collected from various biological and genomic databases on the basis ofthe established procedure. First, an initial draft consensus model was con-structed by compiling the annotated metabolic genes and their correspondingbiochemical reactions from the central model, RiceCyc (Dharmawardhanaet al., 2013), and KEGG (Kanehisa and Goto, 2000). Second, each reaction in thedraft network was corrected for reaction directionalities based on informationfrom BRENDA (Schomburg et al., 2002) and MetaCyc (Caspi et al., 2012), ele-mental and charge balanced, and mapped with appropriate genes to deviseproper GPR relationships. Charge balancing was done for each reaction basedon their chemical formula and charge using the corresponding acid dissociationconstant value for a pH of 7.2 (http://www.chemaxon.com/product/pka.html). Third, each pathway in the draft network was manually curated usingavailable literature sources for establishing the presence of particular enzymesand associated reactions in rice, because the list obtained from RiceCyc con-tained several reactions from other plants and a large number of nonplant re-actions as well. The reactions that did not have sufficient literature evidencewere removed from the metabolic network. Additionally, few rice-specific re-actions, such as oryzanol and oryzalaexin biosynthesis, from published articlesas the draft network did not contain these pathways. Literature sources wereagain used to localize the individual reactions in draft network to appropriatesubcellular compartments. If the subcellular localizations of certain reactionsare not available in published articles, then the Plant-mPLoc (Chou and Shen,2010) localization prediction software was used to predict putative cellularcompartment. After each reaction in the draft network was assigned to a certainsubcellular compartment, the intracellular metabolite transport reactions werethen added based on the evidence found in the literature and TransportDB data-base (Ren et al., 2004). The connectivity of the draft network was then checkedusing the GapFind algorithm to find the gaps (Satish Kumar et al., 2007). Theidentifiedmissing linkswere filled by either adding reactions from other plants toclose the knowledge gaps or addition of sink reactions to allow the material ex-change between the cell and its surrounding environment. Here, it should benoted that we added unique reactions during gap filling only if sufficient litera-ture evidence was available to substantiate the presence of the enzymes, or oth-erwise, the gap was left unclosed. Furthermore, to confirm the presence of addedreactions in the ricemetabolic network,wealsoperformed aBLASTp search in theNational Center for Biotechnology Information database for the enzymes addedduring gap filling using their amino acid sequences collected from various otherorganisms against the nonredundant protein sequences of the rice ‘Nipponbare’genome. Finally, apart from the manual quality control steps for network con-nectivity, modeling-based gap filling was also performed using constraints-basedflux analysis, adding few reactions essential for in silico growth.

Analysis of Differentially Expressed Genes

Differentially expressed genes between any two conditions (i.e. B-W, R-W,D-W, and G-W) were identified using the limma package in R computing

environment, which is based on a modified t statistic (Wettenhall and Smyth,2004). Subsequently, the P values were adjusted for multiple hypotheses testingusing the method by Benjamini and Hochberg (1995), and the false discoveryrate was controlled at 5%.

Analysis of Differentially Expressed Metabolic Pathways

To analyze the differential expression of individual metabolic pathways iniOS2164, we first mapped the genes from microarray data with the rice model.Subsequently, the differential expression of metabolic pathways was estimatedusing the method previously described (Hu et al., 2013). Briefly, the expressionchange of each gene in a particular light treatment relative to W was first es-timated as follows:

DEia ¼ log2x

ia 2 log2y

Wa

wherexia is the expression of gene a in i light treatment other thanW (i.e. R, G, B,or D), and log2y

Wa is the expression of the corresponding gene in W. Next, the

Wilcoxon signed rank test of ΔEi for all genes within a metabolic pathway ini light treatment was calculated to determine the significance of up- or down-regulation of the particular pathway compared with W.

Identification of Reporter Metabolites

The reporter metabolites in D versus W, R versus W, and B versus W wereidentified based on previous publication (Patil and Nielsen, 2005). Briefly, eachmetabolite in the iOS2164 was scored based on the P values of neighboringdifferentially expressed enzymes. For this purpose, each enzyme in the modelwas assigned with a P value based on the corresponding genes differentialexpression. In the case of isozymes or enzyme complexes, the lowest P value ofthe isozyme or enzyme subunit was used. Then, the P values are converted intoZ scores for each enzyme i using inverse normal cumulative distribution asfollows:

Zi ¼ u2 1ð12 piÞAfter each enzyme isZ scored, then theZ score for eachmetabolite (Zmetabolite) iniOS2164 was calculated using the aggregated Z scores of k neighboring en-zymes as follows:

Zmetabolite ¼ 1k∑Zi

The metabolite Z scores are corrected for background distribution by sub-tracting mean (mk) and dividing by SD (sk) from the original Z score, Zmetabolite:

Zcorrectedmetabolite ¼

�Zmetabolite 2mk

sk

�

Finally, the corrected Z scores are transformed into P values using normal cu-mulative distribution, andmetabolites with P values less than 0.05 are classifiedas reporter metabolites. In this study, the COBRA toolbox (Schellenberger et al.,2011) was used to identify the reporter metabolites from rice GEM.

Motif Detection and Identification of Putative TFs

The promoter sequences (21,000 and +200 nucleotides) relative to the ex-perimentally verified transcription start site for the up- and down-regulatedgenes that are neighbors to reporter metabolites were extracted from our in-house rice promoter sequence database. Known and unique promoter motifswere detected using the Dragon Motif Builder program (Huang et al., 2005). Ateach time, 30 motifs were detected with a length of 8 to 10 nucleotides at athreshold value of 0.875. Motifs occurrence in over 50% of the sequences, and athreshold e-value of #1023 was considered as statistically overrepresented.Different motif classes were identified using several plant TF binding data-bases, such as TRANSFAC (Matys et al., 2003) and Osiris (Morris et al., 2008).The total enrichment score was calculated by adding up the percentage of oc-currences of all motifs belonging to the same TF family.

Modeling of Light-Using Metabolic Reactions

The light-using metabolic reactions are modeled in a wavelength-specificmanner using a method described earlier (Chang et al., 2011) with slight


Lakshmanan et al.


http://www.chemaxon.com/product/pka.html

http://www.chemaxon.com/product/pka.html


modifications. In this method, the photosynthetic active light spectrum (i.e.400–700 nm) was divided into 15 parts, each denoting a cumulative region.For example, the first part denoted by 410 nm covers the region from 400 to420 nm, the second part denoted by 430 nm covers from 420 to 440 nm, and soon. Such breakdown of active spectrum allows us to accurately model theeffective range of photon wavelengths capable of driving the associated re-action in rice network. Next, the prism reactions denoting the photon contentequivalent of input light source were reconstructed based on the methodsuggested by a previous publication (Chang et al., 2011). It involves digiti-zation of light intensity data for each light source and the integration of underthe curve in the above-mentioned 15 parts of the photosynthetic active-lightspectrum. Such reactions denote the distribution of available photons in anyof the 15 parts of light spectra. Mathematically, it can be represented asfollows:

PhotonVis → C420400 photon410 þ C440

420 photon430 þ C460440 photon450

þ C480460 photon470 þ C500

480 photon490þ C520500 photon510

þ C540520 photon530þ C560


þ C600580 photon590þ C620


þ C660640 photon650 þ C680


where C is the coefficient of photon available in that particular range that iscalculated by integration of total area under the curve as described previously(Chang et al., 2011). The prism reactions of R, G, and B LED light inputs weremodeled as described previously using the manufacturer’s spectral irradiancegraphs. After the prism reactions are modeled, we then drafted photon absor-bance reactions, which will provide the actual metabolic reaction with usablephotons. For this purpose, we analyzed the absorbance spectrum of PSI (Kargulet al., 2003) and PSII (Nield et al., 2000), and the absorptivity graphs were used.For example, the photon absorbance reaction at 410 nm for PSI can bewritten asfollows:

photon410 → x photonPSI þ ð12 xÞ photonDrain

where x is the ratio of incident photon absorbed by PSI at 410 nm. In such away,we modeled PSI and PSII. It should be noted that the protochlorophyllide ox-idoreductase reaction was modeled in a slightly different manner, becauseno absorbance spectra were available for the same. To model the proto-chlorophyllide oxidoreductase reaction, we used the action spectra, which de-note themaximum activity of the corresponding enzyme throughout the visiblespectra. Accordingly, the reaction was written for all 15 regions between 400and 700 nm, and the activity at each level was multiplied with the wholereaction, reflecting the possible differences in reaction conversion acrosswavelengths.

Constraint-Based Flux Analysis

We implemented constraints-based flux analysis to simulate the rice cellgrowth in seed and leaf by manipulating the constraints. The biomass reactionwasmaximized to obtain optimal growth rate as described elsewhere (Lee et al.,2006; Orth et al., 2010). Mathematically, the optimization problem (i.e. maxi-mization of biomass subjected to stoichiometric and capacity constraints) can beformulated as follows:

max Z ¼ ∑jcjvj

s:t: ∑jSijvj ¼ 0 ∀ metabolite i

vminj # vj # vmax

j ∀ reaction j

where Sij refers to the stoichiometric coefficient of metabolite i involved in re-action j, vj denotes the flux or specific rate of metabolic reaction j, vmin

j and vmaxj

represent the lower and upper limits on the flux of reaction j, respectively, andZcorresponds to the cellular objective as a linear function of all of the metabolicreactions where the relative weights are determined by the coefficient cj . In thisstudy, the constraints-based flux analysis problems were solved using COBRAtoolbox (Schellenberger et al., 2011).

To simulate the seed-derived rice cell growth on either Suc or Glc, we firstapplied the regulatory constraints to the network using the Boolean rules asdescribed previously (Lakshmanan et al., 2013). The proteome data from Rice

Proteome Database (Komatsu, 2005) were also used to model the functionallyactive reactions. The carbon source uptake rate was constrained at the ex-perimentally measured values (Lakshmanan et al., 2013). For the aerobicsimulations, the oxygen exchange reaction was also constrained at3.312 mmol g21 DCW d21 based on the literature (Wen and Zhong, 1995). Tosimulate the photorespiring rice leaf cells growth, a similar procedure wasfollowed by first constraining the fluxes of D reactions to zero using Booleanregulatory rules and proteome data. Subsequently, the leaf cell growth un-der individual light colors was simulated by maximizing the leaf biomasswhile constraining the corresponding wavelength’s photon uptake at100 mmol g21 DCW h21. To simulate the photorespiratory behavior at dif-ferent carboxylation to oxygenation ratios (VC:VO), the ratio of flux throughRubisco was varied between 1 and 10 as described previously (Lakshmananet al., 2013). Considering the large size of iOS2164, a set of reactions be-longing to folates metabolism, glycolysis/gluconeogenesis, nitrogen me-tabolism, and sulfate metabolism was also constrained at zero in leaf andcoleoptile simulations to avoid free cofactor recycling. The complete sets ofreactions that are inactivated during both the simulations are listed inSupplemental Data Set S2.

Biomass Composition

The two biomass equations, one representing the germinating cells of riceseeds and the other representing the photorespiring cells of rice leaves, used inconstraints-based flux analysis simulations are adopted from the centralmodel (Lakshmanan et al., 2013) with slight modifications by accounting fornucleotides and fatty acids composition. Lipid compositions were obtainedfrom previous publications (Brown and Beevers, 1987). The overall DNA andRNA composition was also obtained from the literature (Edwards et al.,2012). The individual weights of nucleotides in the DNA and RNA werecalculated based on the reported G + C content of 69.4% (Goff et al., 2002).Detailed information on biomass composition calculations could be found inSupplemental Data Set S2.

Random Sampling

Artificial centering hit and run Monte Carlo sampling (Schellenberger andPalsson, 2009) was used to uniformly sample the metabolic flux solution spaceof rice leafs under different light treatments with appropriate flux constraints.In all treatments, a PPF of 94 mmol g21 DCW h21 was constrained in each of thesimulations, and the solution space was sampled with 100,000 randomly dis-tributed points for 10,000 iterations. It should be noted that the internal reac-tions were constrained in each simulation based on the correspondingtranscriptome data by the E-Flux approach (Colijn et al., 2009). Additionally, wealso applied the regulatory constraints to the network using the Boolean rules asdescribed previously (Lakshmanan et al., 2013) and used the proteome datafrom the Rice Proteome Database (http://gene64.dna.affrc.go.jp/RPD/) tomodel the functionally active reactions (for reactions that are inactivated duringsimulations, see Supplemental Data Set S2). In this study, COBRA toolbox(Schellenberger et al., 2011) was used to implement the random fluxsampling.

After the metabolic network is sampled in each light treatment, the fluxmagnitudes of all of the reactions in each condition were first normalized bydividing the flux value of each sample point by the absolute sum of all fluxesbased on an approach proposed earlier (Nam et al., 2014). The differences inscaled flux samples between any pair of conditions were then quantified using aZ score approach as described previously (Mo et al., 2009). First, two randomflux vectors, one from each sample, were chosen, and the fold change is cal-culated as follows:

vj;fd ¼ �vj;c1 2 vj;c2

�This approachwas repeated 10,000 times to the flux difference sample, vj,fd, with10,000 points. Second, the sample mean mj and SD sj were computed to calculatethe Z score as follows:

Zj ¼mj

ðsj� ffiffiffi

np Þ

The absolute Z scores were then translated to P values using normal cu-mulative distribution function, and the reactions with P values less than0.05 were classified as statistically different between the two conditionsanalyzed.






http://gene64.dna.affrc.go.jp/RPD/



Supplemental Data

The following supplemental materials are available.

Supplemental Figure S1. Morphological phenotype comparison of riceseedlings grown under different light treatments and in darkness.

Supplemental Figure S2. Reconstruction procedure of iOS2164.

Supplemental Figure S3. Comparison of iOS2164 with previous ricegenome-scale model.

Supplemental Figure S4. PCA of all genes’ expressions includingnonmetabolic.

Supplemental Figure S5. Enrichment scores different GO:biological process.

Supplemental Figure S6. Visualization of individual metabolic pathways.

Supplemental Figure S7. The rice central metabolic map showing the foldchange in metabolic fluxes obtained through random sampling beforeapplying the constraints based on E-Flux approach.

Supplemental Figure S8. Photosynthesis performance of rice leaves underB and R colors.

Supplemental Data Set S1. Supplemental text detailing the GEM recon-struction methods, comparisons of this model with previous rice models,and results of photosynthetic performance of rice leaves under R and B.

Supplemental Data Set S2. Spreadsheet file containing the reconstructedrice GEM, detailed information on the biomass composition of rice seedsand straw, a list of references used to reconstruct the rice metabolicnetwork, a list of possible new annotations suggested during reconstruc-tion, a list of reactions inactivated during simulations to avoid futilecycles, and a list of reactions that are differentially expressed betweenB and R.

Supplemental Data Set S3. Spreadsheet file containing a list of reportermetabolites, differentially expressed genes, and motif enrichmentscores.

Supplemental Data Set S4. Systems Biology Markup Language file of thereconstructed rice GEM.

Supplemental Data Set S5. High-resolution network diagram of iOS2164in pdf format.

ACKNOWLEDGMENTS

We thank Drs. Chun Yue Maurice Cheung, Sarantos Kyriakopolous, andIn-Cheol Jang for insightful comments and useful discussions and undergrad-uate final year project students Zhaoyang Zhang and Jerwin Alliguy for help inassembling the draft reconstruction and manually curating the network,respectively.

Received September 9, 2015; accepted October 7, 2015; published October 9,2015.

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unraveling the light-speciﬁc metabolic and regulatory ... · well as in the dark. concurrently,...

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