Metabotyping of rice (Oryza sativa L.) for understanding its intrinsic physiology and potential eating quality

Accepted Manuscript Metabotyping of rice (Oryza sativa L.) for understanding its intrinsic physiology and potential eating quality

Eun-Hye Song, Jaesik Jeong, Clara Yongjoo Park, Han-Yong Kim, Eun-Hee Kim, Eunjung Bang, Young-Shick Hong PII: DOI: Reference:

S0963-9969(18)30359-4 doi:10.1016/j.foodres.2018.05.003 FRIN 7592

To appear in:

Food Research International

Received date: Revised date: Accepted date:

19 February 2018 2 May 2018 3 May 2018

Please cite this article as: Eun-Hye Song, Jaesik Jeong, Clara Yongjoo Park, Han-Yong Kim, Eun-Hee Kim, Eunjung Bang, Young-Shick Hong , Metabotyping of rice (Oryza sativa L.) for understanding its intrinsic physiology and potential eating quality. The address for the corresponding author was captured as affiliation for all authors. Please check if appropriate. Frin(2017), doi:10.1016/j.foodres.2018.05.003

This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.

ACCEPTED MANUSCRIPT Metabotyping of rice (Oryza sativa L.) for understanding its intrinsic physiology and potential eating quality Eun-Hye Songa,1, Jaesik Jeongb,1, Clara Yongjoo Parka, Han-Yong Kim c , Eun-Hee Kim d, Eunjung Bange,* , Young-Shick Honga,* a

Division of Food and Nutrition, Chonnam National University, Yongbong -ro, Buk-gu, Gwangju 500-

757, Republic of Korea

PT

b

Department of Statistics, Chonnam National University, Yongbong-ro, Buk-gu, Gwangju 500-757,

c

RI

Republic of Korea

Department of Applied Plant Science, Chonnam National University, Yongbong-ro, Buk-gu, Gwangju

SC

500-757, Republic of Korea d

Protein Structure Group, Korea Basic Science Institute, Cheongwon -Gu, Cheongju-Si, Chungbuk

NU

363-883, Republic of Korea e

AC C

EP T

ED

MA

Western Seoul Center, Korea Basic Science Institute, Seoul 136-701, Republic of Korea

Abstract Rice (Oryza sativa L.), the major staple food in many countries, has genetic diversity adapted to different environmental conditions. However, metabolic traits about diverse rice 1

ACCEPTED MANUSCRIPT plants are rarely discovered. In the present study, rice leaves and grains were collected at whole growth stages from late (LMC) and early (EMC) maturing cultivars. Metabolic dependences of rice plants on both growth and cultivar were investigated in their leaves and grains through NMR-based metabolomics approach. Rice leaf metabolome were differently regulated between two rice cultivars, thereby affecting variations of rice grain metabolome.

PT

Sucrose levels in leaves of EMC were markedly decreased compared to those in LMC, and more accumulations of sucrose, amino acids and free fatty acids were found in grains of

RI

EMC. These distinct metabolisms between EMC and LMC rice cultivars were associated

SC

with temperature during their growing seasons and might affect the eating quality of rice. The current study highlights that metabolomic approach of rice leaves and grains could lead to

NU

better understanding of the relationship between their distinct metabolisms and

MA

environmental conditions, and provide novel insights to metabolic qualities of rice grains.

AC C

EP T

ED

Keywords: rice, leaf, grain, cultivars, NMR, metabolomics

1. Introduction Rice (Oryza sativa L.) is a major staple crop in agriculture for human nutrition worldwide. It is 2

ACCEPTED MANUSCRIPT a staple food for more than half of the world’s population (Fitzgerald, McCouch, & Hall, 2009). The main components in rice grains such as starch, protein and lipid are closely associated with physicochemical characteristics for determining the quality of cooking and eating rice (Juliano, Bautista, Lugay, & Reyes, 1964; Zhou, Robards, Helliwell, & Blanchard, 2002). Therefore, most research studies on rice have focused on physicochemical properties of

PT

starch in rice grains to determine the rice quality. Small components of rice including amino acids, organic acids and simple sugars also contribute to its quality. In response to

RI

environments during growth of rice plant, these small components or metabolites in rice

SC

leaves and grains are first regulated, followed by regulations of main components such as starch, protein and lipids. Recently, Hu et al. (2014) have reported metabolic variations in

NU

mature rice seeds between Oryza sativa japonica and indica cultivars by non-targeted metabolomics and correlations between metabolic phenotype and geographic origin of rice

MA

seeds. Dependences of intra- or inter-cellular metabolites in rice (Oryza sativa L.) grain on rice cultivars through 1H high-resolution magic angle spinning (HR-MAS) NMR-based

ED

metabolomics approach are also reported (Song et al., 2016). Metabolomics provides comprehensive and holistic information about metabolites related to plant physiology (Kim,

EP T

Choi, & Verpoorte, 2011). It is thus useful for exploring association of global metabolite variations with rice qualities. Investigation of diverse global metabolite composition according

AC C

to different conditions is important to provide new insights into their association with rice grain quality and improve our understanding of rice physiology. Rice subspecies can further be divided into early (EMC) and late (LMC) maturing rice cultivars. Phenotypes of these cultivars are characterized according to the heading date related to harvesting point, in which their genetic diversity can be determined through quantitative trait loci (QTL) analysis (Endo-Higashi, & Izawa, 2011). The physiological status of rice plants can also be affected by different environmental conditions such as temperature 3

ACCEPTED MANUSCRIPT and photoperiod during their growth (Vegrara, 1976). Earlier heading will lead to sooner harvesting of rice. This is an important point in rice breeding to develop new rice cultivars due to differences in regional and seasonal adaptation of each rice cultivar (Wassmann et al., 2009). Various issues including climates, cultivars, regions, and seasons can affect intrinsic metabolism of rice plants and lead to distinct metabolite compositions of rice grains.

PT

In the present study, we cultivated two different cultivars (EMC and LMC) of rice to monitor comprehensive metabolite changes during their growth and investigate metabolic

RI

consequences from the interaction between rice plant and environmental conditions through 1

AC C

EP T

ED

MA

NU

SC

H-NMR based metabolomics approach.

4

ACCEPTED MANUSCRIPT 2. Materials and Methods 2.1. Samples of rice leaves and grains Two rice cultivars, early maturing rice cultivar (EMC, Oryza sativa L. Jomyeong) and late maturing cultivar (LMC, Oryza sativa L. Saenuri), were identified according to harvest

PT

season. They were cultivated in a rice paddy field at Chonnam National University (Gwangju, South Korea) in 2015 and used in the present study. Rice plants were planted 25th April for

RI

EMC and 29th May for LMC. Rice plants including leaves and grains were randomly chosen

SC

at five different growing stages: of panicle formation (P), heading (H), milk ripe (M), dough (D), and full ripe (F) stages. The sampling or harvesting date for EMC was 23rd June, 10th

NU

July, 28th July, 4th August and 18th August, and for LMC was 28th July, 18th August, 15th September, 22nd September and 8th October. Rice plants were collected at 10 different

MA

parcels, separated into rice leaves and grains, and immediately stored at –80 oC until analysis. Rice leaves and grains were individually ground into fine powder using a mortar

EP T

ED

and a pestle with liquid nitrogen and freeze-dried for 48 h.

2.2. 1H NMR analysis of rice leaf extracts In general, 1 H solution NMR is involved in comprehensive metabolite profiling of plant leaf

AC C

extracts, as well described by Kim et al. (2010). Therefore, 1H solution NMR was used for metabolite profiling of rice plant leaves in the current study. However, metabolites in rice grains were analyzed by 1H HR-MAS NMR due to poor detection of fatty acids, one of main metabolites in rice grains, in their extracts, which have been described in detail in our previous study (Song et al., 2016). Ten milligrams of freeze-dried rice leaves were dissolved in a mixture of methanol-d4 (CD3OD, 490 μL) and deuterium water (D2O, 210 μL) containing 5

ACCEPTED MANUSCRIPT 0.05% (wt) 3-(trimethylsilyl) [2,2,3,3-2 H4] propionate (TSP) in a 1.5 mL Eppendorf tube. The mixture was sonicated for 20 min at 4 oC to extract rice leaf metabolites and then centrifuged at 13,000 rpm for 15 min at 4 oC. The supernatant of each rice leaf extract (550 μL) was transferred into a 5 mm NMR tube. D 2O in the mixture provided a field frequency lock and TSP was used as a chemical shift reference ( 1 H, 0.00). 1 H NMR spectra were acquired on a

PT

Bruker Avance 700 spectrometer (Bruker Biospin, Rheinstetten, Germany) operated at a frequency of 700.39 MHz 1H and a temperature of 300 K using a cryogenic triple-resonance

RI

probe and a Bruker automatic injector. A one-dimensional (1D) nuclear overhauser effect

representative samples

was

SC

spectroscopy (NOESY) was used to suppress residual water signal. Signal assignment for facilitated by two-dimensional (2D)

total correlation

NU

spectroscopy (TOCSY) and heteronuclear single-quantum correlation (HSQC), and spiking

MA

experiments.

ED

2.3. 1H HR-MAS NMR spectroscopic analysis of rice grain powder Ten milligrams of ground rice sample were inserted into a disposable insert kit BL4 (Bruker

EP T

Biospin, Rheinstetten, Germany) placed in a 4mm ZrO 2 rotor. D2O containing 0.05% (wt) TSP acted as an internal reference was mixed with rice grain samples for a field-frequency lock during measurement of NMR spectra. Next, the rotor was covered with a Kel-F rotor cap.

AC C

Details on 1 H high resolution-magic angle spinning (HR-MAS) NMR experiment have been described in a previous study (Song et al., 2016). 1

H HR-MAS NMR spectra of the rice grain powder were acquired on a Bruker AV-700 NMR

spectrometer (Rheinstetten, Germany) operating at 700.13 MHz for 1 H and a temperature of 300 K, equipped with a HR-MAS probe at a spin rate of 6000 Hz. A 1D Carr-PurcellMeiboom-Gill (CPMG) pulse sequence with water presaturation was applied for acquisition 6

ACCEPTED MANUSCRIPT of HR-MAS spectra. For each sample, 128 transients were collected into 32 K data points using a spectrum width of 14097.7 Hz with spin-spin relaxation delay, 2n, of 252 ms. The recycle delay was 4 s. 1H HR-MAS NMR spectra were manually corrected for phase and baseline distortions using Topspin software 3.2 (Bruker Analytik, Rheinstetten, Germany). For assignment or identification of rice grain metabolites, two-dimensional (2D) total

PT

correlation spectroscopy (TOCSY) and heteronuclear single quantum correlation (HSQC) NMR spectra were acquired for selected samples. 1D statistical TOCSY (STOCSY) (Cloarec

SC

RI

et al., 2005a) was also used for assignment of rice metabolite.

NU

2.4. 1H NMR data processing and multivariate statistical analysis Phases and baselines of all 1H NMR and 1 H HR-MAS NMR spectra for rice leaves and

MA

grains were corrected manually and then converted to ASCII format. ASCII format files were then imported into MATLAB R2010b (The Mathworks, Inc., Natick, MA, USA). All spectra

ED

were aligned by the icoshift method (Savorani, Tomasi, & Engelsen, 2010) and normalized using probabilistic quotient normalization method (Dieterle, Ross, & Senn, 2006). Regions

EP T

corresponding to water (δ 4.7-4.9 ppm), TSP (δ -5.0-0.5 ppm), and methanol (δ 3.28-3.34 ppm) were removed prior to normalization and spectrum alignment. The resulting data were

AC C

imported into SIMCA-P version 12.0 (Umetrics, Umea, Sweden) for multivariate statistical analysis. Principal component analysis (PCA) was performed to examine the intrinsic variation in the data set. Orthogonal projection on latent structure discriminant analysis (OPLS-DA) (Bylesi et al., 2006) was then used to extract maximum information on discriminant compounds for the data. To identify variables or metabolites responsible for the discrimination between two groups the in the model, OPLS-DA coefficient or loading plot was obtained and back transformed as described by Cloarec et al. (2005b). The quality of 7

ACCEPTED MANUSCRIPT the OPLS-DA model was described by R 2X and Q2 values. R2X was defined as the proportion of variance in the data explained by the models and indicated goodness of fit. Q2 was defined as the proportion of variance in the data predictable by the model and indicated predictability.

PT

2.5. Statistical analysis

RI

All statistic data analyses were conducted using IBM SPSS Statistical software (IBM Corp,

SC

Version. 21). Duncan's multiple range test of analysis of variance (ANOVA) was used for data analysis of metabolites in rice leaves and grains for different growth stages of the same

NU

cultivar. Paired student's t-test was used to determine the statistical significance of integral area of each rice metabolites according to two different rice cultivars at each same growth

ED

MA

stage. Statistical significance was considered at P < 0.05.

2.6. Chemicals

EP T

Deuterium oxide (D2O, 99.9% 2H) containing 0.05% (wt) 3-(trimethylsilyl) [2,2,3,3-2 H4] propionate (TSP) and methanol-d4 (CD3OD, 99.8% 4H) were purchased from Sigma (St.

AC C

Louis, MO, USA).

8

ACCEPTED MANUSCRIPT 3. Results

3.1. Metabolites of rice leaves and grains identified by 1H NMR analysis Representative 1 H solution NMR and 1H HR-MAS NMR spectra of rice leaves and rice grains

PT

obtained from two different rice cultivars at each growth stage are shown in Figs. S1 and S2, respectively. Rice leaves consisted of saturated fatty acids (SFAs), unsaturated fatty acids

RI

(UFAs), amino acids (valine, isoleucine, leucine, threonine, alanine, glutamate, glutamine,

SC

tyrosine, γ-aminobutyrate (GABA), and phenylalanine), carbohydrates (glucose and sucrose), organic acids (acetate and formate), choline, ethanol, guanosine, ethanolamine, and

NU

trigonelline. In rice grains, SFAs, UFAs, amino acids (valine, isoleucine, leucine, glutamate, glutamine, asparagine and GABA), carbohydrates (glucose and sucrose), organic acids

MA

(acetate, succinate, and fumarate), uracil, choline, and ethanolamine were identified by 1 H HR-MAS NMR spectroscopy. These metabolites in rice grains have also been identified in

EP T

ED

our previous study (Song et al., 2016).

3.2.Metabolic differentiation of rice by multivariate statistical analysis

AC C

Whole 1H NMR spectra of rice leaves and rice grains were applied to multivariate statistical analysis such as PCA and OPLS-DA. All rice leaf samples from the two different rice cultivars (LMC and EMC) collected at each growing stage (panicle formation stage (P) to full ripe stage (F)) were plotted in PCA (Fig. 1A). OPLS-DA models were further applied to LMC and EMC rice leaves as shown in Fig. 1B and 1C, respectively. Rice grain samples collected at milk ripe (M), dough (D), and full ripe (F) stages were also plotted in PCA (Fig. 1D). Indeed, OPLS-DA models with 1H HR-MAS NMR spectra of rice grains from LMC and EMC 9

ACCEPTED MANUSCRIPT cultivars were also generated (Fig. 1E, F). Two samples collected at panicle formation stage of LMC rice leaves and at dough stage of EMC rice grains were markedly outlied due to poor spectrum shimming during the acquisition of 1H NMR spectrum (Fig. 1B, D). Therefore, they were excluded from further analysis. As shown in Fig. 1B and 1C, clear metabolic differentiations of rice leaves until milk ripe stage were observed both in LMC and EMC rice

PT

cultivars, demonstrating marked changes of the rice leaf metabolites at early growing stage. Different patterns of metabolic evolutions in rice leaves between LMC and EMC rice cultivars

RI

were observed when rice plants grew from panicle formation stage to milk ripe stage (Fig.

SC

1B, C). Moreover, the dependence of rice grain metabolites on rice cultivars was more obvious at full ripe stage than that at other growing stages (Fig. 1D). Such growth-

NU

dependence of rice grain metabolites was found in both LMC and EMC rice cultivars (Fig.

MA

1E, F).

ED

3.3. Identification of metabolite changes according to growing stage of rice leaves Fig. 2 shows pairwise OPLS-DA models for identification of rice leaf metabolites dependent

EP T

on growing stage, typically at panicle formation (P), milk ripe (M) stage and full ripe (F) stage in LMC (Fig. 2A – 2D) and EMC (Fig. 2E – 4H) rice cultivars. All models were validated with high fitness and strong predictability as indicated by R2X and Q2 values, respectively. In the

AC C

OPLS-DA loading plot of rice growing stages, higher levels of sucrose and threonine were observed in rice leaves at milk ripe stage compared to panicle formation stage of LMC rice cultivars (Fig. 2B), whereas levels of UFAs, valine, γ-aminobutyrate (GABA), choline, phenylalanine and trigonelline in rice leaves were lowered at milk ripe stage. Interestingly, only a few metabolites in rice leaves were different between milk ripe and full ripe stages of LMC rice cultivar. That is, levels of sucrose and UFAs were decreased at full ripe stage 10

ACCEPTED MANUSCRIPT compared to those at milk ripe stage of LMC cultivar (Fig. 2D). In rice leaves between panicle formation and milk ripe stages of the EMC rice cultivar, sucrose, GABA, and trigonelline levels were decreased at milk ripe stage whereas threonine levels were increased (Fig. 2F). GABA, UFAs, valine, leucine, isoleucine, and phenylalanine levels were also decreased at full ripe stage of EMC rice cultivars compared to those at milk ripe stage,

RI

PT

while sucrose and threonine levels were increased at full ripe stage (Fig. 2H).

SC

3.4. Dependence of rice leaf metabolite on rice cultivar

Differences in rice leaf metabolites between late (LMC) and early (EMC) rice cultivars at

NU

panicle (P), milk (M) ripe and full ripe (F) stages were identified (Fig. 3). These two rice cultivars were clearly differentiated at growing stages of panicle, milk ripe, and full ripe,

MA

which were shown in OPLS-DA score plots with good fitness and high predictability (Fig. 3A, C and E). At panicle formation stage, levels of sucrose, glucose, and tyrosine were higher in

ED

leaves of EMC rice cultivar compared to those in LMC rice cultivar (Fig. 3B). At milk ripe stage, levels of acetate, valine, isoleucine, leucine, phenylalanine, GABA, and UFAs in

EP T

leaves of EMC rice cultivar were higher than those in LMC rice cultivar, whereas levels of sucrose were lower in leaves of EMC rice cultivar (Fig. 3D). At full ripe stage, levels of tyrosine in leaves of EMC rice cultivar were higher than those in LMC rice cultivar, but levels

AC C

of sucrose and alanine were lower in rice leaves of EMC rice cultivar than those in LMC rice cultivar (Fig. 3F).

3.5. Dependence of rice grain metabolite on growing stage and cultivar Since metabolic differentiations in rice grains among rice cultivars and growth stages were 11

ACCEPTED MANUSCRIPT also observed (Fig. 1D - F), pairwise OPLS-DA models between growth stages and between rice cultivars were generated to identify metabolites in rice grains varied according to growth and cultivar, respectively (Figs. 4 and 5). In the comparison between milk and full ripe stages of LMC rice cultivar, sucrose levels were found to be increased in rice grain at full ripe stage compared to those at milk ripe stage, while levels of valine, isoleucine, leucine,

PT

GABA, succinate, choline, uracil, and fumarate were decreased in rice grain at full ripe stage (Fig. 4B). These metabolic differences between milk and full ripe stages in EMC rice cultivar

RI

were very similar to the results observed in the LMC rice cultivar. However, levels of fatty

SC

acids such as SFAs and UFAs were further increased while glucose levels were decreased in rice grains at full ripe stage than those at milk ripe state of EMC rice cultivar (Fig. 4D). As

NU

shown in OPLS-DA models generated for pairwise comparison of rice grain metabolite between EMC and LMC rice cultivars at milk ripe stage (Fig. 5A, B), dough stage (Fig. 5C,

MA

D) and full ripe stage (Fig. 5E, F), glucose, fatty acids (UFAs and SFAs), valine, isoleucine, leucine, GABA, glutamine, glutamate, choline, ethanolamine (EA), uracil, and fumarate were

ED

more accumulated in rice grains of EMC than those of LMC at milk and dough stages. However, asparagine was less accumulated. In particular, fatty acids were markedly more

EP T

accumulated in rice grains of EMC rice cultivars at full ripe stage (Fig. 5F).

AC C

3.6. Quantitative comparison of metabolite levels in rice leaves and grains Fig. 6 shows statistical differences in relative levels of individual metabolites, which were calculated by integral area of

1

H NMR and HR-MAS NMR peaks corresponding to

metabolites in rice leaves and grains. Most of rice leaf and grain metabolites such as amino acids and organic acids were decreased until the final growth stage. However, sucrose was accumulated in both rice leaves and grains after the milk ripe stages. 12

ACCEPTED MANUSCRIPT

4. Discussion In the present study, we collected samples of rice leaves and grains from two rice cultivars

PT

(EMC and LMC) at various growth stages (panicle formation stage (P), heading stage (H), milk ripe stage (M), dough stage (D), and full ripe stage (F) to investigate progressive

RI

changes of rice metabolites and identify rice cultivar-dependent metabolites through 1H NMR

SC

analysis coupled with multivariate statistical analysis. These EMC and LMC rice cultivars were used to assess their metabolic traits and to explore their metabolic correlations with

NU

environmental conditions. The length of basic vegetative phase (early panicle formation stage) in rice is dependent on genetic diversity. It is the most important growth stage to

MA

characterize EMC and LMC rice cultivars (Luh B. S., 1991). Panicle primordium development, which means formation of young panicle, is also influenced by environmental

ED

factors such as temperature and photoperiod. For rice plant, high temperature before heading stage naturally leads to early formation of panicle, consequently affecting milk ripe

EP T

stage (Yamakawa, Hirose, Kuroda, & Yamaguchi, 2007). When rice grain is exposed to abnormal high temperature during the ripening period, rice grain will become chalky,

AC C

followed by induction of low density of starch granule, low grain yield, and reduction of grain weight (Liu et al., 2010). Chalk is one of important quality characteristic of rice grains because its presence or absence affects the cooking quality (Lisle, Martin, & Fitzgerald, 2000). In general, sucrose in rice plant dramatically changes as it grows. Sucrose is synthesized by photosynthesis, the major method to produce plant tissue such as leaf. It is then delivered from photosynthetic tissues (source) to non-photosynthetic tissues (sink) such as grain and flower through the phloem. Therefore, the rate of photosynthesis is closely 13

ACCEPTED MANUSCRIPT related to levels of sucrose in plants (Turgeon, 1989; Paul, & Pellny, 2003; Smyth, Repetto, & Seidel, 1986; Wind, Smeekens, & Hanson, 2010). Carbohydrate metabolism in rice is an important feature not only for leaf development and senescence, but also for storage in sucrose into starch (Tognetti, Pontis, & Martinez-Noël, 2013). Sucrose, the known precursor of starch, is one of the highest carbohydrates in rice. Starch synthase is a key enzyme for

PT

transforming sucrose into starch. Most soluble sugars are converted into starch by starch synthase. They consist of 70 to 80 % of mature rice grain and thus, only a few sucrose

RI

remains in soluble state (Smyth, Repetto, & Seidel, 1986). Large amounts of intracellular

SC

sugars and fatty acids in rice grains were observed by 1 H HR-MAS NMR analysis (Song et al., 2016). This suggests that most sugars and fatty acids observed in rice grains of the

NU

current study might be from cytosols of the cells in rice grains.

In the present study, the up-regulation and down-regulation of sucrose was observed in rice

MA

leaves of LMC and EMC rice cultivars, respectively, until milk ripe growing stage (Fig. 6B), demonstrating distinct metabolism between LMC and EMC rice cultivars. Abnormal high

ED

temperature during the whole period was observed for EMC and LMC rice cultivar in the present study (Fig. 7A). However, mean temperature was increased when EMC rice cultivar

EP T

was cultivated whereas mean temperature was decreased during cultivation of LMC rice cultivar. The rapid down-regulation of sucrose until milk ripe stage in leaves of EMC rice

AC C

cultivar might have led to synthesis of other metabolites such as isoleucine, leucine, and phenylalanine. However, these amino acids were not changed in leaves of LMC rice cultivar (Fig. 6). This metabolic behavior of sucrose in rice leaves has also been observed in leaves of soybean (both Glycine max and Glycine gracilis) until full flowering stages of whole growth stage (Yun et al., 2016). Therefore, rapid reductions of sucrose levels in plant leaves might be a normal metabolic phenomenon as temperature increases until the middle of growth stage, for example, milk ripe stage in rice plant and full flowering stage in soybean plant. 14

ACCEPTED MANUSCRIPT This common metabolic behavior of sucrose in plant might lead to normal metabolic flow to other metabolic metabolisms such as synthesis of amino acids and the secondary metabolites, the former of which was observed in rice leaves (Fig. 6) while the latter of which was observed in leaves of soybean (Yun et al., 2016). In contrast, sucrose in leaves of LMC rice cultivar was continuously accumulated until the milk ripe stage (Fig. 7A). It was

PT

negatively correlated with average temperature. This abnormal metabolism of sucrose in LMC rice cultivar might indicate the poor role of sucrose in the metabolic flow to other

RI

metabolic mechanisms, causing lower levels of glucose, sucrose, amino acids, and fatty

SC

acids in rice grains of LMC rice cultivar than those in rice grains of EMC rice cultivar (Figs. 5 and 6). In particular, marked differences in levels of sucrose, fatty acids, and amino acids in

NU

rice grains between EMC and LMC cultivars were noted, likely resulting from distinct metabolism of sucrose in rice leaves until milk rice stage (Fig. 7A, B). No significant

MA

correlation of rice leaf or grain metabolome with sun-exposure time or rainfall was observed (Fig. 7C, D).

ED

Alterations of fatty acids in a plant are associated with leaf growth, leaf senescence, and maturing of rice grain. Lipid metabolism in rice plays an important role in both leaf and grain

EP T

because lipid provides energy to senescence leaf (Troncoso-Ponce, Cao, Yang, & Ohlrogge, 2013) while carbon and nitrogen sources are important for the ripening of grain (Buchanan-

AC C

Wollaston, 1997). In the present study, although 1 H NMR peaks corresponding to individual lipids were mostly overlapped, saturated (SFAs) and unsaturated (UFAs) fatty acids were identified in rice leaves and grains. Similar results have been reported in soybean leaves (Yun et al., 2016) and rice grains in our previous study (Song et al., 2016). Rice grain lipids consist of non-starch and starch lipids and free fatty acids, including arranged oleic (18:1), linoleic (18:2), palmitic (16:0) and linolenic (18:3) acids (Zhou, Robards, Helliwell, & Blanchard, 2002; Choudhury & Juliano, 1980a; Aibara et al., 1986). Fatty acids in rice grains 15

ACCEPTED MANUSCRIPT are known to be rapidly accumulated during early ripening stage (Choudhury & Juliano, 1980b). Elevated levels of SFAs in rice grains of EMC rice cultivar were observed at full ripe stage (Fig. 6p). Kitta et al. (2005) have reported that fatty acids in rice grains are closely associated with temperature during ripening stages of non-glutinous rice cultivars. For example, some free fatty acids such as oleic and palmitic acids showed positive correlation

PT

with mean temperature whereas some free fatty acids such as linoleic and linolenic acids had negative correlation with mean temperature (Kitta et al., 2005). Therefore, increase in

RI

mean temperature might lead to more accumulation of lipids in rice grains of EMC rice

SC

cultivar than those in LMC rice cultivar (Figs. 6p and 7). However, the genetic dependence of lipid metabolism in rice grains on the rice cultivar remained for further study.

NU

Amino acids are also important factor for the growth of rice plant. Nitrogen is an essential element affecting the yield for rice (Mae, 1997). Nitrogen of rice leaf blades is the major

MA

nitrogen source for rice grain (Mae & Ohira, 1981). Remobilization of nitrogen accelerates leaf senescence and rapidly descreases photosynthetic activity (Mae, 1997). Amino acids in

ED

rice grain are accumulated for energy during germination to be used for sources of nitrogen (He, Han, Yao, Shen, & Yang, 2011). Rice grain and milk ripe stage are the most sensitive

EP T

organ and growth stage to high temperature (Yamakawa & Hakata, 2010). In general, accumulation of protein in developing rice endosperms results from conversion of amino

AC C

acids during the milk ripe stage (Lial, Zhou, Zhang, Zhong, & Huang, 2014) Increase of amino acid biosynthesis from carbohydrates and decrease of translation occur in developing grains (Liu et al., 2010; Yamakawa, & Hakata, 2010) Naturally, most amino acids are accumulated as temperature increases, which were observed in rice leaves of EMC rice cultivar until milk ripe state, but not in those of LMC rice cultivar (Fig. 6). Glutamate is the most abundant free amino acid in rice leaf blade. Glutamine and asparagine are also plenty in rice (Kamachi, Yamaya, Mae, & Ojima, 1991). These amino acids act as nitrogen donors 16

ACCEPTED MANUSCRIPT for the biosynthesis of compounds, including nucleotides. Chlorophyll contents are closely connected with senescence (García-Gutiérrez, et al., 1998). GABA is a four-carbon nonprotein amino acid. It is also an important metabolite as temporary nitrogen source (GarcíaGutiérrez, et al., 1998; Shelp, Bown, & McLean, 1999; Kinnersley, & Turano, 2000). GABA is synthesized from glutamate by glutamate decarboxylase through enzymatic reaction

PT

including GABA shunt. It is important for scavenging of hydrogen peroxide under stress conditions (Satya Narayan & Nair, 1990). In the present study, rice leaves and grains had

RI

the highest levels of GABA in both LMC and EMC cultivars at the earliest growth stage (Fig.

SC

6). Moreover, levels of branched-chain amino acids (BCAAs) such as valine, isoleucine, and leucine in rice leaves were increased until milk ripe stage. They were then decreased until

NU

the final stage in EMC cultivar (Fig. 6). The metabolic behaviors of BCAAs were negatively correlated with those of sucrose in leaves of EMC rice cultivar. However, levels of these

MA

amino acids did not change significantly in LMC cultivar. Therefore, these results demonstrate that BCAAs metabolism depends on rice cultivar and climatic conditions.

ED

EMC rice cultivar is developed to avoid a decline in production yield caused by typhoons in East Asia. It provides newly harvested rice with high quality before Korean Thanksgiving Day.

EP T

It is generally accepted that low amylose contents are positively correlated with eating quality of rice grains due to low hardness and high stickiness (Nakamura et al., 2016).

AC C

According to our previous study, rice grains with low amylose contents have more amounts of free fatty acids, sucrose, and several amino acids compared to rice grains with high amylose contents (Song et al., 2016). Therefore, EMC rice cultivar, which had markedly higher amounts of fatty acids, sucrose, and amino acids in its grains than LMC rice cultivar, would provide high eating quality of rice grains. These results demonstrate potential role of metabolomics in assessing the relationship between metabolome and quality of rice grains . In conclusion, the present study exhibited that metabolism of early and late maturing rice 17

ACCEPTED MANUSCRIPT cultivars was correlated with temperature during their growth periods. The distinct metabolism between the two rice cultivars might lead to different metabolic consequences in rice leaves, which could further affect the quality of rice grains. Therefore, metabolomics studies can identify distinct metabolisms of rice cultivars and improve our understanding of the associations between metabolisms and environmental conditions of rice and might be

AC C

EP T

ED

MA

NU

SC

RI

PT

used to predict rice quality.

18

ACCEPTED MANUSCRIPT Associated content Supporting Information. Representative 700MHz 1H NMR spectra of rice leaf extracts collected at panicle formation stage (A and C) and full ripe stage (B and D) of late matureing cultivar (A and B) and early maturing cultivar (C and D) of rice (Fig. S1). Representative 700MHz 1H HR MAS NMR spectra of rice grains from two different cultivars of late maturing

PT

cultivar (A and B) and early maturing cultivar (C and D) collected at milk ripe stage (A and C)

RI

and full ripe stage (B and D) (Fig. S2).

SC

Author information

NU

Corresponding Author

(Y.S. Hong) Mail: Division of Food and Nutrition, Chonnam National University, Yongbong-ro,

MA

Buk-gu, Gwangju 500-757, Republic of Korea. Phone: (82) 62 530 1331. Fax: (82) 62 530 1339. E-mail: [email protected] or [email protected]

ED

(E. Bang) Mail: Western Seoul Center, Korea Basic Science Institute, Seoul 136-701, Republic of Korea. Phone: (82) 2 6908 6231. E-mail: [email protected]

These authors equally contributed to this work.

EP T

1

Conflict of interest

AC C

The authors declare no conflict of financial interest.

Acknowledgements This work was financially supported by Chonnam National University in 2014. We would like to thank the Korea Basic Science Institute (KBSI) for excellent technical assistance with 700 MHz NMR and 700 MHz HR-MAS NMR experiments.

19

AC C

EP T

ED

MA

NU

SC

RI

PT

ACCEPTED MANUSCRIPT

20

ACCEPTED MANUSCRIPT References Aibara, S., Ismail, I.A., Yamashita, H., Ohta, H., Sekiyama, F., & Morita, Y. (1986). Changes in rice bran lipids and fatty acids during storage. Agricultural and Biological Chemistry, 50,

PT

665-673.

Buchanan-Wollaston, V. (1997). The molecular biology of leaf senescence. Journal of

SC

RI

Experiment Botany, 48, 181− 199.

NU

Bylesj, M., Rantalainen, M., Cloarec, O., Nicholson, J.K., Holmes, E., & Trygg, J. (2006). OPLS

MA

discriminant analysis: combining the strengths of PLS-DA and SIMCA classification.

ED

Journal of Chemometrics, 20, 341-351.

EP T

Choudhury, N.H. & Juliano, B.O. (1980a). Effect of amylose content on the lipids of

AC C

mature rice grain. Phytochemistry, 19, 1385− 1389.

Choudhury, N.H. & Juliano, B. O. (1980b). Lipids in developing and mature rice grain.

Phytochemistry, 19, 1063− 1069.

Cloarec, O., Dumas, M.E., Craig, A., Barton, R.H., Trygg, J., Hudson, J., Blancher, C.,

21

ACCEPTED MANUSCRIPT Gauguier, D., Lindon, J.C., Holmes, E., & Nicholson, J.K. (2005a). Statistical total correlation spectroscopy: An exploratory approach for latent biomarker identification from metabolic 1H NMR data sets. Analytical Chemistry, 77, 1282-1289.

PT

Cloarec, O., Dumas, M.E., Trygg, J., Craig, A., Barton, R.H., Lindon, J.C., Nicholson, J.K., Holmes, E. (2005b). Evaluation of the orthogonal projection on latent structure model

RI

limitations caused by chemical shift variability and improved visualization of biomarker

SC

changes in H-1 NMR spectroscopic metabonomic studies. Analytical Chemistry, 77, 517-

MA

NU

526.

Dieterle, F., Ross, A., Schlotterbeck, G., & Senn, H. (2006). Probabilistic quotient

ED

normalization as robust method to account for dilution of complex biological mixtures.

EP T

Application in H-1 NMR metabonomics. Analytical Chemistry, 78, 4281-4290.

Endo-Higashi, N., & Izawa, T. (2011). Flowering time genes Heading date 1 and Early

AC C

heading date 1 together control panicle development in rice. Plant and Cell Physiology,

52, 1083-1094.

Fitzgerald, M.A., McCouch, S.R., & Hall, R.D. (2009). Not just a grain of rice: the quest for quality. Trends in Plant Sciences, 14, 133− 139. 22

ACCEPTED MANUSCRIPT

García-Gutiérrez, A., Dubois, F., Cantón, F.R., Gallardo, F., Sangwan, R.S., & Cánovas, F.M. (1998). Two different modes of early development and nitrogen assimilation in

PT

gymnosperm seedlings. The Plant Journal, 13, 187–199.

RI

He, D., Han, C., Yao, J., Shen, S., & Yang, P. (2011). Constructing the metabolic and

SC

regulatory pathways in germinating rice seeds through proteomic approach. Proteomics,

NU

11(13), 2693-2713.

MA

Hu, C.Y., Shi, J.X., Quan, S., Cui, B., Kleessen, S., Nikoloski, Z., Tohge, T., Alexander, D., Guo, L.N., Lin, H., Wang, J., Cui, X., Rao, J., Luo, Q., Zhao, X.X., Fernie, A.R., & Zhang, D.B. (2014).

ED

Metabolic variation between japonica and indica rice cultivars as revealed by non -

EP T

targeted metabolomics. Scientific Reports, 4, 5067.

AC C

Juliano, B.O., Bautista, G.M., Lugay, J.C., & Reyes, A.C.J. (1964). Studies on the physiochemical properties of rice. Journal of Agricultural and Food Chemistry, 12, 131– 138.

Kamachi, K., Yamaya, T., Mae, T., & Ojima, K. (1991). A role for glutamine synthetase in the remobilization of leaf nitrogen during natural senescence in rice leaves. Plant 23

ACCEPTED MANUSCRIPT Physiology, 96, 411–417.

Kim, H. K., Choi, Y. H., & Verpoorte, R. (2010). NMR-based metabolomic analysis of plants.

PT

Nature Protocols. 5, 536−549.

Kim, H. K., Choi, Y. H., & Verpoorte, R. (2011). NMR-based plant metabolomics: where do

SC

RI

we stand, where do we go? Trends in Biotechnology, 29, 267-275.

NU

Kinnersley, A.M., & Turano, F.J. (2000). γ-Aminobutyric acid (GABA) and plant responses to

MA

stress. Critical Reviews in Plant Sciences, 19, 479− 509.

ED

Kitta, K., Ebihara, M., Iizuka, T., Yoshikawa, R., Isshiki, K., & Kawamoto, S. (2005). Variations in lipid content and fatty acid composition of major non-glutinous rice cultivars in Japan.

AC C

EP T

Journal of Food Composition and Analysis, 18, 269− 278.

Lial, J.L., Zhou, H.W., Zhang, H.Y., Zhong, P.A., & Huang, Y.J. (2014). Comparative proteomic analysis of differentially expressed proteins in the early milky stage of rice grains during high temperature stress. Journal of Experimental Botany, 65(2), 655-671.

Lisle, A.J., Martin, M., & Fitzgerald, M.A. (2000). Chalky and translucent rice grains differ in 24

ACCEPTED MANUSCRIPT starch composition and structure and cooking properties. Cereal Chemistry, 77, 627-632.

Liu, X., Guo, T., Wan, X., Wang, H., Zhu, M., Li, A., Su, N., Shen, Y., Mao, B., Zhai, H., Mao, L., & Wan, J. (2010). Transcriptome analysis of grain- filling caryopses reveals involvement

RI

PT

of multiple regulatory pathways in chalky grain formation in rice. BMC Genomics, 11, 730.

Mae,

T.

(1997).

Physiological

nitrogen

NU

SC

Luh, B. S. (1991). Rice, Volume 1: Production (2nd ed.). New York: Springer-Verlag US.

efficiency

in

rice:

nitrogen

utilization,

ED

MA

photosynthesis, and yield potential. Plant and Soil, 196(2), 201-210.

Mae, T., & Ohira, L. (1981). The Remobilization of Nitrogen Related to Leaf Growth and

EP T

Senescence in Rice Plants (Oryza sativa L.). Plant and Cell Physiology, 22(6), 1067-1074.

AC C

Nakamura, S., Cui, J., Zhang, X., Yang, F., Xu, X., Sheng, H., & Ohtsubo, K. (2016), Comparison of eating quality and physiochemical properties between Japanese and Chinese rice cultivars. Bioscience, Biotechnology, and Biochemistry, 80(12), 2437-2449.

Paul, M.J., & Pellny, T.K. (2003). Carbon metabolite feedback regulation of leaf 25

ACCEPTED MANUSCRIPT photosynthesis and development. Journal of Experimental Botany, 54(382), 539–547.

Satya Narayan, V., & Nair, P.M. (1990). Metabolism, enzymology and possible roles of 4-

PT

aminobutyrate in higher plants. Phytochemistry, 29, 367− 375.

RI

Savorani, F., Tomasi, G., & Engelsen, S.B. (2010). icoshift: A versatile tool for the rapid

NU

SC

alignment of 1D NMR spectra. Journal of Magnetic Resonance, 202, 190-202.

Shelp, B.J., Bown, A.W., & McLean, M.D. (1999). Metabolism and functions of gamma-

ED

MA

aminobutyric acid. Trends in Plant Science, 4, 446–452.

Smyth, D.A., Repetto, B.M., & Seidel, N.E. (1986). Cultivar differences in soluble sugar

EP T

content of mature rice grain. Physiologia Plantarum, 68, 367–374.

AC C

Song, E.H., Kim, H.J., Jeong, J., Chung, H.J., Kim, H.Y., Bang, E.J., & Hong, Y.S. (2016). 1H HR-MAS NMR-based metabolomics study for metabolic characterization of rice grain from various Oryza sativa L. cultivars. Journal of Agricultural and Food Chemistry, 64(15), 3009-3016.

26

ACCEPTED MANUSCRIPT Tognetti, J.A., Pontis, H.G., & Martinez-Noël, G.M.A. (2013). Sucrose signaling in plants. A world yet to be explored. Plant Signaling & Behavior, 8(3), e23316.

Troncoso-Ponce, M.A., Cao, X., Yang, Z., & Ohlrogge, J.B. (2013). Lipid turnover during

RI

PT

senescence. Plant Science, 205− 206, 13− 19.

SC

Turgeon, R. (1989). The sink–source transition in leaves. Annual Review of Plant

NU

Physiology and Plant Molecular Biology, 40, 119–138.

MA

Vergara, B. S. (1976). Physiological and morphological adaptability of rice varieties to

EP T

83), Los Banos: Philippines.

ED

climate. Proceedings at the symposium of International Rice Research Institute (pp. 67-

Wassmann, R., Jagadish, S.V.K., Sumfleth, K., Pathak, H., Howell, G., Ismail, A., Serraj, R.,

AC C

Redona, E., Singh, R.K., & Heuer, S. (2009). Regional vulnerability of climate change impacts on Asian rice production and scope for adaptation. Advances in Agronomy, 102, 91-133.

Wind, J., Smeekens, S., & Hanson, J. (2010). Sucrose: Metabolite and signaling molecule.

27

ACCEPTED MANUSCRIPT Phytochemistry, 71 (14− 15), 1510− 1614.

Yamakawa, H., & Hakata, M. (2010). Atlas of rice grain filling-related metabolism under high temperature: Joint analysis of metabolome and transcriptome demonstrated

PT

inhibition of starch accumulation and induction of amino acid accumulation. Plant and

SC

RI

Cell Physiology, 51(5), 795-809.

Yamakawa, H., Hirose, T., Kuroda, M., & Yamaguchi, T. (2007). Comprehensive expression

NU

profiling of rice grain filling-related genes under high temperature using DNA microarray.

MA

Plant Physiology, 144, 258− 277.

ED

Yun, D.Y., Kang, Y.G., Yun, B., Kim, E.H., Kim, M., Park, J.S., Lee, J.H., & Hong, Y.S. (2016).

EP T

Distinctive Metabolism of Flavonoid between Cultivated and Semiwild Soybean Unveiled through Metabolomics Approach. Journal of Agricultural and Food Chemistry, 64 (29),

AC C

5573-5783.

Zhou, Z., Robards, K., Helliwell, S., & Blanchard, C. (2002). Composition and functional properties of rice. Internal Journal of Food Science and Technology, 37, 849–868.

28

ED

MA

NU

SC

RI

PT

ACCEPTED MANUSCRIPT

EP T

Fig. 1. PCA (A and D) and OPLS-DA (B, C, E and F) score plots derived from 700 MHz 1H NMR rice leaf spectra (A - C) and 700 MHz 1 H HR-MAS NMR rice grain spectra (D - F) of late maturing cultivar (LMC) and early maturing cultivar (EMC) of rice at each growth stage,

AC C

demonstrating the dependence of rice leaf and grain metabolome on growing stage and cultivar. All OPLS-DA models were validated by permutation test (data not shown).

29

AC C

EP T

ED

MA

NU

SC

RI

PT

ACCEPTED MANUSCRIPT

Fig. 2. OPLS-DA score (A, C, E and G) and loading (B, D, F and H) plots generated with 1H NMR spectra of rice leaves for pairwise comparison of metabolic difference between different stages: panicle ripe and milk ripe stages (A and B, and E and F), and milk ripe and full ripe stages (C and D, and G and H) in late maturing cultivar (LMC, A – D) and early 30

ACCEPTED MANUSCRIPT maturing cultivar (EMC, E – H) cultivar of rice. All OPLS-DA models were generated with one predictive component and one orthogonal component and validated by permutation test (data not shown). Good fitness and predictability of each model were indicated by R2X and Q2 values, respectively. Val, Valine; Ile, Isoleucine; Leu, Leucine; GABA, -aminobutyrate;

AC C

EP T

ED

MA

NU

SC

RI

PT

UFAs, Unsaturated fatty acids; *, unknown compounds.

31

EP T

ED

MA

NU

SC

RI

PT

ACCEPTED MANUSCRIPT

AC C

Fig. 3. OPLS-DA score (A, C and E) and OPLS-DA loading (B, D and F) plots for identification of rice leaf metabolite dependent on rice cultivars at panicle formation (A and B), milk ripe (C and D), and full ripe (E and F) stages, demonstrating difference in metabolism between the late maturing cultivar (LMC) and early (EMC) maturing cultivar of rice. Val, Valine; Ile, Isoleucine; Leu, Leucine; GABA, -aminobutyrate; UFAs, Unsaturated fatty acids; *, unknown compounds.

32

MA

NU

SC

RI

PT

ACCEPTED MANUSCRIPT

Fig. 4. OPLS-DA score (A and C) and OPLS-DA loading (B and D) plots derived from 700MHz

1

H HR-MAS NMR spectra of rice grains for the identification of rice grain

metabolites dependent on growing stage in late maturing cultivar (LMC) and early maturing

ED

rice cultivar (EMC) of rice at milk ripe (M) and full ripe (F) stages. GABA; -aminobutyrate;

AC C

EP T

SFAs, Saturated fatty acids; UFAs, Unsaturated fatty acids.

33

EP T

ED

MA

NU

SC

RI

PT

ACCEPTED MANUSCRIPT

1

AC C

Fig. 5. OPLS-DA score (A, C, and E) and loading (B, D, and F) plots derived from 700MHz H HR-MAS NMR spectra for the identification of rice grain metabolites dependent on rice

cultivar through a pairwise comparison of rice grain between late maturing cultivar (LMC) and early maturing cultivar (EMC) of rice at milk ripe (A and B), dough (C and D) and full ripe (E and F) stages. Val, Valine; Ile, Isoleucine; Leu, Leucine; Glu, Glutamate; Gln, Glutamine; GABA,-aminobutyrate; EA, Ethanolamine; FAs, Saturated fatty acids; UFAs, Unsaturated fatty acids.

34

AC C

EP T

ED

MA

NU

SC

RI

PT

ACCEPTED MANUSCRIPT

35

ACCEPTED MANUSCRIPT Fig. 6. Relative quantification of rice plant metabolites in rice leaves and grains of late maturing cultivar (LMC) and early maturing cultivar (EMC) of rice. Different letters above bars indicate significant difference among growth stages of grain determined by Duncan’s multiple test at P < 0.05. *P < 0.05; **P < 0.01; ***P < 0.001; (independent Student’s t-test

AC C

EP T

ED

MA

NU

SC

RI

PT

between LMC and EMC at given growing stage)

36

MA

NU

SC

RI

PT

ACCEPTED MANUSCRIPT

ED

Fig. 7. Changes in average temperature (A), total-exposure time (B), total rainfall (D), and sucrose levels in rice leaves (A) and sucrose and glucose levels in rice grains (B) during growing of early maturing cultivar (EMC) and late maturing cultivar (LMC) of rice. All climatic

AC C

EP T

parameters were calculated from values between each growth stage.

37

ACCEPTED MANUSCRIPT

AC C

EP T

ED

MA

NU

SC

RI

PT

Graphical Abstracts

38

ACCEPTED MANUSCRIPT Highlights Dependences of rice leaf and grain metabolome on growth and cultivar were found.



Metabolic variations of rice leaves and grains were associated with temperature.



Metabolism in rice leaf affects rice grain metabolism.



Rice grain metabolome could predict eating quality of rice.

AC C

EP T

ED

MA

NU

SC

RI

PT



39