Transcriptome profiling characterizes phosphate deficiency effects on carbohydrate metabolism in rice leaves

Journal of Plant Physiology 169 (2012) 193–205

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Transcriptome profiling characterizes phosphate deficiency effects on carbohydrate metabolism in rice leaves Myoung Ryoul Park a,b , So-Hyeon Baek c , Benildo G. de los Reyes d , Song Joong Yun a , Karl H. Hasenstein b,∗ a

Institute of Agricultural Science & Technology, Chonbuk National University, Jeonju, Jeollabuk-do 561-756, Republic of Korea Department of Biology, University of Louisiana, Lafayette, LA 70504, USA c Department of Rice and Winter Cereal Crop, NICS, RDA, Iksan 570-080, Republic of Korea d School of Biology and Ecology, University of Maine, Orono, ME 04469, USA b

a r t i c l e

i n f o

Article history: Received 1 July 2011 Received in revised form 19 August 2011 Accepted 13 September 2011 Keywords: Carbohydrate Metabolic pathway Microarray Phosphate Rice

a b s t r a c t Phosphorus (P) is a structural component of nucleic acids and phospholipids and plays important roles in plant growth and development. P accumulation was significantly reduced (about 35%) in rice leaves from plants grown under low (32 ␮M) P compared to 320 ␮M P grown plants. Genome response to low P was examined using the rice 60K oligonucleotide DNA microarrays. At the threshold significance of |log2 | fold > 2.0, 21,033 genes (about 33.7% of all genes on the microarray) were affected by P deficiency. Among all genes on the microarray, 4271 genes were sorted into 51 metabolic pathways. Low P affected 1494 (35.0%) genes and the largest category of genes was related to sucrose degradation to ethanol and lactate pathway. To survey the role of P in rice, 25 pathways were selected based on number of affected genes. Among these pathways, cytosolic glycolysis contained the least number of upregulated but most down-regulated genes. Low P decreased glucose, pyruvate and chlorophyll, and genes related to carbon metabolism and chlorophyllide a biosynthesis. However, sucrose and starch levels increased. These results indicate that P nutrition affects diverse metabolic pathways mostly related to glucose, pyruvate, sucrose, starch, and chlorophyll a. © 2011 Elsevier GmbH. All rights reserved.

Introduction Plant growth and development are closely linked to primary metabolism and respond to natural variation. The phenotype of an organism is the result of interaction between its genotype and the environment (Lisec et al., 2009). Studies of plant metabolomics show that metabolite levels can be the controlling element in plant growth (Fiehn, 2006). Living cells require millimolar amounts of phosphorus (P) to satisfy demands for nucleic acids, phospholipids, and other metabolites. Plant cell metabolism and its regulation by inorganic phosphate (Pi) are complex. P affects signal transduction, photosynthesis and respiration and regulation of enzyme activity through reversible phosphorylation. P is likely to affect the

Abbreviations: P, phosphorus; Pi, inorganic phosphate; AGPase, EC 2.7.7.27, ADPglucose pyrophosphorylase; HXK, EC 2.7.1.1, hexokinase; PGM, phosphoglucomutase; pPGM, EC 5.4.2.2, plastid-localized; cPGM, EC 5.4.2.2, cytosolic isoforms; UGPase, EC 2.7.7.9, UDP-glucose pyrophosphorylase; SSU, small subunits; LSU, large subunits; SBE, 1,4-␣-glucan-branching enzyme, EC 2.4.1.18, starch branching enzyme; TPI, d-glyceraldehyde-3-phosphate ketol isomerase, EC 5.3.1.1, triosephosphate isomerase; PFK, EC 2.7.1.11, phosphofructokinase. ∗ Corresponding author. Tel.: +1 337 482 6750; fax: +1 337 482 5834. E-mail address: [email protected] (K.H. Hasenstein). 0176-1617/$ – see front matter © 2011 Elsevier GmbH. All rights reserved. doi:10.1016/j.jplph.2011.09.002

equilibrium between ADP and ATP. It is also a structural component of nucleic acids and phospholipids (Plaxton and Carswell, 1999). Throughout the life cycle of rice, photosynthesis is important for growth, metabolism, and development. P deficiency in plants significantly affects photosynthesis and carbon metabolisms (Rao, 1996). Decreased photosynthesis as a consequence of P deficiency has been reported in barley (Foyer and Spencer, 1986), soybean (Fredeen et al., 1989), and sugar beet (Rao and Terry, 1995). Photosynthetic CO2 assimilation is reduced by P deficiency as a result of decreased RuBP pool size in soybean (Fredeen et al., 1989), Helianthus annuus (Jacob and Lawlor, 1992) and rice (Li et al., 2006). In chloroplasts glucose is stored mainly in the form of starch granules. In Arabidopsis, most of P-limiting conditions result in down-regulated genes related to functional groups that are required for photosynthesis and nitrogen assimilation (Ma et al., 2001; Wu et al., 2003). Starch is a significant plant product and used as a source of renewable energy, e.g., for ethanolic fermentation. Starch is also the most important carbohydrate in the human diet and constitutes the bulk of biomass in potatoes, wheat, maize, and rice. Starch can be hydrolyzed into sucrose, glucose, fructose, and pyruvate by various enzymes. Plants accumulate sugars and starch in their leaves under P starvation (Hammond and White, 2008). Previous studies

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showed that P deficiency had significant influence on carbohydrate content (Fredeen et al., 1989; Qiu and Israel, 1994; Nanamori et al., 2004). P deficiency increased sucrose and starch content in Phaseolus (Ciereszko and Barbachowska, 2000) and rice (Li et al., 2006), but the effect of P on sucrose and starch metabolism varies among different plant species. Many challenging problems remain in identifying the role of P deficiency since quantitative data on dynamics and regulation of plant cell metabolism are limited. Moreover, understanding biological processes of P for rice growth and development at the genome level is complex and difficult since P affects multiple metabolic pathways. Microarray technology has become a useful tool for the analysis of gene expression profiles. A genome-scale analysis would be very useful for understanding the mechanism and would provide a basis for characterization of gene expression in response to P starvation. Recently, the effect of P on transcriptome expression has been examined in rice using 9K cDNA microarrys (Wasaki et al., 2003, 2006) and 60K oligo-array (Li et al., 2010). In this study, we combine profiling of expression of genes and measuring contents of carbon metabolites to investigate the effect of low P on carbohydrate partitioning and photosynthesis. Materials and methods Plant growth and Pi deficiency treatment Rice (Oriza sativa, var. Dongjin byeo) seeds were surfacesterilized in a 0.1% (w/v) HgCl2 solution for 1 min (Yoshida et al., 1976) and germinated on filter paper in a petri dish for 3 days at 25 ◦ C in the dark. Uniformly germinated seedlings were transferred to a hydroponic culture system consisting of Styrofoam sheets with holes to accommodate individual seedlings. The Styrofoam sheets floated on containers filled with aerated standard solution. The composition and concentration of the standard solution was as follows: N (1.43 mM), P (0.32 mM), K (0.51 mM), Ca (0.75 mM), Mg (1.64 mM), Fe (59.37 ␮M), B (18.92 ␮M), Mn (9.50 ␮M), Mo (0.10 ␮M), Zn (0.15 ␮M), Cu (0.16 ␮M), and citric acid (70.72 ␮M) (Yoshida et al., 1976). After about three weeks in a greenhouse at 25 ◦ C/18 ◦ C (day/night) under a natural photoperiod (about 16 h light), some of the V3 stage (developed third leaf) seedlings were transferred to a low P solution (the same nutrient solution as above but containing 32 ␮M P) for 5 days. Control plants were maintained in the standard solution for the entire period. The solutions were changed every 3–4 d and the pH adjusted daily to 5.0 with NaOH.

Analysis of gene expression Expression profiles were analyzed in control and low P seedlings using the 60K Rice Whole Genome Microarray (see www.ggbio.com/rice60kchip.html, GreenGene Biotech). Total RNA (100 mg) was prepared from the leaves of rice seedlings grown in either P conditions and mRNA was purified from total RNAs using Qiagen oligotex columns (Qiagen, Valencia, CA). Purified mRNA was converted to cDNA and labeled with Cy3 (control) and Cy5 (low P) using CyScribe Post-Labeling kit (Amersham Biosciences, UK). Change in gene expression was profiled by reading the microarray with a pair of target RNA with three independent biological replicates. Microarray hybridization was performed as described by Ausubel et al. (2000). The microarray was scanned with an ArrayWoRx Biochip Reader-Standard (Applied Precision, USA) and the quality of the chip data was analyzed with Imagene 5.0 (BioDiscovery, USA) and Genesight 3.2 (BioDiscovery, USA). Noncorrelation of signal and background intensities were confirmed by plotting base 2 log background intensity in x axis and base 2 log intensity subtracted with background intensity in y axis. Prior to normalization, normal distribution of Cy3 and Cy5 intensities and the spatial effects on the chip during the hybridization process were tested using Genesight 3.2. Normalization, intensity comparisons, hierarchical clustering was performed using Genesight 3.2 using default settings. High level statistical analyses (T-test, hierarchical and Kmean clustering) were performed with the MeV Bioinformatics Tools (Saeed et al., 2003). Analysis of metabolic pathway and gene ontology To survey a metabolic pathway responding to P deficiency in rice, we classified the genes by category of metabolic pathway based on the RiceCyc database (http://www.gramene.org/ pathway/ricecyc.html) and the Plant Metabolic Network (PMN, http://plantcyc.org/). We also investigated candidate genes with gene ontology (GO) enrichment analysis (http://www. geneontology.org/) and Rice Genome Annotation Project (http:// rice.plantbiology.msu.edu/). Each gene was examined by the locus search tool of the Rice Genome Annotation Project (http://rice. plantbiology.msu.edu/analyses search locus.shtml) including GO Classification, Plant Model Organism and Poaceae Orthologous Group, and Gene Association. Analysis of glucose, fructose, sucrose, pyruvate, and starch contents

Growth rate, P, and chlorophyll content Growth rate of control seedlings and seedlings grown in low P for 3 and 5 days were measured. P contents in the leaves of seedlings exposed to low P for 5 days and controls at V3 stage were analyzed. The samples were dried at 60 ◦ C for 3 d, ground into powder in a mixer mill (MM301, Retsch, Germany). The powder (100 mg) was then wet-digested in 2 mL of a mixture of 70% H2 SO4 and 30% H2 O2 (Lee and Kim, 2001). The sample digests were diluted to 10 mL with sterile triple-distilled water. P was analyzed from aliquots with a sequential plasma spectrometer (ICPS-7500, Shimadzu Corp., Japan). Chlorophyll was measured in three expanded leaves per seedling each subjected to control and low P treatments after five days using a chlorophyll meter (Model SPAD-502, Minolta, USA). The SPAD readings were converted to chlorophyll content (as mg chlorophyll/m2 leaf area) using a linear regression analysis of SPAD versus chlorophyll content on rice leaves as described by Monje and Bugbee (1992). Measurements were taken from at least three different plants.

Tissue samples of control seedlings and seedlings treated with low P for 5 days at V3 stage were powdered in liquid nitrogen and kept at −70 ◦ C until use. Leaf samples (1 g FW) were ground in liquid nitrogen and soluble sugars were extracted with 10 ml 80% EtOH at 75 ◦ C for 8 h (3 times) and starch was extracted with 10 ml of 80% (v/v) ethanol at 80 ◦ C for 5 min. The extracts were evaporated (Concentrator 5301, Eppendorf, Germany) at 60 ◦ C, dissolved in H2 O (Kim, 1995), and used for the analysis of sucrose, glucose, fructose and pyruvate. Sucrose, glucose, and fructose were separated on Shodex NH2P-50 4E column (4.6 mm × 250 mm, Showa Denko Europe GmbH, Germany) with acetonitrile/water (75/25, v/v) mobile phase at a flow rate of 1.0 ml/min and detected with Shodex RI-100 detector (temperature: 40 ◦ C, pressure: 67 bar, Showa Denko Europe GmbH, Germany). Pyruvate was analyzed using the EnzyChromTM Pyruvate assay kit (BioAssay Systems, USA) as described by the manufacturer. Contents of total starch in leaves of rice were measured using the Total Starch (AA/AMG) Assay Kit (Megazyme, Ireland) as described by the manufacturer. All analyses were performed with three replicates.

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Fig. 2. Pie chart illustrating up-regulated (open), down-regulated (solid), and unregulated (gray) genes in leaves of rice seedlings grown under low (32 ␮M) P condition compared to plants grown in 320 ␮M P. An arbitrary cut-off value of |log2 | fold > 2.0 (p < 0.05) was used. Values were determined from three biological replicates.

Global transcriptional change in rice seedlings by low P

Fig. 1. Phosphorus content (A) in V3 stage rice plants grown for 5 days in control (open bar, 320 ␮M) and low (solid bar, 32 ␮M) P conditions in a hydroponic culture system. Accumulation of fresh weight (B) became noticeably reduced within the first 5 days of the low P treatment period (dashed line). The chlorophyll content (C) was significantly different after 5 days. Least significance difference for P content is 0.38 ␮mol/g DW for the control and low P conditions at p < 0.05 and 8.9 mg/m2 , for chlorophyll content at p < 0.05. Columns labeled with different letters are significantly different (p < 0.05).

Statistical analysis Analysis of variance (ANOVA) was performed with Statistix 9.0 (Statistix, USA) based on three independent biological replicates. Results P content in leaves of seedlings under normal and low P conditions P content in leaves of rice seedlings under control and low P conditions were 52.1 and 33.2 ␮mol/g DW, respectively. Thus, P content of leaves of rice treated with the low P for five days decreased by about 35% (Fig. 1A). The low P treatment reduction of growth became noticeable within three days but did not reach statistically significant differences in biomass accumulation within 5 days (Fig. 1B). Analysis of genes related to chlorophyllide a biosynthesis and contents of chlorophyll To determine if P levels changed the expression of the genes related to chlorophyllide a synthesis and affected the amount of chlorophyll in leaves of rice, we measured chlorophyll in rice seedlings. Content of chlorophyll in control plants was higher than that of low P plants (Fig. 1C). Low P caused down-regulation of 16 of the 18 genes required for chlorophyllide ˛ synthesis (Table 1).

To understand the transcriptional consequences of P-dependent gene expression, we profiled the transcriptome of rice seedlings grown under low P and controls (Gene Expression Omnibus No. GSE21377, http://www.ncbi.nlm.nih.gov/geo/). At the arbitrary threshold of |log2 | fold ≥ 2.0 based on three biological replicates, a total of 21,033 genes were affected by P deficiency. Low P induced upregulation of 8043 genes; 12,990 genes were down-regulated (Fig. 2).

Analysis of genes and metabolic pathways To understand how reduced P affected the metabolism of rice seedlings, we sorted these genes by metabolic pathways. Among all genes on the microarray, 4271 genes were related to metabolic pathways and 35% (1494) of these genes were affected by low P. These genes were classified into 51 metabolic pathways. The largest pathway described sucrose degradation to ethanol and formation of anaerobic lactate. Coenzyme A biosynthesis contained the smallest number of genes affected by low P (Table 2 ). The 51 pathways pertained to numerous metabolites. To characterize the low P response in rice, we selected pathways that had at least 35% of their genes modified 2-fold by low P. The resulting 25 pathways were grouped into tryptophan biosynthesis, carbohydrates biosynthesis (Calvin cycle, UDP-glucose conversion, gluconeogenesis, fructose degradation to pyruvate and lactate (anaerobic), sucrose biosynthesis, and starch biosynthesis), cellulose biosynthesis, hormones biosynthesis (jasmonic acid, cytokinins 7-N-glucoside), oxidative ethanol degradation III, carbohydrates degradation (sucrose III, starch, galactose II, sucrose conversion to ethanol and lactate (anaerobic)), nucleosides and nucleotides degradation and recycling (salvage pathways of purine nucleosides), secondary metabolites degradation of betanidin, detoxification (removal of superoxide radicals),electron transfer (NAD/NADH phosphorylation and dephosphorylation), fermentation (glucose fermentation to lactate II), glycolysis IV (plant cytosol), photosynthesis (chlorophyllide a biosynthesis), aerobic respiration, TCA cycle, and phospholipase activity(Fig. 3 and Table 2). In these pathways, the average percentages of down- and up-regulated genes were 75% and 25%, respectively; all probed

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Table 1 Genes of chlorophyllide a biosynthesis regulated by low P in rice leaves. Enzyme

Gene ID

Oligo ID

Locus ID

GO term(s)

Signal intensity Cy3

Uroporphyrinogen decarboxylase

Os005293 01

OsIRUA002775

LOC Os03g21900.1

Coproporphyrinogen oxidase Protoporphyrinogen oxidase

Mg chelatase

Os013075 Os056262 Os002037 Os017044 Os054140 Os054140 Os017083

OsJRFA066873 OsIFCC018002 OsIRUA000822 OsJRFA072414 LOC Os01g51320.1 OsIFCC036992 OsJRFA072463

LOC LOC LOC LOC LOC LOC LOC

Mg-protoporphyrin IX methyltransferase

Os013398 01 Os055953 01

OsJRFA067323 OsIFCC009334

N/A LOC Os06g04150.1

Mg-protoporphyrin monomethyl ester cyclase

Os003942 Os006572 Os009894 Os018210 Os052200 Os053609 Os053774 Os006577

OsIRUA001927 OsIRUA003527 OsJRFA062071 OsJRFA098980 OsIFCC030972 OsIFCC030972 OsIFCC002363 LOC Os03g22780.1

LOC LOC LOC LOC LOC LOC LOC LOC

3,8-Divinyl protochlorophyllide 8-vinyl reductase (DVR)

01 01 01 01 01 02 01

01 01 01 01 01 01 02 01

Os12g17070.1 Os04g41260.1 Os10g38850.1 Os04g57560.1 Os01g51320.1 Os04g57550.1 Os03g59640.1

Os06g45820.1 Os02g21970.1 Os01g39260.1 Os03g05730.1 Os10g30580.1 Os08g31870.1 Os01g39250.1 Os03g22780.1

GO:0009058, GO:0009987, GO:0003824 GO:0003824 N/A GO:0003824

GO:0009058, GO:0009987, GO:0009536, GO:0003824, GO:0019748 N/A GO:0009058, GO:0009987, GO:0009536, GO:0003824, GO:0019748 GO:0005634, GO:0006950, GO:0005622, GO:0009579, GO:0005737, GO:0005488, GO:0016787, GO:0005515, GO:0019538, GO:0000166, GO:0009987, GO:0009056, GO:0009536 GO:0009058, GO:0009987, GO:0003824, GO:0019748

Fold(SD)

Cy5 −2(0)

108

45

261 2295 717 152 11 50 243

57 302 105 38 106 1240 71

−5(1) −8(3) −7(2) −4(1) 10(2) 25(12) −3(1)

130 4758

65 485

−2(0) −10(4)

24 398 70 227 1401 203 76 262

7 124 19 87 8 3 29 49

−4(2) −3(1) −4(1) −3(0) −178(105) −78(39) −3(1) −5(2)

Fold(SD): fold value (standard deviation).

genes on the microarray showed down regulation in 62% and up regulation in 38% of genes affected by low P (Fig. 3). The largest pathway of down-regulated genes by low P belonged to glycolysis IV; the largest up-regulated pathway was related to sucrose biosynthesis. Pathways of glycolysis IV and sucrose biosynthesis in the cytosol of photosynthetic tissue are subpathways of the superpathway of sucrose and starch metabolism II (TAIR; http://www.arabidopsis.org/ and PMN, http://plantcyc.org/, 2008) similar to the starch biosynthesis in chloroplasts, which has also been identified as a subpathway. Based on our data and available information (information http://plantcyc.org/, 2008), we designed a new superpathway of carbohydrate metabolism (Fig. 4). Low P up-regulated genes related to starch degradation, starch biosynthesis, and sucrose biosynthesis, by 18, 36 and 41%, respectively, while 8% of genes affected by low P in glycolysis IV pathway were up-regulated (Fig. 3 and Table 3 ).

Change in contents of sugar, pyruvate, and starch by low P If gene expression is modified and effective in altering the pool of metabolites, low P should affect related metabolites. To confirm that a decrease in P in rice was responsible for the changed expression of genes related to metabolic pathways, we measured glucose, fructose, pyruvate and starch in rice leaves after low P treatment and controls. Because glucose and fructose can be generated from sucrose, we also measured the sucrose content. This concept was shown to be correct because contents of starch and sucrose significantly increased, but glucose and pyruvate decreased (Fig. 5). ␣-d-Glucose-6-phosphateis produced during starch degradation, and converted to fructose 1,6-bisphosphate but also used for sucrose biosynthesis (Fig. 4). Starch is eventually converted to pyruvate (Fig. 4). During these steps, intermediates such as glucose and fructose phosphate are produced.

Fig. 3. Percentage of down-regulated (open bars) and up-regulated (solid bars) genes sorted by metabolic pathways. 1, average of all affected genes; 2, all genes related to metabolic pathway; 3, glycolysis IV; 4, UDP-glucose conversion; 5, chlorophyllide ˛ biosynthesis; 6, phospholipases; 7, cellulose biosynthesis; 8, purine nucleotides de novo biosynthesis II; 9, TCA cycle; 10, jasmonic acid biosynthesis; 11, gluconeogenesis; 12, NAD/NADH phosphorylation and dephosphorylation; 13, starch degradation; 14, glucose fermentation to lactate II; 15, sucrose degradation III; 16, betanidin degradation; 17, aerobic respiration; 18, sucrose degradation to ethanol and lactate (anaerobic); 19, oxidative ethanol degradation III; 20, fructose degradation to pyruvate and lactate (anaerobic); 21, Calvin cycle; 22, galactose degradation II; 23, starch biosynthesis; 24, salvage pathways of purine nucleosides; 25, tryptophan biosynthesis; 26, removal of superoxide radicals; 27, sucrose biosynthesis.

Table 2 Genes in rice leaves affected by low P and sorted by metabolic’ pathways. General function

Metabolic step

Pathway ID

Specific pathway

No. of genes Whole (A)

Biosynthesis

TRPSYN-PWY PWY-2941 PWY-702 PWY-801

Aminoacyl-tRNA charging Carbohydrates biosynthesis

TRNA-CHARGING-PWY CALVIN-PWY P61-PWY GLUCONEO-PWY ANAEROFRUCAT

Cell structures biosynthesis Cofactors, prosthetic groups, electron carriers biosynthesis Fatty acids and lipids biosynthesis Hormones biosynthesis

Nucleosides and nucleotides biosynthesis

SUCSYN-PWY PWY-622 PWY-1001 PHOSLIPSYN-PWY COA-PWY PWY0-862 PWY-735 PWY-2881 PWY-2582 PWY-841 PWY0-162

Secondary metabolites biosynthesis

PWY-5121 PWY-922 UDPNACETYLGALSYNPWY PWY-66

Total (B)

D(Md)

U(Md)

Tryptophan biosynthesis Lysine biosynthesis II Methionine biosynthesis II Homocysteine and cysteine interconversion tRNA charging pathway Calvin cycle UDP-glucose conversion Gluconeogenesis Fructose degradation to pyruvate and lactate (anaerobic) Sucrose biosynthesis Starch biosynthesis Cellulose biosynthesis Phospholipid biosynthesis I Coenzyme A biosynthesis

35 35 43 23

13 12 12 6

8(−4) 10(−10) 10(−5) 4(−46)

5(11) 2(119) 2(41) 2(36)

37 34 28 26

121 113 114 134 185

42 51 49 49 65

34(−11) 34(−8) 44(−7) 41(−21) 45(−16)

8(60) 17(36) 5(48) 8(90) 20(55)

35 45 43 37 35

51 30 35 71 12

22 14 15 24 2

13(−6) 9(−3) 13(−15) 21(−7) 1(−10)

9(15) 5(12) 2(93) 3(45) 1(4)

43 47 43 34 17

Fatty acid elongation (unsaturated II) Jasmonic acid biosynthesis Cytokinins 7-N-glucoside biosynthesis Brassinosteroid biosynthesisII Purine nucleotides de novo biosynthesis II De novo biosynthesis of pyrimidine ribonucleotides Geranylgeranyldiphosphate biosynthesis II (plastidic) Mevalonate pathway UDP-N-acetylgalactosamine biosynthesis GDP-l-fucose biosynthesis I (from GDP-d-mannose)

30

9

7(−8)

2(61)

30

75 189

31 71

26(−21) 59(−5)

5(61) 12(46)

41 38

125 64

42 22

35(−15) 19(−7)

7(51) 3(21)

34 34

42

13

9(−9)

4(40)

31

30

10

8(−4)

2(15)

33

28 54

9 15

6(−13) 9(−7)

3(32) 6(15)

32 28

22

6

5(−24)

1(2)

27

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Amino acids biosynthesis

B/A, % Regulated

197

198

Table 2 (Continued) General function

Metabolic step

Pathway ID

Specific pathway

No. of genes Whole (A)

Degradation/utilization/ assimilation

Alcohols degradation Amino acids degradation

Carbohydrates degradation

PWY-621 PWY-842 PWY-3821 PWY-3801

Nucleosides and nucleotides degradation and recycling

P121-PWY P1-PWY PWY0-163

Secondary metabolites degradation Detoxification Generation of precursor metabolites and energy

Other Electron transfer Fermentation

Glycolysis Other Photosynthesis Respiration

TCA cycle U: upregulated; D: downregulated; Md: median fold value.

PWY-5461 LIPAS-PWY PWY-1081 DETOX1-PWY PWY-5083 P124-PWY P127-PWY FERMENTATION-PWY GLYCOLYSIS LIPASYN-PWY CHLOROPHYLL-SYN PWY-3781 ANARESPDON-PWY ANARESP1-PWY TCA

Oxidative ethanol degradation III Oxidative ethanol degradation I Valine degradation II Leucine degradation I De novo biosynthesis of pyrimidine deoxyribonucleotides Sucrose degradation III Starch degradation Galactose degradation II Sucrose degradation to ethanol and lactate (anaerobic) Salvage pathways of purine nucleosides Salvage pathways of purine and pyrimidine nucleotides Salvage pathways of pyrimidine ribonucleotides Betanidin degradation Triacylglycerol degradation Homogalacturonan degradation Removal of superoxide radicals NAD/NADH phosphorylation and dephosphorylation Glucose fermentation to lactate II Ethanol fermentation to acetate Mixed acid fermentation Glycolysis IV (plant cytosol) Phospholipases Chlorophyllide a biosynthesis Aerobic respiration Aerobic respiration Respiration (anaerobic) TCA cycle

Regulated Total (B)

D(Md)

U(Md)

20 67 75 96 31

10 22 23 27 8

7(−14) 18(−6) 18(−26) 13(−6) 6(−4)

3(58) 4(48) 5(34) 14(34) 2(40)

50 33 31 28 26

84 106 88 298

35 44 32 108

27(−16) 36(−4) 21(−17) 77(−7)

8(49) 8(24) 11(35) 31(49)

42 42 36 36

52

19

12(−7)

7(38)

37

104

25

5(−8)

20(50)

24

57

10

9(−16)

1(159)

18

249 129 149 61 29

89 38 43 26 12

64(−15) 23(−11) 31(−45) 16(−22) 10(−7)

25(37) 15(83) 12(51) 10(102) 2(148)

36 29 29 43 41

148 78 118 125 62 50 79 44 112 99

53 24 34 48 25 18 32 14 35 36

42(−36) 15(−7) 27(−35) 44(−7) 22(−16) 16(−4) 23(−36) 10(−28) 24(−17) 31(−27)

11(63) 9(14) 7(27) 4(38) 3(65) 2(8) 9(50) 4(37) 11(48) 5(96)

36 31 29 38 40 37 41 32 31 36

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PWY66-162 PWY66-21 PWY-5057 LEU-DEG2-PWY PWY0-166

B/A, %

Table 3 Genes sorted by pathways (starch degradation, glycolysis IV, sucrose biosynthesis, and starch biosynthesis) regulated by low P in rice leaves. Enzyme

Gene ID

Oligo ID

Starch degradation

␣-Amylase 4-␣-Glucanotransferase

OsIFCC027048 OsIRUA002902 OsJRFA101900 OsIRUA003666 OsJRFA103367 LOC Os03g55090.1 OsJRFA065487 OsIFCC011832 OsJRFA105449 OsIFCC012398 OsJRFA068968 OsIFCC000914 OsIRUA003269 LOC Os10g41550.1 LOC Os03g22790.1 LOC Os02g03690 OsIFCC006498 OsIFCC024731 OsIFCC031096 LOC Os10g32810.1 OsJRFA064590 OsJRFA064011 OsIRUA003216 LOC Os01g71320.1 OsIFSB002014 OsIFSB002013 OsIRUA000160 OsJRFA107494 CB630867 OsJRFA068061 OsJRFA060343 OsJRFA066618 OsJRFA071036 OsIFCC026862 OsIRUA005866 OsJRFA065052 OsIRUA004450 OsIRUA000772 AU090544 OsIRUA003288 OsJRFA059771 OsIFCC026862 OsIRUA003571 OsIRUA003540 OsJRFA064893 OsIRUA004199 OsJRFA068502

Os052086 Os005480 Os020038 Os006787 Os021048 Os054687 Os012072 Os003651 Os021775 Os001998 Os014590 Os052837 Os006168 Os001087 Os002571 Os057261 Os000347 Os051347 Os054634 Os057558 Os011417 Os011016 Os006069 Os004257 Os038719 Os038718 Os000495 Os023219 Os005122 Os013918 Os009330 Os012905 Os016121 Os000926 Os056941 Os011755 Os008066 Os001928 Os009960 Os006197 Os009102 Os005402 Os006636 Os006594 Os011642 Os007657 Os014249

␣-Glucan phosphorylase ␣-Glucosidase

␤-Amylase

Hexokinase

Glucose-6phosphate isomerase

Phosphofructokinase

Sucrose biosynthesis

Phosphoglucomutase

01 01 01 01 01 01 01 01 01 01 01 01 01 01 01 02 01 01 01 01 01 01 01 01 01 01 01 01 01 01 01 01 01 01 02 01 01 01 01 01 01 01 01 01 01 01 01

Locus ID

GO term(s)

LOC LOC LOC LOC LOC LOC LOC LOC LOC LOC LOC LOC LOC LOC LOC LOC LOC LOC LOC LOC LOC LOC LOC LOC LOC LOC LOC LOC LOC LOC LOC LOC LOC LOC LOC LOC LOC LOC LOC LOC LOC N/A N/A N/A N/A N/A LOC

N/A N/A GO:0016740 GO:0016740, GO:0030246 GO:0006950, GO:0016740, GO:0009536, GO:0009628 GO:0005783, GO:0016787

Os08g36910.1 Os10g35020.1 Os09g25460.1 Os07g43390.1 Os01g63270.1 Os03g55090.1 Os03g11720.1 Os06g46284.1 Os06g46340.1 Os09g07480.1 Os05g38250.1 Os01g62850.1 Os01g13550.1 Os10g41550.1 Os03g22790.1 Os02g03690.1 Os03g04770.1 Os07g35940.1 Os07g47120.1 Os10g32810.1 Os05g45590.1 Os01g09460.1 Os07g09890.1 Os01g71320.1 Os01g52450.1 Os01g53930.1 Os03g56460.1 Os08g37380.1 Os08g37380.1 Os09g29070.1 Os01g07730.1 Os08g25624.2 Os09g12600.1 Os08g34050.1 Os08g25720.1 Os06g22060.1 Os10g26570.1 Os06g05860.1 Os01g09570.1 Os02g48360.1 Os02g41590.1

Os06g28194.1

GO:0016020, GO:0003674

GO:0016787

GO:0007165, GO:0016301, GO:0006950, GO:0009628, GO:0009719, GO:0000166, GO:0005739, GO:0009536, GO:0009987 GO:0009058, GO:0006091, GO:0005975, GO:0009987, GO:0009056, GO:0003824 GO:0016020, GO:0006810, GO:0009536, GO:0005739, GO:0005215 GO:0005575, GO:0016301, GO:0005829, GO:0006091, GO:0005975, GO:0009056

GO:0016301 N/A N/A N/A N/A N/A GO:0005975, GO:0003824

Signal Intensity Cy3

Cy5

63 188 691 25 86 12 281 388 682 86 1053 29 648 84 447 1261 1055 18 4 3 103 196 26 1334 4 10 99 96 348 413 117 133 206 787 6062 59 123 35 162 833 298 36 28 146 65 201 875

182 52 223 3840 13 98 12 102 262 34 421 12 8 15 121 420 377 393 90 248 34 68 10 606 69 671 6 43 85 103 25 32 59 10 183 13 27 8 51 362 35 7 7 42 1 56 20

Fold(SD)

3(1) −4(1) −3(1) 153(45) −7(2) 8(4) −23(15) −4(2) −3(2) −3(2) −3(1) −2(0) −77(45) −6(1) −4(2) −3(1) −3(1) 22(13) 26(9) 73(32) −3(0) −3(1) −3(2) −2(1) 16(13) 69(23) −18(7) −2(0) −4(1) −4(1) −5(2) −4(2) −4(1) −77(23) −33(23) −5(3) −5(1) −4(2) −3(2) −2(1) −9(6) −5(1) −4(1) −4(2) −65(12) −4(2) −44(22)

M.R. Park et al. / Journal of Plant Physiology 169 (2012) 193–205

Pathway

199

200

Table 3 (Continued) Pathway

Enzyme

UDP-d-glucose pyrophosphorylase

Sucrose-phosphate synthase

Aldolase

Triosephosphate isomerase

Glyceraldehyde-3-phosphate dehydrogenase

Oligo ID

Locus ID

OsJRFA064872 OsIRUA001470 OsIRUA002901 OsIRUA002509 OsIRUA000145 LOC Os03g16150.1 LOC Os09g38030.1 OsJRFA065780 OsJRFA065273 OsIRUA004367 OsIFCC039038 OsIRUA000786 OsJRFA063304 OsJRFA100306 OsIRUA007382 LOC Os02g58480.1 OsJRFA102158 OsIRUA001724 OsIFCC006836 OsIRUA001317 OsIRUA007243

Os011632 Os003194 Os005479 Os004888 Os000475 Os007519 Os004023 Os012299 Os011898 Os007926 Os037828 Os001960 Os010591 Os018886 Os005864 Os005336 Os020216 Os003611 Os051978 Os002953 Os004593

01 01 01 01 01 01 01 01 01 01 01 01 01 01 01 01 01 01 01 01 01

LOC LOC LOC LOC LOC LOC LOC LOC LOC LOC LOC LOC LOC LOC LOC LOC LOC LOC N/A LOC LOC

Os07g26610.1 Os07g09720.1 Os10g11140.1 Os03g50480.1 Os08g13930.1 Os03g16150.1 Os09g38030.1 Os01g15910.1 Os02g09170.1 Os08g20660.1 Os11g12810.1 Os06g43630.1 Os04g24430.1 Os07g42490.1 Os06g09450.1 Os02g58480.1 Os03g22120.1 Os03g28330.1

OsIFCC021360 OsIRUA003050 OsIRUA002885 OsJRFA068321 OsJRFA067794

Os002337 Os005786 Os005448 Os014110 Os013720

01 01 01 01 01

LOC LOC LOC LOC LOC

Os05g36270.1 Os03g16050.1 Os04g38530.1 Os10g06720.1 Os06g14740.1

OsIFCC004362 OsIRUA006925 OsJRFA105927 OsJRFA104952 OsIRUA006279 OsIRUA005975 OsIFCC000333 OsIRUA004372 OsIFCC005045 OsIFCC017765 OsIFCC017975 OsJRFA070032 OsJRFA066880 OsJRFA062559 OsJRFA073318 OsJRFA070366 OsJRFA063231

Os054415 Os027669 Os022055 Os021496 Os057735 Os057249 Os054476 Os007935 Os055294 Os054468 Os054392 Os015381 Os013081 Os010113 Os017696 Os015623 Os010533

01 01 01 01 02 02 01 01 01 01 01 01 01 01 01 01 01

LOC LOC LOC LOC LOC LOC LOC LOC LOC LOC LOC LOC LOC LOC LOC LOC LOC

Os01g67860.1 Os05g33380.1 Os11g07020.1 Os09g02540.1 Os01g02880.1 Os09g36450.1 Os01g05490.1 Os01g62420.1 Os03g03720.1 Os04g38600.1 Os04g40950.1 Os02g07490.1 Os05g41590.1 Os08g34210.1 Os01g58740.1 Os01g74000.1 Os02g18784.1

Os09g36030.1 Os08g14330.1

GO term(s)

GO:0005739 GO:0006950, GO:0005975, GO:0003824, GO:0009628, GO:0005737 GO:0009058, GO:0016740

GO:0005975, GO:0016740, GO:0009987

GO:0005975, GO:0016740, GO:0009987

N/A N/A GO:0005975, GO:0009987, GO:0003824, GO:0005886 GO:0016787, GO:0006950, GO:0005975, GO:0009987, GO:0009628 GO:0005975, GO:0009987, GO:0003824 GO:0006091, GO:0005975, GO:0009056, GO:0006139, GO:0003824 GO:0006091, GO:0005975, GO:0009536, GO:0009056, GO:0006139, GO:0005739, GO:0003824 GO:0005975, GO:0009536, GO:0005739, GO:0003824, GO:0015979 GO:0006950, GO:0006091, GO:0003824, GO:0009628, GO:0009579, GO:0005975, GO:0005739, GO:0009056, GO:0009536, GO:0015979 GO:0008152, GO:0009987, GO:0003824, GO:0005737

N/A

Signal Intensity

Fold(SD)

Cy3

Cy5

252 52 62 229 243 298 1494 290 74 15 11 20 155 175 175 203 542 83 7 202 569

63 16 1039 8647 28 85 4481 1101 12 54 608 1476 13 28 28 36 115 457 114 22 19

−4(1) −3(2) 17(12) 38(11) −9(6) -4(2) 3(1) 4(2) −6(3) 4(2) 54(21) 73(22) −12(9) −6(1) −6(4) −6(1) −5(3) 6(2) 15(6) −9(7) −30(27)

227 880 76 218 108

25 33 5 51 11

−9(4) −27(22) −14(12) −4(2) −9(3)

4811 81 841 159 7 286 1277 209 1776 2849 1634 173 242 544 110 81 893

89 32 350 72 113 3 55 31 16 30 23 31 28 81 21 17 425

−54(23) −3(1) −2(1) −2(0) 17(12) −114(97) −23(20) −7(5) −111(65) −95(45) −72(69) −6(3) −9(4) −7(5) −5(2) −5(3) −2(1)

M.R. Park et al. / Journal of Plant Physiology 169 (2012) 193–205

Sucrose synthase

Gene ID

Table 3 (Continued) Pathway

Glycolysis IV

Enzyme

Oligo ID

Locus ID

LOC Os03g01250.4 OsJRFA100921 OsIRUA001718 OsJRFA070705 OsIRUA003559

Os000293 Os019343 Os003603 Os015880 Os006618

01 01 01 01 01

LOC LOC LOC LOC LOC

Os03g01250.4 Os02g57400.1 Os09g39870.1 Os06g45710.1 Os02g07260.1

Phosphoglycerate mutase

AU108653 CB636655 OsIFCC015992 AU197241

Os001991 Os004156 Os001513 Os053520

01 01 01 01

LOC LOC LOC LOC

Os04g01230.1 Os08g37140.1 Os01g47190.1 Os08g34720.1

OsIFSB002964 OsIFCC030036 OsIRUA002155 OsJRFA073611 OsIRUA007098 OsIRUA005386 OsJRFA103463

Os042763 Os051944 Os004317 Os017908 Os002605 Os027081 Os021112

01 01 01 01 01 01 01

LOC LOC LOC LOC LOC LOC LOC

Os02g22000.1 Os09g20820.1 Os10g08550.1 Os06g04510.1 Os03g14450.1 Os10g14040.1 Os09g22410.1

OsIRUA007416 OsIRUA002145 OsJRFA070512 OsIFCC005931 OsIRUA000757 OsJRFA058235 OsJRFA059179 OsIFCC031763 OsJRFA071497 OsIRUA002503 OsIFCC042241 OsIRUA006280 OsJRFA071826 OsIRUA000216 LOC Os07g22930.1 OsIRUA004686 OsIRUA007090 OsIRUA000588 OsJRUA000766 OsJRFA068920 OsIRUA003289 OsIFCC017437

Os006578 Os004300 Os015739 Os041359 Os001895 Os008536 Os008886 Os055524 Os016442 Os004879 Os052471 Os057737 Os016661 Os000647 Os057871 Os008509 Os002439 Os001449 Os054414 Os014555 Os006200 Os054559

01 01 01 01 01 01 01 01 01 01 01 02 01 01 02 01 01 01 01 01 01 01

LOC LOC LOC LOC LOC LOC LOC LOC LOC LOC LOC LOC LOC LOC LOC LOC LOC LOC LOC LOC LOC LOC

Os03g46910.1 Os01g16960.1 Os07g08340.1 Os01g47080.1 Os04g58110.1 Os11g05110.1 Os11g10980.1 Os10g42100.1 Os09g12660.1 Os03g52460.1 Os07g13980.1 Os01g44220.1 Os08g25734.1 Os01g52250.1 Os07g22930.1 Os08g09230.1 Os05g45720.1 Os06g12450.1 Os06g04200.1 Os06g51084.1 Os06g26234.1 Os02g32660.1

Pyruvate kinase

ADP-glucose pyrophosphorylase

Starch synthase

1,4-␣-Glucan-branching enzyme

GO term(s)

N/A N/A N/A GO:0016301, GO:0006950, GO:0006091, GO:0005975, GO:0009056, GO:0005739, GO:0009628, GO:0005737 GO:0008152, GO:0003824

GO:0009605, GO:0009058, GO:0006950, GO:0009536, GO:0005739, GO:0003824, GO:0006519 N/A GO:0005739, GO:0003824

N/A GO:0016301, GO:0006091, GO:0005975, GO:0009056 GO:0016301, GO:0006091, GO:0005975, GO:0009056, GO:0005739

GO:0016740

GO:0005975, GO:0016740, GO:0009987

Signal Intensity

Fold(SD)

Cy3

Cy5

329 109 48 1054 55

16 27 24 125 18

−20(11) −4(1) −2(1) −8(3) −3(2)

159 158 1713 12

17 38 779 667

−9(7) −4(1) −2(0) 58(32)

7 841 301 244 893 278 189

411 9 84 94 357 93 9

61(41) −95(38) −4(1) −3(1) −3(1) −3(2) −21(17)

86 137 128 193 288 607 706 10 105 68 130 19 65 236 53 954 101 17 143 128 688 9

10 20 23 5 10 225 282 77 11 21 56 190 652 36 20 397 44 198 4897 5 255 151

−9(4) −7(3) −6(1) −36(21) −29(22) −3(1) −3(1) 8(3) −10(4) −3(2) −2(1) 10(2) 10(4) −7(5) −3(1) −2(0) −2(0) 12(3) 34(27) −26(16) −3(1) 17(14)

M.R. Park et al. / Journal of Plant Physiology 169 (2012) 193–205

Phosphoglycerate kinase

Enolase

Starch biosynthesis

Gene ID

Fold(SD): fold value(standard deviation).

201

202

M.R. Park et al. / Journal of Plant Physiology 169 (2012) 193–205

Fig. 4. Superpathway of carbohydrate metabolites in photosynthetic tissue, modified by superpathway of sucrose and starch metabolism II (PMN, http://plantcyc.org/). Double arrows show reversible reactions. Each field indicates individual reactions of each subpathway and the number of genes per subpathway corresponds to the area. The number shown in each field corresponds to the number of genes down regulated (D) and up regulated (U) by low P in the respective sub-pathway.

Discussion Our data show a substantial response of rice to low P conditions. Most of the affected genes are involved in general metabolic reactions similar to the results of Ma et al. (2001) and Wu et al. (2003), who reported approximately 29% of Arabidopsis genes changed 2fold or more during P starvation. However, our results showed that about 34% of the examined genes were affected by low P. This large percentage suggests that rice may be more sensitive to P starvation than Arabidopsis but also be increased by pseudogenes included

in the array (see www.ggbio.com/rice60kchip.html, GreenGene Biotech). Our classification by metabolic pathways confirmed that genes related to metabolic pathways were more susceptible to low P than other genes. According to Wu et al. (2003), genes related to photosynthesis and general carbon metabolism were significantly affected by P deficiency in Arabidopsis. Our results also show that genes related to photosynthesis and carbon metabolism were more affected by low P in rice leaves compared to other genes. Li et al. (2010) reported that genes involved in phosphate (Pi) transport, phosphatases, and genes pertaining to primary and secondary

M.R. Park et al. / Journal of Plant Physiology 169 (2012) 193–205

203

Fig. 5. Contents of pyruvate, fructose, glucose, sucrose, and starch in leaves of rice seedlings grown in a hydroponic culture system. V3 plants were either grown at control levels of P (open bars, 320 ␮M P) or exposed for five days to low P conditions (black bars, 32 ␮M P). Columns labeled with different letters are significantly different (p < 0.05).

metabolism were differentially affected by P deficiency in rice roots. The discrepancy between the data by Li et al. (2010) and our data likely stems from a different number of replicates per time point (6 vs. 3), a less stringent p-threshold (0.01 vs. 0.05), and a different choice of tissue (roots vs. leaves). The reduced stringency lead to a higher number of affected genes (Fig. 2) and the difference in tissue also is likely to reflect different gene expression. Lastly, the lower number of replicates is likely to increase the overall uncertainty. Further, phosphate use efficiency differs based on the tissue type: shoots (this paper) vs. roots (Li et al., 2010). Sucrose and starch contents in rice leaves increased significantly under low P conditions (Fig. 5), similar to results by Li et al. (2006). ␣-Amylase (EC 3.2.1.1) I-1 plays an important role in the starch degradation in chloroplasts through the endoplasmic reticulum–Golgi system in rice (Asatsuma et al., 2005); this gene (OsIFCC027048) was up-regulated by low P (Table 3). Degradation of transitory starch mainly results in the formation of glucose and maltose; neutral sugars are transported into the cytosol via respective translocators. The cytosolic metabolism of neutral sugars is catalyzed by the action of hexokinase (HXK; EC 2.7.1.1), phosphoglucomutase, and transglucosidase (EC 2.4.1.24) that utilize high molecular weight glycans. HXK has dual functions; it phosphorylates hexose to hexose 6-phosphate and is important for sugar sensing and signaling (Jang et al., 1997). However, HXK function is complex because rice contains ten isoforms, OsHXK1 through OsHXK10. Sugars induced the expression of three OsHXKs, OsHXK2, OsHXK5, and OsHXK6, in excised leaves, but suppressed OsHXK7 expression in excised leaves and immature seeds (Cho et al., 2006). Our results show strong upregulation by low P of OsHXK2 (OsIFSB002013) and OsHXK5 (OsIFSB002014), while OsJRFA067988 and OsIRUA003216, which encode OsHXK8 and are expressed during early seed development (Cho et al., 2006), were down-regulated by low P (Table 3). Because ␣-amylase I-1, OsHXK2 andOsHXK5, which contribute to starch degradation, were up-regulated by low P, we infer that P deficiency leads to degradation of starch in rice. PGMs in plants consist of plastid-localized (pPGM; EC 5.4.2.2) and cytosolic isoforms (cPGM, EC 5.4.2.2) (Herbert et al., 1979; Gottlieb, 1982). cPGM interconverts glucose-6-phosphate and glucose-1-phosphate and is essential for energy metabolism. The upregulation of cPGM genes by low P compared with other PGMs suggests that cytosolic glucose-1-phosphate is required for UDP-dglucose production, which serves as a precursor for the synthesis of sucrose and cell wall constituents, including cellulose and callose (Koch, 2004). UDP-glucose pyrophosphorylase (UGPase, EC 2.7.7.9) is a key enzyme in plant carbohydrate metabolism and cell wall biosynthesis. This enzyme catalyzes the reversible formation of UDP-glucose and pyrophosphate from glucose-1-phosphate and uridine triphosphate. The rice genome contains two homologous UGPase genes, OsUgp1 and OsUgp2. The OsUgp1 gene is

expressed in various rice tissues (Chen et al., 2007). OsUgp isoform derived from the OsUgp2 gene occurs preferentially in pollen and is developmentally regulated (Mu et al., 2009). Expression of OsUpg1 (OsJRFA065780) and OsUgp2 (LOC Os09g38030.1) was enhanced by low P. Low P increased sucrose by upregulated cPGM and UGPase genes. Because concentrations of leaf sucrose were not related to the activities of sucrose phosphate synthase (Hawker and Smith, 1984; Huber, 1983), low P leads to an accumulation of sucrose not because of inhibited degradation but because of increased synthesis (Fig. 5). Cytosolic ␣-d-glucose-1-phosphate is converted into ADPglucose by the activity of ADPglucose pyrophosphorylase (AGPase; EC 2.7.7.27). This substance is imported into plastids via an ADP-glucose/ADP antiporter (ADP-glucose transporter) in the plastid envelope. Within plastids, glucose 6-phosphate is converted to glucose 1-phosphate by pPGM, which is then converted to ADP d-glucose by AGPase from glucose 1-phosphate and ATP. However, cytosolic ADP-glucose is also imported into plastids for starch synthesis via an ADP-glucose/ADP antiporter (ADPglucose transporter) in the plastid envelope (Comparot-Moss and Denyer, 2009). As AGPase activity is governed by the ratio of the activator 3-phosphoglycerate (3-PGA) and inhibitory inorganic P; its activity is one of the rate-limiting steps of starch biosynthesis (Nagai et al., 2009). The rice AGPase gene family consists of genes encoding small subunits (SSU) and large subunits (LSU) of the AGPase heterotetrameric complex. SSU (OsAGPS2a and OsAGPS1) and LSU (OsAGPL1, OsAGPL3, and OsAGPL4) are targeted to plastids, but OsAGPS2b and OsAGPL2 are localized in the cytosol (Lee et al., 2007). We show that OsAGPS1 (OsJRFA071826) and OsAGPL3 (OsIRUA006280) was upregulated in rice leaves under low P. The starch branching enzyme (SBE, 1,4-␣-glucan-branching enzyme, EC 2.4.1.18) catalyzes the formation of the ␣-1,6 linkages within the polymer and the SBE IIa gene (OsIFCC017437) was upregulated 17-fold by low P. Although the absence of SBE IIa in maize lead to a modification of the structure of photosynthetic starch it had no effect on endosperm starch (Stinard et al., 1993; Blauth et al., 2001, 2002; Yao et al., 2004). Based on these results we assume that decreased P increases starch in rice leaves. Our results also show that low P enhanced expression of genes for sucrose and starch biosynthesis and increased sucrose and starch contents in rice leaves. Thus, the increase in gene expression related to carbohydrate metabolisms affects the equilibrium between sucrose and starch. In plants, glycolysis occurs in two different locations: the plastids (glycolysis I) and cytosol (glycolysis IV, http://www.plantcyc.org/). As part of glycolysis, triosephosphate isomerase (TPI; d-glyceraldehyde-3-phosphate ketol isomerase; EC 5.3.1.1) catalyzes the conversion of dihydroxyacetone phosphate

204

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and d-glyceraldehyde-3-phosphate, an essential isomerization reaction in the glycolytic pathway. This enzyme also plays a central role in other metabolic pathways involving triosephosphate, such as gluconeogenesis, fatty acid biosynthesis, pentosephosphate pathway, and photosynthetic carbon fixation (Miernyk, 1990). All TPI genes were down-regulated by low P (Fig. 3 and Table 3) as were about 84% of the 49 genes related to gluconeogenesis (Fig. 3). Phosphofructokinase (PFK, EC 2.7.1.11) catalyzes the rate-limiting step in glycolysis IV (Turner and Turner, 1980) and is the most important control point. It is also the first irreversible step that is unique to the glycolytic pathway. Low P down-regulated all PFK genes (Table 3). Based on these results, we speculate that the low-P down-regulated genes for glycloysis IV reduce the efficiency of glycolysis and lead to a reduced content of intermediates and pyruvate compared with control plants. The content of starch is closely related to rate of photosynthesis in chloroplasts and photosynthesis is positively related to the content of chlorophyll (Hu et al., 2009). Although CO2 assimilation decreased, a large amount of starch in leaves was retained possibly because the low availability of P inhibits the triose phosphate translocator (Pieters et al., 2001). Carbon is retained in chloroplasts as starch; a process that was facilitated by increased activation of AGPase in Nicotiana tabacum (Nielsen et al., 1998). Starch may also accumulate as a consequence of decreased demand for growth (cell wall biosynthesis) and respiration (Pieters et al., 2001). In our previous study, the rate of carbon assimilation and content of chlorophyll in rice were positively correlated with phosphate (Park et al., 2010). The relationship between P and chlorophyll seems universal as the chlorophyll content of pytoplankton was also lowered as a result of nitrogen and phosphorus deficiency (Riemann et al., 1989). Chlorophyll is essential for photosynthesis and is linked to photosynthetic ATP production. The conversion of chlorophyllide ˛ to chlorophyll depends on uroporphyrinogen decarboxylase (Mock and Grimm, 1997), Mg chelatase (Jung et al., 2003a), Mg protoporphyrin IX methyltransferase (Pontier et al., 2007), Mg-protoporphyrin IX monomethyl ester cyclase (Bang et al., 2008; Hung et al., 2010), and 3,8-divinyl(proto) chlorophyll(ide) a 8-vinyl reductase (Wang et al., 2010). However, the chlorophyll content was not significantly changed by the activity of protoporphyrinogen oxidase in rice (Jung et al., 2003b). Most of genes related to chlorophyllide a biosynthesis were down-regulated by low P in rice except for two protoporphyrinogen oxidase genes (Fig. 3 and Table 1). The content of chlorophyll in rice grown under low P decreased compared to controls, indicating that the chlorophyll content is controlled by P and that the decrease in gene expression reduced chlorophyll content. In summary, many genes in diverse metabolic pathways responded to reduced P in rice. Especially the genes related to carbohydrate metabolism (starch degradation and biosynthesis, sucrose biosynthesis and glycolysis IV) were more sensitive than genes related to other pathways. Moreover, metabolites such as pyruvate, glucose, fructose, and chlorophyll were negatively affected, while sucrose and starch content increased in low P rice plants. Our results indicate that low P in rice induces sucrose and starch but reduces chlorophyll. Thus, phosphorus nutrition is essential for sustained the carbohydrate pool, growth, and (endosperm) development and nutritional value of rice. Taken together, our study of the genome-wide relationship between P nutrition and metabolic responses characterizes the intricate balance between gene expression and P-related metabolites after short-term P starvation. The down-regulation of genes resulted in a modification of the metabolite pool but the temporal response and long-term effects of P starvation remain to be investigated.

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