Open Access

iTRAQ-based proteome profile analysis of superior and inferior Spikelets at early grain filling stage in japonica Rice

BMC Plant BiologyBMC series – open, inclusive and trusted201717:100

https://doi.org/10.1186/s12870-017-1050-2

Received: 12 January 2017

Accepted: 29 May 2017

Published: 7 June 2017

Abstract

Background

Large-panicle rice varieties often fail to achieve their yield potential due to poor grain filling of late-flowering inferior spikelets (IS). The physiological and molecular mechanisms of poor IS grain filling, and whether an increase in assimilate supply could regulate protein abundance and consequently improve IS grain filling for japonica rice with large panicles is still partially understood.

Results

A field experiment was performed with two spikelet removal treatments at anthesis in the large-panicle japonica rice line W1844, including removal of the top 1/3 of spikelets (T1) and removal of the top 2/3 of spikelets (T2), with no spikelet removal as a control (T0). The size, weight, setting rate, and grain filling rate of IS were significantly increased after spikelet removing. The biological functions of the differentially expressed proteins (DEPs) between superior and inferior spikelets as well as the response of IS to the removal of superior spikelets (SS) were investigated by using iTRAQ at 10 days post anthesis. A total of 159, 87, and 28 DEPs were identified from group A (T0-SS/T0-IS), group B (T0-SS/T2-IS), and group C (T2-IS/T0-IS), respectively. Among these, 104, 63, and 22 proteins were up-regulated, and 55, 24, and 6 proteins were down-regulated, respectively. Approximately half of these DEPs were involved in carbohydrate metabolism (sucrose-to-starch metabolism and energy metabolism) and protein metabolism (protein synthesis, folding, degradation, and storage).

Conclusions

Reduced endosperm cell division and decreased activities of key enzymes associated with sucrose-starch metabolism and nitrogen metabolism are mainly attributed to the poor sink strength of IS. In addition, due to weakened photosynthesis and respiration, IS are unable to obtain a timely supply of materials and energy after fertilization, which might be resulted in the stagnation of IS development. Finally, an increased abundance of 14–3-3 protein in IS could be involved in the inhibition of starch synthesis. The removal of SS contributed to transfer of assimilates to IS and enhanced enzymatic activities of carbon metabolism (sucrose synthase, starch branching enzyme, soluble starch synthase, and pullulanase) and nitrogen metabolism (aspartate amino transferase and alanine amino transferase), promoting starch and protein synthesis in IS. In addition, improvements in energy metabolism (greater abundance of pyrophosphate-fructose 6-phosphate 1-phosphotransferase) might be played a vital role in inducing the initiation of grain filling. These results collectively demonstrate that carbohydrate supply is the main cause of poor IS grain filling.

Keywords

Rice Removal of superior spikelets Inferior spikelets Grain filling iTRAQ Proteome

Background

Rice is a major staple food crop worldwide, and its consumption is increasing as the world’s population grows. Improving the output per unit area is therefore necessary for producing more rice on a limited land area [1]. Many efforts have been made to reach this target by breeders, who have attempted to expand the sink capacity by increasing the number of spikelets per panicle, creating extra-heavy panicle types or large-panicle rice varieties [2]. These cultivars with larger sink capacities, however, generally do not produce the expected yield due to the low seed setting rate and grain weight of inferior spikelets (IS) [3, 4]. Within the rice panicle, spikelets are grouped into superior spikelets (SS) and IS according to their location on the branch and the time of flowering [5]. Generally, SS are located on upper primary branches, and they flower earlier, fill more quickly, and produce larger and heavier grains. The IS are located on the lower secondary branches, and they flower later, fill more slowly, and produce smaller grains [6]. Therefore, improving IS grain filling is important for achieving a high yield potential of large-panicle rice varieties.

There are many explanations that may account for poor IS grain filling, including reduced activities of the enzymes involved in sucrose-to-starch conversion [7, 8], hormone imbalances [4], assimilate transportation obstacles [3, 9], and the differential expression of genes associated with cell growth and signal transduction [10]. However, whether the assimilate supply is a crucial factor for poor IS grain filling remains controversy [3, 6, 11]. In order to clarify this question, previous studies have normally used leaf- and flower-thinning methods to regulate the source-sink balance [12, 13]. For example, Xu et al. [14] found that IS grain weight and grain setting rate were significantly increased following removal of SS. However, Kato [15] reported that IS filling was not significantly improved by SS removal. Our previous study showed that SS removal could force assimilate transport to IS, promoting IS grain filling, and the possible physiological mechanisms underlying this process have been discussed [16]. However, rice grain filling is a highly complex biological process, and previous studies have primarily focused on the relationship between spikelet removal and grain weight or the underlying physiological mechanisms. Thus, the effect of SS removal on protein abundances in IS and how these interact with IS grain filling remains unclear.

In recent years, proteomics has become an essential technique for revealing the mechanisms of poor IS grain filling. Proteomics contributes to a greater understanding of complex biological systems as it allows for the simultaneous analysis of changes in multiple proteins [17]. There have been numerous studies that have attempted to resolving the problem of poor IS grain filling by reporting differences in protein abundance between SS and IS. Zhang et al. [18] employed two-dimensional gel electrophoresis (2-DE)-based comparative proteomic and phosphoproteomic analyses to explore differentially expressed proteins in IS following spraying with abscisic acid (ABA); a total of 111 differentially expressed proteins (DEPs) were found to be associated with defense response, carbohydrate, protein, amino acid, energy, secondary metabolism, cell development, and photosynthesis, demonstrating that IS grain filling was improved by ABA through proteins and phosphoproteins that participate in carbon, nitrogen, and energy metabolisms. Furthermore, Zhang et al. [19] reported that the 14–3-3 protein plays an important role in the signaling networks of IS development, especially in developmental stagnancy. Chen et al. [20] also compared differential protein expression between SS and IS using the 2-DE method and found that the dramatic down-regulation of functional proteins related to photosynthesis, carbohydrate and energy metabolism, amino acids metabolism, and defense responses was the main cause of poor IS grain filling. In addition, they found that post-anthesis alternate wetting and moderate soil drying could improve grain filling by regulating protein expression, especially in IS. Although 2-DE could separate thousands of different proteins and provide visual information of the proteome including distinct protein isoforms resulting from changes in Mr. (relative molecular mass) and pI (isoelectric point), it is not suitable for detection of low-abundance proteins and more accurate quantification. Isobaric tags for relative and absolute quantitation (iTRAQ) is a mass spectrometry-based quantitative approach that has become prevalent in developmental grain proteomics, as it simultaneously identifies and quantifies proteins from multiple samples with high coverage [21]. It has been reported that lower sink strength and smaller sink sizes result in reduced decomposition, conversion of photoassimilate, and slower cell division in hybrid rice [22]. However, previous studies were only made on hybrid rice or an indica varieties, and little proteomic information has documented using iTRAQ regarding SS and IS and the response of IS to SS removal in homozygous japonica rice.

This study investigated whether an increase in assimilate supply could regulate protein abundance and consequently improve IS grain filling for japonica rice with large panicles. Transfer of assimilates toward IS was forced by removal of SS, and we examined subsequent changes in grain weight, seed setting rate, and grain filling rate of IS during the grain filling period. Additionally, iTRAQ technology was used to identify DEPs between SS and IS under different treatments and their biological functions, and then we analyzed the relationship between these proteins and grain development to reveal the underlying causes of differences in grain filling between SS and IS as well as the response of IS to SS removal at proteomic level.

Methods

Plant materials

The experiment was conducted in 2015 at the Danyang Experimental Base of the Nanjing Agricultural University, Jiangsu Province, China (31°54′31″N, 119°28′21″E) during the rice growing season. In order to analyze the mechanisms of poor IS grain filling at the molecular level, the experiment was conducted using the homozygous large-panicle japonica rice line W1844, which is an inbred line and not a hydrid or transgenetic line. Moreover, W1844 is the intermediate material of breeding, but its genetic characteristics have stabilized. The seeds of W1844 were provided by the professor jian-min wan of the State Key Laboratory of Rice Genetics and Germplasm Innovation, Nanjing Agricultural University, Jiangsu, China. W1844 has 265 grains per panicle and thus is typical of large-panicle rice varieties. Its plant height, panicle length, thousand-grain weight, and seed setting rate are 99.8 cm, 18.1 cm, 23.6 g, and 92.1% respectively. Seeds were sown on May 28th, 2015, and seedlings were transplanted to the field on June 18th at a hill spacing of 13.3 cm × 30 cm. The trials were designed in randomized plots with three replicates, and each plot was 5 m × 10 m. The soil type was clay loam, and 280 kg·ha−1 nitrogen was applied during the growing season. Nitrogen fertilizer was converted into urea according to the nitrogen content and was applied according to the ratio of base fertilizer to panicle fertilizer (5:5). The base fertilizer was applied before seedling transplantation, and the panicle fertilizer was applied when the leaf-age remainder was 3.5. Cultivation and management measures were applied according to the technical requirements of the local high-yield field.

Experimental design

A total of 800 single stems (panicles) with similar growth patterns that flowered on the same day were labeled during heading-blooming stage. Once most labeled panicles had withdrawn from flag leaf sheath completely, two spikelet thinning treatments were performed: T0 was control treatment with no spikelet thinning, T1 plants had the upper 1/3 of spikelets removed, and T2 plants had the upper 2/3 of spikelets removed. Spikelet thinning involved removal of the primary branch. The primary branches of each panicle were equally divided into three parts: upper, middle, and lower. If the number of primary branches could not be divided equally, a number of spikelets equal to the integer of the average branch number was included in each of the upper and lower parts, and the remaining branches were included in the middle part. SS were considered to be the grains on the three primary branches on the upper part of the panicle, while medium spikelets (MS) were defined as the grains on the three primary branches in the middle part of the panicle, and IS were the grains on the three primary branches in the lower part.

Sampling and measurement

Determination of the grain filling rate

From anthesis to maturity, 50 tagged panicles from each plot were collected every 5 days. SS, MS, and IS samples were collected from the T0 group; MS and IS were collected from the T1 group, and only IS were collected from the T2 group. Two-fifths of the sampled grains were frozen in liquid nitrogen and stored at −80 °C for protein extraction. The remaining grains were deactivated at 105 °C for 0.5 h and dried at 80 °C until they reached a constant weight. They were then weighed to determine grain dry weights. Richards’s growth eq. [23] was used for grain filling process fitting and grain filling rate calculation:
$$ W=\frac{A}{{\left(1+{Be}^{- kt}\right)}^{1/ N}} $$
(1)
The grain filling rate (R) was calculated as the derivative of the Eq. (1)
$$ R=\frac{AkBe^{- kt}}{N{\left(1+ Be{-}^{\mathrm{kt}}\right)}^{\left( N+1\right)/ N}} $$
(2)

where W is the grain weight (mg); A is the final grain weight (mg); t is the time after anthesis (days); and B, k, and N are coefficients established from the regression of the equation.

Protein extraction

Protein extraction was performed according to Isaacson et al. [24] with some modifications. About 0.1 g dehulled grains were homogenized with a pestle in a pre-cooled mortar containing ice-cold 10% (w/v) trichloroacetic acid in acetone. They were incubated at −20 °C for 1 h, followed by centrifugation at 15000 g for 15 min at 4 °C in a refrigerated high-speed centrifuge, after which the precipitate was collected. After vacuum drying, adding an equal volume of phenol saturated with Tris-HCl (pH 7.5), and centrifugation at 5000 g for 30 min at 4 °C, we collected the upper phenolic phase. Five volumes of pre-cooled 0.1 M ammonium acetate in methanol were added to collected phenol phase, followed by centrifugation at 10000 g for 10 min at 4 °C, after which the precipitate was collected; this process was repeated three times. Protein concentration was determined by the BCA method [25].

Protein digestion and iTRAQ labeling

Protein digestion was performed according to the method of FASP [26]. Five volumes of cold acetone were added to 100 μg protein from each sample and centrifuged at 12000 rpm for 10 min at 4 °C, collected the precipitate and dried by speed vacuum concentrator. 50 μL dissolution buffer was added for dissolve protein precipitation, and added 4 μL reducing reagent, incubated at 60 °C for 1 h, then added 2 μL cysteine-blocking reagent at room temperature for 10 min. Clean the protein solution by using 10 KDa ultrafiltration tube to centrifuge at 12000 rpm for 20 min, and discarded the solution at the bottom of the collection tube; 100 μL dissolution buffer was added to the ultrafiltration tube, then centrifuged at 12000 rpm for 15 min, discarded the solution at the bottom of the collection tube and repeat this step three times. Replace a new collection tube, 50 μL sequencing-grade trypsin (50 ng/μL) was placed into the ultrafiltration tube, incubated at 37 °C for 12 h, and centrifuged at 12000 rpm for 20 min, then collected the peptides. Transfered the filter units to new collection tube and added 50 μL dissolution buffer to centrifuge the tube again, and combined the two filter solution, which contained peptides. The peptides were dried in a centrifugal speed vacuum concentrator.

Two biological replicates were performed for each sample for iTRAQ analysis. The peptides of each sample were labeled using iTRAQ 8-plex kits according to the manufacturer’s manual (AB SCIEX Inc., USA). Labelling was performed by adding one reagent vial, containing an isobaric tag, to 110 μg of dried peptides for each sample. The labelling reaction proceeded for 3 h at room temperature after which all the samples were pooled before application of separation techniques and mass spectrometry analysis. The labelling scheme was as follows: Tags 113 and 117, T0-SS; Tags 114 and 118, T0-IS; Tags 115 and 119, T2-IS.

Two dimensional liquid chromatography tandem mass spectrometry (2D–LC-MSMS) analysis

After labeling, all samples were pooled and purified using a strong cation exchange chromatography (SCX) column by Agilent 1200 HPLC (Agilent). The HPLC column was purchased from Agilent, and its parameters were as follows: the Analytical Guard Column 4.6 × 12.5 mm 5-Micron; Narrow-Bore 2.1 × 150 mm 5 μm with 215 nm and 280 nm UV detection. Separation was performed at 0.3 mL/min using a nonlinear binary gradient. Collected the first peptides from 0 to 5 mins, then collected each peptides with 4.5 mins interval for the 6–45 min, and for the last peptides from 46 to 50 mins, with a total of 10 peptides. Dried every peptides in a vacuum freezed dryer for LC-MSMS Analysis.

The dried peptides were re-suspended with Nano-RPLC buffer A (0.1% formic acid, 2% acetonitrile). The online Nano-RPLC was employed on the Eksigent nanoLC-Ultra™ 2D System (AB SCIEX). The samples were loaded on C18 NanoLC trap column (100 μm × 3 cm, C18, 3 μm, 150 Å) and washed by Nano-RPLC Buffer A at 2 μL/min for 10 mins. An elution gradient of 5–35% acetonitrile (0.1% formic acid) in 70 mins gradient was used on an analytical ChromXP C18 column (75 μm × 15 cm, C18, 3 μm 120 Å) with spray tip. The LC fractions were analyzed using a Triple TOF 5600 mass spectrometer.

Mass spectrometer data acquisition was performed with a Triple TOF 5600 System (AB SCIEX, USA) fitted with a Nanospray III source (AB SCIEX, USA) and a pulled quartz tip as the emitter (New Objectives, USA). Data were acquired using an ion spray voltage of 2.5 kV, curtain gas of 30 PSI, nebulizer gas of 5 PSI, and an interface heater temperature of 150 °C. For information dependent acquisition (IDA), survey scans were acquired in 250 ms and as many as 35 product ion scans were collected if they exceeded a threshold of 150 counts per second (counts/s) with a 2+ to 5+ charge-state. The total cycle time was fixed to 2.5 s. A rolling collision energy setting was applied to all precursor ions for collision-induced dissociation (CID). Dynamic exclusion was set for 1/2 of peak width (18 s), and the precursor was then refreshed off the exclusion list.

Bioinformatics analysis

The proteins were identified in two biological replicates using the iTRAQ technique. Data were processed with Protein Pilot Software v. 5.0 (AB SCIEX, USA) against Oryza sativa database of UniProt using the Paragon algorithm [27]. The experimental data from tandem mass spectrometry were matched against theoretical data for protein identification. The iTRAQ 8-plex was chosen for protein quantification with unique peptides during the search. According to the abundances of proteins and the results of comparison among groups, the screening criteria for authentic proteins was an FDR ≤ 1% and a unique peptide ≥1. The screening criteria for DEPs was a fold change >1.5 or <0.67 and a p-value <0.05. The bioinformatics data analysis tool, OmicsBean, was used to analyze the obtained proteomics data (http://www.omicsbean.cn/), in which distributions in biological functions, cell component and molecular functions were assigned to each protein based on Gene Ontology (GO) categories. The Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis was performed in order to enrich high-level functions in the defined biological systems.

Assessment of panicle characteristics

Approximately 90 tagged panicles from each treatment were harvested at maturity. The SS, MS, and IS were collected from T0 group, MS and IS were collected from T1 group, and IS were collected from T2 group. The samples were naturally dried, and the grain weight and seed setting rate were measured. The seed setting rate was determined using the method of Kobata et al. [28].

Statistical analysis

For all statistical analyses, at least three biological replicates were used for each treatment and control. Statistical analyses of the data were accomplished by the standard analysis of variance (ANOVA) and mean values were tested by least significant difference (LSD) at the 5% level using SPSS16.0.

Results

Grain weight and grain setting rate

Grain weights and seed setting rates were significantly different among the SS, MS, and IS of W1844 under T0 treatment, while SS has the highest and IS has the lowest values (Table 1). Compared with T0 group, the grain weights and seed setting rates of MS and IS in T1 group, as well as those of the IS in T2 group were all increased, with the greatest improvement in seed setting under T2 treatment. Therefore, in the subsequent analyses, we focused on the effect of T2 treatment on IS grain filling. As shown in Table 1, the grain weight and seed setting rate of IS under T2 were the same as those of SS under T0, demonstrating that SS removal significantly improved IS grain weight and seed setting rate.
Table 1

Grain weight and seed setting rate under different treatments

Treatments

T0

T1

T2

Superior

Medium

Inferior

Medium

Inferior

Inferior

Grain weight (mg/grain)

26.6 ± 0.13a

23.2 ± 0.53c

20.9 ± 0.39d

26.2 ± 0.23b

25.6 ± 0.45b

28.2 ± 0.12a

Seed setting rate (%)

97.1 ± 0.47a

93.5 ± 0.39b

85.7 ± 0.26c

96.8 ± 0.26a

92.5 ± 1.10b

96.6 ± 0.76a

T0: control treatment with no spikelet thinning; T1: the upper 1/3 of spikelets were removed; T2: the upper 2/3 of spikelets were removed. Values are means ± S.D. of three replications. The different lowercase letters labeled after the data from the same character indicate significant differences at the 0.05 level

Grain filling of SS and IS

The dynamic changes in grain weight and grain filling rate in W1844 during the grain filling period were shown in Fig. 1. The IS grain weight was consistently lower than that of SS throughout the filling process, while under T2 treatment, the IS grain weight began to increase and reached the SS level at 30 days post anthesis (DPA) (Fig. 1-a). We found that the initial and maximum grain filling rates of IS were consistently lower than those of SS, and peak grain filling also appeared later in IS than that in SS (Fig. 1-b). Compared with T0 treatment, T2 treatment significantly increased the initial and maximum grain filling rates of IS. Moreover, the peak value of IS grain filling rate under T2 was higher and occurred 5 days earlier than that of IS in T0 group. Changes in grain weight and grain filling rate indicated that removal of SS significantly improved IS grain filling.
Fig. 1

Grain weight and grain filling rate of SS and IS of rice during grain-filling period. T0 represent control treatment with no spikelet thinning and T2 represent treatment with the upper 2/3 of spikelets were removed. The black square represent superior spikelets under the T0 treatment, the asterisk represent inferior spikelets under the T0 treatment, and the black triangle represent inferior spikelets under the T2 treatment. Vertical bars, where values exceed size of symbol, represent ± SEM (n = 3)

Grain morphology of SS and IS

Changes in the kernel development dynamics of SS and IS under different treatments are shown in Fig. 2. We observed that the SS first elongated and then widened after flowering, and SS grain size showed a rapid increase. However, the IS developed slowly during the early stage of grain filling (days 5–15), and its grain morphology changed greatly at 20 DPA. Compared to IS under T0, grain size and grain weight of IS under T2 treatment increased significantly at 10 DPA (Fig. 1-a), indicating that important changes occurred within the kernel during this time and affected the development of the IS. Some studies have shown that the physiological activities of grain are significantly positively correlated with grain filling at the beginning of the filling stage [29, 30]. Therefore, the subsequent experiment studied protein expression in the grains under different treatments at 10 DPA.
Fig. 2

The morphology of SS and IS in rice during grain filling period under different treatments (observed under stereoscope × 6.3). T0 represent control treatment with no spikelet thinning and T2 represent treatment with the upper 2/3 of spikelets were removed

DEPs in SS and IS at 10 DPA under different treatments

In order to further study the reason behind the grain filling difference between SS and IS, as well as molecular mechanism of IS response to SS removal, we used comparative proteomics to analyze protein expression in SS and IS. A total of 4631 proteins were identified in two biological replicates using the iTRAQ technique and were subjected to comparative analysis. Protein abundances that changed by more than 1.5-fold or less than 0.67-fold were selected. Following this criterion, a total of 174 types of proteins were detected which showed that there were differentially abundant between SS and IS under different treatments at 10 DPA.

Table 2 lists these DEPs between SS and IS under different treatments, providing the accession numbers and names of these proteins according to the Uniprot database as well as their fold changes in abundance. The numbers of DEPs and their changes in abundance are listed in Fig. 3. As Fig. 3 shows, in the T0-SS/T0-IS comparison, 159 DEPs were identified, of which 104 proteins (65.4%) were up-regulated and 55 proteins (34.6%) were down-regulated; in the T0-SS/T2-IS comparison, 87 DEPs were identified, of which 63 proteins (72.4%) were up-regulated and 24 proteins (27.6%) were down-regulated; and in the T2-IS/T0-IS comparison, 28 DEPs were identified, of which 22 proteins (78.6%) were up-regulated and 6 proteins (21.4%) were down-regulated.
Table 2

Identification of 159, 87 and 28 differentially expressed proteins (≥ 1.5 fold) between SS and IS at 10 DAP in group A (T0-SS/T0-IS), B (T0-SS/T2-IS) and C (T2-IS/T0-IS)

Protein No.

Accession

Uniprot date accession no.

Protein name UniprotKB database

Fold-change (≥1.5-fold)

T0-SS/T0-IS (A)

T0-SS/T2-IS (B)

T2-IS/T0-IS (C)

Cell growth/division

 36

Os02g0753800

Q6Z6A7

Annexin

3.01 ± 0.71

ns

ns

 91

Os05g0438800

Q75HX0

Actin

2.89 ± 0.12

1.76 ± 0.08

ns

 154

Os07g0249700

Q8H3C8

IAA-amino acid hydrolase ILR1-like 8

3.52 ± 0.47

2.06 ± 0.52

ns

Sugar metabolism

 14

Os03g0758100

Q9AUV8

Alpha-1,4 glucan phosphorylase

4.70 ± 0.48

3.18 ± 1.26

ns

 19

Os06g0194900

P30298

Sucrose synthase 2

6.04 ± 1.05

ns

3.57 ± 1.79

 26

Os03g0278000

Q8W3J0

Os03g0278000 protein

0.35 ± 0.04

ns

ns

 33

Os03g0703000

Q75I93

Beta-glucosidase 7

0.40 ± 0.02

ns

ns

 38

Os01g0944700

Q94CR1

Beta 1,3-glucanase

2.82 ± 0.20

2.45 ± 0.27

ns

 50

Os10g0340600

Q7XFK2

Beta-galactosidase 14

0.40 ± 0.13

ns

ns

 54

Os02g0752200

Q6Z8I7

Os02g0752200 protein

0.37 ± 0.09

ns

ns

 65

Os08g0509200

Q84YK7

Beta-glucosidase 27

0.45 ± 0.02

ns

0.57 ± 0.02

 121

Os06g0172800

Q5SNC5

Putative seed imbibition protein

2.52 ± 0.32

ns

ns

 136

Os03g0340500

Q10LP5

Sucrose synthase 4

5.38 ± 1.07

2.02 ± 0.19

ns

Starch biosynthesis

 6

Os04g0409200

Q0JDF0

Os04g0409200 protein

0.48 ± 0.04

ns

ns

 40

Os08g0345800

P15280

Glucose-1-phosphate adenylyltransferase small subunit, chloroplastic/amyloplastic

4.66 ± 0.51

ns

ns

 41

Os05g0580000

Q688T8

Glucose-1-phosphate adenylyltransferase

0.21 ± 0.08

0.21 ± 0.08

ns

 42

Os01g0130400

Q9LGC6

Putative alpha-glucosidase

0.48 ± 0.04

ns

ns

 44

Os01g0633100

Q7G065

ADP-glucose pyrophosphorylase/AGPase

3.36 ± 0.44

ns

ns

 45

Os01g0894300

Q0JGZ6

Fructokinase-1

0.53 ± 0.04

ns

ns

 46

Os01g0841600

Q8LR75

Triosephosphate isomerase

ns

0.33 ± 0.05

ns

 49

Os06g0675700

Q0DA62

Probable alpha-glucosidase Os06g0675700

4.71 ± 1.86

2.51 ± 0.76

ns

 84

Os09g0553200

Q93X08

Os09g0553200 protein

3.23 ± 0.63

ns

ns

 86

Os05g0482700

Q5KQH5

Putative 2,3-bisphosphoglycerate-independent phosphoglycerate mutase

0.60 ± 0.05

ns

ns

 94

Os08g0520900

Q0J4C6

Os08g0520900 protein

3.19 ± 0.38

2.50 ± 0.36

ns

 118

Os08g0191433

Q6Z1D6

Putative starch synthase DULL1

2.45 ± 0.16

ns

2.43 ± 0.19

 132

Os04g0164900

Q7X834

OSJNBa0019G23.2 protein/pullulanase

20.08 ± 1.58

5.66 ± 0.45

3.84 ± 0.56

 152

Os04g0526600

Q0JBL0

Alpha-amylase/subtilisin inhibitor

6.59 ± 0.45

3.02 ± 0.06

ns

 158

Os02g0528200

Q6H6P8

Branching enzyme-3/SBE3

2.36 ± 0.36

ns

ns

 159

Os06g0726400

Q0D9D0

Os06g0726400 protein/SBE1

15.54 ± 0.72

3.34 ± 0.24

5.00 ± 0.42

Respration (Glycolysis,TCA and Fermentation)

 4

Os01g0905800

Q5N725

Fructose-bisphosphate aldolase

3.99 ± 0.62

ns

ns

 27

Os01g0926300

Q5JK10

Os01g0926300 protein

0.37 ± 0.07

ns

ns

 30

Os02g0601300

Q6K5G8

Glyceraldehyde-3-phosphate dehydrogenase 3, cytosolic

3.76 ± 0.30

ns

ns

 59

Os02g0169300

Q6H6C7

Phosphoglycerate kinase

4.84 ± 0.51

ns

ns

 69

Os06g0668200

Q655T1

Phosphoglycerate kinase

2.56 ± 0.42

ns

ns

 76

Os10g0478200

Q7XDC8

Malate dehydrogenase, cytoplasmic

3.51 ± 0.12

ns

ns

 82

Os08g0191700

Q0J7H9

Lactoylglutathione lyase

3.30 ± 0.40

3.14 ± 1.07

ns

 89

Os11g0210500

Q0ITW7

Alcohol dehydrogenase 2

5.38 ± 1.61

ns

ns

 104

Os06g0486800

Q0DC43

Formate dehydrogenase

3.25 ± 0.54

2.41 ± 0.74

ns

 120

Os08g0545200

Q6ZBH2

Os08g0545200 protein/Sorbitol dehydrogenase

12.37 ± 2.25

2.49 ± 0.24

5.09 ± 0.97

 122

Os03g0293500

Q10MW3

Pyruvate decarboxylase 2

3.96 ± 0.28

2.72 ± 1.11

ns

 125

Os06g0326400

Q69T78

Pyrophosphate-fructose 6-phosphate 1-phosphotransferase subunit alpha

2.05 ± 0.21

ns

2.27 ± 0.28

 127

Os07g0187200

Q7XI14

Probable D-2-hydroxyglutarate dehydrogenase, mitochondrial

4.60 ± 0.81

ns

ns

 173

Os04g0486950

Q7XUG1

Malate synthase

5.85 ± 1.70

4.64 ± 0.51

ns

Photosynthesis

 1

Os01g0711000

Q8S7T5

ATP synthase subunit alpha

ns

4.23 ± 1.46

ns

 3

Os10g0356000

P0C512

Ribulose bisphosphate carboxylase large chain

2.74 ± 0.09

2.40 ± 0.75

ns

 7

Q8S6G5[a]

Q8S6G5

Photosystem II CP43 reaction center protein

ns

1.92 ± 0.09

ns

 23

Q69VC8[b]

Q69VC8

Photosystem II CP47 reaction center protein

ns

3.17 ± 1.27

ns

 43

Os03g0563300

Q53RM0

Magnesium-chelatase subunit ChlI, chloroplastic

0.46 ± 0.01

ns

ns

 57

Os10g0492000

Q9FWV2

Putative chloroplast inner envelope protein

2.36 ± 0.57

ns

ns

Material transport

 22

Os07g0448800

Q8H5N9

Probable aquaporin PIP2–1

ns

3.21 ± 0.71

ns

 64

Os08g0513600

Q6Z8M9

Os08g0513600 protein

6.26 ± 2.17

ns

ns

 75

Os02g0202400

Q6Z782

Os02g0202400 protein

3.04 ± 0.84

ns

ns

 78

Os11g0644100

Q2R0I6

Leucine Rich Repeat family protein, expressed

0.24 ± 0.02

ns

ns

 111

Os03g0271200

Q10NF2

Protein TOC75, chloroplastic

4.63 ± 0.36

1.95 ± 0.32

2.45 ± 0.41

 123

Os05g0111200

Q65XV6

Os05g0111200 protein

3.77 ± 0.18

2.85 ± 0.23

ns

 126

Os03g0240500

Q10PB3

Translocase of chloroplast

2.27 ± 0.17

ns

ns

Signal transduction

 2

Os03g0710800

Q10E23

14–3-3-like protein GF14-F

0.53 ± 0.01

ns

ns

 101

Os06g0110100

Q8H684

OSEYA1

4.56 ± 1.10

1.86 ± 0.18

2.51 ± 0.58

 129

Os01g0356800

Q0JMV9

Os01g0356800 protein/ GTP binding protein

35.53 ± 10.52

2.45 ± 0.06

12.89 ± 1.52

 138

Os02g0799000

Q69QZ0

Probable protein phosphatase 2C 27

5.05 ± 0.74

ns

ns

 Stress and defense

 5

Os07g0186000

Q0D840

Thioredoxin H1

ns

3.65 ± 0.82

ns

 8

Os02g0115700

Q0E4K1

Catalase isozyme A

0.28 ± 0.01

ns

0.49 ± 0.07

 10

Os05g0116100

Q65XA0

Dehydroascorbate reductase

2.15 ± 0.13

2.97 ± 0.40

ns

 11

Os05g0323900

Q43008

Superoxide dismutase [Mn], mitochondrial

1.77 ± 0.05

3.29 ± 0.33

ns

 21

Os04g0508300

P55142

Glutaredoxin-C6

2.95 ± 0.11

3.44 ± 0.66

ns

 24

Os05g0157200

Q75M01

Os05g0157200 protein

ns

2.97 ± 0.28

ns

 35

Os12g0244100

Q2QV45

70 kDa heat shock protein

5.12 ± 1.21

2.18 ± 0.62

ns

 58

Os05g0453700

Q7XXS5

Os05g0453700 protein

4.37 ± 0.28

3.23 ± 0.16

ns

 67

Os07g0624600

Q7XI41

Probable trehalose-phosphate phosphatase 3

0.35 ± 0.03

0.29 ± 0.08

ns

 73

Os12g0514500

Q0IN14

Hsp90 protein, expressed

0.36 ± 0.05

0.32 ± 0.05

ns

 81

Os01g0270100

Q0JNR2

Cysteine proteinase inhibitor 12

2.14 ± 0.21

ns

ns

 113

Os07g0694700

Q0D3B8

Ascorbate peroxidase

0.51 ± 0.06

ns

ns

 134

Os01g0663400

Q0JKM8

Os01g0663400 protein

8.82 ± 0.50

2.32 ± 0.43

3.68 ± 0.78

 166

Os07g0638300

P0C5C9

1-Cys peroxiredoxin A

14.91 ± 5.52

3.85 ± 0.39

3.59 ± 1.15

 174

Os03g0245800

Q10P60

26.7 kDa heat shock protein, chloroplastic

4.84 ± 0.88

2.16 ± 0.53

ns

Protein synthesis and destination

 12

Os04g0685200

Q7XPU2

OSJNBa0088H09.14 protein

0.38 ± 0.04

ns

ns

 16

Os06g0687700

Q653F6

Putative t-complex protein 1 theta chain

0.40 ± 0.01

ns

ns

 25

Os12g0277500

Q2QU06

60 kDa chaperonin alpha subunit

0.21 ± 0.03

0.23 ± 0.05

ns

 34

Os03g0804800

Q75HJ3

Putative TCP-1/cpn60 chaperonin family protein

0.50 ± 0.06

0.61 ± 0.10

ns

 48

Os02g0332200

Q6YUK5

T-complex protein 1 subunit delta

0.27 ± 0.03

0.32 ± 0.05

ns

 68

Os07g0578300

Q6ZL89

Os07g0578300 protein

0.05 ± 0.01

0.09 ± 0.03

0.38 ± 0.01

 71

Os03g0619400

Q6AV23

Putative TCP-1/cpn60 chaperonin family protein

0.40 ± 0.05

ns

ns

 79

Os02g0717400

Q6ZGV8

Clustered mitochondria protein homolog

ns

0.30 ± 0.08

ns

 95

Os03g0565500

Q10I39

Elongation factor G, mitochondrial, putative, expressed

0.32 ± 0.08

0.33 ± 0.08

ns

 103

Os09g0491772

B9G4B3

Os09g0491772 protein

0.16 ± 0.04

0.15 ± 0.04

ns

 110

Os02g0100100

Q67IX6

Protein disulfide isomerase-like 1–4

5.62 ± 0.64

ns

3.37 ± 0.67

 117

Os01g0185200

Q5VRX8

Os01g0185200 protein

3.21 ± 0.26

ns

ns

 119

Os01g0752700

Q5JMX4

Os01g0752700 protein

0.31 ± 0.04

0.28 ± 0.02

ns

 130

Os09g0451500

Q67UF5

Protein disulfide isomerase-like 2–3

2.15 ± 0.47

ns

ns

 135

Os02g0506500

Q6K6K7

Ubiquitin-like modifier-activating enzyme 5

3.28 ± 1.07

ns

ns

 143

Os09g0252800

Q6K3Y7

Putative ubiquitin-protein ligase 1

ns

0.53 ± 0.04

ns

 144

Os02g0115900

Q6Z7B0

Dnak-type molecular chaperone Bip

6.30 ± 1.31

ns

3.84 ± 1.04

 147

Os05g0557200

Q6I605

Os05g0557200 protein

2.13 ± 0.15

ns

ns

 148

Os07g0215500

Q0D7S0

Allergen RA5B

22.26 ± 5.08

4.59 ± 0.37

ns

 153

Os07g0213800

Q8H4M4

Allergenic protein

ns

5.68 ± 0.20

ns

 155

Os11g0199200

Q53LQ0

Protein disulfide isomerase-like 1–1

4.62 ± 0.40

ns

ns

 161

Os03g0610650

Q75H81

Serpin-ZXA

3.53 ± 0.52

2.30 ± 0.33

ns

 170

Os05g0519700

Q6F2Y7

Chaperone protein ClpB1

4.54 ± 0.18

2.35 ± 0.39

1.98 ± 0.35

Storage proteins

 97

Os07g0609000

Q6YTX6

Seed protein

19.17 ± 2.41

6.65 ± 2.22

ns

 99

Os01g0762500

Q0JJ36

Glutelin

1.54 ± 0.02

ns

ns

 128

Os03g0793700

Q852L2

Cupin family protein, expressed

8.40 ± 1.03

4.63 ± 0.42

ns

 141

Os05g0499100

Q0DH05

Alpha-globulin

39.03 ± 4.70

4.02 ± 0.47

ns

 145

Os02g0242600

Q6ESW6

Glutelin

19.54 ± 5.20

ns

9.51 ± 1.33

 151

Os03g0197300

Q0DUA3

Os03g0197300 protein

25.94 ± 10.52

6.59 ± 1.98

ns

 157

Os02g0456100

Q6K7K6

Glutelin

36.64 ± 4.06

3.66 ± 0.31

11.87 ± 0.71

 163

Os03g0336100

Q0DS36

Os03g0336100 protein

23.65 ± 1.52

3.60 ± 0.61

ns

 165

Os02g0249900

Q0E2D2

Glutelin

ns

4.24 ± 0.89

ns

 167

Os07g0214300

Q0D7S4

Seed allergenic protein RAG2

12.44 ± 2.51

6.26 ± 0.42

ns

 169

Os02g0249000

Q6K508

Glutelin

28.90 ± 5.78

1.86 ± 0.30

14.62 ± 1.20

 171

Os03g0663800

Q75GX9

Cupin family protein, expressed

31.11 ± 3.49

ns

ns

 172

Os02g0268300

Q0E261

Glutelin

56.76 ± 1.25

4.28 ± 0.71

ns

Amino acid metabolism

 9

Os08g0447000

Q6ZAA5

D-3-phosphoglycerate dehydrogenase

0.18 ± 0.06

0.28 ± 0.11

ns

 20

Os11g0216900

Q0ITU1

Methylthioribose-1-phosphate isomerase

0.31 ± 0.01

ns

ns

 52

Os03g0738400

Q7Y1F0

Serine hydroxymethyltransferase

1.99 ± 0.05

ns

ns

 66

Os03g0223400

Q10PS4

Glutamine synthetase

5.20 ± 0.10

3.93 ± 1.24

ns

 70

Os09g0255400

Q8H3R5

Putative indole-3-glycerol phosphate synthase

3.82 ± 0.69

ns

ns

 85

Os12g0235800

Q2QVC1

Argininosuccinate synthase, chloroplast, putative, expressed

2.74 ± 0.13

ns

ns

 92

Os03g0136200

Q10S41

Methyltransferase

0.42 ± 0.04

ns

ns

 93

Os12g0607000

Q2QME6

Homocysteine S-methyltransferase 3

3.83 ± 0.20

ns

ns

 105

Os12g0138900

Q2QXY9

2-isopropylmalate synthase B, putative, expressed

2.75 ± 0.17

1.81 ± 0.27

ns

 114

Os02g0783625

Q6K7D6

Putative lysine-ketoglutarate reductase/saccharopine dehydrogenase bifunctional enzyme

3.13 ± 0.56

ns

ns

 140

Os12g0145100

Q2QXS4

Os12g0145100 protein

ns

ns

2.63 ± 0.68

 142

Os10g0390500

Q94HC5

Putative alanine amino transferase

3.07 ± 0.38

ns

3.02 ± 0.44

 149

Os12g0578200

Q2QN58

Chorismate mutase, chloroplast, putative, expressed

18.67 ± 2.19

1.92 ± 0.11

8.26 ± 0.90

 150

Os03g0171900

Q10R45

Alanine-glyoxylate aminotransferase 2, mitochondrial, putative, expressed

3.68 ± 0.78

ns

ns

 160

Os04g0389800

Q0E0Z3

Acetolactate synthase

3.41 ± 0.30

1.80 ± 0.24

ns

 162

Os01g0760600

Q0JJ47

Aspartate aminotransferase

3.82 ± 0.36

ns

3.30 ± 0.31

Nucleotides

 29

Os10g0539500

Q7XUC9

Histone H4

0.39 ± 0.03

ns

ns

 37

Os01g0550000

Q5JK84

DEAD-box ATP-dependent RNA helicase 15

0.54 ± 0.06

ns

ns

 60

Os01g0275600

Q9SDG8

Protein argonaute 4A

0.32 ± 0.06

0.43 ± 0.09

ns

 63

Os02g0736400

Q6Z744

Dihydropyrimidine dehydrogenase

2.25 ± 0.17

ns

ns

 87

Os03g0158500

Q8H8C1

Putative RNA-binding protein

ns

0.31 ± 0.05

ns

 88

Os02g0214500

Q6H8A9

NAC6

4.80 ± 0.64

ns

ns

 102

Os07g0471300

Q69UP6

Protein argonaute 18

0.37 ± 0.08

0.41 ± 0.09

ns

 107

Os02g0137400

Q6YXY3

Putative splicing factor 3b, subunit 3, 130 kDa

0.56 ± 0.03

ns

ns

 109

Os02g0821800

Q6AT27

Putative fibrillarin

0.42 ± 0.08

ns

ns

 115

Os07g0212300

Q8H4U7

Mut T-like protein

10.59 ± 1.42

ns

ns

 131

Os02g0523500

Q6H547

Os02g0523500 protein

2.18 ± 0.24

ns

1.95 ± 0.34

Lipid metabolism

 28

Os05g0295300

B9FK36

Acetyl-CoA carboxylase 2

0.64 ± 0.01

ns

ns

 51

Os11g0558300

Q2R2L5

AMP-binding enzyme family protein, expressed

0.34 ± 0.14

ns

ns

 56

Os03g0181500

Q8H7L2

3-ketoacyl-CoA synthase

0.17 ± 0.02

0.25 ± 0.01

ns

 62

Os06g0156700

Q5VMA4

Os06g0156700 protein

3.49 ± 0.43

ns

ns

 77

Os05g0567100

Q0DFW1

Aspartic proteinase oryzasin 1

2.58 ± 0.49

2.54 ± 0.40

ns

 96

Os01g0880800

Q8LJJ9

Stearoyl-[acyl-carrier-protein] 9-desaturase 1, chloroplastic

ns

0.35 ± 0.05

ns

 108

Os07g0188800

Q6Z4E4

Methylmalonate semi-aldehyde dehydrogenase

2.13 ± 0.19

2.04 ± 0.40

ns

 124

Os06g0260500

Q5Z7E7

3-ketoacyl-CoA synthase

0.24 ± 0.10

0.26 ± 0.12

ns

 146

Os01g0348600

Q94CN1

Os01g0348600 protein

0.38 ± 0.15

ns

ns

Secondary metabolism

 31

Os07g0529600

Q7XXS4

Thiamine biosynthetic enzyme

0.30 ± 0.02

0.30 ± 0.02

ns

 47

Os08g0157500

Q6ZD89

Flavone 3′-O-methyltransferase 1

4.73 ± 0.80

2.59 ± 0.69

ns

 55

Os08g0498400

Q7F8T6

Tricin synthase 2

3.56 ± 0.56

2.19 ± 0.30

ns

 116

Os09g0446800

Q0J1E1

Os09g0446800 protein

2.55 ± 0.03

1.67 ± 0.18

ns

 139

Os03g0192700

Q10QK8

Inositol-3-phosphate synthase

2.06 ± 0.18

ns

ns

 164

Os08g0189100

Q6YZA9

Germin-like protein 8–2

7.61 ± 0.71

5.34 ± 0.86

ns

Unknown

 13

Os12g0555500

Q2QNS7

Os12g0555500 protein

0.49 ± 0.03

ns

ns

 15

Os06g0646500

Q67W57

Os06g0646500 protein

ns

ns

0.57 ± 0.04

 17

Os03g0278200

Q10N92

Os03g0278200 protein

0.46 ± 0.05

ns

ns

 18

Os11g0687100

Q2QZH3

Os11g0687100 protein

0.08 ± 0.03

0.21 ± 0.11

0.31 ± 0.05

 32

Os12g0182200

Q2QWU7

Dihydrolipoamide S-acetyltransferase, putative, expressed

0.53 ± 0.05

ns

ns

 39

Os06g0613000

Q69WY2

Uncharacterized protein

0.36 ± 0.04

ns

ns

 53

Os01g0916600

Q7F2X8

Os01g0916600 protein/OsGRP2

0.24 ± 0.01

0.28 ± 0.01

ns

 61

Os11g0687200

Q2QZH2

Expressed protein

0.30 ± 0.05

ns

0.54 ± 0.03

 72

Os07g0409100

Q7XTM4

OSJNBa0033G05.21 protein

0.53 ± 0.05

ns

ns

 74

Os07g0638100

Q8GVH2

Os07g0638100 protein

0.53 ± 0.03

ns

ns

 80

Os01g0128400

Q9LGA3

Os01g0128400 protein

2.00 ± 0.06

ns

ns

 83

Os04g0531900

Q7X8W6

OSJNBa0081C01.20 protein

5.73 ± 1.54

4.34 ± 0.33

ns

 90

Os10g0463200

Q8H906

Putative early nodulin gene (Enod) related protein

0.36 ± 0.03

0.46 ± 0.04

ns

 98

Os03g0327600

Q10M12

Expressed protein

2.03 ± 0.08

ns

ns

 100

Os07g0568700

Q0D5C7

Os07g0568700 protein

0.44 ± 0.03

ns

ns

 106

Os04g0482800

Q7XUP3

OSJNBb0011N17.20 protein

3.73 ± 0.15

2.14 ± 0.10

ns

 112

Os02g0783700

Q0DX00

Os02g0783700 protein

2.05 ± 0.19

ns

ns

 133

Os05g0132100

Q0DL03

Os05g0132100 protein

0.34 ± 0.05

ns

ns

 137

Os06g0214300

Q69Y21

Os06g0214300 protein

2.85 ± 0.43

1.68 ± 0.16

ns

 156

Os04g0404400

Q7X6I8

OJ000315_02.8 protein

16.73 ± 7.88

5.11 ± 1.18

ns

 168

Os10g0463800

Q337M4

Os10g0463800 protein

3.60 ± 0.15

2.59 ± 0.29

ns

Accession: the code of the identified protein in RAP database (http://rapdb.dna.affrc.go.jp/); [a] and [b]: the code of these two identified proteins in RAP database were not found; ns means no significant change of protein abundance between the two compared samples; T0: control treatment with no spikelet thinning; T2: the upper 2/3 of spikelets were removed. SS: Superior spikelets; IS: Inferior spikelets; Values are means ± S.D. of two replications. The screening criteria for differentially expressed proteins was a fold change >1.5 or <0.67 and a p-value <0.05

Fig. 3

Patterns of change on differentially expressed proteins of a (T0-SS / T0-IS), b (T0-SS / T2-IS) and c (T2-IS / T0-IS). T0 represent control treatment with no spikelet thinning and T2 represent treatment with the upper 2/3 of spikelets were removed

Functional classification of DEPs between SS and IS under different treatments

DEPs were classified according to their biological functions and were divided into 11 categories, including carbohydrate metabolism, protein metabolism, secondary metabolism, lipid metabolism, nucleotide metabolism, amino acid metabolism, photosynthesis, cell growth/division, material transport, signal transduction, and stress/defense (Table 2, Fig. 4). In this study, proteins with unknown biological functions or those that could not be attributed to these 11 categories were classified into an unknown protein category. Among the 11 major functional categories, carbohydrate metabolism includes glucose metabolism, starch biosynthesis, glycolysis, tricarboxylic acid (TCA) cycle, and fermentation, while protein metabolism includes protein synthesis, proteolysis, protein folding, and storage.
Fig. 4

Functional classifications of the differentially expressed proteins in groups a (T0-SS / T0-IS), b (T0-SS / T2-IS) and c (T2-IS / T0-IS). T0 represent control treatment with no spikelet thinning and T2 represent treatment with the upper 2/3 of spikelets were removed

The T0-SS/T0-IS comparison resulted in the greatest number of DEPs (159), which mainly participated in physiological and biochemical processes including carbohydrate metabolism (24.5%), protein metabolism (20.13%), stress/defense (8.18%), and amino acid metabolism (9.43%) (Fig. 4-a). Relatively fewer DEPs were identified in the T0-SS/T2-IS comparison (87), and these mainly participated in the same metabolic processes as those in the T0-SS/T0-IS comparison. Among these, 17.24% were related to carbohydrate metabolism and 26.44% were associated with protein metabolism (Fig. 4-b). The T2-IS/T0-IS comparison resulted in the fewest DEPs (28), but 25% of these were involved in carbohydrate metabolism and 25% were involved in protein metabolism. Moreover, 7.14% of DEPs in this group were associated with signal transduction, which was significantly more than 2.52% in the T0-SS/T0-IS comparison and 2.3% in the T0-SS/T2-IS comparison (Fig. 4-c).

Together, the above experimental results showed that carbohydrate metabolism and protein metabolism play key roles in the differential development of SS and IS. Among the three comparisons, starch synthesis and protein storage functions were relatively prevalent among carbohydrate metabolism and protein metabolism functions (Fig. 4), suggesting that the supply of carbohydrates to IS increased after SS removing [16], starch and protein synthesis in the grains are significantly enhanced. It is worth noting that the signal transduction function showed the greatest influence on IS development after SS removal, which may be one of the reasons for the increase in IS grain filling after SS removal treatment.

Discussion

Physiological differences between SS and IS under different treatments

The phenomena of low seed setting rates and poor plumpness are common in large-panicle rice varieties, and this is mainly due to poor IS grain filling and the formation of empty and blighted grains of rice [31]. These phenomena were also observed in this study. The grain weight and seed setting rate were significant different between SS and IS. SS elongated rapidly and grew well at 10 DPA, while IS were in a state of developmental stagnation. After the removal of SS, the IS grain size and grain weight significantly increased, indicating that 10 DPA was the end of the stagnant grain filling period and the beginning of the grain filling initiation period. Limited assimilate supply was generally considered to be the main cause of poor IS grain filling [7, 11]. The results from our previous studies [16] and this study support this view as well, as SS removal significantly improved IS grain size, sucrose content, grain weight, and grain filling rate in W1844. Since grain filling is a highly complex process, its molecular mechanisms need to be further elucidated.

Low expression proteins associated with endosperm cell growth and division leading to small sink capacity

A positive correlation between endosperm cell numbers and grain weight has been found in rice [32], wheat [33], and maize [34]. Previous reports showed that SS had a large number of endosperm cells, and thus a large sink size [5]. However, IS endosperm cell division was stagnant at the early grain filling stage, which limited IS sink establishment. In the present study, three cell division-related proteins, actin, annexin, and IAA-amino acid hydrolase ILR1-like 8 were identified. Significant differences in the expressions of these three proteins between SS and IS were considered to be very important for endosperm cell division.

The actin cytoskeleton provides a structural framework for defining cell shape and polarity. Its dynamic properties provide the driving force for cells to move and to divide [35]. Annexins are thought to be associated with cell proliferation and differentiation [36]. In this study, actin and annexin were identifed and their abundances in T0-SS showed significantly higher up-regulation compared with those in T0-IS, this matched well with differences in endosperm cell division between SS and IS. Although SS removal treatment improved IS grain filling, IS grain weight was still lower under T2 than that of T0-SS. Actin, which is involved in endosperm cell division, was 1.76-fold higher in T0-SS than in T2-IS, indicating that compared with T0-SS, sink capacity was smaller in T2-IS, thus explaining the low grain weight in T2-IS at the protein expression level.

Amide-linked conjugates of indole-3-acetic acid (IAA) may serve as reservoirs of inactive IAA that can be hydrolyzed by IAA-conjugate hydrolases, releasing free IAA from the conjugate form. Thus, IAA-conjugate hydrolases are likely to play an important role in regulating free IAA levels [37, 38]. For example, in maize germination, conjugate hydrolysis provides free IAA to the developing seedling [39]. IAA-amino acid hydrolase ILR1-like 8 is an IAA-conjugate hydrolase, and increasing its abundance could elevate levels of free IAA. IAA is an important signal in cereal endosperm development [40]. Low IAA leads to low endosperm cell division in rice IS [41]. Based on these findings, the expression of IAA-amino acid hydrolase ILR1-like 8 may be important for endosperm cell division. Our comparative proteomic results showed that the abundance of IAA-amino acid hydrolase ILR1-like 8 in T0-IS was 3.52-fold lower than that in T0-SS, suggesting that levels of free IAA in T0-IS were lower than those in T0-SS. Thus, T0-IS resulted in poor endosperm cell division, as well as low sink capacity and grain weight. While in T2-IS, the abundance of IAA-amino acid hydrolase ILR1-like 8 was still lower than in T0-SS, and the kernel development of T2-IS also poorer than that in T0-SS. These results indicate that the abundances of actin, annexins, and IAA-amino acid hydrolase ILR1-like 8 in rice are important for the establishment of grain sink.

Low activities of key enzymes associated with sucrose-starch metabolism leading to poor starch synthesis

Grain filling is actually a process of starch biosynthesis and accumulation [42]. Grain filling materials are transported from the source to the grain mainly in the form of sucrose and are converted to starch through a series of enzymatically catalyzed reactions. Among these, sucrose synthase (SuSase) catalyzes and degrades sucrose to produce uridine diphosphoglucose (UDPG) and fructose, and its activity is an index of the rice sink strength [43]. In this study, the abundances of SuSase in SS were higher than those in IS at 10 DPA, which may be attributed to the high sucrose content of SS that needs to be decomposed. In the T2-IS/T0-IS and T0-SS/T2-IS comparisons, the abundance of SuSase was up-regulated and down-regulated, respectively, consistent with sucrose content. Studies have also shown that sucrose exerts a regulatory effect on SuSase activity [7, 44]. Under SS removal, a large amount of assimilate is supplied to the IS, increasing its sucrose content and inducing an increase in SuSase abundance. Therefore, the improvement in IS grain filling after the removal of SS may be attributed to increased assimilates and a stronger capacity for sugar decomposition.

Many enzymes involved in starch synthesis were identified in this study, such as ADP-glucose pyrophosphorylase (AGPase), starch branching enzyme (SBE), OSJNBa0019G23.2 protein (pullulanase), and putative starch synthase DULL1 (SSS). Among these, AGPase is a key enzyme controlling starch accumulation rate, and its up-regulation can achieve high yields [45]. SBE is a key enzyme controlling amylopectin synthesis, and its enzymatic activity is significantly positively correlated with the amylopectin accumulation rate [46], while SSS plays an important role in amylose synthesis [47]. In this study, compared to T0-IS at 10 DPA, the protein abundances of AGPase, SSS, SBE, and pullulanase in T0-SS were up-regulated. This result is consistent with the proteomic results from Zhang et al. [48]. Futhermore, compared to levels in T0-IS, alpha-glucosidase (AGS), which is involved in starch hydrolysis, was down-regulated in T0-SS, while the alpha-amylase/subtilisin inhibitor (ASI), involved in the inhibition of starch hydrolysis, was up-regulated in T0-SS. This facilitated starch accumulation in SS. In general, the results from this study and from previous studies [49] show that reduced activity of the enzymes associated with starch synthesis is the main reason for poor IS grain filling.

As Fig. 5 shows, the DEPs related to carbohydrate metabolism in the T2-IS/T0-IS comparison mainly participate in starch synthesis. Compared to T0-IS, the abundances of SSS, SBE, and pullulanase in T2-IS were all up-regulated, which may be due to the increasing supply of sucrose to the IS after SS removal. Similar to the findings of previous studies, we showed that expressions of SSS and SBE were up-regulated by an increase in sucrose [50]. Therefore, improving IS grain filling after SS removal may be achieved through an increase in the sucrose content, which in turn induces the up-regulation of SBE and SSS, thus promoting starch synthesis in IS.
Fig. 5

The differentially expressed proteins onto carbohydrate metabolism of rice grain. Note: A, B and C represent the groups of T0-SS / T0-IS, T0-SS / T2-IS and T2-IS / T0-IS. Red and green indicate p ≤ 0.05 (red denotes significant up-regulation in the endosperm, green significant down-regulation); Light grey indicate no significant difference in the level of p ≤ 0.05. SuSase, sucrose synthase; SBE, starch branching enzyme; UDPG, uridine diphosphate glucose; ADPG, adenosine diphosphate glucose; DHAP, dihydroxyacetone phosphate; GA-3P, glyceraldehyde-3-phosphate; 1,3-DPG, 1,3-diphosphoglycerate; 3-PGA, 3-phosphoglycerate; 2-PGA, 2-phosphoglycerate; PEP, phosphoenolpyruvate; FK, fructokinase; PFP, pyrophosphate-fructose 6-phosphate 1-phosphotransferase; PGM, phosphoglucomutase; AGPase, adenosine diphosphoglucose pyrophosphprylase; PULL, pullulanase; DULL1, putative starch synthase DULL1; FBA, fructose-bisphosphate aldolase; TPI, triosephosphate isomerase; AGS, alpha-glucosidase; ASI, alpha-amylase/subtilisin inhibitor; GAPDH, glyceraldehyde-3-phosphate dehydrogenase; PGK, phosphoglycerate kinase; PDC, pyruvate decarboxylase; ADH, alcohol dehydrogenase; MS, malate synthase; MDH, malate dehydrogenase, cytoplasmic; ANT, adenylate transporter; GPT, glucose phosphate translocator; TPT, triose phosphate translocator

Weakened photosynthesis and respiration resulting in stagnation of grain development

Carbohydrate metabolism mainly includes glycolysis and the TCA cycle, which provides energy and material for the transformation and synthesis of metabolites [51]. In this study, proteins associated with carbohydrate metabolism were identified (Fig. 5), including proteins involved in glycolysis, such as fructose-bisphosphate aldolase (FBA), glyceraldehyde-3-phosphate dehydrogenase 3 (GAPDH), lactoylglutathione lyase, and phosphoglycerate kinase (PGK), as well as proteins participating in the TCA cycle, such as malate dehydrogenase (MDH). The abundances of these proteins were lower in IS than in SS, which is consistent with the results of Zhang et al. [48]. Reduced glycolysis and TCA cycle activity in the IS at 10 DPA is not able to supply enough material and energy for cell expansion and starch synthesis, and thus affects the formation of the grain sink. Under hypoxic conditions, enzymes in the alcohol fermentation pathway are important for the formation of ATP, which is required to maintain starch synthesis, including pyruvate decarboxylase 2 (PDC 2) and alcohol dehydrogenase 2 (ADH2). The abundances of these two enzymes in the IS were lower than those in the SS, and this indicated that the down-regulation of alcohol fermentation in the IS resulted in a decrease in ATP, thus affecting the initiation of IS grain filling and restricting normal starch synthesis.

We also identified other proteins associated with energy metabolism, such as D-2-hydroxyglutarate dehydrogenase (D-2HGDH), which catalyzes the formation of 2-ketoglutarate from D-2-hydroxyglutarate in the mitochondria and releases energy [52], and formate dehydrogenase (FDH), which catalyzes the oxidation of formic acid to CO2 and reduces NAD+ to NADH [53, 54]. Our proteomic study indicated that these two proteins were down-regulated in the IS at 10 DPA. Ribulose bisphosphate carboxylase large chain (Rubisco), a key enzyme for CO2 fixation during plant photosynthesis [55], was identified in the present study, and its abundance in SS was 2.74-fold higher than that in IS. It may be inferred therefore that photosynthesis was more productive in SS than in IS, producing more carbohydrates used for grain filling. The reduced abundances of these energy metabolism-related proteins in the IS therefore explains poor IS grain filling at the proteomic level.

Pyrophosphate-fructose 6-phosphate 1-phosphotransferase (PFP) can reversibly catalyze the conversion between fructose-6-phosphate (F6P) and fructose 1,6-bisphosphate (F-1,6-P2) by phosphorylation and dephosphorylation [56]. However, in vivo, the positive reaction from F6P to F-1,6-P2 is catalyzed by the irreversible enzyme phosphofructokinase (PFK). It is worth noting that, unlike PFK, the positive reaction catalyzed by PFP does not require consumption of ATP. Therefore, in higher plants, oxygen-free glycolysis is dependent on PFP, which is more economical from the standpoint of energy transformation. PFP also stores energy in a PPi (phosphate group) from the perspective of gluconeogenesis [57]. In this study, we found that the PFP abundance of T2-IS was 2.27-fold higher than that of T0-IS, which demonstrated that PFP plays an important role in the processes of glycolysis and gluconeogenesis in IS after SS removal and that its activity is conducive to the economical utilization of energy.

Low abundances of proteins associated with proteins metabolism (protein synthesis, folding, and storage) leading to poor protein synthesis

Rice protein formation is closely related to the nitrogen nutritional status of the plant [58], which is regulated by nitrogen metabolism. Transamination is a crucial process of nitrogen metabolism, that involves a variety of enzymes, including aspartate amino transferase (GOT) and alanine amino transferase (GPT). In higher plants, inorganic nitrogen is converted to amino acids by catalysis with these two transaminases, thus providing a variety of amino acid donors for the synthesis and metabolism of grain proteins [59]. In this study, the abundances of GOT and GPT in SS were 3.82-fold and 3.07-fold higher, respectively, than those in IS, which may be due to the fact that in the early filling stage, less material was supplied to the IS, resulting in poor nitrogen metabolism. A proteomic study by Zhang et al. [48] has shown that the abundances of GPT in IS are down-regulated, probably owing to the lack of nitrogen and accelerated aging of the rice plants at later stages. The results also showed that the abundances of GOT and GPT in T2-IS were higher than those in T0-IS, which suggested that IS grain filling was improved after SS removal, probably due to the up-regulation of GPT and GOT, promoting IS protein formation.

Molecular chaperones are effective in regulating the proper folding of polypeptide chains, thereby forming active proteins [60]. In this study, molecular chaperones, such as the DnaK-type molecular chaperone Bip and the chaperone protein ClpB1, were found to be differentially expressed between SS and IS. The abundances of these two proteins in T0-SS were 6.30-fold and 4.54-fold higher than those in T0-IS, while they were all higher in T2-IS than those in T0-IS. Thus, well-developed rice grains probably require high abundances of molecular chaperones, which regulate the proper folding of polypeptide chains.

The formation or isomerism of disulfide bonds plays an important role in protein folding and metabolic regulation [61]. Protein disulfide isomerase (PDI) and protein disulfide isomerase-like (PDILs) can catalyze the formation of disulfide bonds in proteins [62]. Shimoni et al. [63] was the first to report that PDI was involved in the folding of storage proteins during endosperm formation. In this study, the abundances of PDIL1–1, PDIL1–4, and PDIL2–3 in SS were all higher than those in IS. Johnson et al. [64] demonstrated that in wheat, PDIL1–1 was essential for accurate assembly and distribution of gliadin and glutelin in the endoplasmic reticulum. Moreover, PDIL1–1 was found to control endosperm development by regulating the quantity and composition of proteins in rice seeds [65]. The results of this study indicated that the synthesis of storage proteins in SS is elevated compared to that in IS during the formation of the seed endosperm, and this may be one of the reasons for poor IS grain filling. Additionally, the abundance of PDIL1–4 in T2-IS was higher than that in T0-IS, which may be attributed to a significant increase in the nitrogen compounds supplied to IS after SS removal. The quantity of storage proteins in IS was increased by up-regulating PDIL abundance during the formation of the seed endosperm, thereby improving IS grain filling.

Storage proteins are mainly found in the rice endosperm and can be divided into glutelin, globulin, albumin, and prolamin according to their solubility. The contents and proportions of these proteins affect the quality of rice. In this study, we identified a large number of differentially expressed storage proteins between SS and IS, such as glutelin, globulin, and vegetative storage proteins (cupin family protein). The abundances of glutelin (Nos. 145, 157, 169) were 9.51-fold, 11.87-fold, and 14.62-fold higher in T2-IS than those in T0-IS, respectively. Ma et al. [66] showed that high-temperature stress significantly increased glutelin abundance in rice grains, but there was no effect of the application of panicle fertilizer. Dong et al. [22] suggested that drought stress may change the abundances of storage proteins in rice grains. The results of this study showed that during grain filling, SS removal could also affect the abundance of glutelin in the IS, though the specific regulatory mechanism involved requires further study.

GTP binding protein, PP2C and IAA-amino acid hydrolase ILR1-like 8, in signaling networks involved in IS development

The growth and development of plants are mainly regulated by genetic and environmental information. The transmission of changing environmental information, namely, cellular signal transduction, regulates carbohydrate and energy metabolisms, as well as physiological and biochemical reactions. GTP binding protein participates in a series of signal transduction process in cells, such as the signal transduction of transmembrane messengers, light signal transduction, protein biosynthesis, and cytoskeletal structure formation [67]. In this study, the abundance of GTP-binding protein was 35.53-fold higher in T0-SS than that in T0-IS, indicating that the rate of signal transduction in T0-SS was higher than that in T0-IS. In addition, the abundance of GTP-binding protein in T2-IS was also increased, and it was 12.89-fold higher compared to that in T0-IS, which may be one of the reasons for the improvement in IS grain filling after SS removal.

Protein phosphorylation/dephosphorylation is one of the most important methods of biological signal transmission, and it occurs mainly through the activities of two types of protein with mutually antagonistic biochemical properties: protein kinases and protein phosphatases. Protein phosphatase 2C (PP2C) plays an important role in biological signal transduction and is involved in various ABA signaling pathways in higher plants [68]. ABA is a key hormone involved in the regulation of grain filling, and ABA levels are significantly positively correlated with the grain filling rate [4]. In this study, the abundance of PP2C was 5.05-fold higher in SS than that in IS. Therefore, we suspect that poor IS grain filling may be associated with poor ABA signal transduction. The increase in IAA-amino acid hydrolase ILR1-like 8 abundance may increase the active IAA level in grains. Seth et al. [69] argued that IAA as a signaling substance could control grain growth by regulating the distribution of assimilation products. The main role of IAA in grain filling is to increase the “pull” of its position to assimilates, so that assimilates are supplied primarily to locations with high IAA levels [70]. In this study, the abundance of IAA-amino acid hydrolase ILR1-like 8 in T0-SS was higher than that in T0-IS, suggesting that assimilates were preferentially supplied to the SS and that IS are unable to obtain a timely supply of nutrients after fertilization, resulting in a relative lag in IS grain filling.

Increased abundance of 14–3-3 protein in IS inhibits starch synthesis

In the process of plant development, 14–3-3 proteins participate in plant signal transduction, substance metabolism, stress response, and other regulatory processes by interacting with other proteins [71]. In recent years, great progress has been made in the study of plant 14–3-3, and it was found that 14–3-3 also plays an important role in starch metabolism. A high content of 14–3-3 proteins in the wheat endosperm inhibits the activity of sucrose synthase [72]. In Arabidopsis, inhibition of 14–3-3 activity leads to an increase in starch accumulation [73]. These results suggest that 14–3-3 may inhibit starch synthesis. In this study, the abundance of 14–3-3 protein (14–3-3-like protein GF14-F) in IS was higher than that in SS, consistent with the results from previous studies [48]. Therefore, the higher abundance of 14–3-3 protein in IS could be an important factor leading to the poor development of IS.

Unknown proteins

Through bioinformatics comparison, we identified many unknown proteins, such as Nos. 83, 106, 156, and 168, which were over 3-fold up-regulated in T0-SS/T0-IS and T0-SS/T2-IS at 10 DPA. However, even using bioinformatics analysis methods, the roles of these proteins could not be identified, and their functions remain unclear.

Conclusions

A large-panicle japonica rice line W1844 was suitable to explore the physiological and molecular mechanism of poor IS grain filling, for there exists great difference in kernel development between SS and IS. Compared with SS, the IS exhibited more weaker endosperm cell division and lower activity of key enzymes related to sucrose-starch metabolism, carbohydrate metabolism and nitrogen metabolism. In addition, the weakened photosynthesis and respiration could not timely provide enough materials and energy for cell expansion and grain filling, which may result in the stagnation of IS development. Moreover, a higher abundance of 14–3-3 protein in IS could be involved in the inhibition of starch synthesis. However, the removal of SS significantly improved IS grain filling primarily by increasing carbohydrate supply, which increased the activities of key enzymes involved in sucrose-to-starch metabolism and nitrogen metabolism, promoting the starch and protein synthesis. Additionally, the energy metabolism was also improved as the more carbohydrate in IS. Therefore, we argued that a limitation in the assimilate supply may be the main cause of poor IS grain filling. The poor IS grain filling is a complex process and this is confirmed by the proteomic analysis in present study. An integrated method using a combination of omics platforms such as metabolomic and transcriptomic will be needed to understand this mechanism comprehensively.

Abbreviations

2-DE: 

two-dimensional gel electrophoresis

ABA: 

Abscisic acid

ADPG: 

Adenosine diphosphate glucose

AGPase: 

ADP-glucose pyrophosphorylase

DEPs: 

Differentially expressed proteins

DPA: 

Days post anthesis

GOT: 

Aspartate amino transferase

GPT: 

Alanine amino transferase

IAA: 

Indole-3-acetic acid

IS: 

Inferior spikelets

ITRAQ: 

Isobaric tags for relative and absolute quantitation

MS: 

Medium spikelets

PFP: 

Pyrophosphate-fructose 6-phosphate 1-phosphotransferase

PULL: 

Pullulanase

SBE: 

Starch branching enzyme

SS: 

Superior spikelets

SSS: 

Soluble starch synthase

SuSase: 

Sucrose synthase

TCA: 

Tricarboxylic acid

UDPG: 

Uridine diphosphoglucose

Declarations

Acknowledgements

We thank the staff of the Laboratory of Crop Physiology and Ecology in Anhui Agriculture University and the Key Laboratory of Crop Physiology and Ecology in Southern China, Nanjing Agriculture University.

Funding

This work was supported by grants from the National Key Research and Development Program (2016YFD0300505; 2016YFD0300608) and Anhui Scientific and Technological Research Plan (1501031088).

Availability of data and materials

The data sets supporting the conclusions of this article are included within the article and its additional files. The data of matched proteins in Additional file 1 are from UniProt database. (http://www.uniprot.org/uniprot/).

Authors’ contributions

CY, LW and SW designed the experiments; LC and HH performed part of the experiments; CY and YD analyzed experimental results; CY, LW, SW and CM prepared the manuscript. All authors have read and approved the final manuscript.

Competing interests

All the authors declare that they have no conflict of interest.

Consent for publication

Not applicable.

Ethics approval and consent to participate

Not applicable.

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Authors’ Affiliations

(1)
College of Agronomy, Anhui Agricultural University
(2)
College of Agronomy, Nanjing Agricultural University/Key Laboratory of Crop Physiology Ecology and Production Management, Ministry of Agriculture
(3)
Jiangsu Collaborative Innovation Center for Modern Crop Production

References

  1. Khush GS. What it will take to feed 50 billion rice consumers in 2030. Plant Mol Biol. 2005;59:1–6.View ArticlePubMedGoogle Scholar
  2. Kato T, Shinmura D, Taniguchi A. Activities of enzymes for sucrose-starch conversion in developing endosperm of rice and their association with grain filling in extra-heavy panicle types. Plant Prod Sci. 2007;10:442–50.View ArticleGoogle Scholar
  3. Yang J, Zhang J. Grain filling problem in “super” rice. J exp Bot. 2010;61:1–5.View ArticlePubMedGoogle Scholar
  4. Zhang H, Tan G, Yang L, Yang J, Zhang J, Zhao B. Hormones in the grains and roots in relation to post-anthesis development of inferior and superior spikelets in japonica/indica hybrid rice. Plant Physiol Biochem. 2009;47:195–204.View ArticlePubMedGoogle Scholar
  5. Ishimaru T, Matsuda T, Ohsugi R, Yamagishi T. Morphological development of rice caryopses located at the different positions in a panicle from early to middle stage of grain filling. Funct Plant Biol. 2003;30:1139–49.View ArticleGoogle Scholar
  6. Mohapatra PK, Patel R, Sahu SK. Time of flowering affects grain quality and spikelet partitioning within the rice panicle. Aust J Plant Physiol. 1993;1993(20):231–42.View ArticleGoogle Scholar
  7. Tang T, Hong X, Yu X, Bing L, Jian S. The effect of sucrose and abscisic acid interaction on sucrose synthase and its relationship to grain filling of rice (Oryza sativa L.). J exp Bot. 2009;60(9):2641–52.View ArticlePubMedGoogle Scholar
  8. Wang Z, Xu Y, Chen T, Zhang H, Yang J, Zhang J. Abscisic acid and the key enzymes and genes in sucrose-to-starch conversion in rice spikelets in response to soil drying during grain filling. Planta. 2015;241(5):1091–107.View ArticlePubMedGoogle Scholar
  9. Fu J, Huang Z, Wang Z, Yang J, Zhang J. Pre-anthesis non-structural carbohydrate reserve in the stem enhances the sink strength of inferior spikelets during grain filling of rice. Field Crops res. 2011;123:170–82.View ArticleGoogle Scholar
  10. Peng T, Lv Q, Zhang J, Li JZ, Du YX, Zhao QZ. Differential expression of the microRNAs in superior and inferior spikelets in rice (Oryza sativa). J exp Bot. 2011;62(14):4943–54.View ArticlePubMedGoogle Scholar
  11. Ishimaru T, Hirose T, Matsuda T, Goto A, Takahashi K, Sasaki H, et al. Expression patterns of genes encoding carbohydrate-metabolizing enzymes and their relationship to grain filling in Rice (Oryza sativa L.): comparison of caryopses located at different positions in a panicle. Plant Cell Physiol. 2005;46(4):620–8.View ArticlePubMedGoogle Scholar
  12. Xu FX, Xiong H, Zhu YC, Wang GX. Effect of source-sink ratio on grain filling and the source-sink characteristics of high yield varieties of mid-season hybrid rice. Scientia Agric sin. 2005;38(2):265–71.Google Scholar
  13. Seo SW. The effects of the limiting of the number of the ripening grains per panicle on the changing of the grain weight and the protein content in brown rice during the ripening period. Jpn J Crop Sci. 1980;49:8–14.View ArticleGoogle Scholar
  14. Xu FX, Guo XY, Zhang L, Xiong H, Zhu YC, Liu M, et al. Effects of sink-source structures on filling of superior and inferior spikelets of mid-season hybrid rice. J Agr Sci Tech. 2013;15(1):96–101.Google Scholar
  15. Kato T. Effect of spikelet removal on the grain filling of Akenohoshi, a rice cultivar with numerous spikelets in a panicle. J Agric Sci. 2004;142:177–81.View ArticleGoogle Scholar
  16. You CC, Zhu HL, Xu BB, Huang WX, Wang SH, Ding YF, et al. Effect of removing superior Spikelets on grain filling of inferior Spikelets in Rice. Front Plant Sci. 2016;7:1161.PubMedPubMed CentralGoogle Scholar
  17. Zieske LR. A perspective on the use of iTRAQ™ reagent technology for protein complex and profiling studies. J exp Bot. 2006;57(7):1501–8.View ArticlePubMedGoogle Scholar
  18. Zhang ZX, Chen J, Lin SS, Li Z, Cheng RH, Fang CX, et al. Proteomic and phosphoproteomic determination of ABA's effects on grain-filling of Oryza sativa L. Inferior spikelets. Plant Sci. 2012;185-186:259–73.View ArticlePubMedGoogle Scholar
  19. Zhang ZX, Tang J, Du TW, Zhao H, Li Z, Li Z, et al. Mechanism of developmental stagnancy of rice inferior spikelets at early grain-filling stage as revealed by proteomic analysis. Plant Mol Biol rep. 2015;33(6):1844–63.View ArticleGoogle Scholar
  20. Chen TT, Xu GW, Wang ZQ, Zhang H, Yang JC, Zhang JH. Expression of proteins in superior and inferior spikelets of rice during grain filling under different irrigation regimes. Proteomics. 2015;16:102–21.View ArticlePubMedGoogle Scholar
  21. Robbins ML, Roy A, Wang PH, Gaffoor I, Sekhon RS, de O. Buanafina MM, et al. Comparative proteomics analysis by DIGE and iTRAQ provides insight into the regulation of phenylpropanoids in maize. J Proteome. 2013;20(93):254–75.View ArticleGoogle Scholar
  22. Dong MH, Gu JR, Zhang L, Chen PF, Liu TF, Deng JH, et al. Comparative proteomics analysis of superior and inferior spikelets in hybrid rice during grain filling and response of inferior spikelets to drought stress using isobaric tags for relative and absolute quantification. J Proteome. 2014;109:382–99.View ArticleGoogle Scholar
  23. Richards F. A flexible growth function for empirical use. J exp Bot. 1959;10(29):290–300.View ArticleGoogle Scholar
  24. Isaacson T, Damasceno CM, Saravanan RS, He Y, Catalá C, Saladié M, et al. Sample extraction techniques for enhanced proteomic analysis of plant tissues. Nat Protoc. 2006;1(2):769–74.View ArticlePubMedGoogle Scholar
  25. Smith PK, Krohn RI, Hermanson GT, Mallia AK, Gartner FH, Provenzano MD, et al. Measurement of protein using bicinchoninic acid. Anal Biochem. 1985;150(1):76–85.View ArticlePubMedGoogle Scholar
  26. Wiśniewski JR, Zougman A, Nagaraj N, Mann M. Universal sample preparation method for proteome analysis. Nat Methods. 2009;6:359–62.View ArticlePubMedGoogle Scholar
  27. Shilov IV, Seymour SL, Patel AA, Loboda A, Tang WH, Keating SP, et al. The paragon algorithm, a next generation search engine that uses sequence temperature values and feature probabilities to identify peptides from tandem mass spectra. Mol Cell Proteomics. 2007;6(9):1638–55.View ArticlePubMedGoogle Scholar
  28. Kobata T, Akiyama Y, Kawaoka T. Convenient estimation of unfertilized grains in rice. Plant Production Sci. 2010;13(3):289–96.View ArticleGoogle Scholar
  29. Yang JC, Zhang WH, Wang ZQ, Liu LJ, Zhu QS. Source-sink characteristics and the translocation of assimilates in new plant type and intersubspecific hybrid rice. J Integr Agr. 2002b;1(2):155–62.Google Scholar
  30. Xie GH, Yang JC, Wang ZQ, Zhu QS. Grain filling characteristics of rice and their relationships to physiological activities of grains. Acta Agron sin. 2001;27(5):557–65.Google Scholar
  31. Yang J, Zhang J. Grain filling of cereals under soil drying. New Phytol. 2006;169:223–36.View ArticlePubMedGoogle Scholar
  32. Yang JC, Zhang JH, Huang Z, Wang ZQ, Zhu QS, Liu L. Correlation of cytokinin levels in the endosperms and roots with cell number and cell division activity during endosperm development in rice. Ann Bot. 2002a;90:369–77.View ArticlePubMedPubMed CentralGoogle Scholar
  33. Singh BK, Jenner CF. Association between concentration of organic nutrients in the grain, endosperm cell number and grain dry weight within the ear of wheat. Aust J Plant Physiol. 1982;9(1):83–95.View ArticleGoogle Scholar
  34. Jones R, Roessler J, Ouattar S. Thermal environment during endosperm cell division in maize: effects on number of endosperm cells and starch granules. Crop Sci. 1985;25:830–4.View ArticleGoogle Scholar
  35. Hall A. Rho GTPases and the Actin cytoskeleton. Science. 1998;279(5350):509–14.View ArticlePubMedGoogle Scholar
  36. Emoto K, Sawada H, Yamada Y, Fujimoto H, Takahama Y, Ueno M, et al. Annexin II overexpression is correlated with poor prognosis in human gastric carcinoma. Anticancer res. 2001;21(2B):1339–45.PubMedGoogle Scholar
  37. Bartel B, Fink GR. ILR1, an amidohydrolase that releases active indole-3-acetic acid from conjugates. Science. 1995;268:1745–8.View ArticlePubMedGoogle Scholar
  38. Ludwig-Müller J, Epstein E, Hilgenberg W. Auxinconjugate hydrolysis in Chinese cabbage: characterization of an amidohydrolase and its role during infection with clubroot disease. Physiol Plant. 1996;96:627–34.View ArticleGoogle Scholar
  39. Epstein E, Cohen JD, Bandurski RS. Concentration and metabolic turnover of indoles in germinating kernels of Zea mays L. Plant Physiol. 1980;65:415–21.View ArticlePubMedPubMed CentralGoogle Scholar
  40. Lur HS, Setter TL. Role of auxin in maize endosperm development (timing of nuclear DNA endoreduplication, zein expression, and cytokinin). Plant Physiol. 1993;103:273–80.View ArticlePubMedPubMed CentralGoogle Scholar
  41. Yang JC, Zhang JH, Wang ZQ, Zhu QS. Hormones in the grains in relation to sink strength and postanthesis development of spikelets in rice. Plant Growth Regul. 2003;41:185–95.View ArticleGoogle Scholar
  42. Yoshida S. Physiological aspects of grain yield. Annu rev Plant Physiol. 1972;23:437–64.View ArticleGoogle Scholar
  43. Liang J, Zhang J, Cao X. Grain sink strength may be related to the poor grain filling of indica-japonica (Oryza sativa) hybrids. Physiol Plant. 2001;112:470–7.View ArticlePubMedGoogle Scholar
  44. Ross HA, Davies HV. Sucrose metabolism in tubers of potato (Solanum tuberosum L.). Effects of sink removal and sucrose flux on sucrose-degrading enzyme. Plant Physiol. 1992;98:287–93.View ArticlePubMedPubMed CentralGoogle Scholar
  45. Kang GZ, Liu GQ, Peng XQ, Wei L, Wang CY, Zhu YJ, et al. Increasing the starch content and grain weight of common wheat by over-expression of the cytosolic AGPase large subunit gene. Plant Physiol Biochem. 2013;73:93–8.View ArticlePubMedGoogle Scholar
  46. Mizuno K, Kimura K, Arai Y, Kawasaki T, Shimada H, Baba T. Starch branching enzymes from immature rice seeds. Biochem J. 1992;112(5):643–51.View ArticleGoogle Scholar
  47. Keeling P, Bacon P, Holt D. Elevated temperature reduces starch deposition in wheat endosperm by reducing the activity of soluble starch synthase. Planta. 1993;191:342–8.View ArticleGoogle Scholar
  48. Zhang ZX, Zhao H, Tang J, Li Z, Li Z, Chen DM, et al. A proteomic study on molecular mechanism of poor grain-filling of rice (Oryza sativa L.) inferior spikelets. PLoS One. 2014;9(2):1–19.Google Scholar
  49. Fu J, Xu YJ, Chen L, Yuan LM, Wang ZQ, Yang JC. Changes in enzyme activities involved in starch synthesis and hormone concentrations in superior and inferior spikelets and their association with grain filling of super rice. Rice Sci. 2013;20:120–8.View ArticleGoogle Scholar
  50. Ahn YO, Kim SH, Kim CY, Lee JS, Kwak SS, Lee HS. Exogenous sucrose utilization and starch biosynthesis among sweet potato cultivars. Carbohydr res. 2010;345(1):55–60.View ArticlePubMedGoogle Scholar
  51. Fernie AR, Carrari F, Sweetlove LJ. Respiratory metabolism: glycolysis, the TCA cycle and mitochondrial electron transport. Curr Opin Plant Biol. 2004;7:254–61.View ArticlePubMedGoogle Scholar
  52. Achouri Y, Noël G, Vertommen D, Rider MH, Veiga-da-cunha M, Schaftingen EV. Identification of a dehydrogenase acting on D-2-hydroxyglutarate. Biochem J. 2004;381(1):35–42.View ArticlePubMedPubMed CentralGoogle Scholar
  53. Bykova NV, Stensballe A, Egsgaard H, Jensen ON, Moller IM. Phosphorylation of formate dehydrogenase in potato tuber mitochondria. J Biol Chem. 2003;278(28):26021–30.View ArticlePubMedGoogle Scholar
  54. Andreadeli A, Flemetakis E, Axarli I, Dimou M, Udvardi MK, Katinakis P, et al. Cloning and characterization of Lotus Japonicus formate dehydrogenase: a possible correlation with hypoxia. Biochim Biophys Acta. 2009;1794(6):976–84.View ArticlePubMedGoogle Scholar
  55. Lundqvist T, Schneider G. Crystal structure of activated ribulose-1,5-bisphosphate carboxylase complexed with its substrate, ribulose-1,5-bisphosphate*. J Biol Chem. 1991;266:12604–11.PubMedGoogle Scholar
  56. Kowalczyk S, Januszewska B, Cymerska E, Maslowski P. The occurrence of inorganic pyrophosphate: D-fructose-6-phosphate 1-phosphotransferase in higher plants. Physiol Plant. 1984;60(1):31–7.View ArticleGoogle Scholar
  57. Botha AM, Botha FC. Effect of anoxia on the expression and molecular form of the pyrophosphate dependent phosphofructokinase. Plant Cell Physiol. 1991;32(8):1299–302.Google Scholar
  58. Jiang LG, Dai TB, Wei SQ, Gan XQ, Xu JY, Cao WX. Genotypic differences and valuation in nitrogen uptake and utilization efficiency in rice. Acta Phytoecologica Sinica. 2003;27(4):466–71.Google Scholar
  59. Zhao XQ, Shi WM. Expression analysis of the glutamine synthetase and glutamate synthase gene families in young rice (Oryza sativa) seedlings. Plant Sci. 2006;170:748–54.View ArticleGoogle Scholar
  60. Levinthal C. Are there pathways for protein folding? J Chim Phys. 1968;65:44–5.View ArticleGoogle Scholar
  61. Kaminaka H, Morita S, Yokoi H, Masumura T, Tanaka K. Molecular cloning and characterization of a cDNA for plastidic copper / zinc-superoxide dismutase in rice (Oryza sativa L.). Plant Cell Physiol. 1997;38(1):65–9.View ArticlePubMedGoogle Scholar
  62. Freedman RB, Hirst TR, Tuite MF. Protein disulphide isomerase : building bridges in protein folding. Trends Biochem Sci. 1994;19:331–6.View ArticlePubMedGoogle Scholar
  63. Shimoni Y, Segal G, Zhu XZ, Galili G. Nucleotide sequence of a wheat cDNA encoding protein disulfide isomerase. Plant Physiol 1995, 107 (1): 281-281.Google Scholar
  64. Johnson JC, Appels R, Bhave M. The PDI genes of wheat and their syntenic relationship to the esp2 locus of rice. Funct Integr Genomic. 2006;6:104–21.View ArticleGoogle Scholar
  65. Kim YJ, Yeu SY, Park BS, Koh HJ, Song JT, Seo HS. Protein disulfide isomerase-like protein 1-1 controls endosperm development through regulation of the amount and composition of seed proteins in rice. PLoS One. 2012;7(9):e44493.View ArticlePubMedPubMed CentralGoogle Scholar
  66. Ma QL, Li YS, Tian XH, Yan SZ, Lei WC, Noboru N. Influence of high temperature stress on composition and accumulation configuration of storage protein in rice. Sci Agric sin. 2009;42(2):714–8.Google Scholar
  67. Yoshito K, Hiroshi I, Tohruk k, Masato N, Takaya S. Structure and function of signal-transducting GTP-binding proteins. Annu rev Biochem 1991, 60:349-400.Google Scholar
  68. Murata Y, Pei ZM, Mori IC, Schroeder J. Abscisic acid activation of plasma membrane Ca2+ channels in guard cells requires cytosolic NAD(P)H and is differentially disrupted upstream and downstream of reactive oxygen species production in abi1-1 and abi2-1 protein phosphatase 2C mutants. Plant Cell. 2001;13(11):2513–23.PubMedPubMed CentralGoogle Scholar
  69. Seth A, Waering PE. Hormone-directed transport of metabolites and its possible role in plant senescence. J exp Bot. 1967;18:65–77.View ArticleGoogle Scholar
  70. Tian S, Wang X. The relationship between IAA and grain developing of Indica-japonica hybrid rice and regulation with S-07. Chinese J Rice Sci. 1998;12(2):99–104.Google Scholar
  71. Aitken A. 14-3-3 pproteins: a historic overview. Semin Cancer Biol. 2006;16(3):162–72.View ArticlePubMedGoogle Scholar
  72. Song JM, Dai S, Li HS, Liu AF, Cheng DG, Chu XS, et al. Expression of a wheat endosperm 14-3-3 protein and its interactions with starch biosynthetic enzymes in amyloplasts. Acta Agron sin. 2009;35(8):1445–50.View ArticleGoogle Scholar
  73. Sehnke PC, Chung HJ, Wu K, Ferl RJ. Regulation of starch accumulation by granule-associated plant 14-3-3 proteins. Proc Natl Acad Sci U SA. 2001;98:765–70.View ArticleGoogle Scholar

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