iTRAQ-based quantitative proteome analysis reveals metabolic changes between a cleistogamous wheat mutant and its wild-type wheat counterpart

Plant material

The cleistogamy mutant line ZK001 was isolated from a mutagenized population of wheat variety YM18, using an SMFs of 7 Tesla for 5 h. After mutagenesis, it propagated via self-pollination until the cleistogamous phenotype was completely stable. All wheat seeds were stored at the Hefei Institutes of Physical Science, Chinese Academy of Sciences (CASHIPS), Anhui, P. R. China. SM3, Aikang 58 (AK58), YM18, and ZK001 were grown in a greenhouse in an experimental field (31°54′N, 117°10′E) at CASHIPS. AK58 and YM18, which are both varieties in the northern China, were not resistant to FHB infection (Yu et al., 2019). Therefore, AK58 was used as susceptible control. Fertilizer and weed management were similar to methods used in the process for wheat breeding (Li et al., 2014). The spikelets and lodicules of YM18 and ZK001, which had three biological replicates, were harvested during the white anther stage (WAS), green anther stage (GAS), yellow anther stage (YAS), and anthesis stage (AS) (Zadoks, Chang & Konzak, 1974; Kirby & Appleyard, 1987; Guo & Schnurbusch, 2015). These samples were collected and frozen in liquid nitrogen and preserved at −80 °C.

Starch and total soluble sugar content

A total of 20 pairs of lodicules with three biological replications from YM18 and ZK001 were sampled and snap frozen in liquid nitrogen at the four flower development stages. The samples were ground using Tissuelyser-24 (Shanghai Jingxin Industrial Development Co., Ltd., Shanghai, China) for 45 s at 50 Hz.

Starch and total soluble sugars were extracted following the instructions included with the Starch Content and Plant Soluble Sugar Content test kits (Nanjing Jiancheng Bioengineering Institute, Nanjing, China). The starch and total soluble sugars in the supernatant were determined using a UV–VIS spectrophotometer (Lambda 365; PerkinElmer, Waltham, MA, USA) with a wavelength of 620 nm. The starch and total soluble sugar contents were calculated using the following formulas: $$C_{\text{starch}}=\frac{{\text{OD}}_{\text{sample}}-{\text{OD}}_{\text{blank}}}{{\text{OD}}_{\text{standard}}-{\text{OD}}_{\text{blank}}}\times \frac{C_{\text{standard }1}\times V_{\text{pretreatment}}\times \text{Dilution ratio}}{N_{\text{total PL}}}$$ $C_{\text{total souble sugar}}=\frac{{\text{OD}}_{\text{sample}}-{\text{OD}}_{\text{blank}}}{{\text{OD}}_{\text{standard}}-{\text{OD}}_{\text{blank}}}\times \frac{C_{\text{standard }2}\times \text{Dilution ratio}}{10\times V_{\text{distilled water}}\times N_{\text{total PL}}}$

Note: PL, pair of lodicules; C_starch, the starch content in lodicule (μg∙PL⁻¹); C_{total souble sugar}, the total souble sugar content in lodicule (μg∙PL⁻¹); C_{standard 1}, standard solution 1 concentration = 200 μg∙mL⁻¹; C_{standard 2}, standard solution 2 concentration = 100 μg∙mL⁻¹, Dilution ratio = 1; V_pretreatment, the volume of pretreatment solution = 1.7 mL; V_{distilled water}, the volume of distilled water used for homogenising = one mL; N_{total PL}, the total number of PL which were sampled = 20.

Observation of spikes and lodicules

Spike images of YM18 and ZK001 were photographed (D90; Nikon, Tokyo Metropolis, Japan) at the AS. After 90 min, the lodicules of YM18 and ZK001, which were separated from the central young spikes in triplicate during GAS and cultured on Murashige and Skoog (MS) medium, were observed with an upright fluorescence stereomicroscope (SZX10; Olympus, Tokyo, Japan) and photographed (DP72; Olympus, Tokyo, Japan).

FHB resistance testing

Fusarium head blight resistance testing was performed during the flowering stage of SM3, AK58, YM18, and ZK001 in the greenhouse by spraying the FHB spore F0601 (Fusarium graminearum Schw. cv. F0601) in both 2013–2014 and 2014–2015. The inoculum (50 μL at 105 spores per mL) was deposited by spraying both sides of the ears. The diseased spikelet rate was calculated using the following formula: $\text{Diseased spikelets rate}=\frac{N_{\text{infected spikelets}}}{N_{\text{total spikelets}}}\times 100\text{\%.}$

Note: N_{infected spikelets}, the number of infected spikelets; N_{total spikelets}, the number of total spikelets.

Protein extraction and iTRAQ labelling

Total soluble proteins were extracted according to a published procedure (Yang et al., 2013) with slight modifications. Briefly, moderate amounts of the samples were separately frozen using liquid N₂ and ground in −20 °C pre-cooled pestles and mortars with urea extraction buffer containing 150 mM Tris–HCl (pH 7.6), 8M urea, 0.5% SDS, 1.2% Triton X-100, 20 mM EDTA, 20 mM EGTA, 50 mM NaF, 1% glycerol 2-phosphate, five mM DTT, and 0.5% phosphatase inhibitor mixture 2 (Sigma, Darmstadt, Germany). The mixtures were centrifuged at 10,000×g for 1 h at 4 °C, then the supernatants were mixed with pre-cooled acetone/methanol and incubated for 1 h at −20 °C. The mixtures were centrifuged at 15,000×g for 15 min at 4 °C. The pellets were washed twice with cold acetone. Pellets were dried and solubilized in lysis buffer containing 50 mM Tris–HCl (pH 6.8), 8M urea, five mM DTT, 1% SDS, and 10 mM EDTA. Protein concentrations of the samples were estimated using the Bradford method (Bradford, 1976) (Table S1) and the samples were stored at −80 °C for further use. All protein samples were checked via sodium dodecyl sulfate polyacrylamide gel electrophoresis (SDS–PAGE) according to the Schägger protocol (Schägger, 2006). SDS-PAGE gels (Fig. S1) were stained with Coomassie Brilliant Blue staining solution (Coomassie Blue Fast Staining Solution, Beijing Dingguo Changsheng Biotechnology Co., Ltd., Beijing, China) (Kang et al., 2002).

After determining the protein concentration, we digested the samples with trypsin (V5113; Promega, Madison, WI, USA) and then incubated them for 12–16 h at 37 °C. Approximately 100 μg of peptides of the different samples were labelled with iTRAQ based on the protocol of Unwin, Griffiths & Whetton (2010). The peptides of the different samples were labelled with iTRAQ reagents (isobaric tags 113, 114, 115, 116, 117, 118, 119, and 121 for groups YM18-WAS, ZK001-WAS, YM18-GAS, ZK001-GAS, YM18-YAS, ZK001-YAS, YM18-AS, and ZK001-AS, respectively) according to the manufacturer’s instructions (Applied Biosystems, Foster City, CA, USA).

HPLC grading of C₁₈ columns at high pH and LC-electrospray ionization-MS/MS analysis

The lyophilized peptide mixture was reconstituted with 100 μL of solution A (2% acetonitrile (ACN) and 20 mM ammonium formate, pH 10). Then, the samples were loaded onto a reverse-phase column (C₁₈ column, 1.9 μm (particle size), 150 μm (inner diameter) × 120 mm (length), Waters) and eluted using a step linear elution program (Table S2). The samples were collected every 1.5 min and centrifuged at 14,000×g for 5–90 min. The 60 collected fractions were dried and re-dissolved with five μL 0.5% formic acid (FA). The collected fractions were finally combined into 10 pools and centrifuged at 14,000×g for 10 min.

The reconstituted peptides were analyzed with a Q-Exactive HF mass spectrometer (Thermo Fisher Scientific, Waltham, MA, USA) coupled with a nano high-performance liquid chromatography system (1260 Infinity II; Agilent, Santa Clara, CA, USA) (Scheltema et al., 2014). The peptides were loaded onto a C₁₈ reversed-phase column (C₁₈ column, three μm (particle size), 100 μm (inner diameter) × 200 mm (length), Thermo Scientific, Waltham, MA, USA) using mobile phases A (0.1% FA/H₂O) and B (0.08% FA, 80% ACN) (Table S3). The HPLC effluent was directly electrosprayed into the mass spectrometer and analyzed based on pre-set parameters (Fig. S2).

Data analysis

The raw mass data were processed for peptide identification using Proteome Discoverer 1.4 (ver. 1.4.0.288; Thermo Fisher Scientific, Waltham, MA, USA) with specific parameters (Table S4) for searching the UniProt Triticum database. A false discovery rate (FDR) of ≤0.01 was estimated for protein identification using a target-decoy search strategy (Elias & Gygi, 2007). The mass spectrometry proteomics data have been deposited in the ProteomeXchange Consortium (http://proteomecentral.proteomexchange.org) via the PRIDE partner repository (Vizcaino et al., 2016) with the dataset identifier < PXD010188 >. Increasing and decreasing abundant proteins were determined based on 1.5-fold-changes and peptides spectral matches (PSMs) ≥ 2 (Sharma et al., 2017) between ZK001-WAS and YM18-WAS (Group 1), ZK001-GAS and YM18-GAS (Group 2), ZK001-YAS and YM18-YAS (Group 3), ZK001-AS and YM18-AS (Group 4), YM18-WAS and YM18-GAS (Group 5), YM18-WAS and YM18-YAS (Group 6), YM18-WAS and YM18-AS (Group 7), ZK001-WAS and ZK001-GAS (Group 8), ZK001-WAS and ZK001-YAS (Group 9), and ZK001-WAS and ZK001-AS (Group 10).

Protein annotation was conducted by a BLAST search against NCBI and UniProt databases. Protein function was classified based on the following databases: Gene Ontology (http://www.geneontology.org/, GO), and Kyoto Encyclopedia of Genes and Genomes (http://www.genome.jp/kegg/, KEGG). For analysis of differentially abundant proteins (DAPs), significant GO enrichment and KEGG enrichment were defined as a corrected FDR with a P-value less than 0.01 (Benjamini & Hochberg, 1995). Proteins containing at least two PSMs per protein and fold change ratios ≥1.5 or ≤0.67 were considered more abundant or less abundant proteins, respectively. In order to validate the DAPs profile, we searched the EnsemblPlants database (http://plants.ensembl.org/index.html) for corresponding DNA sequences. A total of 10 DAPs involved in carbohydrate metabolism and calcium ion binding and transport were selected for Quantitative reverse transcription polymerase chain reaction (qRT-PCR) validation.

Quantitative real-time PCR validation

Total RNA was isolated using a Plant RNA kit (R6827; Omega, Norcross, America) according to the manufacturer’s instructions. The quality of each RNA sample was checked on 1% agarose gels. Measurement of the concentration of RNA samples was performed using a NanoDrop 2000 spectrophotometer bioanalyzer (Thermo Fisher Scientific, Waltham, MA, USA). cDNAs were synthesized using TransScript One-Step gDNA Removal and cDNA Synthesis SuperMix (Transgen Biotech, Beijing, China) according to the manufacturer’s protocol. qRT-PCR was used to measure the transcript levels of the proteins of interest. Each experiment was performed in three technical replicates with three biological replicates. Target gene-specific primers (Table S5) were designed using the online software Primer 3 version 0.4.0 (http://bioinfo.ut.ee/primer3-0.4.0/primer3/) (Untergasser et al., 2012). qRT-PCR was performed according to the manufacturer’s instructions for the FastStart Essential DNA Green Master (Roche, Basel, Switzerland), run on the Roche LightCycler^® 96 Instrument. The glyceraldehyde-3-phosphate dehydrogenase gene from T. aestivum (TaGAPDH, GI: 7579063) served as an internal control and the relative expression of target genes was calculated using the 2^−ΔΔCT method (Livak & Schmittgen, 2001).

Statistical data analysis

The experimental data values represented the average of the measurements conducted from three independent assays and were expressed as the mean ± standard error of the mean. The data were further analyzed using ANOVA followed by Duncan’s test (SPSS 18.0; IBM, Somers, NY, USA). The level of significance was set at P ≤ 0.05.

Comparative resistance to Fusarium head blight

The results of FHB resistance testing showed that the infection rate in SM3 and ZK001 were 7.03% and 9.39% in 2013–2014 and 8.61% and 17.60% in 2014–2015, respectively (Table 1). Compared to SM3 and ZK001, YM18, and AK58 were highly susceptible: the FHB infection rate was 35.16% and 38.12% in 2014–2015, respectively. However, the diseased spikelet rates for YM18 and AK58 were 15.20% and 20.41% in 2013–2014, respectively, which was half the rate in 2014–2015 (Table 1). This indicates that the FHB infection rate is greatly influenced by environmental factors. These results suggest that cleistogamous cultivars have a lower FHB infection rate than chasmogamous cultivars.

Comparison of flowering in YM18 and ZK001

In accordance with previous reports, the exserted anthers increased the incidence of FHB (Sage & De Isturiz, 1974; Parry, Jenkinson & McLeod, 1995). Furthermore, the anthers of cleistogamy wheat were detained in glums during the flowering stage. Although the individual lines of YM18 and ZK001 were grown under the same growth and environmental conditions, the morphological differences were obvious. In YM18, the anthers extruded from the palea and lemma at the AS, whereas, no anthers were observed in ZK001 at all flower development stages (Fig. 1A). The morphology of the lodicules of YM18 and ZK001 was also obviously different. In order to investigate their morphology, we harvested the lodicules of YM18 and ZK001 at the GAS and cultured them for 90 min on MS medium. The width of the YM18 lodicules (Fig. 1B) was greater than that of the ZK001 lodicules (Figs. 1C and 1D).

Physiological characteristics of lodicules in YM18 and ZK001

To reveal the cause of the lodicules difference between YM18 and ZK001, we measured the starch and total soluble sugar contents in the lodicules of YM18 and ZK001 at the four flower development stages. Lodicule starch (Fig. 2A; Table S6) and total soluble sugar (Fig. 2B; Table S6) contents showed an overall increase from the WAS to the AS for YM18 and ZK001. No significant differences in the starch and soluble sugar contents in the lodicule were observed between YM18 and ZK001 during the WAS or GAS. Additionally, the starch and total soluble sugar contents in ZK001 during the YAS significantly decreased 2.40- and 1.75-fold, respectively (both P < 0.05), compared with those in YM18, detected in one pair of lodicules (Fig. 2; Table S6). In contrast, the starch and total soluble sugar contents in ZK001 during the AS remarkably increased 3.57- and 1.52-fold, respectively (both P < 0.05), compared with those in YM18, detected in one pair of lodicules (Fig. 2; Table S6).

Overview of quantitative proteome analysis

In order to study the protein expression patterns in YM18 and ZK001, we examined and quantitatively catalogued the proteomes of YM18 and ZK001 in the four flower development stages using iTRAQ technology. In this study, 19,422 peptides were matched to 4,497 proteins in the samples (Table S7); in addition, 11,603 unique peptides were found, and 2,172 proteins were identified with more than two unique peptide sequences excluding post-translational modifications. As shown in Fig. 3A, more than 99% of the peptides covered proteins within the 36 peptides, and protein quantity decreased as the number of matching peptides increased. In terms of protein mass distribution, good coverage (an average of 10–18% of total proteins in each protein-mass group) was obtained for proteins >10 kDa and <60 kDa (Fig. 3B). The length of the identified peptides was between 10 and 13 amino acids at the peak and approximately 93% of the peptide length was within 24 amino acids (Fig. 3C). Over 77% of the proteins had >5% sequence coverage. Additionally, sequence coverage distribution was high in most of the identified proteins: More than 58% had over >10% coverage and more than 37% had over 20% coverage (Fig. 3D). These results indicate that the identified peptides were sufficient for protein identification.

Cluster analysis of protein expression at four developmental stages

In order to identify more DAPs, we compared DAPs in YM18 and ZK001 in the flowering development process, ZK001-WAS vs YM18-WAS (Group 1), ZK001-GAS vs YM18-GAS (Group 2), ZK001-YAS vs YM18-YAS (Group 3), ZK001-AS vs YM18-AS (Group 4), YM18-WAS vs YM18-GAS (Group 5), YM18-WAS vs YM18-YAS (Group 6), YM18-WAS vs YM18-AS (Group 7), ZK001-WAS vs ZK001-GAS (Group 8), ZK001-WAS vs ZK001-YAS (Group 9), and ZK001-WAS vs ZK001-AS (Group 10). Increasing abundance and decreasing abundance proteins were determined based on fold-changes (FC) of >1.5 or <0.667 for expression difference comparison. For further screening, approximately 16, 47, 2, 0, 11, 124, 105, 15, 298, and 188 DAPs were identified with a corrected P-value for GO KEGG enrichment less than 0.01 in groups 1 to 10 (Table 2). A Venn diagram of the DAPs and their overlap in Group 1 and Group 2 showed that two common DAPs were increased-abundance and one common DAP was decreased-abundance (Fig. S3). Group 3 and Group 4 showed no overlap with Group 1 or Group 2 (Fig. S3). Venn diagrams indicated that 6, 10, 32, 206, 57, and 140 DAPs were specific DAPs of Groups 5, 8, 6, 9, 7, and 10, respectively (Fig. S3).

Functional classification and subcellular localization of proteins

Gene ontology analysis showed that all of the identified proteins in YM18 and ZK001 were involved in 11 subgroups of MF, 19 subgroups of BP, and 14 subgroups of CC (Fig. S4; Table S7). Significant GO enrichment was employed to analyze the DAPs with a corrected FDR P-value less than 0.01 and an FC ratio of more than 1.5. Based on GO annotations and enrichments, the DAPs of Group 1 were enriched in molecular function terms for lipid binding (100%) (Fig. 4A) as well as biological process terms for lipid transport (14.06%), lipid localization (14.06%), macromolecule localization (20.31%), organic substance transport (20.31%), single-organism transport (15.63%), and single-organism localization (15.63%) (Fig. 4B). GO classification of Group 2 revealed that the DAPs were enriched in the biological process, cellular component, and molecular function (Fig. 4C). No protein was enriched in Group 3 or Group 4. The DAPs of Groups 5 to 10 were also classified into biological process (Fig. S5A), cellular component (Fig. S5B) and molecular function (Fig. S5C). DAPs involved in carbohydrate metabolism and transport, calcium ion binding and protein transport, and fatty acid biosynthesis were further used in cluster analyses.

Accumulation patterns of DAPs and verification of DAPs of interest

Based on the above analyses, 11 genes which corresponded to DAPs of interest were chosen for qRT-PCR analyses using gene-specific primers (Table S5) to explore the expression profile at the transcription level.

The lodicule morphology showed significant differences between YM18 and ZK001 (Fig. 1). We performed qRT-PCR using the RNA of YM18 and ZK001 lodicules to study the transcript profiles of the 11 genes corresponding to the selected DAPs (Fig. 5). The results of qRT-PCR indicated that the expression level of the gene A0A1D6CCI3 (encoding the bidirectional sugar transporter SWEET) was expressed in the lodicules at the WAS and GAS of both YM18 and ZK001, with almost no expression at the YAS and AS (Fig. 3; Table S7); (Fig. 5A). Additionally, the expression level of the gene A0A1D5WGA3 (encoding a nutrient reservoir-related protein) was extremely down-regulated from the WAS to the GAS in both YM18 and ZK001 (Fig. 5B). Though the relative expression level in ZK001 was significantly higher than that in YM18 in the GAS (P < 0.001), the relative expression levels were all less than 0.1 in the GAS, YAS or AS in YM18 or ZK001 (Fig. 5B). Compared with the relative expression levels of the gene encoding beta-amylase A0A1D5RR02 in YM18, the gene in ZK001 was down-regulated during the WAS (P < 0.001), GAS (P < 0.05), YAS (P < 0.05), and AS (P < 0.05) (Fig. 5C). The relative expression levels of A0A1D5RR02 were all down-regulated from the WAS to the YAS in YM18 and ZK001, but there was no significant difference from the YAS to the AS in YM18 or ZK001 (both P > 0.05) (Fig. 5C). Additionally, compared with its expression in the lodicules of YM18, P93594 (beta-amylase)-encoding mRNA was all up-regulated (P < 0.001) in the lodicules of ZK001, except that in the YAS (Fig. 5D). Interestingly, the mRNA level of sucrose synthase W5B5R3 in ZK001 indicated up-regulation compared with the levels in YM18 at the GAS, YAS, and AS (P < 0.001), and down-regulation at the WAS (P < 0.05) (Fig. 5E). The mRNA levels of A0A1D5SYC3 (a gene encoding cellular glucose homeostasis-related proteins) and A0A1D5VEI9 (a gene encoding cellular glucose homeostasis-related proteins) showed almost the same expression profile as W5B5R3, except for that of A0A1D5SYC3 in the WAS and that of A0A1D5VEI9 in the AS (Figs. 5E–5G). The relative expression level of A0A1D5RVB4 (a gene encoding cellular glucose homeostasis-related proteins) and W5B5R3 were similar (Figs. 5E and 5H). Compared with expression levels in YM18, the gene expression of beta-glucosidase activity-related protein A0A077S2F2 in ZK001 was down-regulated (P < 0.05) during the WAS and GAS and up-regulated (P < 0.001) in the AS (Fig. 5I).

Calcium ions play a key role in the development of plants. Therefore, the relative expression of the genes encoding calcium ion binding-related protein A0A1D5TN57 and annexin A0A1D5RRV7 were also evaluated to determine the profile during flower development. Compared with levels in YM18, the relative gene expression levels of A0A1D5TN57 in ZK001 were up-regulated during the WAS and YAS (Fig. 5K), and those of A0A1D5RRV7 were up-regulated during the WAS, YAS, and AS (Fig. 5J). However, the expression levels of A0A1D5RRV7 and A0A1D5TN57 were all down-regulated (P < 0.05) from the WAS to the GAS in ZK001 (Figs. 5J and 5K).

Cleistogamy provides structural barriers for diseases of Fusarium head blight

From the physiological point of view, the flowering stage is regarded as the most susceptible period for primary infection of wheat spikes by FHB because of the opening of wheat florets and the extension of anthers (Pugh, Johann & Dickson, 1933; Schroeder & Christensen, 1963; Gilsinger et al., 2005; Schuster & Ellner, 2008). Barley is a plant that self-fertilizes with permanently closed flowers, but chasmogamous barley varieties are easily infected with Fusarium (Yoshida, Kawada & Tohnooka, 2005; Culley & Klooster, 2007). Table 1 shows that the diseased spikelet rate in 2014–2015 was more severe than that in 2013–2014 except in SM3, possibly because of the resistance gene Fhb1 (Rawat et al., 2016). Compared with YM18, which is a wild-type chasmogamous cultivar, the diseased spikelet rate for ZK001, a mutant cleistogamous cultivar, decreased by 38.22% and 50.00% in 2013–2014 and 2014–2015, respectively (Table 1). This indicates that although the diseased spikelet rate is greatly influenced by environmental factors, cleistogamous cultivars that flower partially or completely may have a lower risk of FHB infection than chasmogamous cultivars (Kubo et al., 2010; Wang et al., 2013). Therefore, we further verified the hypothesis that cleistogamous wheat cultivars might have lower Fusarium susceptibility. A practical strategy for controlling FHB would be to introduce the cleistogamous character into other varieties that are suitable for production and promotion but sensitive to FHB through hybridization.

Lodicules play an important role in glume opening/closing in wheat

The molecular mechanism for cleistogamy has been intensively studied in rice (Maeng et al., 2006; Yoshida et al., 2007; Ohmori et al., 2012; Ni et al., 2014; Lombardo et al., 2017) and barley (Turuspekov et al., 2004; Hori et al., 2005; Nair et al., 2010; Wang et al., 2013, 2015; Zhang et al., 2016). However, the molecular mechanism for cleistogamy in wheat remains unclear, though it is known that the lodicule is a key factor in glume opening/closing in the monocotyledon. The abnormal lodicules may lack the ability to push the lemma and palea apart during the flowering stage in the cleistogamous mutant ZK001 (Fig. 1). This phenomenon is similar to that occurring in barley (Nair et al., 2010).

Carbohydrates and calcium are the main factors regulating lodicule osmotic pressure

Sucrose is the primary form of sugar transported for photosynthetic carbon assimilation (Chen et al., 2012). The A0A1D6CCI3 gene was expressed in the lodicules of both YM18 and ZK001 (Fig. 5A; Table S8). This indicates that carbohydrates can be transferred normally to the lodicules of both YM18 and ZK001. Nevertheless, the starch (Fig. 2A; Table S6) and soluble sugar (Fig. 2B; Table S6) contents of the YM18 lodicules increased dramatically from the GAS to the YAS. Liu et al. (2017) suggested that retarded lodicule expansion in ZS97A was caused by reduced water accumulation because of diminished accumulation of osmotic regulation substances. In contrast, the lower soluble sugar content in the lodicules of ZK001 prevented the accumulation of water during the YAS (Fig. 2B; Table S6). The lodicule size of YM18 was larger than that of ZK001 because the starch and soluble sugar contents in the lodicules of ZK001 decreased from the GAS to the YAS, leading to little water transfer to the lodicules. The lodicules of wheat swell extensively and subsequently contract after rapid autolysis of the tissues (Craig & O’Brien, 1975). Accordingly, the starch and soluble sugar contents in the lodicule of YM18 were lower than those in ZK001 at the AS (Figs. 2A and 2B; Table S6).

Cytosolic calcium is an important secondary messenger in plants and plays important roles in the response to both environmental and internal signals (Poovaiah & Reddy, 1993; Liao, Zheng & Guo, 2017). Plant annexins are calcium-dependent phospholipid binding proteins with many biological functions; for instance, they participate in calcium ion channel formation, membrane dynamics, plant growth, and the stress response (Mortimer et al., 2008; Laohavisit & Davies, 2011). In this study, the relative gene expression levels of annexin (A0A1D5RRV7) and the calcium ion binding-related protein (A0A1D5TN57) in the lodicules of ZK001 were up-regulated during the WAS compared to those of in the lodicules of YM18 (Figs. 5J and 5K). Therefore, we infer that the WAS is a critical period for the lodicules.

An overview of the pathways for proteome metabolic changes in lodicules

Many substances regulate the osmotic pressure of lodicules, such as soluble sugar (Zee & O’Brien, 1971; Wang, Gu & Gao, 1991; Yan et al., 2017), starch (Pissarek, 1971), calcium (Qin, Yang & Zhao, 2005; Chen et al., 2016), and potassium (Heslop-Harrison & Heslop-Harrison, 1996; Chen et al., 2016; Liu et al., 2017). Our findings, together with those of previous studies, provide an overview of the metabolic pathways involving the carbohydrates that regulate the osmotic pressure of the lodicules. As shown in Fig. 6, sucrose is transferred into the lodicules from the extra-cellular environment through a bidirectional sugar transporter (A0A1D6CCI3), and is converted into D-fructose-6P by hexokinase (A0A1D5SYC3, A0A1D5RVB4, and A0A1D5VEI9) after being broken down into D-fructose. D-fructose-6P converted to α-D-glucose-1P, which can be converted to D-glucose by β-glucosidase (A0A077S2F2) and synthesized into amylose. The starch formed from amylose can be broken down into D-glucose under the action of β-amylase (A0A1D5RR02 and P93594). UDP-glucose formed from amino sugar and nucleotide sugar can also be converted into D-glucose under the action of β-glucosidase (A0A077S2F2). The accumulation of D-glucose leads to a change in osmotic pressure in the lodicules. Additionally, α-D-glucose-1P can enter the pentose phosphate pathway and the fatty acid biosynthesis pathway through the formation of α-D-glucose-6P and β-D-glucose-6P. Soluble sugar can also enter and exit cells through the bidirectional sugar transporter. Furthermore, annexin (A0A1D5RRV7) can trigger calcium ion influx, increasing the osmotic pressure. Once the osmotic pressure changes, water accumulates in/is excreted from the cells of the lodicules and induces the expansion/shrinkage of the lodicules.