Integrated proteome analyses of wheat glume and awn reveal central drought response proteins under water deficit conditions

Accepted Manuscript Title: Integrated proteome analyses of wheat glume and awn reveal central drought response proteins under water deficit conditions Authors: Xiong Deng, Shoumin Zhen, Dongmiao Liu, Yue Liu, Mengfei Li, Nannan Liu, Yueming Yan PII: DOI: Reference:

S0176-1617(18)30439-5 https://doi.org/10.1016/j.jplph.2018.11.011 JPLPH 52894

To appear in: Received date: Revised date: Accepted date:

24 July 2018 12 November 2018 12 November 2018

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Integrated proteome analyses of wheat glume and awn reveal central drought response proteins under water deficit conditions

Mengfei Li1, Nannan Liu1 and Yueming Yan1*

College of Life Science, Capital Normal University, 100048 Beijing, China.

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Contributed equally to this work.

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E-mail:

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Xiong Deng: [email protected]

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Dongmiao Liu: [email protected]

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Shoumin Zhen: [email protected]

Yue Liu: [email protected]

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Xiong Deng1,2, Shoumin Zhen1,2, Dongmiao Liu1,2, Yue Liu1,

Mengfei Li: [email protected]

*Corresponding

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Nannan Liu: [email protected] authors:

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E-mail Addresses: [email protected] (Y. Yan)

Abstract Main conclusion Integrated proteome analyses revealed differentially accumulated proteins in the non-leaf green organs in wheat glume and awn that play important roles in photosynthesis and drought resistance. 1

Two non-leaf green organs in wheat, glume and awn, have photosynthetic potential, contribute to grain yield, and also play roles in resistance to adverse conditions. We performed the first integrated proteome analysis of wheat glume and awn in response to water deficit. Water deficit caused a significant decrease in important agronomic traits and grain yield. A total of 120 and 77 differentially accumulated protein (DAP) spots, representing 100 and 67 unique proteins responsive to water deficit, were identified by two-dimensional

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difference gel electrophoresis (2D-DIGE) in glumes and awns, respectively, of the elite Chinese bread wheat cultivar Zhongmai 175. The DAPs of both organs showed similar

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functional classification and proportion and were mainly involved in photosynthesis,

detoxification/defense, carbon/energy metabolism, and proteometabolism. Comparative proteome analyses revealed many more drought-responsive DAP spots in glumes than in

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awns, which indicate that glumes underwent more proteome changes in response to water

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deficit. The main DAPs involved in photosynthesis and carbon metabolism were significantly

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downregulated, whereas those related to detoxification/defense and energy metabolism were markedly upregulated under water deficit. The potential functions of the identified DAPs

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revealed an intricate interaction network that responds synergistically to drought stress during

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grain development. Our results from the proteome perspective illustrate the potential roles of wheat non-leaf green organs glume and awn in photosynthetic and defensive responses under

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Abbreviation

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drought stress.

2D-DIGE, two-dimensional fluorescence difference gel electrophoresis; 6PGD, 6-phosphogluconate dehydrogenase; ALDH, aldehyde dehydrogenase;

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APX; ascorbate peroxidase; AsA-GSH, ascorbate-glutathione; atpA, ATP synthase CF1 alpha subunit; atpB, ATP synthase CF1 beta subunit; AXAH, arabinoxylanarabinofuranohydrolaseisoenzyme; CAT, catalase; CBPs, chlorophyll-binding proteins; 2

CD4B, ATP-dependent Clp protease ATP-binding subunit clpA-like protein; CK, control group; CPN-60, plastid chaperonin 60; Cytb6-f, cytochrome b6-f complex iron-sulfur subunit, chloroplastic pet C; DAO, diamine oxidases; DAPs, differentially accumulated proteins; DHA, dehydroascorbate;

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DHAR, dehydroascorbate reductase; DPA, days post-anthesis; DS, drought treatment group;

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ELIPs, Early light inducible proteins; F6P, fructose-6-phosphate; FBA, chloroplast fructose-bisphosphatealdolase; FBPase, fructose 1,6-bisphosphatase;

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FNR, ferredoxin-NADP(H) oxidoreductase;

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G3P, glyceraldehyde-3-phosphate; GAPDH, glyceraldehyde-3-phosphate dehydrogenase;

GLDC, glycine dehydrogenase;

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GLP 8-14, germin-like protein 8-14;

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GIII, glucan endo-1,3-beta-glucosidase;

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GAPDH-B, glyceraldehyde-3-phosphate dehydrogenase B;

GSTs, glutathione S-transferases; Hsp 70, heat shock protein 70;

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IPP, isomerase;

LHCP, light-harvesting chlorophyll a/b-binding protein;

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MDH, malate dehydrogenase;

mitETC, mitochondrial electron transport chain MS, methionine synthase 1 enzyme; MTK1, methylthioribose kinase 1;

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NADP-ME, NADP-malic enzyme; NADP-IDH, isocitrate dehydrogenase [NADP]; O2-, superoxide anion radical; OxO 2, oxalate oxidase 2; PAO, polyamine oxidases; PAL, phenylalanine ammonia-lyase; PAs, polyamines; 3

PCA, Principal component analysis; PEP, phosphoenolpyruvate; PEPCase, phosphoenolpyruvate carboxylase; PGK, phosphoglycerate kinase, chloroplastic; PGM, phosphoglycerate mutase; PMM, phosphomannomutase; POD, peroxidase; Pn, photosynthetic rate;

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PPDK, pyruvate orthophosphate dikinase PPO, polyphenol oxidase; PsbO, the 33 kDa protein;

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PsbP, the 23 kDa protein; PSII, oxygen-evolving photosystem II;

Rbcl, ribulose-1,5-bisphosphate carboxylase/oxygenase large subunit;

Rbcs, ribulose-1,5-bisphosphate carboxylase/oxygenase small subunit;

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ROS, reactive oxygen species;

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Rubisco, ribulose-1, 5-bisphosphate carboxylase/oxygenase;

SDS-PAGE, sodium dodecyl sulfate-polyacrylamide gel electrophoresis;

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SOD, superoxide dismutase; TCA, tricarboxylic acid;

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TPI, triose phosphate isomerase;

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SRWC, Soil relative water content;

V-H(+)-ATPase, vacuolar proton-ATPase;

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WUE, water use efficiency.

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Keywords: Bread wheat, glume, awn, 2D-DIGE, proteome, water deficit

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INTRODUCTION

Wheat production is highly sensitive to changes in the environment and climate. Various abiotic stresses, such as drought, high salinity, and heat, significantly impair plant growth and grain yields (Porter and Semenov, 2005). Drought is a major abiotic stress that limits yields of many crop species during grain filling. In northern China, water is the main limiting factor for winter wheat production because drought always occurs during the wheat growing season. 4

The effects of abiotic stresses on crop production are exacerbated by global warming and climate change: A temperature increase of 0.5–2.0 °C can result in a decrease in yield of up to 2.4%–7.9% (Xiao et al., 2016). Drought stress disrupts photosynthesis and the transfer of stored carbohydrates into grains during flowering and reduces grain number and weight, ultimately decreasing grain yield (Richards et al., 2011). In addition to leaf tissue, non-leaf green organs have actual or potential photosynthetic

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capability (Nilsen, 1995; Guido and Hardy, 2003; Hu et al., 2012). For example, the non-leaf green organs ear, internode, and sheath possess the capability of assimilating CO2 by the C4

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pathway of photosynthesis, or the C3-C4 intermediate type of photosynthesis (Singal et al., 1986; Blanke, 1989; Jia et al., 2015). Together with the flag leaf, the various photosynthetic

parts of the ear make major contributions to wheat grain carbohydrates (Singal et al., 1986).

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The contribution of the ear accounts for 10–44% of total wheat photosynthesis depending on

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the variety, environment, and experimental procedures (Kriedemann, 1966). At least 59% of

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materials in wheat grain come from photosynthesis of ear tissue, according to C isotope technology (Araus et al., 1993). Especially under water-deficit conditions, the ears may

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2004; Eduardo et al., 2007).

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become the main photosynthetic contributors to grain filling (Bort et al., 1994; Abbad et al.,

The glume and awn are two important components of the wheat ear. In response to drought, each resists stress and has higher water use efficiency (WUE) than leaves (Wang et

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al., 2001; Martinez et al., 2003; Araus et al., 2005). In botanical terms, a glume is a bract (leaf-like structure) below a spikelet in the inflorescence (flower cluster) of grasses (Poaceae)

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or the flowers of sedges (Cyperaceae). It is the main green tissue of the spike in wheat and facilitates photosynthesis and delays aging under drought (Jia et al., 2015). The awn, located at the top of the wheat plant, also has stomata and can fix CO2 (Kriedemann, 1966). In a

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previous study, when single awns on basal florets of spikelets in the central part of the ear were dosed with 14CO2, 99% of the carbon-14 recovered was in the spikelet bearing the awn (Olugbemi, 2010). Thus, the glume and awn are two important non-leaf green organs that play an active role in wheat yield via photosynthesis (Slafer et al., 1990). Drought generally induces a series of responses such as repression of photosynthesis and cell growth, a reduction in carbon assimilation and utilization capacity, activation of 5

respiration, and oxidative stress. A major effect of drought is reduced photosynthesis, which is often, but not always, related to stomatal responses (Cornic, 2000). Drought-induced stomatal closure, which directly decreases the influx of CO2, mainly occurs in leaves (Wahid and Rasul, 2005; Farooq, 2009). Furthermore, drought stress changes photosynthetic pigments and components (Anjum et al., 2003), damages the photosynthetic apparatus (Fu and Huang, 2001), and diminishes the activities of Calvin cycle enzymes, which are all

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important causes of reduced crop yields (Monakhova and Chernyadèv, 2002). Another important effect of drought that suppresses the photosynthetic abilities and growth of plants is

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an imbalance in the generation and removal of reactive oxygen species (ROS; Vivekanandan et al., 2004; Choudhury et al., 2017). ROS-induced injury to biological macromolecules under drought stress is a major deterrent to growth (Vivekanandan et al., 2004).

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Plants also respond and adapt to water deficits at the cellular and molecular levels (e.g.,

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by accumulating ROS and proteins involved in drought stress tolerance). High levels of ROS,

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which may include singlet oxygen molecules, superoxide anion radicals (O2-), hydrogen peroxide (H2O2), and hydroxyl radicals (OH), activate a series of enzymes involved in the

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ROS-scavenging system (Zhu, 2002; Drazkiewicz et al., 2007). The ascorbate–glutathione

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(AsA-GSH) cycle is an important part of this ROS response. AsA and GSH are not consumed in this pathway but participate in the cyclic transfer of reducing equivalents, a process that involves ascorbate peroxidase (APX), monodehydroascorbate reductase (MDHAR),

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dehydroascorbate reductase (DHAR), and glutathione reductase (GR) and leads to the reduction of H2O2 to H2O (Noctor and Foyer, 1998). Simultaneously, ROS can be scavenged

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by other antioxidant compounds such as superoxide dismutase (SOD), peroxidase (POD), catalase (CAT), oxalate oxidase (OxO), and glutathione S-transferases (GSTs). In addition, synthesis of stress-related proteins is a ubiquitous response to prevailing stressful conditions,

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including water deficit. Molecular chaperones, which are important stress-related proteins, protect cellular proteins from denaturation (Mahajan and Tuteja, 2005). In recent years, extensive studies on the molecular basis of drought response and tolerance have been performed using proteomic approaches in different plant species, including Arabidopsis (Reumann and Singhal, 2014), rice (Wan and Liu, 2008; Mirzaei, 2012; Zerzucha, 2012; Reumann and Singhal, 2014), soybean (Das et al., 2016), and Brassica 6

napus (Jin et al., 2015). Most of the work in wheat has focused on PEG-induced osmotic stress in seedlings (Kang et al., 2012; Hao et al., 2015) and greenhouse-based water-deficit treatment (Jiang et al., 2012; Ge et al., 2012). Few studies have reported the proteomic responses of different plant organs to field drought stress during grain filling, such as developing grains (Zhang et al., 2014), embryo and endosperm (Gu et al., 2015), and developing flag leaves and grains (Deng et al., 2018). Field experiments can directly reflect

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natural drought conditions; thus, experimental data from such experiments have extremely

high practical value. However, proteome responses to field drought stress in non-leaf green

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organs of wheat, in particular glume and awn, have not been investigated.

In this study, we performed the first comprehensive proteome analysis of the glume and awn of wheat plants exposed to water-deficit treatment in the field. We identified a

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considerable number of differentially accumulated proteins (DAPs) involved in

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photosynthesis and stress defense in both organs. Our results provide new evidence to further

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understand the molecular mechanisms of plant responses and adaptation to drought stress.

MATERIALS AND METHODS

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Plant materials, field drought treatment, and sampling The elite Chinese bread wheat cultivar “Zhongmai 175” (Triticum aestivum L.), one of the

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most widely planted commercial winter wheat cultivars in the North China Plain, was used as material and planted at the experimental station of China Agricultural University (CAU),

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Wuqiao, Hebei Province (116°37′23″E and 37°16′02″N) during the 2015–2016 wheat growing season. The topsoil (0–20 cm) of the experimental plots contained organic matter 12.1 g kg−1, total nitrogen 1.0 g kg−1, hydrolysable nitrogen 106.7 mg kg−1, available

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phosphorus 33.8 mg kg−1, and potassium 183.4 mg kg−1. The precipitation during the winter wheat growing seasons was recorded. According to the reported irrigation method (Chu et al., 2016), supplemental irrigation

was performed before sowing. About 80% field capacity targets the relative soil water content of 0-200 cm soil layer, and so soil water content was irrigated to 80.7% of field water capacity in the 2015-2016 growing seasons before sowing. Crop developmental stages were 7

categorized using the Zadoks scale (Zadoks et al., 1974). Two irrigation treatments (75 mm water each) were established as follows: two-irrigation at jointing and anthesis stages for the control (CK) and no-irrigation after sowing for drought stress treatment (DS). Each experimental plot was 8 m × 4 m with rows spaced at 0.16 m, and the experiment used a random complete block design with three replications. An unirrigated zone of 1 m width was set to minimize the effects of adjacent plots. A flow meter was used to measure the amount of

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water applied.

Soil samples were collected and measured from 0 to 2 m in layer segments of 0.20 m by

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using a soil corer at the beginning of anthesis and at maturity. The soil water content was

determined using the oven-drying method (Gardner, 1986). Plants were marked after flowering, and then wheat glume and awn from five periods (10, 15, 20, 25, and 30 days

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post-anthesis, DPA) in three biological replicates were collected. All collected samples were

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immediately transferred to liquid nitrogen for storage prior to analysis.

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Agronomic trait measurements

Plant, spikelet, glume, and awn phenotypes in the control and drought treatment groups

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were assessed at the indicated developmental stages. Total starch content was tested using

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total starch assay kits (Megazyme Int. Ireland, Ireland) according to the manufacturer’s protocols and Chen et al. (2016). Grain yield (with 13% water content) was measured from an area of 4 m-2. The major agronomic traits of mature plants were tested, including plant

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height, ear length, tiller number, number of spikelets, number of infertile spikelets, spike number (10,000/ha), grain number per spike, 1,000-grain weight (g), and grain yield.

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Statistical analyses were carried out using independent Student’s t-tests with SPSS statistics software (ver. 19.0; SPSS Inc., Chicago, IL, USA). Protein extraction, 2D-DIGE, and 2-DE

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The total protein of glume and awn from three biological replicates was extracted according to

Cascardo et al. (2001) with minor modifications. Approximately 5 g of fresh glume and awn from each biological replicate was ground into a fine power in liquid nitrogen. Then 5 ml of extraction buffer (0.9 M sucrose, 0.1 M pH 8.0 Tris-HCl, 50 mM EDTA-Na2, 2% SDS, 1% PVPP, 20 mM DTT), 10 µL 0.1 M PMSF and 0.1 M dithiothreitol (DTT) was added. Samples were mixed vigorously for 10 min at room temperature and centrifuged twice at 13,000 rpm 8

and 4 °C for 15 min each. Subsequently, the supernatants were precipitated by adding 100 mM cold ethanolamine.methanol solution at 20 °C overnight, followed by centrifuging for 15 min at 13,000 rpm and 4 °C. The pellets were rinsed three times with 1 mL chilled (–20 °C) acetone containing 20 mM DTT and centrifuged at 1,000 rpm and 4 °C for 10 min between rinses. After freeze-drying, the pellets were added to 200 μL of solubilization buffer (7 M urea, 2 M thiourea, 1% DTT (w/v), and 4% CHAPS) at room temperature for 2 h. Finally,

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protein concentration was determined with a 2-D Quant Kit (GE Healthcare, USA). The final protein solution was stored at 80 °C for later use. The experimental design for 2D-DIGE

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analysis was based on Rollins et al. (2013) and Sikulu et al. (2015). Protein labeling and

2D-DIGE were performed according to Bian et al. (2017) and Li et al. (2017) with minor modifications. Pairs of Cy3- and Cy5-labeled protein samples and a Cy2-labeled internal

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standard were mixed and subjected to 2D-DIGE. The DIGE images were visualized using

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TyphoonTM 9500 scanner, with filters for the excitation/emission wave lengths of each dye:

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Cy2 (Blue, 520 nm), Cy3 (Green, 580 nm), and Cy5 (Red, 670 nm), and then analyzed using DeCyder software (ver. 6.5; Amersham, Little Chalfont, UK) according to Rollins et al. (2013)

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and Cao et al. (2016). All samples were carried out in three biological replicates, and only those with significant and biologically reproducible changes (abundance variation at least

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two-fold, Student’s t-Test, p < 0.05) were considered as DAP spots. The details of 2D-DIGE experiments for DAP identification and expression analysis are listed in Supporting

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Information Table S1. Based on the results of 2D-DIGE, 2-DE was performed to test the dynamic changes of the identified DAP spots and to facilitate tandem mass spectrometry

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analysis based on Bian et al. (2017). The 2-DE gels were analyzed using Image Master 2D Platinum 7.0 (GE Healthcare, USA). The DAP spots on the preparative gels were used for MS/MS analysis after image analysis. Three biological replicates were conducted for 2-DE,

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and the gel imaging and analysis were performed according to Bian et al. (2017). Those with significant and biological reproducible changes (abundance variation at least two-fold, Student’s t-Test, p value of < 0.05) were considered as candidate DAP spots. Only those consistent with 2D-DIGE were used as final DAPs for subsequent tandem mass spectrometry analysis. Principal component analysis (PCA) 9

The multivariate method PCA was performed in the R Language and Environment for Statistical Computing (version 3.0.2, Auckland, New Zealand) (Valledor and Jorrín, 2011) to find the main variations and reveal hidden structures present in the identified proteome and qRT data set. Whole data sets and DAP spot data sets in glume and awn, at five developmental stages in the CK and DS groups, were analyzed by PCA. Afterwards, the transcription expression level change of 11 DAPs genes at five developmental stages in the

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CK and DS groups of two organs was measured via PCA analysis (SPSS v. 19, SPSS Inc.,

Chicago, IL, United States). The KMO and Bartlett’s test were selected for dimension

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reduction and factor analysis, and the results were displayed in the loading plot and scatter

plot, respectively. The loading plot and scatter plot of PCA were calculated or displayed with the average center value.

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Protein identification using MALDI-TOF/TOF-MS

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The screened DAP spots were manually excised from the 2-DE gels and digested with trypsin

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in centrifuge tubes (2.0 mL) according to Lv et al. (2014). Tandem mass spectrometry identification of the DAP spots was performed using an ABI 4800 Proteomics Analyzer

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matrix-assisted laser desorption/ionization time-of-flight/time-of-flight mass spectrometer

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(MALDI-TOF/TOF-MS) operating in result-dependent acquisition mode. Peptide mass maps were acquired in positive ion reflector mode (20 kV accelerating voltage) with 1,000 laser shots per spectrum. Monoisotopic peak masses were automatically determined within the

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mass range 800–4,000 Da with a minimum signal-to-noise ratio of 10 and a local noise window width of m/z 250. Up to 10 of the most intense ion peaks (minimum signal-to-noise

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ratio, 50) were selected as precursors for MS/MS acquisition, excluding common trypsin autolysis peaks and matrix ion signals. In MS/MS-positive ion mode, spectra were averaged, the collision energy was 2 kV, and default calibration was set. The MS/MS spectra were

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searched against the nonredundant National Center for Biotechnology Information (NCBI) wheat protein database (77,037 entries, as described by Lv et al. (2014) using software MASCOT version 2.1 (Matrix Science) with the following parameter settings: trypsin cleavage, one missed cleavage allowed, carbamidomethylation set as fixed modification, oxidation of methionines allowed as variable modification, peptide mass tolerance set to 100 ppm, and fragment tolerance set to ± 0.3 Da. All searches were evaluated based on the 10

significant scores obtained from MASCOT. The Protein Score C. I. % and Total Ion Score C. I. % were both set above 95% and the significance threshold was p < 0.05 for the MS/MS. Venn diagram and function classification A Venn diagram analysis was based on the online software ‘Venny’ (http://bioinfogp. cnb.csic.es/tools/venny/indexhtml). Protein function classification was performed according to the annotation of UniProt (Wang et al., 2015).

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Subcellular localization

First, subcellular locations of the identified DAPs were predicted according to the integration

Plant-mPLoc

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of prediction results of the WoLF PSORT (http://www.genscript.com/ wolf-psort.html), (http://www.csbio.sjtu.edu.cn/bioinf/plant-multi/),

(http://cello.life.nctu.edu.tw/),

FUEL-mLoc

Server

CELLO

version

2.5

(http://bioinfo.eie.polyu.

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edu.hk/FUEL-mLoc/), and UniProtKB. The consistent results from at least three prediction

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methods were used. Then the subcellular locations of the representative DAPs were

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determined to further verify the predicted results by transient expression in Arabidopsis mesophyll protoplasts. Different recombined plasmids were constructed, including

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pCambia1302-35S-PGM::GFP, pCambia1302-35S-DHAR::GFP, pCambia1302-35S-GSTs::

Cytb6-f::GFP,

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GFP, pCambia1302-35S-Enolase::GFP, pCambia1302-35S-PsbO::GFP, pCambia1302-35SpCambia1302-35S-CD4B::GFP,

pCambia1302-35S-GLP8-14::GFP,

and

pCambia1302-35S-GFP. These plasmids were introduced into Arabidopsis mesophyll

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protoplasts by the PEG-mediated transfection procedure (Wang et al., 2015). Protoplast cells were resuspended in 200 μL W5 wash solution containing 154 mM NaCl, 125 mM CaCl2, 5

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mM MgCl2, and 2 mM MES adjusted to pH 5.7 with KOH and incubated overnight at 26°C in the dark. Confocal images were captured using a Zeiss LSM 780 fluorescence confocal microscopy. The fluorescence signals were excited at 488 nm for GFP.

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Hierarchical clustering analysis Hierarchical clustering analysis of the DAP spots during different grain developmental

stages was performed using Cluster 3.0 software according to the method described by Eisen et al. (1998). The relative ratios of DAP spots in CK group and DS group were calculated via log2 transformation, and then the Euclidean distance similarity metric was used to define the similarity and the hierarchical clusters were assembled using the complete linkage clustering 11

method.

The

clustering

results

were

visualized

through

TreeView

software

(http://sourceforge.net/projects/jtreeview/). Protein-protein interaction (PPI) network analysis and Yeast two-hybrid (Y2H) assays The protein sequences of the DAPs were collected by BLAST analysis with the NCBI, which was used for PPI analysis by the Search Tool for the Retrieval of Interacting Genes/Proteins (STRING) database (version 9.1, http://string-db.org) (Szklarczyk et al.,

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2011). Brachypodium distachyon L., a model plant for economically important crop species

including wheat and barley was selected as the studied organism, and a PPI network with a

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confidence score of at least 0.700 was constructed (Suo et al., 2015; Qi et al., 2015) and displayed using Cytoscape (version 3.0.2) software (Shannon et al., 2003).

Y2H was used to verify the predicted results of PPI network. The full-length coding

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sequence of the representative DAPs were cloned from wheat cDNA. Rbcl and atpA were

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inserted into pGBKT7 Vector. Meanwhile, Rbcs, Rbcs 1, and atpB were inserted into

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pGADT7 Vector. The primers used are summarized in Table S8. Recombinant vector for Y2H assays were co-transformed into yeast strain Y2HGold by the lithium acetate method (Ramer

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et al., 1992). Different yeast strains were all grown on DDO (SD/-Leu/-Trp) plate medium for five days at 30°C. When the yeast cells grew larger than 3 mm, the co-transformed cell was

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cultured in DDO liquid medium and adjusted to OD600 > 1 with sterilized water, and 5 μL of the yeast cells was spotted onto DDO and QDO (SD/-Leu/-Trp/-Ade/-His) plates containing

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40μg/mL X-α-gal and 42 mM aureobasidin A (AbA) for three to five days at 30°C. Western blotting

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The total proteins of 15 µg extracted from each sample were subjected to sodium dodecyl sulfate-polyacrylamide gel electrophoresis (SDS-PAGE) and transferred to a PVDF (polyvinylidene difluoride) membrane by electroblotting. Western blots were processed using

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appropriate primary antibodies and were then incubated with the Goat anti-Rabbit IgG(H+L) HRP-linked secondary antibodies (KPL, Gaithersburg, MD 20878 USA). ECL prime reagents were acquired from GE Healthcare Life Science. Chemiluminescence was accurately quantified using the LAS3000 Imaging System. The Rbcl, L-ascorbate peroxidase (APX), heat shock protein 70 (HSP 70), and actin were detected with anti-Rubisco large subunit (AS03 037), anti-APX (AS08 368), anti-HSP70 (AS08 371), and anti-actin (AS13 2640) 12

antibody (Agrisera, Stockholm, Sweden), respectively. Total mRNA extraction and qRT-PCR Total mRNA of the glume and awn samples from eight developmental periods (8, 10, 13, 15, 17, 20, 25, and 30 DPA) was isolated using TRIzol Reagent (Invitrogen) according to the methods of Cao et al. (2016). Genomic DNA removal and cDNA synthesis were performed using a PrimeScript ® RT Reagent Kit with gDNA Eraser (TaKaRa, Shiga, Japan) according

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to the manufacturer’s instructions. Gene-specific primers of the selected DAPs genes were

designed using Primer3Plus (http://www.bioinformatics.nl/cgi-bin/primer3plus/primer3plus.

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cgi) (Untergasser et al., 2007), and their specificities were checked by melting curve analysis

of RT-PCR products and the corresponding bands in agarose gels. qRT-PCR was performed as described previously (Bian et al., 2017). Ubiquitin was selected as the reference gene. The

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optimal parameters yielded a correlation coefficient (R2) of 0.994–0.999 and PCR

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amplification efficiency (E) of 90–110%. Three biological replicates were performed for each

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sample. RESULTS

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An overview of the experimental design is shown in Fig. S1. Field experiments involved a

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control group (with irrigation at jointing and anthesis) and a drought stress (DS) group (with no irrigation after sowing). The main agronomic traits after maturity were measured. DAP spots between glume and awn of Zhongmai 175 were detected at 20 DPA using 2D-DIGE,

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and their dynamic accumulation patterns at 10, 15, 20, 25, and 30 DPA were determined by 2-DE and analyzed by hierarchical clustering analyses. In combination with principal

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component analyses (PCA), DAPs were identified by matrix-assisted laser desorption ionization–time of flight/time of flight mass spectrometry (MALDI-TOF/TOF-MS). Subcellular localization and protein–protein interactions (PPIs) were predicted and verified

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by confocal microscopy and yeast two-hybrid (Y2H) assays, respectively. Expression of the representative DAPs at both the transcriptional and translational levels was detected using qRT-PCR and Western blotting, respectively. Soil water content and changes in agronomic traits under water deficit The total precipitation during the 2015–2016 wheat growing season at the Wuqiao experimental station was 134.4 mm, which is approximately 25–40% of the total water 13

requirement of winter wheat (Wang, 2017; Xu et al., 2018). Changes in the relative water content of different soil layers in each group after supplemental irrigation are shown in Fig. 1A-B. The relative water content increased gradually with depth of soil layer, but the DS group had significantly less water than controls in most soil layers at both anthesis and maturity. Based on the grade of agricultural drought (GB/T 32136-2015; Lv et al., 2015), severe drought occurred in the 0–100 cm soil layers of the DS group.

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Water deficit generally expedited plant growth and shortened grain filling. Glumes and

awns turned more yellow under water deficit than under normal irrigation conditions from

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flowering to grain maturity (Fig. 1C). Analyses of major agronomic and yield traits showed that water-deficit treatment significantly decreased plant height, spike number, grain number per spike, and grain starch content; and increased the number of infertile spikelets, ultimately

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resulting in a 7.33% reduction in starch content and 19.22% reduction in grain yield (Fig. 1D,

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Table S2).

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Differential proteome identification in glumes and awns under water deficit The proteomes of the CK and DS groups at 20 DPA were separated and DAP spots were

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detected by 2D-DIGE. A total of 537 and 650 protein spots in glumes and awns, respectively,

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were separated by 2D-DIGE, of which 231 spots (136 in glumes and 95 in awns) met the threshold of significance of P < 0.05, showing two-fold expression (Fig. 2). Subsequently, 2-DE was used to separate these spots at five stages of grain development. All of these spots

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were reproducibly detected and well matched at different developmental stages in both glumes and awns (Fig. S2).

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To test the correctness of DAPs selection, we performed PCA using all protein spots (537

in glumes and 650 in awns) and DAP spots (136 in glumes and 95 in awns), as in previous studies (Kristiansen et al., 2010; Valledor et al., 2010; Valledor and Jorrín, 2011). In glume,

A

the first three PCs accounted for over 85% of the biological variability, the number that was reduced to the first two components in the DAP spots data set (Table S3). Likewise, the first fove PCs in awn accounted for over 85% of the biological variability, the number that was reduced to the first three components in the DAP spots data set (Table S4). The employment of these components, plotting PC1 and PC2, allowed the effective separation of samples into their data set. Better separation and a greater sum for plotting PC1 and PC2 values from DAP 14

spot data sets (Fig. 3A–B) than whole spot data sets (Fig. 3C–D) in both glume and awn were found, which reflects the strong selection force that was applied to the original data set. As shown in Fig. 3B, DAP spots in glumes that showed a higher loading with PC2 were proteins related to drought developmental stages; thus, PC2 was named “developmental stage.” By contrast, spots in awns with a higher correlation with PC2 were parameters related to treatment; thus, PC2 was named “treatment” (Fig. 3D). Drought treatment had a greater

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influence on awns, as revealed by their distinct grouping in the PCA plot.

All DAP spots were identified by MALDI-TOF/TOF-MS (Fig. S2 and Tables S5 and S6).

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Detailed information and peptide sequences are listed in Tables S7 and S8. A total of 120

(88.24%) and 77 (81.05%) DAP spots, representing 100 and 67 unique DAPs in glumes and awns, respectively, were successfully identified. Venn diagram analyses revealed that 42 DAP

U

spots (27.1%) corresponding to 35 unique proteins were present in both organs, whereas 78

N

DAP spots (50.32%) corresponding to 65 unique proteins and 35 DAP spots (22.58%)

(Fig.

4A).

Therefore,

drought-responsive DAPs than awns.

glumes

contained

a

greater

abundance

of

M

respectively

A

corresponding to 32 unique proteins were specifically identified in glumes and awns,

ED

All unique DAPs in both glumes and awns were divided into seven functional classes (Fig. 4B): photosynthesis (accounting for 27% in glumes and 25.9% in awns), carbon metabolism (19% and 18.4%), detoxification and defense (15% and 15.9%), energy metabolism (13% and

PT

15.6%), amino acid metabolism and proteometabolism (15% and 13.4%), transcription and translation (6% and 3.3%), and other proteins (5% and 7.5%). Both glumes and awns similarly

CC E

exhibited

large

proportions

of

proteins

related

to

photosynthesis,

detoxification/defense, and carbon metabolism. Prediction of subcellular localization by five online software programs showed that

A

DAPs were primarily localized in chloroplasts (50% of DAPs in glumes and 45.4% in awns), followed by the cytoplasm (35.8% and 33.8%; Fig. 4C–D). In addition, 10.4% and 5.2% of DAPs in awns were localized in the mitochondria and peroxisomes, respectively; these proportions are much larger than the proportions of DAPs in the mitochondria (5.8%) and peroxisomes (1.7%) in glumes (Fig. 4C–D). The majority of enzymes participating in photosynthesis were located in chloroplasts, and those involved in carbohydrate metabolism 15

and detoxification/defense were located in the cytoplasm. To verify the prediction results, we selected eight representative proteins for further subcellular localization analyses using transient expression with green fluorescent protein (GFP) fusion proteins. These representative proteins included four chloroplast proteins, three cytoplasm proteins, and one plasma membrane protein. Different recombinant plasmids were established (DHAR::GFP, PGM::GFP, Enolase::GFP, GSTs::GFP, Cytb6-f::GFP, PsbO::GFP,

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GLP8-14::GFP, and CD4B::GFP) under the control of the 35S promoter and transiently expressed in Arabidopsis suspension culture cells. The primers used are summarized in Table

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S9. The green fluorescence signals of PGM::GFP, DHAR::GFP, GSTs::GFP, and

Enolase::GFP were particularly strong in the cytoplasm (Fig. 5). Cytb6-f::GFP, PsbO::GFP, and CD4B::GFP displayed very strong signals in chloroplasts, and strong GLP8-14::GFP

U

fluorescence was found in the plasma membrane (Fig. 5). The results were consistent with

N

those from Web-based prediction tools.

A

Dynamic accumulation patterns of DAPs in response to water deficit Dynamic accumulation patterns of the identified DAPs in response to water deficit were

M

detected using hierarchical cluster analyses. Two hierarchical clusters corresponding to

ED

glumes (Fig. S3A) and awns (Fig. S3B) were constructed. All DAP spots in both organs were divided into six expression patterns (Cluster I–VI). According to the hierarchical cluster analyses results, these six expression patterns are more clearly shown in Fig. S4 using a line

PT

chart. In Cluster I, DS group showed a significant downregulation from 20 DPA to 30 DPA, and these proteins were mainly involved in photosynthesis and carbohydrate metabolism.

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Moreover, Cluster II (down) and Cluster IV (down-up) exhibited the same expression patterns between the CK and DS group. Likewise, the expression level of DAPs in DS group significantly increased compared with CK group in Cluster III, especially at 10 and 15 DPA.

A

These proteins mainly participated in detoxification/defense and photosynthesis. In Cluster V, there was a significant upregulation at 20 DPA in CK group but at 25 DPA in DS group. These proteins were mainly involved in energy metabolism and photosynthesis. Cluster VI was markedly downregulated in the DS group at all five different development stages, which mainly included proteins related to photosynthesis. Thus, water deficit generally triggered the upregulation of proteins related to detoxification/defense and energy metabolism and the 16

downregulation of proteins related to photosynthesis and carbohydrate metabolism. Verification of three key DAPs by Western blotting To further verify the reliability of the DAP accumulation patterns, we detected the expression of three key DAPs at five different developmental stages in each organ using Western blotting (Fig. 6): Rbcl (C66 and A29), APX (C37 and A77), and HSP70 (C13). All three proteins exhibited significant changes in expression in response to water deficit. Actin was selected as

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the reference protein, as its expression was relatively constant across different treatments and stages. Quantitative evaluation of the Rbcl, APX, and HSP70 bands using Image J software

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(NIH, Bethesda, MD, USA) showed that the relative signal intensity and relative protein

expression levels of Rbcl under water deficit were downregulated in both glumes and awns. By contrast, APX showed upregulated expression in both organs, especially glumes.

U

Compared to controls, the relative signal intensity and protein expression level of Hsp 70

N

displayed an upregulation–downregulation–upregulation pattern in glumes, consistent with

A

the proteome accumulation patterns. Uncropped blots are shown in Fig. S5. PPI networks of key drought-responsive proteins and Y2H assays

M

To better understand the biological functions of the DAPs in glumes and awns, we

ED

constructed PPI networks for the key drought-responsive proteins using the STRING database. The PPI networks and the potential substrates were extracted from the whole interaction network and reconstructed using Cytoscape software. As shown in Fig. 7A-B, the

PT

drought-responsive DAPs in the PPI networks were mainly classified into five functional groups involving photosynthesis, amino acid metabolism and proteometabolism, carbon

CC E

metabolism, energy metabolism, and detoxification/defense. A complex interaction network was exhibited among proteins related to photosynthesis, carbon metabolism, and energy production.

A

Y2H assays were performed to further confirm the PPI networks (Fig. 7C). Interactions were found between Rbcl (gi|61378618) and Rbcs (gi|11990897), and Rbcl and Rbcsl (gi|474416311). This indicates that Rbcl, along with Rbcs and Rbcsl, participates in the formation of a multimer closely related to photosynthesis (Spreitzer and Salvucci, 2002). An interaction between atpA (gi|193075554) and atpB (gi|21684927), which is closely related to energy metabolism, was also confirmed. 17

Dynamic transcription expression profiles of key DAP genes Based on the proteome analyses, we selected 18 key DAP genes whose dynamic transcription expression profiles during different developmental stages were detected using qRT-PCR: 10 DAP genes common to both organs (Rbcl, Cytb6-f, FNR, MTK1, FBA, DHAR, GLDC, GAPDH, atpA, and V-H[+]-ATPase), 5 in glumes (Rbcs, NADP-IDH, PGK, HSP70, and OXO 2), and 4 in awns (MS, MDH, HSP90, and GAPDH-B). The primer sequences for the

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qRT-PCR assays are listed in Table S10. These DAPs were closely related to photosynthesis

(PGK, Cytb6-f, Rbcl, and Rbcs), carbon metabolism (GAPDH, MDH, NADP-IDH, and

SC R

GAPDH-B), energy metabolism (FBA and atpA), detoxification/defense (OxO2, HSP70, HSP90, FNR, and DHAR), amino acid metabolism and proteometabolism (MTK1, GLDC, and MS), and transport (V-H[+]-ATPase).

U

Based on the expression changes of these DAP genes under water deficit (Fig. S6), five

N

primary expression patterns were differentiated and shown in Fig. S7. As shown in Fig. S7A,

A

pattern I was downregulated at early and mid-development stages (8-17 DPA), but upregulated at late development stages (20-30 DPA). Pattern II exhibited the up (early

M

stage)-down (middle stage)-up (late stage) expression pattern under water deficit (Fig. S7B

ED

and D). Afterwards, DAP genes showed the up-regulation expression, except 8 DPA in pattern III (Fig. S7C), whereas the genes of pattern IV were downregulated, except 8 and 10 DPA (Fig. S7E). Pattern V showed a down-regulated expression pattern at almost all five

PT

stages under water deficit (Fig. S7F). In addition, six DAP genes (MTK1, atpA, V-H[+]-ATPase, NADP-IDH, GAPDH, and DHAR) in glume showed high consistency

CC E

between transcriptional and translational levels (Fig. S8A), in which these genes showed consistent expression patterns with protein level for at least three stages. The other glume DAP genes showed similar (HSP70, FNR, Cytb6-f, GLDC, FBA, and OXO2) or poor (Rbcl,

A

Rbcs, and PGK) consistency (Fig. S8B-C), possibly owing to posttranslational modifications (Guo et al., 2012). Likewise, MTK1, FNR, GAPDH, GAPDH-B, and HSP90 in awn showed high consistency (Fig. S8A). Nevertheless, five genes (FBA, Rbcl, atpA, V-H[+]-ATPase, and DHAR) and four genes (Cytb6-f, GLDC, MDH, and MS) showed similar and poor consistency, respectively (Fig. S8B-C). These results are generally consistent with previous reports (Jiang et al., 2012; Bian et al., 2017). The standard curve, correlation coefficient (R2), PCR 18

amplification efficiency (E), and melt peak are shown in Fig. S9. PCA was carried out to separate sample clusters based on levels of expression of the 11 common DAP genes. First, we used the different stages and treatments as variables in the PCA analyses (Fig. 8A). According to factor analyses, the first two factors (PC1 and PC2) explained much of the variation between the two organs. Glumes and awns were notably differentiated by factor loadings for PC1. The spots in glumes that showed a higher loading

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with PC1 were genes related to developmental stages, whereas the spots in awns that showed a higher loading with PC2 were genes related to treatment (Fig. 8A). In sum, the

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transcriptional differences in the 11 genes between glumes and awns were discriminated by factor loadings, and drought stress induced a greater response in awns. Second, these 11 genes were selected as variables (see Fig. S10). Differences in their expression levels in both

U

organs at eight different developmental stages in each group were discriminated by factor

N

loadings to factor scores. Fig. 8B presents a scatter plot of the factor scores showing the

A

interrelationships between functional groups. Detoxification/defense, energy metabolism, and amino acid metabolism and proteometabolism showed relatively close associations;

M

differences were mainly due to genes related to photosynthesis. Therefore, drought-stress

ED

treatment induced marked changes in genes related to photosynthesis at the transcriptional

DISCUSSION

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level that differed significantly from changes in the other functional groups.

Both glumes and awns exhibited considerable numbers of DAPs in response to water deficit,

CC E

which suggests that both organs play important roles in drought resistance and grain development. Here we further discuss key DAPs and their potential functions, in particular those involved in photosynthesis and detoxification/stress defense. These DAPs could be

A

used as protein markers for further crop drought-resistance improvement and field cultivation practices. Then, a putative metabolic pathway of drought-responsive proteins in wheat glumes and awns is proposed. DAPs involved in photosynthesis/carbon metabolism under water deficit Water deficit generally has serious impacts on photosynthesis. These effects can be direct, such as decreased availability of CO2 due to limited diffusion through the stomata and the 19

mesophyll (Flexas et al., 2007; Daszkowska-Golec and Szarejko, 2013) or changes in photosynthetic metabolism (Lawlor and Cornic, 2002), or indirect, namely, oxidative stress. The former is the major determinant of reduced photosynthesis under drought stress in leaves (Cornic, 2000). Non-leaf green organs in wheat are relatively resistant to abiotic stress, such as drought and high temperature, due to their structure and stable water potential (Maydup et al., 2014; Eduardo et al., 2007). Wheat glumes and awns are less affected by changes to

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stomata, perhaps because of their composition and lower overall numbers of stomata. Therefore, they have significant photosynthetic potential and contribute to grain yield even

SC R

when subjected to drought stress (Zhang et al., 2011).

Our proteome data identified a series of Rubisco subunits, including large and small subunits, in glumes and awns. The rate of photosynthesis in higher plants depends on Rubisco

U

activity (Chaitanya et al., 2002; Parry et al., 2002). Under drought stress, the relative

N

expression of large and small Rubisco subunits at both the protein and transcriptional levels

A

was downregulated in glumes and awns (Fig. 6). Meanwhile, Y2H assays showed that Rbcl interacted with Rbcs and Rbcsl (Fig. 7), which suggests that Rbcl, along with Rbcs and Rbcsl,

M

participated in the formation of a Rubisco holoenzyme. Water deficit likely affects the

ED

stability among subunits, thus influencing enzyme activity. In addition, CBP 8, one of the chlorophyll-binding proteins (CBPs) that constitute a large family of proteins with diverse functions in both light-harvesting and photoprotection (Xia et al., 2012), was downregulated

PT

in awn under drought stress, which may affect the rates of light-harvesting. In addition to CBP 8, the other two photosystem II proteins, PsbO and PsbP, were downregulated in glumes

CC E

and awns, which likely resulted in reduced rates of photosynthetic oxygen evolution. Moreover, Cytb6-f, a key protein in the electron transfer chain located in thylakoid membranes (Liberton et al., 2016), was downregulated due to heat stress in the leaves of

A

clonal plants of two lines (ColxCB169 and ColxCB190) in a previous study (Liberton et al., 2016). Similarly, expression of Cytb6-f was significantly downregulated in both wheat glumes and awns under drought stress, which most likely affected photosynthetic electron transport, thus influencing plant growth, development, and yield formation. When plants remain in a disadvantageous environment with water deficit, carbohydrate metabolism will self-regulate to maintain normal growth and development (Hossain and 20

Komatsu, 2012). Carbohydrates are the basic building blocks of starch, and their formation, breakdown, and interconversion in living organisms constitutes carbon metabolism. Enzymes related to carbon metabolism and the synthesis and transportation of carbohydrates are affected by water deficit, eventually causing loss of yield. In this study, some DAPs related to carbon metabolism, in particular isocitrate dehydrogenase

(NADP-IDH),

malate

dehydrogenase

(MDH),

and

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glyceraldehyde-3-phosphate dehydrogenase (GAPDH), displayed downregulated expression

patterns at the protein level under water-deficit conditions, with more than three-fold

SC R

downregulation at 15, 20, and 25 DPA. Our previous study also found that these proteins related to carbon metabolism in flag leaf were downregulated significantly under water deficit (Deng et al., 2018). NADP-IDH, the key enzyme linking carbon and nitrogen

U

metabolism, was downregulated at both the protein and transcriptional levels in glumes (Fig.

N

S6). Carbon and nitrogen metabolism are linked because they share organic carbon and

A

energy supplied directly from photosynthetic electron transport and CO2 fixation or from the respiration of fixed carbon via glycolysis, the TCA cycle, and mitochondrial electron

M

transport chain (mitETC; And and Turpin, 1994; Mittler et al., 2004). This linkage plays an

ED

important role in plant growth and development. MDH catalyzes the conversion of oxaloacetate and malate. This reaction is important in cellular metabolism, and it is coupled with easily detectable cofactor oxidation/reduction. Kalir and Poljakoffmayber (1981)

PT

reported that, when subjected to high concentrations of sodium chloride (higher than 0.5 M), MDH activity was inhibited in Halimione portulacoides leaves. We found that water deficit

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significantly reduced MDH at both the protein and transcriptional levels in wheat awns. GAPDH is involved in gluconeogenesis, glycolysis, and the pentose phosphate pathway through catalyzing the reversible oxidative phosphorylation of glyceraldehyde 3-phosphate.

A

Carbon reduction regulated by GAPDH could be the key metabolic process imparting genetic variation in Pn (photosynthetic rate) response to drought stress (Xu et al., 2013). Analogously, our results identified a response of GAPDH to drought stress in both glumes and awns, indicating its downregulation at both transcriptional and translational levels. This could affect carbohydrate biosynthesis and interconversion. In addition, some proteins related to energy metabolism were significantly induced by 21

water-deficit treatment, in particular some subunits participating in ATP synthase formation. ATP synthase creates the energy storage molecule ATP, the most commonly used energy currency of cells in most organisms. In this study, atpA, atpB, and atp1-2 accumulated significantly in both glumes and awns, at 10, 15, and 20 DPA in particular. Previous research has also shown that ATP content and the ATP/ADP ratio are markedly increased in spring wheat plants under drought conditions, which indicates that energy metabolism is expedited

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under drought stress (Chen et al., 2004). Respiration of stored and/or translocated photosynthate provides energy in heterotrophic tissues, whereas photosynthesis usually

SC R

supplies energy in photosynthetic cells (And and Turpin, 1994). Compared to leaves, energy consumption in the cells of glumes and awns is very low; thus, most energy substances synthesized by photosynthesis can be transferred to grains, which is beneficial for grain

U

development and yield formation.

N

Detoxification and stress defense

A

Over evolutionary time, plants have formed a series of mechanisms to respond to drought stress. The ROS scavenging system, an important drought defense mechanism, maintains the

M

dynamic equilibrium of ROS. Unfavorable conditions such as water deficit prompt the

ED

accumulation of ROS in plants. These ROS participate in many biological processes, such as the stress response, hormone signaling, cell growth, and development (Mittler et al., 2004; Baileyserres and Mittler, 2006; Pei et al., 2000; Fujita et al., 2006; Guo et al., 2012). Plants

PT

experience oxidative stress due to an imbalance in the generation and removal of ROS under drought stress but are equipped with a ROS scavenging system composed of a series of

CC E

enzymes to mitigate this (Zhang and Kirkham, 1994; Drazkiewicz et al., 2007). In this study, five of these enzymes were identified: GSTs, SOD, APX, DHAR, and OxO. GSTs participate in many biotic and abiotic interactions between plants and their environment. Previous

A

research has shown that upregulated expression of GST8 by drought-associated oxidative stress counteracts the effects of higher ROS production in stressed plants (Bianchi et al., 2002). We also found that GSTs accumulated significantly in awns under water deficit. Similarly, SOD was upregulated in glumes under drought stress, at 15, 20, and 25 DPA in particular. SOD catalyzes the dismutation of O2- to H2O2 and O2 (Smirnoff, 1993) and plays important roles in abiotic stress tolerance in different plant species, including tobacco (Faize 22

et al., 2011), arbuscular mycorrhizal Lactuca sativa plants (Ruiz-Lozano et al., 1996), rice (Deus et al., 2015), and wheat (Zhang and Kirkham, 1994). The AsA-GSH cycle is an important part of the ROS scavenging system in which AsA and GSH are not consumed but participate in the cyclic transfer of reducing equivalents. The AsA-GSH cycle involves four enzymes and consumes H2O2 to generate H2O using electrons derived from NAD(P)H (Noctor and Foyer, 1998). Two of these enzymes, APX and DHAR,

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were identified in our study. APX uses two molecules of AsA to reduce H2O2 to water, with concomitant generation of two molecules of monodehydroascorbate (MDHA), which is

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converted into AsA and DHA (Noctor and Foyer, 1998). Then DHA is reduced to AsA by DHAR using GSH as the reducing substrate (Foyer and Halliwell, 1976). This reaction generates glutathione disulfide (GSSG), which is in turn re-reduced to GSH by NADPH in a

U

reaction catalyzed by glutathione reductase. In our study, APX increased more than five-fold

N

in glumes and three-fold in awns at 25 and 30 DPA (Fig. 6). DHAR was more than three-fold

A

upregulated in both glumes and awns at 20, 25, and 30 DPA under drought stress. The significant upregulation of these proteins could significantly improve the removal efficiency

M

of H2O2 and strongly maintain the AsA-GSH dynamic balance. Moreover, signaling induced by drought stress activates the ABA signaling pathway and

ED

causes excess Ca2+ fluxes that initiate the activation of various MAP kinases (Shanker et al., 2014), which regulates stress tolerance in part by modulating the expression of

PT

stress-responsive genes such as Hsp70, PAO, PPO, and PAL. In natural states, Brownian movement can induce protein misfolding, and this phenomenon is exacerbated when plants

CC E

are subjected to unfriendly environmental stresses such as water deficit (Hartl, 1996). HSP70, which was significantly upregulated in glumes under water deficit (Fig. 6), can prevent misfolded proteins from self-associating and help them recover their natural state (Hartl,

A

1996). In addition, polyamines (PAs) enhance tolerance to environmental stresses such as drought (Takahashi and Kakehi, 2010; Bouchereau et al., 1999; Alcázar et al., 2006), and their catabolism is crucial in regulating PA levels in cells. PAs are oxidatively catabolized by amine oxidases, including PAO and DAO, and they are widespread in all living organisms (Paschalidis and Roubelakis-Angelakis, 2005; Cona et al., 2006). In this study, PAO expression was significantly upregulated in awns under drought stress. PPOs may function in 23

the Mehler reaction, which involves photoreduction of molecular oxygen by PSI (Vaughn et al., 1988). Previous research has shown that water-stressed plants with suppressed PPO exhibit photooxidative damage, and plants with elevated PPO may show increased stress tolerance (Biehler and Fock, 1996). Phenylalanine ammonia-lyase (PAL), a key enzyme in the phenylpropanoid pathway, was upregulated significantly in glume at 20, 25, and 30 DPA under drought stress. Simultaneously, a transcriptomics studies about wheat glume have also

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found that the PAL gene was upregulated significantly under water deficit (Liu et al., 2017). We identified highly expressed PAL proteins in glumes, which may improve drought

SC R

tolerance.

A putative metabolic pathway of drought-responsive proteins in wheat glumes and awns Here we propose a putative metabolic pathway of the regulation of drought resistance in

U

glumes and awns based on our results and previous reports (Fig. 9). When plants were

N

subjected to water deficit, ROS accumulation elevated intracellular Ca2+ concentrations,

A

activated CDPK, and triggered signaling cascades that regulated the expression of stress-responsive genes. ROS induced oxidative stress and activated the ROS scavenging Simultaneously,

oxidative

stress

M

system.

impaired

photosynthesis

and

inhibited

ED

photosynthetic metabolism. In addition, drought stress affected the expression and activities of enzymes involved in energy metabolism and inhibited the photosynthetic carbon reduction

and yield.

PT

cycle and enzyme activities related to carbon metabolism, ultimately decreasing grain weight

CONCLUSIONS

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In this study, we provided an overview of the proteome in glume and awn, non-leaf green organs in wheat, under water deficit conditions. In total, 100 and 67 unique DAPs in glumes and awns were identified, respectively. Both in glume and awn, most of DAPs related to

A

photosynthesis and carbon metabolism were significantly downregulated, whereas those associated with detoxification and stress defense were significantly upregulated. Our proteomic profiling combined with the agronomic characters analysis shows that water deficit significantly inhibited photosynthesis and carbon metabolism, leading to significant decreases in starch biosynthesis and grain yield, and the glumes and awns of wheat respond to drought stress by modulating the expression of large numbers of proteins and genes involved in 24

diverse functions. These findings provide useful information for further understanding of the molecular mechanisms of the response to, and defense against, drought stress in higher plants. AUTHOR CONTRIBUTIONS XD and ZS performed all of the experiments and data analysis. XD wrote the paper. LD performed PCA analysis and data collection. YL contributed to qRT-PCR, and LM performed

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Western blotting. LN revised the manuscript. YY designed and supervised experiments. ACKNOWLEDGMENTS

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This research was financially supported by grants from National Key R & D Program of

China (2016YFD0100502) and the National Natural Science Foundation of China (31471485). The English in this document has been checked by at least two professional both

native

speakers

of

English.

For

a

certificate,

please

see:

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editors,

N

http://www.textcheck.com/certificate/xYDP4L.

A

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Figure 1. Soil relative water content (SRWC), glume/awn morphological changes, and

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yield trait of bread wheat cultivar Zhongmai 175 under water deficit.

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(A) SRWC at anthesis. (B) SRWC at maturity stage. (C) The morphological changes of glume and awn at five different development stages. (D) Changes of grain yield and its three

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major determinants (number of spikelets, grain number per spike, and 1,000-grain weight) at maturity stage. CK and DS indicate the control group (irrigation at jointing and anthesis

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stages) and water-deficit treatment group (no-irrigation after sowing), respectively. Error bars indicate standard errors of three biological replicates. Statistically significant differences

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compared to the control were calculated based on an independent Student's t-tests: *p < 0.05;

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**p < 0.01.

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Figure 2. 2D-DIGE images of glume and awn at 20 DPA under water deficit condition.

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The DAP spots identified by 2D-DIGE and MALDI-TOF/TOF-MS with significant accumulation changes under water deficit in glume and awn are numbered, respectively. (A)

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Glume gel 1, samples were extracted from the glume and electro focused on an 18 cm pH 4–7

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linear IPG strip. (B) Awn gel 1, samples were extracted from the awn and electro focused on

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an 18 cm pH 3–10 linear IPG strip.

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Figure 3. PCA analysis of all protein spots and DAP spots data sets from glume and awn

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of Zhongmai 175.

(A) PCA of all protein spots from glume. (B) PCA of DAP spots from glume. (C) PCA of all

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protein spots in awn; (D) PCA of DAP spots in awn. CK10DPA, CK15DPA, CK20DPA,

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CK25DPA, and CK30DPA represent five different developmental stages: 10, 15, 20, 25, and 30 DPA in CK group, respectively; DS10DPA, DS15DPA, DS20DPA, DS25DPA, and DS30DPA represent five different developmental stages: 10, 15, 20, 25, and 30 DPA in DS

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group, respectively.

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Figure 4. Venn diagram analysis, functional classification, and subcellular localization

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of DAPs from glume and awn of Zhongmai 175.

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(A) Venn diagram analysis of DAP spots in glume and awn under drought stress. The black

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number represents the number of DAPs identified; the red number in brackets represents the

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number of unique protein species identified. (B) Functional classification of DAPs from glume and awn. (C) Subcellular localization of DAPs from glume. (D) Subcellular

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localization of DAPs from awn.

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Figure 5. Subcellular localization of eight representative proteins in Arabidopsis thaliana

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protoplasts.

Eight representative proteins included DHAR, PGM, Enolase, GSTs, Cytb6-f, PsbO, and

CD4B.

GFP:

GFP fluorescence

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GLP8-14,

signal;

Chlorophyll:

chlorophyll

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autofluorescence signal; bright light: field of bright light; Merged: emergence of the GFP fluorescence signal, chlorophyll autofluorescence signal, and bright light field; Negative:

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Wild-type (Clo) Arabidopsis protoplast cell.

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Figure 6. Verification of three key DAPs present in both glume and awn by Western

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blotting.

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Immunoblot analysis of Rbcl (A) and APX (B) both in glume and awn, and Hsp70 protein (C) in glume at different developmental stages in CK and DS groups by using anti-Rbcl,

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anti-APX, and anti-Hsp 70 antibody, respectively. Equal protein loading was confirmed by immunoblotting with an antibody against rice actin (D). The line chart represents

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quantification of the Rbcl bands (E), APX bands (F), and Hsp 70 bands (G) by Image J. Rbcl, APX, and Hsp70 protein levels are expressed as a ratio of Rbcl, APX, and Hsp70 to actin,

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respectively. The dynamic accumulation profiles of Rbcl (H), APX (I), and Hsp70 (J) were detected by 2-DE. Error bars indicate standard errors of three biological replicates. Asterisks

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indicate p < 0.05 (*) and p < 0.01 (**) in Student’s t test analysis.

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Figure 7. Protein-protein interaction (PPI) networks analysis and Y2H assays

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PPI network of the key Drought-responsive DAPs in glume (A) and awn (B) (confidence score: 0.700). Nodes with green background represent the photosynthesis-related DAPs, red

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background represents the detoxification and defense-related DAPs, yellow background represents the amino acid metabolism and proteometabolism-related DPAs, pink background

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represents the energy metabolism-related DAPs, and blue background represents the carbon metabolism-related DAPs. Blue lines represent the interactions between three pairs of

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proteins validated by Y2H assay. (C) Demonstration of the physical interaction: Rbcl and rbcs, Rbcl and RbcsL, and atpA and atpB by Y2H assay. Positive control: pGBKT7-53/pGADT7-T, negative control: pGBKT7-Lam/pGADT7-T. The indicated

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combinations of plasmids were co-transformed into yeast reporter strain Y2HGold, and the interaction of Rbcl with rbcs, Rbcl with Rbcs1, and atpA with atpB were assessed by growth on DDO and QDO (SD/-Leu/-Trp/-Ade/-His) plate containing 40μg/mL X-α-gal and 42 mM aureobasidin A (AbA). pGADT7: activation domain; pGBKT7: binding domain.

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Figure 8. Multivariate analysis based on the expression levels of eleven common DAP

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genes present in both glume and awn at different grain developmental stages of

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Zhongmai 175.

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(A) PCA analysis using different developmental stages and water-deficit treatment as variables. GCK8d, GCK10d, GCK13d, GCK15d, GCK17d, GCK20d, GCK25d, and

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GCK30d represent eight different developmental stages of glume: 8, 10, 13, 15, 17, 20, 25, and 30 DPA in CK group, respectively; GDS8d, GDS10d, GDS13d, GDS15d,

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GDS17d, GDS20d, GDS25d, and GDS30d represent eight different developmental stages of glume: 8, 10, 13, 15, 17, 20, 25 and 30 DPA in the DS group, respectively;

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ACK8d, ACK10d, ACK13d, ACK15d, ACK17d, ACK20d, ACK25d, and ACK30d represent eight different developmental stages of awn: 8, 10, 13, 15, 17, 20, 25, and 30 DPA in CK group, respectively; ADS8d, ADS10d, ADS13d, ADS15d, ADS17d,

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ADS20d, ADS25d, and ADS30d represent eight different developmental stages of awn: 8, 10, 13, 15, 17, 20, 25, and 30 DPA in DS group, respectively. (B) Principle component regression using eleven common genes as variables. Number 1, 2, 3, 4, 5, 6, 7, and 8 represent eight different developmental stages of glume at 8, 10, 13, 15, 17, 20, 25, and 30 DPA in CK group, respectively; Number 9, 10, 11, 12, 13, 14, 15, and 16 represent eight different developmental stages of glume at 8, 10, 13, 15, 17, 42

20, 25, and 30 DPA in DS group, respectively; Number 17, 18, 19, 20, 21, 22, 23, and 24 represent eight different developmental stages of awn at 8, 10, 13, 15, 17, 20, 25, and 30 DPA in CK group, respectively; Number 25, 26, 27, 28, 29, 30, 31, and 32 represent eight different developmental stages of awn at 8, 10, 13, 15, 17, 20, 25,

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and 30 DPA in DS group, respectively.

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Figure 9. A putative metabolic pathway of drought stress responses in wheat glumes and

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awns. The proteins with red font are up-regulated under water deficit condition. The proteins

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with blue font are down-regulated under water deficit condition.

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