Shotgun Label-Free Quantitative Proteomics of Water-Deficit-Stressed

Article pubs.acs.org/jpr

Shotgun Label-Free Quantitative Proteomics of Water-DeficitStressed Midmature Peanut (Arachis hypogaea L.) Seed Kameswara Rao Kottapalli,† Masoud Zabet-Moghaddam,† Diane Rowland,‡ Wilson Faircloth,§ Mehdi Mirzaei,∥ Paul A. Haynes,⊥ and Paxton Payton*,# †

Center for Biotechnology and Genomics, Texas Tech University, Canton & Main, Experimental Sciences Building, Room 101, Lubbock, Texas 79409, United States ‡ Agronomy Department, University of Florida, 3105 McCarty Hall B, Gainesville, Florida 32611, United States § National Peanut Research Laboratory, United States Department of Agriculture, 1011 Forrester Drive Southeast, Dawson, Georgia 39842, United States ∥ The Australian School of Advanced Medicine, Macquarie University, Building F10A, Groundfloor, 2 Technology Place, NSW 2109, Australia ⊥ Department of Chemistry and Biomolecular Sciences, Macquarie University, Building F7B, NSW 2109, Australia # Cropping Systems Research Laboratory, United States Department of Agriculture, 3810 Fourth Street, Lubbock, Texas 79415, United States S Supporting Information *

ABSTRACT: Legume seeds and peanuts, in particular, are an inexpensive source of plant proteins and edible oil. A comprehensive understanding of seed metabolism and the effects of water-deficit stress on the incorporation of the main storage reserves in seeds, such as proteins, fatty acids, starch, and secondary metabolites, will enhance our ability to improve seed quality and yield through molecular breeding programs. In the present study, we employed a label-free quantitative proteomics approach to study the functional proteins altered in the midmature (65−70 days postanthesis) peanut seed grown under water-deficit stress conditions. We created a pod-specific proteome database and identified 93 nonredundant, statistically significant, and differentially expressed proteins between well-watered and drought-stressed seeds. Mapping of these differential proteins revealed three candidate biological pathways (glycolysis, sucrose and starch metabolism, and fatty acid metabolism) that were significantly altered due to water-deficit stress. Differential accumulation of proteins from these pathways provides insight into the molecular mechanisms underlying the observed physiological changes, which include reductions in pod yield and biomass, reduced germination, reduced vigor, decreased seed membrane integrity, increase in storage proteins, and decreased total fatty acid content. Some of the proteins encoding rate limiting enzymes of biosynthetic pathways could be utilized by breeders to improve peanut seed production during water-deficit conditions in the field. The data have been deposited to the ProteomeXchange with identifier PXD000308. KEYWORDS: peanut, water-deficit stress, pod development, label-free proteomics

1. INTRODUCTION

1000 counties in 26 states as disaster struck due to drought, the largest natural disaster area in the history of the United States.1 Peanut production in these disaster areas was severely affected, leading to a steep increase in price for peanut commodities like peanut butter and other peanut-based foods.2 Numerous studies conducted during the past several decades on the effects of drought on peanut seed emphasized poor yield and seed quality;3 reduced seed size, seed number, lower oil content, shelling percentage, and delayed maturity;4 inadequate

Peanut (Arachis hypogaea L.) is the second most important crop legume, cultivated across the world on 21.8 million hectares with an annual production of 38.6 million tons (FAOSTAT 2011; http://faostat.fao.org/ (accessed June 28, 2013)). Peanut is mainly grown for its seed in the United States but represents a rich source of edible fats and a primary source of oil in many parts of the world. Whereas peanut is moderately adaptive to water-deficit conditions, prolonged drought and limited water resources are threatening sustainable peanut crop production. Reductions in water supply generate water-deficit stress in peanut and subsequent loss of yield and quality. In 2012, the U.S. Department of Agriculture declared more than © XXXX American Chemical Society

Special Issue: Agricultural and Environmental Proteomics Received: July 4, 2013

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dx.doi.org/10.1021/pr400936d | J. Proteome Res. XXXX, XXX, XXX−XXX

Journal of Proteome Research

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1.8 m deep. Each 5.5 m × 12.2 m plot was protected from rainfall by an automated shelter that covers the entire plot within 30 s of the onset of rainfall. Under dry conditions, the automated shelters remain open so that all plots receive full sunlight during the growing season. Plots were irrigated through the use of sprinkler irrigation mounted to the previously described automated shelter. Soil in the ECPF was a 1:2.75 mixture of locally obtained Tifton series sandy loam and Terrell sand. The drought-prone soil mixture used had adequate levels of tested soil nutrients (mainly P, K, and Ca) and pH 6.1. Soil was fumigated with dazomet at 305 kg/ha approximately 45 days prior to planting. Aldicarb was included in-furrow for systemic insect control, and the herbicides pendimethalin, metolachlor, and glyphosate were applied for preemergence weed control. General management of the study was based on extension service recommendations and included hand-weeding and scheduled fungicide application for both foliar and soil-borne diseases. The mean daily high and low temperatures from June to August were 33 and 20 °C, respectively. After aeration, soil was shallowly tilled, and a smooth seedbed was prepared for planting. Seeds of Georgia Green, a popular runner variety grown in the southeastern U.S., were sown in four-row plots, 5.5 m long with a row spacing of 0.6 m and a plant density of 10 seed m−1 of row. Seeds were sown into a full soil moisture profile to establish uniform germination and plant stand. Fourteen days after germination, two irrigation regimes (full and deficit) were established where full irrigation plots received 50 mm of water per week in a single irrigation event and deficit plots received 25 mm per week. Approximately 110 days after sowing, four to five plants from three replicate plots in each treatment were harvested, and pods were binned into size classes (surrogate for developmental stages), frozen in liquid nitrogen, and stored at −80 °C until protein extraction. Because peanut has asynchronous flowering, harvesting pods at any given time point allows for a range of pod ages to be collected. To capture the representative variability within a subplot and harvest enough material for early developmental stages, multiple plants had to be pooled within a plot and were defined as n = 1. Three groups were generated for each developmental stage (bin) to give n = 3. For this study, mid-mature pods (65−70 days postanthesis) were used for proteomics analysis. On the basis of previous data, the midmature stage of seed development appears to be most susceptible to water-deficit stress.14

supply of assimilates5 or calcium;6 and altered protein, total oil content, and fatty acid (FA) composition.7 However, to our knowledge, in peanut or any grain legume there are no reports of systematic evaluation of transcript or protein responses to water-deficit stress on developing seeds. In maize, water deficit at 9 days after planting (DAP) caused significant differential transcriptional response in placenta and endosperm tissues.8 Placenta tissue showed lower water status than endosperm and a concomitant induction of several known stress tolerance genes. These genes included HSPs, chaperonins, and major intrinsic proteins. In addition, placenta accumulated a greater than four-fold higher concentration of ABA and showed a decrease in sugar flux during stress. In contrast, genes for cell division and growth and cell-wall-degrading enzymes were downregulated in endosperm, suggesting a strong inhibition of cell proliferation during water stress.9 Water deficit also upregulated a homeodomain leucine zipper transcription factor (TF) (ZmOCL5), which was proposed to provide tissuespecific stress regulation in kernels.8 In cereals, heat stress generally results in decrease in the overall synthesis of starch and storage proteins and ultimately reduction in grain yield.10−12 In severe cases, it also leads to grain abortion. An interesting finding is that several heat-stress responses in Arabidopsis shoots and drought-stress responses in barley are conserved.11 In barley, short-term heat stress consistently induced HSP-mediated protein folding, reactive oxygen species (ROS) scavenging, and the biosynthesis of compatible solutes.11 In parallel, genes involved in embryo development, hormone biosynthesis, and cell signaling were altered, indicating rapid sensing, signal transduction, and adaptation of central processes to abiotic stress. In addition, physiology and development of barley caryopsis were negatively affected, as evident from decreased starch biosynthesis, lipid metabolism, and amino acid metabolism immediately after heat stress. Interestingly, but perhaps not surprisingly, several abiotic-stress responses in Arabidopsis and barley are conserved.11 Much of what we know today regarding the molecular response of seeds to water-deficit stress comes from the basic studies carried out in model legumes and Arabidopsis. However, several differences exist between the developmental programs of peanut seeds and those of Arabidopsis and even other legumes, especially when plants are grown under production conditions. The primary difference is that peanut seeds develop underground and have a large endosperm tissue accumulating proteins and lipids, while Arabidopsis and most model legume seeds develop aerially in green pods with large embryos. Because seeds harbor the nutritional reserves to support life of humans and other living organisms, it is important to understand the genetic and biochemical mechanisms altered due to stress resulting in differential accumulation of main seed reserves, such as proteins, carbohydrates, and lipids in peanut seeds. We discuss the effect of water-deficit stress on seed metabolism and identify candidate pathways that are our focus for the development of stress-tolerant peanut cultivars.

2.2. Crude Protein and Total Fatty Acid Quantification

Crude protein analysis was conducted by the Dumas combustion method on a Leco model FP-228 as previously described.15 In this procedure, 100 mg peanut seed sample was transferred to a tin container and placed in a combustion chamber (850 °C) of an automated FP 228. The mixture of gases released during combustion in this method was catalytically converted to N2 quantitatively by passing the gas through a conductivity cell. Total FA composition was measured by FAME analysis. Extracts were obtained from 100 mg of ground seeds and analyzed by gas chromatography with an Agilent 6890 series gas chromatograph outfitted with a 30 × 0.53 mm EC-WAX column and a flame ionization detector. The peak areas of all individual FAs were determined to establish the amount of total FA in well-watered and waterdeficit stressed samples.

2. MATERIALS AND METHODS 2.1. Seed Material and Water-Deficit Stress Treatment

Experimental field plots were established at the USDA-ARS National Peanut Research Laboratory’s Environmental Control Plot Facility in Dawson, Georgia (31.7733° N, 84.4467° W).13 In brief, the Environmental Control Plot Facility (ECPF) contains a series of sheltered plots with an artificial soil profile B



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2.3. Protein Extraction and One-Dimensional Gel Electrophoresis (1-DGE)

trometer (Thermo, CA). Chromatographic separation of the peptides was performed using a Dionex nano-HPLC (Ultimate 3000) with a trapping column (C18, 3 μm, 100 Å, 75 μm × 2 cm), followed by a reverse phase column (C18, 2 μm, 100 Å, 75 μm × 15 cm, nanoViper). Peptides were first injected onto the trapping column, which was equilibrated with 1% ACN and 0.1% formic acid in water and washed for 10 min with the same solvent at a flow rate of 300 nL min−1. After washing, the trapping column was switched to the reverse-phase analytical column and bound peptides were eluted using solvents A (2% ACN, 0.1% formic acid in water) and B (98% ACN, 2% water, 0.1% formic acid). The gradient was kept constant for the first 10 min at 4% solvent B, followed by a linear increase to 30% solvent B in 20 min. Solvent B was further increased to 60% in 40 min, followed by a fast increase in solvent B to 90% over 5 min. The eluted peptides were directed into the nanospray ionization source of the LTQ-XL with a capillary voltage of ∼2 kV. The collected spectra were scanned over the mass range of 300−2000 atomic mass units. Data-dependent scan settings were defined to choose the six most intense ions with dynamic exclusion list size of 100, exclusion duration of 30 s, repeat count of 2, and repeat duration of 15 s. MS/MS spectra were generated by collision-induced dissociation of the peptide ions at a normalized collision energy of 35%.

Total pod protein was extracted using the phenol-extraction protocol previously described.16 In brief, 100 mg of freshly ground pod tissue, pooled from five plants, was added to an extraction medium containing 0.9 M sucrose, 0.1 M Tris·HCl (pH 8.8), 10 mM EDTA (pH 8.0), 0.4% (v/v) 2mercaptoethanol, and Tris-buffered phenol (pH 8.8) and gently mixed at room temperature (RT). Proteins were precipitated by incubating the phenolic phase with 0.1 M ammonium acetate-methanol at −20 °C overnight, followed by precipitation and washing of the proteins serially in three organic solvents (methanol, acetone, and ethanol) to give a highly purified protein pellet. The protein content was measured by Bradford assay using bovine serum albumin Fraction V as the standard.17 One hundred and fifty micrograms of protein from each replicate was incubated for 10 min at 95 °C in sample buffer containing 62 mM Tris-HCL (pH 6.8) containing 10% (v/v) glycerol, 2.5% (w/v) sodium dodecyl sulfate (SDS), 5% (v/v) 2-mercaptoethanol, and a drop of bromophenol blue, then cooled to RT. After incubation at RT for 10 min, the mixture was centrifuged, and the supernatant was used for SDS-polyacrylamide gel electrophoresis on 10% Mini-PROTEAN TGX precast gels (Bio-Rad, Hercules, CA). The proteins were electrophoresed at 70 V in a running buffer of 1× triglycine sulfate (TGS) (250 mM Trizma base, 1.92 mM glycine, 1% (w/v) SDS; pH 8.3). On completion of electrophoresis, gels were placed in fixing solution (50% ethanol, 10% acetic acid, 40% Milli-Q water) for 1 h to fix the protein bands and then stained for 15 min in staining solution (10% phosphoric acid, 10% ammonium sulfate, 20% methanol, and 0.12% Coomassie G-250). The gel was washed twice with water to remove the excess stain. Each sample lane was cut into 16 equal pieces that were subsequently transferred to a 96-well flat bottom plate. These gel pieces were decolored by washing with 50% acetonitrile (ACN) in 100 mM ammonium bicarbonate at 37 °C for 30 min. This wash was repeated three more times to sufficiently decolor the gel pieces.

2.6. Generation of a Peanut Proteome Database

RNA was isolated from well-watered control and water-deficitstressed pod tissue, and TruSeq RNA libraries were prepared as per manufacturer’s protocol (Illumina). Transcriptome sequencing of control and stressed samples was performed by paired-end sequencing on and Illumina Mi-Seq. De novo assembly of the fastq files from each library using N-Gen software (DNAStar) generated 20 209 and 16 004 contigs from control and stressed tissue, respectively. Furthermore, assembly of the contigs from stressed and control tissue using CAP3 software19 generated 24 483 nonredundant consensus sequences. The consensus sequences were translated in all six frames to create a pod proteome database containing 146 898 amino acid sequences for protein identification (outlined in Supplementary Figure S1 in the Supporting Information).

2.4. Tryptic in-Gel Digestion

2.7. Protein Identification and Quantitative Data Analysis

Protein samples were separated on 1D PAGE. Each sample lane was cut into 16 slices, and each slice was placed into 96-well plate wells. In-gel digestion was performed on each slice as previously described.18 In brief, the gel pieces were washed with a 1:1 mixture of ACN/100 mM NH4HCO3 twice for 10 min to destain the gels. Reduction was performed by adding 50 μL of dithiothreitol (DTT) solution (10 mM) for 1 h at 56 °C. After reduction, the spots were alkylated by adding 50 μL of iodoacetamide solution (55 mM in 40 mM NH4HCO3) and incubating in the dark for 30 min. The gel pieces were washed one more time with ACN/100 mM NH4HCO3 and then dehydrated by adding 100% ACN and air-dried. The digestion was started with 30 μL of trypsin solution (12.5 ng/μL in 25 mM NH4HCO3) and left overnight at 37 °C. Peptide extraction was performed twice using 1:1 mix of ACN/water, 0.1% formic acid solution. The extracted peptide solutions were dried in the speed vacuum centrifuge, and the peptides were resuspended in 20 μL of 0.1% formic acid for the nanoflow liquid tandem mass spectrometry (nano-LC−MS/MS) analysis.

The RAW files of LC−MS/MS runs were converted to mzXML format using the ReAdW program (http://tools. proteomecenter.org/wiki/index.php?title=Software:ReAdW). The mzXML spectra files were searched using GPM (Global Proteome Machine, version 2.1.1; http://www.thegpm.org)20 software against our in-house protein database generated from a pod-specific transcriptome, as previously described. Each mzXML spectra file was also searched against a reversedsequence database to calculate the false discovery rate (FDR) as protein FDR = (# reverse proteins identified)/(total protein identifications) × 100.21 Additionally, the peptide FDR was calculated as peptide FDR = 2*(# reverse peptide identifications)/(total peptide identifications) × 100.22 The 16 output files for each replicate were combined to create a single merged result file, and only proteins with FDR less than 1% were used for the analysis. The following parameters were used for the search: enzyme: trypsin; allowed missed cleavage: 2; variable modification: methionine oxidation; fixed modification: carbamidomethylation of cysteine; MS and MS/MS mass tolerance: ± 2 and ±0.2 Da, respectively. For the quantitative analysis, the normalized spectral abundance factors (NSAFs) were used to study protein

2.5. Nano-LC−MS/MS

The peptides obtained from in-gel digestion were analyzed by nano-LC−MS/MS using an LTQ-XL ion trap mass specC



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Table 1. Summary of Proteins Identified by Global Proteome Machine (GPM) search low stringency no. of proteins identifieda treatments

R1

R2

R3

RSD %b

reproducibly identified proteinsc

protein FDR (%)d

peptide FDR (%)

well-watered control water-deficit stress

3186 3185

3452 3237

3682 3506

7.2 5.2

648 613

0.46 0.66

0.02 0.03

a

R1, R2, and R3 denote replicates 1, 2, and 3, respectively. bRelative standard deviation. cProteins common to all three replicates. dFalse discovery rate.

present our findings on the impact of water-deficit stress on midmaturity stage peanut pods grown under field conditions and discuss the possible mechanisms involved in the stress response and potential targets for genetic manipulation and selection to mitigate the negative effects of late-season water deficit stress. Figure 1 shows that water-deficit stress resulted in a significant increase in crude protein content and a decrease in

abundance as previously described.23 For each identified protein, k, in sample i, the number of spectral counts was divided by the length of the identified protein. NSAFi values for each sample i were obtained by normalizing SpCk/lengthk values to the total by dividing by the sum (SpCk/lengthk) over all proteins. The NSAF mean values for all replicates were applied to calculate protein abundance. A spectral fraction of 0.5 was added to the entire spectral counts for each protein to compensate for null values and allow for log transformation of the NSAF data prior to statistical analysis.24 2.8. Statistical Analysis

A two-sample unpaired t test was used to identify the differentially accumulated proteins between control and water-deficit stressed samples. Only the proteins present in all three replicates with the minimum total spectral count of five for either control or stressed condition were included in the data set.23 Log-transformed NSAF data were used for the t test, and the proteins with a p value ≤0.05 were considered to be differentially expressed. 2.9. Annotation and Mapping

Differentially expressed proteins were subsequently annotated using Mercator-MapMan annotation tool.25 The resulting mapping file from Mercator along with relative protein abundance values were imported into MapMan software version 3.5 to identify pathways altered due to stress.

3. RESULTS AND DISCUSSION 3.1. Analysis of Label-Free Quantitative Proteomics Data

A total of 761 nonredundant proteins were identified under control and stressed conditions. Table 1 provides a summary of proteins identified in all three replicates of well-watered control and water-deficit stress treatments. The number of proteins reproducibly identified from control and stress treatments were 647 and 612, respectively. Low FDR values were observed for both treatments, indicating a high stringency in selection of the data set; average protein FDR was 0.56% and average peptide FDR was 0.025%. Supplementary Tables S3 and S4 contain details of the proteins identified in each treatment, including their NSAF values (Supporting Information).

Figure 1. Total fatty acid and crude protein content of water-deficit stressed seeds and well-water control plants. The values represent the mean of six replicates from each treatment. Bars represent mean values ± standard error. * indicates significance at p value of 0.006. ** indicates significance at p value of 0.05.

total FA content of stressed pods compared with well-watered control pods. This is similar to the results previously reported,7 where they showed significant reductions in total oil and specific FAs and significantly increased total protein in pods exposed to late season (80 days after sowing) water-deficit stress. To gain insight into the proteome response to water deficit in these samples, we employed shotgun label-free proteomics and identified 761 nonredundant proteins, out of which 93 proteins were statistically significant and differentially accumulated between well-watered and drought-stressed samples. To date, peanut lacks a publicly available version of the tetraploid genome sequence. Therefore, we created a podspecific transcriptome using next-generation sequencing and subsequently translated the assembled transcriptome in all six

3.2. Effect of Water-Deficit Stress on Peanut Seed Composition

Water-deficit stress during flowering and seed development results in significant reductions in peanut pod yield and pod biomass, reduced germination, reduced vigor, decreased seed membrane integrity, and reduced embryo RNA content depending on the genotype and intensity of the stress.4,16,26−28 Seeds from the moisture-stressed plants when germinated had low chlorophyll and dehydrogenase activity in cotyledons, which greatly influenced the growth potential of plants.27 Skelton and Shear6 reported that water deficit reduced the calcium content of both peanut seed and the pericarp. Here we D



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Figure 2. Overview of water-deficit stress response in seeds. Proteins encoding enzymes, signaling hormones, and different biological intermediates induced due to stress are represented in red and those reduced are marked in green. Proteins were annotated by Mercator tool and binned into functional categories and pathways by MapMan version 3.5.

Figure 3. Overview of glycolysis pathway including acetyl-CoA biosynthesis and alcoholic fermentation. Proteins significantly reduced are marked in green boxes. PD, pyruvate dikinase; AD, alcohol dehydrogenase.

differentially accumulated proteins. Subsequently, MapMan analysis of the differential proteins resulted in the identification of a number of primary processes and metabolic pathways that were altered due to stress. Figure 2 provides an overview of water-deficit stress response in peanut pods used for this study. Upon stress recognition, significant changes in ROS scavenging capacity were seen. These changes can occur due to disruption of electron transport mechanisms or due to respiratory burst.29,30 Downregulation of proteins like PDIL1-4 and glutaredoxin suggests an elevated ER stress and has been shown to affect starch

frames to create a pod proteome database for protein identification (outlined in Supplementary Figure S1 in the Supporting Information). Mercator tool was used to functionally categorize the differentially accumulated proteins into 35 different classes (Supplementary Figure S2 in the Supporting Information). Among these functional categories, the majority belonged to the unassigned/unknown group containing mostly different forms of storage proteins. The second largest class included development-related proteins, and, not surprisingly, stressrelated proteins represented the fourth largest group of E



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Figure 4. MapMan-generated schematic representation of sucrose and starch metabolism pathways. Proteins significantly reduced by stress are marked in green boxes. Other proteins indicated in either green (reduced) or red (induced) were not statistically significant. Proteins SBS, sucrose binding protein; SUS4, sucrose synthase 4; AI, acid invertase; AGPase, ADP-glucose pyrophosphorylase; GP, glucan phosphorylase.

synthesis31 and cause a decrease in highly disulfide-bonded storage proteins.32 A primary mechanism for transmission of stress signals to the nucleus is by hormones and other signaling peptides. We measured significant increases in proteins involved in the synthesis of brassinosteroids (BRs) and reduction in jasmonic acid (JA) biosynthesis. BR is known to induce stress tolerance by downstream increases in heat shock proteins,33 compensate for biomass reduction,34 and help in increased water uptake, membrane stability, higher CO2, and nitrogen assimilation during stress.35 JA has been shown to regulate late embryogenesis abundant (LEA) protein content36 and induce the accumulation of storage proteins.37 Interestingly, in this study, the decrease in JA biosynthesis was correlated with a decrease in LEA proteins and the storage proteins legumin and glycinin (Supplementary Table S3 in the Supporting Information). However, we have observed a large increase in Arah1, a predominant peanut storage protein, in response to waterdeficit stress. Downstream of TFs in the water-deficit stress response cascade are stress-responsive proteins including the proteolytic ubiquitin family of proteins and heat shock proteins. Ubiquitination plays a primary role in post-translational regulation of expression and can affect protein stability, enzyme activity, and cellular targeting.38−40 We observed significant changes in two ubiquitin proteins, UBQ6 and UBQ14 (Figure 2 and Supplementary Table S3 in the Supporting Information). Interestingly, these two proteins showed opposite trends in response to water-deficit stress, UBQ6 was reduced, and UBQ14 was induced. Several polyubiquitin genes have been shown to increase in response to environmental stress.38,41−43 Sun and Callis43 reported induction of both UBQ14 and HSP70. Here we measured an increase in HSP 17.4 but reduced levels of HSP 70. We also observed a reduction in UBQ6 protein, which was shown to negatively regulate ethylene signaling.44 Although the changes were not statistically

significant, we observed an increase in ethylene responsive proteins, which could be a result of reduced UBQ6 levels. 3.3. Major Biological Pathways Altered in Seed Due to Water-Deficit Stress

3.3.1. Glycolysis. In Glycolysis, carbohydrates are converted to hexose phosphates, which are then split into two triose phosphates. In a subsequent energy-conserving phase, the triose phosphates are oxidized and rearranged to yield two molecules of pyruvate. In the presence of oxygen, pyruvate then enters the citric acid cycle and undergoes oxidation, producing energy. In the absence of oxygen, plants metabolize pyruvate by alcoholic fermentation, producing ethanol, carbon dioxide, and energy. We measured a significant reduction in pyruvate dikinase and alcohol dehydrogenase (Figure 3) in response to water-deficit stress, suggesting a movement of carbohydrate toward acetyl-CoA, ultimately resulting in increased energy in the form of ATP generated by the TCA cycle. The increase in ATP supply may serve to meet increased stress-related energy demands. 3.3.2. Sucrose and Starch Metabolism. Drought stress considerably decreases photosynthetic rate and disrupts carbohydrate metabolism in soybean and peanut leaves and carbon export to sink tissues.16,45,46 Because sucrose is both the principal and the preferred form of photosynthate for longdistance transport to sink organs, its concentration in leaves represents the current availability of assimilates for reproductive development.47 In pigeon pea, drought stress resulted in a sharp decline in leaf sucrose and starch concentrations, while glucose and fructose levels increased significantly.48 Furthermore, it has been shown that starch and sucrose in plant leaves are depleted due to drought, and a subsequent increase in hexose may be involved in feedback regulation of photosynthesis,49 causing a source limitation. Drought can also affect carbohydrate metabolism in sink tissues with a subsequent increase in sucrose concentrations in reproductive organs of drought-stressed plants.9 Sucrose must be cleaved into hexoses F



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Figure 5. Simplified pathway of triacylglycerol (TAG) biosynthesis in seeds. Significantly reduced proteins coding enzymes involved in FA synthesis in plastids are indicated in green boxes. All other enzymes of FA synthesis and FA modification in ER are indicated in gray boxes. Abbreviations: Glc6-P, glucose-6-phosphate; Fru-1,6-P, fructose-1,6-biphosphate; DHAP, dihydroxyacetone-3-phosphate; GAP, glyceraldehydes-3-phosphate; 1,3-BPG, 1,3-biphosphoglycerate; 3-PGA, 3-phosphoglycerate; 2-PGA, 2-phosphoglycerate; 6-PGL, 6-phosphogluconolactone; 6-PG, 6-phosphogluconate; Ru-5-P, ribulose-5-phosphate; Ru-1,5-P, ribulose-1,5-biphosphate; PGK, phosphoglycerate kinase; ENOp, plastidial enolase; PEP, phosphoenolpyruvate; PKp, plastidial pyruvate kinase; PDC, pyruvate dehydrogenase complex; Ac-CoA, acetyl-coenzyme A; ACCase, acetyl-CoA carboxylase; ACP, acyl carrier protein; MAT, malonyl-CoA:ACP transacylase; KAS, 3-ketoacyl-ACP synthase; KAR, 3-ketoacyl-ACP reductase; HD, 3-hydoxyacyl-ACP dehydratase; ENR, enoyl-ACP reductase; SAD, stearoyl-ACP desaturase; FAT, fatty acyl-ACP thioesterases; FAE, fatty acid elongase complex; and DAGAT, diacylglycerol acyltransferase.

for cellular metabolism. Two major enzymes responsible for cleavage are sucrose synthase and acid invertase. We observed a decrease in sucrose synthase and a corresponding increase in acid invertase protein (Figure 4). Additionally, sucrose-binding protein, glucan phosphorylase, and starch transporter protein showed reduced levels in response to stress (Figure 4). This suggests a general reduction in starch synthesis and an increase in sucrose metabolism and hexose production, which may maintain the sucrose gradient between seed and source tissues.50 Apart from reduced sucrose degradation, there was a reduction in sucrose-binding protein, a sucrose transporter, suggesting a further imbalance in soluble sugar levels potentially leading to retardation of seed development.51−53 3.3.3. Fatty Acid Metabolism. FAs in seeds of oilseed crops are stored in oil bodies (OBs) as triacylglycerol (TAG). In seeds, they not only act as reserves of carbon and energy but also determine the economic value of seeds in many crops.54

Production of TAG commences during the maturation phase of seed development and results in a steady increase in seed dry weight. TAGs are esters of glycerol in which FAs are esterified to each of the three hydroxyl groups of the glycerol backbone. Once synthesized, TAGs are accumulated in subcellular organelles called OBs, surrounded by phospholipid monolayers.55,56 In seeds, sucrose is imported into embryo cells and cleaved by the enzymes, invertase, or SUS.57,58 Sucrose cleavage generates hexose phosphates, which are metabolized through the oxidative pentose phosphate pathway (OPPP) and glycolytic pathway, providing precursors for FA production in the form of acetyl-CoA. In Arabidopsis, a multisubunit heteromeric acetyl-CoA carboxylase (ACCase) catalyzes the first committed step of the FA biosynthetic pathway (Figure 5).59 FAs are produced from acetyl-CoA and malonyl-ACP, where acetyl-CoA is used as the starting unit and malonyl-CoA is used as the elongator. The malonyl thioesters formed in the G



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above reaction enter into a series of condensation reactions with acetyl-CoA and acyl-ACP acceptors. Three separate condensing enzymes, namely, 3-ketoacyl-ACP synthases (KAS) I, II, and III, are necessary for the production of an 18-carbon FA. The initial condensation reaction is catalyzed by KASIII, while KASI is responsible for producing chain lengths from 6 to 16 carbons. KASII finally elongates 16:0-ACP to 18:0-ACP.60 Three additional reactions are required after each condensation step to obtain a saturated FA that is two carbons longer than at the start of the cycle. These reactions are catalyzed by 3-ketoacyl-ACP reductase (KAR), 3-hydroxyacylACP dehydratase (HD), and enoyl-ACP reductase (ENR).61 Very long chain FAs (VLCFAs) are synthesized by the multienzyme fatty acid elongase (FAE) complex in the ER. Drought stress reduced three proteins encoding enzymes of the FA biosynthesis pathway in peanut seed (Figure 5). The most significant among them was ACCase, the rate-limiting enzyme of FA metabolism. We have also seen reduction in phosphoglycerate kinase possibly limiting the supply of acetyl Coal. In addition, there was a significant decrease in an oleosin, protein suggesting an altered OB structure and lipid accumulation (Siloto et al., 2006) due to stress. Together, the proteomics data obtained in this study potentially explain the molecular basis of reduction in total FA content measured in stressed peanut seeds (Figure 1).

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ASSOCIATED CONTENT

S Supporting Information *

Supplementary Figure S1: An overview of the label-free quantitative proteomics approach. We have sequenced the midmature peanut-pod transcriptome and generated an inhouse pod specific proteome. Supplementary Figure S2: Functional categorization of nonredundant proteins identified in this study. MapMan-Mercator tool was used to annotate and functionally bin the proteins into different categories. Supplementary Table S3: The complete list of 761 proteins identified from control and stress samples in this study. The Table also includes the numbers of peptides assigned to each protein for each replicate as well as the NSAF values. The statistically significant proteins are colored red (for upregulated proteins) and green (for down-regulated proteins), which were obtained from t-test analysis. Supplementary Table S4: The separate lists of identified proteins for control sample well as stress with their corresponding peptides numbers and NSAF values. This material is available free of charge via the Internet at http://pubs.acs.org.

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AUTHOR INFORMATION

Corresponding Author

*E-mail: [email protected]. Phone: 806-749-5560. Fax: 806-723-5272. Notes

The authors declare no competing financial interest.

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4. CONCLUDING REMARKS Peanut is an important dietary staple and biologically presents a unique model crop in terms of its seed development. However, compared with other legumes, little is known about the molecular mechanism of seed development and the impact of abiotic stresses on development and quality. In this study, we have utilized a quantitative shotgun proteomics approach to gain insight into the molecular events underlying the physiological responses of peanut seeds to water-deficit stress. Under adverse growing conditions, peanut seeds sense the external stimuli by signaling hormones BR and JA. Additionally, changes in polyubiquitination and induction of heat shock proteins may help to maintain cellular metabolism under stress. Stress induction of glycolysis and inhibition of alcoholic fermentation could provide additional energy to meet increased metabolic demands under water-deficit stress. Under waterdeficit stress, sucrose mobility/export and starch synthesis are suppressed along with a concomitant increase in sucrose degradation by acid invertase, possibly explaining the molecular basis of reduction in seed size, biomass, and viability. Drought alters peanut seed composition by reducing the total FA content due to suppression of FA metabolism. In future studies we will adopt an integrated approach of agronomic, genetic, and biochemical methods coupled to emerging -omics data for a deeper insight into the molecular basis of drought tolerance. This integrative approach will be the foundation of our efforts in developing peanut germplasm for production in low-input regions or for specific nutritive and quality traits. The mass spectrometry proteomics data have been deposited to the ProteomeXchange Consortium (http://proteomecentral. proteomexchange.org) via the PRIDE partner repository62 with the data set identifier PXD000308 (username: review27605; password: 4m8cyH4b).

ACKNOWLEDGMENTS We thank Marie Syapin and Pratibha Kottapalli for technical help. We also thank Susan San Francisco for valuable suggestions during the preparation of the manuscript. This research was supported by grants from the Ogallala Aquifer Initiative and USDA-ARS CRIS 6208-21000-012-00D.

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