Comparative Proteomics Analysis of Selenium Responses in

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Comparative Proteomics Analysis of Selenium Responses in Selenium-Enriched Rice Grains Yu-Dong Wang, Xu Wang, Sai-ming Ngai, and Yum-Shing Wong J. Proteome Res., Just Accepted Manuscript • DOI: 10.1021/pr300878y • Publication Date (Web): 17 Dec 2012 Downloaded from http://pubs.acs.org on December 31, 2012

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Journal of Proteome Research

Comparative Proteomics Analysis of Selenium Responses in Selenium-Enriched Rice Grains Yu-Dong Wang,1, a, * Xu Wang,1, a Sai-ming Ngai,1 Yum-shing Wong,1, *

1

School of Life Sciences, The Chinese University of Hong Kong, Shatin, N.T., Hong Kong SAR, China

a

Both authors contributed equally to this work

CORRESPONDING AUTHOR FOOTNOTE *

To whom correspondence may be addressed:

Yu-Dong Wang, E-mail: [email protected]; Tel.: +852 9422 0286; Fax: +852 2603 5745. Yum-Shing Wong, E-mail: [email protected]; Tel.: +852 2609 6389; Fax: +852 2603 5745.

1

ACS Paragon Plus Environment


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ABSTRACT By foliar fortification with selenite, selenium (Se)-enriched rice with higher Se content and grain yield has been generated. However, the regulatory mechanisms of Se response in rice grains remain unknown, therefore we carried out a comparative proteomics study in Se-enriched rice grains by using two approaches including 2-dimensional gel electrophoresis (2-DE) coupled MALDI-TOF/TOF MS and 1-DE/LC-FT-ICR MS coupled label-free quantification. By comparison between Se treatment and control, 62 and 250 abundance changed proteins were identified from 2-DE and 1-DE, respectively. By functional classification, proteins involved in metabolism, cell redox regulation, and seed nutritional storage were the most highly affected by Se accumulation. The up-regulation of late embryogenesis abundant (LEA) proteins as well as proteins involved in sucrose synthesis and other metabolism pathways may contribute to the earlier maturation and higher yield of the Se-enriched rice. In addition, there have been 6 proteins identified to contain selenoamino acid modification which is the first identification of selenoproteins in higher plants. In conclusion, our study provided novel insights into Se response in rice grains at the proteome level, which are expected to be highly useful for dissecting the Se response pathways in rice and for the production of Se-enriched rice in the future.

KEYWORDS: plant proteomics; 2-DE; label-free quantification; selenium; rice grain; redox regulation

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INTRODUCTION

Selenium (Se) is an essential nutrient for humans as being a crucial component of iodothyrine 5’-deiodinase, glutathione peroxidase (GPX1), and thioredoxin reductase whereas the beneficial range of dietary Se intake for humans is relatively narrow and has been conferred as 55-200 µg/g dry food.1 An adequate intake of 200 µg Se/day has been shown to reduce the risk of cancers2, 3 while outside this range, excess Se intake may induce chronic selenosis, the major effects of which are dermal and neurological, such as hair loss, unsteady gait, and paralysis.1, 4 In addition, Se deficiency can seriously affect human health by leading to heart disease, hypothyroidism, and a weakened immune system.5 Around the world, it was estimated that for up to 1 billion people, insufficient dietary intake of Se is a serious health constraint.6 Since the organic Se species from food are considered to be the most bioavailable for human beings,7 the American Dietetic Association recommends to consume this nutrient through foods whenever possible.8 As a vital component of the world’s diet, rice (Oryza sativa L.) is also a major source of Se;6 however, Se concentration in rice grains is generally low compared to other crops.9 Therefore, to solve the problem of Se deficiency, optimizing Se levels in rice can have great impacts on human Se status. There are no reports that conclusively demonstrate that Se is essential for higher plants, even though Se is an essential micronutrient for animals, microorganisms and some other eukaryotes.10 However, plants do take up and assimilate Se by sharing the same transporters and metabolic pathways with sulfur (S) due to their chemical similarities.11, 12 In addition, there have been reports showing the beneficial effects of Se on plant growth since Se can protect plants from abiotic and biotic stresses.13-15 Therefore, application of Se fertilizers in the beneficial range to rice plant is well accepted for both agronomic biofortification of Se and elevation of rice yield.15 Plant leaves can absorb micronutrients as roots do, and foliar application of selenium fertilizers has been approved as a safe, low cost, effective, and convenient means to increase the selenium content in rice.16-18 In our recent study (unpublished results), foliar application of sodium selenite (10.5 g Se/ha) to the rice cultivar Jing 9983 (Oryza sativa 3



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L. ssp. Japonica) increased grain yield by up to 1.24 times and Se content in grains by up to 51 times. By Se speciation analysis and antioxidant assays, high Se bioaccessibility and bioavailability can be found in this Se-enriched rice, which suggests enormous potential for Se supplementation in humans. Despite the progress, to avoid potential environment pollution by Se fertilizer, genetic engineering of Se enriched rice could be a promising strategy. Better knowledge of Se metabolism and regulatory pathways in rice is thus an essential prerequisite. Until now, much has been known about Se metabolism in plants as Se shares the same metabolic pathways with S; whereas, the regulatory mechanisms of Se response in higher plants remain elusive. To uncover these mysteries and elucidate the mechanisms underlining the beneficial effect of Se on rice production, we have carried out a comprehensive proteomic study of Se responses in rice seedlings and found that lower Se treatments can promote rice seedling growth by not only activating antioxidative system to enhance stress resistance, but also up-regulating proteins involved in photosynthesis, carbohydrate, and protein metabolism.15 Therefore, to fully understand the mechanisms underlining Se biofortification in rice, further study is necessary to examine proteome changes by Se accumulation in Se-enriched rice grains. Nowadays, quantitative proteomics has been intensively applied for plant biology studies.19 Although suffering protein losses at both extremes of isoelectric point (pI) and low molecular weight (Mw) proteins, as well as highly hydrophobic proteins, two-dimensional gel electrophoresis (2-DE) with immobilized pH gradients (IPGs) combined with protein identification by mass spectrometry (MS) is currently the workhorse for proteomic studies.20 2-DE delivers a map of intact proteins, which reflects changes at protein expression level, isoforms or post-translational modifications and enables the separation of complex mixtures of proteins according to their pI and Mw. In addition, identifying and quantifying peptides in mixtures of varying complexity have emerged to use LC-MS-based quantification methods, which cover proteins with different pI and Mw. Therefore, both methods are believed to deliver largely complementary protein identifications, suggesting that a combination of these methods is necessary to achieve comprehensive proteome coverage.19 4


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There are commonly two approaches used for peptide quantification in MS-based proteomics, including isotope labeling 21-25 and label-free approach.26 Although isotope labeling methods provide the greatest accuracy, they still have several disadvantages such as relatively high cost, procedural complexity, and the potential danger of artifacts.27 In recent years, label-free quantitative proteomic approaches have progressed and are considered reliable and efficient methods to study protein abundance changes in complex mixtures.28, 29 Many bioinformatic tools have been developed for the label-free quantification of MS data, either by spectral counting or by peptide MS1 signal intensity measurement.26 In the latter field, IDEAL-Q software was newly introduced as an efficient and robust quantification tool.30 It employs an efficient algorithm to predict the elution time of a given peptide unidentified in a specific LC-MS/MS run but identified in other runs. Then, the predicted elution time is used to detect peak clusters of the assigned peptide. Detected peptide peaks are processed by statistical methods and further validated by stringent criteria to filter out noisy data. Therefore, IDEAL-Q shows sufficient sensitivity and reproducibility to meet the requirements of stability of intensity, mass measurement, and retention time for label-free quantitative MS analysis. Here, we wanted to examine the proteome changes induced by Se accumulation in mature Se-enriched rice grains by combining 2-DE/MALDI-TOF/TOF (matrix-assisted laser desorption inoization-tandem time of flight) MS and 1-DE/LC-FT-ICR (Fourier transform-ion cyclotron resonance) MS analyses. Since the hull of rice grain contains mainly fiber and has the lowest protein content among the milling fractions of rice,31 only dehulled rice grains (brown rice) were thus used as plant material. In this study, not only biological pathways regulated by Se accumulation in rice grains have been revealed, but also selenoproteins in the proteome of higher plants have been identified for the first time. MATERIALS AND METHODS Plant Materials and Growth Conditions Rice seeds (Oryza sativa L. ssp. japonica, Jing 9983) were sterilized in 15% commercial chlorate for 10 min, and then fully washed with Milli-Q grade water. The seeds were germinated on wet filter 5



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paper in 13.5 cm petri dishes at 30 °C in the dark. After 5 days of germination, the seedlings were transferred into plastic pots with vermiculite below and the plant density was set 50 per pot. The seedlings were grown in a growth chamber at 26 °C, light/dark cycle of 16/8 h, relative humidity of 70%, illumination of 154 MJ·m-2·s-1 and supplied with 2 mg/L of sodium selenite (Na2SeO3), respectively. The sodium selenite solution was changed every two days. After a further growth of 11 days, 2×50 seedlings were randomly selected and paddy transplanted in a greenhouse (north latitude 22° 42′, east longitude 114° 21′). During the rice growth period (3 independent experiments), the temperature in the greenhouse ranged from 20 °C (night minimum) to 37 °C (day maximum) and the humidity ranged from 40% (day minimum) to 80% (night maximum). The soil (~12 m2 for each treatment) was maintained under flooded condition. Other treatments were performed by standard farmers’ practices. After cultivation for 7 days, 100 ml of 0 and 2 mg/L sodium selenite were used to spray the leaves of control (1×50) and Se fortified (1×50) rice plants, respectively, as foliar application of Se fertilizers every 15 days during the vegetative growth stage. After heading, 100 ml of 0 and 40 mg/L sodium selenite (namely 0 and 1.5 g Se/ha, as 78/173, or about 45% of the sodium selenite is Se) were applied to control and Se fortified rice plants, respectively, every 15 days until the grains were mature (applied totally 7 times, namely 0 and 10.5 g Se/ha). To avoid the interferences from sodium, sodium chloride (NaCl) was added in control to make the concentration of sodium constant. At physiological maturity, the rice grains (14% moisture content) were harvested and stored at 4 °C. Total Protein Extraction Total proteins were extracted from brown rice by a trichloroacetic acid (TCA)/acetone method as previously described.32, 33 Briefly, dehulled rice grains were homogenized with a mortar and pestle under liquid nitrogen. 0.5 g of powder were mixed with 4 ml of precooled homogenization buffer [20 mM Tris/HCl (pH 7.5), 250 mM sucrose, 10 mM EDTA, 5 mM ethyleneglycol-bis (β-aminoethyl ether)-N,N,N’,N’-tetraacetic acid (EGTA), 1 mM phenylmethylsulfonyl fluoride (PMSF), 1 mM dithiothreitol (DTT), and 1% Triton X-100]. After homogenization, the homogenates were centrifuged 6


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at 15,000 ×g for 15 min at 4 °C. The resulting supernatants were mixed with 1/4 volume of 50% cold TCA solubilized in acetone and kept at -20 °C for 30 min. Then all mixtures were centrifuged at 15,000×g for 15 min at 4 °C and the pellets were washed three times with cold acetone. After the second centrifugation, the protein pellets were vacuum-dried and solubilized in lysis buffer [7 M urea, 2 M thiourea, 4% CHAPS, 20 mM DTT and 0.5% IPG buffer, pH 4-7 (GE Healthcare)]. After incubation at 30 °C for 2 h with gentle mixing, the suspension was centrifuged at 15,000×g for 30 min at 24 °C to remove the insoluble materials. Protein concentration was measured using the Bradford method (Bio-Rad protein assay). 2-DE Separation, Gel Staining and Image Analysis Three biological replicates (each with two technical replicates) of 2-DE were performed using different plant materials. IEF was carried out using an IPGphor II electrophoresis system (GE Healthcare) and 18-cm immobiline dry strips, with linear pH gradient of 4–7 (GE Healthcare) as mentioned before.15 900 µg of protein samples were loaded for 14 h’s rehydration. IEF was then performed by ramping to 500 V over 1 h, holding at 500 V for 1 h, and 1000 V for 1 h successively, ramping to 8000 V over 1 h, and holding at 8000 V, until a total of 32 kVh were reached. Prior to the second dimension electrophoresis, the gel strips were equilibrated in equilibration buffer [6 M urea, 30% (w/v) glycerol, 2% (w/v) SDS, 50 mM Tris-HCl, pH 8.0], first with 1% DTT and then with 2.5% iodoacetamide each for 15 min. After equilibration, the strips were applied to 12.5% vertical SDS-polyacrylamide gels for the second dimension electrophoresis using an Ettan DALTsix Large Vertical System (GE Healthcare). SDS-PAGE was run at 1 watt/gel for 2 h and then 3 watts/gel for 12–16 h until the bromphenol blue dye front reached the gel end. After electrophoresis, gels were stained with CBB G-250.34 The stained gels were scanned at a 300 dpi resolution using ImageScanner (GE Healthcare). The comparison between gels of control (no Se addition) and Se fortification were made with the ImageMaster 2D Platinum software (version 5.0, GE Healthcare) according to its user manual. Calibration and background subtraction of gel images were 7



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done fully automatically. Minimal manual editing was performed to correct the mismatched and unmatched spots between gels. To accurately compare the abundance of a spot across gels, the spot abundance was normalized as a proportion of the given spot volume to the volume of all spots on the particular gel (Vol%). Using paired Student’s t-test in GraphPAD Software v5.01, only spots showing significant changes (%Vol varied more than 1.5-fold, p < 0.05) were selected for further analysis. Trypsin Digestion, Mass Spectrometry and Protein Identification Protein spots, which showed significant abundance changes after Se treatments, were excised from the CBB stained gels. Each protein spot was cut into small pieces and destained with destaining solution (50 mM NH4HCO3 in 50% ACN).15 Trypsin digestion of each protein spot was performed using sequencing grade modified trypsin (Promega).15 MS analysis was carried out using an ABI 4700 MALDI-TOF/TOF tandem mass spectrometer (Applied Biosystems, Foster City, USA). For acquisition of mass spectra, 0.5 µl samples were spotted onto a MALDI plate, followed by 0.5 µl matrix solution (4 mg/ml α-cyano-4- hydroxycinnamic acid in 35 % ACN and 1% TFA). Mass data acquisitions were piloted by 4000 Series Explorer Software v3.0 using batched-processing and automatic switching between MS and MS/MS modes. All MS survey scans were acquired over the mass range 800-3500 m/z in the reflectron positive-ion mode and accumulated from 2000 laser shots with acceleration of 20 kV. The MS spectra were internally calibrated using porcine trypsin autolytic products (m/z 842.509, m/z 1045.564, m/z 1940.935 and m/z 2211.104) resulted in mass errors of less than 30 ppm. The MS peaks (MH+) were detected on minimum S/N ratio ≥20 and cluster area S/N threshold ≥25 without smoothing and raw spectrum filtering. Peptide precursor ions corresponding to contaminants including keratins and the trypsin autolytic products were excluded in a mass tolerance of ± 0.2 Dalton. The filtered precursor ions with a user-defined threshold (S/N ratio ≥50) were selected for the MS/MS scan. Fragmentation of 8


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precursor ions was performed using MS-MS 1kV positive mode with CID on and argon as the collision gas. MS/MS spectra were accumulated from 3000 laser shots using default calibration with Glu-Fibrinopeptide B from 4700 Calibration Mixture (Applied Biosystems, USA). The MS/MS peaks were detected on minimum S/N ratio ≥3 and cluster area S/N threshold ≥15 with smoothing. The MS and MS/MS data were loaded into GPS Explorer version 3.5 (Applied Biosystems, USA) and searched against the NCBInr database (release on March 1, 2011; including 14,478,394 sequences, 4,957,987,209 residues) with species restriction to Oryza sativa (134,656 sequences) by Mascot search engine version 2.2 (Matrix science, London, UK) using combined MS (peptide-mass-fingerprint approach) with MS/MS (DeNovo sequencing approach) analysis for protein identification. The following search parameters were used: monoisotopic peptide mass (MH+); 800-3500 Dalton; one missed cleavage per peptide; enzyme, trypsin; taxonomy, Oryza sativa; pI (isoelectric point), 0-14; precursor-ion mass tolerance, 50 ppm; MS/MS fragment-ion mass tolerance, 0.1 Da; variable modifications, carbamidomethylation for cysteine and oxidation for methionine were allowed. Known contaminant ions corresponding to trypsin and keratins were excluded from the peak lists before database searching. Top six hits for each protein search were reported. To assure confident protein identification, only proteins with Mascot protein scores (based on both MS and MS/MS spectra) above the statistically significant threshold (55, p

Comparative Proteomics Analysis of Selenium Responses in

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