Comparative Proteomic Analysis of Brassica napus in Response to

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Comparative proteomic analysis of Brassica napus in response to drought stress Jin Koh, Gang Chen, Mi-Jeong Yoo, Ning Zhu, Daniel Dufresne, John E. Erickson, Hongbo Shao, and Sixue Chen J. Proteome Res., Just Accepted Manuscript • DOI: 10.1021/pr501323d • Publication Date (Web): 18 Jun 2015 Downloaded from http://pubs.acs.org on June 22, 2015

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Journal of Proteome Research

Comparative proteomic analysis of Brassica napus in response to drought stress Jin Koh†,§, Gang Chen ‡, ¥,§, Mi-Jeong Yoo ‡, Ning Zhu ‡, Daniel Dufresne&, John E. Erickson#, Hongbo Shao$, Sixue Chen†,‡,¶,* †

Proteomics and Mass Spectrometry, Interdisciplinary Center for Biotechnology Research,

University of Florida, Gainesville, FL 32610, USA ‡

Department of Biology, Genetics Institute, University of Florida, Gainesville, FL 32610, USA

¥

Yangzhou University, Yangzhou 225009, Jiangsu, China

&

Palm Beach Central High School, Wellington, FL 33411, USA

#

Agronomy Department, University of Florida, Gainesville, FL 32611, USA

$

Yantai Institute of Costal Zone Research, Chinese Academy of Sciences, Yantai 264003,

Shandong, China ¶

Plant Molecular and Cellular Biology, University of Florida, Gainesville, FL 32611, USA

§

These authors contributed equally.

* To whom correspondence should be addressed: Sixue Chen, Ph.D. CGRC Room 438, University of Florida 2033 Mowry Road, Gainesville, FL 32610, USA Tel: +1 (352) 273-8330, Fax: +1 (352) 273-8024 Email: [email protected]

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ABSTRACT:

Drought is one of the most widespread stresses leading to retardation of plant growth and development, and ultimately low crop yield. Here we examined the proteome changes of an important oil seed crop, canola (Brassica napus L.), under drought stress over a 14 day period. Using iTRAQ LC-MS/MS, we identified 1,976 proteins expressed during drought stress. Among them, 417 proteins showed significant changes in abundance under stress, and 136, 244, 286, and 213 proteins were differentially expressed in the 3rd, 7th, 10th, and 14th day of drought stress, respectively. Functional analysis of the 417 proteins indicated that the number of proteins associated with metabolism, protein folding & degradation, and signaling decreased, while those related to energy (photosynthesis), protein synthesis, and stress and defense increased in response to drought stress. In particular, the proteome profiles at the 7th and 10th day were similar to each other, although there were much more post-translational modifications (PTMs) at the 10th day of drought stress. Interestingly, 181 proteins underwent PTMs. Forty-nine of the PTMs were differentially changed in drought-stressed plants, and 33 were observed at the 10th day of drought. Furthermore, comparison of protein expression changes with those of gene transcription showed that there was positive correlation in B. napus, although different patterns between transcripts and proteins were observed at each time point. Under drought stress, most of the protein abundance changes may be attributed to gene transcription, and PTMs clearly contribute to the protein diversity and functions.

KEYWORDS: drought stress, Brassica napus, proteomics, iTRAQ, PTMs, transcription


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INTRODUCTION Plants are frequently exposed to various abiotic stresses, such as drought, salinity, extreme temperatures, chemical toxicity, and oxidative stress. These stresses often cause an imbalance in cellular homeostasis that leads to a series of morphological, physiological, and molecular changes1. Drought, salt stress, and low temperature are among the most important challenges in agriculture because they disturb plant growth and development from its genetic potential2 and cause a loss of more than 50% of grain yield in many crops3, 4. Although drought stress is an important type of water stress, most studies on water stress signaling have focused on salt stress primarily because plant responses to salt and drought are closely related and the response mechanisms to the stresses overlap2, 5-7. Drought has been a serious problem in the US and around the world, and it may become more frequent and more severe with changing climate8. Therefore, a better understanding of crop drought tolerance at many different levels, especially changes to the proteome under prolonged water deficit, is needed. Drought is a situation of low water potential and turgor in plants where cytoplasmic components and the cell contents are concentrated in the cytosol and extracellular matrices become increasingly viscous due to loss of water7, 9. Effects of drought on plants have been studied for several decades, and changes induced by insufficient water supply have been examined from the whole plant/plant population levels to biochemical and molecular levels. Plants can either avoid drought stress by accelerating their life cycle with early flowering, reducing water loss, or enhancing water uptake through morphological modifications. Plants have also evolved drought tolerance mechanisms. Many studies have shown that plants cope with drought stress by modulating a myriad of molecular, biochemical, and physiological processes. For example, plants respond to drought stress by manipulating physiological


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processes like photosynthesis, respiration, water relations, turgor, and osmotic adjustment10, 11. Drought causes stomatal closure, which in turn decreases CO2 intake and net photosynthesis10, 11, eventually leading to reduced growth. Meanwhile, there are a number of biochemical and molecular responses to drought stress, including decreased efficiency of RuBisCO, accumulation of abscisic acid (ABA)12, reactive oxygen species (ROS)13, and stress metabolites, such as glutathione14, betaine15, proline16, raffinose, and galactinol17, and an increase of ROS-scavenging enzymes such as superoxide dismutase (SOD), ascorbate peroxidase (APX), catalase (CAT), glutathione reductase (GR), and peroxidase (POD)18, 19. In particular, ABA and ROS affect several signal transduction pathways that trigger stress response gene expression related to accumulation of osmoprotectants, antioxidants and ROS scavengers, which may eventually protect plants from being damaged by the stress20. Hundreds of genes have been identified in modulating the processes of drought response and tolerance using genetic, genomic, and molecular approaches. There are a few studies at the proteome level in crops under drought stress, such as rice (Oryza sativa L.21-23), maize (Zea mays L.24-26), wheat (Triticum aestivum L.27-30), barley (Hordeum vulgare L.31-33), cotton (Gossypium herbaceum L.34, G. hirsutum L.35), chickpea (Cicer arietinum L.36-38), common bean (Phaseolus vulgaris L.39), sugar beet (Beta vulgaris L.40), and sunflower (Helianthus annuus L.41, 42). As to canola (B. napus L.), there are many proteomics studies on the effects of abiotic stresses43-50. However, there is only one proteomic study that provides limited information on drought tolerance due to technological limitations of two dimensional gel electrophoresis (2-DE)51 and a single time point analysis of drought stress52. Canola is an important oil seed crop whose production has grown rapidly over the past 40 years, and is now a large feed meal and vegetable oil crop in the United States. Canola grows


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mainly in arid and semiarid areas where its yield is often restricted by water deficit and high temperature during the reproductive growth. The effect of drought stress on early vegetative growth has been well-studied using physiological and biochemical approaches53-55. However, as stated above, limited studies have been performed at the molecular level, particularly at the proteomic level. Therefore, investigating the proteome profiles under drought stress can provide detailed information on specific protein changes related to drought responses. In addition, the results may be applicable to other agronomically important brassica crops, such as mustard (B. juncea L.), cabbage (B. oleracea L.), and turnip rape (B. rapa L.). Here we examined the effects of gradual drought stress conditions on the phenotype, physiological, and biochemical aspects of B. napus seedlings. Based on the information, we further investigated the proteome profiles in response to gradual drought stress over 14 days. In particular, we have analyzed the post-translational modification (PTM) of proteins under drought stress, and this work provides evidence for the role of PTMs in modulating plant stress responses.

EXPERIMENTAL PROCEDURES Plant Materials Seedlings of Brassica napus var. Global were grown on a Metro-Mix 500 potting mixture (The Scotts Co., Marysville, OH, USA) in a growth chamber under a photosynthetic photon flux of 160 µmol of photons m-2s-1 and a short-day condition (8 h light at 22°C /16 h darkness at 20°C). Six-week-old plants were treated with drought via no watering, and leaves were harvested at 3, 5, 7, 10, 12 and 14 days after treatment along with control groups and immediately frozen in liquid nitrogen. Three independent biological replicates for each treatment were conducted.


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Physiological Analysis of Control and Drought Stressed Plants Relative water content of leaves: After different periods of drought treatment, fresh weight was measured immediately after leaves were harvested, and dry weight was obtained after drying the samples at 75°C for 48 hours. Turgor weight was determined by subjecting leaves to rehydration for 2 hours after their fresh weight was determined. For each experiment, relative water content (RWC) was determined according to a previous method56: RWC(%) = (fresh weight- dry weight) / (turgor weight – dry weight) × 100. Photosynthesis and chlorophyll fluorescence analysis: Photosynthesis rate (Pn), stomatal conductance (Cond), intercellular CO2 concentration (Ci), and transpiration rate (Tr) were determined under light in the growth chamber (see Plant Materials) with a LI-6400XT photosynthesis system (LI-COR Inc., Lincoln, USA), and respiration rate was determined in the dark in the growth chamber. Water-use efficiency (WUE) was calculated from Pn divided by Tr57. The chlorophyll fluorescence parameters (Fv/Fm and Fv/Fo) were measured using a Handy PEA portable fluorescence spectrometer (Hansatech Instruments, Ltd., King’s Lynn, UK)58. Biochemical Analysis of Control and Drought Stressed Plants Analysis of soluble sugar, proline, and betaine contents: After sampling, fresh leaves were lyophilized and ground into a fine powder for proline and sugar analyses. Proline and total soluble sugar contents were determined using a ninhydrin reaction and an anthrone reagent, respectively59. Betaine content was determined with fresh leaves using Reinecke salt as previously described60.


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Antioxidant enzyme activity assay: Leaves are homogenized in 100 mM potassium phosphate buffer (0.1 mM EDTA, 1 % (w/v) PVP, 0.5 % (v/v) Triton X-100, 5 mM ascorbate, pH7.8). The homogenate is centrifuged at 10,000 g for 20 min at 4°C. The supernatant is collected for measurement of the activities of antioxidant enzymes (SOD61, APX62, glutathione S-transferase (GST)63, and CAT64) using the published methods. Statistical Analysis of Physiological and Biochemical Data ANOVA was performed using JMP 10.0.2 (SAS Institute, Cary, NC, USA) to determine whether there were differences in the data across different time points. A two-sample t-test was performed to assess whether control and drought-stressed samples differ at each time point. Protein Extraction, Digestion, iTRAQ Labeling and LC-MS/MS Proteins were extracted and quantified as previously described65, and dissolved in 0.1% SDS (w/v), 0.5 M triethylammonium bicarbonate, pH 8.5. For each sample, a total of 100 µg of protein were reduced, alkylated, trypsin-digested, and labeled according to the manufacturer’s instructions (AB Sciex Inc., Foster City, CA, USA). The control samples of 3, 7, 10, and 14 days were labeled with iTRAQ tags 113, 115, 117 and 119, and the corresponding drought-stressed samples were labeled with iTRAQ tags 114, 116, 118 and 121, respectively. Labeled peptides were desalted with C18-solid phase extraction and dissolved in strong cation exchange (SCX) solvent A (25% (v/v) acetonitrile, 10 mM ammonium formate, and 0.1% (v/v) formic acid, pH 2.8). The peptides were fractionated using an Agilent HPLC 1260 with a polysulfoethyl A column (2.1 × 100 mm, 5 µm, 300 Å; PolyLC, Columbia, MD, USA). Peptides were eluted with a linear gradient of 0–20% solvent B (25% (v/v) acetonitrile and 500 mM ammonium formate, pH 6.8) over 50 min followed by ramping up to 100% solvent B in 5 min. The absorbance at 280


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nm was monitored and a total of 16 fractions were collected. The fractions were lyophilized and resuspended in LC solvent A (0.1% formic acid in 97% water (v/v), 3% acetonitrile (v/v)). A hybrid quadrupole Orbitrap (Q Exactive) MS system (Thermo Fisher Scientific, Bremen, Germany) was used with high energy collision dissociation (HCD) in each MS and MS/MS cycle. The instrument was run in data dependent mode with a full MS (400 - 2000 m/z) resolution of 70,000 and five MS/MS experiments (HCD NCE=28%, isolation width = 3 Th, first mass = 105 Th., 5% underfill ratio, peptide match set to ‘preferred’, and an AGC target of 1e6). Dynamic exclusion for 10 s was used to prevent repeated analysis of the same peptides, and a lock mass of m/z 445.12003 (polysiloxane ion) was used for real-time internal calibration. The MS system was interfaced with an automated Easy-nLC 1000 system (Thermo Fisher Scientific, Bremen, Germany). Each sample fraction was loaded onto an Acclaim Pepmap 100 pre-column (20 mm × 75 µm; 3 µm-C18) and separated on a PepMap RSLC analytical column (250 mm × 75 µm; 2 µm-C18) at a flow rate at 300 nl/min during a linear gradient from solvent A (0.1% formic acid (v/v)) to 25% solvent B (0.1% formic acid (v/v) for 80 min and to 99.9% acetonitrile (v/v)) for additional 15 min. Proteomics Data Analysis The raw MS/MS data files were processed by a thorough database searching approach considering biological modification and amino acid substitution against a non-redundant Brassica database with decoy sequences (135,954 entries), which was constructed from 45,768 B. oleracea and 41,019 B. rapa sequences, using ProteoIQ v2.7 (Premier Biosoft, Palo Alto, CA, USA), Proteome Discoverer v1.4 (Thermo Fisher Scientific, Bremen, Germany) with the SEQUEST algorithm66, and ProteinPilot v4.5 with the Paragon algorithm67. The following parameters were used for all the searching: peptide tolerance at 10 ppm, tandem MS tolerance at


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± 0.01 Da, peptide charges of 2+ to 5+, trypsin as the enzyme, allowing one missed cleavage, iTRAQ label and methyl methanethiosulfonate (C) as fixed modifications, and oxidation (M) and phosphorylation (S, T, Y) as variable modifications. In addition, ProteinPilot software automatically considers over 171 different modifications67 (Supplemental Table 1 in the Supporting Information). Peptide and protein were filtered using ProteoIQ 2.7 with strict peptide and protein probabilities, 0.8 and 0.95, respectively. Peptide probability was applied to filter peptide assignments obtained from MS/MS database searching results using predictable false identification error rate68. Protein probability was used to filter proteins with the null hypothesis that the database matching is random and taking into account of the peptide probability for all the peptides apportioned to that protein69. For peptide confidence, we adopted the following cutoff values of Xcorr that are commonly used for the SEQUEST algorithm66: 2.31 for 2+, 2.41 for 3+, and 2.6 for 4+ and 5+ peptides. For protein quantification, only MS/MS spectra that were unique to a particular protein and where the sum of the signal-to-noise ratios for all the peak pairs > 9 were used for quantification. To be identified as being significantly differentially expressed, a protein should be quantified with at least four unique peptides in both experimental replicates, a p-value 1.5 or

Comparative Proteomic Analysis of Brassica napus in Response to

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