Characterization of proteins in soybean roots under flooding and drought stresses MyeongWon Oh, Setsuko Komatsu PII: DOI: Reference:
S1874-3919(14)00526-0 doi: 10.1016/j.jprot.2014.11.008 JPROT 1996
To appear in:
Journal of Proteomics
Received date: Accepted date:
23 August 2014 7 November 2014
Please cite this article as: Oh MyeongWon, Komatsu Setsuko, Characterization of proteins in soybean roots under flooding and drought stresses, Journal of Proteomics (2014), doi: 10.1016/j.jprot.2014.11.008
This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.
ACCEPTED MANUSCRIPT
Characterization of proteins in soybean roots under flooding and drought stresses
IP
T
MyeongWon Oh1,2, Setsuko Komatsu1,2
SC R
1 Graduate School of Life and Environmental Sciences, University of Tsukuba, Tsukuba 305-8572, Japan
MA
Organization, Tsukuba 305-8518, Japan
NU
2 National Institute of Crop Science, National Agriculture and Food Research
*Address correspondence to this author: Setsuko Komatsu, National Institute of Crop
D
Science, National Agriculture and Food Research Organization, Kannondai 2-1-18,
CE P
[email protected]
TE
Tsukuba 305-8518, Japan. Tel: +81-29-838-8693, Fax: +81-29-838-8694, Email:
AC
Running title: Soybean-root proteins under flooding and drought.
Abbreviations: LC, liquid chromatography; MS, mass spectrometry; qRT-PCR, Quantitative reverse transcription polymerase chain reaction
1
ACCEPTED MANUSCRIPT
Abstract: Flooding and drought affect soybean growth because soybean is stress-sensitive crop.
IP
T
In 2-day-old plants exposed to 2-day flooding or drought, the fresh weight of roots was markedly suppressed, although the root morphology clearly differed between two
SC R
conditions. To understand the response mechanisms of soybean to flooding and drought stresses, gel-free proteomic technique was used. A total of 97 and 48 proteins
NU
were significantly changed in response to flooding and drought stresses, respectively. Proteins involved in protein synthesis were decreased by flooding stress and increased
MA
by drought. Glycolysis-related proteins were increased in roots both flooding and drought stresses. Fermentation, stress, and cell wall-related proteins were increased in
D
response to flooding stress, whereas cell organization and redox-related proteins were
TE
increased under drought stress. Among the identified proteins, three
CE P
S-adenosylmethionine synthetases were commonly decreased and increased in response to flooding and drought stresses, respectively. The mRNA expression levels of S-adenosylmethionine synthetase genes displayed a similar tendency to the changes
AC
in protein abundance. These results suggest that S-adenosylmethionine synthetase is involved in the regulation of stress response because it was changed in response to flooding and drought stresses.
Keywords: soybean, root, flooding stress, drought stress, proteomics
2
ACCEPTED MANUSCRIPT
Introduction Soybean is an important dicot crop for human consumption and animal feeds due
IP
T
to the large amounts of protein and oil contained in its seeds (1). Like other legume crops, soybean is sensitive to a variety of abiotic stresses, including flooding and
SC R
drought. As increased flooding and drought is an inevitable result of climate change (2), the development of stress-tolerant crop cultivars is required to maintain yields and
NU
quality. In soybean, flooding and drought stresses induce completely different responses (3). The abundance and activity of the reactive oxygen species-scavenging enzyme
MA
ascorbate peroxidase are increased under drought and decreased under flooding (3). Despite the identification of various differentially changed proteins, the underlying
D
mechanisms of these different responses are not well understood at the molecular and
TE
biochemical levels.
CE P
Drought is one of the major causes of crop failure in many regions of the world. Under drought stress, the photosynthetic rate of soybean was reduced, although the respiration rate was not significantly affected (4). Decreased photosynthesis was
AC
associated with stomatal closure with impairment of ribulose 1,5-bisphosphate carboxylase/oxygenase activity (5). The metabolites, such as proline and pinitol, were accumulated in soybean under drought stress (6). Proteins involved in osmotic adjustment, cell wall modification, signal transduction, carbohydrate/nitrogen metabolism, defense signaling, and programmed cell death were important for regulating soybean responses to drought (7). Mohammadi et al. (8) reported that the decreased methionine synthase in response to drought stress might impair the soybean growth. In addition, caffeoyl-CoA-O-methyltransferase and 20S proteasome alpha subunit A played key roles in the responses to osmotic stress in the early stages of soybean-root growth (9). However, the role of proteins in drought-responsive 3
ACCEPTED MANUSCRIPT
mechanism has not been clearly elucidated. Flooding is another form of water stress that results from excess water in the root
IP
T
environment. Most crop plants, including soybean, are sensitive to flooding stress (10). As gases diffuse 10,000 times faster in air than in water (11), the main constraint for
SC R
normal growth under flooding is hypoxia, which causes a shift from aerobic to anaerobic pathways of energy generation in soybean. Several studies were reported that
NU
the exposure of soybean seedlings to flooding increases the abundance of proteins involved in glycolysis and fermentation (12-14). Genes associated with alcohol
MA
fermentation, ethylene biosynthesis, pathogen defense, and cell wall loosening are significantly upregulated in flooded soybean (12). Glycolysis/fermentation related
D
enzymes and inducers of heat shock proteins are key elements in the early responses of
TE
soybean to flooding (14). These findings indicate that glycolysis and fermentation
CE P
pathways are critical for adaptation to flooding conditions. Roots are the first organ to encounter drought and flooding stresses. Plants initially respond to flooding by reducing root permeability, water absorption, and
AC
mineral uptake, followed by decreasing photosynthesis, altering hormonal balance, and developing aerenchyma and adventitious roots (15). These responses are mediated by the accumulation of ethylene under the flooding-induced hypoxic conditions. Under flooding stress, increased levels of class 1 hemoglobin improve the redox and energy status of the flooded plant (16). Plant roots are also able to sense decreases in soil water content (17,18). During drought stress, the production of abscisic acid in roots and its transport to leaves is considered to be a key signal leading to stomatal closure and reduced growth. pH, cytokinins, ethylene precursor, malate, and other unidentified factors are involved in root-to-shoot communications under drought stress (19). Recently, a number of proteins involved in protein synthesis/modification were found to 4
ACCEPTED MANUSCRIPT
be activated in the root tips of soybean during germination (20). These observations indicate that the root is an important organ to understand the regulatory mechanisms of
IP
T
soybean against stresses.
Although a number of crops such as rice have distinct response mechanisms to
SC R
flooding and drought stresses (21), stress-response mechanisms have not been elucidated in soybean at the molecular level. In this study, a gel-free proteomics
NU
technique was used to identify proteins that are differentially changed in soybean roots under flooding and drought stresses. Furthermore, the levels of key enzymes identified
MA
in the proteomic analysis were analyzed in a time-dependent manner by quantitative
TE
2. Materials and methods
D
reverse transcription polymerase chain reaction (qRT-PCR).
CE P
2.1. Plant material and treatments
Seeds of soybean (Glycine max L. cultivar Enrei) were sterilized with 1% sodium hypochlorite solution, rinsed in water, and sown on 500 mL silica sand saturated with
AC
150 mL water in a plastic case (180 x 140 x 45 mm). Soybeans were grown in a growth chamber illuminated with white fluorescent light (160 μmol m-2s-1, 16 h light period/day) at 25 C and 70% relative humidity. Two-day-old soybeans were either exposed to flooding by adding excess water or to drought by withholding watering. For morphological analysis, roots and hypocotyls were collected. For proteomics analysis, roots were collected as samples. For qRT-PCR analysis, roots, hypocotyls, and cotyledons were collected for the extraction of RNA. Three independent experiments were performed as biological replicates for all experiments.
2.2. Protein extraction 5
ACCEPTED MANUSCRIPT
A portion (500 mg) of each root sample was ground to a powder in liquid nitrogen using a mortar and pestle. The powder was transferred into a solution of 10%
T
trichloroacetic acid and 0.07% 2-mercaptoethanol in acetone, and mixed thoroughly by
IP
vortexing. The suspension was sonicated for 10 min, incubated for 60 min at -20 C.,
SC R
and then centrifuged at 9,000 x g for 20 min at 4 C. The resulting supernatant was discarded and the pellet was washed twice with 0.07% 2-mercaptoethanol in acetone.
NU
The final pellet was dried using a Speed-Vac concentrator (Savant Instruments, Hicksville, NY, USA) and resuspended in lysis buffer consisting of 8 M urea, 2 M
MA
thiourea, 5% CHAPS, and 2 mM tributylphosphine by vortexing for 1 h at 25 C. The suspension was then centrifuged at 20,000 x g for 20 min at 25C and the supernatant
D
was collected as protein extract for gel-free proteomics analysis. Protein concentration
TE
was determined using the Bradford method (22) with bovine serum albumin as the
CE P
standard.
2.3. Protein purification for mass spectrometry
AC
Extracted proteins (100 µg) were purified with methanol and chloroform to remove detergent from the sample, as described by Nanjo et al. (23). For gel-free proteomic analysis, purified proteins were digested with trypsin, reduced with 25 mM dithiothreitol for 1 h at 37 °C, and then alkylated with 30 mM iodoacetamide for 1 h at room temperature in darkness. Alkylated proteins were further digested with trypsin and lysyl endopeptidase (Wako, Osaka, Japan) at 1:100 enzyme/protein ratio at 37 °C for 16 h. The resulting tryptic peptides were acidified with 2 µL of 20% formic acid (pH < 3), and were then desalted with a C18-pipette tip (Nikkyo Technos, Tokyo, Japan). The sample was analyzed by nano-liquid chromatography (LC) mass spectrometry (MS)/MS. 6
ACCEPTED MANUSCRIPT
2.4. Protein identification by mass spectrometry
IP
T
The purified peptide samples were separated using an Ultimate 300 nano LC system (Dionex, Germering, Germany), and the peptide ions were detected using a
SC R
nanospray LTQ Orbitrap Discovery mass spectrometry (Thermo Fisher Scientific, San Jose, CA, USA) in data-dependent acquisition mode with the installed Xcalibur
NU
software (version 2.0.7, Thermo Fisher Scientific). For the separation, the peptide samples were loaded onto a C18 PepMap trap column (300 µm ID x 5 mm, Thermo
MA
Fisher Scientific) equilibrated with 0.1% formic acid and were eluted from the trap column with a linear acetonitrile gradient in 0.1% formic acid at a flow rate of 200
D
nL/min. The eluted peptides were loaded and separated on a C18 NANO HPLC
TE
NTTC-360/75-3 capillary tip column (75 µm ID x 120 mm, Nikkyo Technos) with a
CE P
spray voltage of 1.5 kV. Full-scan mass spectra were acquired in the orbitrap MS over 400-1,500 m/z with a resolution of 30,000. The top ten most intense precursor ions were selected for collision-induced fragmentation in the linear ion trap at a normalized
AC
collision energy of 35%. Dynamic exclusion was employed within 90 s to prevent the repetitive selection of peptides (24).
2.5. Data acquisition by mass spectrometry analysis Proteins identification was performed using Mascot search engines (version 2.4.1, Matrix Science, London, UK) with a soybean peptide database (55,787 sequences) constructed from the soybean genome database (Phytozome version 9.1, http://www.phytozome.net/soybean) (25). The acquired raw data files were processed using Proteome Discover software (version 1.4, Thermo Fisher Scientific). In the Mascot searches, the carbamidomethylation of cysteine was set as a fixed modification 7
ACCEPTED MANUSCRIPT
and oxidation of methionine was set as a variable modification. Trypsin was specified as the proteolytic enzyme and one missed cleavage was allowed. Peptide mass
IP
T
tolerance was set at 10 ppm, fragment mass tolerance was set at 0.5 Da, and peptide charge was set at +2, +3, and +4. An automatic decoy database search was performed
SC R
as part of the search. Mascot results were filtered with the Percolator function to improve the accuracy and sensitivity of peptide identification. The acquired Mascot
NU
results were imported for SIEVE analysis (version 2.1, Thermo Fisher Scientific).
MA
2.6. Analysis of differentially abundant proteins using mass spectrometry data For the differential analysis of relative abundances of peptides and proteins
D
between control and experimental groups, the commercial label-free quantification
TE
package SIEVE was used. The chromatographic peaks detected by MS were aligned,
CE P
and the peptide peaks were detected as a frame on all parent ions scanned by MS/MS using the following settings: 5 min of frame time width and 10 ppm of frame m/z width. Chromatographic peak areas within a frame were compared for each sample, and the
AC
ratios between two sample groups in a frame were determined. The frames detected in the MS/MS scan were matched to the imported Mascot results. The ratio of peptides between samples was determined from the variance-weighted average of the ratios in frames that matched the peptides in the MS/MS spectrum. To determine the ratio of the corresponding protein, the ratios of peptides were further integrated with Ingenuity Pathways Analysis which is a widely-adopted application for complex biological and chemical systems. In the differential analysis of protein abundance, total ion current was used for normalization. The minimum requirements for the identification of a protein were two matched peptides. Significant changes in the abundance of proteins between the control and stress treatments were analyzed (p < 0.05). 8
ACCEPTED MANUSCRIPT
2.7. Analysis of function and localization
IP
T
Protein functions were categorized using MapMan bin codes as previously described (http://mapman.gabipd.org/) (26). Protein localization was analyzed using
SC R
the intracellular targeting predication programs of YLoc
(http://abi.inf.unituebingen.de/Services/YLoc/webloc.cgi) (27) Pathway mapping of
NU
identified proteins was performed using the Kyoto Encyclopedia of Genes and
MA
Genomes (KEGG) database (http://www.genome.jp/kegg/) (28).
2.8. RNA extraction and quantitative reverse transcription-polymerase chain reaction
D
A portion (100 mg) of each tissue sample was ground into fine powder in liquid
TE
nitrogen using a sterilized mortar and pestle, and total RNA was then extracted from the
CE P
powdered tissue using an RNeasy Plant Mini kit (Qiagen, Valencia, CA, USA). Extracted RNA was reverse-transcribed to cDNA using iScript Reverse Transcription Supermix (Bio-Rad, Hercules, CA, USA) according to the manufacturer’s instructions.
AC
qRT-PCR was performed in a 10 μL reaction using SsoAdvanced SYBR Green Supermix (Bio-Rad) on a MyiQ Single-Color Real-Time PCR Detection system (Bio-Rad). The PCR conditions were as follows: 95 °C for 30 s, followed by 45 cycles of 95 °C for 10 s and 60 °C for 30 s. Gene expression was normalized using the 18S rRNA gene (X02623.1) as an internal control. The qRT-PCR primers were designed using the Primer3 web interface (http://frodo.wi.mit.edu) (Supplemental Table 1). Primer specificity was checked by BLASTN searches against the Phytozome soybean genome database with the designed primers as queries, by melt curve analysis, and by agarose gel electrophoresis of the amplified fragments.
9
ACCEPTED MANUSCRIPT
2.9. Statistical analysis Statistical significance of the results was evaluated with the Student’s t-test when
IP
T
only two groups were compared, was evaluated using the One-Way ANOVA test for comparisons between multiple groups. All calculations were performed using SPSS
SC R
software (version 22.0). A p value of Gel-free proteomic technique was used to identify soybean proteins changed under
SC R
flooding and drought.
>Protein synthesis related proteins were decreased by flooding and increased by
NU
drought.
MA
>Glycolysis-related proteins were increased in roots both flooding and drought
D
stresses.
CE P
flooding stress.
TE
>Fermentation, stress, and cell wall-related proteins were increased in response to
AC
>Cell organization and redox-related proteins were increased under drought stress.
> S-adenosylmethionine synthetase is involved in the regulation in response to flooding and drought stresses.
65
S1874-3919(14)00526-0 doi: 10.1016/j.jprot.2014.11.008 JPROT 1996
To appear in:
Journal of Proteomics
Received date: Accepted date:
23 August 2014 7 November 2014
Please cite this article as: Oh MyeongWon, Komatsu Setsuko, Characterization of proteins in soybean roots under flooding and drought stresses, Journal of Proteomics (2014), doi: 10.1016/j.jprot.2014.11.008
This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.
ACCEPTED MANUSCRIPT
Characterization of proteins in soybean roots under flooding and drought stresses
IP
T
MyeongWon Oh1,2, Setsuko Komatsu1,2
SC R
1 Graduate School of Life and Environmental Sciences, University of Tsukuba, Tsukuba 305-8572, Japan
MA
Organization, Tsukuba 305-8518, Japan
NU
2 National Institute of Crop Science, National Agriculture and Food Research
*Address correspondence to this author: Setsuko Komatsu, National Institute of Crop
D
Science, National Agriculture and Food Research Organization, Kannondai 2-1-18,
CE P
[email protected]
TE
Tsukuba 305-8518, Japan. Tel: +81-29-838-8693, Fax: +81-29-838-8694, Email:
AC
Running title: Soybean-root proteins under flooding and drought.
Abbreviations: LC, liquid chromatography; MS, mass spectrometry; qRT-PCR, Quantitative reverse transcription polymerase chain reaction
1
ACCEPTED MANUSCRIPT
Abstract: Flooding and drought affect soybean growth because soybean is stress-sensitive crop.
IP
T
In 2-day-old plants exposed to 2-day flooding or drought, the fresh weight of roots was markedly suppressed, although the root morphology clearly differed between two
SC R
conditions. To understand the response mechanisms of soybean to flooding and drought stresses, gel-free proteomic technique was used. A total of 97 and 48 proteins
NU
were significantly changed in response to flooding and drought stresses, respectively. Proteins involved in protein synthesis were decreased by flooding stress and increased
MA
by drought. Glycolysis-related proteins were increased in roots both flooding and drought stresses. Fermentation, stress, and cell wall-related proteins were increased in
D
response to flooding stress, whereas cell organization and redox-related proteins were
TE
increased under drought stress. Among the identified proteins, three
CE P
S-adenosylmethionine synthetases were commonly decreased and increased in response to flooding and drought stresses, respectively. The mRNA expression levels of S-adenosylmethionine synthetase genes displayed a similar tendency to the changes
AC
in protein abundance. These results suggest that S-adenosylmethionine synthetase is involved in the regulation of stress response because it was changed in response to flooding and drought stresses.
Keywords: soybean, root, flooding stress, drought stress, proteomics
2
ACCEPTED MANUSCRIPT
Introduction Soybean is an important dicot crop for human consumption and animal feeds due
IP
T
to the large amounts of protein and oil contained in its seeds (1). Like other legume crops, soybean is sensitive to a variety of abiotic stresses, including flooding and
SC R
drought. As increased flooding and drought is an inevitable result of climate change (2), the development of stress-tolerant crop cultivars is required to maintain yields and
NU
quality. In soybean, flooding and drought stresses induce completely different responses (3). The abundance and activity of the reactive oxygen species-scavenging enzyme
MA
ascorbate peroxidase are increased under drought and decreased under flooding (3). Despite the identification of various differentially changed proteins, the underlying
D
mechanisms of these different responses are not well understood at the molecular and
TE
biochemical levels.
CE P
Drought is one of the major causes of crop failure in many regions of the world. Under drought stress, the photosynthetic rate of soybean was reduced, although the respiration rate was not significantly affected (4). Decreased photosynthesis was
AC
associated with stomatal closure with impairment of ribulose 1,5-bisphosphate carboxylase/oxygenase activity (5). The metabolites, such as proline and pinitol, were accumulated in soybean under drought stress (6). Proteins involved in osmotic adjustment, cell wall modification, signal transduction, carbohydrate/nitrogen metabolism, defense signaling, and programmed cell death were important for regulating soybean responses to drought (7). Mohammadi et al. (8) reported that the decreased methionine synthase in response to drought stress might impair the soybean growth. In addition, caffeoyl-CoA-O-methyltransferase and 20S proteasome alpha subunit A played key roles in the responses to osmotic stress in the early stages of soybean-root growth (9). However, the role of proteins in drought-responsive 3
ACCEPTED MANUSCRIPT
mechanism has not been clearly elucidated. Flooding is another form of water stress that results from excess water in the root
IP
T
environment. Most crop plants, including soybean, are sensitive to flooding stress (10). As gases diffuse 10,000 times faster in air than in water (11), the main constraint for
SC R
normal growth under flooding is hypoxia, which causes a shift from aerobic to anaerobic pathways of energy generation in soybean. Several studies were reported that
NU
the exposure of soybean seedlings to flooding increases the abundance of proteins involved in glycolysis and fermentation (12-14). Genes associated with alcohol
MA
fermentation, ethylene biosynthesis, pathogen defense, and cell wall loosening are significantly upregulated in flooded soybean (12). Glycolysis/fermentation related
D
enzymes and inducers of heat shock proteins are key elements in the early responses of
TE
soybean to flooding (14). These findings indicate that glycolysis and fermentation
CE P
pathways are critical for adaptation to flooding conditions. Roots are the first organ to encounter drought and flooding stresses. Plants initially respond to flooding by reducing root permeability, water absorption, and
AC
mineral uptake, followed by decreasing photosynthesis, altering hormonal balance, and developing aerenchyma and adventitious roots (15). These responses are mediated by the accumulation of ethylene under the flooding-induced hypoxic conditions. Under flooding stress, increased levels of class 1 hemoglobin improve the redox and energy status of the flooded plant (16). Plant roots are also able to sense decreases in soil water content (17,18). During drought stress, the production of abscisic acid in roots and its transport to leaves is considered to be a key signal leading to stomatal closure and reduced growth. pH, cytokinins, ethylene precursor, malate, and other unidentified factors are involved in root-to-shoot communications under drought stress (19). Recently, a number of proteins involved in protein synthesis/modification were found to 4
ACCEPTED MANUSCRIPT
be activated in the root tips of soybean during germination (20). These observations indicate that the root is an important organ to understand the regulatory mechanisms of
IP
T
soybean against stresses.
Although a number of crops such as rice have distinct response mechanisms to
SC R
flooding and drought stresses (21), stress-response mechanisms have not been elucidated in soybean at the molecular level. In this study, a gel-free proteomics
NU
technique was used to identify proteins that are differentially changed in soybean roots under flooding and drought stresses. Furthermore, the levels of key enzymes identified
MA
in the proteomic analysis were analyzed in a time-dependent manner by quantitative
TE
2. Materials and methods
D
reverse transcription polymerase chain reaction (qRT-PCR).
CE P
2.1. Plant material and treatments
Seeds of soybean (Glycine max L. cultivar Enrei) were sterilized with 1% sodium hypochlorite solution, rinsed in water, and sown on 500 mL silica sand saturated with
AC
150 mL water in a plastic case (180 x 140 x 45 mm). Soybeans were grown in a growth chamber illuminated with white fluorescent light (160 μmol m-2s-1, 16 h light period/day) at 25 C and 70% relative humidity. Two-day-old soybeans were either exposed to flooding by adding excess water or to drought by withholding watering. For morphological analysis, roots and hypocotyls were collected. For proteomics analysis, roots were collected as samples. For qRT-PCR analysis, roots, hypocotyls, and cotyledons were collected for the extraction of RNA. Three independent experiments were performed as biological replicates for all experiments.
2.2. Protein extraction 5
ACCEPTED MANUSCRIPT
A portion (500 mg) of each root sample was ground to a powder in liquid nitrogen using a mortar and pestle. The powder was transferred into a solution of 10%
T
trichloroacetic acid and 0.07% 2-mercaptoethanol in acetone, and mixed thoroughly by
IP
vortexing. The suspension was sonicated for 10 min, incubated for 60 min at -20 C.,
SC R
and then centrifuged at 9,000 x g for 20 min at 4 C. The resulting supernatant was discarded and the pellet was washed twice with 0.07% 2-mercaptoethanol in acetone.
NU
The final pellet was dried using a Speed-Vac concentrator (Savant Instruments, Hicksville, NY, USA) and resuspended in lysis buffer consisting of 8 M urea, 2 M
MA
thiourea, 5% CHAPS, and 2 mM tributylphosphine by vortexing for 1 h at 25 C. The suspension was then centrifuged at 20,000 x g for 20 min at 25C and the supernatant
D
was collected as protein extract for gel-free proteomics analysis. Protein concentration
TE
was determined using the Bradford method (22) with bovine serum albumin as the
CE P
standard.
2.3. Protein purification for mass spectrometry
AC
Extracted proteins (100 µg) were purified with methanol and chloroform to remove detergent from the sample, as described by Nanjo et al. (23). For gel-free proteomic analysis, purified proteins were digested with trypsin, reduced with 25 mM dithiothreitol for 1 h at 37 °C, and then alkylated with 30 mM iodoacetamide for 1 h at room temperature in darkness. Alkylated proteins were further digested with trypsin and lysyl endopeptidase (Wako, Osaka, Japan) at 1:100 enzyme/protein ratio at 37 °C for 16 h. The resulting tryptic peptides were acidified with 2 µL of 20% formic acid (pH < 3), and were then desalted with a C18-pipette tip (Nikkyo Technos, Tokyo, Japan). The sample was analyzed by nano-liquid chromatography (LC) mass spectrometry (MS)/MS. 6
ACCEPTED MANUSCRIPT
2.4. Protein identification by mass spectrometry
IP
T
The purified peptide samples were separated using an Ultimate 300 nano LC system (Dionex, Germering, Germany), and the peptide ions were detected using a
SC R
nanospray LTQ Orbitrap Discovery mass spectrometry (Thermo Fisher Scientific, San Jose, CA, USA) in data-dependent acquisition mode with the installed Xcalibur
NU
software (version 2.0.7, Thermo Fisher Scientific). For the separation, the peptide samples were loaded onto a C18 PepMap trap column (300 µm ID x 5 mm, Thermo
MA
Fisher Scientific) equilibrated with 0.1% formic acid and were eluted from the trap column with a linear acetonitrile gradient in 0.1% formic acid at a flow rate of 200
D
nL/min. The eluted peptides were loaded and separated on a C18 NANO HPLC
TE
NTTC-360/75-3 capillary tip column (75 µm ID x 120 mm, Nikkyo Technos) with a
CE P
spray voltage of 1.5 kV. Full-scan mass spectra were acquired in the orbitrap MS over 400-1,500 m/z with a resolution of 30,000. The top ten most intense precursor ions were selected for collision-induced fragmentation in the linear ion trap at a normalized
AC
collision energy of 35%. Dynamic exclusion was employed within 90 s to prevent the repetitive selection of peptides (24).
2.5. Data acquisition by mass spectrometry analysis Proteins identification was performed using Mascot search engines (version 2.4.1, Matrix Science, London, UK) with a soybean peptide database (55,787 sequences) constructed from the soybean genome database (Phytozome version 9.1, http://www.phytozome.net/soybean) (25). The acquired raw data files were processed using Proteome Discover software (version 1.4, Thermo Fisher Scientific). In the Mascot searches, the carbamidomethylation of cysteine was set as a fixed modification 7
ACCEPTED MANUSCRIPT
and oxidation of methionine was set as a variable modification. Trypsin was specified as the proteolytic enzyme and one missed cleavage was allowed. Peptide mass
IP
T
tolerance was set at 10 ppm, fragment mass tolerance was set at 0.5 Da, and peptide charge was set at +2, +3, and +4. An automatic decoy database search was performed
SC R
as part of the search. Mascot results were filtered with the Percolator function to improve the accuracy and sensitivity of peptide identification. The acquired Mascot
NU
results were imported for SIEVE analysis (version 2.1, Thermo Fisher Scientific).
MA
2.6. Analysis of differentially abundant proteins using mass spectrometry data For the differential analysis of relative abundances of peptides and proteins
D
between control and experimental groups, the commercial label-free quantification
TE
package SIEVE was used. The chromatographic peaks detected by MS were aligned,
CE P
and the peptide peaks were detected as a frame on all parent ions scanned by MS/MS using the following settings: 5 min of frame time width and 10 ppm of frame m/z width. Chromatographic peak areas within a frame were compared for each sample, and the
AC
ratios between two sample groups in a frame were determined. The frames detected in the MS/MS scan were matched to the imported Mascot results. The ratio of peptides between samples was determined from the variance-weighted average of the ratios in frames that matched the peptides in the MS/MS spectrum. To determine the ratio of the corresponding protein, the ratios of peptides were further integrated with Ingenuity Pathways Analysis which is a widely-adopted application for complex biological and chemical systems. In the differential analysis of protein abundance, total ion current was used for normalization. The minimum requirements for the identification of a protein were two matched peptides. Significant changes in the abundance of proteins between the control and stress treatments were analyzed (p < 0.05). 8
ACCEPTED MANUSCRIPT
2.7. Analysis of function and localization
IP
T
Protein functions were categorized using MapMan bin codes as previously described (http://mapman.gabipd.org/) (26). Protein localization was analyzed using
SC R
the intracellular targeting predication programs of YLoc
(http://abi.inf.unituebingen.de/Services/YLoc/webloc.cgi) (27) Pathway mapping of
NU
identified proteins was performed using the Kyoto Encyclopedia of Genes and
MA
Genomes (KEGG) database (http://www.genome.jp/kegg/) (28).
2.8. RNA extraction and quantitative reverse transcription-polymerase chain reaction
D
A portion (100 mg) of each tissue sample was ground into fine powder in liquid
TE
nitrogen using a sterilized mortar and pestle, and total RNA was then extracted from the
CE P
powdered tissue using an RNeasy Plant Mini kit (Qiagen, Valencia, CA, USA). Extracted RNA was reverse-transcribed to cDNA using iScript Reverse Transcription Supermix (Bio-Rad, Hercules, CA, USA) according to the manufacturer’s instructions.
AC
qRT-PCR was performed in a 10 μL reaction using SsoAdvanced SYBR Green Supermix (Bio-Rad) on a MyiQ Single-Color Real-Time PCR Detection system (Bio-Rad). The PCR conditions were as follows: 95 °C for 30 s, followed by 45 cycles of 95 °C for 10 s and 60 °C for 30 s. Gene expression was normalized using the 18S rRNA gene (X02623.1) as an internal control. The qRT-PCR primers were designed using the Primer3 web interface (http://frodo.wi.mit.edu) (Supplemental Table 1). Primer specificity was checked by BLASTN searches against the Phytozome soybean genome database with the designed primers as queries, by melt curve analysis, and by agarose gel electrophoresis of the amplified fragments.
9
ACCEPTED MANUSCRIPT
2.9. Statistical analysis Statistical significance of the results was evaluated with the Student’s t-test when
IP
T
only two groups were compared, was evaluated using the One-Way ANOVA test for comparisons between multiple groups. All calculations were performed using SPSS
SC R
software (version 22.0). A p value of Gel-free proteomic technique was used to identify soybean proteins changed under
SC R
flooding and drought.
>Protein synthesis related proteins were decreased by flooding and increased by
NU
drought.
MA
>Glycolysis-related proteins were increased in roots both flooding and drought
D
stresses.
CE P
flooding stress.
TE
>Fermentation, stress, and cell wall-related proteins were increased in response to
AC
>Cell organization and redox-related proteins were increased under drought stress.
> S-adenosylmethionine synthetase is involved in the regulation in response to flooding and drought stresses.
65
