Characterization of Metaproteomics in Crop Rhizospheric Soil

ARTICLE pubs.acs.org/jpr

Characterization of Metaproteomics in Crop Rhizospheric Soil Hai-Bin Wang,†,§,# Zhi-Xing Zhang,†,§,# Hui Li,‡,# Hai-Bin He,†,§ Chang-Xun Fang,†,§ Ai-Jia Zhang,§,|| Qi-Song Li,†,§ Rong-Shan Chen,†,§ Xu-Kui Guo,†,§ Hui-Feng Lin,†,§ Lin-Kun Wu,†,§ Sheng Lin,†,§ Ting Chen,†,§ Rui-Yu Lin,†,§ Xuan-Xian Peng,‡ and Wen-Xiong Lin*,†,§ School of Life Sciences, §Institute of Agricultural Ecology, and Ministry of Agriculture Key Laboratory for Sugarcane Genetic Improvement, Fujian Agricultural and Forestry University, Fuzhou 35002, P. R. China ‡ Center for Proteomics, State Key Laboratory of Bio-Control, School of Life Sciences, Sun at-Sen University, University City, Guangzhou 510006, P. R. China )

†

bS Supporting Information ABSTRACT: Soil rhizospheric metaproteomics is a powerful scientific tool to uncover the interactions between plants and microorganisms in the soil ecosystem. The present study established an extraction method suitable for different soils that could increase the extracted protein content. Close to 1000 separate spots with high reproducibility could be identified in the stained 2-DE gels. Among the spots, 189 spots representing 122 proteins on a 2-DE gel of rice soil samples were successfully identified by MALDI-TOF/TOF-MS. These proteins mainly originated from rice and microorganisms. They were involved in protein, energy, nucleotide, and secondary metabolisms, as well as signal transduction and resistance. Three characteristics of the crop rhizospheric metaproteomics seemed apparent: (1) approximately one-third of the protein spots could not be identified by MALDI-TOF/TOF/MS, (2) the conservative proteins from plants formed a feature distribution of crop rhizospheric metaproteome, and (3) there were very complex interactions between plants and microorganisms existing in a crop rhizospheric soil. Further functional analysis on the identified proteins unveiled various metabolic pathways and signal transductions involved in the soil biotic community. This study provides a paradigm for metaproteomic research on soil biology. KEYWORDS: soil protein extraction, 2DE-based proteomics, crop rhizosphere, soil metaproteomics, plant-microbe interaction, soil biodiversity

’ INTRODUCTION In recent years, there has been an increasing interest in the biological properties of rhizosphere in situ.1 The complexity of the rhizospheric soil ecosystem is caused by the numerous and diverse interactions among its physical, chemical, and biological components.2 Rhizosphere represents a poorly defined zone of soil with a microbiological gradient in which maximum changes in the population of microflora in soil is evident adjacent to the root and declines with distance away from it.3 Microorganisms form an indivisible entity, in which the relationships are regulated by complex molecular signaling. This entity plays a role in rhizosphere interactions with plant roots. Meanwhile, there has been a coevolution between plants and soil microbes resulting in microbial responses to plant exudation and vice versa.4,5 The increased microbial activity around roots can be ascribed to the root exudates, sloughed senescent root cells, and mucigel, which have been described as rhizodeposition.6,7 On the other hand, changes in root exudates affect the microbial community.6,8 These dynamic interactions show the closeness between the roots and microorganisms. r 2010 American Chemical Society

Several techniques, such as the Terminal Restriction Fragment Length Polymorphism (T-RFLP),9 the Denaturing Gradient Gel Electrophoresis (DGGE),10 the Single-Strand Conformation Polymorphism (SSCP),11 and the Reverse Transcription-Polymerase Chain Reaction (RT-PCR)12 have been employed to investigate the diversity of soil microbes at the genomic and transcriptomic level. These techniques have greatly improved our knowledge on the microbial diversity in rhizospheric soil. However, the function of microbial diversity still remains unknown, since the mRNA abundance correlates too weakly with protein abundance, and the post-translational modifications cannot currently be predicted by mRNA and DNA as well.13,14 Furthermore, biological process is driven by not only microbes, but also fauna and plants. A large-scale study to identify the soil proteins would significantly help to elucidate the soil ecological processes and understand the environmental factors.15 In this Received: March 2, 2010 Published: December 13, 2010 932

dx.doi.org/10.1021/pr100981r | J. Proteome Res. 2011, 10, 932–940

Journal of Proteome Research

ARTICLE

Table 1. Basic Parameters of Different Soil Samples soil sampled from different sites Fujian (Southeast China) basic parameter Organic matter (%) Available nitrogen (mg/kg)

rice 5.65 29.6

P. heterophylla

sugar cane

Henan (Middle China)

Yunnan (Southwest China)

Rehmanniae sp

tobacco

11.03

8.72

6.74

21.3

37.85

75.74

Available phosphorus (mg/kg)

126.6

10.42

Available potassium (mg/kg)

354.6

30

2.58 66.1

46.52

23.91

80.7

287.23

187.14

252.7

pH value

5.5

5.7

6.5

6.8

7.37

Cation exchange capacity (cmol/kg)

6.29

7.17

6.46

6.8

5.74

regard, a new proteomic approach would be necessary. Metaproteomics may aid in the understanding of biological properties of the rhizospheric soil. Metaproteomics is the study of all proteins recovered directly from environmental samples at a given point of time.16 Several studies have attempted to extract proteins from diverse systems, including greenhouse soil,17 metal contaminated soil,18 crop soil,19 freshwater,20 wastewater,21 marine sediments,22 and human gut.23 These reports showed evidence of different protein expression in these environments by using SDS-PAGE or 2D-PAGE analysis, but they failed to identify the extracted proteins. Wilmes et al. introduced a method which successfully extracted and purified proteome from a laboratory sludge system, and identified 46 proteins from approximately 630 protein spots that matched across gels by MALDI-TOF/MS and Q-TOF/MS/MS.24 Benndorf et al. developed a method for protein extraction from contaminated soil and groundwater. The extracted proteins from groundwater were separated by SDS-PAGE and 2D-PAGE. Twentynine proteins were identified from 1D gels, and 26 from 2D gels by LC-ESI/MS.25 Subsequently, they used the 2DE-based proteomic approach to investigate the metaproteome of anaerobic benzene community in sediments. About 240 protein spots were detected in a 2DE gel.26 However, these studies were all laboratory-scale and the target microbial communities were artificially enriched. The methods they employed were suitable for investigating microbes that can be cultured, but the majority of the environmental microbial communities cannot be artificially produced. Recently, Chen et al. reported a sequential extraction method with combination of 0.25 M citrate buffer, 1% SDS buffer, and phenol extraction (C-S-P-M) to obtain the extra- and intracellular proteins from different crop soils.19 Despite this preliminary tool, the C-S-P-M method has limited use in crop soil metaproteomics due to the low resolution of 2-DE separation. In addition, protein identification was not included in the report. In the present study, the C-S-P-M method was refined. The modified method shortened the extraction time and increased the extracted protein content of different crop rhizospheric soil (CRS) samples meeting the requirement for a metaproteomic analysis. Our study provided a new way to investigate metaproteomics of CRS, and an unprecedented insight into the crop-microorganism interactions in the soil.

Figure 1. Scheme of extraction and purification of soil proteins.

fields in different cropping areas. The fields were located in Fuzhou, Fujian in southeast China (26.05N, 119.18E); Zhengzhou, Henan in central China (35.19N, 113.51E); and Yuxi, Yunnan in southwest China (24.47N, 102.48E), respectively. After removing plant roots, leaves, and insects, the soil was dried, pulverized, and sieved through a 2 mm mesh as described previously.19 The physical and chemical properties of soil samples were determined (Table 1). Protein Extraction

The C-S-P-M method reported by Chen et al. did not produce enough proteins for metaproteomics analysis.19 An optimized extraction method, named C/S-P-M, was developed in the present study. The modified C/S-P-M method overcame the problem by replacing the sequential citrate and SDS buffer extractions with a single treatment of the two buffers. The basic extraction procedures (Figure 1) included: (1) recovering proteins from soil with citrate and SDS buffers; (2) extracting proteins from the above solution with phenol; and (3) precipitating proteins with methanol and cold acetone. In detail, 1 g of dried soil was homogenized with 5 mL of SDS solvent (1.25% (w/v) SDS,

’ MATERIALS AND METHODS Soil Sample Collection and Preparation

Different rhizospheric soils were sampled from rice, sugar cane, Pseudostellariae heterophylla, Rehmanniae sp., and tobacco 933

dx.doi.org/10.1021/pr100981r |J. Proteome Res. 2011, 10, 932–940


ARTICLE

consisting of a saturated solution of R-cyano-4-hydroxy-transcinnamic acid in 30% acetonitrile containing 0.1% trifluoroacetic acid. Aliquots of 1 μL were spotted onto stainless steel sample target plates.

0.1 M Tris-HCl, pH 6.8, 20 mM dithiothreitol (DTT)) or 5 mL of citrate buffer (0.25 M, pH 8). For SDS buffer, the homogenate was shaken at 1200 rpm for 1 h at room temperature (RT). For citrate buffer, the homogenate was shaken for 3 h under the same conditions. Subsequently, the homogenate was centrifuged for 15 min at 12 000 rpm at 4 °C. The supernatants were filtered through a nylon mesh (0.45 μm) and shaken for 30 min with 2 mL of buffered phenol (pH 8) at RT. The two phases were separated by centrifugation for 30 min at 12 000 rpm at 4 °C. The proteins in the lower phenol phase were precipitated with 6-fold volume of 0.1 M ammonium acetate dissolved in methanol at -20 °C for 6 h. Proteins were recovered by centrifugation for 25 min at 12 000 rpm at 4 °C. The pellet was washed once with cold methanol and twice with cold acetone, then air-dried and stored at -80 °C or solubilized in sample buffer for further use. Proteins obtained by C/S-P-M and C-S-P-M method were analyzed using SDS-PAGE for comparison.

Tandem Mass Spectrometric Analysis

Peptide mass spectra were obtained on a Bruker UltraFlex III MALDI TOF/TOF mass spectrometer (Bruker Daltonics, Karlsruhe, Germany). Data were acquired in positive MS reflector mode using six external standards to calibrate the instrument (Peptide Calibration Standard II, Bruker Daltonics). Mass spectra were obtained from each sample spot by accumulation of 600-800 laser shots in an 800-5000 Da mass range. For MS/ MS spectra, the 5 most abundant precursor ions per sample were selected for subsequent fragmentation and 1000-1200 Da laser shots were accumulated per precursor ion. The criterion for precursor selection was a minimum S/N of 50. Both the MS and MS/MS data were interpreted and processed by using Flexanalysis 3.0 (Bruker Daltonics), then the obtained MS and MS/MS spectra per spot were combined and submitted to MASCOT search engine (V2.3, Matrix Science, London, U.K.) by Biotools 3.1 (Bruker Daltonics) and searched with the following parameters: the NCBI (National Center for Biotechnology Information) in SwissProt (http://www.matrixscience.com/search_form_ select.html), one missed cleavage site, carbamidomethyl as fixed modification of cysteine and oxidation of methionine as a variable modification, MS tolerance of 100 ppm, MS/MS tolerance of 0.6 Da. Known contaminant ions (keratin) were excluded. With the vast varieties of the sources for our soil protein samples, the mass spectra were searched against databases step by step. First, the “all entries” in NCBInr, which include all organisms, was selected for the search. Then, the “Bacteria” and “Fungi” databases were separately selected to avoid the failed matching when “all entries” was used. The above strategy alleviated the problem of missing some of the mass spectra for matches in searching against “all entries”, and allowed significant matching results by searching against “Bacteria” and “Fungi” databases. The reason for such difficulty was possibly due to the limitation in the available information on the genome sequences for soil microorganisms. The “Green Plant” database was not used for the following search as there were sufficient plant genomes for successful matching. Both MS/MS and MS data were utilized for the protein identification based on widely recognized criteria. Proteins sharing an equal searching by MS/MS and MS were determined with priority. Then, proteins matching at least two MS/MS spectra or three Peptide Mass Fingerprintings (PMFs) were identified. The protein with the highest score and similar predicated molecular mass was selected when more than one protein were matched with significant scores.

SDS-PAGE, 2D-PAGE, and Image Analysis

SDS-PAGE and 2D-PAGE were performed as described previously.27,28 Briefly, for SDS-PAGE, proteins were dissolved in a buffer containing 0.1 M Tris-HCl (pH 6.8), 2% 2-mercaptoethanol, 4% SDS, 20% glycerol, 0.2% bromophenol blue and incubated for 5 min at 90 °C, followed by loading on the SDS-gels (5% acrylamide stacking gel, 10% acrylamide separating gel). For 2D-PAGE, proteins were dissolved in a buffer containing 7 M urea, 2 M thiourea, 5% CHAPS, 2% 2-mercaptoethanol, and 5% ampholines pH 3.5-10. The prepared protein samples were further separated, in the first dimension by IEF tube gels and in the second dimension by SDS-PAGE. IEF tube gel (17 cm 0.02 cm) was prepared with 8 M urea, 3.5% acrylamide, 2% NP40, 2% ampholines (GE Healthcare, Uppsala, Sweden; pH 3.5-10 for a linear gel; the ratio of pH 3.5-10/pH 5-8 was 1:5 for a nonlinear gel). The focusing was carried out at 200, 300, 400, 500, and 600 V for 30 min, 800 V for 16 h, and 1000 V for 6 h. SDS-PAGE of the second-dimension electrophoresis was performed using a 5% acrylamide stacking and 10% acrylamide separating gel. Gels were stained with silver nitrate, scanned on ImageScanner III (GE Healthcare, Bio-Sciences, Uppsala, Sweden) and analyzed with Imagemaster 5.0 software (GE Healthcare, Bio-Sciences, Uppsala, Sweden). In-Gel Protein Digestion

In-gel protein digestion was performed as described previously with modifications.29 In brief, protein spots separated by 2D-PAGE were finely excised and transferred into siliconized 0.5 mL Eppendorf tubes. Each gel piece was rinsed twice with deionized water, destained in 25 mM ammonium bicarbonate in water/acetonitrile (50/50) solution, and treated with 1:1 solution of 30 mM potassium ferricyanide and 100 mM sodium thiosulfate and then equilibrated in 50 mM ammonium bicarbonate (pH 8). After dehydrating with acetonitrile and drying in a Speed-Vac centrifuge (Thermo Fisher Scientific, Waltham, MA), the gel spots were rehydrated in a minimal volume of trypsin (Promega) solution (12.5 μg/mL in 25 mM NH4HCO3) and incubated at 37 °C overnight. The supernatants were transferred into a 200 μL microcentrifuge tube and the gels were extracted once with extraction buffer (67% acetonitrile containing 2.5% trifluoroacetic acid). The peptide extract and the supernatant of the gel spot were combined and then completely dried in a Speed-Vac centrifuge. Protein digestion extracts (tryptic peptides) were resuspended with 5 μL of 0.1% trifluoroacetic acid and then the peptide samples were combined (1:1 ratio) with a mixture

Soil DNA Extraction

Soil DNA was extracted from 1 g aliquots (fresh weight) of rice soil according to previous reports.29,30 The experimental procedures were: (1) 2.7 mL of extraction buffer (0.1 M, pH 8) and 20 μL of protease K were added to each sample and mixed thoroughly. Then, the homogenates were shaken at 225 rpm for 30 min at 37 °C. (2) A total of 300 μL of 20% SDS was added and incubated at 65 °C for 2 h. Then, the homogenates were frozen in liquid nitrogen and melted in boiled water 3 times at intervals of 20 min. (3) The lysate was clarified by centrifugation for 10 min at 8000 rpm at 4 °C. The supernatant was transferred to a 10 mL tube and extracted twice with equal volume of chloroform/ isoamyl alcohol (24:1). (4) After centrifugation for 5 min at 934



ARTICLE

Figure 2. Proteins extracted from different crop rhizospheric soil separated by SDS-PAGE. (A) Pattern comparison of soil protein extracted by C/S-P-M (C/M) and C-S-P-M (C-M). (B) Proteins extracted from different crop rhizospheric soil by C/S-P-M.

5000 rpm at 4 °C, 0.6 vol of isopropyl alcohol was added to the supernatant. DNA was precipitated at RT for 2 h. (5) After centrifugation for 10 min at 10 000 rpm at 4 °C, the precipitate was washed with 2 mL of 70% ethanol (containing 0.1 M sodium acetate). Finally, the DNA pellet was dissolved in 200 μL of TE buffer (pH 8). For further purification, DNA was recovered using a Gel Extraction Kit (Sangon, Shanghai, China).

’ RESULTS Effects of Optimized Soil Protein Extraction

Compared with C-S-P-M, more and clearer bands with lighter background were obtained in SDS-PAGE gels of 3 crop soil protein samples (rice, sugar cane, Rehmanniae sp.) extracted by C/S-P-M, especially for the high and low molecular weight proteins (Figure 2A). Moreover, the modified C/S-P-M method took only 11.5 h to complete one extraction, while the C-S-P-M required at least 7 h more. Comparing the data from 5 soil samples collected in 3 different regions in China, the universality of the new method was confirmed (Table 1). Similar patterns were detected in all samples with only slight differences in protein abundance (Figure 2B). Thus, C/S-P-M method developed in the present study was considered simple, rapid, effective, and potentially applicable for protein extraction of different soils.

Analysis of Terminal Restriction Fragment Length Polymorphism (T-RFLP)

As previously described,31 16S rRNA gene sequences were amplified by PCR. The primers were 6-carboxyflurescein-labeled 27F (50 -AGA GTT TGA TCC TGG CTC AG-30 ) and 907R (50 -CCG TCA ATT CCT TTR AGT TT-30 ). The PCR assay was 30 μL containing 10 Taq buffer, 0.25 mM dNTP, 50 pM each primer, and 10 U of Taq DNA polymerase (TAKARA BIO, Otsu, Japan). Further purification of the amplified DNA was done by the Gel Extraction Kit. PCR products were digested for 5 h at 37 °C with MspI and HaeIII, respectively. The digestion assay was 20 μL containing 2 μL of 10 buffer, 2 μL of 10 bovine serum albumin acetylated (TAKARA BIO, Otsu, Japan), and 1 μL of restriction endonuclease (5 U). Two microliters of the digest product was then mixed with 12 μL of formamide and 0.5 μL of size standard (GeneScan-1000ROX, Applied Biosystems). After denaturing at 96 °C for 4 min, samples were stored on ice. Then, the length of the restricted fragments was determined by using an automated ABI DNA sequencer (model 3130, Applied Biosystems). According to the SoftGenetics Application Note July 2006, the fluorescently labeled 50 terminal restriction fragments were detected and analyzed by the GeneMarker Version 1.2 (Applied Biosystems). Terminal restriction fragments were detected in a size range between 50 and 600 bp for a given T-RFLP patterns. Microbe species were obtained by comparing the results with the Ribosomal Database Project II (http://wdcm. nig.ac.jp/RDP/trflp/#program).

2-DE Separation of Soil Proteins

In the present metaproteomic analysis, the protein samples were separated using 2-DE. Linear and nonlinear gels with ampholyte of pH 3.5-10 were selected for the separation. Initially, linear gels were used. The soil proteins were mostly found near the acidic pole (data not shown). To improve the resolution, the nonlinear gels were employed. As a result, approximately 1000 individual protein spots were identifiable on each of the gels (data not shown). This indicated that the nonlinear gel of hand-cast IEF was more suitable for the separation than linear gels. To evaluate the reproducibility of the methods, 3 replications were carried out for each protein sample. Highly reproducible 2-DE maps with significant correlations of scatter plots were obtained (Supporting Information Figure s1). About 900 protein spots were detected from the 2-DE gels of rice, sugar cane, or Rehmanniae sp. soil protein samples. Among them, 538 spots matched across all these gels. The effective protein separation and the high reproducibility suggested that the C/S-P-M method could be satisfactorily applied for soil protein extraction and metaproteomic analysis. 935



ARTICLE

Table 2. Conservative Proteins Identified by MS (MALDI TOF/TOF) MW(Da)/ GI no.a

protein

PIb

Gi|115459078

Glyceraldehyde-3-phosphate

36921/6.34

PMF/

MASCOT

identified

coveraged

score (MS-MS)

peptidese

species

R-130

164

20/64%

369

3

Oryza sativa

192

20/57%

299

3

Oryza sativa

S-130

140

15/51%

125

2

Oryza sativa

R-222

122

15/53%

263

2

Oryza sativa

Re-222

112

16/57%

113

1

Oryza sativa

S-222

100

13/38%

208

2

Oryza sativa

R-227 Re-227

161 165

16/54% 12/42%

594 374

5 4

Oryza sativa Oryza sativa

S-227

115

11/36%

163

3

Oryza sativa

R-230

132

13/60%

404

4

Oryza sativa

aldolase cytoplasmic

Re-230

115

11/43%

397

4

Oryza sativa

isozyme

S-230

124

12/56%

145

3

Oryza sativa

R-245

130

13/43%

196

3

Oryza sativa

Re-245

136

16/50%

154

3

Oryza sativa

S-245

158

15/47%

127

3

Oryza sativa

Glutamine synthetase

39435/6.12

root isozyme A Gi|968996

Glyceraldehyde-3-phosphate dehydrogenase

36641/6.61

Gi|78099751

Fructose-bisphosphate

39238/6.96

Gi|108706511

MASCOT score (PMF)

Re-130

dehydrogenase Gi|121333

spot IDc

Proteasome subunit alpha type 6

32472/7.05

a

GI number in NCBI. b Theoretical pI and molecular weight. c Numbers correspond to the 2-DE gel in Figure 5. R: 2-DE gels of rice soil. Re: 2-DE gels of Rehmanniae soil. S: 2-DE gels of sugar cane soil. d Number of peptides identified by MS/sequence percentage coverage. e Number of peptides identified by MS/MS.

(Supporting Information Tables s4-s7). Additionally, 82 proteins matched across these gels (Figure 3) were studied. They were cut from the 2-DE gel of the rice rhizospheric soil protein sample and subjected to the MS analysis. Out of them, 47 (57.32%) were successfully identified with a specific function. They belonged to rice (32 proteins) and microbes (15 proteins) (Figure 4). The results indicated that most of these conservative proteins might come from the root exudates, and could be utilized as biomakers in studying crop rhizospheric metaproteome. The other 35 spots were related to proteins with unknown functions.

Profile of Metaproteomics

The 2-DE map of rice rhizospheric soil proteins was chosen as a representative sample for the metaproteomic investigation. Two hundred and eighty-seven protein spots were randomly selected to be identified by MS, which contained more than onefourth of the protein spots in the gel and could satisfy the requirement for setting up the method and metaproteomic characterization. Out of them, 189 spots representing 122 proteins were successfully identified. The protein identification was carried out in three ways, that is, (1) 41 protein spots (21.69%) by equal MS/MS and MS searching, (2) 64 protein spots (33.86%) by MS/MS data with at least 2 matching peptides, and (3) 84 protein spots (44.44%) by MS data with at least 3 matching PMFs. Only the proteins with the highest score and similar predicted molecular mass were selected. Detailed results on MS and MS/MS analyses and protein identification are presented in Supporting Information Tables s1, s2, and s3. Ninety-eight protein spots could not be identified by MS or MS/MS. This might be attributed to the incomplete genome information available on environmental microbes. As mentioned above, highly similar protein profiles on the 2-DE gels of different CRS protein samples were observed. It might be assumed that these proteins represented conservative proteins in CRS. Thus, 5 protein spots matched across 2-DE gels of rice, Rehmanniae sp. and sugar cane samples, 15 in total, were randomly selected and subjected to MS/MS analysis. Four proteins were identified, and each of them represented the same protein in all 3 gels (Table 2). They were glyceraldehyde-3phosphate dehydrogenase, glutamine synthetase root isozyme A, fructose-bisphosphate aldolase cytoplasmic isozyme, and proteasome subunit alpha type 6. These proteins are highly conserved in plants. To check our results, the gene sequence encoding these 4 proteins was searched in the Basic Local Alignment Search Tool (BLAST) at http://blast.ncbi.nlm.nih.gov/. These genes were isolated from a wide variety of plant species, including Monocotyledoneae and Dicotyledoneae. The results showed a high similarity in the coding sequence and at the amino acid level

Functions of Soil Proteins

The database search of the 189 identified protein spots from rice rhizospheric soil sample showed that 107 proteins originated from plants, 72 proteins from microflora (bacteria or fungi), and 10 proteins from fauna, respectively. Functional analysis revealed that the identified plant proteins mainly undertook six biological functions (Tables s1, s2 and s3). They were (1) energy metabolism (including the glucose-6-phosphate dehydrogenase and ATP synthase F0 subunit 1); (2) protein turnover and amino acid biosynthesis (including the 20S proteasome alpha 6 subunit and glutamate dehydrogenase); (3) secondary metabolism (including the phenylalanine ammonia-lyase); (4) nucleotide metabolism (including the inosine monophosphate dehydrogenase); (5) signal transduction (including elongation factor EF-2); and (6) proteins involved in resistance mechanisms (including superoxide, dismutase and catalase). Twenty-nine identified proteins were determined to be originated from fungi which participated in five soil biological processes: (1) energy metabolism, (2) protein metabolism, (3) secondary metabolism, (4) nucleotide metabolism, and (5) signal transduction. Forty-three identified proteins were from bacteria which were involved in five soil biological processes: (1) protein metabolism, (2) secondary metabolism, (3) nucleotide metabolism, (4) signal transduction, and (5) resistance. Only 10 identified soil proteins were originated from fauna. Five of them were related to resistance, one participated in signal transduction, one associated with transduction regulation, 936



ARTICLE

Figure 3. Representative silver stained 2-DE maps. (A) sugar cane soil; (B) Rehmanniae sp. soil; (C) rice soil. Numbered spots matching in all the compared gels analyzed.

one participated in nitrogen metabolism, one was involved in virus infection, and one was unknown. Among the total identified proteins in the rice rhizospheric soil, bacterial proteins represented a large proportion. These proteins could be classified into 12 groups according to their microbial sources, that is, Proteobacteria (44.19%), Actinobacteridae (13.95%), Bacilli (6.98%), Bacteroidetes (6.98%), Flavobacteria (4.65%), Sphingobacteria (4.65%), Fusobacteria (4.65%), Verrucomicrobiae (4.65%), Spirochaetes (2.33%), Deferribacteres (2.33%), Cyanophyceae (2.33%), and Clostridia (2.33%) (Figure 5). To confirm the results obtained from the metaproteomic analysis, as well as to test the reliability of metaproteomics for its application on soil biology research, T-RFLP analysis, a technique widely used for studying microbial community in the environment, was carried out. The results showed that the bacterial microflora could be divided into 12 groups according to their taxonomic properties, that is, Proteobacteria (48%), Bacilli (25%), Clostridia (8.4%), Actinomycetes (4.8%), Planctomycetacia (2.4%), Sphingobacteria (2.4%), Spirochaetes (2.4%), Mollicutes (2.4%), Flavobacteria (1.2%), Nitrospira (1.2%), Fibrobacteres (1.2%), and unknown bacteria (0.2%) (Figure 6). The results obtained from the two approaches confirmed that Proteobacteria was the dominant species in rhizospheric microbial community. It was also found that 50% of bacterial groups determined by T-RFLP was not detected by proteomics analysis and vice versa. This might be attributed to incomplete proteomic and genomic information available to date on the environmental microbes. This finding further suggests that it is necessary to combine results from different approaches in reason to get as much information as possible.

Figure 4. Composition of conserved proteins in crop rhizospheric soil.

they failed in recovering high molecular weight proteins. In addition, the method could only be carried out in laboratory, since it needed artificial cultivation of the target microbial community.12 Taylor et al. introduced a combination of two methods for soil proteins extraction, that is, the direct extraction of bulk proteins and the density gradient centrifugation of microbial proteins. Only 6-10 bands of proteins were visible in SDSPAGE by the direct extraction. More bands were obtained by the density gradient centrifugation method, but the bands only represented microbial proteins, which ignored the crucial interactions between microbes and other microorganisms.15 Chen et al. modified these methods and established C-S-P-M assay to extract CRS proteins. The soil proteins obtained could satisfy the SDS-PAGE analysis, but failed to meet the requirement of metaproteomic study. In addition, protein identification was not included in this report.19 Consequently, none of the abovementioned methods is satisfactory for the in situ metaproteomics investigation of soil biotic community. The optimized C/S-P-M method developed in the present study appeared to be efficient, time-saving, and highly reproducible. The advantages might include: (1) the use of two independent extractions by citrate and SDS buffer separately to obtain the cellular and extracellular proteins avoiding the solvent interferences that occurred in the C-S-P-M method, (2) a time saving of more than 7 h, and (3) greater amount of proteins extracted for the subsequent metaproteomic analysis. On the basis of 1-D SDS-PAGE, 2-DE separation, and protein identification, the applicability of the C/S-P-M method for soil metaproteomic analysis was confirmed. The crop rhizospheric metaproteomics is a powerful tool for elucidating the mechanisms, which encompass complex interactions between the crop and microorganisms in soil. Previous reports on CRS metaproteome

’ DISCUSSION For the first time, a complete in situ metaproteomic research of a soil biotic community was carried out and reported. This study focused on CRS for the reason that CRS is able to show the dynamic interactions among microflora, fauna, and crop roots. On the other hand, CRS contains large amounts of interfering compounds, and the existence of complex microorganism metabolites as well as rhizodeposition requires adequate protein extraction to yield sufficient protein for analysis. Previously, Singleton et al. used an extraction method on metal-contaminated soil. Only high molecular weight protein bands ranging from 90 to 110 kDa and few blurry lower molecular weight bands were detected by SDS-PAGE in their study.32 Benndorf et al. developed a method to extract proteins from river sediments, but 937



ARTICLE

Figure 5. Schematic representation of the bacterial community in rice rhizospheric soil according the results of MALDI-TOF/TOF/MS.

Figure 6. Schematic representation of the bacterial community in rice rhizospheric soil according the results of T-RFLP.

obtained on the 2-DE maps for the CRS metaproteome with those methods. It cannot adequately meet the requirement for a crop

were limited to protein extraction and subsequent 1-DE and 2-DE separations.30 Approximately 250 protein spots at most could be 938


Journal of Proteome Research rhizospheric metoproteomic analysis. The newly established C/S-P-M method allowed a satisfactory 2-DE separation. Highly reproducible 2-DE maps of different CRS samples with close to 1000 protein spots could be achieved on the gel. Very similar protein profiles in the maps of 3 different soil protein samples were obtained. The 2-DE map of the rice rhizospheric soil protein sample was used as the material for protein identification in this study. Approximately two-thirds of the randomly selected spots were successfully identified by MS. Moreover, our tests confirmed that each protein spot, which matched across gels of different soil protein samples, represented the same highly conservative protein in plants. In addition, the distribution of bacterial proteins originating from 6 bacterial groups determined by proteomics analysis was confirmed and better defined by T-RFLP analysis. On the basis of our findings, the peculiarities of crop rhizospheric metaproteome were unveiled for the first time. The characterization can be summarized as follows. (1) About one-third of the protein spots could not be identified by MALDI-TOF/TOF/MS. It was postulated that most of these unidentified proteins spots originated from microorganisms, since the rice rhizospheric soil was relatively free from other plants and the sequence of rice genome is available for matching purpose. (2) Conservative plant proteins formed a distribution in the crop rhizospheric metaproteome, which might be related to similar rhizospheric metabolisms or processes in all green plants. This finding could serve to identify hypothetical biomarkers for investigating the interactions between crops and microorganisms in crop soil. Thereby, detailed analysis might uncover crop species-specific biomarkers for typifying a specific crop. In addition, these conservative proteins could provide a method for analyzing data from different laboratories. (3) Proteins in CRS might originate from plants, microbes, and fauna. Therefore, they can suggest specific types of interactions occurring in the analyzed soil. Metaproteomic research on CRS provides in fact direct evidence for the biological processes that are ongoing in the crop rhizosphere at the sampling time. These processes can be very different among organisms showing the extremely complex relationships existing in natural environment. In the present study, the identified proteins of plants, microbes, and fauna were related to several metabolic pathways such as the energy production, protein biosynthesis and turnover, defense machinery, and secondary metabolism. Most of these pathways are associated with the soil nutrient cycles, including carbon and nitrogen cycling.1 Numerous proteins from plants, microbes, and fauna relating to the signal transduction were detected in CRS. Communication between plants, microbes, and fauna is an essential biological process in rhizospheric soil. These proteins might play vital roles in the cross-talking process and induce metabolic changes inside the organisms. To better understand the functions of these proteins relating to the signal transduction, further research will be performed in the future. Despite the current accomplishment, there were 98 protein spots, about one-third of the total protein spots analyzed, which remain to be unidentified, and we also found that 50% of bacterial groups determined by proteomics analysis were not found in genomic counterpart in T-RFLP analysis. Further researches in metagenomics and of new analytical methods will undoubtedly be helpful for the diffusion of the environmental metaproteomics.

ARTICLE

’ ASSOCIATED CONTENT

bS

Supporting Information Supplemental Figure s1, silver stained 2-DE maps for investigation of repeatability from protein samples extracted from sugar cane soil in three repeat assays. Supplemental Figure s2, a typical 2-DE gel of rice soil proteins. Supplemental Table s1, proteins identified by equal MS/MS and MS searching. Supplemental Table s2, proteins identified by MS/MS. Supplemental Table s3, proteins identified by PMFs. Supplemental Table s4, sequence comparative analysis of the glutamine synthetase root isozyme (spot R-222). Supplemental Table s5, sequence comparative analysis of the fructose-bisphosphate aldolase cytoplasmic isozyme (spot R-230). Supplemental Table s6, sequence comparative analysis of the proteasome subunit alpha type 6(spot R-245). Supplemental, Table s7, sequence comparative analysis of glyceraldehyde-3-phosphate dehydrogenase (spot R-130, spot R-227). This material is available free of charge via the Internet at http://pubs.acs.org.

’ AUTHOR INFORMATION Corresponding Author

*School of Life Sciences, Fujian Agriculture and Forestry University, Fuzhou 35002, P. R. China. Tel: (86)-591-837-69440. Fax: (86)-591-837-69440. E-mail: [email protected]. Author Contributions #

These authors contributed equally to this work.

’ ACKNOWLEDGMENT This work was supported by the National Natural Science Foundation of China (Grant nos. 30471028, 30070068, 30200170, 30671220, 31070447, 30772729) and the earmarked fund for Modern Agro-industry Technology Research System. ’ REFERENCES (1) Mukerji, K. Rhizosphere Biology. In Microbial Activity in the Rhizosphere; Mukerji, K. G., Manoharachary, C., Singh, J., Eds.; SpringerVerlag : Berlin Heidelberg, Germany, 2006, pp 1-39. (2) Buscot, F. What are soils? In Microorganisms in Soils: Roles in Genesis and Functions; Varma, A., Buscot, F., Eds.; Springer-Verlage: Berlin Heidelberg, Germany, 2005; pp 3-18. (3) Harwkes, C.; Deangelis, K.; Firestore, M. Root Interactions with soil Microbial Communities and Process. In The Rhizosphere: An Ecological Perspective; Cardon, Z., Whitbeck, J., Eds.; Elsevier Academic: Burlington, MA, 2007; pp 1-25. (4) Atkinson, D.; Watson, C. The beneficial rhizosphere: a dynamic entity. Appl. Soil Ecol. 2000, 15, 99–104. (5) Sturz, A.; Nowak, J. Endophytic communities of rhizobacteria and the strategies required to create yield enhancing associations with crops. Appl. Soil Ecol. 2000, 15, 183–190. (6) Mukerji, K.; Chamola, B.; Sharma, M. Mycorrhiza in control of plant pathogens. In Management of Threatening Plant Diseases of National Importance; Agnihotri,V., Sarbhoy, A., Singh, D., Eds.; MPH: New Delhi, 1997; pp 297-314. (7) Bansal, M.; Chamola, B.; Sarwar, N.; Mukerji, K. Mycorrhizosphere: Interactions between rhizosphere microflora and VAM fungi. In Mycorrhizal Biology; Mukerji,K., Chamola, B., Singh, J., Eds.; SpringerVerlage: Berlin Heidelberg, Germany, 2000; pp 143-152. (8) Varma, A.; Verma, S.; Sudha, N.; Btehorn, B.; Franken, P. Piriformospora indica, a cultivable plant-growth-promoting root endophyte. Appl. Environ. Microb. 1999, 65, 2741–2744. 939



ARTICLE

(9) Euringer, K.; Lueders, T. An optimised PCR/T-RFLP fingerprinting approach for the investigation of protistan communities in groundwater environments. J. Microbiol. Meth. 2008, 75, 262–268. (10) Ying, Y.; Lv, Z.; Min, H.; Cheng, J. Dynamic changes of microbial community diversity in a photohydrogen producing reactor monitored by PCR-DGGE. J. Environ. Sci. 2008, 20, 1118–1125. (11) Schmalenberger, A.; Tebbe, C.; Kertesz, M.; Drake, H.; Ksel, K. Two-dimensional single strand conformation polymorphism (SSCP) of 16S rRNA gene fragments reveals highly dissimilar bacterial communities in an acidic fen. Eur. J. soil biol. 2008, 44, 495–500. (12) Jacobsen, C.; Holben, W. Quantification of mRNA in Salmonella sp. seeded soil and chicken manure using magnetic capture hybridization RT-PCR. J. Microbiol. Methods 2007, 69, 315–321. (13) Griffin, T.; Gygi, S.; Ideker, T.; Rist, B.; Eng, J.; Hood, L.; Aebersold, R. Complementary profiling of gene expression at the transcriptome and proteome levels in Saccharomyces cerevisiae. Mol. Cell. Proteomics 2002, 1, 323–333. (14) Nie, L.; Wu, G.; Zhang, W. Correlation between mRNA and protein abundance in Desulfovibrio vulgaris: a multiple regression to identify sources of variations. Biochem. Biophys. Res. Commun. 2006, 339, 603–610. (15) Taylor, E.; Williams, M. Microbial protein in soil: Influence of extraction method and C amendment on extraction and recovery. Microb. Ecol. 2010, 59, 390–399. (16) Wilmes, P.; Bond, P. The application of two-dimensional polyacrylamide gel electrophoresis and downstream analyses to a mixed community of prokaryotic microorganisms. Environ. Microbiol. 2004, 6, 911–920. (17) Murase, A.; Yoneda, M.; Ueno, R.; Yonebayashi, K. Isolation of extracellular protein from greenhouse soil. Soil Biol. Biochem. 2003, 35, 733–736. (18) Singleton, I.; Merrington, G.; Colvan, S.; Delahunty, J. The potential of soil protein-based methods to indicate metal contamination. Appl. Soil Ecol. 2003, 23, 25–32. (19) Chen, S.; Rillig, M.; Wang, W. Improving soil protein extraction for metaproteome analysis and glomalin-related soil protein detection. Proteomics 2009, 9, 4970–4973. (20) Pierre-Alain, M.; Christophe, M.; Severine, S.; Houria, A.; Philippe, L.; Lionel, R. Protein extraction and fingerprinting optimization of bacterial communities in natural environment. Microb. Ecol. 2007, 53, 426–434. (21) Abram, F.; Gunnigle, E.; O’Flaherty, V. Optimisation of protein extraction and 2-DE for metaproteomics of microbial communities from anaerobic wastewater treatment biofilms. Electrophoresis 2009, 30, 4149–4151. (22) Keil, B. A comparison of non-hydrolytic methods for extracting amino acids and proteins from coastal marine sediments. Mar. Chem. 2006, 98, 31–42. (23) Verberkmoes, N.; Russell., A; Shah, M.; Godzik, A.; Rosenquist, M.; Halfvarson, J.; Lefsrud, M.; Apajalahti, J.; Tysk, c.; Hettich, R.; Jansson, J. Shotgun metaproteomics of the human distal gut microbiota. ISME J. 2008, 23, 179–189. (24) Wilmes, P.; Wexler, M.; Bond, P. Metaproteomics provides functional insight into activated sludge wastewater treatment. PLoS One 2008, 3, e1778. (25) Benndorf, D.; Balcke, G.; Harms, H.; Von Bergen, M. Functional metaproteome analysis of protein extracts from contaminated soil and groundwater. ISME J. 2007, 1, 224–234. (26) Benndorf, D.; Vogt, C.; Jehmlich, N.; Schmidt, Y.; Thomas, H.; Woffendin, G.; Shevchenko, A.; Richnow, H.; von Bergen, M. Improving protein extraction and separation methods for investigating the metaproteome of anaerobic benzene communities within sediments. Biodegradation 2009, 20, 737–750. (27) Kim, S.; Cho, K.; Jang, Y.; Kang, K. Two-dimensional electrophoretic analysis of rice proteins by polyethylene glycol fractionation for protein arrays. Electrophoresis 2001, 22, 2103–2109. (28) Lin, X. M.; Wu, L. N.; Li, H.; Peng, X. X. Down-regulation of Tsx and OmpW and up-regulation of OmpX are required for iron homeostasis in Escherichia coli. J. Proteome Res. 2008, 7, 1235–1243.

(29) Pan, J.; Li, H.; Ma, Y.; Chen, P.; Zhao, P.; Wang, S.; Peng, X. Complexome of Escherichia coli envelope proteins under normal physiological conditions. J. Proteome Res. 2010, 9, 3730–3740. (30) Robe, P.; Nalin, R.; Capellano, C.; Vogel, T.; Simonet, P. Extraction of DNA from soil. Eur. J. Soil Biol. 2003, 39, 183–190. (31) Donn, S.; Griffiths, B.; Neilson, R.; Daniell, T. DNA extraction from soil nematodes for multi-sample community studies. Appl. Soil Ecol. 2008, 38, 20–26. (32) Smalla, K.; Oros-Sichler, M.; Milling, A.; Heuer, H.; Baumgarte, S.; Becker, R.; Neuber, G.; Kropf, S.; Ulrich, A.; Tebbe, C. Bacterial diversity of soils assessed by DGGE, T-RFLP and SSCP fingerprints of PCR-amplified 16S rRNA gene fragments: do the different methods provide similar results? J. Microbiol. Methods 2007, 69, 470–479.

940


Characterization of Metaproteomics in Crop Rhizospheric Soil

Recommend Documents