Metaproteomic analysis of ratoon sugarcane rhizospheric soil
- Wenxiong Lin†1, 2Email author,
- Linkun Wu†1, 2,
- Sheng Lin1, 2,
- Aijia Zhang1, 2,
- Mingming Zhou1, 2,
- Rui Lin3,
- Haibin Wang1, 2,
- Jun Chen1, 2,
- Zhixing Zhang1, 2 and
- Ruiyu Lin1, 2
© Lin et al.; licensee BioMed Central Ltd. 2013
Received: 2 February 2013
Accepted: 30 May 2013
Published: 17 June 2013
The current study was undertaken to elucidate the mechanism of yield decline in ratoon sugarcane using soil metaproteomics combined with community level physiological profiles (CLPP) analysis.
The available stalk number, stalk diameter, single stalk weight and theoretical yield of ratoon cane (RS) were found to be significantly lower than those of plant cane (NS). The activities of several carbon, nitrogen and phosphorus processing enzymes, including invertase, peroxidase, urease and phosphomonoesterase were found to be significantly lower in RS soil than in NS soil. BIOLOG analysis indicated a significant decline in average well-color development (AWCD), Shannon’s diversity and evenness indices in RS soil as compared to NS soil. To profile the rhizospheric metaproteome, 109 soil protein spots with high resolution and repeatability were successfully identified. These proteins were found to be involved in carbohydrate/energy, amino acid, protein, nucleotide, auxin and secondary metabolisms, membrane transport, signal transduction and resistance, etc. Comparative metaproteomics analysis revealed that 38 proteins were differentially expressed in the RS soil as compared to the control soil or NS soil. Among these, most of the plant proteins related to carbohydrate and amino acid metabolism and stress response were up-regulated in RS soil. Furthermore, several microbial proteins related to membrane transport and signal transduction were up-regulated in RS soil. These proteins were speculated to function in root colonization by microbes.
Our experiments revealed that sugarcane ratooning practice induced significant changes in the soil enzyme activities, the catabolic diversity of microbial community, and the expression level of soil proteins. They influenced the biochemical processes in the rhizosphere ecosystem and mediated the interactions between plants and soil microbes.
KeywordsCLPP 2D-electrophoresis Soil enzyme Soil metaproteomics Soil protein extraction Sugarcane
Sugarcane (Saccharum L. spp. hybrids) is of tremendous economic importance not just for the sugar industry but also for its impact on sustainable energy production. The ratoon sugarcane is the regenerated crop plant from the germinating bud of the stubble from the previous crop . Ratooning practice saves cost on preparatory tillage and planting material and benefits from the residual manure and moisture. In addition, the ratoon sugarcane matures earlier than the newly planted sugarcane (plant sugarcane). However, there is a decline in the yield of ratoon sugarcane in the successive years under normal conditions . This has become one of the major problems in the high-yielding cultivation of sugarcane.
The expansion of crop monoculture has led to the simplification of the agroecosystem, and the loss and fragmentation of habitat . Large-scale monocultures result in a decline in the biological diversity, destroy the capability of self-adjustment of the ecosystem, and cause diseases, which further increases the production cost and pollute the environment because more pesticides and better fertilizers are required . The yield decline has been defined as the loss of productive capacity of sugarcane soils under long-term monocultures . Gascho et al.  found that productivity of the ratoon sugarcane was 33 percent less than the plant sugarcane due to increased mortality of stalks, reduction in soil nutrition status and abundance of pests and diseases in the soil. Current evidences suggests that several factors (including the long-term sugarcane monoculture, excessive tillage and mechanical harvesting and haul-out with heavy machinery, etc.) are responsible for the degradation of physical, chemical and microbial properties of sugarcane growing soils [6, 7]. Recent studies have revealed that crop rotation breaks and organic amendments greatly influence the structure and microbial populations of the sugarcane rhizospheric soil [2, 8, 9]. Our previous study showed that ratooning cane, intercropped with legumes, enhanced the functional diversity of rhizospheric microbial community and increased cane yield (Data not shown). Plant-soil organism interactions, especially plant-microbial interactions play crucial roles in soil quality, and crop health and yield [10, 11]. There has been an increasing interest in the biological properties of rhizosphere in situ. However, there is no report hitherto focusing on the relationship among the soil ecosystem, soil organism community and sugarcane ratooning practice from a proteomic perspective.
Various DNA-dependent strategies, such as terminal restriction fragment length polymorphism , denaturing gradient gel electrophoresis  and reverse transcription-polymerase chain reaction  have been used to elucidate the biological information from microbial communities in the soil ecosystem. However, since the mRNA expression and protein expression do not always correlate directly, the function of microbial diversity still remains unknown . Moreover, the biological processes in rhizosphere soil are not only driven by the microbes but also by the plants and the fauna in the ecosystem . Extended soil protein identification is essential for understanding the soil ecological processes and the environmental factors that affect the functioning of the rhizospheric soil ecosystem [18, 19]. Two community-based measurements, community level physiological profiles (CLPP) and soil metaproteomics were used in this work. The assessment of microbial functional diversity by using BIOLOG sole carbon (C) substrate utilization tests is a rapid, sensitive approach to detect modifications in diversity due to soil management, disturbance, stress or succession . Soil rhizospheric metaproteomics is a powerful scientific tool to account for functional gene expression in microbial ecosystems and can uncover the interactions between plants and soil microorganisms .
It was speculated that the yield decline in ratoon sugarcane is closely related to the dynamics and genetic diversity of the community members (i.e., bacteria, fungi and fauna). Therefore, in this study, we aimed to: (i) determine differences between the soil protein abundance in plant sugarcane and ratoon sugarcane rhizospheric soils, and (ii) analyze interactions between the root system and the rhizospheric soil organisms based on our data from soil enzyme assays, CLPP analysis, and a limited number of identified soil proteins.
Sucrose content and theoretical production
The agronomic characters, theoretical sugar content and yield of plant cane and ratoon cane
Sucrose content (%)
Available stalk number (hm-2)
Stalk height (cm)
Stalk diameter (cm)
Single stalk weight (kg)
Theoretical production (kg/hm2)
Soil enzyme activity
Soil enzyme activities in rhizospheric soils from the study sites
Polyphenol oxidase d
Plant cane soil
Ratoon cane soil
Microbial community dynamics assessed by BIOLOG analysis
Diversity and evenness indices, and mean optical density of grouped substrates (six groups) at 96 h incubation time in different treatments
Plant cane soil
Ratoon cane soil
Shannon’s diversity index
Profile analysis of metaproteome in rhizospheric soils
Approximately 759, 788, and 844 protein spots were detected on silver-stained gel of proteins extracted from the control soil, plant cane soil, and ratoon cane soil respectively (Additional file 2: Figure S1). Highly reproducible 2-DE maps were obtained from the three different soil samples with significant correlations among scatter plots. The correlation index between the control soils and the newly planted sugarcane soils was found to be 0.868, and the correlation index between the control soils and the one year ratoon sugarcane soils were was 0.761.
To obtain a metaproteomic profile for the sugarcane rhizospheric soil, 143 protein spots with high resolution and repeatability, including all 38 differentially expressed proteins and 105 constitutively expressed proteins, were selected for identification and 109 protein spots were successfully analyzed by MALDI TOF-TOF MS (Additional file 3: Figure S2; Additional file 4: Table S2).
Differentially expressed proteins and their roles in rhizospheric soils
Differentially expressed proteins identified by MALDI TOF-TOF MS
Spot no. a)
GI no. b)
Score (PMF) c)
Score (MS-MS) f)
Succinate dehydrogenase [ubiquinone] flavoprotein subunit, mitochondrial
Glyceraldehyde-3-phosphate dehydrogenase, cytosolic 3
Proteasome beta type-1
Betaine aldehyde dehydrogenase
Amino acid metabolism
2,3-bisphosphoglycerate-independent phosphoglycerate mutase
Heat shock 70 kDa protein, mitochondrial precursor
NADP dependent malic enzyme
Cyclase family protein
2,3-bisphosphoglycerate-independent phosphoglycerate mutase
Fumarate hydratase 1, mitochondrial precursor, putative, expressed
S-adenosylmethionine synthase 2
Amino acid metabolism
Beta-D-glucan exohydrolase, isoenzyme ExoII
NAD-dependent isocitrate dehydrogenase
Putative sugar ABC transporter, periplasmic component
Mitochondrial N-glycosylase/DNA lyase
ABC transporter ATP-binding subunit
Phosphoribosylformimino-5-aminoimidazole carboxamide ribotide isomerase
Amino acid metabolism
Elongation factor EF-2
Radical SAM domain protein
Amino acid metabolism
Succinate dehydrogenase/fumarate reductase, Fe-S protein subunit
Related to kinesin-like protein
Nitrate reductase, alpha subunit
Conjugal transfer protein A
Two-component system sensor kinase
Isocitrate dehydrogenase [NADP], mitochondrial precursor
Tubulin, gamma complex associated protein 2
Among the microbe-originating differentially expressed proteins, most of them were associated with the carbohydrate/energy metabolism (22.22%) and signal transduction (22.22%) (Figure 5). Several microbial proteins were found related to the root-colonizing ability of microorganisms (including spot 30, two-component system sensor kinase) and the utilization of root exudates (including spot 2, sugar ABC transporter and spot 5, ABC transporter ATP-binding subunit) were up-regulated in the ratoon cane soil, as compared to the plant cane and control soil (Table 4), which might be a response of microbes to the rhizodeposition of ratoon cane. Furthermore, most of proteins originated from fungi (including spot 3, mitochondrial N-glycosylase/DNA lyase; spot 7, ORP1; spot 20, kinesin-like protein and spot 34, isocitrate dehydrogenase) were up-regulated in the ratoon cane soil (Table 4). Besides, one cytoskeleton protein (spot 38, i.e. tubulin gamma) originated from the fauna was identified as well. Therefore, sugarcane ratooning induced the alteration of the expression of soil proteins from the plants, microbes and fauna.
The consecutive monocultures for many medicinal plants and crop plants, such as Rehmannia glutinosa and soybea , etc., result in a significant reduction in the yield and quality of the harvest. This phenomenon is known as soil sickness (replanting disease)  or consecutive monoculture problems . In the present study, the available stalk number per hectare, stalk diameter, single stalk weight) and theoretical production of ratoon cane were found to be significantly (P ≤ 0.05) lower than those of plant cane (Table 1). Hunsigi  indicated that ratooning practice decreased soil fertility under consecutive sugarcane cropping. Several researchers developed a ‘farming systems’ approach to address the problem of sugarcane cultivation with a major focus on the introduction of rotation breaks and organic amendments and found that these practices induced remarkable changes in the commnunity composition and structure of the soil biota (bacteria, fungi and nematodes, etc.) [8, 27, 28]. Enzyme activity in soil is a measure of the soil microbial activity and plays an important role in nutrient cycles and transformations. Therefore, it is used as an indicator of changes into determine changes in quality and productivity of soil [29, 30]. In the present study, five soil enzymes activities involved in nutrition cycling and stress response were assayed. Our data showed that the activities of soil enzymes such as invertase, urease, phosphomonoesterase and peroxidase were significantly lower (P < 0.05) in ratoon cane soil than in plant cane soil (Table 2).
The assessment of microbial functional diversity by carbon substrate utilization patterns has been reported to be a sensitive approach to detect variability in metabolic potential due to soil management . In the current work, the BIOLOG results showed that ratooning practice led to significant decreases (P < 0.05) in AWCD, Shannon’s diversity, and evenness indices in soil as compared to the plant cane soil (Table 3). Particularly, there were significantly lower levels (P < 0.05) of carboxyhydrates, amines and amino acids used in ratoon cane soil than in plant cane soil (Table 3). Principal component analysis allowed the differentiation of ratoon cane soil from the control and the plant cane soil. However, the use of BIOLOG ECO microplates to analyze the metabolic diversity of the microbial community represents only the in situ phenomena where only the fast growing microbes are involved, and ignores the catabolic profiles of functionally inactive microorganisms . Preston-Mafham et al.  claimed that BIOLOG measurements should be applied in community comparisons rather than in community characterization. The trophic structure and the relationship between its components in soil are still poorly understood as the soil food web and biochemical processes are extraordinarily complex. Comparative metaproteomics was used to study the differences in functional gene expression that are mediated by sugarcane ratooning practice in the rhizosphere ecosystem. These differentially expressed proteins were related to various metabolic pathways such as carbohydrate/energy metabolism, amino acid metabolism, signal transduction, membrane transport, and stress/defense response etc.. These results might help to unravel the intricate interactions among plant root systems, root exudates, and rhizospheric microflora.
Differentially expressed plant proteins under ratooning practice
Our metaproteomic analysis showed that the 6 proteins (spot 12, succinate dehydrogenase; spot 13, phosphofructokinase; spots 16 and 35, glyceraldehyde-3-phosphate dehydrogenase and spot 32, fumarate hydratase 1) linked to the glycolysis (EMP) / tricarboxylic acid (TCA) cycle and one protein (spot 25, betaine aldehyde hydrogenase) involved in glycine, serine and threonine metabolism were highly expressed in the ratoon cane soil, as compared to the plant cane and control soils (Table 4). These proteins are probably associated with the release of root exudates from plants. Many root exudates (such as malate, fumarate, oxalate, malonate, citrate, aconitate, arginine, histidine and lysine) are mostly the intermediates of the TCA cycle or amino acid metabolism. Singh and Mukerji  suggested that these root exudates were the determinants of rhizospheric microbial biodiversity. Root exudates act as chemo-attractants that function to attract bacteria towards roots . The qualitative and quantitative composition of root exudates is affected by various environmental factors (such as pH, soil type, oxygen status, nutrient availability, etc.) and the presence of microorganisms. The up-regulation of these proteins involved in the carbohydrate and amino acid metabolism might be explained by a change in the composition of root exudates possibly resulting from soil disturbances which might be caused by ratooning.
In this study, three proteins linked to plant stress/defense response (including spot 4, catalase; spot 23, PrMC3 and spot 27, heat shock 70 kDa protein) showed higher expression levels in the ratoon cane soil than in the plant cane and control soils (Table 4). Catalase and heat shock protein 70 (Hsp 70) have been proven to be critical for various abiotic and biotic stress responses [36–38]. The above mentioned proteins are rapidly up-regulated in pathogen infection and play a central role in defense against pathogens [39, 40]. PrMC3 is a member of a family of proteins that all contain a Ser-hydrolase motif (GxSxG) and is similar to the tobacco protein hsr203J . Hsr203J is rapidly and specifically expressed in the hypersensitive response to various pathogens in tobacco . Furthermore, Zhou et al.  found that the gene expression of PrMC3 was up-regulated in the plant leaves infected by the bacterial pathogen Xanthomonas oryzae pv. Oryzicola. Therefore, the up-regulation of catalase, PrMC3 and Hsp70 might imply that ratoon cane was confronted with environmental stress in the soil, which possibly results from the presence of certain pathogens (pathogenic microbes or root-infecting nematodes) [44, 45] or other abiotic stresses in the ratooning system.
Differentially expressed microbial proteins under ratooning practice
The results from our experiments showed that the two proteins (spots 2, sugar ABC transporter and spot 5, ABC transporter ATP-binding subunit) linked to the membrane transport and one protein (spot 30, two-component system sensor kinase) related to signal transduction had higher expression levels in the ratoon cane soil, as compared to the plant cane and control soils (Table 4). ABC transporters are multicomponent systems, which include one or two integral membrane proteins that constitute the channel across the membrane, an ATP-binding protein that hydrolyzes ATP and drives the transport, and in most cases, an extracellular solute-binding protein . ABC transport systems play an important role in many different aspects of bacterial physiology, facilitating the import of nutrients, and in the extrusion of toxins and antimicrobial agents . Sugar ABC transporters facilitate the transport of a variety of sugars. Some microorganisms utilize highly efficient sugar ABC transporters to survive when substrate concentrations are extremely low . The two-component system sensor kinase (spot 30) was also found to be up-regulated in our study. The two-component system is one of the signal transduction systems in microorganisms that consists of a sensor histidine kinase (SK) and a response regulator (RR). This system responds to a large number of environmental signals  and is postulated to play an important role in root colonization . The up-regulation of the proteins involved in membrane transport and signal transduction might be related to the utilization of rhizodeposition by root-associated bacteria. This probably facilitates root colonization by these bacteria. Besides, most of proteins originated from fungi (including spot 3, mitochondrial N-glycosylase/DNA lyase; spot 7, ORP1; spot 20, kinesin-like protein and spot 34, isocitrate dehydrogenase) showed higher expression levels in ratoon cane soil than in the plant cane and control soils (Table 4). The functional gene expression differences in soil microbial communities are probably mediated by a change in the amount and composition of root exudates [51, 52].
Despite the limited number of soil proteins identified, our metaproteomic analysis results, combined with soil enzyme assays and CLPP analysis, provide a solid foundation to understand the interactions between the soil organisms and plants in the soil ecosystem. Environmental metaproteomics has been demonstrated to be a useful tool for structural and functional characterization of microbial communities in their natural habitat [53, 54], with an increasing improvement in MS performance  and soil protein extraction . Metaproteomics is most powerful when combined with metagenomics or when using unmatched metagenomic datasets .
Our experiments revealed that ratooning practice significantly decreased the activity of soil enzymes, catabolic activity, and Shannon’s diversity and evenness indices. The comparative soil metaproteomics revealed that sugarcane ratooning induced changes in the expression levels of soil proteins originated from the plants, microbes and fauna. A majority of up-regulated plant proteins were found to be related to carbohydrate and amino acid metabolism and stress response, while most of up-regulated microbial proteins were involved in membrane transport and signal transduction. In conclusion, sugarcane ratooning practice induced great changes in the soil enzyme activities, the catabolic diversity of microbial community and the expression level of soil proteins. These changes were found to influence the biochemical processes in the rhizosphere ecosystem and mediated the interactions between plants and soil microbes. The soil proteins identified in our study are almost certainly a small part of the diversity of proteins that were expressed by the plants and soil microbial communities. Yet, environmental metaproteomics is a powerful tool to study plant-microbe interactions in soil.
Site description and soil sampling
The sugarcane cultivar ‘Gan-nang’ was used in this study. The study plots were completely randomized and located at the experimental farm (26°09′N, 119°23′E) of Ministry of Agriculture Key Laboratory for Sugarcane Genetic Improvement, Fujian Agriculture and Forestry University, Fuzhou, P. R. China. The first site (defined as ‘RS’) was a field used for ratoon sugarcane cultivation, in which sugarcane was newly planted on February 15, 2009 and then ratooned in 2010. The second site (defined as ‘NS’) was a field kept fallow in 2009 and then used for newly planted sugarcane cultivation on February 15, 2010. The field with no sugarcane crop (bare fallow) during the entire experimental period of 2 years was used as a control (defined as ‘CK’). These three different treatments (‘CK’, ‘NS’ and ‘RS’) were organized within a single field site and under the same field management conditions during the entire experimental period. Three replicates were taken for each treatment. Approximately, 150 grams of soil samples from 3 cultivation patterns were obtained from 5 random locations on each plot in September 15, 2010. Soil sampling of all three treatments was carried out at the same time in order to minimize the effect of year-to-year environmental variability. The plot samples were mixed to make composite samples. The intact roots were carefully uprooted with a forked spade and shaken to remove loosely attached soil. The rhizospheric soil tightly attached to the roots was collected and then sieved through 2 mm mesh to remove plant roots, leaf remains, insects, etc. The fresh soil samples were immediately used for soil enzyme and BIOLOG analysis. For protein extraction, the soil samples were dried at 70°C for 2 h, pulverized in a mortar, and sieved through a 0.45 mm mesh to extract soil proteins.
Sucrose content and theoretical production determination
The sucrose content was determined by Extech Portable Sucrose Brix Refractometer (Mid-State Instruments, CA, USA) on December 15, 2010 and calculated by using the formula : sucrose (%) = brix (%)×1.0825-7.703. Meanwhile, the theoretical production of sugarcane was calculated according to the following equations : (1) Single stalk weight (kg) = [stalk diameter (cm)]2×[stalk height (cm)-30]×1 (g/cm3)×0.7854/1000; (2) Theoretical production (kg/hm2) = single stalk weight (kg)×productive stem numbers (hm-2).
Soil enzyme assays
The activities of five soil enzymes involved in the cycling of carbon, nitrogen, and phosphorus and stress responses, i.e., invertase (E.C. 22.214.171.124), urease (E.C. 126.96.36.199), acid phosphomonoesterase (E.C. 188.8.131.52), polyphenol oxidase (E.C. 184.108.40.206) and peroxidase (E.C. 220.127.116.11) were determined immediately from freshly sampled soil. Invertase and urease activities were measured following the method of Wang et al.  with 8% sucrose and 10% urea (w/v) as substrates, respectively. Acid phosphomonoesterase was assayed with 50 mM p-nitrophenyl phosphate (PNP) as substrate according to the method of Carine et al. . Polyphenol oxidase and peroxidase activities were determined as described by Yu et al.  using 1% pyrogallic acid as substrate. Three replicates for each soil sample were taken to perform enzyme assays.
Community level physiological profiles (CLPP) were assessed by the BIOLOG Eco MicroPlate™ system (Biolog Inc., CA, USA) according to the method of Lin et al. . Three technical replicates were performed for each treatment. The plates were incubated at 25°C for 168 h, and the color development in each well was recorded as optical density (OD) at 590 nm with a plate reader (Thermo Scientific Multiskan MK3, Shanghai, China) at regular 24 h-intervals.
Microbial activity in each microplate, expressed as average well-color development (AWCD) was determined as follows: AWCD = ∑(C-R)/31, where C is the optical density within each well, R is the absorbance value of the plate control well. The 31 carbon substrates in ECO microplates were subdivided into six categories (polymers, carbohydrates, carboxylic acids, amino acids, amines and phenolic compounds) following Choi et al.’s method . The optical density at 96 h incubation time was used to calculate diversity and evenness indices as well as principal component analysis , since it was the shortest incubation time that provided the best resolution for all treatments .
Protein extraction and purification
The soil proteins from cultivated samples were extracted and purified by the following protocol developed in our lab . Briefly, 1 g of dry cultivated soil powder were extracted using 5 mL of 0.05 M citrate buffer (pH 8.0) and 5 mL of 1.25% SDS buffer (1.25% w/v SDS, 0.1 M Tris-HCl, pH 6.8, 20 mM DTT), respectively. Citrate extract and SDS extract were shaken for 30 min with 2 mL of buffered phenol (pH 8.0). 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. The washed pellets obtained from citrate extraction and SDS extraction were mixed, air-dried and stored at -80°C until further use.
2D-polyacrylamide gel electrophoresis (2D-PAGE) of extracted proteins
The protein pellets were dissolved in appropriate lysis solution (7 M urea, 2 M thiourea, 65 mM DTT, 4% CHAPS, 0.05% v/v ampholytes pH 3.5-10). Protein concentration was determined by Bradford assay using dilutions of bovine serum albumin as standards. 2-D gel electrophoresis (2-DE) was performed as described by Wang et al. . The prepared protein samples were separated by isoelectric focusing (IEF, pH 5–8) in the first dimension, and SDS-PAGE (5% acrylamide stacking gel and a 10% acrylamide separating gel) in the second dimension. After electrophoresis, 2-DE gels were stained with silver nitrate . The gels were scanned using the Image Master (version 5.0, GE Healthcare, Uppsala, Sweden) and analyzed with ImageMaster™ 2D Platinum software (version 5.0, GE Healthcare, Uppsala, Sweden). Repeatability analysis of 2-DE maps of soil proteins was carried out through scatter plots with ImageMaster™ 2D Platinum according to the manufacturer’s instructions. To compensate for subtle differences in sample loading, gel staining, and destaining, the volume of each spot (i.e., spot abundance) was normalized as a relative volume, that is, the spot volume was divided by the total volume over the whole set of gel spots. Standard deviation (SD) was calculated from spots of the gels from three independent experiments and used as error bars. Only those with significant and reproducible changes were considered to be differentially expressed proteins (differing by > 1.5-fold).
MALDI-MS and protein identification
The interesting protein spots were excised manually from gels for mass spectrometric analysis and the in-gel digestion of proteins were performed as described by Wang et al. . Thereafter, 1 μl of the abovementioned solution was spotted onto stainless steel sample target plates. Peptide mass spectra were obtained on a Bruker UltraFlex III MALDI TOF/TOF mass spectrometer (Bruker Daltonics, Karlsruhe, Germany). Data were acquired in the positive MS reflector mode using 6 external standards for the instrument calibration (Peptide Calibration Standard II, Bruker Daltonics). Mass spectra were obtained for each sampled spot by accumulating of 600-800 laser shots in an 800-5,000 Da mass range. For the MS/MS spectra, 5 most abundant precursor ions per sample were selected for subsequent fragmentation, and 1,000-1,200 Da laser shots were accumulated per precursor ion. The criterion for precursor selection was a minimum S/N of 50.
BioTools 3.1 and the MASCOT 2.2.03 search engine were used to automate database searching. Both MS/MS and MS data were used for the identification of proteins with the following selection criteria: NCBInr database (release 20110409, 13655082 sequences; 4686307983 residues), taxonomy of all entries followed by ‘Bacteria’ or ‘Fungi’ database, trypsin of the digestion enzyme, one missed cleavage site, parent ion mass tolerance at 100 ppm, MS/MS mass tolerance of 0.5 Da, carbamidomethylation of cysteine (global modification), and methionine oxidation (variable modification). The probability score (95% confidence interval) calculated by the software was used as criteria for correct identification .
Due to the vast varieties of soil sample sources, the mass spectra were searched against databases step by step. Firstly, ‘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. Both MS/MS and MS data were used for the identification of proteins. The proteins sharing equal searches by MS/MS and MS were preferentially selected. Furthermore, the proteins that matched at least two MS/MS peptides or six peptide mass fingerprintings (PMFs) were subjected to further identification. Only the proteins with the highest score and similar predicted molecular mass were selected.
Gene ontology and KEGG pathway analysis
Gene Ontology (GO) annotations for the identified soil proteins were assigned according to those reported in the uniprot database . WEGO (Web Gene Ontology Annotation Plotting) tool  was used for plotting GO annotation results at GO level 2 as described by Ye et al. . Furthermore, these proteins were used to search KEGG database  to obtain reference pathways.
Average well-color development
Control (with no sugarcane crop)
Community level physiological profiles
2D-polyacrylamide gel electrophoresis
Kyoto Encyclopedia of Genes and Genomes
Principal component analysis
Sodium dodecyl sulfate polyacrylamide gel electrophoresis
Web Gene Ontology Annotation Plotting.
This work was supported by the National Natural Science Foundation of China (Grant nos. 30772729, 30671220, 31070403), the National Key Basic Research Program of China (Grant nos. 2012CB126309, U1205021) and the earmarked fund for Modern Agro-industry Technology Research System projected by Ministry of Agriculture, China.
- Crane DR, Spreen TH: A model of the stubble replacement decision for Florida sugarcane growers. South J Agr Econ. 1980, 12: 55-63.Google Scholar
- Shukla SK, Yadav RL, Suman A, Singh PN: Improving rhizospheric environment and sugarcane ratoon yield through bioagents amended farm yard manure in udic ustochrept soil. Soil Till Res. 2008, 99: 158-168. 10.1016/j.still.2008.02.007.View ArticleGoogle Scholar
- Lin WX, Chen T, Zhou MM: New dimensions in agroecology. Chinese Journal of Eco-Agriculture. 2012, 20: 253-264. 10.3724/SP.J.1011.2012.00253.View ArticleGoogle Scholar
- Garside AL, Smith MA, Chapman LS, Hurney AP, Magarey RC: The yield plateau in the Australian sugar industry: 1970–1990. Intensive sugarcane production, meeting the challenges beyond 2000. Edited by: Keating BA, Wilson JR. 1997, Wallingford: CAB International, 103-124.Google Scholar
- Gascho GJ, Ruelke OC, West SH: Residual effect of germination temperature in sugarcane. Crop Sci. 1973, 13: 274-276. 10.2135/cropsci1973.0011183X001300020037x.View ArticleGoogle Scholar
- Magarey RC: Microbiological aspects of sugarcane yield decline. Aust J Agr Res. 1996, 47: 307-322. 10.1071/AR9960307.View ArticleGoogle Scholar
- Pankhurst CE, Magarey RC, Stirling GR, Blair BL, Bell MJ, Garside AL: Management practices to improve soil health and reduce the effects of detrimental soil biota associated with yield decline of sugarcane in Queensland. Soil Till Res. 2003, 72: 125-137. 10.1016/S0167-1987(03)00083-7.View ArticleGoogle Scholar
- Pankhurst CE, Blair BL, Magarey RC, Stirling GR, Bell MJ, Garside AL: Effect of rotation breaks and organic matter amendments on the capacity of soils to develop biological suppression towards soil organisms associated with yield decline of sugarcane. Appl Soil Ecol. 2005, 28: 271-282. 10.1016/j.apsoil.2004.07.010.View ArticleGoogle Scholar
- Stirling GR, Blair BL, Pattemore JA, Garside AL, Bell MJ: Changes in nematode populations on sugarcane following fallow, fumigation and crop rotation, and implications for the role of nematodes in yield decline. Australas Plant Path. 2001, 30: 323-335. 10.1071/AP01044.View ArticleGoogle Scholar
- Marschner P: Plant-microbe interactions in the rhizosphere and nutrient cycling. Nutrient cycling in terrestrial ecosystems. Volume 10. Edited by: Marschner P, Rengel Z. 2007, Berlin: Springer, 159-182.View ArticleGoogle Scholar
- Pini F, Frascella A, Santopolo L, Bazzicalupo M, Biondi EG, Scotti C, Mengoni A: Exploring the plant-associated bacterial communities in Medicago sativa L. BMC Microbiol. 2012, 12: 78-10.1186/1471-2180-12-78.PubMedView ArticleGoogle Scholar
- Mukerji KG, Manoharachary C: Rhizosphere biology- an overview. Microbial activity in the rhizosphere. Volume 7. Edited by: Mukerji KG, Manoharachary C, Singh J. 2006, Berlin: Springer, 1-39.View ArticleGoogle Scholar
- Fernàndez-Guerra A, Buchan A, Mou X, Casamayor EO, González JM: T-RFPred: a nucleotide sequence size prediction tool for microbial community description based on terminal-restriction fragment length polymorphism chromatograms. BMC Microbiol. 2010, 10: 262-10.1186/1471-2180-10-262.PubMedPubMed CentralView ArticleGoogle Scholar
- 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. 10.1016/S1001-0742(08)62158-0.View ArticleGoogle Scholar
- 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. 10.1016/j.mimet.2007.02.001.PubMedView ArticleGoogle Scholar
- Griffin TJ, Gygi SP, 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. 10.1074/mcp.M200001-MCP200.PubMedView ArticleGoogle Scholar
- Wang HB, Zhang ZX, Li H, He HB, Fang CX, Zhang AJ, Li QS, Chen RS, Guo XK, Lin HF, Wu LK, Lin S, Chen T, Lin RY, Peng XX, Lin WX: Characterization of metaproteomics in crop rhizospheric soil. J Proteome Res. 2011, 10: 932-940. 10.1021/pr100981r.PubMedView ArticleGoogle Scholar
- Maron PA, Ranjard L, Mougel C, Lemanceau P: Metaproteomics: a new approach for studying functional microbial ecology. Microb Ecol. 2007, 53: 486-493. 10.1007/s00248-006-9196-8.PubMedView ArticleGoogle Scholar
- 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. 10.1007/s00248-009-9593-x.PubMedView ArticleGoogle Scholar
- Gomez E, Ferreras L, Toresani S: Soil bacterial functional diversity as influenced by organic amendment application. Bioresour Technol. 2006, 97: 1484-1489. 10.1016/j.biortech.2005.06.021.PubMedView ArticleGoogle Scholar
- Palviainen M, Raekallio M, Vainionpää M, Kosonen S, Vainio O: Proteomic profiling of dog urine after European adder (Vipera berus berus) envenomation by two-dimensional difference gel electrophoresis. Toxicon. 2012, 60: 1228-1234. 10.1016/j.toxicon.2012.08.010.PubMedView ArticleGoogle Scholar
- Qi JJ, Yao HY, Ma XJ, Zhou LL, Li XN: Soil microbial community composition and diversity in the rhizosphere of a Chinese medicinal plant. Commun Soil Sci Plan. 2009, 40: 1462-1482. 10.1080/00103620902818104.View ArticleGoogle Scholar
- Li CG, Li XM, Kong WD, Wu Y, Wang JG: Effect of monoculture soybean on soil microbial community in the Northeast China. Plant Soil. 2010, 330: 423-433. 10.1007/s11104-009-0216-6.View ArticleGoogle Scholar
- Qu XH, Wang JG: Effect of amendments with different phenolic acids on soil microbial biomass, activity, and community diversity. Appl Soil Ecol. 2008, 39: 172-179. 10.1016/j.apsoil.2007.12.007.View ArticleGoogle Scholar
- Wu FZ, Wang XZ, Xue CY: Effect of cinnamic acid on soil microbial characteristics in the cucumber rhizosphere. Eur J Soil Biol. 2009, 45: 356-362. 10.1016/j.ejsobi.2009.04.001.View ArticleGoogle Scholar
- Hunsigi G: Sugarcane in Agriculture and Industry. 2001, Bangalore: Prism Books Pvt LtdGoogle Scholar
- Pankhurst CE, Magarey RC, Stirling GR, Holt JA, Brown JD: Rotation-induced changes in soil biological properties and their effect on yield decline in sugarcane. Proc Aust Soc Sugar Cane Technol. 1999, 21: 79-86.Google Scholar
- Whipps JM: Microbial interactions and biocontrol in the rhizosphere. J Exp Bot. 2001, 52: 487-511. 10.1093/jexbot/52.suppl_1.487.PubMedView ArticleGoogle Scholar
- Gómez-Luna BE, De la Luz Ruiz-Aguilar GM, Vázquez-Marrufo G, Dendooven L, Olalde-Portugal V: Enzyme activities and metabolic profiles of soil microorganisms at KILN sites in Quercus spp. temperate forests of central Mexico. Appl Soil Ecol. 2012, 52: 48-55.View ArticleGoogle Scholar
- Puglisi E, Del Re AAM, Rao MA, Gianfreda L: Development and validation of numerical indexes integrating enzyme activities of soils. Soil Biol Biochem. 2006, 38: 1673-1681. 10.1016/j.soilbio.2005.11.021.View ArticleGoogle Scholar
- Gomez E, Garland J, Conti M: Reproducibility in the response of soil bacterial community-level physiological profiles from a land use intensification gradient. Appl Soil Ecol. 2004, 26: 21-30. 10.1016/j.apsoil.2003.10.007.View ArticleGoogle Scholar
- Papatheodorou EM, Efthimiadou E, Stamou GP: Functional diversity of soil bacteria as affected by management practices and phenological stage of Phaseolus vulgaris. Eur J Soil Biol. 2008, 44: 429-436. 10.1016/j.ejsobi.2008.06.002.View ArticleGoogle Scholar
- Preston-Mafham J, Boddy L, Randerson PF: Analysis of microbial community functional diversity using sole-carbonsource utilization profiles – a critique. FEMS Microb Ecol. 2002, 42: 1-14.Google Scholar
- Singh G, Mukerji KG: Root Exudates as determinant of rhizospheric microbial biodiversity. Microbial activity in the rhizosphere. Volume 7. Edited by: Mukerji KG, Manoharachary C, Singh J. 2006, Berlin: Springer, 39-53.View ArticleGoogle Scholar
- Hadacek F, Gunther FF: Plant root carbohydrates affect growth behaviour of endophytic microfungi. FEMS Microbiol Ecol. 2002, 41: 161-170. 10.1111/j.1574-6941.2002.tb00977.x.PubMedView ArticleGoogle Scholar
- Foyer CH, Mullineaux PM: Causes of photooxidative stres and amelioration of defense dystems in Plants. 1994, Boca Raton: CRC PressGoogle Scholar
- Baker CJ, Orlandi EW: Active oxygen in plant pathogenesis. Annu Rev Phytopathol. 1995, 33: 299-321. 10.1146/annurev.py.33.090195.001503.PubMedView ArticleGoogle Scholar
- Härndahl U, Hall RB, Osteryoung KW, Vierling E, Bornman JF, Sundby C: The chloroplast small heat shock protein undergoes oxidation-dependent conformational changes and may protect plants from oxidative stress. Cell Stress Chaperon. 1999, 4: 129-138. 10.1379/1466-1268(1999)004<0129:TCSHSP>2.3.CO;2.View ArticleGoogle Scholar
- Groom QJ, Torres MA, Fordham-Skelton AP, Hammond-Kosack KE, Robinson NJ, Jones JD: rbohA, a rice homologue of the mammalian gp91phox respiratory burst oxidase gene. Plant J. 1996, 10: 515-522. 10.1046/j.1365-313X.1996.10030515.x.PubMedView ArticleGoogle Scholar
- Jelenska J, van Hal JA, Greenberg JT: Pseudomonas syringae hijacks plant stress chaperone machinery for virulence. PNAS. 2010, 107: 13177-13182. 10.1073/pnas.0910943107.PubMedPubMed CentralView ArticleGoogle Scholar
- Walden AR, Walter C, Gardner RC: Genes expressed in pinus radiata male cones include homologs to anther-specific and pathogenesis response genes. Plant Physiol. 1999, 121: 1103-1116. 10.1104/pp.121.4.1103.PubMedPubMed CentralView ArticleGoogle Scholar
- Pontier D, Godiard L, Marco Y, Roby D: hsr203J, a tobacco gene whose activation is rapid, highly localized and specific for incompatible plant/pathogen interactions. Plant J. 1994, 5: 507-521. 10.1046/j.1365-313X.1994.5040507.x.PubMedView ArticleGoogle Scholar
- Zhou YL, Xu MR, Zhao MF, Xie XW, Zhu LH, Fu BY, Li ZK: Genome-wide gene responses in a transgenic rice line carrying the maize resistance geneRxo1to the rice bacterial streak pathogen, Xanthomonas oryzaepv. Oryzicola.BMC Genomics. 2010, 11: 78-10.1186/1471-2164-11-78.PubMedPubMed CentralView ArticleGoogle Scholar
- Magarey RC: Yield decline of sugarcane. Current trends in sugarcane pathology. Edited by: Rao GP, Gillaspie AGJr, Upadhyaya PP, Bergamin A, Agnihotri VP, Chen CT. 1994, Pitampura: International Books and Periodicals Supply Service, 393-412.Google Scholar
- Stirling GR, Blair BL, Whittle PJL: Nematode pests: their role in yield decline of sugarcane and opportunities for improved management practices. Sugarcane: Research towards efficient and sustainable production. Edited by: Wilson JR, Hogarth DM, Campbell JA, Garside AL. 1996, Brisbane: CSIRO Division of Tropical Crops and Pastures, 228-229.Google Scholar
- Nataf Y, Yaron S, Stahl F, Lamed R, Bayer EA, Scheper TH, Sonenshein AL, Shoham Y: Cellodextrin and laminaribiose ABC transporters in Clostridium thermocellum. J Bacteriol. 2009, 191: 203-209. 10.1128/JB.01190-08.PubMedPubMed CentralView ArticleGoogle Scholar
- Davidson AL, Chen J: ATP-binding cassette transporters in bacteria. Annu Rev Biochem. 2004, 73: 241-268. 10.1146/annurev.biochem.73.011303.073626.PubMedView ArticleGoogle Scholar
- Elferink MG, Albers SV, Konings WN, Driessen AJ: Sugar transport in Sulfolobus solfataricus is mediated by two families of binding protein-dependent ABC transporters. Mol Microbiol. 2001, 39: 1494-1503. 10.1046/j.1365-2958.2001.02336.x.PubMedView ArticleGoogle Scholar
- Barrett JF, Hoch JA: Two-component signal transduction as a target for microbial anti-infective therapy. Antimicrob Agents Ch. 1998, 42: 1529-1536.Google Scholar
- Dekkers LC, Bloemendaal CJP, de Weger LA, Wijffelman CA, Spaink HP, Lugtenberg BJ: A two-component system plays an important role in the root-colonizing ability of Pseudomonas fluorescens strain WCS365. Mol Plant Microbe Interact. 1998, 11: 45-56. 10.1094/MPMI.1918.104.22.168.PubMedView ArticleGoogle Scholar
- Chaparro JM, Badri DV, Bakker MG, Sugiyama A, Manter DK, Vivanco JM: Root exudation of phytochemicals in Arabidopsis follows specific patterns that are developmentally programmed and correlate with soil microbial functions. PLoS ONE. 2013, 8: e55731-10.1371/journal.pone.0055731.PubMedPubMed CentralView ArticleGoogle Scholar
- Trivedi P, He Z, Van Nostrand JD, Albrigo G, Zhou J, Wang N: Huanglongbing alters the structure and functional diversity of microbial communities associated with citrus rhizosphere. The ISME Journal. 2012, 6: 363-383. 10.1038/ismej.2011.100.PubMedPubMed CentralView ArticleGoogle Scholar
- Schneider T, Keiblinger KM, Schmid E, Sterflinger-Gleixner K, Ellersdorfer G, Roschitzki B, Richter A, Eberl L, Zechmeister-Boltenstern S, Riedel K: Who is who in litter decomposition? Metaproteomics reveals major microbial players and their biogeochemical functions. ISME J. 2012, 6: 1749-1762. 10.1038/ismej.2012.11.PubMedPubMed CentralView ArticleGoogle Scholar
- Hettich RL, Sharma R, Chourey K, Giannone RJ: Microbial metaproteomics: identifying the repertoire of proteins that microorganisms use to compete and cooperate in complex environmental communities. Curr Opin Microbiol. 2012, 15: 373-380. 10.1016/j.mib.2012.04.008.PubMedView ArticleGoogle Scholar
- Hettich RL, Pan C, Chourey K, Giannone RJ: Metaproteomics: Harnessing the power of high performance mass spectrometry to identify the suite of proteins that control metabolic activities in microbial communities. Anal Chem. 2013, 85: 4203-4214. 10.1021/ac303053e.PubMedPubMed CentralView ArticleGoogle Scholar
- Keiblinger KM, Wilhartitz IC, Schneider T, Roschitzki B, Schmid E, Eberl L, Riedel K, Zechmeister-Boltenstern S: Soil metaproteomics - Comparative evaluation of protein extraction protocols. Soil Biol Biochem. 2012, 54: 14-24.PubMedPubMed CentralView ArticleGoogle Scholar
- Erickson AR, Cantarel BL, Lamendella R, Darzi Y, Mongodin EF, Pan C, Shah M, Halfvarson J, Tysk C, Henrissat B, Raes J, Verberkmoes NC, Fraser CM, Hettich RL, Jansson JK: Integrated metagenomics/metaproteomics reveals human host-microbiota signatures of Crohn's disease. PLoS ONE. 2012, 7: e49138-10.1371/journal.pone.0049138.PubMedPubMed CentralView ArticleGoogle Scholar
- Chen RK, Lin YQ, Zhang YB, Sun ZC: One hundred questions for sugarcane technique. 2009, Beijing: China Agriculture PressGoogle Scholar
- Wang QY, Zhou DM, Cang L: Microbial and enzyme properties of apple orchard soil as affected by long-term application of copper fungicide. Soil Biol Biochem. 2009, 41: 1504-1509. 10.1016/j.soilbio.2009.04.010.View ArticleGoogle Scholar
- Carine F, Capowiez Y, Stéven C: Enzyme activities in apple orchard agroecosystems: How are they affected by management strategy and soil properties. Soil Biol Biochem. 2009, 41: 61-68. 10.1016/j.soilbio.2008.09.018.View ArticleGoogle Scholar
- Yu XZ, Wu SC, Wu FY, Wong MH: Enhanced dissipation of PAHs from soil using mycorrhizal ryegrass and PAH-degrading bacteria. J Hazard Mater. 2011, 186: 1206-1217. 10.1016/j.jhazmat.2010.11.116.PubMedView ArticleGoogle Scholar
- Lin RY, Rong H, Zhou JJ, Yu CP, Ye CY, Chen LS, Lin WX: Impact of allelopathic rice seedlings on rhizospheric microbial populations and their functional diversity. Acta Ecologica Sinica. 2007, 27: 3644-3654. 10.1016/S1872-2032(07)60078-X.View ArticleGoogle Scholar
- Choi KH, Dobbs FC: Comparison of two kinds of Biolog microplates (GN and ECO) in their ability to distinguish among aquatic microbial communities. J Microbiol Meth. 1999, 36: 203-213. 10.1016/S0167-7012(99)00034-2.View ArticleGoogle Scholar
- Blum H, Beiers H, Gross HJ: Improved silver staining of plant proteins, RNA and DNA in polyacrylamide gels. Electrophoresis. 1987, 8: 93-99. 10.1002/elps.1150080203.View ArticleGoogle Scholar
- Uniprot Database. http://www.uniprot.org,
- WEGO-Web Gene Ontology Annotation Plotting. http://wego.genomics.org.cn/cgi-bin/wego/index.pl,
- Ye J, Fang L, Zheng H, Zhang Y, Chen J, Zhang Z, Wang J, Li S, Li R, Bolund L, Wang J: WEGO: a web tool for plotting GO annotations. Nucleic Acids Res. 2006, 34: 293-297. 10.1093/nar/gkl031.View ArticleGoogle Scholar
- KEGG-Kyoto Encyclopedia of Genes and Genomes. http://www.genome.jp/kegg,
This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.