Global transcriptome profiling of wild soybean (Glycine soja) roots under NaHCO3treatment
- Ying Ge†1,
- Yong Li†1,
- Yan-Ming Zhu1Email author,
- Xi Bai1,
- De-Kang Lv1,
- Dianjing Guo2Email author,
- Wei Ji1 and
- Hua Cai1
© Ge et al; licensee BioMed Central Ltd. 2010
Received: 25 November 2009
Accepted: 26 July 2010
Published: 26 July 2010
Plant roots are the primary site of perception and injury for saline-alkaline stress. The current knowledge of saline-alkaline stress transcriptome is mostly focused on saline (NaCl) stress and only limited information on alkaline (NaHCO3) stress is available.
Using Affymetrix® Soybean GeneChip®, we conducted transcriptional profiling on Glycine soja roots subjected to 50 mmol/L NaHCO3 treatment. In a total of 7088 probe sets, 3307 were up-regulated and 5720 were down-regulated at various time points. The number of significantly stress regulated genes increased dramatically after 3 h stress treatment and peaked at 6 h. GO enrichment test revealed that most of the differentially expressed genes were involved in signal transduction, energy, transcription, secondary metabolism, transporter, disease and defence response. We also detected 11 microRNAs regulated by NaHCO3 stress.
This is the first comprehensive wild soybean root transcriptome analysis under alkaline stress. These analyses have identified an inventory of genes with altered expression regulated by alkaline stress. The data extend the current understanding of wild soybean alkali stress response by providing a set of robustly selected, differentially expressed genes for further investigation.
Soil salinity-alkalinity is one of the major environmental challenges limiting crop productivity globally. For example, the western Songnen Plain of China, which has 3.73 million ha of sodic land, is one of the three major contiguous sodic soil regions in the world. Understanding the molecular basis of plant response under saline-alkaline conditions will facilitate biotechnology efforts to breed crop plants with enhanced tolerance to high saline-alkaline. Root is an important organ for carrying water and mineral nutrients to the rest of the plant. As the primary site of perception and injury for salinity and alkaline stress, roots provide an ideal target for study of the molecular mechanism underlying plant saline-alkaline stress tolerance and adaptation .
Soybean is rich in nutraceutical compounds, e.g., isoflavone and saponins. Its high symbiotic nitrogen fixing capacity (100 Kg/ha/year; FAO data 1984) helps to replenish soil nitrogen. Therefore, soybean is an ideal crop for crop rotation and intercropping. Wild soybean exhibits much higher adaptability to suboptimal (i.e. stressful) natural environment compared to the cultivated soybean. The wild soybean (Glycine soja) line used in this study can germinate and set seed in the sodic soil at pH9.02 and survive in the nutrient solution with 50 mmol/L NaHCO3. The physiological stress response of wild soybean has been described previously . The obvious advantage of wild soybean over other extremophile model plants is that it can be directly compared with soybean cultivar to generate useful information for elucidation of plant stress tolerance and adaptation.
High throughput technologies, such as microarray, have been used to examine the gene expression patterns under various environmental cues in Arabidopsis [1, 3–5], rice , wheat [7, 8], grape  and soybean . Although studies on plant sodic stress has been conducted in perennial plant Leymus chinensis , Puccinellia tenuiora [12, 13], Limonium bicolor  and Tamarix hispida  using cDNA array, the dynamic expression change under sodic stress is not yet available. Currently, commercialization microarrays are only available for a small number of species. Therefore, hybridization using a microarray for a closely related species was used and has demonstrated feasible, without discernible loss of information . Ji has demonstrated that feasible to investigate the wild soybean's gene expression profile using the Affymetrix® Soybean Genome Genechip® based on the high similarity between the two allied species by comparison between the EST sequences of Glycine soja and Glycine max .
In the present study, we analyzed the transcriptome changes in Glycine soja roots under NaHCO3 treatments using Affymetrix® Soybean Genome Array. Our objectives were threefold: (1) to identify genes regulated by alkaline stress, (2) to identify genes co-regulated in a similar pattern and their dynamic change over the course of stress treatment, and (3) to identify the expression feature of gene family and their function category.
Results and Discussion
Transcriptome profiling data
The data discussed in this publication have been deposited in NCBI's Gene Expression Omnibus  and are accessible through GEO Series accession number GSE17883.
The assessment of duplicated microarray experiments using correlation analysis was shown in Additional file 1. The Pearson coefficients ranged from 0.953 to 0.993. A total of 23849 probe sets were considered Present, among which 23741 showing consistent expression patterns in the replicates (0 < median (SD/mean) < 0.5) were used in the following analysis.
Validation of microarray data by real-time quantitative PCR
Identification of genes differentially expressed throughout the NaHCO3stress
Co-expression analysis of stress regulated genes
Time course analysis using a method described by Storey  revealed 1592 probe sets (q < 0.001) displaying significantly changed expression (Additional file 3). Hierarchical clustering of averaged expression value from two biological replicates identified 8 distinctive patterns for the 1592 genes (Additional file 4).
Functional categorization and pathway of differentially expressed genes
Genes involved in metabolism, secondary metabolism, disease and defence, cell structure, intracellular traffic were induced after 6 h and genes involved in energy production were decreases after 6 h. Furthermore, genes responsible for signal transduction, transcription and transporter were decreased at later time point (12 h). Signal transduction and intracellular traffic were decreased at 24 h, while energy was induced. These observations were further supported by a more specific comparison of metabolism using MapMan. This analysis showed up-regulation of several biosynthetic pathways at 6 h, such as terpenes, flavonoids, phenylpropanoids & phenolics, TCA, sucrose metabolism, lignin and lignans and non-mevalonate (MVA) pathway (Additional file 6A). The number of genes participate in secondary metabolism were found more at 6 h than that at 3 h (Figure 7). A further investigation was done to the JA synthesis (Additional file 6B). It was observed that lipoxygenases and oxophytodienoate reductase were up-regulated at 6 h, indicating that the JA synthesis pathway participates in the early response to sodic stress. Several biosynthetic pathways were down-regulated at 12 h, such as cell wall modification, flavonoids, phenylpropanoids & phenolics and lipids metabolism pathways (Additional file 6A). A custom MapMan pathway image was generated and AP2-EREBP, WRKY, bZIP, MYB and MYB related, C2C2 and C2C2-CO-like transcript factors were decreased at 12 h (Additional file 6C and 6D). Protein synthesis were induced after 12 h, and most of them were plastidic, misc and proteins in the nucleotide (Additional file 6E).
The above analysis revealed a cascade process: 1) Firstly, signal transduction and secondary metabolism were induced at 3 h; 2) As a result, metabolism, disease defense, cell structure and intracellular traffic were induced at 6 h; 3) After that, signal transduction, transcription and transporter decreased after 12 h; 4) Later on, signal transduction, secondary metabolism and intracellular traffic was induced at 6 h, and decreased at 24 h; 5) After a long period of stress treatment, protein synthesis and energy were induced at 12 h and 24 h, respectively.
Detailed descriptions of genes participate in signal transduction and transcription are as follows:
Approximately, 122 probe sets representing various signalling proteins, such as 14-3-3, protein phosphatase, small GTPases, and protein kinases, calmodulin-binding family proteins, were up-regulated at 3 h and down-regulated after 12 h. Stress tolerance or susceptibility in plants is a coordinated action of various genes including those signalling pathway components [29–31]. As expected, protein phosphatase and protein kinase were over-represented at earlier time points because reversible protein phosphorylation is a central mechanism in cellular signal transduction and transcriptional regulation . Calmodulin-binding family protein, such as calcium-dependent protein kinase or calmodulin-like domain protein kinases (CDPKs) are essential sensor-transducers of calcium signalling pathways in plants . Their up-regulation at the early stage endorsed the trigger of downstream components to cope with the stressful condition.
It is noteworthy that members of the 14-3-3 family protein were also up-regulated at the early stage of NaHCO3 stress. 14-3-3 family proteins, for its specific phosphoserine/phosphothreonine-binding activity , are thought to be involved in a large range of abiotic signalling processes and to interact with many regulatory proteins like transcription factors, plasma membrane H+-ATPase, ion channels, ascorbate peroxidase (APX) and abscisic acid (ABA) [35–38].
Plant WRKY transcription factor superfamily are known to be involved in biotic  and abiotic stress  response, and in developmental processes . However, their roles in mediating plant alkaline stress response are largely unknown. Recently, 64 GmWRKY genes were identified from soybean , and 30 probe sets representing 15 WRKY family members were quickly induced at early time point before decreasing at later time points. This pattern was similar to WRKYs expression pattern in response to other biotic or abiotic stresses in numerous plant species .
Similar to the WRKY superfamily, AP2-EREBP family are well known for their important functions in plant growth and development, especially in hormonal regulation and in environmental stress response [43, 44]. Our results showed that transcripts encoding AP2-EREBP family proteins increased drastically after 3 h, and rapidly decreased after 12 h of stress treatment.
Although a member of GRAS family proteins seems to be involved in development and other processes, such as rhizobial Nod factor-induce , SHORT-ROOT movement, GA3 induction  and drought stress , very little is known about their physiological roles under saline or alkaline stress. The stress modulated expression of GRAS genes suggested they may be important in NaHCO3 stress response. A full list of GRAS family proteins in soybean still needs to be identified systematically.
In addition, the BZR1 and BES1 protein regulate subsets of BR-responsive genes as downstream signalling components  and are considered to mediate responses to other stimuli as well. The ethylene-insensitive3-like (EIL) transcription factor, which participates in ethylene signalling pathway , was also induced at the early stage of NaHCO3 stress treatment.
14-3-3 proteins are known to regulate several cellular processes and therefore are called as General Regulatory Factors (GRFs) . We found that GRF family genes were up-regulated from 0.5 to 6 h, and decreased after 12 h. Recent investigation of 14-3-3 gene expression profile showed that they are also regulated by salt stress [52–54] and alkaline stress [11, 12].
Several stress-specific microRNAs have been identified in plants under various abiotic stresses, including nutrient deficiency [55, 56], drought [57, 58], cold , high salinity [58, 60, 61], UV-B radiation  and mechanical stress . Some microRNA targets are stress-related genes, suggesting that microRNAs play important roles in plant stress response .
This is the first comprehensive transcriptome profiling analysis of wild soybean root under alkaline stress. The current knowledge about plant alkaline stress response is limited and we provide a list of genes showing dynamic expression change under NaHCO3 stress. Functional characterization of these genes highlights the common and distinctive mechanisms underlying plant response to alkaline and other abiotic stress. Most of the alkaline-modulated genes are involved in metabolism, energy, signal transduction and transcription. Some molecular processes, such as signal transduction, secondary metabolism, and regulation of transcription, were induced at earlier time points. Genes involved in these processes accomplished their regulatory mission and decreased after 12 h. As a result, protein synthesis and energy metabolism were induced. These data indicate that the cellular pathways respond to the NaHCO3 stress as a cascade process.
Plant material, growth conditions, and stress treatments
Glycine soja L. seeds were grown in a culture room with the following settings: 60% relative humidity, 24°C and a light regime of 16 h light/8 h dark. The light source SON-T ARGO 400 W generated constant illumination of 30000 lx. Before sowing, seeds of Glycine soja L. G07256 were shaken for 10 min in 98% sulfuric acid. Subsequently, seeds were washed five times with sterile water. Thirty seeds were placed on each petri dish to accelerate germination for 2 days. Germinated seedlings were then transferred into the growth boxes containing 1/4 strength Hoagland's solution. Nineteen days after sowing, seedlings in the stress treatment group were transferred into 1/4 strength Hoagland's solution with 50 mmol/L NaHCO3 (pH 8.5) before exposure to light condition for 3 h.
Tissue harvest and RNA isolation
Roots from 3 cm root apex were harvested in two independent biological replicates after 0, 0.5, 1, 3, 6, 12 and 24 h treatment with 50 mmol/L NaHCO3 stress under the same light condition. Samples were immediately frozen in liquid nitrogen, and stored at -80°C. To minimize biological variance, roots from three plants originating from the same experiment, condition and cultivar were pooled, and the extracted RNA was used for microarray hybridization. Total RNA was extracted from frozen roots with TRIzol (Invitrogen, Carlsbad, CA) according to the instructions from the manufacturer. RNA integrity was evaluated on agarose gels electrophoresis and absorbance 260/280 ratios between 1.8 and 2.2 were typically obtained.
For QRT-PCR experiments, reverse transcription was carried out using the SuperScript® III First-Strand Synthesis System (SKU# 18080-051, Invitrogen) according to the manufacturer's instructions. Prior to the QRT-PCR assays, the quality of the cDNA was assessed by PCR with gapdh-specific primers to test for genomic DNA contamination.
DNA chip hybridization
GeneChip® Soybean Genome Array (Cat. # 900526; Affymetrix®; Santa Clara, CA, USA) containing 37,744 Glycine max probe sets (35,611 transcripts) was used for microarray analysis. This high-density array consists of 11-probe pair (25 bp per oligonucleotide) and provides multiple independent measurements for each individual transcript. cDNA labelling and Affymetrix® hybridization was carried out by Gene Tech Biotechnology Company Limited (Shanghai, China) according to a Affymetrix® protocol (Affymetrix®, Santa Clara, CA) outlined in .
Microarray Data Analysis
The computation of expression values were conducted using dChip software  (Cheng Li Lab, Harvard). We adopted a sample wise normalization to the median probe cell intensity (CEL) of all 14 arrays. For each sample, the median CEL intensity of one replicate was scaled to the median CEL intensity of all arrays and defined as baseline. The remaining replicates of each sample were normalized to the baseline applying an Invariant Set Normalization Method . Model-based gene expression was obtained from normalized CEL intensities based on a Perfect Match-only model . The quality of each repeated experiment was tested by performing a Pearson's Correlation of signal intensities. Present/Absent/Marginal calls were generated from scanned arrays using Affymetrix® GCOS 1.4 software. Only genes present at least in one of the two biological replicates of each time point were considered as Present .
Two types of analysis were conducted to identify differentially expressed genes. First, two-sample t-test was used to evaluate differential expression of genes between each time point (P < 0.05) . The data were further filtered based on the False Discovery Rate (FDR, q value < 0.15) [78, 79]. Second, Edge [80, 81] time course methodology was used to test for genes with changed expression changes over time (q value < 0.001). Hour was chosen for class variable and covariate giving time points; Differential Expression Type was Time course; Spline type was Natural cubic spline.
Pearson correlation Hierarchical Clustering and K-Means Clustering were performed with TM4: MeV 4.3 [82, 83]. Details of the GeneChip® soybean genome array are available at the Affymetrix® website . The annotation and functional categories for these transcripts were assigned based on the Soybean GeneChip® annotation file (Updated Oct. 2007) and Arabidopsis ATH1 array annotation file (Updated Sept. 2007) . To assess the significance of over-represented GO terms or the transcription factor families in the list of the regulated genes against the genome, Fisher's Exact Test (p < 0.01)  and Benjamini and Hochberg method (FDR < 0.05)  were used. The visualization of profiling data sets in the context of existing knowledge (pathway) was performed with MapMan [23, 24]. The mapping file is Gmax_AFFY_09 (1.0).
Real-time quantitative PCR
The glyceraldehyde-3-phosphate dehydrogenase (gapdh, AFFX-r2-Gma-gapdh-M_at, accession # DQ355800) was used to normalize all values in the QRT-PCR assays, because it exhibited the lowest variation in expression values throughout the NaHCO3 treatment (average fold change = 1.096, coefficient of variation = 0.114). Primers for QRT-PCR were designed using Primer3 software . Primer sequences were listed in Additional file 8.
QRT-PCR reactions based on SYBR Green fluorescence were performed using SYBR GreenER™ using qPCR SuperMix Universal (SKU# 11762-500, Invitrogen) on a Bio-rad iQ5 Real-Time PCR Detection System with iQ™5 Optical System Software Version 2.0 (BIO-RAD, HERCULES, CA, USA) following the manufacturer's instructions. One microliter of synthesized cDNA (diluted 1:10) was used as template. The preset cycling parameters for a SYBR Green experiment with a dissociation curve were used. The analysis term settings were set at an amplification-based threshold, an adaptive baseline, and a moving average. The amplification efficiencies were determined by analyzing the standard curves generated from triplicate series of five cDNA template dilutions. The iQ™ 5 Optical System Software Version 2.0 plotted the known starting quantities against the measured Ct values and generate the standard curve. The amplification reactions were consisted of a 2-min denaturing step at 95°C, followed by 40 cycles at 95°C for 10 s, 60°C for 30 s and 70°C for 30 s, end with melting curve program 70°C for 30 s. Three replicate reactions per sample were used to ensure statistical significance. The RNA from each sample was analyzed simultaneously. Expression levels for all candidate genes were computed based on the stable expression level of the reference gene according to Pfaffl method .
This project was supported by grant from National Natural Science Foundation of China (30570990), the Key Research Plan of Heilongjiang Province (GB05B104), the Innovation Research Group of NEAU (CXT004), the "863" project (2006AA100104-18) and University Grants Committee, Hong Kong UGC AoE plant Agricultural Biotechnology Project (AoE B-07/09).
- Jiang Y, Deyholos MK: Comprehensive transcriptional profiling of NaCl-stressed Arabidopsis roots reveals novel classes of responsive genes. BMC Plant Biology. 2006, 6: 25-10.1186/1471-2229-6-25.PubMedPubMed CentralView ArticleGoogle Scholar
- Ge Y, Zhu YM, Lv DK, Dong TT, Wang WS, Tan SJ, Liu CH, Zou P: Research on responses of wild soybean to alkaline stress. Pratacultural Science. 2009, 26 (2): 47-52.Google Scholar
- Kilian J, Whitehead D, Horak J, Wanke D, Weinl S, Batistic O, D'Angelo C, Bornberg-Bauer E, Kudla J, Harter K: The AtGenExpress global stress expression data set: protocols, evaluation and model data analysis of UV-B light, drought and cold stress responses. Plant Journal. 2007, 50 (2): 347-363. 10.1111/j.1365-313X.2007.03052.x.PubMedView ArticleGoogle Scholar
- Seki M, Narusaka M, Ishida J, Nanjo T, Fujita M, Oono Y, Kamiya A, Nakajima M, Enju A, Sakurai T: Monitoring the expression profiles of 7000 Arabidopsis genes under drought, cold and high-salinity stresses using a full-length cDNA microarray. Plant Journal. 2002, 31 (3): 279-292. 10.1046/j.1365-313X.2002.01359.x.PubMedView ArticleGoogle Scholar
- Takahashi S, Seki M, Ishida J, Satou M, Sakurai T, Narusaka M, Kamiya A, Nakajima M, Enju A, Akiyama K: Monitoring the expression profiles of genes induced by hyperosmotic, high salinity, and oxidative stress and abscisic acid treatment in Arabidopsis cell culture using a full-length cDNA microarray. Plant Molecular Biology. 2004, 56: 29-55. 10.1007/s11103-004-2200-0.PubMedView ArticleGoogle Scholar
- Kawasaki S, Borchert C, Deyholos M, Wang H, Brazille S, Kawai K, Galbraith D, Bohnert HJ: Gene expression profiles during the initial phase of salt stress in rice. Plant Cell Online. 2001, 13: 889-906. 10.1105/tpc.13.4.889.View ArticleGoogle Scholar
- Kawaura K, Mochida K, Yamazaki Y, Ogihara Y: Transcriptome analysis of salinity stress responses in common wheat using a 22 k oligo-DNA microarray. Functional & Integrative Genomics. 2006, 6 (2): 132-142.View ArticleGoogle Scholar
- Kawaura K, Mochida K, Ogihara Y: Genome-wide analysis for identification of salt-responsive genes in common wheat. Functional & Integrative Genomics. 2008, 8 (3): 277-286.View ArticleGoogle Scholar
- Tattersall E, Grimplet J, DeLuc L, Wheatley M, Vincent D, Osborne C, Ergül A, Lomen E, Blank R, Schlauch K, et al: Transcript abundance profiles reveal larger and more complex responses of grapevine to chilling compared to osmotic and salinity stress. Functional & Integrative Genomics. 2007, 7 (4): 317-333.View ArticleGoogle Scholar
- Irsigler A, Costa M, Zhang P, Reis P, Dewey R, Boston R, Fontes E: Expression profiling on soybean leaves reveals integration of ER- and osmotic-stress pathways. BMC Genomics. 2007, 8: 431-10.1186/1471-2164-8-431.PubMedPubMed CentralView ArticleGoogle Scholar
- Jin H, Plaha P, Park JY, Hong CP, Lee IS, Yang ZH, Jiang GB, Kwak SS, Liu SK, Lee JS, Kim YA, Lim YP: Comparative EST profiles of leaf and root of Leymus chinensis, a xerophilous grass adapted to high pH sodic soil. Plant Science. 2006, 170 (6): 1081-1086. 10.1016/j.plantsci.2006.01.002.View ArticleGoogle Scholar
- Wang Y, Yang C, Liu G, Zhang G, Ban Q: Microarray and suppression subtractive hybridization analyses of gene expression in Puccinellia tenuiora after exposure to NaHCO3. Plant Science. 2007, 173 (3): 309-320. 10.1016/j.plantsci.2007.06.011.View ArticleGoogle Scholar
- Wang Y, Yang C, Liu G, Jiang J: Development of a cDNA microarray to identify gene expression of Puccinellia tenuiora under saline-alkali stress. Plant Physiology And Biochemistry. 2007, 45 (8): 567-576. 10.1016/j.plaphy.2007.05.006.PubMedView ArticleGoogle Scholar
- Wang Y, Ma H, Liu G, Xu C, Zhang D, Ban Q: Analysis of gene expression profile of Limonium bicolor under NaHCO3 stress using cDNA microarray. Plant Molecular Biology Reporter. 2008, 26 (3): 241-254. 10.1007/s11105-008-0037-4.View ArticleGoogle Scholar
- Li H, Wang Y, Jiang J, Liu G, Gao C, Yang C: Identification of genes responsive to salt stress on Tamarix hispida roots. Gene. 2009, 433 (1-2): 65-71. 10.1016/j.gene.2008.12.007.PubMedView ArticleGoogle Scholar
- Oshlack A, Chabot A, Smyth G, Gilad Y: Using DNA microarrays to study gene expression in closely related species. Bioinformatics. 2007, 23 (10): 1235-1242. 10.1093/bioinformatics/btm111.PubMedView ArticleGoogle Scholar
- Ji W, Li Y, Li J, Dai C, Wang X, Bai X, Cai H, Yang L, Zhu Y: Generation and analysis of expressed sequence tags from NaCl-treated Glycine soja. BMC Plant Biology. 2006, 6: 4-10.1186/1471-2229-6-4.PubMedPubMed CentralView ArticleGoogle Scholar
- Edgar R, Domrachev M, Lash AE: Gene Expression Omnibus: NCBI gene expression and hybridization array data repository. Nucl Acids Res. 2002, 30: 207-210. 10.1093/nar/30.1.207.PubMedPubMed CentralView ArticleGoogle Scholar
- Janes K, Gaudet S, Albeck J, Nielsen U, Lauffenburger D, Sorger P: The response of human epithelial cells to TNF involves an inducible autocrine cascade. Cell. 2006, 124 (6): 1225-1239. 10.1016/j.cell.2006.01.041.PubMedView ArticleGoogle Scholar
- Busch H, Camacho-Trullio D, Rogon Z, Breuhahn K, Angel P, Eils R, Szabowski A: Gene network dynamics controlling keratinocyte migration. 2008Google Scholar
- Xu W, Sato S, Clemente T, Chollet R: The PEP-carboxylase kinase gene family in Glycine max (GmPpcK1-4): an in-depth molecular analysis with nodulated, non-transgenic and transgenic plants. Plant Journal. 2007, 49 (5): 910-923. 10.1111/j.1365-313X.2006.03006.x.PubMedView ArticleGoogle Scholar
- Chen Z, Jenkins G, Nimmo H: pH and carbon supply control the expression of phosphoenolpyruvate carboxylase kinase genes in Arabidopsis thaliana. Plant, Cell & Environment. 2008, 31 (12): 1844-1850.View ArticleGoogle Scholar
- Enoch T, Nurse P: Mutation of fission yeast cell cycle control genes abolishes dependence of mitosis on DNA replication. Cell. 1990, 60 (4): 665-673. 10.1016/0092-8674(90)90669-6.PubMedView ArticleGoogle Scholar
- Storey JD, Xiao W, Leek JT, Tompkins RG, Davis RW: Significance analysis of time course microarray experiments. Proceedings of the National Academy of Sciences of the United States of America. 2005, 102 (36): 12837-12842. 10.1073/pnas.0504609102.PubMedPubMed CentralView ArticleGoogle Scholar
- Thimm O, Blasing O, Gibon Y, Nagel A, Meyer S, Kruger P, Selbig J, Muller L, Rhee S, Stitt M: MAPMAN: a user-driven tool to display genomics data sets onto diagrams of metabolic pathways and other biological processes. Plant Journal. 2004, 37 (6): 914-10.1111/j.1365-313X.2004.02016.x.PubMedView ArticleGoogle Scholar
- Usadel B, Nagel A, Thimm O, Redestig H, Blaesing OE, Palacios-Rojas N, Selbig J, Hannemann J, Piques MC, Steinhauser D, Scheible WR, Gibon Y, Morcuende R, Weicht D, Meyer S, Stitt M: Extension of the Visualization Tool MapMan to Allow Statistical Analysis of Arrays, Display of Coresponding Genes, and Comparison with Known Responses. Plant Physiol. 2005, 138 (3): 1195-1204. 10.1104/pp.105.060459.PubMedPubMed CentralView ArticleGoogle Scholar
- Societies A: Contrasting response mechanisms to root-zone salinity in three co-occurring Mediterranean woody evergreens: a physiological and biochemical study. Functional Plant Biology. 2009, 36 (6): 551-563. 10.1071/FP09054.View ArticleGoogle Scholar
- Dixon R, Paiva N: Stress-induced phenylpropanoid metabolism. The Plant Cell. 1995, 7 (7): 1085-10.2307/3870059.PubMedPubMed CentralView ArticleGoogle Scholar
- Xiong L, Schumaker K, Zhu J: Cell signaling during cold, drought, and salt stress. Plant Cell Online. 2002, 14: 165-183. 10.1105/tpc.010278.View ArticleGoogle Scholar
- Yamaguchi-Shinozaki K, Shinozaki K: Transcriptional regulatory networks in cellular responses and tolerance to dehydration and cold stresses. Annual Review of Plant Biology. 2006, 57: 781-803. 10.1146/annurev.arplant.57.032905.105444.PubMedView ArticleGoogle Scholar
- Mahajan S, Pandey G, Tuteja N: Calcium-and salt-stress signaling in plants: shedding light on SOS pathway. Archives of biochemistry and biophysics. 2008, 471 (2): 146-158. 10.1016/j.abb.2008.01.010.PubMedView ArticleGoogle Scholar
- Ueda A, Li P, Feng Y, Vikram M, Kim S, Kang CH, Kang JS, Bahk JD, Lee SY, Fukuhara T, Staswick PE, Pepper AE, Koiwa H: The Arabidopsis thaliana carboxyl-terminal domain phosphatase-like 2 regulates plant growth, stress and auxin responses. Plant molecular biology. 2008, 67 (6): 683-697. 10.1007/s11103-008-9348-y.PubMedView ArticleGoogle Scholar
- Harper J, Breton G, Harmon A: Decoding Ca2+ signals through plant protein kinases. Annual review of plant biology. 2004, 55: 263-288. 10.1146/annurev.arplant.55.031903.141627.PubMedView ArticleGoogle Scholar
- Muslin A, Tanner J, Allen P, Shaw A: Interaction of 14-3-3 with signaling proteins is mediated by the recognition of phosphoserine. Cell. 1996, 84 (6): 889-898. 10.1016/S0092-8674(00)81067-3.PubMedView ArticleGoogle Scholar
- Ferl R, Manak M, Reyes M: The 14-3-3s. Genome Biology. 2002, 3 (7): reviews3010.1-reviews3010.7. 10.1186/gb-2002-3-7-reviews3010.View ArticleGoogle Scholar
- Finnie C, Andersen C, Borch J, Gjetting S, Christensen A, de Boer A, Thordal-Christensen H, Collinge D: Do 14-3-3 proteins and plasma membrane H+-ATPases interact in the barley epidermis in response to the barley powdery mildew fungus?. Plant molecular biology. 2002, 49 (2): 137-147. 10.1023/A:1014938417267.View ArticleGoogle Scholar
- Yan J, He C, Wang J, Mao Z, Holaday S, Allen R, Zhang H: Overexpression of the Arabidopsis 14-3-3 protein GF14 lambda in cotton leads to a "stay-green" phenotype and improves stress tolerance under moderate drought conditions. Plant and Cell Physiology. 2004, 45 (8): 1007-1014. 10.1093/pcp/pch115.PubMedView ArticleGoogle Scholar
- Wijngaard P, Sinnige M, Roobeek I, Reumer A, Schoonheim P, Mol J, Wang M, De Boer A: Abscisic acid and 14-3-3 proteins control K+ channel activity in barley embryonic root. The Plant Journal. 2005, 41: 43-55. 10.1111/j.1365-313X.2004.02273.x.PubMedView ArticleGoogle Scholar
- Arabidopsis Gene Regulatory Information Server. [http://biodatabase.org/index.php/AGRIS_-_Arabidopsis_Gene_Regulatory_Information_Server]
- Davuluri R, Sun H, Palaniswamy S, Matthews N, Molina C, Kurtz M, Grotewold E: AGRIS: Arabidopsis gene regulatory information server, an information resource of Arabidopsis cis-regulatory elements and transcription factors. BMC bioinformatics. 2003, 4: 25-10.1186/1471-2105-4-25.PubMedPubMed CentralView ArticleGoogle Scholar
- Pandey SP, Somssich IE: The role of WRKY transcription factors in plant immunity. Plant Physiology. 2009, 150 (4): 1648-1655. 10.1104/pp.109.138990.PubMedPubMed CentralView ArticleGoogle Scholar
- Ülker B, Somssich I: WRKY transcription factors: from DNA binding towards biological function. Current Opinion in Plant Biology. 2004, 7 (5): 491-498. 10.1016/j.pbi.2004.07.012.PubMedView ArticleGoogle Scholar
- Zhou Q, Tian A, Zou H, Xie Z, Lei G, Huang J, Wang C, Wang H, Zhang J, Chen S: Soybean WRKY-type transcription factor genes, GmWRKY13, GmWRKY21, and GmWRKY54, confer differential tolerance to abiotic stresses in transgenic Arabidopsis plants. Plant Biotechnology Journal. 2008, 6 (5): 486-503. 10.1111/j.1467-7652.2008.00336.x.PubMedView ArticleGoogle Scholar
- Nakashima K, Ito Y, Yamaguchi-Shinozaki K: Transcriptional regulatory networks in response to abiotic stresses in Arabidopsis and grasses. Plant Physiology. 2009, 149: 88-95. 10.1104/pp.108.129791.PubMedPubMed CentralView ArticleGoogle Scholar
- Smit P, Raedts J, Portyanko V, Debelle F, Gough C, Bisseling T, Geurts R: NSP1 of the GRAS protein family is essential for rhizobial Nod factor-induced transcription. Science. 2005, 308 (5729): 1789-1791. 10.1126/science.1111025.PubMedView ArticleGoogle Scholar
- Gallagher K, Benfey P: Both the conserved GRAS domain and nuclear localization are required for SHORT-ROOT movement. The Plant Journal. 2009, 57 (5): 785-797. 10.1111/j.1365-313X.2008.03735.x.PubMedPubMed CentralView ArticleGoogle Scholar
- Itoh H, Shimada A, Ueguchi-Tanaka M, Kamiya N, Hasegawa Y, Ashikari M, Matsuoka M: Overexpression of a GRAS protein lacking the DELLA domain confers altered gibberellin responses in rice. Plant Journal. 2005, 44 (4): 669-679. 10.1111/j.1365-313X.2005.02562.x.PubMedView ArticleGoogle Scholar
- Guo H, Jiao Y, Di C, Yao D, Gaihua Z, Zheng X, Lan L, Qunlian Z, Guo A, Su Z: Discovery of Arabidopsis GRAS family genes in response to osmotic and drought stresses. Chinese Bulletin of Botany. 2009, 44 (3): 290-299.Google Scholar
- Yin Y, Wang Z, Mora-Garcia S, Li J, Yoshida S, Asami T, Chory J: BES1 accumulates in the nucleus in response to brassinosteroids to regulate gene expression and promote stem elongation. Cell. 2002, 109 (2): 181-191. 10.1016/S0092-8674(02)00721-3.PubMedView ArticleGoogle Scholar
- Chao Q, Rothenberg M, Solano R, Roman G, Terzaghi W, Ecker J: Activation of the ethylene gas response pathway in Arabidopsis by the nuclear protein ETHYLENE-INSENSITIVE3 and related proteins. Cell. 1997, 89 (7): 1133-1144. 10.1016/S0092-8674(00)80300-1.PubMedView ArticleGoogle Scholar
- Rooney M, Ferl R: Sequences of three Arabidopsis general regulatory factor genes encoding GF14 (14-3-3) proteins. Plant Physiology. 1995, 107: 283-284. 10.1104/pp.107.1.283.PubMedPubMed CentralView ArticleGoogle Scholar
- Chen F, Li Q, Sun L, He Z: The rice 14-3-3 gene family and its involvement in responses to biotic and abiotic stress. DNA research. 2006, 13 (2): 53-63. 10.1093/dnares/dsl001.PubMedView ArticleGoogle Scholar
- Xu W, Shi W: Expression profiling of the 14-3-3 gene family in response to salt stress and potassium and iron deficiencies in young tomato (Solanum lycopersicum) roots: Analysis by real-time RTPCR. Annals of Botany. 2006, 98 (5): 965-974. 10.1093/aob/mcl189.PubMedPubMed CentralView ArticleGoogle Scholar
- Wei X, Zhang Z, Li Y, Wang X, Shao S, Chen L, Li X: Expression analysis of two novel cotton 14-3-3 genes in root development and in response to salt stress. Progress in Natural Science. 2009, 19 (2): 173-178. 10.1016/j.pnsc.2008.06.016.View ArticleGoogle Scholar
- Fujii H, Chiou T, Lin S, Aung K, Zhu J: A miRNA involved in phosphate-starvation response in Arabidopsis. Current Biology. 2005, 15 (22): 2038-2043. 10.1016/j.cub.2005.10.016.PubMedView ArticleGoogle Scholar
- Sunkar R, Chinnusamy V, Zhu J, Zhu J: Small RNAs as big players in plant abiotic stress responses and nutrient deprivation. Trends in plant science. 2007, 12 (7): 301-309. 10.1016/j.tplants.2007.05.001.PubMedView ArticleGoogle Scholar
- Zhao B, Liang R, Ge L, Li W, Xiao H, Lin H, Ruan K, Jin Y: Identification of drought-induced microRNAs in rice. Biochemical and Biophysical Research Communications. 2007, 354 (2): 585-590. 10.1016/j.bbrc.2007.01.022.PubMedView ArticleGoogle Scholar
- Liu H, Tian X, Li Y, Wu C, Zheng C: Microarray-based analysis of stress-regulated microRNAs in Arabidopsis thaliana. RNA. 2008, 14 (5): 836-843. 10.1261/rna.895308.PubMedPubMed CentralView ArticleGoogle Scholar
- Zhou X, Wang G, Sutoh K, Zhu J, Zhang W: Identification of cold-inducible microRNAs in plants by transcriptome analysis. BBA-Gene Regulatory Mechanisms. 2008, 1779 (11): 780-788.PubMedGoogle Scholar
- Sunkar R, Zhou X, Zheng Y, Zhang W, Zhu J: Identification of novel and candidate miRNAs in rice by high throughput sequencing. BMC Plant Biology. 2008, 8: 25-10.1186/1471-2229-8-25.PubMedPubMed CentralView ArticleGoogle Scholar
- Ding D, Zhang L, Wang H, Liu Z, Zhang Z, Zheng Y: Differential expression of miRNAs in response to salt stress in maize roots. Annals of Botany. 2009, 103: 29-38. 10.1093/aob/mcn205.PubMedPubMed CentralView ArticleGoogle Scholar
- Zhou X, Wang G, Zhang W: UV-B responsive microRNA genes in Arabidopsis thaliana. Molecular Systems Biology. 2007, 3: 103-10.1038/msb4100143.PubMedPubMed CentralView ArticleGoogle Scholar
- Lu S, Sun Y, Shi R, Clark C, Li L, Chiang V: Novel and mechanical stress-responsive microRNAs in Populus trichocarpa that are absent from Arabidopsis. The Plant Cell Online. 2005, 17 (8): 2186-2203. 10.1105/tpc.105.033456.View ArticleGoogle Scholar
- Phillips J, Dalmay T, Bartels D: The role of small RNAs in abiotic stress. FEBS letters. 2007, 581 (19): 3592-3597. 10.1016/j.febslet.2007.04.007.PubMedView ArticleGoogle Scholar
- Altschul S, Gish W, Miller W, Myers E, Lipman D: Basic local alignment search tool. J mol Biol. 1990, 215 (3): 403-410.PubMedView ArticleGoogle Scholar
- miRBase. [http://microrna.sanger.ac.uk/sequences/]
- Griffiths-Jones S, Saini HK, van Dongen S, Enright AJ: miRBase: tools for microRNA genomics. Nucleic Acids Research. 2008, 36: D154-158. 10.1093/nar/gkm952.PubMedPubMed CentralView ArticleGoogle Scholar
- Griffiths-Jones S, Grocock RJ, van Dongen S, Bateman A, Enright AJ: miRBase: microRNA sequences, targets and gene nomenclature. Nucleic Acids Research. 2006, 34: D140-144. 10.1093/nar/gkj112.PubMedPubMed CentralView ArticleGoogle Scholar
- Griffiths-Jones S: The microRNA Registry. Nucleic Acids Research. 2004, 32: D109-111. 10.1093/nar/gkh023.PubMedPubMed CentralView ArticleGoogle Scholar
- Sunkar R, Kapoor A, Zhu J: Posttranscriptional induction of two Cu/Zn superoxide dismutase genes in Arabidopsis is mediated by downregulation of miR398 and important for oxidative stress tolerance. The Plant Cell Online. 2006, 18 (8): 2051-2065. 10.1105/tpc.106.041673.View ArticleGoogle Scholar
- Wang Y, Li P, Cao X, Wang X, Zhang A, Li X: Identification and expression analysis of miRNAs from nitrogen-fixing soybean nodules. Biochemical and Biophysical Research Communications. 2009, 378 (4): 799-803. 10.1016/j.bbrc.2008.11.140.PubMedView ArticleGoogle Scholar
- Miyashima S, Hashimoto T, Nakajima K: ARGONAUTE1 Acts in Arabidopsis root radial pattern formation independently of the SHR/SCR pathway. Plant Cell Physiol. 2009, 50 (3): 626-634. 10.1093/pcp/pcp020.PubMedView ArticleGoogle Scholar
- Affymetrix® GeneChip® Expression Analysis Technical Manual. [http://www.affymetrix.com/support/downloads/manuals/expressionanalysistechnicalmanual.pdf]
- dChip Software. [http://biosun1.harvard.edu/complab/dchip/]
- Li C, Wong WH: Model-based analysis of oligonucleotide arrays: Expression index computation and outlier detection. Proceedings of the National Academy of Sciences of the United States of America. 2001, 98: 31-36. 10.1073/pnas.011404098.PubMedPubMed CentralView ArticleGoogle Scholar
- McClintick J, Edenberg H: Effects of filtering by Present call on analysis of microarray experiments. BMC Bioinformatics. 2006, 7: 49-10.1186/1471-2105-7-49.PubMedPubMed CentralView ArticleGoogle Scholar
- Huber W, von Heydebreck A, Sultmann H, Poustka A, Vingron M: Variance stabilization applied to microarray data calibration and to the quantification of differential expression. Bioinformatics. 2002, 18 (Suppl 1): S96-S104.PubMedView ArticleGoogle Scholar
- Storey JD, Tibshirani R: Statistical significance for genome wide studies. Proceedings of the National Academy of Sciences of the United States of America. 2003, 100 (16): 9440-9445. 10.1073/pnas.1530509100.PubMedPubMed CentralView ArticleGoogle Scholar
- Benjamini Y, Hochberg Y: Controlling the false discovery rate: a practical and powerful approach to multiple testing. Journal of the Royal Statistical Society. Series B (Methodological). 1995, 57: 289-300.Google Scholar
- EDGE. [http://www.genomine.org/edge/]
- Leek JT, Monsen E, Dabney AR, Storey JD: EDGE: extraction and analysis of differential gene expression. Bioinformatics. 2006, 22 (4): 507-508. 10.1093/bioinformatics/btk005.PubMedView ArticleGoogle Scholar
- TM4: MeV. [http://www.tm4.org/mev.html]
- Saeed A, Sharov V, White J, Li J, Liang W, Bhagabati N, Braisted J, Klapa M, Currier T, Thiagarajan M: TM4: a free, open-source system for microarray data management and analysis. Biotechniques. 2003, 34 (2): 374-378.PubMedGoogle Scholar
- Affymetrix® website. [http://affymetrix.com/index.affx]
- Soybean GeneChip® and Arabidopsis ATH1 array annotation file. [http://seedgenenetwork.net/annotate]
- Agresti A: A survey of exact inference for contingency tables. Statistical Science. 1992, 131-153. 10.1214/ss/1177011454.Google Scholar
- Rozen S, Skaletsky H: Primer3 on the WWW for general users and for biologist programmers. Methods in Molecular Biology. 2000, 132: 365-386.PubMedGoogle Scholar
- Pfaffl MW: A new mathematical model for relative quantification in real-time RT-PCR. Nucleic Acids Research. 2001, 29: 2002-2007. 10.1093/nar/29.9.e45.View ArticleGoogle Scholar
- Feng J, Liu D, Pan Y, Gong W, Ma L, Luo J, Deng X, Zhu Y: An annotation update via cDNA sequence analysis and comprehensive profiling of developmental, hormonal or environmental responsiveness of the Arabidopsis AP2/EREBP transcription factor gene family. Plant molecular biology. 2005, 59 (6): 853-868. 10.1007/s11103-005-1511-0.PubMedView ArticleGoogle Scholar
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