Literature DB >> 26484247

Transcriptomics analyses of soybean leaf and root samples during water-deficit.

Prateek Tripathi1, Roel C Rabara2, Qingxi J Shen3, Paul J Rushton2.   

Abstract

Drought being a major challenge for crop productivity and yield affects multigenic and quantitative traits. It is also well documented that water stress shows a cross talk with other abiotic stresses such as high temperature and high light intensities (Tripathi et al., 2013) [1]. In this report, we documented the details of the methods and quality controls used and considered in our time course-based transcriptome profile of soybean plants under water deficit conditions using microarray technology. The findings of this study are recently published by the Rushton lab in BMC Genomics for a comparative study of tobacco and Soybean (Rabara et al., 2015) [2]. The raw microarray data set is deposited in GEO database with accession number GSE49537.

Entities:  

Keywords:  Drought; Genomics; Microarray; Soybean; Transcriptomics; WRKY

Year:  2015        PMID: 26484247      PMCID: PMC4583655          DOI: 10.1016/j.gdata.2015.05.036

Source DB:  PubMed          Journal:  Genom Data        ISSN: 2213-5960


Direct link to deposited data

Deposited dataset can be found here : http://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE49537.

Experimental design, materials and methods

Plant material and growth conditions

Soybean (Glycine max L.) W-82 seeds were soaked in water for 10 min and viable seeds were used for sowing. The seeds were sowed on a vermiculite–perlite mix (1:1) and after 2 weeks of growth plantlets were transferred to a hydroponics set-up with 0.5 × Hogland solution, pH 5.8 in a growth chamber (ConvironR) with a 16 h/8 h day/night cycle at 25 °C and 50% RH. The tissues (leaf and root) were harvested after 30 days of total growth when the second tri-foliate becomes fully visible. Plants were allowed to dehydrate in the growth chamber by transferring them to empty boxes for 6 time points (0 min, 30 min, 1 h, 2 h, 3 h and 5 h) of dehydration (Fig. 1) and harvested without actually touching the plants to nullify any possibility of wounding. Nine independent plants were utilized (three replicates per time point and three plants per replicate) for the study and after harvesting were immediately stored in − 80 °C. These samples were utilized for the transcriptomics purposes.
Fig. 1

Schematics of the experimental set-up.

Sample collection and RNA preparation

An equal amount of tissue from each nine plants per time-point was pooled for RNA isolation. Tissue was homogenized using ceramic mortar and pestle in liquid nitrogen and RNA was isolated from both the tissues using QIAGEN© RNeasy-MIDI kit as per the user manual instructions. To get rid off the possible DNase contamination, DNase treatment was performed using Ambion's TURBO DNA-free™ Kit.

RNA quantification and quality check

Quality of the total RNA was checked using Nanodrop and AGILENT© Bioanalyzer-2100 using RNA600 chip as per the user manual instruction. 10 μg of total RNA from each tissue per time-point was used for microarray analysis. Our samples had high quality RNA as per the microarray analysis standards. Any sample with RNA Integrity Number (RIN) less than 7.0 and 260/230 ratio less than 1.7 were not used for the microarray analysis (Table 1).
Table 1

Concentration and RIN values of total RNA isolated from Soybean shoot & root samples.

S. noSample IDTotal RNA (μg)RNA integrity number (RIN)
Leaves
10 min 1213.98.1
20 min 2151.58.4
30 min 3141.98.4
430 min 187.159.1
530 min 2128.79.5
630 min 356.259.2
71 h 1120.39.3
81 h 273.28.9
91 h 3148.058.4
102 h 1708.6
112 h 259.48.4
122 h 3140.258.6
133 h 155.657.9
143 h 260.759.1
153 h 335.78.5
165 h 1358.9
175 h 298.78.8
185 h 3678



Roots
10 min 166.458.6
20 min273.358.1
30 min 363.158.8
430 min 191.359.1
530 min 269.159.4
630 min 387.159
71 h 1122.858.9
81 h 2101.78.8
91 h 3100.88.7
102 h 193.98.7
112 h 296.157.8
122 h 3107.858.6
133 h 188.88.4
143 h 249.058.7
153 h 3498.4
165 h 131.28.4
175 h 282.958.1
185 h 345.38.4

Microarray data and data analysis

Microarray analyses was performed using a custom based 12 × oligo chip designed by NIBMELGEN, which constitutes 60mer of each high and low confidence gene from GLYMAv1.0 of soybean genome release from phytozome [3] along with manually curated 179 soybean WRKY genes obtained using bio-informatics pipeline described in Rushton et al. [4] and Tripathi et al. [5]. Oligoarray experiments were performed for 36 samples (18/tissue) at MOGENE, LC (St. Louis, MO). Data analysis was performed using ArrayStar v4 software package from DNASTAR (DNASTAR Inc., Madison, WI, USA). Differential regulation was calculated with FDR correction at 5%.

Discussion

In this briefing, we described a unique and a robust dataset of trancriptomic analyses of roots and leaf samples of soybean under water stress. This experimental set-up leads us to find some novel candidates, which provide many novel insights into the roles of WRKY transcription factors during water stress in soybean towards system-wide understanding of water-stress signaling as also discussed in Tripathi et al. [1]. This dataset has been recently used in a comparative study of soybean and tobacco under drought conditions [2]. We hope the design and dataset may also be useful for the different groups and investigations related to other transcription factors for crop improvement under drought conditions.

Conflict of interest

The authors declare no conflicts if interest.
Specifications
Organism/cell line/tissueSoybean (Glycine max.), Four weeks old plants
SexNA
Sequencer or array typeNimblegen custom based MicroarrayNimbleGen Glycine max Array [100526_Brach_MoGene_exp]
Data formatRaw Data
Experimental factorsDehydrated and Un-dehydrated time course samples (roots and shoots)
Experimental featuresFour week old hydrophonically grown plants were transferred to empty boxes (without touching the plants) and samples (roots and shoots separately) were collected at 0 h (control), 30 min, 1 h, 2 h, 3 h and 5 h and frozen immediately in liquid nitrogen for further processing.
ConsentNA
Sample source locationBrookings, South Dakota, USA
  5 in total

Review 1.  A systems biology perspective on the role of WRKY transcription factors in drought responses in plants.

Authors:  Prateek Tripathi; Roel C Rabara; Paul J Rushton
Journal:  Planta       Date:  2013-10-22       Impact factor: 4.116

2.  The WRKY transcription factor family in Brachypodium distachyon.

Authors:  Prateek Tripathi; Roel C Rabara; Tanner J Langum; Ashley K Boken; Deena L Rushton; Darius D Boomsma; Charles I Rinerson; Jennifer Rabara; R Neil Reese; Xianfeng Chen; Jai S Rohila; Paul J Rushton
Journal:  BMC Genomics       Date:  2012-06-22       Impact factor: 3.969

3.  Phytozome: a comparative platform for green plant genomics.

Authors:  David M Goodstein; Shengqiang Shu; Russell Howson; Rochak Neupane; Richard D Hayes; Joni Fazo; Therese Mitros; William Dirks; Uffe Hellsten; Nicholas Putnam; Daniel S Rokhsar
Journal:  Nucleic Acids Res       Date:  2011-11-22       Impact factor: 16.971

4.  Tobacco drought stress responses reveal new targets for Solanaceae crop improvement.

Authors:  Roel C Rabara; Prateek Tripathi; R Neil Reese; Deena L Rushton; Danny Alexander; Michael P Timko; Qingxi J Shen; Paul J Rushton
Journal:  BMC Genomics       Date:  2015-06-30       Impact factor: 3.969

5.  TOBFAC: the database of tobacco transcription factors.

Authors:  Paul J Rushton; Marta T Bokowiec; Thomas W Laudeman; Jennifer F Brannock; Xianfeng Chen; Michael P Timko
Journal:  BMC Bioinformatics       Date:  2008-01-25       Impact factor: 3.169

  5 in total
  7 in total

1.  Genome-wide characterization and expression analysis of TOPP-type protein phosphatases in soybean (Glycine max L.) reveal the role of GmTOPP13 in drought tolerance.

Authors:  Sibo Wang; Jingsong Guo; Ying Zhang; Yushuang Guo; Wei Ji
Journal:  Genes Genomics       Date:  2021-04-17       Impact factor: 1.839

2.  A toolbox of genes, proteins, metabolites and promoters for improving drought tolerance in soybean includes the metabolite coumestrol and stomatal development genes.

Authors:  Prateek Tripathi; Roel C Rabara; R Neil Reese; Marissa A Miller; Jai S Rohila; Senthil Subramanian; Qingxi J Shen; Dominique Morandi; Heike Bücking; Vladimir Shulaev; Paul J Rushton
Journal:  BMC Genomics       Date:  2016-02-09       Impact factor: 3.969

3.  Genome-wide Identification and Structural, Functional and Evolutionary Analysis of WRKY Components of Mulberry.

Authors:  Vinay Kumar Baranwal; Nisha Negi; Paramjit Khurana
Journal:  Sci Rep       Date:  2016-08-01       Impact factor: 4.379

4.  Comparative Metabolome Profile between Tobacco and Soybean Grown under Water-Stressed Conditions.

Authors:  Roel C Rabara; Prateek Tripathi; Paul J Rushton
Journal:  Biomed Res Int       Date:  2017-01-03       Impact factor: 3.411

5.  Phenotypic and transcriptomic responses of cultivated sunflower seedlings (Helianthus annuus L.) to four abiotic stresses.

Authors:  Max H Barnhart; Rishi R Masalia; Liana J Mosley; John M Burke
Journal:  PLoS One       Date:  2022-09-30       Impact factor: 3.752

6.  Understanding Water-Stress Responses in Soybean Using Hydroponics System-A Systems Biology Perspective.

Authors:  Prateek Tripathi; Roel C Rabara; Vladimir Shulaev; Qingxi J Shen; Paul J Rushton
Journal:  Front Plant Sci       Date:  2015-12-21       Impact factor: 5.753

7.  Comprehensive mapping of abiotic stress inputs into the soybean circadian clock.

Authors:  Meina Li; Lijun Cao; Musoki Mwimba; Yan Zhou; Ling Li; Mian Zhou; Patrick S Schnable; Jamie A O'Rourke; Xinnian Dong; Wei Wang
Journal:  Proc Natl Acad Sci U S A       Date:  2019-11-01       Impact factor: 11.205

  7 in total

北京卡尤迪生物科技股份有限公司 © 2022-2023.