Gene network modules associated with abiotic stress response in tolerant rice genotypes identified by transcriptome meta-analysis


Abiotic stress tolerance is a complex trait regulated by multiple genes and gene networks in plants. A range of abiotic stresses are known to limit rice productivity. Meta-transcriptomics has emerged as a powerful approach to decipher stress-associated molecular network in model crops. However, retaining specificity of gene expression in tolerant and susceptible genotypes during meta-transcriptome analysis is important for understanding genotype-dependent stress tolerance mechanisms. Addressing this aspect, we describe here “abiotic stress tolerant” (ASTR) genes and networks specifically and differentially expressing in tolerant rice genotypes in response to different abiotic stress conditions. We identified 6,956 ASTR genes, key hub regulatory genes, transcription factors, and functional modules having significant association with abiotic stress–related ontologies and cis-motifs. Out of the 6956 ASTR genes, 73 were co-located within the boundary of previously identified abiotic stress trait–related quantitative trait loci. Functional annotation of 14 uncharacterized ASTR genes is proposed using multiple computational methods. Around 65% of the top ASTR genes were found to be differentially expressed in at least one of the tolerant genotypes under different stress conditions (cold, salt, drought, or heat) from publicly available RNAseq data comparison. The candidate ASTR genes specifically associated with tolerance could be utilized for engineering rice and possibly other crops for broad-spectrum tolerance to abiotic stresses.

This is a preview of subscription content, log in to check access.

Access options

Buy single article

Instant unlimited access to the full article PDF.

US$ 39.95

Price includes VAT for USA

Subscribe to journal

Immediate online access to all issues from 2019. Subscription will auto renew annually.

US$ 99

This is the net price. Taxes to be calculated in checkout.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8


  1. Allen JD, Xie Y, Chen M, Girard L, Xiao G (2012) Comparing statistical methods for constructing large scale gene networks. PLoS One 7:e29348.

  2. Alm E, Arkin AP (2003) Biological networks. Curr Opin Struct Biol 13:193–202

  3. Aoki K, Ogata Y, Shibata D (2007) Approaches for extracting practical information from gene co-expression networks in plant biology. Plant Cell Physiol 48:381–390.

  4. Bagnaresi P, Biselli C, Orrù L, Urso S, Crispino L, Abbruscato P, Piffanelli P, Lupotto E, Cattivelli L, Valè G (2012) Comparative transcriptome profiling of the early response to magnaporthe oryzae in durable resistant vs susceptible rice (Oryza sativa L.) Genotypes. PLoS One 7:e51609.

  5. Bailey TL, Williams N, Misleh C, Li WW (2006) MEME: discovering and analyzing DNA and protein sequence motifs. Nucleic Acids Res 34:W369–W373

  6. Balazadeh S, Riaño-Pachón DM, Mueller-Roeber B (2008) Transcription factors regulating leaf senescence in Arabidopsis thaliana. Plant Biol 10:63–75.

  7. Barabási A-L, Oltvai ZN (2004) Network biology: understanding the cell’s functional organization. Nat Rev Genet 5:101–113.

  8. Benjamini Y, Hochberg Y (1995) Controlling the false discovery rate: a practical and powerful approach to multiple testing. J R Stat Soc Ser B Methodol 57:289–300

  9. Bindea G, Mlecnik B, Hackl H, Charoentong P, Tosolini M, Kirilovsky A, Fridman WH, Pagès F, Trajanoski Z, Galon J (2009) ClueGO: a Cytoscape plug-in to decipher functionally grouped gene ontology and pathway annotation networks. Bioinformatics 25:1091–1093.

  10. Bodén M, Bailey TL (2008) Associating transcription factor-binding site motifs with target GO terms and target genes. Nucleic Acids Res 36:4108–4117.

  11. Brown RL, Kazan K, McGrath KC, Maclean DJ, Manners JM (2003) A role for the GCC-Box in Jasmonate-mediated activation of the PDF1.2 Gene of Arabidopsis. Plant Physiol 132:1020–1032.

  12. Chakravarthy S, Tuori RP, D’Ascenzo MD et al (2003) The tomato transcription factor Pti4 regulates defense-related gene expression via GCC box and non-GCC box cis elements. Plant Cell 15:3033–3050.

  13. Chen R, Cheng Y, Han S, van Handel B, Dong L, Li X, Xie X (2017) Whole genome sequencing and comparative transcriptome analysis of a novel seawater adapted, salt-resistant rice cultivar – sea rice 86. BMC Genomics 18:655.

  14. Childs KL, Davidson RM, Buell CR (2011) Gene coexpression network analysis as a source of functional annotation for rice genes. PLoS One 6:e22196.

  15. Cohen SP, Liu H, Argueso CT et al (2017) RNA-Seq analysis reveals insight into enhanced rice Xa7-mediated bacterial blight resistance at high temperature. PLoS One 12.

  16. Cotsaftis O, Plett D, Johnson AAT, Walia H, Wilson C, Ismail AM, Close TJ, Tester M, Baumann U (2011) Root-specific transcript profiling of contrasting rice genotypes in response to salinity stress. Mol Plant 4:25–41.

  17. Cramer GR, Urano K, Delrot S, Pezzotti M, Shinozaki K (2011) Effects of abiotic stress on plants: a systems biology perspective. BMC Plant Biol 11:163

  18. Dametto A, Buffon G, Blasi ÉADR, Sperotto RA (2015) Ubiquitination pathway as a target to develop abiotic stress tolerance in rice. Plant Signal Behav 10:e1057369.

  19. Degenkolbe T, Do PT, Zuther E, Repsilber D, Walther D, Hincha DK, Köhl KI (2009) Expression profiling of rice cultivars differing in their tolerance to long-term drought stress. Plant Mol Biol 69:133–153.

  20. Ding X, Li X, Xiong L (2013) Insight into differential responses of upland and paddy rice to drought stress by comparative expression profiling analysis. Int J Mol Sci 14:5214–5238.

  21. Eyidogan F, Oz MT, Yucel M, Oktem HA (2012) Signal transduction of phytohormones under abiotic stresses. In: Khan NA, Nazar R, Iqbal N, Anjum NA (eds) Phytohormones and Abiotic Stress Tolerance in Plants. Springer, Berlin Heidelberg, pp 1–48

  22. Fang C, Dou L, Liu Y, Yu J, Tu J (2018) Heat stress-responsive transcriptome analysis in heat susceptible and tolerant rice by high-throughput sequencing. Ecol Genet Genomics 6:33–40.

  23. Ficklin SP, Feltus FA (2011) gene coexpression network alignment and conservation of gene modules between two grass species: maize and rice. Plant Physiol 156:1244–1256.

  24. Fini A, Brunetti C, Di Ferdinando M et al (2011) Stress-induced flavonoid biosynthesis and the antioxidant machinery of plants. Plant Signal Behav 6:709–711.

  25. Formentin E, Sudiro C, Perin G, Riccadonna S, Barizza E, Baldoni E, Lavezzo E, Stevanato P, Sacchi GA, Fontana P, Toppo S, Morosinotto T, Zottini M, Lo Schiavo F (2018) Transcriptome and cell physiological analyses in different rice cultivars provide new insights into adaptive and salinity stress responses. Front Plant Sci 9:204.

  26. Friedel CC, Zimmer R (2007) Influence of degree correlations on network structure and stability in protein-protein interaction networks. BMC Bioinformatics 8:297.

  27. Garcia AV, Al-Yousif M, Hirt H (2012) Role of AGC kinases in plant growth and stress responses. Cell Mol Life Sci 69:3259–3267.

  28. Gillis J, Pavlidis P (2011) The impact of multifunctional genes on “guilt by association” analysis. PLoS One 6:e17258.

  29. Goffard N, Frickey T, Weiller G (2009) PathExpress update: the enzyme neighbourhood method of associating gene-expression data with metabolic pathways. Nucleic Acids Res 37:W335–W339.

  30. Golldack D, Li C, Mohan H, Probst N (2014) Tolerance to drought and salt stress in plants: unraveling the signaling networks. Plant Genet Genomics 5:151.

  31. Hartwell LH, Hopfield JJ, Leibler S, Murray AW (1999) From molecular to modular cell biology. Nature 402:C47–C52.

  32. Hazen SP, Pathan MS, Sanchez A, Baxter I, Dunn M, Estes B, Chang HS, Zhu T, Kreps JA, Nguyen HT (2005) Expression profiling of rice segregating for drought tolerance QTLs using a rice genome array. Funct Integr Genomics 5:104–116.

  33. Hirschmann F, Krause F, Papenbrock J (2014) The multi-protein family of sulfotransferases in plants: composition, occurrence, substrate specificity, and functions. Front Plant Sci 5.

  34. Hong CY, Kao CH (2008) NaCl-induced expression of ascorbate peroxidase 8 in roots of rice (Oryza sativa L.) seedlings is not associated with osmotic component. Plant Signal Behav 3:199–201

  35. Hu W, Hu G, Han B (2009) Genome-wide survey and expression profiling of heat shock proteins and heat shock factors revealed overlapped and stress specific response under abiotic stresses in rice. Plant Sci 176:583–590.

  36. Hu X, Wu L, Zhao F, Zhang D, Li N, Zhu G, Li C, Wang W (2015) Phosphoproteomic analysis of the response of maize leaves to drought, heat and their combination stress. Front Plant Sci 6.

  37. Izumikawa T, Dejima K, Watamoto Y, Nomura KH, Kanaki N, Rikitake M, Tou M, Murata D, Yanagita E, Kano A, Mitani S, Nomura K, Kitagawa H (2016) Chondroitin 4-O-sulfotransferase is indispensable for sulfation of chondroitin and plays an important role in maintaining normal life span and oxidative stress responses in nematodes. J Biol Chem 291:23294–23304.

  38. Jain M, Nijhawan A, Arora R, Agarwal P, Ray S, Sharma P, Kapoor S, Tyagi AK, Khurana JP (2007) F-box proteins in rice. Genome-wide analysis, classification, temporal and spatial gene expression during panicle and seed development, and regulation by light and abiotic stress. Plant Physiol 143:1467–1483.

  39. Jiang Y, Cai Z, Xie W, Long T, Yu H, Zhang Q (2012) Rice functional genomics research: progress and implications for crop genetic improvement. Biotechnol Adv 30:1059–1070.

  40. Jin J, He K, Tang X, Li Z, Lv L, Zhao Y, Luo J, Gao G (2015) An Arabidopsis transcriptional regulatory map reveals distinct functional and evolutionary features of novel transcription factors. Mol Biol Evol 32:1767–1773.

  41. Katiyar A, Smita S, Lenka SK, Rajwanshi R, Chinnusamy V, Bansal K (2012) Genome-wide classification and expression analysis of MYB transcription factor families in rice and Arabidopsis. BMC Genomics 13:544.

  42. Ku WL, Duggal G, Li Y, Girvan M, Ott E (2012) Interpreting patterns of gene expression: signatures of coregulation, the data processing inequality, and triplet motifs. PLoS One 7:e31969.

  43. Lan H, Carson R, Provart NJ, Bonner AJ (2007) Combining classifiers to predict gene function in Arabidopsis thaliana using large-scale gene expression measurements. BMC Bioinformatics 8:358.

  44. Lenka SK, Katiyar A, Chinnusamy V, Bansal KC (2011) Comparative analysis of drought-responsive transcriptome in Indica rice genotypes with contrasting drought tolerance. Plant Biotechnol J 9:315–327

  45. Lenka SK, Muthusamy SK, Chinnusamy V, Bansal KC (2018) Ectopic expression of rice PYL3 enhances cold and drought tolerance in Arabidopsis thaliana. Mol Biotechnol 60:350–361.

  46. Li H-W, Zang B-S, Deng X-W, Wang X-P (2011) Overexpression of the trehalose-6-phosphate synthase gene OsTPS1 enhances abiotic stress tolerance in rice. Planta 234:1007–1018.

  47. Liao JC, Boscolo R, Yang Y-L, Tran LM, Sabatti C, Roychowdhury VP (2003) Network component analysis: reconstruction of regulatory signals in biological systems. Proc Natl Acad Sci U S A 100:15522–15527.

  48. Liu F, Wang Z, Ren H, Shen C, Li Y, Ling HQ, Wu C, Lian X, Wu P (2010) OsSPX1 suppresses the function of OsPHR2 in the regulation of expression of OsPT2 and phosphate homeostasis in shoots of rice. Plant J Cell Mol Biol 62:508–517.

  49. Ma S, Bohnert HJ (2007) Integration of Arabidopsis thaliana stress-related transcript profiles, promoter structures, and cell-specific expression. Genome Biol 8:R49.

  50. Ma Q, Dai X, Xu Y, Guo J, Liu Y, Chen N, Xiao J, Zhang D, Xu Z, Zhang X, Chong K (2009) Enhanced tolerance to chilling stress in OsMYB3R-2 transgenic rice is mediated by alteration in cell cycle and ectopic expression of stress genes. Plant Physiol 150:244–256.

  51. Mao L, Van Hemert JL, Dash S, Dickerson JA (2009) Arabidopsis gene co-expression network and its functional modules. BMC Bioinformatics 10:346.

  52. Matys V, Kel-Margoulis OV, Fricke E, Liebich I, Land S, Barre-Dirrie A, Reuter I, Chekmenev D, Krull M, Hornischer K, Voss N, Stegmaier P, Lewicki-Potapov B, Saxel H, Kel AE, Wingender E (2006) TRANSFAC® and its module TRANSCompel®: transcriptional gene regulation in eukaryotes. Nucleic Acids Res 34:D108–D110.

  53. Merico D, Gfeller D, Bader GD (2009) How to visually interpret biological data using networks. Nat Biotechnol 27:921–924.

  54. Mikami T, Kitagawa H (2013) Biosynthesis and function of chondroitin sulfate. Biochim Biophys Acta 1830:4719–4733.

  55. Mittal D, Madhyastha DA, Grover A (2012) Genome-wide transcriptional profiles during temperature and oxidative stress reveal coordinated expression patterns and overlapping regulons in rice. PLoS One 7:e40899.

  56. Mochida K, Uehara-Yamaguchi Y, Yoshida T, Sakurai T, Shinozaki K (2011) Global landscape of a co-expressed gene network in barley and its application to gene discovery in Triticeae crops. Plant Cell Physiol 52:785–803.

  57. Mustroph A, Lee SC, Oosumi T, Zanetti ME, Yang H, Ma K, Yaghoubi-Masihi A, Fukao T, Bailey-Serres J (2010) Cross-kingdom comparison of transcriptomic adjustments to low-oxygen stress highlights conserved and plant-specific responses. Plant Physiol 152:1484–1500.

  58. Narsai R, Wang C, Chen J, Wu J, Shou H, Whelan J (2013) Antagonistic, overlapping and distinct responses to biotic stress in rice (Oryza sativa) and interactions with abiotic stress. BMC Genomics 14:93.

  59. Neumann PM (2008) Coping mechanisms for crop plants in drought-prone environments. Ann Bot 101:901–907.

  60. Ni J, Pujar A, Youens-Clark K et al (2009) Gramene QTL database: development, content and applications. Database 2009:bap005.

  61. Ohara K, Kokado Y, Yamamoto H, Sato F, Yazaki K (2004) Engineering of ubiquinone biosynthesis using the yeast coq2 gene confers oxidative stress tolerance in transgenic tobacco. Plant J Cell Mol Biol 40:734–743.

  62. Ouyang S-Q, Liu Y-F, Liu P, Lei G, He SJ, Ma B, Zhang WK, Zhang JS, Chen SY (2010) Receptor-like kinase OsSIK1 improves drought and salt stress tolerance in rice (Oryza sativa) plants. Plant J Cell Mol Biol 62:316–329.

  63. Peleg Z, Reguera M, Tumimbang E, Walia H, Blumwald E (2011) Cytokinin-mediated source/sink modifications improve drought tolerance and increase grain yield in rice under water-stress. Plant Biotechnol J 9:747–758.

  64. Plessis A, Hafemeister C, Wilkins O et al (2015) Multiple abiotic stimuli are integrated in the regulation of rice gene expression under field conditions. eLife 4:e08411.

  65. Qi X, Bakht S, Qin B, Leggett M, Hemmings A, Mellon F, Eagles J, Werck-Reichhart D, Schaller H, Lesot A, Melton R, Osbourn A (2006) A different function for a member of an ancient and highly conserved cytochrome P450 family: From essential sterols to plant defense. Proc Natl Acad Sci 103:18848–18853.

  66. Ransbotyn V, Yeger-Lotem E, Basha O, Acuna T, Verduyn C, Gordon M, Chalifa-Caspi V, Hannah MA, Barak S (2015) A combination of gene expression ranking and co-expression network analysis increases discovery rate in large-scale mutant screens for novel Arabidopsis thaliana abiotic stress genes. Plant Biotechnol J 13:501–513.

  67. Raorane ML, Pabuayon IM, Varadarajan AR et al (2015) Proteomic insights into the role of the large-effect QTL qDTY12.1 for rice yield under drought. Mol Breed 35:139.

  68. Reguera M, Peleg Z, Abdel-Tawab YM, Tumimbang EB, Delatorre CA, Blumwald E (2013) Stress-induced cytokinin synthesis increases drought tolerance through the coordinated regulation of carbon and nitrogen assimilation in rice. Plant Physiol 163:1609–1622.

  69. Reimand J, Arak T, Vilo J (2011) g:Profiler--a web server for functional interpretation of gene lists (2011 update). Nucleic Acids Res 39:W307–W315.

  70. Reményi A, Schöler HR, Wilmanns M (2004) Combinatorial control of gene expression. Nat Struct Mol Biol 11:812–815.

  71. Rhee SY, Mutwil M (2014) Towards revealing the functions of all genes in plants. Trends Plant Sci 19:212–221.

  72. Seki M, Narusaka M, Ishida J, Nanjo T, Fujita M, Oono Y, Kamiya A, Nakajima M, Enju A, Sakurai T, Satou M, Akiyama K, Taji T, Yamaguchi-Shinozaki K, Carninci P, Kawai J, Hayashizaki Y, Shinozaki K (2002) Monitoring the expression profiles of 7000 Arabidopsis genes under drought, cold and high-salinity stresses using a full-length cDNA microarray. Plant J Cell Mol Biol 31:279–292

  73. Serin EAR, Nijveen H, Hilhorst HWM, Ligterink W (2016) Learning from co-expression networks: possibilities and challenges. Front Plant Sci 7.

  74. Shaik R, Ramakrishna W (2013) Genes and co-expression modules common to drought and bacterial stress responses in Arabidopsis and rice. PLoS One 8:e77261.

  75. Shaik R, Ramakrishna W (2014) Machine learning approaches distinguish multiple stress conditions using stress-responsive genes and identify candidate genes for broad resistance in rice. Plant Physiol 164:481–495.

  76. Shankar R, Bhattacharjee A, Jain M (2016) Transcriptome analysis in different rice cultivars provides novel insights into desiccation and salinity stress responses. Sci Rep 6:23719.

  77. Shao H, Wang H, Tang X (2015) NAC transcription factors in plant multiple abiotic stress responses: progress and prospects. Plant Physiol 902.

  78. Sharma R, De Vleesschauwer D, Sharma MK, Ronald PC (2013) Recent advances in dissecting stress-regulatory crosstalk in rice. Mol Plant 6:250–260.

  79. Shen C, Li D, He R, Fang Z, Xia Y, Gao J, Shen H, Cao M (2014) Comparative transcriptome analysis of RNA-seq data for cold-tolerant and cold-sensitive rice genotypes under cold stress. J Plant Biol 57:337–348.

  80. Shi J, Yan B, Lou X, Ma H, Ruan S (2017) Comparative transcriptome analysis reveals the transcriptional alterations in heat-resistant and heat-sensitive sweet maize (Zea mays L.) varieties under heat stress. BMC Plant Biol 17:26.

  81. Shinozaki K, Yamaguchi-Shinozaki K, Seki M (2003) Regulatory network of gene expression in the drought and cold stress responses. Curr Opin Plant Biol 6:410–417.

  82. Singh A, Giri J, Kapoor S, Tyagi AK, Pandey GK (2010) Protein phosphatase complement in rice: genome-wide identification and transcriptional analysis under abiotic stress conditions and reproductive development. BMC Genomics 11:435.

  83. Smita S, Lenka SK, Katiyar A, Jaiswal P, Preece J, Bansal KC (2011) QlicRice: a web interface for abiotic stress responsive QTL and loci interaction channels in rice. Database J Biol Databases Curation 2011.

  84. Smita S, Katiyar A, Pandey DM et al (2013) Identification of conserved drought stress responsive gene-network across tissues and developmental stages in rice. Bioinformation 9:72–78

  85. Smita S, Katiyar A, Chinnusamy V, Pandey DM, Bansal KC (2015) Transcriptional regulatory network analysis of MYB transcription factor family genes in rice. Front Plant Sci 6.

  86. Smyth GK (2004) Linear models and empirical bayes methods for assessing differential expression in microarray experiments. Stat Appl Genet Mol Biol 3:3.

  87. Sottosanto JB, Gelli A, Blumwald E (2004) DNA array analyses of Arabidopsis thaliana lacking a vacuolar Na+/H+ antiporter: impact of AtNHX1 on gene expression. Plant J 40:752–771.

  88. Tian X, Wang Z, Li X, Lv T, Liu H, Wang L, Niu H, Bu Q (2015) Characterization and functional analysis of pyrabactin resistance-like abscisic acid receptor family in rice. Rice 8:28.

  89. Todaka D, Nakashima K, Shinozaki K, Yamaguchi-Shinozaki K (2012) Toward understanding transcriptional regulatory networks in abiotic stress responses and tolerance in rice. Rice (N Y) 5:6.

  90. Usadel B, Obayashi T, Mutwil M et al (2009) Co-expression tools for plant biology: opportunities for hypothesis generation and caveats. Plant Cell Environ 32:1633–1651.

  91. Van Dongen S (2008) Graph clustering via a discrete uncoupling process. SIAM J Matrix Anal Appl 30:121–141

  92. Walia H, Wilson C, Condamine P, Liu X, Ismail AM, Zeng L, Wanamaker SI, Mandal J, Xu J, Cui X, Close TJ (2005) Comparative transcriptional profiling of two contrasting rice genotypes under salinity stress during the vegetative growth stage. Plant Physiol 139:822–835.

  93. Walia H, Wilson C, Zeng L, Ismail AM, Condamine P, Close TJ (2007) Genome-wide transcriptional analysis of salinity stressed japonica and indica rice genotypes during panicle initiation stage. Plant Mol Biol 63:609–623.

  94. Walia H, Wilson C, Ismail AM, Close TJ, Cui X (2009) Comparing genomic expression patterns across plant species reveals highly diverged transcriptional dynamics in response to salt stress. BMC Genomics 10:398.

  95. Wang L, Pei Z, Tian Y, He C (2005) OsLSD1, a rice zinc finger protein, regulates programmed cell death and callus differentiation. Mol Plant-Microbe Interact 18:375–384.

  96. Wang X, Haberer G, Mayer KFX (2009) Discovery of cis-elements between sorghum and rice using co-expression and evolutionary conservation. BMC Genomics 10:284.

  97. Wang D, Pan Y, Zhao X, Zhu L, Fu B, Li Z (2011) Genome-wide temporal-spatial gene expression profiling of drought responsiveness in rice. BMC Genomics 12:149.

  98. Wang A, Hu J, Huang X et al (2016) Comparative transcriptome analysis reveals heat-responsive genes in chinese cabbage (Brassica rapa ssp. chinensis). Front Plant Sci 7.

  99. Wani SH, Kumar V, Shriram V, Sah SK (2016) Phytohormones and their metabolic engineering for abiotic stress tolerance in crop plants. Crop J 4:162–176.

  100. Wilkins O, Hafemeister C, Plessis A, Holloway-Phillips MM, Pham GM, Nicotra AB, Gregorio GB, Jagadish SVK, Septiningsih EM, Bonneau R, Purugganan M (2016) EGRINs (Environmental Gene Regulatory Influence Networks) in rice that function in the response to water deficit, high temperature, and agricultural environments. Plant Cell 28:2365–2384.

  101. Wong DC, Sweetman C, Drew DP, Ford CM (2013) VTCdb: a gene co-expression database for the crop species Vitis vinifera (grapevine). BMC Genomics 14:882.

  102. Wong DC, Sweetman C, Ford CM (2014) Annotation of gene function in citrus using gene expression information and co-expression networks. BMC Plant Biol 14:186.

  103. Xing H, Fu X, Yang C, Tang X, Guo L, Li C, Xu C, Luo K (2018) Genome-wide investigation of pentatricopeptide repeat gene family in poplar and their expression analysis in response to biotic and abiotic stresses. Sci Rep 8:2817.

  104. Yang T, Poovaiah BW (2002) A calmodulin-binding/CGCG box DNA-binding protein family involved in multiple signaling pathways in plants. J Biol Chem 277:45049–45058.

  105. Yoo Y-H, Nalini Chandran AK, Park J-C, Gho YS, Lee SW, An G, Jung KH (2017) OsPhyB-mediating novel regulatory pathway for drought tolerance in rice root identified by a global RNA-Seq transcriptome analysis of rice genes in response to water deficiencies. Front Plant Sci 8:580.

  106. You J, Zong W, Hu H et al (2014) A SNAC1-regulated protein phosphatase gene OsPP18 modulates drought and oxidative stress tolerance through ABA-independent reactive oxygen species scavenging in rice. Plant Physiol:114.251116.

  107. Yue B, Xue W, Xiong L, Yu X, Luo L, Cui K, Jin D, Xing Y, Zhang Q (2006) Genetic basis of drought resistance at reproductive stage in rice: separation of drought tolerance from drought avoidance. Genetics 172:1213–1228.

  108. Yusuf MA, Kumar D, Rajwanshi R, Strasser RJ, Tsimilli-Michael M, Govindjee, Sarin NB (2010) Overexpression of γ-tocopherol methyl transferase gene in transgenic Brassica juncea plants alleviates abiotic stress: Physiological and chlorophyll a fluorescence measurements. Biochim Biophys Acta Bioenerg 1797:1428–1438.

  109. Zhang F, Huang L, Wang W, Zhao X, Zhu L, Fu B, Li Z (2012a) Genome-wide gene expression profiling of introgressed indica rice alleles associated with seedling cold tolerance improvement in a japonica rice background. BMC Genomics 13:461.

  110. Zhang L, Yu S, Zuo K, Luo L, Tang K (2012b) Identification of gene modules associated with drought response in rice by network-based analysis. PloS One 7.

  111. Zhang F, Zhou Y, Zhang M, Luo X, Xie J (2017a) Effects of drought stress on global gene expression profile in leaf and root samples of Dongxiang wild rice (Oryza rufipogon). Biosci Rep 37:BSR20160509.

  112. Zhang T, Huang L, Wang Y, Wang W, Zhao X, Zhang S, Zhang J, Hu F, Fu B, Li Z (2017b) Differential transcriptome profiling of chilling stress response between shoots and rhizomes of Oryza longistaminata using RNA sequencing. PLoS One 12:e0188625.

  113. Zhu Y-N, Shi D-Q, Ruan M-B, Zhang LL, Meng ZH, Liu J, Yang WC (2013) Transcriptome analysis reveals crosstalk of responsive genes to multiple abiotic stresses in cotton (Gossypium hirsutum L.). PLoS One 8:e80218.

  114. Zou X, Qin Z, Zhang C et al (2015) Over-expression of an S-domain receptor-like kinase extracellular domain improves panicle architecture and grain yield in rice. J Exp Bot 66:7197–7209.

Download references


The authors would like to thank the Indian Council of Agricultural Research (ICAR) for supporting this work through the ICAR-sponsored project on the National Initiative on Climate Resilient Agriculture (NICRA) project. VC was supported by NASF (ICAR) grant No. Phen 2015/2011-12. Cis-element GRN analysis was performed using Computational facilities provided by the BTISNET program of the DBT, Govt. of India Grant No. BT/BI/04/069/2006. Use of computational resources of Bioinformatics Center, NII, and New Delhi for TRANSFAC analysis is gratefully acknowledged.

Author information

SS, VC, and KCB conceived and designed the experiments. SS performed the experiments and analyzed the data. SS, SKL, and GY interpreted the data. AKa performed computational analysis. SKM and AKu performed promoter extraction and cis-regulatory element in ASTR-GCN genes. SS, VC, DMP, GY, SKL, MD, and KCB wrote the paper. All authors read and approved the final manuscript.

Correspondence to Viswanathan Chinnusamy or Kailash Chander Bansal.

Ethics declarations

Competing interests

The authors declare that they have no competing interests.

Additional information

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Electronic supplementary material

Fig. S1

Node degree distribution of abiotic stress tolerant (ASTR) genes network in rice. (PDF 137 kb)

Fig. S2

Functional association of identified motifs in the abiotic stress tolerant (ASTR) genes modules in rice. (PDF 162 kb)

Fig. S3

(a) Bar plot shows the frequency of ASTR genes categorized in two groups; (1) ASTR gene differentially expressed in more than 1 experiment (in dark brown bar color) and (2) ASTR genes differentially expressed in one or in none of the experiments (in light brown bar color) plotted over the different categories (range from 2 to 17) of ranking from our microarray data shows on x-axis. Figure shows top ranking ASTR genes are in the top bin from RNAseq data as well. (b) Heatmap showing log2 fold change expression of ASTR genes in different tolerant cultivar under different stress condition in rice from publicly available RNAseq data (Shen et al. 2014; Shankar et al. 2016; Yoo et al. 2017; Zhang et al. 2017a, b; Chen et al. 2017; Cohen et al. 2017; Formentin et al. 2018) (Quantitative data is given in Table S8). Samples information in detailed in column name, formatted as stress name, then tissue type separated by “.” and then cultivar information in “( )”. Note that heat stress data showing here (very few) is for ASTR genes uniquely differentially expressed in heat stress tolerant genotype only. (PDF 37 kb)

(PDF 21 kb)

Table S1

(XLSX 19 kb)

Table S2

(XLSX 10 kb)

Table S3

(XLSX 1067 kb)

Table S4

(XLSX 16 kb)

Table S5

(XLSX 2201 kb)

Table S6

(XLSX 299 kb)

Table S7

(XLSX 23 kb)

Table S8

(XLSX 112 kb)

Table S9

(XLSX 37 kb)

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Smita, S., Katiyar, A., Lenka, S.K. et al. Gene network modules associated with abiotic stress response in tolerant rice genotypes identified by transcriptome meta-analysis. Funct Integr Genomics 20, 29–49 (2020).

Download citation


  • Rice (Oryza sativa)
  • Gene network module
  • Abiotic stress
  • QTLs
  • Tolerant genotype
  • Meta-analysis
  • Transcriptome