Abstract
Gene networks offer a strong perspective for better understanding of gene functions associated with complex biological traits. A gene regulatory network (GRN) is comprised of the regulatory elements which interact together to regulate the transcriptional and translational processes within the cell. GRNs involve the network of genes and proteins and molecular interactions that regulate those genes and proteins. GRNs govern the way a plant responds to various environmental cues. Thus, GRNs are the key to understand the interaction between the plant’s genotype and environment. Elucidation of plant GRNs is important for plant resistance and adaptability under external environmental stressors. Serious attempts are being made to characterize these GRNs and modulate them in order to get the desired trait in crop plants. In this chapter, we will discuss the structure, recent advances, and factors influencing the GRNs under various environmental stresses and major challenges for future researches.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Abbreviations
- ABA1:
-
ABA Deficient 1
- ABI:
-
Abscisic Acid-Insensitive
- AGL15:
-
Agamous-like 15
- AHG3:
-
ABA-hypersensitive Germination 3
- AIL3:
-
Aintegumenta-like 3
- ANL2:
-
Anthocyaninless 2
- AP1:
-
Apetala 1
- ARAB/ABF:
-
ABA-responsive Element Binding Protein/ABRE Binding Factor
- ASG2:
-
Altered Seed Germination 2
- bZIP:
-
basic Leucine Zipper
- CAL:
-
Cauliflower
- CCA1:
-
Circadian Clock Associated 1
- CUC:
-
Cup-Shaped Cotyledon
- CUC3:
-
Cup-Shaped Cotyledon 3
- DOG1:
-
Delay of Germination 1
- DREB1/CBF:
-
Dehydration-responsive Element Binding Protein 1/C-Repeat Binding Factor
- ESR1:
-
Enhancer of Shoot Regeneration 1
- FT:
-
Flowering Locus T
- FUS3:
-
Fusca 3
- GID1:
-
Gibberellin Receptor 1
- HDG11:
-
Homeodomain Glabrous 11
- HSFB1:
-
Heat Stress Factor B1
- HSP17:
-
Heat Shock Protein 17
- JAB:
-
Janus Kinase Binding Protein
- KEG1:
-
Keep on Going 1
- LEC:
-
Leafy Cotyledon
- LFY:
-
Leafy
- LHY:
-
Late Elongated Hypocotyl
- NAC:
-
NAM, ATAF, and CUC
- PDF2:
-
Protodermal Factor 2
- PLT3:
-
Plethora 3
- RGA:
-
Repressor of Ga1-3
- RLT2:
-
Ringlet 2
- SCL14:
-
Scarecrow-like 14
- SCL18 :
-
Scarecrow-like18
- SCR:
-
Scarecrow
- SEP3:
-
Sepallata 3
- TALE:
-
Three Amino Acid Loop Extension
- TFL1:
-
Terminal Flower 1
- TOC1:
-
Timing of Cab Expression 1
- TTG1:
-
Transparent Testa Glabra 1
- VAL:
-
VP1/ABI3-LIKE
- WER:
-
Werewolf
- WOX:
-
WUS/WUS-Related Homeobox
References
Alabadı́ D, Oyama T, Yanovsky MJ, Harmon FG, Más P, Kay SA (2001) Reciprocal regulation between TOC1 and LHY/CCA1 within the arabidopsis circadian clock. Science 293:880–883. https://doi.org/10.1126/science.1061320
Alon U (2007) Network motifs: theory and experimental approaches. Nat Rev Genet 8:450–461. https://doi.org/10.1038/nrg2102
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. https://doi.org/10.1093/pcp/pcm013
Ballouz S, Verleyen W, Gillis J (2015) Guidance for RNA-seq co-expression network construction and analysis: safety in numbers. Bioinformatics 31:2123–2130. https://doi.org/10.1093/bioinformatics/btv118
Balov N, Salzman P (2012) Catnet: categorical Bayesian network inference. R package version, vol 1
Banf M, Rhee SY (2017) Enhancing gene regulatory network inference through data integration with markov random fields. Sci Rep 7:41174. https://doi.org/10.1038/srep41174
Bansal M, Gatta GD, di Bernardo D (2006) Inference of gene regulatory networks and compound mode of action from time course gene expression profiles. Bioinformatics 22:815–822. https://doi.org/10.1093/bioinformatics/btl003
Bassel GW, Lan H, Glaab E, Gibbs DJ, Gerjets T, Krasnogor N, Bonner AJ, Holdsworth MJ, Provart NJ (2011) Genome-wide network model capturing seed germination reveals coordinated regulation of plant cellular phase transitions. PNAS 108:9709–9714. https://doi.org/10.1073/pnas.1100958108
Bellot P, Olsen C, Salembier P, Oliveras-Vergés A, Meyer PE (2015) NetBenchmark: a bioconductor package for reproducible benchmarks of gene regulatory network inference. BMC Bioinform 16:312. https://doi.org/10.1186/s12859-015-0728-4
Bentsink L, Hanson J, Hanhart CJ, Vries HB, Coltrane C, Keizer P, El-Lithy M, Alonso-Blanco C, de Andrés MT, Reymond M, van Eeuwijk F, Smeekens S, Koornneef M (2010) Natural variation for seed dormancy in Arabidopsis is regulated by additive genetic and molecular pathways. PNAS 107:4264–4269. https://doi.org/10.1073/pnas.1000410107
Beyene G, Chauhan RD, Taylor NJ (2017) A rapid virus-induced gene silencing (VIGS) method for assessing resistance and susceptibility to cassava mosaic disease. Virol J 14. https://doi.org/10.1186/s12985-017-0716-6
Bin Z, Steve H (2005) A general framework for weighted gene co-expression network analysis. sagmb 4. https://doi.org/10.2202/1544-6115.1128
Brady SM, Zhang L, Megraw M, Martinez NJ, Jiang E, Yi CS, Liu W, Zeng A, Taylor-Teeples M, Kim D, Ahnert S, Ohler U, Ware D, Walhout AJM, Benfey PN (2011) A stele-enriched gene regulatory network in the Arabidopsis root. Mol Syst Biol 7:459. https://doi.org/10.1038/msb.2010.114
Brooks MD, Cirrone J, Pasquino AV, Alvarez JM, Swift J, Mittal S, Juang C-L, Varala K, Gutiérrez RA, Krouk G, Shasha D, Coruzzi GM (2019) Network Walking charts transcriptional dynamics of nitrogen signaling by integrating validated and predicted genome-wide interactions. Nat Commun 10:1569. https://doi.org/10.1038/s41467-019-09522-1
Bustos R, Castrillo G, Linhares F, Puga MI, Rubio V, Pérez-Pérez J, Solano R, Leyva A, Paz-Ares J (2010) A central regulatory system largely controls transcriptional activation and repression responses to phosphate starvation in Arabidopsis. PLoS Genet 6:e1001102. https://doi.org/10.1371/journal.pgen.1001102
Chalancon G, Babu MM (2013) Structure and evolution of transcriptional regulatory networks. In: Madan Babu M (eds) Bacterial gene regulation and transcriptional networks. Caister Academic Press, UK, p 26
Chan EKF, Rowe HC, Corwin JA, Joseph B, Kliebenstein DJ (2011) Combining genome-wide association mapping and transcriptional networks to identify novel genes controlling glucosinolates in Arabidopsis thaliana. PLoS Biol 9:e1001125. https://doi.org/10.1371/journal.pbio.1001125
Chen D, Yan W, Fu L-Y, Kaufmann K (2018) Architecture of gene regulatory networks controlling flower development in Arabidopsis thaliana. Nat Commun 9:4534. https://doi.org/10.1038/s41467-018-06772-3
Childs KL, Davidson RM, Buell CR (2011) Gene coexpression network analysis as a source of functional annotation for rice genes. PLoS ONE 6:e22196. https://doi.org/10.1371/journal.pone.0022196
Chinnusamy V, Schumaker K, Zhu J-K (2004) Molecular genetic perspectives on cross-talk and specificity in abiotic stress signalling in plants. J Exp Bot 55:225–236. https://doi.org/10.1093/jxb/erh005
Chou K-C, Shen H-B (2007) Recent progress in protein subcellular location prediction. Anal Biochem 370:1–16. https://doi.org/10.1016/j.ab.2007.07.006
Chu D, Zabet NR, Mitavskiy B (2009) Models of transcription factor binding: sensitivity of activation functions to model assumptions. J Theor Biol 257:419–429. https://doi.org/10.1016/j.jtbi.2008.11.026
Coen ES, Meyerowitz EM (1991) The war of the whorls: genetic interactions controlling flower development. Nature 353:31. https://doi.org/10.1038/353031a0
Davidson E, Levin M (2005) Gene regulatory networks. Proc Natl Acad Sci USA 102:4935. https://doi.org/10.1073/pnas.0502024102
de Luis Balaguer MA, Sozzani R (2017) Inferring gene regulatory networks in the Arabidopsis root using a dynamic Bayesian network approach. In: Kaufmann K, Mueller-Roeber B (eds) Plant gene regulatory networks. Springer, New York, NY, pp 331–348
de Luis Balaguer MA, Fisher AP, Clark NM, Fernandez-Espinosa MG, Möller BK, Weijers D, Lohmann JU, Williams C, Lorenzo O, Sozzani R (2017) Predicting gene regulatory networks by combining spatial and temporal gene expression data in Arabidopsis root stem cells. Proc Natl Acad Sci USA 114:E7632–E7640. https://doi.org/10.1073/pnas.1707566114
Defoort J, Van de Peer Y, Vermeirssen V (2018) Function, dynamics and evolution of network motif modules in integrated gene regulatory networks of worm and plant. Nucleic Acids Res 46:6480–6503. https://doi.org/10.1093/nar/gky468
Des Marais David L, Guerrero Rafael F, Lasky Jesse R, Scarpino Samuel V (2017) Topological features of a gene co-expression network predict patterns of natural diversity in environmental response. Proc Roy Soc B: Biol Sci 284:20170914. https://doi.org/10.1098/rspb.2017.0914
Eckardt NA (2007) Positive and negative feedback coordinate regulation of disease resistance gene expression. Plant Cell 19:2700–2702. https://doi.org/10.1105/tpc.107.056226
Espinosa-Soto C, Padilla-Longoria P, Alvarez-Buylla ER (2004) A gene regulatory network model for cell-fate determination during Arabidopsis thaliana flower development that is robust and recovers experimental gene expression profiles. Plant Cell 16:2923–2939. https://doi.org/10.1105/tpc.104.021725
Faith JJ, Hayete B, Thaden JT, Mogno I, Wierzbowski J, Cottarel G, Kasif S, Collins JJ, Gardner TS (2007) Large-scale mapping and validation of Escherichia coli transcriptional regulation from a compendium of expression profiles. PLoS Biol 5:e8. https://doi.org/10.1371/journal.pbio.0050008
Ferrario S, Immink RG, Angenent GC (2004) Conservation and diversity in flower land. Curr Opin Plant Biol 7:84–91. https://doi.org/10.1016/j.pbi.2003.11.003
Ficklin SP, Feltus FA (2011) Gene coexpression network alignment and conservation of gene modules between two grass species: maize and rice[C][W][OA]. Plant Physiol 156:1244–1256. https://doi.org/10.1104/pp.111.173047
Finkelstein RR, Gampala SSL, Rock CD (2002) Abscisic acid signaling in seeds and seedlings. Plant Cell 14:S15–S45. https://doi.org/10.1105/tpc.010441
Fujita Y, Yoshida T, Yamaguchi-Shinozaki K (2013) Pivotal role of the AREB/ABF-SnRK2 pathway in ABRE-mediated transcription in response to osmotic stress in plants. Physiol Plant 147:15–27. https://doi.org/10.1111/j.1399-3054.2012.01635.x
Garg R, Singh VK, Rajkumar MS, Kumar V, Jain M (2017) Global transcriptome and coexpression network analyses reveal cultivar-specific molecular signatures associated with seed development and seed size/weight determination in chickpea. Plant J 91:1088–1107. https://doi.org/10.1111/tpj.13621
Greene CS, Krishnan A, Wong AK, Ricciotti E, Zelaya RA, Himmelstein DS, Zhang R, Hartmann BM, Zaslavsky E, Sealfon SC, Chasman DI, FitzGerald GA, Dolinski K, Grosser T, Troyanskaya OG (2015) Understanding multicellular function and disease with human tissue-specific networks. Nat Genet 47:569–576. https://doi.org/10.1038/ng.3259
Gunasekara C, Zhang K, Deng W, Brown L, Wei H (2018) TGMI: an efficient algorithm for identifying pathway regulators through evaluation of triple-gene mutual interaction. Nucleic Acids Res 46:e67–e67. https://doi.org/10.1093/nar/gky210
Gutierrez L, Van Wuytswinkel O, Castelain M, Bellini C (2007) Combined networks regulating seed maturation. Trends Plant Sci 12:294–300. https://doi.org/10.1016/j.tplants.2007.06.003
Hamada K, Hongo K, Suwabe K, Shimizu A, Nagayama T, Abe R, Kikuchi S, Yamamoto N, Fujii T, Yokoyama K, Tsuchida H, Sano K, Mochizuki T, Oki N, Horiuchi Y, Fujita M, Watanabe M, Matsuoka M, Kurata N, Yano K (2011) OryzaExpress: an integrated database of gene expression networks and omics annotations in rice. Plant Cell Physiol 52:220–229. https://doi.org/10.1093/pcp/pcq195
Haque S, Ahmad JS, Clark NM, Williams CM, Sozzani R (2019) Computational prediction of gene regulatory networks in plant growth and development. Curr Opin Plant Biol 47:96–105. https://doi.org/10.1016/j.pbi.2018.10.005
Holdsworth MJ, Bentsink L, Soppe WJJ (2008) Molecular networks regulating Arabidopsis seed maturation, after-ripening, dormancy and germination. New Phytol 179:33–54. https://doi.org/10.1111/j.1469-8137.2008.02437.x
Hollender CA, Kang C, Darwish O, Geretz A, Matthews BF, Slovin J, Alkharouf N, Liu Z (2014) Floral transcriptomes in woodland strawberry uncover developing receptacle and anther gene networks. Plant Physiol 165:1062–1075. https://doi.org/10.1104/pp.114.237529
Huang DW, Sherman BT, Lempicki RA (2009) Systematic and integrative analysis of large gene lists using DAVID bioinformatics resources. Nat Protoc 4:44
Huang J, Zheng J, Yuan H, McGinnis K (2018) Distinct tissue-specific transcriptional regulation revealed by gene regulatory networks in maize. BMC Plant Biol 18. https://doi.org/10.1186/s12870-018-1329-y
Huynh-Thu VA, Sanguinetti G (2018) Gene regulatory network inference: an introductory survey. arXiv:1801.04087 [q-bio]
Huynh-Thu VA, Irrthum A, Wehenkel L, Geurts P (2010) Inferring regulatory networks from expression data using tree-based methods. PLoS ONE 5:e12776. https://doi.org/10.1371/journal.pone.0012776
Ikeuchi M, Shibata M, Rymen B, Iwase A, Bågman A-M, Watt L, Coleman D, Favero DS, Takahashi T, Ahnert SE, Brady SM, Sugimoto K (2018) A gene regulatory network for cellular reprogramming in plant regeneration. Plant Cell Physiol 59:770–782. https://doi.org/10.1093/pcp/pcy013
Itkin M, Heinig U, Tzfadia O, Bhide AJ, Shinde B, Cardenas PD, Bocobza SE, Unger T, Malitsky S, Finkers R, Tikunov Y, Bovy A, Chikate Y, Singh P, Rogachev I, Beekwilder J, Giri AP, Aharoni A (2013) Biosynthesis of antinutritional alkaloids in solanaceous crops is mediated by clustered genes. Science 341:175–179. https://doi.org/10.1126/science.1240230
Jaeger KE, Pullen N, Lamzin S, Morris RJ, Wigge PA (2013) Interlocking feedback loops govern the dynamic behavior of the floral transition in Arabidopsis[W][OA]. Plant Cell 25:820–833. https://doi.org/10.1105/tpc.113.109355
Kang YH, Kirik V, Hulskamp M, Nam KH, Hagely K, Lee MM, Schiefelbein J (2009) The MYB23 gene provides a positive feedback loop for cell fate specification in the arabidopsis root epidermis. Plant Cell 21:1080–1094. https://doi.org/10.1105/tpc.108.063180
Kolovos P, Knoch TA, Grosveld FG, Cook PR, Papantonis A (2012) Enhancers and silencers: an integrated and simple model for their function. Epigenetics Chromatin 5:1. https://doi.org/10.1186/1756-8935-5-1
Krishnan A, Gupta C, Ambavaram MMR, Pereira A (2017) RECoN: rice environment coexpression network for systems level analysis of abiotic-stress response. Front Plant Sci 8. https://doi.org/10.3389/fpls.2017.01640
Kroj T, Savino G, Valon C, Giraudat J, Parcy F (2003) Regulation of storage protein gene expression in Arabidopsis. Development 130:6065–6073. https://doi.org/10.1242/dev.00814
Kulkarni SR, Vaneechoutte D, Van de Velde J, Vandepoele K (2018) TF2Network: predicting transcription factor regulators and gene regulatory networks in Arabidopsis using publicly available binding site information. Nucleic Acids Res 46:e31. https://doi.org/10.1093/nar/gkx1279
Langfelder P, Horvath S (2008) WGCNA: an R package for weighted correlation network analysis. BMC Bioinform 9:559. https://doi.org/10.1186/1471-2105-9-559
Lara P, Oñate-Sánchez L, Abraham Z, Ferrándiz C, Díaz I, Carbonero P, Vicente-Carbajosa J (2003) Synergistic activation of seed storage protein gene expression in Arabidopsis by ABI3 and two bZIPs related to OPAQUE2. J Biol Chem 278:21003–21011. https://doi.org/10.1074/jbc.M210538200
Lebre S (2013) G1DBN: a package performing dynamic Bayesian network inference. Version
Lee JM, Sonnhammer ELL (2003) Genomic gene clustering analysis of pathways in eukaryotes. Genome Res 13:875–882. https://doi.org/10.1101/gr.737703
Lee I, Ambaru B, Thakkar P, Marcotte EM, Rhee SY (2010) Rational association of genes with traits using a genome-scale gene network for Arabidopsis thaliana. Nat Biotechnol 28:149–156. https://doi.org/10.1038/nbt.1603
Lee I, Seo Y-S, Coltrane D, Hwang S, Oh T, Marcotte EM, Ronald PC (2011) Genetic dissection of the biotic stress response using a genome-scale gene network for rice. PNAS 108:18548–18553. https://doi.org/10.1073/pnas.1110384108
Li Y, Pearl SA, Jackson SA (2015) Gene networks in plant biology: approaches in reconstruction and analysis. Trends Plant Sci 20:664–675. https://doi.org/10.1016/j.tplants.2015.06.013
Lin H, Yu J, Pearce SP, Zhang D, Wilson ZA (2017) RiceAntherNet: a gene co-expression network for identifying anther and pollen development genes. Plant J 92:1076–1091. https://doi.org/10.1111/tpj.13744
Locke JCW, Southern MM, Kozma-Bognár L, Hibberd V, Brown PE, Turner MS, Millar AJ (2005) Extension of a genetic network model by iterative experimentation and mathematical analysis. Mol Syst Biol 1:2005.0013. https://doi.org/10.1038/msb4100018
Locke JCW, Kozma-Bognár L, Gould PD, Fehér B, Kevei É, Nagy F, Turner MS, Hall A, Millar AJ (2006) Experimental validation of a predicted feedback loop in the multi-oscillator clock of Arabidopsis thaliana. Mol Syst Biol 2:59. https://doi.org/10.1038/msb4100102
Ma L, Chen C, Liu X, Jiao Y, Su N, Li L, Wang X, Cao M, Sun N, Zhang X, Bao J, Li J, Pedersen S, Bolund L, Zhao H, Yuan L, Wong GK-S, Wang J, Deng XW, Wang J (2005) A microarray analysis of the rice transcriptome and its comparison to Arabidopsis. Genome Res 15:1274–1283. https://doi.org/10.1101/gr.3657405
Ma S, Ding Z, Li P (2017) Maize network analysis revealed gene modules involved in development, nutrients utilization, metabolism, and stress response. BMC Plant Biol 17:131. https://doi.org/10.1186/s12870-017-1077-4
Ma X, Zhao H, Xu W, You Q, Yan H, Gao Z, Su Z (2018) Co-expression gene network analysis and functional module identification in bamboo growth and development. Front Genet 9. https://doi.org/10.3389/fgene.2018.00574
MacNeil LT, Walhout AJM (2011) Gene regulatory networks and the role of robustness and stochasticity in the control of gene expression. Genome Res 21:645–657. https://doi.org/10.1101/gr.097378.109
Madan Babu M, Teichmann SA (2003) Evolution of transcription factors and the gene regulatory network in Escherichia coli. Nucleic Acids Res 31:1234–1244
Madar A, Greenfield A, Ostrer H, Vanden-Eijnden E, Bonneau R (2009) The inferelator 2.0: a scalable framework for reconstruction of dynamic regulatory network models. In: 2009 annual international conference of the IEEE Engineering in Medicine and Biology Society. IEEE, Minneapolis, MN, pp 5448–5451
Maeda YT, Sano M (2006) Regulatory dynamics of synthetic gene networks with positive feedback. J Mol Biol 359:1107–1124. https://doi.org/10.1016/j.jmb.2006.03.064
Mangan S, Alon U (2003) Structure and function of the feed-forward loop network motif. Proc Natl Acad Sci USA 100:11980–11985. https://doi.org/10.1073/pnas.2133841100
Mao L, Van Hemert JL, Dash S, Dickerson JA (2009) Arabidopsis gene co-expression network and its functional modules. BMC Bioinform 10:346. https://doi.org/10.1186/1471-2105-10-346
Marbach D, Costello JC, Küffner R, Vega N, Prill RJ, Camacho DM, Allison KR, Kellis M, Collins JJ, Stolovitzky G (2012) Wisdom of crowds for robust gene network inference. Nat Methods 9:796–804. https://doi.org/10.1038/nmeth.2016
Margolin AA, Nemenman I, Basso K, Wiggins C, Stolovitzky G, Favera RD, Califano A (2006) ARACNE: an algorithm for the reconstruction of gene regulatory networks in a mammalian cellular context. BMC Bioinform 7:S7. https://doi.org/10.1186/1471-2105-7-S1-S7
Meng Y, Shao C, Chen M (2011) Toward microRNA-mediated gene regulatory networks in plants. Brief Bioinform 12:645–659. https://doi.org/10.1093/bib/bbq091
Mentzen WI, Wurtele ES (2008) Regulon organization of Arabidopsis. BMC Plant Biol 8:99. https://doi.org/10.1186/1471-2229-8-99
Meyer PE, Lafitte F, Bontempi G (2008) minet: a R/Bioconductor package for inferring large transcriptional networks using mutual information. BMC Bioinform 9:461. https://doi.org/10.1186/1471-2105-9-461
Mi H, Muruganujan A, Casagrande JT, Thomas PD (2013) Large-scale gene function analysis with the PANTHER classification system. Nat Protoc 8:1551–1566. https://doi.org/10.1038/nprot.2013.092
Milo R, Shen-Orr S, Itzkovitz S, Kashtan N, Chklovskii D, Alon U (2002) Network motifs: simple building blocks of complex networks. Science 298:824–827. https://doi.org/10.1126/science.298.5594.824
Mitsuda N, Ohme-Takagi M (2009) Functional analysis of transcription factors in Arabidopsis. Plant Cell Physiol 50:1232–1248. https://doi.org/10.1093/pcp/pcp075
Mizoi J, Ohori T, Moriwaki T, Kidokoro S, Todaka D, Maruyama K, Kusakabe K, Osakabe Y, Shinozaki K, Yamaguchi-Shinozaki K (2013) GmDREB2A;2, a canonical DEHYDRATION-RESPONSIVE ELEMENT-BINDING PROTEIN2-Type transcription factor in soybean, is posttranslationally regulated and mediates dehydration-responsive element-dependent gene expression. Plant Physiol 161:346–361. https://doi.org/10.1104/pp.112.204875
Morrissey ER (2011) GRENITS: gene regulatory network inference using time series, p 5
Movahedi S, Van de Peer Y, Vandepoele K (2011) Comparative network analysis reveals that tissue specificity and gene function are important factors influencing the mode of expression evolution in Arabidopsis and rice1[W]. Plant Physiol 156:1316–1330. https://doi.org/10.1104/pp.111.177865
Mutwil M, Klie S, Tohge T, Giorgi FM, Wilkins O, Campbell MM, Fernie AR, Usadel B, Nikoloski Z, Persson S (2011) PlaNet: combined sequence and expression comparisons across plant networks derived from seven species[W][OA]. Plant Cell 23:895–910. https://doi.org/10.1105/tpc.111.083667
Nakamura S, Lynch TJ, Finkelstein RR (2001) Physical interactions between ABA response loci of Arabidopsis. Plant J 26:627–635. https://doi.org/10.1046/j.1365-313x.2001.01069.x
Nakashima K, Yamaguchi-Shinozaki K (2013) ABA signaling in stress-response and seed development. Plant Cell Rep 32:959–970. https://doi.org/10.1007/s00299-013-1418-1
Nakashima K, Ito Y, Yamaguchi-Shinozaki K (2009) Transcriptional regulatory networks in response to abiotic stresses in Arabidopsis and grasses. Plant Physiol 149:88–95. https://doi.org/10.1104/pp.108.129791
Ouma WZ, Pogacar K, Grotewold E (2018) Topological and statistical analyses of gene regulatory networks reveal unifying yet quantitatively different emergent properties. PLoS Comput Biol 14:e1006098. https://doi.org/10.1371/journal.pcbi.1006098
Pruneda-Paz JL, Kay SA (2010) An expanding universe of circadian networks in higher plants. Trends Plant Sci 15:259–265. https://doi.org/10.1016/j.tplants.2010.03.003
Raz V, Bergervoet JH, Koornneef M (2001) Sequential steps for developmental arrest in Arabidopsis seeds. Development 128:243–252
Rebeiz M, Patel NH, Hinman VF (2015) Unraveling the tangled skein: the evolution of transcriptional regulatory networks in development. Annu Rev Genomics Hum Genet 16:103–131. https://doi.org/10.1146/annurev-genom-091212-153423
Reimand J, Arak T, Adler P, Kolberg L, Reisberg S, Peterson H, Vilo J (2016) g:Profiler—a web server for functional interpretation of gene lists (2016 update). Nucleic Acids Res 44:W83–W89. https://doi.org/10.1093/nar/gkw199
Rodríguez-Leal D, Lemmon ZH, Man J, Bartlett ME, Lippman ZB (2017) Engineering quantitative trait variation for crop improvement by genome editing. Cell 171:470–480.e8. https://doi.org/10.1016/j.cell.2017.08.030
Rosenfeld N, Elowitz MB, Alon U (2002) Negative autoregulation speeds the response times of transcription networks. J Mol Biol 323:785–793. https://doi.org/10.1016/S0022-2836(02)00994-4
Ruprecht C, Proost S, Hernandez-Coronado M, Ortiz-Ramirez C, Lang D, Rensing SA, Becker JD, Vandepoele K, Mutwil M (2017) Phylogenomic analysis of gene co-expression networks reveals the evolution of functional modules. Plant J 90:447–465. https://doi.org/10.1111/tpj.13502
Ryngajllo M, Childs L, Lohse M, Giorgi FM, Lude A, Selbig J, Usadel B (2011) SLocX: predicting subcellular localization of arabidopsis proteins leveraging gene expression data. Front Plant Sci 2. https://doi.org/10.3389/fpls.2011.00043
Saddic LA, Huvermann B, Bezhani S, Su Y, Winter CM, Kwon CS, Collum RP, Wagner D (2006) The LEAFY target LMI1 is a meristem identity regulator and acts together with LEAFY to regulate expression of CAULIFLOWER. Development 133:1673–1682. https://doi.org/10.1242/dev.02331
Schäfer J, Opgen-Rhein R (2006) Reverse engineering genetic networks using the GeneNet package, vol 6, p 4
Schafer J, Strimmer K (2005) An empirical Bayes approach to inferring large-scale gene association networks. Bioinformatics 21:754–764. https://doi.org/10.1093/bioinformatics/bti062
Segal E, Shapira M, Regev A, Pe’er D, Botstein D, Koller D, Friedman N (2003) Module networks: identifying regulatory modules and their condition-specific regulators from gene expression data. Nat Genet 34:166. https://doi.org/10.1038/ng1165
Seo CH, Kim J-R, Kim M-S, Cho K-H (2009) Hub genes with positive feedbacks function as master switches in developmental gene regulatory networks. Bioinformatics 25:1898–1904. https://doi.org/10.1093/bioinformatics/btp316
Shahan R, Zawora C, Wight H, Sittmann J, Wang W, Mount SM, Liu Z (2018) Consensus coexpression network analysis identifies key regulators of flower and fruit development in wild strawberry. Plant Physiol 178:202–216. https://doi.org/10.1104/pp.18.00086
Shannon P (2003) Cytoscape: a software environment for integrated models of biomolecular interaction networks. Genome Res 13:2498–2504. https://doi.org/10.1101/gr.1239303
Sharan R, Ulitsky I, Shamir R (2007) Network-based prediction of protein function. Mol Syst Biol 3:88. https://doi.org/10.1038/msb4100129
Sharma R, Upadhyay S, Bhat B, Singh G, Bhattacharya S, Singh A (2019) Abiotic stress induced miRNA-TF-gene regulatory network: a structural perspective. Genomics. https://doi.org/10.1016/j.ygeno.2019.03.004
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. https://doi.org/10.3389/fpls.2015.01157
Smith LM (2018) Identification of woodland strawberry gene coexpression networks. Plant Physiol 178:7–8. https://doi.org/10.1104/pp.18.00880
Smith NC, Matthews JM (2016) Mechanisms of DNA-binding specificity and functional gene regulation by transcription factors. Curr Opin Struct Biol 38:68–74. https://doi.org/10.1016/j.sbi.2016.05.006
Smith VA, Yu J, Smulders T, Hartemink AJ, Jarvis ED (2005) Computational inference of neural information flow networks. PLoS Comput Biol e161. https://doi.org/10.1371/journal.pcbi.0020161.eor (preprint)
Stone SL, Williams LA, Farmer LM, Vierstra RD, Callis J (2006) KEEP ON GOING, a RING E3 ligase essential for Arabidopsis growth and development, is involved in abscisic acid signaling. Plant Cell 18:3415–3428. https://doi.org/10.1105/tpc.106.046532
Sun Y, Dinneny JR (2018) Q&A: how do gene regulatory networks control environmental responses in plants? BMC Biol 16:38. https://doi.org/10.1186/s12915-018-0506-7
Sunkar R, Chinnusamy V, Zhu J, Zhu J-K (2007) Small RNAs as big players in plant abiotic stress responses and nutrient deprivation. Trends Plant Sci 12:301–309. https://doi.org/10.1016/j.tplants.2007.05.001
Suzuki M, Wang HH-Y, McCarty DR (2007) Repression of the LEAFY COTYLEDON 1/B3 regulatory network in plant embryo development by VP1/ABSCISIC ACID INSENSITIVE 3-LIKE B3 genes. Plant Physiol 143:902–911. https://doi.org/10.1104/pp.106.092320
Tai Y, Liu C, Yu S, Yang H, Sun J, Guo C, Huang B, Liu Z, Yuan Y, Xia E, Wei C, Wan X (2018) Gene co-expression network analysis reveals coordinated regulation of three characteristic secondary biosynthetic pathways in tea plant (Camellia sinensis). BMC Genomics 19:616. https://doi.org/10.1186/s12864-018-4999-9
Theocharidis A, van Dongen S, Enright AJ, Freeman TC (2009) Network visualization and analysis of gene expression data using BioLayout Express3D. Nat Protoc 4:1535–1550. https://doi.org/10.1038/nprot.2009.177
Tian W, Skolnick J (2003) How well is enzyme function conserved as a function of pairwise sequence identity? J Mol Biol 333:863–882. https://doi.org/10.1016/j.jmb.2003.08.057
To A, Valon C, Savino G, Guilleminot J, Devic M, Giraudat J, Parcy F (2006) A network of local and redundant gene regulation governs Arabidopsis seed maturation. Plant Cell 18:1642–1651. https://doi.org/10.1105/tpc.105.039925
Tsuchiya Y, Nambara E, Naito S, McCourt P (2004) The FUS3 transcription factor functions through the epidermal regulator TTG1 during embryogenesis in Arabidopsis. Plant J 37:73–81. https://doi.org/10.1046/j.1365-313X.2003.01939.x
van Dam S, Võsa U, van der Graaf A, Franke L, de Magalhães JP (2017) Gene co-expression analysis for functional classification and gene-disease predictions. Brief Bioinform. https://doi.org/10.1093/bib/bbw139
Vandepoele K, de Peer YV (2005) Exploring the plant transcriptome through phylogenetic profiling. Plant Physiol 137:31–42. https://doi.org/10.1104/pp.104.054700
Verdier J, Thompson RD (2008) Transcriptional regulation of storage protein synthesis during dicotyledon seed filling. Plant Cell Physiol 49:1263–1271. https://doi.org/10.1093/pcp/pcn116
Vialette-Guiraud ACM, Andres-Robin A, Chambrier P, Tavares R, Scutt CP (2016) The analysis of gene regulatory networks in plant evo-devo. J Exp Bot 67:2549–2563. https://doi.org/10.1093/jxb/erw119
Vlasblom J, Zuberi K, Rodriguez H, Arnold R, Gagarinova A, Deineko V, Kumar A, Leung E, Rizzolo K, Samanfar B, Chang L, Phanse S, Golshani A, Greenblatt JF, Houry WA, Emili A, Morris Q, Bader G, Babu M (2015) Novel function discovery with GeneMANIA: a new integrated resource for gene function prediction in Escherichia coli. Bioinformatics 31:306–310. https://doi.org/10.1093/bioinformatics/btu671
Wang Z, Xu W, San Lucas FA, Liu Y (2013) Incorporating prior knowledge into gene network study. Bioinformatics 29:2633–2640. https://doi.org/10.1093/bioinformatics/btt443
Wang A, Shu X, Niu X, Zhao W, Ai P, Li P, Zheng A (2018) Comparison of gene co-networks analysis provide a systems view of rice (Oryza sativa L.) response to Tilletia horrida infection. PLOS ONE 13:e0202309. https://doi.org/10.1371/journal.pone.0202309
Weston DJ, Karve AA, Gunter LE, Jawdy SS, Yang X, Allen SM, Wullschleger SD (2011) Comparative physiology and transcriptional networks underlying the heat shock response in Populus trichocarpa, Arabidopsis thaliana and Glycine max. Plant Cell Environ 34:1488–1506. https://doi.org/10.1111/j.1365-3040.2011.02347.x
Wilkins O, Hafemeister C, Plessis A, Holloway-Phillips M-M, 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. https://doi.org/10.1105/tpc.16.00158
Wirojsirasak W, Kalapanulak S, Saithong T (2019) Pan- and core-gene association networks: integrative approaches to understanding biological regulation. PLoS ONE 14:e0210481. https://doi.org/10.1371/journal.pone.0210481
Wu S, Alseekh S, Cuadros-Inostroza Á, Fusari CM, Mutwil M, Kooke R, Keurentjes JB, Fernie AR, Willmitzer L, Brotman Y (2016) Combined use of genome-wide association data and correlation networks unravels key regulators of primary metabolism in Arabidopsis thaliana. PLoS Genet 12:e1006363. https://doi.org/10.1371/journal.pgen.1006363
Xi D-M, Zheng C-C (2011) Transcriptional regulation of seed storage protein genes in Arabidopsis and cereals. Seed Sci Res 21:247–254. https://doi.org/10.1017/S0960258511000237
Yeger-Lotem E, Sattath S, Kashtan N, Itzkovitz S, Milo R, Pinter RY, Alon U, Margalit H (2004) Network motifs in integrated cellular networks of transcription–regulation and protein–protein interaction. Proc Natl Acad Sci USA 101:5934–5939. https://doi.org/10.1073/pnas.0306752101
Yoon MU (2010) Differential equation models and numerical methods for reverse engineering genetic regulatory networks. PhD diss., University of Tennessee, p 164
Yu H, Jiao B, Liang C (2017) High-quality rice RNA-seq-based co-expression network for predicting gene function and regulation. bioRxiv 138040. https://doi.org/10.1101/138040
Zeilinger MN, Farré EM, Taylor SR, Kay SA, Doyle FJ (2006) A novel computational model of the circadian clock in Arabidopsis that incorporates PRR7 and PRR9. Mol Syst Biol 2:58. https://doi.org/10.1038/msb4100101
Zhang L, Yu S, Zuo K, Luo L, Tang K (2012) Identification of gene modules associated with drought response in rice by network-based analysis. PLoS ONE 7:e33748. https://doi.org/10.1371/journal.pone.0033748
Zhu J-Y, Sun Y, Wang Z-Y (2012) Genome-wide identification of transcription factor-binding sites in plants using chromatin immunoprecipitation followed by microarray (ChIP-chip) or sequencing (ChIP-seq). In: Wang Z-Y, Yang Z (eds) Plant signalling networks: methods and protocols. Humana Press, Totowa, NJ, pp 173–188
Zimmermann P (2004) GENEVESTIGATOR. Arabidopsis microarray database and analysis toolbox. Plant Physiol 136:2621–2632. https://doi.org/10.1104/pp.104.046367
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Singapore Pte Ltd.
About this chapter
Cite this chapter
Gupta, P., Singh, S.K. (2019). Gene Regulatory Networks: Current Updates and Applications in Plant Biology. In: Singh, S., Upadhyay, S., Pandey, A., Kumar, S. (eds) Molecular Approaches in Plant Biology and Environmental Challenges. Energy, Environment, and Sustainability. Springer, Singapore. https://doi.org/10.1007/978-981-15-0690-1_18
Download citation
DOI: https://doi.org/10.1007/978-981-15-0690-1_18
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-15-0689-5
Online ISBN: 978-981-15-0690-1
eBook Packages: Biomedical and Life SciencesBiomedical and Life Sciences (R0)