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Q&A: How do gene regulatory networks control environmental responses in plants?

BMC Biology201816:38

https://doi.org/10.1186/s12915-018-0506-7

Published: 11 April 2018

Abstract

A gene regulatory network (GRN) describes the hierarchical relationship between transcription factors, associated proteins, and their target genes. Studying GRNs allows us to understand how a plant’s genotype and environment are integrated to regulate downstream physiological responses. Current efforts in plants have focused on defining the GRNs that regulate functions such as development and stress response and have been performed primarily in genetically tractable model plant species such as Arabidopsis thaliana. Future studies will likely focus on how GRNs function in non-model plants and change over evolutionary time to allow for adaptation to extreme environments. This broader understanding will inform efforts to engineer GRNs to create tailored crop traits.

Question 1: What is a gene regulatory network?

A gene regulatory network (GRN) is composed of molecular regulators such as transcription factors (TFs) that bind to short, non-coding DNA sequences called cis-regulatory elements (CREs), which are typically located in the promoter region of a gene [1, 2]. Transcriptional regulators and their target genes form an interconnected regulatory network that integrates endogenous and environmental cues into changes in gene expression (Fig. 1) [35].
Fig. 1.

Plants exposed to stress in the environment elicit changes in the expression of genes mediated by transcription factors (TF). Interactions between TF and their associated cis-regulatory element (CRE) regulate the abundance of RNA expressed from different genes. Combinations of TF–CRE interaction lead to the establishment of gene regulatory networks (GRNs). Variation in the GRN may lead to different responses of the plants to the environmental stress

Question 2: How will studying GRNs improve our understanding of plant biology?

GRNs are often composed of thousands of connections between TFs and target genes that together, regulate many cellular functions. GRNs are complex and can be differentially regulated across tissue types and organs during plant develop or environmental acclimation [68]. Such complexity can be difficult to tackle experimentally because each part of the GRN requires many experiments to characterize each part of the GRN. To understand the function of this network in the regulation of a process of interest, it can be useful to identify points within the network that are most critical. These points in the network are frequently interaction hubs that target, or are targets of, many other genes and proteins in the network. Functionally characterizing these points within GRNs will likely improve our understanding of the biology of the plant [9].

Question 3: Why have most GRN studies in plants utilized Arabidopsis as a model?

Many studies to understand the function of GRNs have used the model plant Arabidopsis thaliana because of the substantial genetic resources generated by the research community [10]. Arabidopsis is distributed across a wide range of habitats around the globe. Its genetic diversity has contributed to its ability to adapt to local environments. The genetic diversity within Arabidopsis provides an opportunity for understanding how the evolution of GRNs could contribute to environmental adaptations [11, 12]. To date, the genomes of over 1000 natural accessions of Arabidopsis from around the world have been sequenced and can be used to profile the functional effects of sequence variation on plant physiology [13]. Furthermore, relatives of Arabidopsis have been used to understand how environmental response traits may have evolved [14]. Currently, more than 285 plant genomes have been sequenced and span more than nine families of vascular plants, including 14 in the Brassicaceae family to which Arabidopsis belongs [15, 16]. Characterizing gene content within plant genomes has revealed that plants have a large number of TF families, suggesting that they have extensive GRNs like other complex eukaryotes [17].

Question 4: How do we currently study GRNs?

Recent studies of GRNs have focused on defining the genes and proteins that make up the network and the molecular interactions that regulate those genes and proteins. This has been facilitated by the establishment of genome-wide datasets including whole genome sequences and transcriptomic profiling in different tissues and conditions. More recently, high-throughput assays to profile TF binding site preference and chromatin structure has established how TF-DNA interaction influences the expression of genes within a GRN [18]. Construction of a GRN can also focus on TFs and target genes that likely function together in a specific biological pathway. For example, a GRN associated with secondary cell wall biosynthesis was constructed using yeast-1-hybrid assays and led to the discovery of stress-responsive changes in wall composition [19]. Additionally, the global-scale analysis of TF-target interactions using ChIP-Seq established an extensive GRN acting downstream of 21 TFs controlling response to the hormone abscisic acid (ABA) [20]. In vitro biochemical assays such as DNA affinity purification and sequencing (DAP-Seq) have also been used to broadly survey the direct genomic targets of several hundred TFs [21].

Additional computational tools are being developed so GRNs can be utilized to understand dynamic regulatory processes in plants. For example, the Environmental Gene Regulatory Influence Network (EGRIN) uses an algorithm to incorporate genome-scale transcriptome data from controlled and agricultural field experiments, and chromatin accessibility measurements into a model that predicts TF activity in response to changing environmental conditions [22]. Integrating multiple layers of regulation improves the predictive power of GRNs and can identify potential mechanisms for crosstalk between pathways [23]. Incorporating tissue and developmental stage-specific transcriptome data identified TF nodes that function in both stress and developmental signaling pathways [24]. These tools have been powerful in determining how groups of genes within GRNs are being regulated together and improves our knowledge of how genotype determines phenotype.

Question 5: How does genetic variation affect the architecture of GRNs?

Genetic variation within a species can have important effects on a GRN; changes in the coding sequence of a TF can change binding site preference, and sequence variation in promoters can result in the gain or loss of CREs [25]. To understand how sequence variation ultimately leads to differences in downstream physiology, GRNs can be constructed to include sequence differences that exist within a species or across species [26]. Analysis of genetic variation in Arabidopsis has revealed a greater number of polymorphisms in the promoter regions of drought and cold responsive genes than genes with other functions, suggesting that differences in CRE composition may be involved in local adaptation to environmental stress [27]. It is likely that comparing GRNs between species will help identify points in the network where genetic variation contributes to functional differences in gene regulatory mechanisms [26].

Question 6: How can GRNs be experimentally manipulated?

GRNs can be experimentally manipulated using gene knockouts, gene silencing, and editing approaches, such as viral-induced gene silencing (VIGS) and clustered regularly interspaced short palindromic repeats (CRISPR)/CAS9 system, respectively. Functional characterization of genes in GRNs through mutational analysis can help to validate the relationships between TFs, their target genes, and the phenotypes GRNs govern. The development of the (CRISPR)/CAS9 system has also greatly improved the specificity, efficiency, and throughput of genome editing [28]. Recently, the CAS9 system was used to create different promoter isoforms and this led to novel inflorescence architectures that affected tomato yield [29]. Parts of GRNs can also be reconstituted in heterologous systems to identify the necessary components needed to compose a GRN [30]. This has been effectively demonstrated for the auxin signaling pathway, where engineered yeast are able to induce target genes in response to exogenously supplied hormone [31, 32].

Question 7: What are the future opportunities for understanding GRNs?

Our ability to predict the function and dynamical states of GRNs will be enhanced by improvements in computational modeling. Using Bayesian networks, GRNs can be inferred with small false positive rates. Markov models allow stochastic GRN dynamics to be studied. Additionally, neural models with higher learning rate and better predictive power are being used to study all possible gene-to-gene regulatory interactions. The Extreme learning machine is able to reconstruct predictive GRNs from only transcriptomic datasets [33]. Future questions the field may address will include: What datasets are needed to build predictive GRNs? Based solely on the genome of a plant, can we predict the adaptive traits the plant has? How do GRNs change over evolutionary time and during domestication, and can we domesticate plants more efficiently through an understanding of the GRN?

Question 9: Where can I find more information?

  • Gene regulatory networks [13, 26, 34, 35]

  • The diversity of TF families in plants [17]

  • Current updates on synthetic biology [30, 36, 37]

  • Abiotic stress and NaCl stress in plants [3843]

  • Effects of the environment on root systems [44, 45]

  • ABA and signals involved in plant stress response [46]

  • Plant plasticity and evolution of tolerance traits [47, 48]

  • Impact of CRE on stress response [49]

  • Spatio-temporal GRNs [6, 22, 5054]

  • Halophytes and stress tolerant plants [5560]

Declarations

Acknowledgements

Funding was provided by the Carnegie Institution for Science Endowment and an HHMI-Simons Foundation Faculty Scholar award to JRD and a National Science Foundation Graduate Research Fellowship to YS.

Authors’ contributions

YS and JD wrote and edited the paper. Both authors read and approved the final manuscript.

Competing interests

The authors declare that they have no competing interests.

Publisher’s Note

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Authors’ Affiliations

(1)
Department of Biology, Stanford University, Stanford, USA
(2)
Department of Plant Biology, Carnegie Institution for Science, Stanford, USA

References

  1. Geertz M, Maerkl SJ. Experimental strategies for studying transcription factor–DNA binding specificities. Brief Funct Genomics. 2010;9:362–73.View ArticlePubMedPubMed CentralGoogle Scholar
  2. Li Y, Chen C-Y, Kaye AM, Wasserman WW. The identification of cis-regulatory elements: a review from a machine learning perspective. Biosystems. 2015;138:6–17.View ArticlePubMedGoogle Scholar
  3. Macneil LT, Walhout AJM. Gene regulatory networks and the role of robustness and stochasticity in the control of gene expression. Genome Res. 2011;21:645–57.View ArticlePubMedPubMed CentralGoogle Scholar
  4. Carroll SB. Evo-devo and an expanding evolutionary synthesis: a genetic theory of morphological evolution. Cell. 2008;134:25–36. http://www.sciencedirect.com/science/article/pii/S0092867408008179 View ArticlePubMedGoogle Scholar
  5. Yamaguchi-Shinozaki K, Shinozaki K. Transcriptional regulatory networks in cellular responses and tolerance to dehydration and cold stresses. Annu Rev Plant Biol. 2006;57:781–803.View ArticlePubMedGoogle Scholar
  6. Brady SM, Orlando DA, Lee J-Y, Wang JY, Koch J, Dinneny JR, et al. A high-resolution root spatiotemporal map reveals dominant expression patterns. Science. 2007;318:801–6.View ArticlePubMedGoogle Scholar
  7. Dinneny JR, Long TA, Wang JY, Jung JW, Mace D, Pointer S, et al. Cell identity mediates the response of Arabidopsis roots to abiotic stress. Science. 2008;320:942–5.View ArticlePubMedGoogle Scholar
  8. Kilian J, Whitehead D, Horak J, Wanke D, Weinl S, Batistic O, et al. The AtGenExpress global stress expression data set: protocols, evaluation and model data analysis of UV-B light, drought and cold stress responses. Plant J. 2007;50:347–63.View ArticlePubMedGoogle Scholar
  9. Rebeiz M, Patel NH, Hinman VF. Unraveling the Tangled Skein: The Evolution of Transcriptional Regulatory Networks in Development. Annu Rev Genomics Hum Genet. 2015;16:103–31.View ArticlePubMedGoogle Scholar
  10. Provart NJ, Alonso J, Assmann SM, Bergmann D, Brady SM, Brkljacic J, et al. 50 years of Arabidopsis research: highlights and future directions. New Phytol. 2016;209:921–44.View ArticlePubMedGoogle Scholar
  11. Fournier-Level A, Korte A, Cooper MD, Nordborg M, Schmitt J, Wilczek AM. A map of local adaptation in Arabidopsis thaliana. Science. 2011;334:86–9.View ArticlePubMedGoogle Scholar
  12. Weigel D. Natural variation in Arabidopsis: from molecular genetics to ecological genomics. Plant Physiol. 2012; http://www.plantphysiol.org/content/158/1/2.short
  13. 1001Genomes Consortium. 1,135 genomes reveal the global pattern of polymorphism in Arabidopsis thaliana. Cell. 2016;166:481–91.View ArticleGoogle Scholar
  14. Chang C, Bowman JL, Meyerowitz EM. Field guide to plant model systems. Cell. 2016;167:325–39.View ArticlePubMedPubMed CentralGoogle Scholar
  15. NCBI. Genome List. https://www.ncbi.nlm.nih.gov/genome/browse/#!/overview/brassicaceae. Accessed Dec 12 2017.
  16. Cheng F, Liu S, Wu J, Fang L, Sun S, Liu B, et al. BRAD, the genetics and genomics database for Brassica plants. BMC Plant Biol. 2011;11:136.View ArticlePubMedPubMed CentralGoogle Scholar
  17. Lehti-Shiu MD, Panchy N, Wang P, Uygun S, Shiu S-H. Diversity, expansion, and evolutionary novelty of plant DNA-binding transcription factor families. Biochim Biophys Acta. 1860;2017:3–20.Google Scholar
  18. Lister R, Gregory BD, Ecker JR. Next is now: new technologies for sequencing of genomes, transcriptomes, and beyond. Curr Opin Plant Biol. 2009;12:107–18.View ArticlePubMedPubMed CentralGoogle Scholar
  19. Taylor-Teeples M, Lin L, de Lucas M, Turco G, Toal TW, Gaudinier A, et al. An Arabidopsis gene regulatory network for secondary cell wall synthesis. Nature. 2015;517:571–5.View ArticlePubMedGoogle Scholar
  20. Song L, Huang S-SC, Wise A, Castanon R, Nery JR, Chen H, et al. A transcription factor hierarchy defines an environmental stress response network. Science. 2016;354 Available from: https://doi.org/10.1126/science.aag1550
  21. O’Malley RC, Huang S-SC, Song L, Lewsey MG, Bartlett A, Nery JR, et al. Cistrome and epicistrome features shape the regulatory DNA landscape. Cell. 2016;165:1280–92.View ArticlePubMedPubMed CentralGoogle Scholar
  22. Wilkins O, Hafemeister C, Plessis A, Holloway-Phillips M-M, Pham GM, Nicotra AB, et al. EGRINs (environmental gene regulatory influence networks) in rice that function in the response to water deficit, high temperature, and agricultural environments. Plant Cell. 2016;28:2365–84.View ArticlePubMedPubMed CentralGoogle Scholar
  23. Walley JW, Sartor RC, Shen Z, Schmitz RJ, Wu KJ, Urich MA, et al. Integration of omic networks in a developmental atlas of maize. Science. 2016;353:814–8.View ArticlePubMedPubMed CentralGoogle Scholar
  24. Miao Z, Han Z, Zhang T, Chen S, Ma C. A systems approach to a spatio-temporal understanding of the drought stress response in maize. Sci Rep. 2017;7:6590.View ArticlePubMedPubMed CentralGoogle Scholar
  25. Johnson AD. The rewiring of transcription circuits in evolution. Curr Opin Genet Dev. 2017;47:121–7.View ArticlePubMedGoogle Scholar
  26. Thompson D, Regev A, Roy S. Comparative analysis of gene regulatory networks: from network reconstruction to evolution. Annu Rev Cell Dev Biol. 2015;31:399–428.View ArticlePubMedGoogle Scholar
  27. Lasky JR, Des Marais DL, Lowry DB, Povolotskaya I, McKay JK, Richards JH, et al. Natural variation in abiotic stress responsive gene expression and local adaptation to climate in Arabidopsis thaliana. Mol Biol Evol. 2014;31:2283–96.View ArticlePubMedPubMed CentralGoogle Scholar
  28. Zsögön A, Cermak T, Voytas D, Peres LEP. Genome editing as a tool to achieve the crop ideotype and de novo domestication of wild relatives: case study in tomato. Plant Sci. 2017;256:120–30.View ArticlePubMedGoogle Scholar
  29. Rodríguez-Leal D, Lemmon ZH, Man J, Bartlett ME, Lippman ZB. Engineering quantitative trait variation for crop improvement by genome editing. Cell. 2017;171:470–80. e8View ArticlePubMedGoogle Scholar
  30. Nemhauser JL, Torii KU. Plant synthetic biology for molecular engineering of signalling and development. Nat Plants. 2016;2:16010.View ArticlePubMedPubMed CentralGoogle Scholar
  31. Pierre-Jerome E, Jang SS, Havens KA, Nemhauser JL, Klavins E. Recapitulation of the forward nuclear auxin response pathway in yeast. Proc Natl Acad Sci U S A. 2014;111:9407–12.View ArticlePubMedPubMed CentralGoogle Scholar
  32. Pierre-Jerome E, Moss BL, Lanctot A, Hageman A, Nemhauser JL. Functional analysis of molecular interactions in synthetic auxin response circuits. Proc Natl Acad Sci U S A. 2016;113:11354–9.View ArticlePubMedPubMed CentralGoogle Scholar
  33. Rubiolo M, Milone DH, Stegmayer G. Extreme learning machines for reverse engineering of gene regulatory networks from expression time series. Bioinformatics. 2017; https://doi.org/10.1093/bioinformatics/btx730
  34. Sinha NR, Rowland SD, Ichihashi Y. Using gene networks in EvoDevo analyses. Curr Opin Plant Biol. 2016;33:133–9.View ArticlePubMedGoogle Scholar
  35. Wittkopp PJ, Kalay G. Cis-regulatory elements: molecular mechanisms and evolutionary processes underlying divergence. Nat Rev Genet. 2011;13:59–69.View ArticlePubMedGoogle Scholar
  36. Braguy J, Zurbriggen MD. Synthetic strategies for plant signalling studies: molecular toolbox and orthogonal platforms. Plant J. 2016;87:118–38.View ArticlePubMedGoogle Scholar
  37. Samodelov SL, Zurbriggen MD. Quantitatively understanding plant signaling: novel theoretical–experimental approaches. Trends Plant Sci. 2017;22:685–704.View ArticlePubMedGoogle Scholar
  38. Pereira A. Plant abiotic stress challenges from the changing environment. Front Plant Sci. 2016;7:1123.PubMedPubMed CentralGoogle Scholar
  39. Munns R, Tester M. Mechanisms of salinity tolerance. Annu Rev Plant Biol. 2008;59:651–81.View ArticlePubMedGoogle Scholar
  40. Zhu J-K. Abiotic stress signaling and responses in plants. Cell. 2016;167:313–24.View ArticlePubMedPubMed CentralGoogle Scholar
  41. Dinneny JR. Traversing organizational scales in plant salt-stress responses. Curr Opin Plant Biol. 2015;23:70–5.View ArticlePubMedGoogle Scholar
  42. Feng W, Lindner H, Robbins NE 2nd, Dinneny JR. Growing out of stress: the role of cell- and organ-scale growth control in plant water-stress responses. Plant Cell. 2016;28:1769–82.View ArticlePubMedPubMed CentralGoogle Scholar
  43. Hasegawa PM, Bressan RA, Zhu J-K, Bohnert HJ. Plant cellular and molecular responses to high salinity. Annu Rev Plant Physiol Plant Mol Biol. 2000;51:463–99.View ArticlePubMedGoogle Scholar
  44. Rellán-Álvarez R, Lobet G, Dinneny JR. Environmental control of root system biology. Annu Rev Plant Biol. 2016;67:619–42.View ArticlePubMedGoogle Scholar
  45. Brophy JAN, LaRue T. Dinneny JR. Semin Cell Dev Biol: Understanding and engineering plant form; 2017. https://doi.org/10.1016/j.semcdb.2017.08.051 Google Scholar
  46. Zhu J-K. Salt and drought stress signal transduction in plants. Annu Rev Plant Biol. 2002;53:247–73.View ArticlePubMedPubMed CentralGoogle Scholar
  47. Des Marais DL, Juenger TE. Pleiotropy, plasticity, and the evolution of plant abiotic stress tolerance. Ann N Y Acad Sci. 2010;1206:56–79.View ArticlePubMedGoogle Scholar
  48. Nicotra AB, Atkin OK, Bonser SP, Davidson AM, Finnegan EJ, Mathesius U, et al. Plant phenotypic plasticity in a changing climate. Trends Plant Sci. 2010;15:684–92.View ArticlePubMedGoogle Scholar
  49. Zou C, Sun K, Mackaluso JD, Seddon AE, Jin R, Thomashow MF, et al. Cis-regulatory code of stress-responsive transcription in Arabidopsis thaliana. Proc Natl Acad Sci U S A. 2011;108:14992–7.View ArticlePubMedPubMed CentralGoogle Scholar
  50. Birnbaum K, Shasha DE, Wang JY, Jung JW, Lambert GM, Galbraith DW, et al. A gene expression map of the Arabidopsis root. Science. 2003;302:1956–60.View ArticlePubMedGoogle Scholar
  51. Walker L, Boddington C, Jenkins D, Wang Y, Grønlund JT, Hulsmans J, et al. Changes in gene expression in space and time orchestrate environmentally mediated shaping of root architecture. Plant Cell. 2017;29:2393–412.View ArticlePubMedGoogle Scholar
  52. Sonawane AR, Platig J, Fagny M, Chen C-Y, Paulson JN, Lopes-Ramos CM, et al. Understanding tissue-specific gene regulation. Cell Rep. 2017;21:1077–88.View ArticlePubMedGoogle Scholar
  53. Uygun S, Seddon AE, Azodi CB, Shiu S-H. Predictive models of spatial transcriptional response to high salinity. Plant Physiol. 2017;174:450–64.View ArticlePubMedPubMed CentralGoogle Scholar
  54. Krishnan A, Gupta C, Ambavaram MMR, Pereira A. RECoN: Rice environment Coexpression network for systems level analysis of abiotic-stress response. Front Plant Sci. 2017;8:1640.View ArticlePubMedPubMed CentralGoogle Scholar
  55. Wu H-J, Zhang Z, Wang J-Y, Oh D-H, Dassanayake M, Liu B, et al. Insights into salt tolerance from the genome of Thellungiella salsuginea. Proc Natl Acad Sci U S A. 2012;109:12219–24.View ArticlePubMedPubMed CentralGoogle Scholar
  56. Orsini F, D’Urzo MP, Inan G, Serra S, Oh D-H, Mickelbart MV, et al. A comparative study of salt tolerance parameters in 11 wild relatives of Arabidopsis thaliana. J Exp Bot. 2010;61:3787–98.View ArticlePubMedPubMed CentralGoogle Scholar
  57. Oh D-H, Hong H, Lee SY, Yun D-J, Bohnert HJ, Dassanayake M. Genome structures and transcriptomes signify niche adaptation for the multiple-ion-tolerant extremophyte Schrenkiella parvula. Plant Physiol. 2014;164:2123–38.View ArticlePubMedPubMed CentralGoogle Scholar
  58. Dassanayake M, Oh D-H, Hong H, Bohnert HJ, Cheeseman JM. Transcription strength and halophytic lifestyle. Trends Plant Sci. 2011;16:1–3.View ArticlePubMedGoogle Scholar
  59. Oh D-H, Dassanayake M, Haas JS, Kropornika A, Wright C, d’Urzo MP, et al. Genome structures and halophyte-specific gene expression of the extremophile Thellungiella parvula in comparison with Thellungiella salsuginea (Thellungiella halophila) and Arabidopsis. Plant Physiol. 2010;154:1040–52.View ArticlePubMedPubMed CentralGoogle Scholar
  60. Oh D-H. Dassanayake M, Bohnert HJ. Cheeseman JM Life at the extreme: lessons from the genome Genome Biol. 2012;13:241.PubMedGoogle Scholar

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