Abstract
Understanding the systems-level actions of transcriptional responses to hormones provides insight into how the genome is reprogrammed in response to environmental stimuli. Here, we investigated the signalling pathway of the hormone jasmonic acid (JA), which controls a plethora of critically important processes in plants and is orchestrated by the transcription factor MYC2 and its closest relatives in Arabidopsis thaliana. We generated an integrated framework of the response to JA, which spans from the activity of master and secondary regulatory transcription factors, through gene expression outputs and alternative splicing, to protein abundance changes, protein phosphorylation and chromatin remodelling. We integrated time-series transcriptome analysis with (phospho)proteomic data to reconstruct gene regulatory network models. These enabled us to predict previously unknown points of crosstalk of JA to other signalling pathways and to identify new components of the JA regulatory mechanism, which we validated through targeted mutant analysis. These results provide a comprehensive understanding of how a plant hormone remodels cellular functions and plant behaviour, the general principles of which provide a framework for analyses of cross-regulation between other hormone and stress signalling pathways.
This is a preview of subscription content, access via your institution
Access options
Access Nature and 54 other Nature Portfolio journals
Get Nature+, our best-value online-access subscription
$29.99 / 30 days
cancel any time
Subscribe to this journal
Receive 12 digital issues and online access to articles
$119.00 per year
only $9.92 per issue
Buy this article
- Purchase on SpringerLink
- Instant access to full article PDF
Prices may be subject to local taxes which are calculated during checkout
Similar content being viewed by others
Data availability
All described lines can be requested from the corresponding authors. Sequence data can be downloaded from the Gene Expression Omnibus repository (GSE133408). Proteomics data are deposited at the ProteomeXchange under the accession ID PXD013592. Visualized data can be found at http://neomorph.salk.edu/MYC2 and http://signal.salk.edu/interactome/JA.php. Source data for Figs. 1–5 and Extended Data Figs. 1–10 are provided with the paper.
Change history
21 July 2020
A Correction to this paper has been published: https://doi.org/10.1038/s41477-020-0743-y
References
Vanstraelen, M. & Benkova, E. Hormonal interactions in the regulation of plant development. Annu. Rev. Cell Dev. Biol. 28, 463–487 (2012).
Chang, K. N. et al. Temporal transcriptional response to ethylene gas drives growth hormone cross-regulation in Arabidopsis. eLife 2, e00675 (2013).
Song, L. et al. A transcription factor hierarchy defines an environmental stress response network. Science 354, aag1550 (2016).
Hickman, R. et al. Architecture and dynamics of the jasmonic acid gene regulatory network. Plant Cell 29, 2086–2105 (2017).
Pauwels, L. et al. Mapping methyl jasmonate-mediated transcriptional reprogramming of metabolism and cell cycle progression in cultured Arabidopsis cells. Proc. Natl Acad. Sci. USA 105, 1380–1385 (2008).
Wang, C., Liu, Y., Li, S. S. & Han, G. Z. Insights into the origin and evolution of the plant hormone signaling machinery. Plant Physiol. 167, 872–886 (2015).
Huang, H., Liu, B., Liu, L. & Song, S. Jasmonate action in plant growth and development. J. Exp. Bot. 68, 1349–1359 (2017).
Thines, B. et al. JAZ repressor proteins are targets of the SCF(COI1) complex during jasmonate signalling. Nature 448, 661–665 (2007).
Chini, A. et al. The JAZ family of repressors is the missing link in jasmonate signalling. Nature 448, 666–671 (2007).
Fonseca, S. et al. (+)-7-iso-Jasmonoyl-l-isoleucine is the endogenous bioactive jasmonate. Nat. Chem. Biol. 5, 344–350 (2009).
Sheard, L. B. et al. Jasmonate perception by inositol-phosphate-potentiated COI1–JAZ co-receptor. Nature 468, 400–405 (2010).
Xie, D. X. et al. COI1: an Arabidopsis gene required for jasmonate-regulated defense and fertility. Science 280, 1091–1094 (1998).
Fernandez-Calvo, P. et al. The Arabidopsis bHLH transcription factors MYC3 and MYC4 are targets of JAZ repressors and act additively with MYC2 in the activation of jasmonate responses. Plant Cell 23, 701–715 (2011).
Song, S. et al. MYC5 is involved in jasmonate-regulated plant growth, leaf senescence and defense responses. Plant Cell Physiol. 58, 1752–1763 (2017).
Lorenzo, O., Chico, J. M., Sanchez-Serrano, J. J. & Solano, R. JASMONATE-INSENSITIVE1 encodes a MYC transcription factor essential to discriminate between different jasmonate-regulated defense responses in Arabidopsis. Plant Cell 16, 1938–1950 (2004).
Zhang, F. et al. Structural basis of JAZ repression of MYC transcription factors in jasmonate signalling. Nature 525, 269–273 (2015).
Schweizer, F. et al. Arabidopsis basic helix–loop–helix transcription factors MYC2, MYC3, and MYC4 regulate glucosinolate biosynthesis, insect performance, and feeding behavior. Plant Cell 25, 3117–3132 (2013).
Bao, S. et al. Molecular basis of natural variation in photoperiodic flowering responses. Dev. Cell 50, 90–101 (2019).
Du, M. et al. MYC2 orchestrates a hierarchical transcriptional cascade that regulates jasmonate-mediated plant immunity in tomato. Plant Cell 29, 1883–1906 (2017).
Dombrecht, B. et al. MYC2 differentially modulates diverse jasmonate-dependent functions in Arabidopsis. Plant Cell 19, 2225–2245 (2007).
Yadav, V. et al. A basic helix–loop–helix transcription factor in Arabidopsis, MYC2, acts as a repressor of blue light-mediated photomorphogenic growth. Plant Cell 17, 1953–1966 (2005).
Gangappa, S. N. & Chattopadhyay, S. MYC2, a bHLH transcription factor, modulates the adult phenotype of SPA1. Plant Signal. Behav. 5, 1650–1652 (2010).
Zhang, X. et al. Jasmonate-activated MYC2 represses ETHYLENE INSENSITIVE3 activity to antagonize ethylene-promoted apical hook formation in Arabidopsis. Plant Cell 26, 1105–1117 (2014).
Gimenez-Ibanez, S. et al. JAZ2 controls stomata dynamics during bacterial invasion. New Phytol. 213, 1378–1392 (2017).
Fernandez, P. C. et al. Genomic targets of the human c-Myc protein. Genes Dev. 17, 1115–1129 (2003).
Godoy, M. et al. Improved protein-binding microarrays for the identification of DNA-binding specificities of transcription factors. Plant J. 66, 700–711 (2011).
Xie, M. et al. A B-ARR-mediated cytokinin transcriptional network directs hormone cross-regulation and shoot development. Nat. Commun. 9, 1604 (2018).
Liu, Y. et al. MYC2 regulates the termination of jasmonate signaling via an autoregulatory negative feedback loop. Plant Cell 31, 106–127 (2019).
Guo, Q. et al. JAZ repressors of metabolic defense promote growth and reproductive fitness in Arabidopsis. Proc. Natl Acad. Sci. USA 115, E10768–E10777 (2018).
Pauwels, L. et al. NINJA connects the co-repressor TOPLESS to jasmonate signalling. Nature 464, 788–791 (2010).
Huot, B., Yao, J., Montgomery, B. L. & He, S. Y. Growth-defense tradeoffs in plants: a balancing act to optimize fitness. Mol. Plant 7, 1267–1287 (2014).
Chen, X. et al. New perspective of the bHLH–MYB complex in jasmonate-regulated plant fertility in Arabidopsis. Plant Signal. Behav. 11, e1135280 (2016).
Hou, X. et al. DELLAs modulate jasmonate signaling via competitive binding to JAZs. Dev. Cell 19, 884–894 (2010).
Lorenzo, O., Piqueras, R., Sanchez-Serrano, J. J. & Solano, R. ETHYLENE RESPONSE FACTOR1 integrates signals from ethylene and jasmonate pathways in plant defense. Plant Cell 15, 165–178 (2003).
Pre, M. et al. The AP2/ERF domain transcription factor ORA59 integrates jasmonic acid and ethylene signals in plant defense. Plant Physiol. 147, 1347–1357 (2008).
Bu, Q. et al. Role of the Arabidopsis thaliana NAC transcription factors ANAC019 and ANAC055 in regulating jasmonic acid-signaled defense responses. Cell Res. 18, 756–767 (2008).
Gao, Q. M., Venugopal, S., Navarre, D. & Kachroo, A. Low oleic acid-derived repression of jasmonic acid-inducible defense responses requires the WRKY50 and WRKY51 proteins. Plant Physiol. 155, 464–476 (2011).
Pauwels, L. & Goossens, A. Fine-tuning of early events in the jasmonate response. Plant Signal. Behav. 3, 846–847 (2008).
Schweizer, F. et al. Differential contribution of transcription factors to Arabidopsis thaliana defense against Spodoptera littoralis. Front. Plant. Sci. 4, 13 (2013).
Taki, N. et al. 12-Oxo-phytodienoic acid triggers expression of a distinct set of genes and plays a role in wound-induced gene expression in Arabidopsis. Plant Physiol. 139, 1268–1283 (2005).
Xiao, J., Jin, R. & Wagner, D. Developmental transitions: integrating environmental cues with hormonal signaling in the chromatin landscape in plants. Genome Biol. 18, 88 (2017).
Wang, H. et al. MED25 connects enhancer-promoter looping and MYC2-dependent activation of jasmonate signalling. Nat. Plants 5, 616–625 (2019).
Rothbart, S. B. & Strahl, B. D. Interpreting the language of histone and DNA modifications. Biochim. Biophys. Acta 1839, 627–643 (2014).
Coleman-Derr, D. & Zilberman, D. Deposition of histone variant H2A.Z within gene bodies regulates responsive genes. PLoS Genet. 8, e1002988 (2012).
Kawaguchi, R. & Bailey-Serres, J. mRNA sequence features that contribute to translational regulation in Arabidopsis. Nucleic Acids Res. 33, 955–965 (2005).
Walley, J. W. et al. Integration of omic networks in a developmental atlas of maize. Science 353, 814–818 (2016).
Hartmann, L. et al. Alternative splicing substantially diversifies the transcriptome during early photomorphogenesis and correlates with the energy availability in Arabidopsis. Plant Cell 28, 2715–2734 (2016).
Chung, H. S. et al. Alternative splicing expands the repertoire of dominant JAZ repressors of jasmonate signaling. Plant J. 63, 613–622 (2010).
Moreno, J. E. et al. Negative feedback control of jasmonate signaling by an alternative splice variant of JAZ10. Plant Physiol. 162, 1006–1017 (2013).
Nakata, M. et al. A bHLH-type transcription factor, ABA-INDUCIBLE BHLH-TYPE TRANSCRIPTION FACTOR/JA-ASSOCIATED MYC2-LIKE1, acts as a repressor to negatively regulate jasmonate signaling in Arabidopsis. Plant Cell 25, 1641–1656 (2013).
Sasaki-Sekimoto, Y. et al. Basic helix–loop–helix transcription factors JASMONATE-ASSOCIATED MYC2-LIKE1 (JAM1), JAM2, and JAM3 are negative regulators of jasmonate responses in Arabidopsis. Plant Physiol. 163, 291–304 (2013).
Xu, J. et al. Activation of MAPK kinase 9 induces ethylene and camalexin biosynthesis and enhances sensitivity to salt stress in Arabidopsis. J. Biol. Chem. 283, 26996–27006 (2008).
Hentrich, M. et al. The jasmonic acid signaling pathway is linked to auxin homeostasis through the modulation of YUCCA8 and YUCCA9 gene expression. Plant J. 74, 626–637 (2013).
Wild, M. et al. The Arabidopsis DELLA RGA-LIKE3 is a direct target of MYC2 and modulates jasmonate signaling responses. Plant Cell 24, 3307–3319 (2012).
Mittler, R. et al. Gain- and loss-of-function mutations in Zat10 enhance the tolerance of plants to abiotic stress. FEBS Lett. 580, 6537–6542 (2006).
Lozano-Duran, R. et al. The transcriptional regulator BZR1 mediates trade-off between plant innate immunity and growth. eLife 2, e00983 (2013).
Magome, H. et al. The DDF1 transcriptional activator upregulates expression of a gibberellin-deactivating gene, GA2ox7, under high-salinity stress in Arabidopsis. Plant J. 56, 613–626 (2008).
Dubois, M. et al. The ETHYLENE RESPONSE FACTORs ERF6 and ERF11 antagonistically regulate mannitol-induced growth inhibition in Arabidopsis. Plant Physiol. 169, 166–179 (2015).
Zander, M. et al. Repression of the Arabidopsis thaliana jasmonic acid/ethylene-induced defense pathway by TGA-interacting glutaredoxins depends on their C-terminal ALWL motif. Mol. Plant 5, 831–840 (2012).
Ndamukong, I. et al. SA-inducible Arabidopsis glutaredoxin interacts with TGA factors and suppresses JA-responsive PDF1.2 transcription. Plant J. 50, 128–139 (2007).
Shyu, C. et al. JAZ8 lacks a canonical degron and has an EAR motif that mediates transcriptional repression of jasmonate responses in Arabidopsis. Plant Cell 24, 536–550 (2012).
Ren, X. et al. ABO3, a WRKY transcription factor, mediates plant responses to abscisic acid and drought tolerance in Arabidopsis. Plant J. 63, 417–429 (2010).
Alonso, J. M. Genome-wide insertional mutagenesis of Arabidopsis thaliana. Science 301, 653–657 (2003).
Arabidopsis Genome Initiative. Analysis of the genome sequence of the flowering plant Arabidopsis thaliana. Nature 408, 796–815 (2000).
Huang, J., Ghosh, R. & Bankaitis, V. A. Sec14-like phosphatidylinositol transfer proteins and the biological landscape of phosphoinositide signaling in plants. Biochim. Biophys. Acta 1861, 1352–1364 (2016).
Mosblech, A. et al. Jasmonic acid perception by COI1 involves inositol polyphosphates in Arabidopsis thaliana. Plant J. 65, 949–957 (2011).
Zhou, R., Benavente, L. M., Stepanova, A. N. & Alonso, J. M. A recombineering-based gene tagging system for Arabidopsis. Plant J. 66, 712–723 (2011).
Kaufmann, K. et al. Chromatin immunoprecipitation (ChIP) of plant transcription factors followed by sequencing (ChIP-SEQ) or hybridization to whole genome arrays (ChIP–CHIP). Nat. Protoc. 5, 457–472 (2010).
Langmead, B. Aligning short sequencing reads with Bowtie. Curr. Protoc. Bioinformatics Chapter 11, Unit 11 17 (2010).
O’Malley, R. C. et al. Cistrome and epicistrome features shape the regulatory DNA landscape. Cell 165, 1280–1292 (2016).
Bartlett, A. et al. Mapping genome-wide transcription-factor binding sites using DAP-seq. Nat. Protoc. 12, 1659–1672 (2017).
Kim, D. et al. TopHat2: accurate alignment of transcriptomes in the presence of insertions, deletions and gene fusions. Genome Biol. 14, R36 (2013).
Anders, S., Pyl, P. T. & Huber, W. HTSeq—a Python framework to work with high-throughput sequencing data. Bioinformatics 31, 166–169 (2015).
Robinson, M. D., McCarthy, D. J. & Smyth, G. K. edgeR: a Bioconductor package for differential expression analysis of digital gene expression data. Bioinformatics 26, 139–140 (2010).
Lun, A. T., Chen, Y. & Smyth, G. K. It’s DE-licious: a recipe for differential expression analyses of RNA-seq experiments using quasi-likelihood methods in edgeR. Methods Mol. Biol. 1418, 391–416 (2016).
Ernst, J. & Bar-Joseph, Z. STEM: a tool for the analysis of short time series gene expression data. BMC Bioinformatics 7, 191 (2006).
Jin, J. et al. PlantTFDB 4.0: toward a central hub for transcription factors and regulatory interactions in plants. Nucleic Acids Res. 45, D1040–D1045 (2017).
Langmead, B. & Salzberg, S. L. Fast gapped-read alignment with Bowtie 2. Nat. Methods 9, 357–359 (2012).
Kharchenko, P. V., Tolstorukov, M. Y. & Park, P. J. Design and analysis of ChIP-seq experiments for DNA-binding proteins. Nat. Biotechnol. 26, 1351–1359 (2008).
Zhang, Y. et al. Model-based analysis of ChIP-Seq (MACS). Genome Biol. 9, R137 (2008).
Quinlan, A. R. & Hall, I. M. BEDTools: a flexible suite of utilities for comparing genomic features. Bioinformatics 26, 841–842 (2010).
Zhu, L. J. et al. ChIPpeakAnno: a Bioconductor package to annotate ChIP-seq and ChIP-chip data. BMC Bioinformatics 11, 237 (2010).
Khan, A. & Mathelier, A. Intervene: a tool for intersection and visualization of multiple gene or genomic region sets. BMC Bioinformatics 18, 287 (2017).
Machanick, P. & Bailey, T. L. MEME-ChIP: motif analysis of large DNA datasets. Bioinformatics 27, 1696–1697 (2011).
Guo, Y., Mahony, S. & Gifford, D. K. High resolution genome wide binding event finding and motif discovery reveals transcription factor spatial binding constraints. PLoS Comput. Biol. 8, e1002638 (2012).
Zang, C. et al. A clustering approach for identification of enriched domains from histone modification ChIP-Seq data. Bioinformatics 25, 1952–1958 (2009).
Li, H. et al. The sequence alignment/map format and SAMtools. Bioinformatics 25, 2078–2079 (2009).
Yu, G., Wang, L. G., Han, Y. & He, Q.-Y. clusterProfiler: an R package for comparing biological themes among gene clusters. Omics 16, 284–287 (2012).
Krishnakumar, V. et al. Araport: the Arabidopsis information portal. Nucleic Acids Res. 43, D1003–D1009 (2015).
Tyanova, S., Temu, T. & Cox, J. The MaxQuant computational platform for mass spectrometry-based shotgun proteomics. Nat. Protoc. 11, 2301–2319 (2016).
Cox, J. et al. Andromeda: a peptide search engine integrated into the MaxQuant environment. J. Proteome Res. 10, 1794–1805 (2011).
Elias, J. E. & Gygi, S. P. Target-decoy search strategy for mass spectrometry-based proteomics. Methods Mol. Biol. 604, 55–71 (2010).
Li, J., Witten, D. M., Johnstone, I. M. & Tibshirani, R. Normalization, testing, and false discovery rate estimation for RNA-sequencing data. Biostatistics 13, 523–538 (2012).
Patro, R. et al. Salmon provides fast and bias-aware quantification of transcript expression. Nat. Methods 14, 417–419 (2017).
Zhang, R. et al. AtRTD—a comprehensive reference transcript dataset resource for accurate quantification of transcript-specific expression in Arabidopsis thaliana. New Phytol. 208, 96–101 (2015).
Guo, W., Calixto, C. P. G., Brown, J. W. S. & Zhang, R. TSIS: an R package to infer alternative splicing isoform switches for time-series data. Bioinformatics 33, 3308–3310 (2017).
Shibata, M. et al. GTL1 and DF1 regulate root hair growth through transcriptional repression of ROOT HAIR DEFECTIVE 6-LIKE 4 in Arabidopsis. Development 145, dev159707 (2018).
Clark, N. M. et al. Auxin induces widespread proteome remodeling in Arabidopsis seedlings. Proteomics 19, e1900199 (2019).
Giorgino, T. Computing and visualizing dynamic time warping alignments in R: the dtw package. J. Stat. Softw. https://doi.org/10.18637/jss.v031.i07 (2012).
Clark, N. M. et al. Stem-cell-ubiquitous genes spatiotemporally coordinate division through regulation of stem-cell-specific gene networks. Nat. Commun. 10, 5574 (2019).
Alon, U. Network motifs: theory and experimental approaches. Nat. Rev. Genet. 8, 450–461 (2007).
Milo, R. et al. Network motifs: simple building blocks of complex networks. Science 298, 824–827 (2002).
Ingram, P. J., Stumpf, M. P. & Stark, J. Network motifs: structure does not determine function. BMC Genomics 7, 108 (2006).
Acknowledgements
M.Z. was supported by a Deutsche Forschungsgemeinschaft (DFG) research fellowship (Za-730/1-1) and by the Salk Pioneer Postdoctoral Endowment Fund. M.G.L. was supported by an EU Marie Curie FP7 International Outgoing Fellowship (252475). In addition, this work was supported by the Mass Spectrometry Core of the Salk Institute with funding from NIH-NCI CCSG (P30 014195) and the Helmsley Center for Genomic Medicine. This work was supported by grants from the National Science Foundation (NSF) (MCB-1818160 and IOS-1759023 to J.W.W., MCB-1024999 to J.R.E.), the National Institutes of Health (R01GM120316), the Division of Chemical Sciences, Geosciences, and Biosciences, the Office of Basic Energy Sciences of the US Department of Energy (DE-FG02-04ER15517), and the Gordon and Betty Moore Foundation (GBMF3034). Research in the lab of R.S. was supported by grant BIO2016-77216-R (MINECO/FEDER) from the Ministry of Economy, Industry and Competitiveness. J.W.W. is supported as a Faculty Scholar of the ISU Plant Sciences Institute. J.R.E. is an Investigator of the Howard Hughes Medical Institute. We thank the following postdocs, undergraduates and technicians who contributed technical assistance to the project: M. Xie, L. Song, R. Carlos Serrano, C. Sy, L. Tames, J. Park, O. Romero, R. Luong, W. Ho, Y. Koga, S. Hazelton, M. Urich and T. Dabi. We thank S.-s. C. Huang for computational assistance and J. Moresco and J. Diedrich for proteomics support.
Author information
Authors and Affiliations
Contributions
M.Z., M.G.L., R.S. and J.R.E. designed the research. M.Z., M.G.L., A.E.L. and B.J. performed the phenotype screening. M.Z., M.G.L. and J.P.S.G. carried out the RNA-seq and ChIP-seq experiments. M.G.L., E.H. and J.P.S.G. performed the cloning and generation of transgenic constructs. M.G.L., J.R.N., H.C., M.Z. and L.Y. analysed the sequencing data and performed bioinformatics analyses. A.B. carried out DAP-seq experiments. N.M.C. and J.W.W. analysed the proteome and phosphoproteome data. N.M.C., J.W.W., A.W. and Z.B.-J. performed regulatory network analyses. M.Z., M.G.L. and J.R.E. prepared the figures and wrote the manuscript.
Corresponding authors
Ethics declarations
Competing interests
The authors declare no competing interests.
Additional information
Peer review information Nature Plants thanks Pingtao Ding, Jonathan Jones, Chuanyou Li and the other, anonymous, reviewer for their contribution to the peer review of this work.
Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Extended data
Extended Data Fig. 1 Overview of quality metrics of generated ChIP-seq datasets.
a–c, Correlation plot of the respective TF ChIP-seq samples is shown. The MYC2 and MYC3 ChIP-seq replicates are shown together in (a). Clustering is determined by the degree of correlation (Pearson correlation). ChIP-seq data is derived from at least three independent experiments: MYC2 (JA, n = 4), MYC3 (JA, n = 3), ZAT10 (air, n = 3; JA, n = 2), ANAC055 (JA, n = 3). d-i, Cross-correlation (Pearson correlation) plot for the respective TF and histone ChIP-Seq sample is shown. NSC means normalized strand cross-correlation coefficient and RSC means relative strand cross-correlation coefficient. Qtag means quality tag based on thresholded RSC (codes = −2: very low, −1: low, 0: medium, 1: high, 2: very high). All shown TF ChIP-seq replicates are derived from independent experiments: MYC2 (JA, n = 4), MYC3 (JA, n = 3), ZAT10 (air, n = 3; JA, n = 2), ANAC055 (JA, n = 3). Histone ChIP-seq data is derived from a single experiment (n = 1).
Extended Data Fig. 2 Overview of quality metrics of generated RNA-seq and proteome data.
a,b, Multidimensional scaling (MDS) plots of replicate samples of the 24 h JA treatment RNA-seq time-series in WT (a) and the 4 h JA-treatment RNA-seq time-series in WT and myc2 seedlings (b) are shown. Both JA treatment time series consist of three independent samples (n = 3) for each time point and genotype. c, d, Principal component analysis (PCA) plots of independent biological replicate samples analyzed by proteomics (c) and phosphoproteomics (d).
Extended Data Fig. 3 MYC2 and MYC3 act predominantly as activators for a functionally diverse range of target genes.
a,b, Gene ontology (GO) analyses using a hypergeometric distribution of all MYC2 and MYC3 targets (a) as well as MYC2 only and MYC2/MYC3 shared targets (b) are shown. Data is derived from four independent MYC2 (n = 4) and three independent MYC3 (n = 3) ChIP-seq samples. Analyses were conducted using clusterProfiler. c, Bar plots shows the portion of JA-induced and JA-repressed genes that are bound by MYC2 and MYC3. d, e, The CACG[A/C]G motif (286 sites, E = 2*10−52) (d) and the AT[A/T][A/T] [A/T]ATA motif (714 sites, E = 8.9*10−35) (e) were enriched in MYC2 high-confidence target regions that do not contain a G-box or the degenerate G-box motifs CATGTG or CACGTT.
Extended Data Fig. 4 MYC2 and MYC3 regulate the majority of JA signaling pathway components.
a, Schematic overview of known MYC2/MYC3-targeted JA pathway components. Genes that are directly targeted by MYC2/MYC3 are highlighted in orange. b, Binding behavior of MYC2 and MYC3 at known JA genes (Supplementary Table 6) is shown. Known JA genes are grouped into non-differentially expressed and JA differentially expressed genes. c, AnnoJ genome browser screenshot visualizes MYC2 and MYC3 binding at all 13.
Extended Data Fig. 5 MYC2 and MYC3 target a large number of TFs.
a. Cluster analysis revealed the 5 other main clusters in the JA time course experiment. Clusters visualize the log2 fold change expression dynamics over the indicated 24 hours’ time period. The three strongest enriched gene ontology terms for each cluster are shown as well. b, Pie chart indicates the proportions of TFs that are transcriptionally induced by JA, bound by MYC2/MYC3, or both. c,d, Overview of MYC2/MYC3-bound plant hormone genes (c) and TFs (d) is shown. Plant hormones are abbreviated (ET (ethylene), BR (brassinosteroids), GA (gibberellic acid), ABA (abscisic acid), SA (salicylic acid), CK (cytokinin), AUX (Auxin), K (karrikin), SL (strigolactones)).
Extended Data Fig. 6 Overview of MYC-controlled TF network.
a. Significantly enriched (adjusted p < 0.05) gene ontology terms amongst the target of each TF. For each TF the 4 terms with the lowest p-value are shown, some of which are redundant between TFs. No enriched terms were detected for DREB2B targets. ChIP-seq data is indicated by presence of *, all other data was generated by DAP-seq. ChIP-seq data is derived from at least three independent experiments: MYC2 (JA, n = 4), MYC3 (JA, n = 3), ZAT10 (air, n = 3; JA, n = 2), ANAC055 (JA, n = 3). DAP-seq data is derived from a single experiment (n = 1).
Extended Data Fig. 7 MYC2 partially controls expression of JAZ repressors.
a, Individual plots show expression of all JAZ/TIFYs and NINJA in WT (blue) and myc2 (orange) seedlings following JA treatment. log2 fold change (FC) was calculated relative to their respective 0 h (ie. non-treated) control samples. b, Bar chart shows the number of differentially expressed (DE) genes at each time point after JA treatment between WT and myc2 seedlings. The bar chart also indicates how many of these DE genes were direct binding targets of MYC2 (in ChIP-seq assays) and whether they were more highly expressed in WT (blue) or myc2 (orange) seedlings. c, Charts indicates of how MYC2 indirectly affects the expression of downstream genes through secondary TFs. The expression of genes in pairwise comparisons of WT and myc2 transcriptomes at 0, 0.5, 1 and 4 h was assessed. Only genes that were direct targets of the TFs ATAF2, ZAT10, ANACO55 and ERF1, and not direct targets of MYC2, were analyzed which are termed “non-MYC2 target genes”. ATAF2, ZAT10, ANACO55 and ERF1 are themselves direct targets of MYC2 and their expression levels were decreased in myc2 relative to WT, indicating they are directly regulated by MYC2. DE indicates differentially expressed genes.
Extended Data Fig. 8 JA shapes the local chromatin architecture.
a, Bar plot shows the impact of two hours JA treatment on the genome-wide distribution of H3K4me3 and H2A.Z domains. Occupancy was determined in untreated/JA-treated WT and myc2 seedlings using ChIP-seq. SICER was used to identify the number of histone domains that show an increase (blue) or decrease (orange) of enrichment in response to JA. b,c, Heatmaps show the occupancy of H3K4me3 and H2A.Z from 1 kb upstream to 2 kb downstream of the transcriptional start site (TSS) at all Arabidopsis genes (TAIR10). Heatmaps are shown for H3K4me3 (b) and H2A.Z (c) in untreated and JA-treated (4 h) WT and myc2 seedlings. d, Quantification of H3K4me3 occupancy at JAZ2 and GRX480 is shown. It was calculated as the ratio between the respective ChIP-seq sample and the WT IgG control. e,f, Aggregated profiles show the log2 fold change enrichment of H3K4me3 at JA DEGs that are directly (e) and not directly targeted (f) by MYC2 from 2 kb upstream to 2 kb downstream of the transcriptional start site (TSS). g,h, Plot profiles show the log2 fold change enrichment of H2A.Z in WT (g) and myc2 mutants (h) from 2 kb upstream to 2 kb downstream of the transcriptional start site (TSS) at JA-induced and JA-repressed genes.
Extended Data Fig. 9 The JA gene regulatory network.
a, Illustration of JA gene regulatory network for 1, 2 and 4 h time points. Edges were predicted using phosphoproteome (green), proteome (orange) and transcriptome (blue) data. Node sizes are scaled by normalized motif score, with larger nodes indicating greater scores and likely greater importance within the network. Edges predicted early in the time-series transcriptomic data are red (0.25–2 h), edges predicted late are blue (4–24 h). Proteome and phosphoproteome-data-predicted edges are grey and green, respectively.
Extended Data Fig. 10 Gene regulatory network validation against ChIP/DAP-seq data.
a, The MYC2 subnetwork is shown. Edges are directional and red edges exist at early time points (0.25–2 h), blue only at late time points (4–24 h). Thicker edges with chevrons indicate that MYC2 were directly bound to that gene in our ChIP-seq experiments. b, Validated edges are those between TFs and first neighbors in the JA gene regulatory network for which the first neighbor was also a direct target of the TF in ChIP/DAP-seq assays. These edges are indicated by chevrons. Early time-series transcriptome-predicted edges (0.25–2 h) are red and later edges (4–24 h) are blue. Edges detected in the proteomic data are grey and those detected in the phosphoproteomic data are green. c, Bar plot shows quantification of JA-induced root growth inhibition in the indicated T-DNA alleles. Seedlings were grown on LS media with or without 20 µM MeJA. WT seedlings serve as independent controls for each tested T-DNA line. Sample size number n is shown within the respective bars. Samples are derived from three independent experiments. Asterisks represent significant differences between WT (-/ + JA) and indicated T-DNA lines (-/ + JA) (two-way ANOVA with Bonferroni post test, ns (not significant) p > 0.05, *p < 0.05, **p < 0.01, ***p < 0.001). d, Subnetwork of CYP708A2 is shown.
Supplementary information
Supplementary Tables
Workbook containing all 20 supplementary tables. Each tab sheet is one supplementary table. The respective table legends are also included.
Source data
Source Data Fig. 1
Overview of generated, analysed and used sequencing data for Fig. 1.
Source Data Fig. 2
Overview of generated, analysed and used sequencing data for Fig. 2.
Source Data Fig. 3
Overview of generated, analysed and used sequencing data for Fig. 3.
Source Data Fig. 4
Overview of generated, analysed and used sequencing and (phospho)proteome data for Fig. 4.
Source Data Fig. 5
Overview of generated, analysed and used sequencing data for Fig. 5.
Source Data Extended Data Fig. 1
Overview of generated, analysed and used sequencing data for Extended Data Fig. 1.
Source Data Extended Data Fig. 2
Overview of generated, analysed and used sequencing and (phospho)proteome data in Extended Data Fig. 2.
Source Data Extended Data Fig. 3
Overview of generated, analysed and used sequencing data for Extended Data Fig. 3.
Source Data Extended Data Fig. 4
Overview of generated, analysed and used sequencing data for Extended Data Fig. 4.
Source Data Extended Data Fig. 5
Overview of generated, analysed and used sequencing data for Extended Data Fig. 5.
Source Data Extended Data Fig. 6
Overview of generated, analysed and used sequencing data for Extended Data Fig. 6.
Source Data Extended Data Fig. 7
Overview of generated, analysed and used sequencing data for Extended Data Fig. 7.
Source Data Extended Data Fig. 8
Overview of generated, analysed and used sequencing data for Extended Data Fig. 8.
Source Data Extended Data Fig. 9
Overview of generated, analysed and used sequencing and (phospho)proteome data in Extended Data Fig. 9.
Source Data Extended Data Fig. 10
Overview of generated, analysed and used sequencing and (phospho)proteome data in Extended Data Fig. 10.
Rights and permissions
About this article
Cite this article
Zander, M., Lewsey, M.G., Clark, N.M. et al. Integrated multi-omics framework of the plant response to jasmonic acid. Nat. Plants 6, 290–302 (2020). https://doi.org/10.1038/s41477-020-0605-7
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1038/s41477-020-0605-7
This article is cited by
-
Improved pokeweed genome assembly and early gene expression changes in response to jasmonic acid
BMC Plant Biology (2024)
-
The temperature sensor TWA1 is required for thermotolerance in Arabidopsis
Nature (2024)
-
Jasmonic Acid (JA) in Plant Immune Response: Unravelling Complex Molecular Mechanisms and Networking of Defence Signalling Against Pathogens
Journal of Plant Growth Regulation (2024)
-
Advances in functional studies of plant MYC transcription factors
Theoretical and Applied Genetics (2024)
-
Changing turn-over rates regulate abundance of tryptophan, GS biosynthesis, IAA transport and photosynthesis proteins in Arabidopsis growth defense transitions
BMC Biology (2023)
