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Integrated multi-omics framework of the plant response to jasmonic acid

Nature Plants volume 6, pages 290–302 (2020)Cite this article

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A Publisher Correction to this article was published on 21 July 2020

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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.

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Fig. 1: Design of our study and key datasets utilized.
Fig. 2: MYC2 and MYC3 target a large proportion of JA-responsive genes that encode TFs.
Fig. 3: The JA-responsive epigenome.
Fig. 4: Loss of functional MYC2 affects the global proteome and phosphoproteome.
Fig. 5: JA-response genome regulatory model positions: known and new components.

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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.

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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

Author notes

  1. J. Paola Saldierna Guzmán

    Present address: School of Natural Sciences, University of California Merced, Merced, CA, USA

  2. Elizabeth Hann

    Present address: Department of Chemical and Environmental Engineering, Department of Botany and Plant Sciences, University of California, Riverside, CA, USA

  3. These authors contributed equally: Mark Zander and Mathew G. Lewsey.

Authors and Affiliations

  1. Plant Biology Laboratory, Salk Institute for Biological Studies, La Jolla, CA, USA

    Mark Zander, J. Paola Saldierna Guzmán, Elizabeth Hann, Amber E. Langford & Joseph R. Ecker

  2. Genomic Analysis Laboratory, Salk Institute for Biological Studies, La Jolla, CA, USA

    Mark Zander, Anna Bartlett, Bruce Jow, Joseph R. Nery, Huaming Chen & Joseph R. Ecker

  3. Howard Hughes Medical Institute, Salk Institute for Biological Studies, La Jolla, CA, USA

    Mark Zander, Bruce Jow & Joseph R. Ecker

  4. Centre for AgriBioscience, Department of Animal, Plant and Soil Sciences, School of Life Sciences, La Trobe University, Melbourne, Victoria, Australia

    Mathew G. Lewsey & Lingling Yin

  5. Australian Research Council Industrial Transformation Research Hub for Medicinal Agriculture, Centre for AgriBioscience, La Trobe University, Bundoora, Victoria, Australia

    Mathew G. Lewsey & Lingling Yin

  6. Plant Pathology and Microbiology, Iowa State University, Ames, IA, USA

    Natalie M. Clark & Justin W. Walley

  7. Computational Biology Department, School of Computer Science, Carnegie Mellon University, Pittsburgh, PA, USA

    Aaron Wise & Ziv Bar-Joseph

  8. Department of Plant Molecular Genetics, Centro Nacional de Biotecnología, Consejo Superior de Investigaciones Científicas (CNB-CSIC), Madrid, Spain

    Roberto Solano

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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

Correspondence to Mathew G. Lewsey or Joseph R. Ecker.

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).

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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).

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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.

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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.

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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)).

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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).

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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.

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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.

Source data

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.

Source data

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.

Source data

Supplementary information

Reporting Summary

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.

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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

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