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Long-lasting memory of jasmonic acid-dependent immunity requires DNA demethylation and ARGONAUTE1

Nature Plants volume 9, pages 81–95 (2023)Cite this article

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Abstract

Stress can have long-lasting impacts on plants. Here we report the long-term effects of the stress hormone jasmonic acid (JA) on the defence phenotype, transcriptome and DNA methylome of Arabidopsis. Three weeks after transient JA signalling, 5-week-old plants retained induced resistance (IR) against herbivory but showed increased susceptibility to pathogens. Transcriptome analysis revealed long-term priming and/or upregulation of JA-dependent defence genes but repression of ethylene- and salicylic acid-dependent genes. Long-term JA-IR was associated with shifts in glucosinolate composition and required MYC2/3/4 transcription factors, RNA-directed DNA methylation, the DNA demethylase ROS1 and the small RNA (sRNA)-binding protein AGO1. Although methylome analysis did not reveal consistent changes in DNA methylation near MYC2/3/4-controlled genes, JA-treated plants were specifically enriched with hypomethylated ATREP2 transposable elements (TEs). Epigenomic characterization of mutants and transgenic lines revealed that ATREP2 TEs are regulated by RdDM and ROS1 and produce 21 nt sRNAs that bind to nuclear AGO1. Since ATREP2 TEs are enriched with sequences from IR-related defence genes, our results suggest that AGO1-associated sRNAs from hypomethylated ATREP2 TEs trans-regulate long-lasting memory of JA-dependent immunity.

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Fig. 1: Short- and long-term effects of JA on resistance against three different biotic stresses.
Fig. 2: Transcriptome of long-term JA-IR against herbivory and JA-IS against pathogens.
Fig. 3: MYC2/3/4 transcription factors control short- and long-term JA-IR against herbivory.
Fig. 4: Long-term JA-IR against herbivory and associated shifts in glucosinolate profiles require intact DNA methylation homoeostasis.
Fig. 5: The DNA methylome of long-term JA-IR is associated with selective hypomethylation of ATREP2 transposable elements.
Fig. 6: Role of ATREP2 TEs, DNA (de)methylation pathways and AGO1 in long-term JA-IR against herbivory.

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Data availability

The mRNA-seq, WGBS and Nanopore sequencing data discussed in this publication have been deposited in NCBI’s Gene Expression Omnibus and are accessible through GEO SuperSeries accession number GSE163271. The sRNA-seq data analysed in this study were downloaded from the NCBI Sequence Read Archive (https://www.ncbi.nlm.nih.gov/sra; SRR5313815 and SRR5313816). Arabidopsis genome sequence and annotation data were downloaded from TAIR (www.arabidopsis.org) and Ensembl Plants (TAIR10.40; www.plants.ensembl.org). Biological materials are available from the corresponding authors.

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Acknowledgements

We thank T. Turlings for providing Spodoptera littoralis eggs, R. Solano for providing the myc2 myc3 myc4 triple mutant, and L. Smith, L. Furci, D. Pascual-Pardo, E. Vinnicombe and D. Rapley for useful discussions, assistance with experiments and rearing of S. littoralis. The work presented in this publication was supported by a consolidator grant (309944 ‘Prime-A-Plant’) and a proof-of-concept grant (824985, ‘ChemPrime’) from the European Research Council to J.T., a Research Leadership Award (RL-2012-042) from the Leverhulme Trust to J.T., a BBSRC-IPA grant (BB/P006698/1) to J.T., a PROMOS grant (Promo158) from the German Academic Exchange Service (DAAD) and Freie Universität to A.M. and Research Council of Norway grants 249920 and 249958/F20 to P.K. and M.H.M., respectively.

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Authors and Affiliations

  1. Plants, Photosynthesis and Soil, School of Biosciences, Institute for Sustainable Food, The University of Sheffield, Sheffield, UK

    S. W. Wilkinson, A. Hannan Parker, A. Muench, R. S. Wilson, M. A. Henderson, E. K. Moffat, P. S. C. F. Rocha, H. Hipperson, J. H. M. Stassen, A. López Sánchez & J. Ton

  2. Department of Agroecology, Aarhus University, Slagelse, Denmark

    K. Hooshmand & I. S. Fomsgaard

  3. Division for Biotechnology and Plant Health, Norwegian Institute of Bioeconomy Research (NIBIO), Ås, Norway

    P. Krokene & M. H. Mageroy

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Contributions

S.W.W., P.K., M.H.M. and J.T. conceived the idea for the research, which was supervised by J.T. S.W.W. conducted experiments and gathered data with assistance from A.H.P., A.M., R.S.W., M.A.H., E.K.M., P.S.C.F.R., H.H., A.L.S. and M.H.M. K.H. conducted the LC–MS/MS profiling of glucosinolates with assistance from I.S.F. Data analysis was performed by S.W.W. with assistance from A.H.P., A.M., R.S.W., M.A.H., E.K.M., H.H., J.H.M.S., A.L.S., K.H. and J.T. The paper was written by S.W.W. and J.T. with comments and input from all other authors. M.H.M., P.K. and J.T. provided funding for the research.

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Correspondence to S. W. Wilkinson or J. Ton.

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Extended data

Extended Data Fig. 1 Herbivore damage at the seedling stage results in long-term IR against herbivory and long-term IS against necrotrophic pathogen infection.

Effect of feeding damage by Spodoptera littoralis (Sl) larvae at the 2-week-old seedling stage on the resistance of 5-week-old Arabidopsis (Col-0) against herbivory by Sl larvae (left panel) and disease by necrotrophic Plectosphaerella cucumerina (Pc; right panel). Data points represent weights of individual Sl larvae after feeding on individual plants (n = 10–18) or average per plant lesion diameters by Pc (n = 18–21). Asterisks indicate statistically significant differences between seedling treatments (Mann-Whitney test for Sl or two sample t-test for Pc; ** P < 0.01, *** P < 0.001). For more details about experimental design, see legend for Fig. 1.

Extended Data Fig. 2 Selection of genes with expression profiles and predicted functions that correlate with long-term JA-IR against herbivory (left) and long-term JA-IS against hemi-biotrophic (middle) and necrotrophic (right) pathogens.

a, Gene expression profiles were selected from the 2,409 genes with a statistically significant interaction between seedling and challenge treatment (Padj < 0.01), using the criteria displayed above the heatmaps (letters before and after the underscore indicate seedling treatment and challenge, respectively), resulting in 832 (left), 904 (middle) and 395 (right) genes. Replicate samples (n = 4) for mRNA-seq analysis were collected from 5-week-old plants at 4 h after challenge with water (W) or 0.1 mM JA. Plants had been pre-treated with water or 1 mM JA at the seedling stage (2-week-old). Blue and red columns above the heatmaps indicate water and JA treatments, respectively, of seedlings (ST) and 5-week-old plants (Challenge). Heatmap-projected values represent per gene z-scores of transformed read counts from all biological replicates. Numbered boxes next to heatmaps indicate 10 distinct gene expression clusters. b, Selection of defence-related Gene Ontology (GO) terms enriched (Padj < 0.05) within the 10 gene clusters shown in (a). For complete lists of enriched GO terms, see Supplementary Data 3, 7 and 11.

Extended Data Fig. 3 JA seedling treatment reduces plant growth independently of MYC2/3/4 TFs and RdDM- and ROS1-dependent regulation of DNA methylation.

Hyperspectral imaging quantified rosette surface areas of 5-week-old plants (n = 22–24) pre-treated with water (blue) or 1 mM JA (red) at the seedling stage (2-weeks-old). Asterisks indicate statistically significant within genotype differences between treatments (Wilcoxon rank sum test, * P < 0.05).

Extended Data Fig. 4 JA seedling treatment induces long-lasting changes in glucosinolate content that are dependent on the DNA demethylase ROS1.

Long-term effects of water (blue) and 1 mM JA (red) treatments of 2-week-old seedlings on the concentrations (µg/g dry mass) of all glucosinolates detected in the leaf tissue of 5-week-old WT (Col-0) and ros1-4 plants (n = 8). a, Indole glucosinolates. b, Aliphatic glucosinolates. If the seedling treatment (ST) x Genotype (G) interaction term was significant (Two-way ANOVA, P < 0.05), a Tukey post-doc test was conducted with different letters indicating significant differences between means (P < 0.05). I3M: glucobrassicin, 4OHI3M: 4-hydroxyglucobrassicin, 4MOI3M: 4-methoxyglucobrassicin, NMOI3M: neoglucobrassicin, 3msp: glucoiberin, 4mtb: glucoerucin, 4msb: glucoraphanin.

Extended Data Fig. 5 Long-term impacts of JA seedling treatment on global DNA methylation levels and patterning.

a, Long-lasting effects of JA on global weighted cytosine (C) methylation levels at all-C, CG, CHG and CHH contexts (H indicates any nucleotide other than G). Data points indicate biologically replicated samples (n = 3) from 5-week-old plants that had been pre-treated with water (blue) or 1 mM JA (red) at the seedling stage (2-weeks-old). No statistically significant differences between seedling treatments were detected (two-sample t-tests, P > 0.05). b, PCA plots of global methylation at CG, CHG or CHH contexts.

Extended Data Fig. 6 Differentially methylated regions in ATREP2 transposable elements are predominantly hypomethylated and spread across the genome.

a, Numbers and genomic contexts of differentially methylated regions (DMRs) overlapping with ATREP2 transposable elements (TEs). For details about DMR selection, see legend to Fig. 5c. Frequencies of hyper- and hypo-methylated DMRs are indicated by the bars above and below the x-axis, respectively. b, Distribution across the 5 Arabidopsis chromosomes of DMRs overlapping with ATREP2 TEs. Black dots and grey bars indicate centromeres and chromosomes, respectively. ATREP2 TEs labelled in green or grey overlapped with DMRs at all-C or CHH sequence contexts, respectively. Shown are all ATREP2 TEs which overlapped with at least one DMR from one 1JA_vs_3W comparison.

Extended Data Fig. 7 Transposable element (super)families enriched with JA-induced differentially methylated regions.

Shown are transposable element (TE) (super)families enriched with JA-induced differentially methylated regions (DMRs) at CHH (a), all-C (b), CG (c) and CHG (d) sequence contexts. For details about DMR selection, see legend to Fig. 5. Enriched TE families for CHH and all-C contexts are displayed in Fig. 5d,e. Graphs plot statistical significance against the corresponding fold-enrichment, represented by mean –log10(Padj) values (± SEM) and mean fold enrichment values (± SEM), respectively. Enrichment is expressed relative to the background of all genome annotated TEs (TAIR v10). Labelled data points indicate TE (super)families with a mean –log10(Padj) > -log10(0.05) (red dashed line). Brightly coloured data points indicate TE (super)families that were significantly (Padj ≤ 0.05) overrepresented in 1 (red), 2 (yellow) or 3 (green) comparisons, respectively.

Extended Data Fig. 8 RdDM-dependent regulation of ATREP2 TEs and JA-induced DMRs.

a, Numbers of TEs from the ATREP2, ATREP7 and TNAT1A families overlapping with previously characterised Type I and Type II RdDM targets23 (Type I, RdDM is dominant; Type II, ROS1 is dominant). b, Numbers of JA-induced DMRs (all-C and CHH contexts for all three comparisons; see Fig. 5) overlapping with previously characterised Type I and Type II RdDM targets23. c,d,e,f,g,h, Global DNA methylation patterns by whole-genome bisulphite sequencing of shoot tissues from naïve WT (Col-0), nrpe1-11 and ros1-4. c, PCA displaying variation in global DNA methylation at all-C sequence context between the 4 biological replicates of each genotype. d,e,f, Average % DNA methylation at TEs from the ATREP2, ATREP7 and TNAT1A families in CG (d), CHG (e) and CHH (f) contexts. g,h, Average % DNA methylation at JA-induced DMRs in all-C (g) or CHH contexts (h). For each analysis, n indicates the number of members of each TE family (d,e,f) or DMRs of each comparison (g,h) with sufficient coverage across all three genotypes to be included in the analysis (≥ 5 reads for ≥ 50 % of cytosines).

Extended Data Fig. 9 Characterisation of XVE:ROS1-YFP plants.

a, Plasmid diagram for XVE:ROS1-YFP. b, Experimental setup to analyse the impact of estradiol-induced ROS1-YFP on the Arabidopsis methylome. XVE:ROS1-YFP plants were treated with estradiol or DMSO at 14 days (Treatment 1, Tr1) and 18 days (Tr2) post sowing. Plants were harvested 48 h after Tr2. c, RT-qPCR quantification of ROS1 expression at 48 h after Tr2. Data points represent ROS1 expression values of individual biological replicates (n = 3) relative to the mean expression value of the DMSO replicates. Asterisks indicate a statistically significant difference between treatments (Two-sample t-test; ** P < 0.01). d, ROS1-YFP protein accumulation after estradiol or DMSO treatment was imaged by fluorescence microscopy. Images were acquired 24 h after Tr2. For the macroscopic brightfield (BF) and YFP images of leaves, brightness of all raw images was increased by 40% and scale bars = 1 mm. For the microscopic images of leaf cells, brightness and contrast was increased for both the YFP images (40%) and DAPI images (30% and 70%, respectively), DAPI and YFP images were pseudo-coloured blue and yellow, respectively, and scale bars = 25 µm. e,f,g,h, Oxford Nanopore Technology (ONT) sequencing and subsequent cytosine (C) methylation calling was performed on two DNA samples extracted from ~100 estradiol or DMSO treated plants at 48 h post Tr2. e, Numbers of differentially methylated regions (DMRs) between samples at all-C, CG, CHG and CHH contexts (H is any nucleotide other than G). Numbers above and below the zero line indicate hyper- and hypo-methylated DMRs, respectively, in the estradiol sample relative to the DMSO control. f, Average all-C context methylation at individual members of the TE families ATREP2, ATREP7 and TNAT1A. n indicates the number of TEs with sufficient coverage across both treatment groups (DMSO and estradiol) to be included in the analysis (≥ 5 reads for ≥ 50 % of cytosines). g, Numbers of hypermethylated, hypomethylated or unchanged ATREP2, ATREP7 and TNAT1A TEs by estradiol treatment. A difference > 1% was required for a TE to be classed as hyper- or hypo-methylated. TE families which do not share the same letter show significantly different distributions of DMRs (pairwise Chi-squared tests; Padj < 0.05). h, Example of an ATREP2 TE (AT2TE87640) that is targeted for DNA demethylation by ROS1. Bar height represents the proportion of methylated reads at each cytosine within the chromosome 2 region with the approximate coordinates: 19,242,400-19,243,500. The mean methylation level of AT2TE87640 in samples from estradiol- and DMSO-treated plants is indicated by the red dots in f.

Extended Data Fig. 10 Characterisation of ATREP2-derived sRNAs associated with nuclear AGO1.

a, Frequency-size distributions of AGO1-associated sRNAs mapping to TEs from the ATREP2, ATREP7 and TNAT1A TE families. Nuclear AGO1 was extracted from 10-day-old seedlings at 1 h after treatment with 50 µM MeJA or an ethanol control (Ctrl)44. To enrich the dataset with siRNAs, reads from other known classes of RNAs were excluded from the analysis. Counts of sRNAs ranging from 18–30 nucleotides (nt) are displayed as counts per million (cpm) reads. b, MeJA-induced shifts in the proportion of 21 nt and 24 nt AGO1-associated sRNAs from ATREP2, ATREP7 and TNAT1A. Percentages indicate the relative increase in ratio between 21 nt sRNAs and 24 nt sRNAs by MeJA. The asterisk indicates a statistically significant shift towards 21 nt sRNAs (Chi-square test; * P < 0.05; N.S. P > 0.05). c,d, Alignment of two JA-IR-related glucosinolate regulatory genes, AT1G07780 (c) and AT3G16400 (d), to homologous ATREP2 sequences and AGO1-associated sRNAs44. TAIR v10 gene models are shown in black, sRNA normalised coverage tracks (cpm) are displayed for the Ctrl (blue) and MeJA (red) samples described by Liu et al.44. Green lines indicate gene regions with sequence homology to ATREP2 TEs.

Supplementary information

Supplementary Information

Supplementary Methods, Fig. 1, and Tables 1 and 2.

Reporting Summary

Supplementary Data 1–21.

Supplementary Data 1 mRNA-seq read counts for genes displaying an altered response to JA challenge as a result of previous JA seedling treatment. Supplementary Data 2 mRNA-seq read counts for the 832 genes selected by the criteria (W_JA > W_W) and (JA_JA > W_JA). Supplementary Data 3 GO terms that are statistically enriched in gene clusters I–IV of Extended Data Fig. 2a. Supplementary Data 4 mRNA-seq read counts for the 203 genes associated with long-term JA-IR against Sl. Supplementary Data 5 GO terms that are statistically enriched among the 203 genes associated with long-term JA-IR against Sl. Supplementary Data 6 mRNA-seq read counts for the 904 genes selected by the criteria (W_JA < W_W) and (JA_JA < W_JA). Supplementary Data 7 GO terms that are statistically enriched in gene clusters V–VII of Extended Data Fig. 2a. Supplementary Data 8 mRNA-seq read counts for the 796 genes associated with long-term JA-IS to Pst. Supplementary Data 9 GO terms that are statistically enriched among the 796 genes associated with long-term JA-IS to Pst. Supplementary Data 10 mRNA-seq read counts for the 395 genes selected by the criteria (W_JA > W_W) and (JA_JA < W_JA). Supplementary Data 11 GO terms that are statistically enriched in gene clusters VIII–X of Extended Data Fig. 2a. Supplementary Data 12 mRNA-seq read counts for the 144 genes associated with long-term JA-IS to Pc. Supplementary Data 13 GO terms that are statistically enriched among the 144 genes associated with long-term JA-IS to Pc. Supplementary Data 14 Transcription factor DNA-binding motifs that are overrepresented within promoters of the 203 genes associated with long-term JA-IR to Sl. Supplementary Data 15 JA-induced DMRs: locations and statistics. Supplementary Data 16 JA-induced DMRs: summary statistics. Supplementary Data 17 Consensus DMRs. Supplementary Data 18 Estradiol-induced DMRs: locations and statistics. Supplementary Data 19 Raw read data and alignment statistics of the mRNA sequencing analysis. Supplementary Data 20 Raw read data and alignment statistics for the WGBS analysis of 5-week-old Col-0 plants treated as 2-week-old seedlings with water or JA. Supplementary Data 21 Raw read data and alignment statistics for the WGBS analysis of Col-0, nrpe1-11 and ros1-4.

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Wilkinson, S.W., Hannan Parker, A., Muench, A. et al. Long-lasting memory of jasmonic acid-dependent immunity requires DNA demethylation and ARGONAUTE1. Nat. Plants 9, 81–95 (2023). https://doi.org/10.1038/s41477-022-01313-9

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