- Open Access
H2A monoubiquitination in Arabidopsis thaliana is generally independent of LHP1 and PRC2 activity
- Yue Zhou†1,
- Francisco J. Romero-Campero†2,
- Ángeles Gómez-Zambrano3,
- Franziska Turck1Email authorView ORCID ID profile and
- Myriam Calonje3Email author
© The Author(s). 2017
- Received: 3 February 2017
- Accepted: 22 March 2017
- Published: 12 April 2017
Polycomb group complexes PRC1 and PRC2 repress gene expression at the chromatin level in eukaryotes. The classic recruitment model of Polycomb group complexes in which PRC2-mediated H3K27 trimethylation recruits PRC1 for H2A monoubiquitination was recently challenged by data showing that PRC1 activity can also recruit PRC2. However, the prevalence of these two mechanisms is unknown, especially in plants as H2AK121ub marks were examined at only a handful of Polycomb group targets.
By using genome-wide analyses, we show that H2AK121ub marks are surprisingly widespread in Arabidopsis thaliana, often co-localizing with H3K27me3 but also occupying a set of transcriptionally active genes devoid of H3K27me3. Furthermore, by profiling H2AK121ub and H3K27me3 marks in atbmi1a/b/c, clf/swn, and lhp1 mutants we found that PRC2 activity is not required for H2AK121ub marking at most genes. In contrast, loss of AtBMI1 function impacts the incorporation of H3K27me3 marks at most Polycomb group targets.
Our findings show the relationship between H2AK121ub and H3K27me3 marks across the A. thaliana genome and unveil that ubiquitination by PRC1 is largely independent of PRC2 activity in plants, while the inverse is true for H3K27 trimethylation.
- Polycomb group repression mechanism
- Arabidopsis thaliana
Polycomb group (PcG)-mediated epigenetic marks contribute to maintain the transcriptionally repressed state of genes involved in important cellular and developmental processes in eukaryotes [1, 2]. PcG proteins are found in two major protein complexes, Polycomb repressive complex 2 (PRC2), which has histone H3 lysine 27 (H3K27) tri-methyltransferase activity , and PRC1, which has histone H2A E3 ubiquitin ligase activity  as well as other non-enzymatic functions critical for chromatin compaction .
Vertebrate PRC2 comprises EZH2 (or its closely related EZH1), which is the catalytic subunit, EED, SUZ12, and RBBP46 (or RBBP48) [6, 7]. Homologs of these components are also found in Drosophila [6, 7] and plants [8–10]. In Arabidopsis thaliana PRC2 encompasses the EZH2 homologs CURLY LEAF (CLF) , SWINGER (SWN) or MEDEA (MEA) [12, 13], the SUZ12 homologs EMBRYONIC FLOWER 2 (EMF2) , VERNALIZATION 2 (VRN2) or FERTILIZATION INDEPENDENT SEED 2 (FIS2) [15, 16], the EED equivalent FERTILIZATION INDEPENDENT ENDOSPERM (FIE) , and the RBBP46/48 homolog SUPPRESSOR OF IRA 1 (MSI1) . While CLF and SWN are the catalytic subunits of the different combinational PRC2s acting during sporophyte development , MEA confers enzymatic activity to the complex during gametophyte and early seed formation [19, 20].
The vertebrate PRC1 E3 monoubiquitin ligase module comprises RING1B (or RING1A) and one of the six Polycomb RING finger (PCGF) proteins, while the one in Drosophila is constituted by dRing and Psc, Su(z)2, or L(3)73 Ah [7, 21]. The E3 monoubiquitin ligase module can associate with PHC1/2/3 and CBX2/4/6/7/8 (Ph and Pc, respectively, in Drosophila) to constitute canonical PRC1s or with other subunits to form variant PRC1s [6, 7]. In A. thaliana the module includes one of three AtBMI1s (AtBMI1A/B/C) and AtRING1A or AtRING1B [22–24]. Besides these conserved subunits, there are plant-specific proteins that participate in PcG-mediated gene repression, playing a role that is not yet well-defined [25, 26]. Such is the case of LIKE HETEROCHROMATIN PROTEIN 1 (LHP1), which has been proposed to be the functional equivalent to vertebrate CBX proteins or Drosophila Pc due to its ability to bind H3K27me3 [27, 28] and interact with other PRC1 components [22, 29, 30]; however, it also co-purifies with PRC2 [31, 32].
Since the identification of PcG proteins, an immense amount of biochemical work has focused on understanding the PcG repression mechanism. A major issue has been to determine the sequence of events. The recruitment of PcG complexes to specific targets in animals has been widely thought to occur in two steps: first PRC2 incorporates H3K27me3 at a specific gene, and then the PRC1 complex is recruited by its ability to bind to H3K27me3 to mediate H2A monoubiquitination . This classic hierarchical model was also adopted by the plant field despite very limited supporting evidence. However, recent results indicate that PRC1 recruitment may occur via H3K27me3-dependent and -independent mechanisms  and, furthermore, that PRC1, in some cases, recruits PRC2 [24, 35–37]. The prevalence of these possible mechanisms is unclear, especially in plants, as H2AK121ub marks have been examined at only a handful of PcG targets and the interdependence of PRC1 and PRC2 remains an unanswered key question.
Our genome-wide chromatin data in PcG mutants in A. thaliana revealed that PRC2 activity and H3K27me3 marking do not act upstream of H2A monoubiquitination in the regulation of most genes, which strongly argues against the classic model for PcG mark deposition as the prevailing mechanism. Furthermore, LHP1 is fully dispensable for H2A monoubiquitination, indicating that a non-canonical PRC1 is responsible for all H2AK121 monoubiquitination in A. thaliana and that this complex can find target regions independently of H3K27me3. In contrast, the activity of this non-canonical PRC1 is required for H3K27me3 coverage at the majority of PcG target loci since these display reduced levels of both H2AK121ub and H3K27me3 in atbmi1a/b/c mutants.
H2AK121ub marks are widely distributed in the A. thaliana genome, often co-localizing with H3K27me3
We found that H2AK121ub and H3K27me3 peaks often marked the same genes (H2AK121ub/H3K27me3; 4979 genes); however, a surprisingly high number of genes were also exclusively marked with H2AK121ub (only-H2AK121ub; 9109 genes) and a lower but considerable number of genes were only marked with H3K27me3 (only-H3K27me3; 1864 genes) (Fig. 1c, d; Additional file 2: Dataset S1; Additional file 1: Figure S4). These three differently marked subsets of genes have also been recently reported in animals [21, 38]. To identify possible differences between the two subsets of H2AK121ub-marked genes, we compared H2AK121ub coverage at H2AK121ub/H3K27me3- and only-H2AK121ub-marked genes (Additional file 1: Figure S5). We found higher levels of H2AK121ub in gene bodies of H2AK121ub/H3K27me3- compared to only-H2AK121ub-marked genes (p value of 2.2 × 10–16 according to Wilcoxon test), suggesting that H3K27me3 has an effect on H2AK121ub distribution.
Neither PRC2 activity nor LHP1 function are a major determinant for H2A monoubiquitination in A. thaliana
To further evaluate the extent of the increase in H2AK121ub at target genes, we normalized peak read coverage using robust linear regression computed over common peaks with ±20% change (mutant versus WT) in coverage measured as reads per million mapped (RPM). This constitutes a variation of the MAnorm approach  (“Methods”; Additional file 1: Figure S10). The normalized values were then used for quantitative comparison. We found that despite the fact that most H2K121ub/H3K27me3-marked genes showed unaltered or even increased levels in clf28/swn7, there was also a small percentage of genes displaying a considerable reduction (Fig. 3h; Additional file 3: Dataset S2), which was validated by ChIP-quantitative PCR (qPCR) analysis (Additional file 1: Figure S11). These data reveal that PRC2 activity is not required for establishing H2AK121ub at most target genes; however, in the absence of this activity the levels of these marks are not appropriately maintained at some genes.
Since the binding of Pc to H3K27me3 marks has been proposed to recruit PRC1 for H2A monoubiquitination , we compared the profile of H2AK121ub marks in lhp1 mutants and WT (Additional file 1: Figure S8). Metagene plots of H2AK121ub coverage at all marked genes or at H2K121ub/H3K27me3- and only-H2AK121ub-marked genes separately did not show differences between lhp1 mutants and WT (Fig. 3d–f). The same result was obtained when comparing the density heatmap in WT and lhp1 (Additional file 1: Figure S9). In agreement with this, the levels at H2AK121ub peaks after normalization (Additional file 1: Figure S10) were similar in lhp1 and WT (Fig. 3h; Additional file 4: Dataset S3). All together, these data indicate that LHP1 is dispensable for H2AK121ub marking in A. thaliana.
The levels of H2AK121ub and H3K27me3 are significantly affected in atbmi1a/b/c mutants
Next, we examined whether H3K27me3 marking was affected in atbmi1a/b/c mutants by comparing H3K27me3 profiles in WT and mutant plants (Additional file 1: Figure S12). A metagene plot and heatmap of H3K27me3 coverage in WT and atbmi1a/b/c mutants (Fig. 4d; Additional file 1: Figure S18) showed a significant reduction in global H3K27me3 levels in the mutants (p value of 2.2 × 10–16 according to Wilcoxon test), which was confirmed by western blot analysis (Fig. 4g; Additional file 1: Figure S18). A global reduction in levels of H3K27me3 in atbmi1a/b/c was correlated with the decrease of the marks at H2AK121ub/H3K27me3 genes (Fig. 4e, f). To further investigate the impact of the loss of AtBMI1 function on H3K27me3 levels, we quantified the changes of the levels at H3K27me3 peaks in atbmi1a/b/c mutants (Fig. 4i; Additional file 1: Figures S19 and S20; Additional file 6: Dataset S5). Fifty percent of H2AK121ub/H3K27me3 genes displayed, to some extent, decreased levels of H3K27me3 at their associated peaks in the mutants (Fig. 4i). Furthermore, H3K27me3 peaks were severely reduced at some of these genes, indicating that loss of AtBMI1 function affects the deposition or the maintenance of H3K27me3 marks. We also found that 10% of the genes exhibited increased levels of H3K27me3 marks (Fig. 4i). Increased levels of H3K27me3 have been previously reported at some loci in atring1a/b and atbmi1a/b double mutants ; however, it is not known whether this is a consequence of unbalanced PcG activities or an indirect effect of the global gene misregulation experienced by these mutants . Surprisingly, around 20% of only-H3K27me3 genes showed decreased or increased levels of H3K27me3 marks at peaks in atbmi1a/b/c mutants (Fig. 4i; Additional file 6: Dataset S5). It is possible that AtBMI1s target and affect these genes by an H2AK121ub-independent mechanism given that H2A monoubiquitination is dispensable for repression of some PRC1 targets in animals, while other BMI-mediated functions are still required [45, 46].
Levels of H3K27me3 and H2AK121ub are correlated
Our findings show that LHP1 is not required for H2AK121ub marking and that PRC2 activity is dispensable to establish H2AK121ub marks at most genes, which argues against the classic hierarchical model for PcG mark deposition as the prevailing sequence of events in A. thaliana. Nevertheless, we found that around 20% of H2AK121ub/H3K27me3-marked genes showed to some extent decreased H2AK121ub levels in the absence of PRC2 activity. It could be possible that PRC2 activity is required to maintain appropriate H2AK121ub levels at these genes. Alternatively, loss of H2AK121ub might be a consequence of the transcriptional activation of these genes. Moreover, we found that AtBMI1 activity is required for establishing and maintaining proper H3K27me3 levels at H2AK121ub/H3K27me3 genes (Fig. 5d). According to this, H2A monoubiquitination has been shown to promote H3K27me3 . However, the fact that H2AK121ub coverage is more similar to that of H3K27me3 in H3K27me3/H2AK121ub-marked genes than in only-H2AK121ub genes suggests a positive feedback loop for H2A monoubiquitination. Positive feedback loops for generating PcG-repressed chromatin has been previously proposed in animals . Interestingly, loss of AtBMI1 function seems to have an effect on the levels of H3K27me3 at some only-H3K27me3-marked genes, which is a priori surprising but consistent with studies showing that PRC1 ubiquitin-independent functions are required for the repression of some targets in animals [45, 46]. On the other hand, a recent report revealed that the H2A deubiquitinases UBIQUITIN SPECIFIC PROTEASE (UBP) 12 and UBP13 are needed for H3K27me3 marking and repression of a subset of PcG targets in A. thaliana . Similarly, Drosophila Calypso deubiquitinase has been proposed to remove or balance H2A monoubiquitination levels for appropriate repression [48, 49]. Therefore, it might be possible that H2AK121ub marks are initially incorporated at only-H3K27me3 genes but then removed.
Plant material and growth conditions
A. thaliana Col-0 wild type (WT), atbmi1a/b/c , clf28/swn7  and lhp1 (also named tfl2-2 ) mutants were grown under long-day conditions at 21 °C on MS agar plates containing 1.5% sucrose and 0.8% agar for 7 days.
ChIP-seq and ChIP-qPCR
ChIP experiments were performed as previously described . Chromatin was extracted from 7-day-old whole seedlings (150 seedlings). Anti-H2Aub (Cell Signaling Technology, 8240S) and anti-H3K27me3 (Diagenode, C15410069) antibodies were used for chromatin immunoprecipitation. For ChIP-seq, two immunoprecipitations from independent biological replicates were processed for next-generation sequencing library preparation. All libraries were prepared by the Ovation® Ultralow Library Systems (NuGEN) following the manufacturer’s instruction using 80% of a typical ChIP as starting material. After amplification for 16 PCR cycles, DNA of a size range between 200 and 500 bp was purified from an agarose gel. Amplification was confirmed by testing an aliquot of the library before and after amplification by qPCR. Sequencing was carried out as single-end 100-nucleotide reads on an Illumina HiSeq by the Max Planck Genome Centre in Cologne. For ChIP-qPCR, amplification was performed using Sensi FAST SYBR & Fluorescein kit (Bioline) and an iQ5 Biorad system. Samples were normalized to input DNA prepared from a reverse cross-linked aliquot of each chromatin preparation. qPCR data are shown as the means of two replicates from a representative experiment. Primers used for ChIP-qPCR are shown in Additional file 1: Table S2.
Quality control and read mapping
Read quality of each sequenced sample was examined using the software package FASTQC (http://www.bioinformatics.babraham.ac.uk/projects/fastqc/). No quality problem was detected in the sequenced samples. The A. thaliana genome sequence TAIR10 in fasta format and its corresponding gene annotation in GTF format were downloaded from the data base Ensembl plants (http://plants.ensembl.org/) release 23 and used as the reference genome. Read mapping to the A. thaliana reference genome was carried out using the ultrafast, memory-efficient short read aligner bowtie ; the parameters -v 2 --best --strata -m 1 were used to allow at most two mismatches and report only the best alignment when multiple ones were found. High percentages of mapped reads were produced for each sample and no problems were detected during the mapping process (Additional file 1: Table S1). The bowtie output was stored in SAM format. SAM to BAM format conversion, sorting, and indexing were performed with the software package SAMtools .
Peak calling and annotation
The software package MACS2  was used for the identification of read-enriched regions or peaks. The software tool SICER  was used to check the robustness of our results for H2AK121ub. Indeed, 93.7% of the peaks detected by MACS2 were also detected by SICER (Additional file 1: Figure S2). A common input library and default parameters were used for all samples. More specifically, an adjusted p value according to Benjamini–Hochberg of less than 0.01 and a fold change between 5 and 50 were chosen as the enrichment threshold. Conversion to BED format and manipulation of BED files were carried out using BEDTools . Peak annotation or the identification of genes associated with peaks was performed with PeakAnalyzer  according to the Nearest Downstream Gene (NDG) criterion. Specifically, a peak was associated with a gene when it overlapped any of the gene regions or when it was located at most 2 kb upstream of its transcription start site (TSS). A gene was assumed to be marked when at least one peak was found to be associated with it. H2AK121ub- and H3K27me3-marked genes were identified in the WT samples. Each replicate was analyzed separately and the final set of marked genes was determined as those detected in both replicates (Additional file 1: Figure S1).
The Integrative Genome Viewer (IGV)  was used for peak profile visualization. Read counts were RPKM (reads per kilobase and million mapped reads) normalized using the deepTools  utility bamCoverage with a bin size of 10 bp. Scatter plots comparing RPKM normalized peak values for each replicate show high similarity and reproducibility between replicates (Additional file 1: Figures S1, S8, and S12). Metagene plots representing the coverage of H2AK121ub and H3K27me3 marks were generated using the Bioconductor R package ChIPpeakAnno  (http://bioconductor.org/packages/release/bioc/html/ChIPpeakAnno.html). The significance of the overlaps between H2AK121ub or H3K27me3 peaks and A. thaliana gene regions (obtained from the Bioconductor R package TxDb.Athaliana.BioMart.plantsmart28) was determined using the functions shuffle and enrichPeakOverlap from the R Bioconductor package chipseeker . We generated 500 random shuffles of H2AK121ub or H3K27me3 peaks to estimate the background null distribution of the overlap with the following genomic regions: intergenic, promoter (2 kb upstream of the TSS), 5′ UTR, first exon, gene body, and 3′ UTR. P values were corrected for multiple testing using the Benjamini–Hochberg precedure. Percentages of genes showing H2AK121ub and H3K27me3 peaks at annotated genic and intergenic regions in the A. thaliana genome were computed using the Bioconductor R package GenomicRanges (http://bioconductor.org/packages/release/bioc/html/GenomicRanges.html). Heatmaps representing the intensity of H2AK121ub and H3K27me3 marks around peak centers were generated using the Bioconductor R package ChIPpeakAnno . RPKM and total library size (reads per million reads sequenced (RPM)) normalizations produced similar qualitative results with a sharper apparent decrease in the case of total library size normalization when comparing atbmi1a/b/c to WT (Additional file 1: Figures S13 and S18).
Transcriptomic analysis by RNA sequencing
In order to analyze the expression levels of marked genes, RNA-seq was performed in two biological replicates for WT and atbmi1a/b/c mutant plants at 7 DAG. The Qiagen RNAeasy minikit was used for RNA extraction following the manufacturer’s instructions. RNA concentration and purity were tested using nanodrop-photometric quantification (Thermo Scientific). The TruSeq RNA Sample Prep Kit v2 Illumina was used for library preparation following the manufacturer’s recommendations. Sequencing of RNA libraries was carried out with the Illumina HiSeq 2000 sequencer, yielding an average of approximately 15 million 100-nucleotide long paired-end reads for each sample. The high quality of each sample was verified using the software package FASTQC. The number of reads and concurrent pair alignment rate per sequencing sample and scatterplots of pairwise comparison between RNA-seq replicates are shown in Additional file 1: Figure S6. Read mapping to the A. thaliana TAIR10 reference genome, transcript assembly, and differential expression were performed with the software tools TopHat andCufflinks . Differentially expressed genes (DEGs) were selected according to the false discovery rate (FDR) calculation performed by cuffdiff, a tool from the cufflinks package. P values were corrected for multiple testing using the Benjamini–Hochberg procedure. The Bioconductor R package cummeRbund (http://www.bioconductor.org/) was used for result processing and visualization. An FDR of 0.05 was used for DEG selection. Gene expression was measured in FPKM (fragments per kilobase of exon and million mapped reads). A gene was assumed to be expressed when its FPKM was higher than 5. Differentially expressed genes were selected according to false discovery rate calculation and a log-fold change cut-off >|1| in atbmi1a/b/c when compared to Col-0 and a p value <0.05.
Gene Ontology term and transcription factor family enrichment analysis
The R Bioconductor package clusterProfiler  was used for Gene Ontology (GO) term enrichment analysis applying the Singular Enrichment Analysis (SEA) algorithm. The list of transcription factor families in A. thaliana was downloaded from the plant transcription factor database PlantTFDB 3.0 . Transcription factor family enrichment analysis in the sets of marked genes was performed using Fisher’s exact test (Additional file 1: Figure S7).
Quantitative comparison of ChIP-seq samples
In order to quantitatively compare ChIP-enriched regions (peaks) detected in WT to those in atbmi1a/b/c, clf28/swn7, and lhp1 mutants a variant of the MAnorm  approach was taken. MAnorm main assumption states that the true intensities (estimated as read counts) of most commons peaks between the two samples being compared are identical and therefore the detected differences can be used to rescale (using robust linear regression) the intensities of all peaks; however, this does not hold for atbmi1a/b/c since the intensities of most common peaks in these mutants are truly affected (Additional file 1: Figure S15) as global levels of H2AK121ub are substantially decreased  (Fig. 4). Using all common peaks for rescaling produced a bias that resulted in too few detected peaks with decreased and too many with increased levels in the mutants; for instance, genes like WUS, MAGPIE (MGP) KNUCKLES (KNU), or WOX12, which were found to be upregulated in atbmi1a/b/c mutants (RNA-seq data), displayed increased levels of H2AK121ub after normalization. We therefore required peaks for which a symmetric distribution was likely to estimate a correction for the entire dataset. The set of common peaks serving as a reference to build the rescaling model for normalization were restricted to those common peaks exhibiting a change of ±20% in RPKM data compared to WT since we found that a reduction of 20% in the levels of H2AK121ub already had a significant impact on gene expression in atbmi1a/b/c mutants (Additional file 1: Figure S16). For the comparison between atbm1a/b/c and the WT we therefore constrained the set of peaks used for normalization to those whose associated genes were not differentially expressed. Moreover, a high rate of non-significant changes were present in the peaks with a variation smaller than 20% whereas peaks with a reduction greater than 20% exhibited a high rate of significant changes (Additional file 1: Figure S16). Applying this modification, we found that WUS, MGP, KNU, and WOX12 displayed decreased levels (Additional file 5: Dataset S4), which is in line with ChIP-qPCR results (Additional file 1: Figure S16), expression analysis, and previously published results . Additional file 1: Figures S10, S15 and S19 show the selected common peaks used for building the rescaling model and the fold change (M)/mean intensities (A) of all peaks after normalization.
For the selection of differential peaks in atbmi1a/b/c, clf28/swn7, and lhp1 compared to WT, each replicate was analyzed separately. An adjusted p value cutoff of 0.05 was used and peaks were classified into different groups. Peaks exhibiting less than 80% of the WT intensity were assumed to have differentially reduced their intensity whereas peaks exhibiting more than 120% of the WT intensity were assumed to have differentially increased their intensity. The final set of differential peaks was taken as the intersection between the differential peaks found in each replicate.
The annotation of differential peaks was performed with PeakAnalyzer using the NDG criterion as described previously. When several peaks were found to be associated with a gene, only the one exhibiting the biggest decrease in the observed mark was taken into account.
Significance of Venn diagram overlaps
The significance of Venn diagram overlaps was analyzed using Fisher’s exact test. Specifically, the function fisher.test from the R package stats was used.
Western blot analysis
An aliquot of fixed chromatin after sonication was boiled for 10 min in SDS-PAGE buffer. Proteins were separated on 12% SDS-PAGE gel and transferred to a PVDF membrane (Immobilon-P Transfer membrane, Millipore) by semi-dry blotting in 25 mM Tris–HCl, 192 mM glycine, and 10% methanol. The following antibodies were used: anti-H3K27me3 polyclonal antibody (Diagenode, C15410069), anti-H2AUb (Cell-Signalling Technology, 8240S), and anti-H3 (Agrisera, AS10 710). Horseradish peroxidase-conjugated goat anti-rabbit antibody (Sigma-Aldrich, A0545) was used as secondary antibody at 1/10,000 dilution. Chemiluminescence detection was performed with ECL Prime Western Blotting Detection Reagent (GE Healthcare Life Sciences) following the manufacturer’s instructions.
We thank Dr. George Coupland for critical reading of the manuscript.
This work is supported by Marie Curie CIG grant ID 333748 from the European Union and BIO2013-44078-P and BIO2016-76457-P grants from Ministerio de Economía y Competitividad (MINECO). FT and YZ are supported by core funding from the Max Planck Gesellschaft.
Availability of data and materials
The ChIP-seq and RNA-seq data sets generated in this study have been deposited in the Gene Expression Omnibus (GEO) under accession GSE89358.
YZ performed most of the experiments with help from AGZ and MC; FJRC and FT analyzed high-throughput sequencing data; MC and FT conceived the study, interpreted the data and wrote the manuscript. All authors read and approved the final manuscript.
The authors declare that they have no competing interests.
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
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