David Séré

Océane Cassan

Fanny Bellegarde

Cécile Fizames

Jossia Boucherez

Geoffrey Schivre

Jacinthe Azevedo

Thierry Lagrange

Alain Gojon

Antoine Martin
email: antoine.martin@cnrs.fr

Loss of Polycomb proteins CLF and LHP1 leads to excessive RNA degradation in Arabidopsis

Keywords: Arabidopsis, chromatin, nutrition, gene expression, RNA degradation, Polycomb, roots

de niveau recherche, publiés ou non, émanant des établissements d'enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.

A c c e p t e d M a n u s c r i p t Introduction Accurate expression of genes is the result of a range of transcriptional and post-transcriptional mechanisms. At the chromatin level, a large number of regulatory pathways and their associated factors have been identified, and are major determinants of the regulation of genome expression.

Among them, the Polycomb-Group (PcG) proteins have been largely described for their role in the establishment of genomic programs in eukaryotes (Mozgova and Hennig, 2015;[START_REF] Schuettengruber | Trithorax: 70 Years and Counting[END_REF]. In plants, PcG regulation is ensured by two different complexes (Polycomb Repressive Complexes), PRC1 and PCR2 (Mozgova and Hennig, 2015). The enzymatic function of PRC1 is to catalyze the monoubiquitination of the lysine 119 on histone H2A (H2AK119ub), while PRC2 catalyzes the trimethylation of the lysine 27 on histone H3 (H3K27me3) [START_REF] Forderer | The age of multiplexity: recruitment and interactions of Polycomb complexes in plants[END_REF]. The function of these two chromatin modifications is to reduce the expression of genes and to maintain their expression at low levels. Therefore, H2AK119ub, H3K27me3, PRC1 and PRC2 are preferentially associated with genes showing low expression [START_REF] Roudier | Integrative epigenomic mapping defines four main chromatin states in Arabidopsis[END_REF][START_REF] Sequeira-Mendes | The Functional Topography of the Arabidopsis Genome Is Organized in a Reduced Number of Linear Motifs of Chromatin States[END_REF][START_REF] Turck | Arabidopsis TFL2/LHP1 specifically associates with genes marked by trimethylation of histone H3 lysine 27[END_REF]Zhou et al., 2017a). In Arabidopsis, PRC1 is composed of several typical E3 ubiquitin ligases, BMI1A/B/C and RING1A/B, associated with the plant-specific factors LHP1 and EMF1 [START_REF] Wang | Chromatin modulation and gene regulation in plants: insight about PRC1 function[END_REF]. Arabidopsis PRC2 is organized around three H3K27 trimethyl-transferase enzymes, among them CLF, and to a lesser extent SWN, are responsible for H3K27me3 deposition in vegetative tissues like shoots and roots (Mozgova et al., 2015). A large number of studies, in animals and in plants, have drawn a schematic cooperative model for the hierarchical actions of PRC1 and PRC2. This model proposes that PRC2 might first catalyze H3K27me3 on target genes, which will be recognized by H3K27me3 readers such as LHP1 or other PRC1-associated factors [START_REF] Li | Polycomb-mediated gene silencing by the BAH-EMF1 complex in plants[END_REF][START_REF] Turck | Arabidopsis TFL2/LHP1 specifically associates with genes marked by trimethylation of histone H3 lysine 27[END_REF]. This cooperative model has been largely described and yet several recent studies have challenged it, proposing an opposite hierarchy between PRC2 and PRC1, as well as independent functions and genomic targets [START_REF] Merini | PRC1 is taking the lead in PcG repression[END_REF]Zhou et al., 2017a). Nevertheless, several pieces of evidence strongly support the hypothesis of a functional link between PRC2 and PRC1, and especially between CLF and LHP1. First, LHP1 physically interacts with several members of PRC2 A c c e p t e d M a n u s c r i p t 4 [START_REF] Derkacheva | Arabidopsis MSI1 connects LHP1 to PRC2 complexes[END_REF][START_REF] Hecker | The Arabidopsis GAGA-Binding Factor BASIC PENTACYSTEINE6 Recruits the POLYCOMB-REPRESSIVE COMPLEX1 Component LIKE HETEROCHROMATIN PROTEIN1 to GAGA DNA Motifs[END_REF], suggesting that it acts as a bridge between PRC2 and PRC1. In addition, clf and lhp1 mutants have similar developmental phenotypes and significantly overlapping transcriptome, supporting a common function [START_REF] Veluchamy | LHP1 Regulates H3K27me3 Spreading and Shapes the Three-Dimensional Conformation of the Arabidopsis Genome[END_REF][START_REF] Wang | Arabidopsis Flower and Embryo Developmental Genes are Repressed in Seedlings by Different Combinations of Polycomb Group Proteins in Association with Distinct Sets of Cis-regulatory Elements[END_REF]Zhou et al., 2017b). Taken together, these observations mean that PRC1 and PRC2 cooperate, to a certain extent, to down-regulate the expression of genes, and to keep their expression silenced.

However, several studies have demonstrated or suggested more diverse functions for PcG proteins than the strict transcriptional repression of genes. First, recent reports have illustrated PcG functions that are not restricted to genes with low expression. In mammalian cells for instance, a non-canonical PRC1 targets preferentially active loci, associated with metabolic functions [START_REF] Van Den Boom | Non-canonical PRC1.1 Targets Active Genes Independent of H3K27me3 and Is Essential for Leukemogenesis[END_REF]. Likewise, it has been demonstrated in Drosophila that PRC1 could regulate the expression of a subset of active genes, with a large part of this regulation being certainly direct [START_REF] Pherson | Polycomb repressive complex 1 modifies transcription of active genes[END_REF].

In Arabidopsis, a subset of H2AUb-marked genes corresponds to transcriptionally active genes (Zhou et al., 2017a). Interestingly these genes are also associated with metabolic functions, but the impact of PcG proteins regulation on them has not been analyzed. Another study in Arabidopsis described that NRT2.1, which is a highly expressed gene coding for a major and essential root nitrate transporter, is constantly marked by H3K27me3 even under transcriptionally permissive conditions [START_REF] Bellegarde | Polycomb Repressive Complex 2 attenuates the very high expression of the Arabidopsis gene NRT2.1[END_REF]. Loss of CLF function leads to a reduction of H3K27me3 levels at the NRT2.1 locus, increasing its mRNA levels without altering its expression pattern [START_REF] Bellegarde | Polycomb Repressive Complex 2 attenuates the very high expression of the Arabidopsis gene NRT2.1[END_REF]. On another hand, non-transcriptional functions have been also described for PcG proteins. Indeed, different reports have demonstrated that PcG proteins can be directly implicated in cellular processes independently of the regulation of gene expression, including the regulation of cell cycle progression, or the regulation of alternative splicing [START_REF] Brien | A chromatin-independent role of Polycomb-like 1 to stabilize p53 and promote cellular quiescence[END_REF][START_REF] Gonzalez | A lncRNA regulates alternative splicing via establishment of a splicing-specific chromatin signature[END_REF][START_REF] Lecona | Polycomb protein SCML2 regulates the cell cycle by binding and modulating CDK/CYCLIN/p21 complexes[END_REF][START_REF] Mohd-Sarip | Transcription-Independent Function of Polycomb Group Protein PSC in Cell Cycle Control[END_REF]. Therefore, growing evidence show that PcG proteins may have emerging functions that are not strictly associated with repression of gene expression. However, even though the presence of PcG proteins and their associated chromatin marks on transcriptionally active loci have been clearly demonstrated, the implication in their regulation is still elusive and deserves A c c e p t e d M a n u s c r i p t 5 further investigation. Here, we used NRT2.1 as a model gene to further explore the role of PcG proteins in the regulation of transcriptionally active genes in Arabidopsis. We showed that in addition to being controlled by PRC2, NRT2.1 is also regulated by PRC1 members, including LHP1. Concurrent mutations in CLF and LHP1 lead to an unexpected down-regulation of NRT2.1 expression, associated with the accumulation of sRNAs resulting from mRNA degradation. We demonstrated that overaccumulation of sRNAs in a double clf lhp1 mutant concerns a large set of transcriptionally active genes, that are consequently deregulated. We finally observe that mRNA degradation globally increases in clf lhp1 mutant, likely corresponding to a misregulation of actors of RNA degradation machinery.

Material and Methods

Plant material and growth conditions

The Arabidopsis thaliana accession used in this study was Col-0. Mutant alleles and transgenic plants used in this study are clf-29 (Xu andShen, 2008), lhp1-4 (Takada and[START_REF] Takada | TERMINAL FLOWER2, an Arabidopsis Homolog of HETEROCHROMATIN PROTEIN1, Counteracts the Activation of FLOWERING LOCUS T by CONSTANS in the Vascular Tissues of Leaves to Regulate Flowering Time[END_REF], ProNRT2.1:LUC [START_REF] Girin | Identification of Arabidopsis Mutants Impaired in the Systemic Regulation of Root Nitrate Uptake by the Nitrogen Status of the Plant[END_REF]), bmi1a, bmi1b, bmi1c (Merini et al., 2017). Experiments were performed using roots from 5 weeks-old seedlings grown hydroponically under a short-day photoperiod (8 h light and 16 h dark) and supplied with MS/2 solution containing 0,3 mM KNO 3 .

Expression analysis

Root samples were frozen in liquid nitrogen and tissues were disrupted for 1 min at 30 s-1 in a Retsch mixer mill MM301 homogenizer (Retsch, Haan, Germany). Total RNA was extracted using TRI REAGENT (MRC). Subsequently 500 ng of total RNA were treated with DNase (DNase I; SIGMA-ALDRICH, St. Louis, MO, USA) following the manufacturer's instructions. Reverse transcription was achieved with M-MLV reverse transcriptase (RNase H minus, Point Mutant, Promega) using 1 µL of an anchored oligo(dT) 20 primer (50 µM) for mRNA, or specific complementary primers (50 µM) with stem loop secondary structures for miRNAs [START_REF] Varkonyi-Gasic | Protocol: a highly sensitive RT-PCR method for detection and quantification of microRNAs[END_REF]. Accumulation of transcripts A c c e p t e d M a n u s c r i p t 6 was measured by qRT-PCR (LightCycler 480, Roche Diagnostics) using the SYBR R Premix Ex Taq TM (TaKaRa), according to the manufacturer's instructions with 1 µL of cDNA in a total reaction volume of 10 µL. Gene expression was normalized using UBQ10 or ACT2 as internal standards and using the 2 -Ct method [START_REF] Livak | Analysis of Relative Gene Expression Data Using Real-Time Quantitative PCR and the 2-ΔΔCT Method[END_REF]. Sequences of primers used in qPCR for gene expression and stem-loop analysis are listed in Supplemental dataset.

Root nitrate influx

NO 3 -influx was assayed using 0.2 mM 15 NO 3 . Roots were then dried at 70°C for 48 h, and samples were analyzed for total N and atom% 15 N using a continuous flow isotope ratio mass spectrometer coupled with a C/N elemental analyzer (model Euroflash; Eurovector, Pavia, Italy) as previously described [START_REF] Jacquot | NRT2.1 C-terminus phosphorylation prevents root high affinity nitrate uptake activity in Arabidopsis thaliana[END_REF].

Transcriptome profiling

Genome-wide expression analyses were performed on using Arabidopsis Affymetrix Gene 1.1 ST array strips designed to measure whole transcript accumulation of 28,501 genes (or transcripts clusters), based on 600,941 probes designed based on TAIR10 genome annotation. Biotin labeled and fragmented cRNAs were obtained using a GeneChip WT PLUS Reagent kit (902280; Thermo Fisher Scientific) following the manufacturer's instructions. Hybridization on array strips was performed for 16 h at 48°C. The arrays were washed, stained, and scanned using a GeneAtlas HWS Kit (901667;

Thermo Fisher Scientific) on the GeneAtlas Fluidics and Imaging Station. RNA-seq libraries were done from root total RNA using Standard RNA-Seq protocol method (PolyA selection for mRNA species) by the Genwiz company.

Nucleosome density

Root samples were frozen in liquid nitrogen and nuclei were isolated using Nuclei Isolation Buffer Reactions were stopped with 2 µL of EDTA 0.5 mM, treated with RNAse A before DNA purification by phenol-chloroform and precipitation by ethanol. DNA was quantified using qPCR. Primers used for quantification are described in Supplemental dataset. Quantification of digested samples was normalized using non-digested samples and the ratio obtained for sequences located at the relative +1 position of the housekeeping gene AP2M (AT5G46630).

DNA methylation

DNA methylation was quantified by McrBC-qPCR, as described in [START_REF] Martin | A transposon-induced epigenetic change leads to sex determination in melon[END_REF]. DNA was extracted by DNeasy plant mini kit (QIAGEN), and each sample were subjected to two reactions with or without McrBC (NEB) for overnight digestion. The reaction was stopped by 20 min incubation at 65°C. DNA methylation quantification is realized by qPCR using control samples (not treated with enzyme). Percentage of methylated DNA was calculated as 100-2 e (-dCp*100) , where Cp is the threshold cycle; dCp is the difference of Cp between digested and mock samples. Primers used for quantification are described in Supplemental dataset.

Small RNA Northern blot (electrophoresis, labelling, hybridization, quantification)

Northern blot of small RNAs was performed as described in [START_REF] Blevins | Northern Blotting Techniques for Small RNAs[END_REF] with minor modifications.

Total RNA from each sample were loaded on denaturing polyacrylamide-urea gels and separated using 4 hours of migration. Small RNAs were transferred overnight on nylon membrane (Whatman® 0,45µm), and cross-linked with EDC (Thermo Scientific™ Pierce™). Membranes were pre-hybridized 4 hours with PerfectHyb Plus Hybridization Buffer (Sigma), and hybridized overnight with radioactive probes. Probes were generated from denatured PCR products labelled with 32 P-CTP by Klenow enzyme (Fig. 3 and Supplemental Fig. 3), or from single-strand oligonucleotides labelled with 32 P-ATP by T4PNK (Fig. 5). List of primers and oligonucleotides used for probe design is described in A c c e p t e d M a n u s c r i p t 8 Supplemental dataset. Hybridized membranes were washed with 2X SSC 0,1% SDS solution and exposed to CL-XPosure Film (ThermoFisher). Intensity of signal was quantified using ImageJ.

Run-on

Root samples were frozen in liquid nitrogen and nuclei were isolated using NIB described above.

Nuclei were resuspended in 75 µL of 1.3X transcription buffer (65 mM Tris HCl pH 8, 6.5 mM MgCl2, 6.5 mM KCl), and mixed with 25 µL of a solution containing ATP, CTP, GTP (final concentration at 0.75 mM) and biotinylated UTP (Biotin-16-UTP, Roche, final concentration 0.25 mM). Transcription reactions were performed at 30°C for 40 min. Samples RNA were extracted using TRI REAGENT (MRC)

and treated with DNase. Biotinylated RNAs were recovered using streptavidin-coated beads (M-280

Streptavidin Dynabeads, Invitrogen). Following retrotranscription (Superscript III, Invitrogen)), accumulation of nascent transcripts was measured by qRT-PCR (LightCycler 480, Roche Diagnostics) using the SYBR R Premix Ex Taq TM (TaKaRa). Gene expression was normalized using UBQ10 as an internal standard.

Cell fractionation

Root samples were frozen in liquid nitrogen and nuclei were isolated using NIB as described above.

Supernatant were recovered and taken as representative of the cytoplasmic fraction. Nuclei were further purified using a 30% Percoll cushion. RNAs were extracted from both fraction using LS Trizol (Invitrogen) and resuspended in 50% formamide.

sRNAs sequencing

Small RNA sequencing was performed from total RNA by the company Fasteris. Total RNA from each sample was size selected for 18 to 50-nucleotide sRNAs using denaturing polyacrylamide-urea gels.

Sequencing was done on a NextSeq instrument using 50-base single-end mode.

A c c e p t e d M a n u s c r i p t 9

Bioinformatics analysis

Transcriptomics analyses and identification of DEGs. Normalization of the microarray raw intensities was performed using the Robust Multiarray Averaging (RMA) method from the oligo R package of BioConductor. DEGs were identified using the limma R package. A gene was identified as a DEG in a selected genotype when the adjusted P-value was <0.05. Analysis of RNA-seq data. Initial paired-end RNA-Seq reads were pre-processed using fastp. High quality reads from WT and clf-29 lhp1-4 were aligned to the reference genome (A. thaliana TAIR10 release) using STAR. To analyze mis-splicing of mRNA, STAR output bam files were converted to bed format and we selected the reads mapping on both (i) at least 75 bp of the exons and (ii) at most 10bp of their flanking introns, using the intersect function of the bedtools package. The indels & polymorphisms were detected from STAR output bam files with the mpileup function of bcftools. Normalization and visualization functions from DIANE [START_REF] Cassan | Inferring and analyzing gene regulatory networks from multifactorial expression data: a complete and interactive suite[END_REF] were used to normalize, and explore the counts from RNA-Seq data. Venn diagram and intersection of gene lists. Venn diagrams were performed using VennDiagram or ggVennDiagram R packages. Determination whether the overlap between different lists of genes is higher than randomly expected, and its value of significance, was tested and calculated using GeneSect (http://virtualplant.bio.nyu.edu). Genesect uses a bootstrapping-based method, consisting of selecting randomly 1,000 gene lists of Arabidopsis AGI having the same size as the observed lists, and counting the number of times that the intersection size of the random lists is equal or higher than the intersection observed for the two tested gene lists, reaching to a P-value [START_REF] Krouk | Predictive network modeling of the high-resolution dynamic plant transcriptome in response to nitrate[END_REF]. Gene ontologies. Gene ontology enrichment were interrogated using the ClusterProfiler R package, using the biological process function. The enrichment tests correspond to Fisher's exact tests, determining if the proportion of a specific ontology in a list of interest is statistically higher than expected in all Arabidopsis genes. P-values from those tests are corrected for multiple testing, and the threshold for over-representation tests was set to 0.05. In the graphical results, each significant ontology is thus characterized by an adjusted P-value, and a "Count", representing the number of genes associated to this ontology in the list of interest. Small RNA sequencing analysis.

A c c e p t e d M a n u s c r i p t 10 Small RNA-seq was analyzed using the Shortstack package, after filtering the fastq files from 21-, 23, 24 and 33 nt reads. In order to analyze sRNAs associated with degradation of mRNA, cluster of sRNAs obtained by Shortstack were first filtered to only consider for the rest of the analysis those mapping to coding genes. We then applied (i) a coverage filter, with at least 64 reads associated to a coding gene using all the libraries, (ii) a strand specific filter, keeping only clusters of sRNAs for which sRNA strand corresponds to mRNA strand, and (iii) a filter for a minimum complexity at 0.7 provided by Shortstack. Normalization of sRNA-seq read density using miRNAs expression. In order to normalize the density of sRNA reads between WT and clf-29 lhp1-4, we used absolute values of several miRNAs expression measured by stem-loop quantitative PCR. This analysis reports that these miRNAs are not significantly deregulated in clf-29 lhp1-4 (Supplemental Fig. 3). Then we relate these values to the number of reads observed for these miRNAs in the sRNA-seq for the 2 libraries of WT and clf-29 lhp1-4. Compiled together, this leads to a normalization factor of 8.44 that we applied to calculate normalized read density between WT and clf-29 lhp1-4. Metagene profiling. Metagene profiling was performed using computeMatrix function of deepTools, using the transcript location of each gene and bigwig files with normalized density of sRNAs for each genotype as inputs. Ten groups of genes were made according to their level of expression in WT, with 0-10 corresponding to the first 10% (less expressed) and 90-100 corresponding to the last 10% (most expressed The null distribution of this test was simulated as the median degradation scores of 1000 lists of genes randomly sampled genome-wide, the size of the lists being equal to the size of the list of interest. This null distribution served to assess whether the observed median degradation score in marked genes was significantly higher than expected in random lists of genes. The P-values, defined as the number of random lists with a median degradation score above the observed degradation score divided by 1000, were 0 for all markings and both genotypes. The derived Zscores, defined as the number of standard deviations between the observed score and the mean of the null distribution, had values between 6 and 11, depending on the marking and genotype.

Bootstrapping results were plotted with ggplot2 in R.

Results

In order to get new insights into the role of PcG proteins in the regulation of actively transcribed genes, we first measured the expression of NRT2.1 under highly transcription-permissive conditions (low nitrate) in mutants for PcG-related factors, including LHP1 and BMI1 proteins. As we already observed, NRT2.1 expression was slightly higher in clf-29 mutant than in WT (Fig. 1A) [START_REF] Bellegarde | Polycomb Repressive Complex 2 attenuates the very high expression of the Arabidopsis gene NRT2.1[END_REF]. Similarly, we observed that the expression of NRT2.1 was increased in lhp1-4 mutant in comparison to WT, and also to a lesser extent in bmi1 mutants, presumably due to strong redundancy between BMI1 proteins [START_REF] Merini | The Arabidopsis Polycomb Repressive Complex 1 (PRC1) Components AtBMI1A, B, and C Impact Gene Networks throughout All Stages of Plant Development[END_REF] (Fig. 1A). We concluded that NRT2.1 might be targeted by both PRC1 and PRC2. This confirmed what might be suggested by epigenome-wide analyses, where NRT2.1 is found to be marked by both H3K27me3 and H2AUb, and is described as an LHP1

target [START_REF] Veluchamy | LHP1 Regulates H3K27me3 Spreading and Shapes the Three-Dimensional Conformation of the Arabidopsis Genome[END_REF]Zhou et al., 2017a). Several reports showed that mutants for CLF or LHP1 display similar phenotypes, and have thus proposed a functional redundancy between these two PcG-proteins in the regulation of genes with low expression [START_REF] Derkacheva | Arabidopsis MSI1 connects LHP1 to PRC2 complexes[END_REF][START_REF] Veluchamy | LHP1 Regulates H3K27me3 Spreading and Shapes the Three-Dimensional Conformation of the Arabidopsis Genome[END_REF][START_REF] Wang | Arabidopsis Flower and Embryo Developmental Genes are Repressed in Seedlings by Different Combinations of Polycomb Group Proteins in Association with Distinct Sets of Cis-regulatory Elements[END_REF]Zhou et al., 2017b). Therefore, we wondered whether CLF and LHP1 could also have a certain degree of redundancy in the regulation of NRT2.1 and other highly [START_REF] Bratzel | Keeping cell identity in Arabidopsis requires PRC1 RING-finger homologs that catalyze H2A monoubiquitination[END_REF][START_REF] Chanvivattana | Interaction of Polycomb-group proteins controlling flowering in Arabidopsis[END_REF] (Supplemental Fig. 1A,B). In opposition, even though clf-29 lhp1-4 mutant shows strong growth defects, it displays a relatively correct organogenesis including a root system, leaves and the development of flowers and siliques. Concerning the root system, the length of the primary root of clf-29 lhp1-4 was not significantly different of the WT, but the lateral root density was significantly reduced in clf-29 lhp1-4 in comparison to WT (Supplemental Fig. 1C). The root anatomy of clf-29 lhp1-4 mutant appears properly organized, as revealed by propidium iodide staining and confocal microscopy (Supplemental Fig. 1D). Therefore, we measured NRT2.1 expression level in the roots of clf-29 lhp1-4 mutant. Surprisingly, NRT2.1 expression in clf-29 lhp1-4 was largely reduced in comparison to the WT line, in opposition to the increase in expression observed in the two single mutants (Fig. 1A, Supplemental Fig. 2). The same observation was done using the ProNRT2.1:LUC reporter line, that we introduced in the clf-29, lhp1-4 and clf-29 lhp1-4 mutants (Fig. 1B), strongly suggesting again the redundancy between CLF and LHP1 in the regulation of NRT2.1 expression. In agreement with these observations, the root nitrate influx, depending mostly on the function of NRT2.1, was strongly diminished in clf-29 lhp1-4 mutants (Fig. 1C).

To explore the extent of the combined effects of the clf-29 and lhp1-4 mutations on the regulation of transcriptionally active genes, we generated and compared the root transcriptome profiles of WT and the double mutant clf-29 lhp1-4 under low nitrate condition. In agreement with NRT2.1 expression in clf-29 lhp1-4, we found that other highly expressed genes involved in N nutrition and usually coexpressed with NRT2.1, like NRT1.1, NIA1 and NIR1 also display a significantly reduced expression in clf-29 lhp1-4 (Fig. 2A). Genome-wide, concurrent loss of CLF and LHP1 functions leads to the differential expression of almost 3500 genes (adjusted P-value < 0.05), in agreement with the major 1). To understand the effect of CLF and LHP1 concurrent mutations on transcriptionally active genes at a larger scale, we tested if deregulation of genes in clf-29 lhp1-4 was linked to their level of expression in WT. We clearly observed that genes showing high levels of expression in WT, like NRT2.1, tend to be downregulated in clf-29 lhp1-4, in opposition to genes showing low levels of expression which tend to be up-regulated (Fig. 2B). In order to question whether down-regulation of highly expressed genes in clf-29 lhp1-4 correlates with the presence of H3K27me3 and LHP1 at their loci, we isolated among the highly expressed genes those significantly down-regulated in clf-29 lhp1-4, and marked by both H3K27me3 and LHP1. To do so, we used ChIP-seq data from two studies that previously described H3K27me3 and LHP1 target genes [START_REF] Roudier | Integrative epigenomic mapping defines four main chromatin states in Arabidopsis[END_REF][START_REF] Veluchamy | LHP1 Regulates H3K27me3 Spreading and Shapes the Three-Dimensional Conformation of the Arabidopsis Genome[END_REF]. Even if the nitrate conditions are not identical between those studies and ours, it has been demonstrated that H3K27me3 profile is globally very robust in Arabidopsis, and does not change according to nitrate provision [START_REF] Bellegarde | Polycomb Repressive Complex 2 attenuates the very high expression of the Arabidopsis gene NRT2.1[END_REF][START_REF] Roudier | Integrative epigenomic mapping defines four main chromatin states in Arabidopsis[END_REF]. This leads to a relatively low number of 70 genes, as most transcriptionally active genes are under-represented in H3K27me3 and LHP1 markedgenes (Fig. 2C). However, this number was significantly higher than randomly expected, demonstrating that genes highly expressed and marked by H3K27me3 and LHP1 in WT tend to be down-regulated in clf-29 lhp1-4 (Supplemental Fig. 2). Finally, we analyzed the function of these 70 NRT2.1-like genes by looking at gene ontology enrichments. Although we interrogated a relatively low number of genes, we significantly identified several functional categories that were over-represented.

In particular, we observed that "response to nitrate", "inorganic ion transmembrane transport" or "inorganic anion transport" were among the most significantly over-represented functional categories (Fig. 2D). This led us to the conclusion that PcG proteins might target a group of transcriptionally active genes associated to metabolism and nutrition, including nitrate transport, and that the concurrent absence of regulation exerted by CLF and LHP1 lead to their down-regulation.

Excessive expression in plants can lead to the production of aberrant transcripts, which in turn can trigger post-transcriptional regulation like RNAi, or the establishment of transcriptional gene silencing We first compared the chromatin architecture between WT and clf-29 lhp1-4 plants, by profiling nucleosome density and positioning at the NRT2.1 locus. We did not observe any significant difference in NRT2.1 nucleosome density or positioning in clf-29 lhp1-4 (Fig. 3A). We also quantitatively analyzed DNA methylation, which is a hallmark of transcriptional gene silencing, at the However, it revealed in clf-29 lhp1-4 a massive amount of degradation products (Fig. 3C). The signature of NRT2.1 degradation was very intense in clf-29 lhp1-4, and interestingly traces of such degradation, although to a much lesser extent, also appear in the WT line. We also observed that et al., 2016). We observed that NRT2.1 transcription rate in clf-29 lhp1-4 was also higher in clf-29 lhp1-4 than in the WT line, confirming that the decrease in NRT2.1 transcript level was not due to a diminution in transcription (Fig. 3D). Interestingly, we also observed that the increase in NRT2.1 transcription rate in clf-29 lhp1-4 was not in a higher range than the increase in NRT2.1 transcript level in clf-29 or lhp1-4 single mutants (Fig. 1A), suggesting that the concurrent mutations of CLF and LHP1 do not lead to a burst of NRT2.1 expression. Altogether, these observations suggest that CLF and LHP1 might not only control the level of NRT2.1 expression, but also the integrity or the quality of NRT2.1 transcript, as a large part of NRT2.1 mRNAs are subjected to degradation in clf-29 lhp1-4 mutant, leading to a reduced transcript level compared to WT.

Aberrant or defective transcripts are extremely difficult to isolate, as they are instantly degraded to avoid deleterious effect. Therefore, to assess the genome-wide implication of PcG proteins in the regulation of transcript integrity of transcriptionally active genes in Arabidopsis, we focused on the final products of mRNA degradation. We thus analyzed the profile of sRNAs in WT and clf-29 lhp1-4 by performing sequencing of sRNAs (sRNA-seq). First, we observed at the global level that the size distribution of sRNA populations was strikingly different between WT and clf-29 lhp1-4. Indeed, typical sRNA abundant populations in WT, constituted by 21-, 24-or 33-nt sRNAs, were apparently absent in clf-29 lhp1-4 (Fig. 4A). In opposition, clf-29 lhp1-4 mutant line shows a massive increase in non-canonical sRNAs ranging continuously from the lowest to the highest sizes of the sRNA-seq (Fig. 4A). In clf-29 lhp1-4, this population of sRNAs, likely associated with mRNA degradation, represented more than 75% of the sRNA population, in opposition to less than 45% in the WT (Fig 4B). Therefore, the apparent down-representation of 21-, 24-and 33-nt sRNAs in clf-29 lhp1-4 could likely be due to an increase in sRNAs of all other sizes that would affect the whole sequencing representation. To confirm this hypothesis, we performed stem-loop qRT-PCR with primers specific to four representative miRNAs in the genome. The result indicates that there is absolutely no decrease in the abundance of these miRNAs in clf-29 lhp1-4 (Supplemental Fig. 4). Therefore, to perform a correct quantitative comparison of sRNA-seq results between clf-29 lhp1-4 and WT, we used those four miRNAs to generate a normalization factor based on the effective quantification by stem-loop qRT-PCR and on the number of reads obtained in the sRNA-seq data (described in the Methods section). A first targeted analysis of sRNA-seq revealed that genes with an expression profile similar to NRT2.1 in clf-29 lhp1-4 also display a strong accumulation of sRNAs at their locus in this genotype (Fig. 4C).

Moreover, nearly all of these non-canonical sRNAs were oriented in the sense strand of the transcripts, strongly reinforcing the fact that they originate from degraded mRNAs (Supplemental Fig. 5). To properly extract sRNA reads corresponding to RNA degradation, we discarded reads that had the size of canonical sRNAs (21-, 23-, 24-and 33-nt) and used filters for complexity (i.e a broad distribution over a gene coding sequence), read sense (corresponding to the coding strand of the transcript), and minimal coverage of sRNA clusters. Then, we inferred the abundance of sRNA reads corresponding to RNA degradation in WT and clf-29 lhp1-4 at the genome-wide level. We observed an important increase of sRNA abundance over genes in clf-29 lhp1-4, with genes globally accumulating as much as eight times more sRNAs in clf-29 lhp1-4 than in WT (Fig. 4D). This confirmed at the genome-wide level what we observed on NRT2.1 and other co-expressed genes. Then we wanted to compare between WT and clf-29 lhp1-4 the extent of degradation products according to the level of expression in WT. To do so, we performed a metagene analysis representing the density of sRNA reads associated with RNA degradation for subpopulations of genes ranked by their level of expression in WT. In agreement with our previous observations, we could see an important increase in the degradation profile of transcripts in the double mutant, in comparison to the WT. In the WT, only the 10% most expressed genes display a discernable level of RNA degradation. In opposition, we observed for clf-29 lhp1-4 intense profile of RNA degradation, that were especially strong for the A c c e p t e d M a n u s c r i p t 17 most expressed genes (Fig. 4E). The highest gene expression level is, the stronger the effect of clf-29 lhp1-4 mutations is and leads to sRNA accumulation (Fig. 4E). Then we could infer a list of more than 4000 genes that show an increase (at least 2-fold) in their amount of transcript degradation-related reads in clf-29 lhp1-4. Next, we wanted to test whether these genes are, like NRT2.1, overall highly expressed and misregulated in clf-29 lhp1-4. We therefore compared these genes with the list of deregulated genes in clf-29 lhp1-4 and with the 10% most expressed genes in WT. This analysis showed a significant overlap between highly transcribed genes in WT and those degraded and deregulated in clf-29 lhp1-4 (Fig. 4F). These data confirm that the loss of both CLF and LHP1 gives rise to massive transcript degradation on genes that are highly expressed, and that this degradation globally affects the expression level of these genes.

Degradation of transcripts is an important mechanism in eukaryotes that allows suppressing mRNAs with aberrant features that may ultimately compromise cellular functions. Given the excessive amount of transcript degradation related to transcriptionally active genes in clf-29 lhp1-4, we analyzed whether transcripts integrity might be affected following the loss of both CLF and LHP1 function. To do so, we analyzed RNA-seq data performed on WT and clf-29 lhp1-4 lines, and looked at several features associated to premature degradation of mRNAs, including longer transcript, splicing fidelity, presence of small insertions, deletions or unexpected polymorphisms. This analysis shows, for the whole genome as for genes targeted by H3K27me3 or LHP1, that mRNAs from clf-29 lhp1-4 did not show higher signatures of transcript defects compared to those of WT (Supplemental Table 2). This suggests that transcriptional fidelity was not significantly affected in clf-29 lhp1-4. In another hand, we asked whether the excessive production of sRNAs observed in clf-29 lhp1-4 is linked to the chromatin environment mediated by PcG complexes. To do so, we compared the sRNAs abundance on PcG-targeted genes and PcG-non targeted genes in WT and clf-29 lhp1-4. We calculated for each gene of the genome a score of degradation, corresponding to the amount of sRNAs at each locus, normalized by the level of expression. The score of RNA degradation was compared between the list of H3K27me3-or/and LHP1-targeted genes and lists of randomly selected A c c e p t e d M a n u s c r i p t 18 genes. In WT, we observed strikingly that genes marked by H3K27me3, LHP1, or both, have a significantly higher score of RNA degradation than the rest of the genome (Fig. 5A). This, surprisingly, shows that PcG-targeted genes are generally prone to mRNAs degradation in comparison to the rest of the genome. In another hand, two important observations emerged from the corresponding analysis in clf-29 lhp1-4. First, the score of degradation was much higher than in the WT, confirming that degradation of mRNAs globally increases in clf-29 lhp1-4. However, we observed that the score of degradation increased for PcG-targeted genes but also for the rest of the genome, to a very similar extent (Fig. 5A). This strongly suggests that the increase of mRNAs degradation in clf-29 lhp1-4 is not solely affecting PcG-targeted genes but the whole genome, and therefore means that accumulation of sRNAs is not directly mediated by the loss of PcG-related chromatin environment, but rather indirectly by another mechanism. Interestingly, when looking more carefully at the expression of genes annotated with the function "exoribonuclease activity and mRNA catabolic process", we found that some of these genes, such as XRN4, XRN3 or XRN2, could be slightly induced in clf-29 lhp1-4 (Fig. 5B). This suggests that the machinery of mRNAs degradation could be altered and boosted in clf-29 lhp1-4. However, this would also be an indirect explanation, as XRN2, XRN3 and XRN4 are not targeted by PcG chromatin marks [START_REF] Roudier | Integrative epigenomic mapping defines four main chromatin states in Arabidopsis[END_REF]. To test the origin of sRNAs associated to excessive RNA degradation in clf-29 lhp1-4, we performed sRNA Northern blots targeting NRT2.1 transcript on nuclear and cytoplasmic compartments in WT and clf-29 lhp1-4. This analysis showed that NRT2.1-associated sRNAs accumulate in clf-29 lhp1-4 in both nuclear and cytoplasmic compartments, with a stronger accumulation in the cytoplasm (Fig. 5C). These results suggest that sRNAs associated with increased RNA degradation in clf-29 lhp1-4 certainly originate from the cytoplasm and the nuclei. Therefore, we concluded that excessive mRNA degradation caused by the loss of both CLF and LHP1 are presumably due to a misregulation of RNA degradation machinery, affecting the expression of genes with major physiological functions. These genes are associated with metabolic functions, but the impact of PcG proteins regulation on them has not been analyzed yet. PcG proteins have usually been described for their roles in keeping the expression level of developmental genes to a minimum, creating a so called facultative heterochromatin. However, it has already been shown that PcG proteins do not only target genes with low expression. In our data, the list of genes that are highly expressed and targeted by PcG proteins is not enriched in development related loci but in genes involved in metabolism. Indeed, active PcG targets are highly enriched in genes involved in the transport of diverse nutrients such as nitrate, amino acids, sulfate, phosphate, potassium and water. The main model gene used in this study, NRT2.1, is also involved in metabolism. Loss of expression of the gene encoding the main nitrate transporter in clf-29 lhp1-4 reduces dramatically the nitrate influx capacity of the root system.

Transcriptionally active PcG proteins targets have already been characterized for their enrichment in metabolic function [START_REF] Van Den Boom | Non-canonical PRC1.1 Targets Active Genes Independent of H3K27me3 and Is Essential for Leukemogenesis[END_REF]Zhou et al., 2017a). This suggests that PcG proteins are not only important to maintain organ identity by repressing homeotic genes but also to ensure the integrity of active transcripts involved in major metabolic functions. Our previous study also described that NRT2.1 is constantly marked by H3K27me3 even under transcriptionally permissive conditions [START_REF] Bellegarde | Polycomb Repressive Complex 2 attenuates the very high expression of the Arabidopsis gene NRT2.1[END_REF]. Loss of CLF function leads to a reduction of H3K27me3 levels at the NRT2.1 locus, increasing its mRNA levels without altering its expression pattern [START_REF] Bellegarde | Polycomb Repressive Complex 2 attenuates the very high expression of the Arabidopsis gene NRT2.1[END_REF]. Here, using NRT2.1 as a model for active genes, we showed that concurrent loss of LHP1 and This might suggest a link between PcG-mediated chromatin environment and RQC mechanisms.

Indeed, a recent study in yeast showed that the LSM2-8 XRN2 associated complex, localized in the nucleus, is involved in the repression of gene expression applied by PcG proteins (Mattout et al. 2020). They could show that the LSM2-8 complex and XRN2 are specifically degrading transcripts coming from genes subjected to PcG repression to enhance and ensure their transcriptional silencing.

In our study, we observed, whatever the genotype, that PcG-targeted genes are more prone to mRNA degradation than the rest of the genome. However, by differentiating PcG-targeted genes and PcGnon targeted genes, we showed that the mechanism for degradation of mRNAs in PcG mutants is likely indirect, as genes that are not marked by H3K27me3 and LHP1 are also subjected to a similar enhancement of mRNA degradation in clf-29 lhp1-4. This suggests that the global enhancement of mRNA degradation in clf-29 lhp1-4 might be due to a deregulation of the expression of genes important for RQC mechanisms. In agreement, we observed that key genes involved in mRNA

(

  [START_REF] Christie | RNA decay and RNA silencing in plants: competition or collaboration[END_REF][START_REF] Liu | RNA Quality Control as a Key to Suppressing RNA Silencing of Endogenous Genes in Plants[END_REF]. Given the redundancy between CLF and LHP1 and the very high expression of NRT2.1 transcript level in clf-29 and lhp1-4 single mutants, we reasoned that the decrease of NRT2.1 transcript level in clf-29 lhp1-4 might be due to the occurrence of a silencing mechanism in this genetic background. To understand which mechanism could drive the decrease in NRT2.1 transcript level in clf-29 lhp1-4 double mutant, we analyzed several chromatin-based signatures of transcriptional gene silencing or post-transcriptional gene silencing at the NRT2.1 locus.

NRT2. 1

 1 locus using McrBC digestion combined with quantitative PCR. Again, no difference could be found in the DNA methylation levels at NRT2.1 in clf-29 lhp1-4 when compared to WT (Fig. 3B). Then, in order to look for a possible post-transcriptional regulation directed to NRT2.1 transcripts, we analyzed the small RNAs (sRNAs) profile associated to NRT2.1 by performing sRNAs Northen blot in WT and clf-29 lhp1-4. Several RNA-directed regulatory processes exist in plants to target and degrade aberrant endogenous or exogenous RNAs, including messenger RNAs (mRNAs) surveillance by RNA quality control (RQC) mechanisms, regulation by microRNAs (miRNAs), or double stranded RNAmediated post-transcriptional gene silencing (Martinez de Alba et al., 2013). The sRNAs profile associated to NRT2.1 in WT and clf-29 lhp1-4 did not show the presence of 21-, 23-or 24-nt sRNAs, that are typical of double stranded RNA-mediated post-transcriptional gene silencing mechanisms.

NRT2. 1

 1 degradation was likely established in clf-29 and lhp1-4 simple mutants, although the amount of degradation was clearly lower than in clf-29 lhp1-4 (Supplemental Fig.3). Interestingly, the moderate levels of NRT2.1 RNA degradation observed in clf-29 and lhp1-4 single mutants compared Downloaded from https://academic.oup.com/jxb/advance-article/doi/10.1093/jxb/erac216/6589967 by INIST-CNRS INSU user on 30 May 2022 A c c e p t e d M a n u s c r i p t 15 to that of clf-29 lhp1-4 double mutant appears to be in line with their redundancy observed at the level of the regulation of NRT2.1 gene expression. To confirm that the observed reduction of NRT2.1 transcript level in clf-29 lhp1-4 was due to a post-transcriptional mechanism, we performed run-on experiments in WT and clf-29 lhp1-4. We used SOC1, which is a typical target of PcG proteins, as a positive control of run-on experiments, showing an increase in transcription rate in clf-29 lhp1-4 in comparison to the WT (del Olmo

AFigure 1 :

 1 Figure Legends

Figure 2 :

 2 Figure 2: Transcriptomic behavior of active genes in WT and clf-29 lhp1-4. A. Expression of NRT2.1,

Figure 3 :

 3 Figure 3: clf-29 lhp1-4 mutations lead to the accumulation of NRT2.1 transcript degradation

  Downloaded from https://academic.oup.com/jxb/advance-article/doi/10.1093/jxb/erac216/6589967 by INIST-CNRS INSU user on 30 May 2022 Surprisingly, the effect of concurrent loss of function for CLF and LHP1 has not been described yet in the literature. To do so, we generated a double clf-29 lhp1-4 mutant. This double mutant yields very few to almost no seeds, and was thus maintained at the heterozygous state for clf-29 mutation. However, clf-29 lhp1-4 mutant does not display severe PcG phenotype like callus-like structures observed in clf swn or bmia/b/c mutants

	expressed genes.
	M a n u s c r i p t
	A c c e p t e d
	12

  Downloaded from https://academic.oup.com/jxb/advance-article/doi/10.1093/jxb/erac216/6589967 by INIST-CNRS INSU user on 30 May 2022 PcG protein in the regulation of gene expression (Supplemental Table

	function of
	M a n u s c r i p t
	A c c e p t e d
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  expression. Defects in NRT2.1 expression could be due to morphological changes, that are known to be frequent in Polycomb mutants. However, several observations are not in favor of this possibility. Indeed, we observed that the root development and anatomy does not seem particularly disrupted in clf-29 lhp1-4. In addition, we previously demonstrated that the regulation mediated by CLF does not modify the tissue-specific expression of NRT2.1[START_REF] Bellegarde | Polycomb Repressive Complex 2 attenuates the very high expression of the Arabidopsis gene NRT2.1[END_REF]. and most notably, we observed a striking association between the down-regulation of NRT2.1 in clf-29 lhp1-4 and the accumulation of sRNAs originating from the NRT2.1 transcript. Additional experiments will be helpful to characterize the cellular and morphological defects in clf-29 lhp1-4, but our results strongly suggest that NRT2.1 down-regulation is due to excessive RNA degradation that occurs in clf-29 lhp1-4. At the genome-wide level, loss of CLF and LHP1 function also leads to the down-regulation of numerous metabolism-related active genes. The analysis of sRNAs profile at NRT2.1 and genomewide revealed that down-regulation of expression of active genes following loss of LHP1 and CLF is associated to the accumulation of sRNAs, resulting from post-transcriptional degradation of mRNAs.

	CLF, which are members of PRC1 and PRC2 respectively, leads surprisingly to down-regulation of
	NRT2.1
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  21degradation are induced in clf-29 lhp1-4. Yet, this link between PcG and the regulation of mRNAs degradation genes also appears indirect, as potential candidates, such as XRN genes, are not targeted by PcG-related chromatin marks. Further experiments should be undertaken in order to identify the link between PcG proteins and the homeostasis of mRNA degradation. In conclusion, our study highlights that transcriptionally active genes and especially those targeted by PcG are prone to mRNAs degradation, and that PcG function is also essential to maintain an accurate level of mRNAs degradation to ensure a correct expression level of genes. Downloaded from https://academic.oup.com/jxb/advance-article/doi/10.1093/jxb/erac216/6589967 by INIST-CNRS INSU user on 30 May 2022

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Funding

DS and FB were supported by a fellowship from the French Ministry of Higher Education and Research. OC were supported by a fellowship from the CNRS (Centre National de la Recherche Scientifique). AG and AM were supported by a fellowship from ANR (Agence Nationale de la Recherche) (ANR14-CE19-0008 IMANA). Downloaded from https://academic.oup.com/jxb/advance-article/doi/10.1093/jxb/erac216/6589967 by INIST-CNRS INSU user on 30 May 2022

Data availability

Data generated within this paper are available on the ArrayExpress website under the accession numbers E-MTAB-9792, E-MTAB-9793 and E-MTAB-11263.

Author Contribution

DS performed most of the experiments. OC and CF performed most of the bioinformatics analyses.

FB and AG contributed to the initiation of the project. FB, GS and JB contributed to the generation and the first characterization of the double clf-29 lhp1-4 mutant. JA and TL contributed to the analysis of sRNAs after cell fractionation. AM and DS conceived the project and designed the experiments. AM and DS interpreted the data with the help of every authors, and wrote the manuscript.

Conflict of Interest

The authors have no conflicts to declare