Ammonium Transporter

Thomas Roulé

Martin Crespi
email: martin.crespi@ips2.universite-paris-saclay.fr

Thomas Blein

Coline Balzergue

Marc Gabriel

Laetitia Scalisi

Céline Sorin

Aurélie Christ

Christian Godon

AGO ARGONAUTE

Keywords: lncRNA, root growth, root development, plant nutrition, stresses Oxygen Species, SCR, SCARECROW, siRNA, small-interfering RNA, sRNA, small RNA, VIGS, Virus-Induced Gene Silencing, Xist, X-inactive specific transcript, ZAT6, lncRNA, enhancer, cluster, chromatin conformation, LHP1, ABA, seed germination, epigenetics, marneral

Aux galériens de Nantes

Quelle chance j'ai de vous avoir ! Les moments de détente en votre compagnie ont été, sont et seront toujours un vrai plaisir. Merci d'être là.

A ma famille

Maman, merci pour ton écoute, ton intérêt pour mon sujet, ma carrière professionnelle et personnelle. Tu as toujours été à 100% derrière moi, malgré mes quelques bêtises et étourderies, et je t'en remercie. Mes soeurs, neveux et nièces, merci pour votre bonne humeur, votre soutien et votre présence. J'attends toujours avec un sourire aux lèvres les moments de convivialité où nous nous réunissons tous.

A ma femme (et oui !)

Enfin, je tenais à te remercier Mohena, pour les trop nombreuses fois où je n'ai pas vu l'horloge tourner, pour supporter mon humeur parfois bancale, mais également pour tes « quelques » venus à l'IPS2 et même à l'UCR. Merci de m'accompagner dans cette aventure, jonché d'imprévus. Merci pour ton amour.

A mon père

Je dédie cette thèse à mon père, peut-être l'unique personne sur terre qui aurait été capable de lire l'intégralité de ce manuscrit, références y compris, avec passion et application malgré un niveau d'anglais A1. J'ai grâce à toi acquis une détermination, motivation et passion pour la science qui j'espère me portera le plus loin possible.

List of Figures and Tables

Introduction Title Page

Figure 1 RNA Directed DNA methylation pathways.

Figure 2

The miRNA pathway.

Figure 3

The pha-siRNA pathway.

Figure 4

The different classes of lncRNAs.

Figure 5

LncRNA specificities compared to coding mRNAs.

Figure 6

Control of gene expression by promoter' cis-regulatory motif.

Figure 7

Polycomb-mediated chromatin condensation.

Figure 8

Chromosome arrangements within the nucleus.

Figure 9

Short range interactions.

Figure 10

Pol-mediated regulation of gene transcription by lncRNAs.

Figure 11

Epigenetic-mediated regulation of gene transcription by lncRNAs.

Figure 12

Chromatin loop-mediated regulation of gene expression by lncRNAs.

Figure 13

Post-transcriptional regulation of gene expression by lncRNAs.

Review

Regulatory long non-coding RNAs in root growth and development

Figure 1

LncRNAs involved root growth and development and its response to environmental stresses.

Results

Title Page

The non-coding transcriptome from two Arabidopsis ecotypes Publication Landscape of the Noncoding Transcriptome Response of Two Arabidopsis Ecotypes to Phosphate Starvation Figure 1 Identification of the transcripts and their occurrence across the two ecotypes.

Figure 2

Characterization of transcripts at the DNA level.

Figure 3

LncRNAs as sRNA precursors.

Figure 4

Differentially expressed genes according to ecotype and kinetic effects.

Figure 5

Expression of strongly deregulated lncRNAs between the two ecotypes.

Figure 6

Expression of selected lncRNAs in Col and Ler.

Figure 7

Overexpression of the lincRNAs NPC48 and NPC72 affects primary root growth.

Figure 8

Differentially expressed genes in plants overexpressing NPC48.

LATERALINC, new regulator of lateral root growth in Arabidopsis

Figure 1 Identification of the LATERALINC lincRNA.

Figure 2 LATERALINC shaped the root architecture.

Figure 3 LATERALINC does not regulate IAA14 but its expression correlates with the one of IAA1.

Table 1 List of Gene Ontology related to the root growth and development used for LATERALINC identification.

Table 2

List of primer used in LATERALINC study.

Preprint

The lncRNA MARS modulates the epigenetic reprogramming of the marneral cluster in response to ABA Figure 1 AT5G00580 is a lncRNA transcribed from the marneral cluster locus and its expression correlates with its neighboring genes. MARS influences chromatin condensation of MRN1 gene through its interaction with LHP1 protein.

Figure 5

An LHP1-dependent chromatin loop approaches the MRN1 locus with a putative enhancer element in response to ABA.

Figure 6

Regulation of metabolic clusters in plants by lncRNA.

MARS, a lncRNA implicated in the transcriptional regulation of an embedded gene cluster Table 1

List of the putative cis-acting-Col-0-enriched lincRNA and their putative target.

Figure 1

Identification of the MARS lincRNA.

Figure 2

Differentially expressed genes in RNAi MARS 1.

Figure 3 MARS downregulation impaired gene implicated in carbon/nitrogen equilibrium and cell oxidation status.

Figure 4

The MARS lncRNA physically interacts with the marneral cluster genomic region.

I Introduction 1. Non coding RNAs: from junk to utility

The availability of genome sequences of numerous living organisms markedly changed our vision of the genome. Until the 1940s, it was proposed that genome length could positively correlate with the organism's complexity [START_REF] Elliott | What's in a genome? The C-value enigma and the evolution of eukaryotic genome content[END_REF]. Nevertheless, the strong differences of genome length observed between closely related organisms and the existence of very long genomes within simple organisms, noted in the early 1950s, give rise to a convolutional quandary, the so-called "C-value paradox" [START_REF] Thomas | The genetic organization of chromosomes[END_REF]. Later, the uncovering of large non-protein-coding DNA regions within the genome resolved the lack of correlation between genome length and species complexity. Anyhow, this finding raises multiple questions concerning the relevance of these non-protein-coding sequences: How these sequences appear? Are they functional? Why don't they exist in all organisms? Together, these inquiries spawn the "C-value enigma" [START_REF] Gregory | Coincidence, coevolution, or causation? DNA content, cellsize, and the C-value enigma[END_REF]. The extent advances of RNA sequencing methods revealed that more than 90% of eukaryotic genomes are transcribed to RNA with only 2-10% of individual transcripts translated into protein (Gil and Ulitsky, 2020). As expected, this tremendous quantity of transcripts is significantly different from each other, some are constitutively activated and participate in the basal function of cell life, such as gene transcription or traduction; others are specifically activated upon particular stimuli or tissues, presenting regulatory function.

In this first part, I will describe the different types of transcripts found within a cell, their characteristics and specificity, and focus on a specific class of transcripts: the long non-coding RNAs (lncRNAs).

A large diversity of transcripts 1.Cooperation of non-coding and coding transcripts for protein synthesis

The protein composition within a cell directly influences its fate. For coding genes, the final step of gene expression is the mRNA translation, in which the genetic information contained within the transcript is decoded into a protein [START_REF] Cramer | Organization and regulation of gene transcription[END_REF].

First, the gene is transcribed into an RNA molecule which is getting mature through the addition of a poly(A) tail and a N7-methylated guanosine linked to the first nucleotide in 5'.

These modifications increased the RNA stability and translation efficiency [START_REF] Shatkin | Capping of eucaryotic mRNAs[END_REF]. The linear RNA sequence can then be modified, or spliced, by the spliceosome, leading to the eviction of some portion of the transcript [START_REF] Filichkin | Alternative splicing in plants: Directing traffic at the crossroads of adaptation and environmental stress[END_REF]. In humans, 73 mutations impairing the splicing process has been linked to cancer predisposition [START_REF] Tate | COSMIC: The Catalogue Of Somatic Mutations In Cancer[END_REF], strengthening the importance of this mechanism for cell life. Most of the time, during splicing all intronic regions are removed from the transcript. However, certain introns can be retained and certain exons can be skipped, leading to different mature RNA from one locus through Alternative Splicing (AS). Interestingly, in plants the majority of AS relates to intron retention, whereas in humans it consists in exon skipping [START_REF] Sammeth | A general definition and nomenclature for alternative splicing events[END_REF][START_REF] Wang | Alternative isoform regulation in human tissue transcriptomes[END_REF]. The spliceosome is constituted of small nuclear RNAs (snRNAs) that bind to proteins which together form a ribonucleoprotein complex and catalyze the splicing [START_REF] Wahl | The Spliceosome: Design Principles of a Dynamic RNP Machine[END_REF].

Notably, more than 90 percent of pre-mRNA transcripts are spliced in humans, many in a tissue-dependent or developmental-related manner [START_REF] Wang | Alternative isoform regulation in human tissue transcriptomes[END_REF]. Similarly, in higher plants, more than 60 % of transcripts containing intron undergo AS [START_REF] Chaudhary | Alternative splicing and protein diversity: Plants versus animals[END_REF].

Once mature, the coding transcripts exit the nucleus and are bound and decoded by the ribosomal complex, a large complex form with multiple proteins and ribosomal non-coding RNAs (rRNAs). The ribosomal complex is made of two protein subunits heterogeneous in size, together with four rRNAs implicated in the assembly and functionality of the ribosomes [START_REF] Merchante | Translation regulation in plants: an interesting past, an exciting present and a promising future[END_REF]. This complex is able to recognize specific sequences from the coding transcripts and produce the corresponding protein with the help of another type of non-coding transcripts: the transfer RNA (tRNA), which harbors the amino-acids [START_REF] Sanchita | Updates on plant long non-coding RNAs (lncRNAs): the regulatory components[END_REF].

Strikingly, it has been estimated that one amino acid is transferred every 60ms to the forming polypeptides chain [START_REF] Zaher | Fidelity at the Molecular Level: Lessons from Protein Synthesis[END_REF]. This process is called mRNA translation and it is realized through the cooperation of proteins and non-coding transcripts. In addition, small nucleolar RNAs are another class of structural RNAs that conduct chemical modifications, such as methylation and pseudouridylation, of snRNAs, rRNAs and tRNAs, participating in their maturation [START_REF] Streit | The Arabidopsis 2′-O-Ribose-Methylation and Pseudouridylation Landscape of rRNA in Comparison to Human and Yeast[END_REF]. Together, mRNA splicing and translation are fascinating examples showing the importance of non-coding transcriptional units cooperating with proteins for the one basal function of the cell: the protein synthesis.

The small non-coding regulatory transcripts

In addition to these house-keeping non-coding RNAs, new regulatory RNAs have been identified in recent years. Small regulatory non-coding RNAs or small RNAs (sRNAs) are defined as transcripts with a length in-between 19-25nt and not translated into protein. They are issued from double strands (ds) of RNA. Depending on their biogenesis and their mechanism of action they can be classified in different major types: small interfering RNAs (siRNAs), microRNAs (miRNAs) and phasiRNAs [START_REF] Brant | Plant small non-coding RNAs and their roles in biotic stresses[END_REF], all of which, fine-tune the transcriptional or post-transcriptional gene activity [START_REF] Voinnet | Origin, Biogenesis, and Activity of Plant MicroRNAs[END_REF][START_REF] Budak | Plant miRNAs: biogenesis, organization and origins[END_REF].

The siRNAs

SiRNAs are 21-24nt length RNAs which inhibit transcription through DNA methylation via RNA dependent DNA methylation (RdDM). This mechanism is notably critical for the silencing of Transposable Element (TE) activity in both animals and plants [START_REF] Zemach | Evolution of eukaryotic DNA methylation and the pursuit of safer sex[END_REF]. Also, RdDM-deficient Arabidopsis mutants present higher TE activity, even though transposition is still pretty rare [START_REF] Ito | An siRNA pathway prevents transgenerational retrotransposition in plants subjected to stress[END_REF]. In the TE-rich maize genome, methylated CHH islands are found in-between activated genes and silent-TE, preventing the spreading of active euchromatin to inactive heterochromatin (Li et al., 2015a).

RdDM action is a well described process involving siRNAs that arise from genes transcribed by either RNA Polymerase II (Pol II) or Pol IV (Figure 1). Interestingly, Pol IV is a plant-specific Polymerase that shares similar subunits with Pol II [START_REF] Haag | Multisubunit RNA polymerases IV and V: Purveyors of non-coding RNA for plant gene silencing[END_REF] and specifically implicated in siRNA biogenesis (Matzke and Mosher, 2014;[START_REF] Matzke | RNA-directed DNA methylation: The evolution of a complex epigenetic pathway in flowering plants[END_REF].

Upon Pol II transcription, the single strand (ss) siRNA-gene RNA is converted to a dsRNA through transcription of its corresponding strand by the RNA DEPENDENT RNA POLYMERASE6 (RDR6). Dicer-Like2 (DCL2) or DCL4 cut the dsRNA into 21 and 22nt, respectively. One of the strands is finally included in the RNA-Induced Silencing Complex (RISC) through its interaction with Argonaute6 (AGO6) [START_REF] Wu | Roles of DICER-LIKE and ARGONAUTE proteins in TASderived small interfering RNA-triggered DNA methylation[END_REF][START_REF] Nuthikattu | The initiation of epigenetic silencing of active transposable elements is triggered by RDR6 and 21-22 nucleotide small interfering RNAs[END_REF][START_REF] Mccue | ARGONAUTE 6 bridges transposable element m RNA -derived si RNA s to the establishment of DNA methylation[END_REF].

Interestingly, Pol II transcripts can also naturally generate hairpin structures through base complementarity leading to a dsRNA-like structure without the need of RDR6. In this case, the dsRNA is cleaved by DCL3 every 24nt and one of the strands is included in the RISC through its interaction with AGO4 or AGO6 (Figure 1; Pol II-dependent siRNA production). On the other hand, Pol IV dependent transcripts are converted into dsRNA with RDR2 [START_REF] Smith | An SNF2 protein associated with nuclear RNA silencing and the spread of a silencing signal between cells in Arabidopsis[END_REF][START_REF] Law | Polymerase IV occupancy at RNA-directed DNA methylation sites requires SHH1[END_REF]. Then, the dsRNA is processed into 21-24nt through cleavage with DCL2/3 or DCL4. Interestingly, an important proportion of RdDM targets are still methylated within dcl1/dcl2/dcl3/dcl4 quadruple mutants, indicating a DCL-independent dsRNA cleavage [START_REF] Yang | Dicer-independent RNA-directed DNA methylation in Arabidopsis[END_REF]. Anyhow, one of the strands is loaded into the RISC through interaction with AGO4 or AGO6 (Figure 1; Pol IV-dependent siRNA production).

Once the RISC is formed, it associates with the chromatin through base complementarity in-between the ssRNA associated with AGO and the DNA and mediates DNA methylation with the help of other proteins and co-factors. More precisely, for both Pol II or

Pol IV dependent siRNA synthesis, the single strand RNA directs the RISC through base complementarity with a nascent Pol V transcript on the region where the DNA methylation occurs. Finally, RISC is able to recruit the DNA methyltransferase DOMAINS REARRANGED METHYLASE2 (DRM2) which methylate the DNA with the help of RNA-DIRECTED DNA METHYLATION1 (RDM1) which bind to both AGO4/AGO6 and DRM2. Interestingly, to maintain Pol V transcript next to the chromatin and facilitate DNA methylation, the yeast and mammalian homolog Ribosomal RNA processing 6 (RRP6), retain the RNA close to the active Pol V. In addition, the Pol V RNA-AGO RNA interaction is strengthened by the INVOLVED IN DE NOVO 2 (IDN2)-IDN2 PARALOGUE (IDP) complex that bind SWIB3 (part of the SWI/SNF complex) participating in chromatin decondensation, facilitating Pol V activity (Zhang et al., 2018a) (Figure 1). Methylated DNA positively correlates with a condensed chromatin and a low transcriptional activity. The effect of DNA methylation on gene expression is detailed further in section 2.2.1.

Figure 1: RNA Directed DNA methylation pathways (Adapted from Zhang et al., 2018). For Pol-IV dependent siRNA production, the Pol IV recruitment to the chromatin is facilitated by SAWADEE HOMEODOMAIN HOMOLOGUE 1 (SHH1), which binds dimethylated histone H3 lysine 9 (H3K9me2) [START_REF] Law | Polymerase IV occupancy at RNA-directed DNA methylation sites requires SHH1[END_REF][START_REF] Zhang | Long non-coding genes implicated in response to stripe rust pathogen stress in wheat (Triticum aestivum L.)[END_REF]. Also, Pol IV processing is facilitated by the chromatin remodeler SNF2 DOMAIN-CONTAINING PROTEIN CLASSY 1 (CLSY1) [START_REF] Zhang | Long non-coding genes implicated in response to stripe rust pathogen stress in wheat (Triticum aestivum L.)[END_REF][START_REF] Smith | An SNF2 protein associated with nuclear RNA silencing and the spread of a silencing signal between cells in Arabidopsis[END_REF]. Pol IV dependent non-coding RNAs (P4 RNAs) are transformed into a dsRNA via the RNA-DEPENDENT RNA POLYMERASE 2 (RDR2). The dsRNA is either cleaved by DICER-LIKE PROTEIN 2 (DCL2), DCL3 and DCL4 (pathway 1), or cleaved by non-DCL proteins (pathway 2), both leading to mainly 24nt siRNAs. These siRNAs interact with ARGONAUTE 4 (AGO4) or AGO6 and pair with a Pol V dependent scaffold transcript which together recruit DOMAINS REARRANGED METHYLASE 2 (DRM2), which methylates the DNA. Interestingly, Pol II can also generate RdDM-related siRNAs. On one hand, Pol II transcripts can harbor a stem loop structure which is cleaved by DCL3 generating 24nt siRNAs (pathway 3). On the other hand, Pol II transcripts can serve as template for RDR6 mediated dsRNA synthesis [START_REF] Wu | Roles of DICER-LIKE and ARGONAUTE proteins in TASderived small interfering RNA-triggered DNA methylation[END_REF][START_REF] Nuthikattu | The initiation of epigenetic silencing of active transposable elements is triggered by RDR6 and 21-22 nucleotide small interfering RNAs[END_REF][START_REF] Mccue | ARGONAUTE 6 bridges transposable element m RNA -derived si RNA s to the establishment of DNA methylation[END_REF], which is then cleaved by DCL2 and DCL4 generating 21-22nt siRNAs (pathway 4). Finally, the Pol II dependent siRNAs transcripts interact with AGO4 or AGO6. The association between the AGOs complex and Pol V is facilitated by RNA-DIRECTED DNA METHYLATION 3 (RDM3) (He et al., 2009;Bies-Etheve et al., 2009). The production of Pol V dependent scaffold RNA needs the DDR complex, comprising the chromatin remodeler DEFECTIVE IN RNA-DIRECTED DNA METHYLATION 1, DEFECTIVE IN MERISTEM SILENCING 3 and RDM1. The DDR complex interacts with AGO4, DRM2, SUPPRESSOR OF VARIEGATION 3-9 HOMOLOGUE PROTEIN 2 (SUVH2) and SUVH9, and bind single-stranded methylated DNA and recruit Pol V (Gao et al., 2010;Kanno et al., 2004;Kanno et al., 2008;Law et al., 2010;Zhong et al., 2012;[START_REF] Liu | The miR9863 Family Regulates Distinct Mla Alleles in Barley to Attenuate NLR Receptor-Triggered Disease Resistance and Cell-Death Signaling[END_REF]Johnson et al., 2014). Finally, the retention of Pol V transcripts is mediated by the RNA-binding proteins RRP6-LIKE 1 (RRP6L1 (Zhang et al., 2014)) and the INVOLVED IN DE NOVO 2 (IDN2)-IDN2 PARALOGUE (IDP) complex, which interacts with the SWITCH/SUCROSE NONFERMENTING (SWI/SNF), a chromatin-remodeling complex (Ausin et al., 2009;Zheng et al., 2010;Ausin et al., 2012;Zhang et al., 2012;Finke et al., 2012;Xie et al., 2012;[START_REF] Guan | Heat stress induction of miR398 triggers a regulatory loop that is critical for thermotolerance in Arabidopsis[END_REF].

The miRNAs

MicroRNAs (miRNAs) are 19-24nt length RNAs which inhibit gene expression at the post-transcriptional level. Using their base complementarity, they can direct a RISC to other RNAs triggering their cleavage or avoiding their translation into protein (Figure 2). Strikingly, it has been estimated that one third of the human genome is regulated by miRNA [START_REF] Hammond | An overview of microRNAs[END_REF]. Rationally, in plants miRNA are implicated in almost all biological processes such as development and responses to biotic and abiotic stimuli [START_REF] Budak | Plant miRNAs: biogenesis, organization and origins[END_REF]. MiRNA genes generate 100-200nt length pri-miRNA transcripts that naturally form a stem-loop structure through base complementarity. In plants, a complex composed notably of DCL1, the dsRNA binding protein HYPONASTIC LEAVES1 (HYL1), nuclear cap binding complex (CBC) and a C2H2-type zinc finger (SERRATE) processed the pri-miRNA by removing 15nt from each extremity of the RNA stem-loop to form the precursor miRNA (pre-miRNA) [START_REF] Axtell | Revisiting criteria for plant microRNA annotation in the Era of big data[END_REF]. Then, the pre-miRNA is cleaved into a 20-24nt fragment to form a miRNA/miRNA* duplex. This duplex is methylated at the 3' end by HEN1 which protects them from degradation.

Finally, one of the strands is taken by AGO1 to form the RISC, which together mediates mRNA cleavage or translational repression through base complementarity binding of the miRNA complex with the RNA target (Figure 2).

Initially, it was thought that plant miRNAs mainly act by transcript cleavage thanks to the high sequence complementarity to their targets. Nevertheless, plant miRNAs are also found enriched on membrane-bound polysomes (Li et al., 2016a) increasing evidence that they also act through translation repression as mammal's miRNA [START_REF] Millar | Plant and animal microRNAs: Similarities and differences[END_REF]. For example, SQUAMOSA PROMOTER BINDING PROTEIN-LIKE 3 (SPL3), REVOLUTA, SCARECROW-LIKE PROTEIN 4 (SCL4), APETALA 2 (AP2), and COPPER/ZINC SUPEROXIDE DISMUTASE 1 (CSD1) and CSD2 are all mRNAs targeted by miRNA and subjected to both transcript cleavage and repression of the translation [START_REF] Yu | The 'how' and 'where' of plant microRNAs[END_REF]. Also, as miRNA are relatively small, they may target different mRNA that share similar sequences, likely belonging to the same gene family.

For example, in Arabidopsis thaliana miR156 regulates the transition from vegetative to flowering phase by changing the expression of several SPL genes [START_REF] Wang | Regulation of flowering time by the miR156-mediated age pathway[END_REF], and the miR167 targets various ARF genes implicated in auxin signaling [START_REF] Xia | Extensive families of miRNAs and PHAS loci in Norway spruce demonstrate the origins of complex phasiRNA networks in seed plants[END_REF]. Also, miRNA evolution may recruit new targets in one species. For example, the miR396-GRFs regulation is conserved in angiosperms and gymnosperms [START_REF] Chorostecki | Identification of new microRNA-regulated genes by conserved targeting in plant species[END_REF][START_REF] Debernardi | Functional specialization of the plant miR396 regulatory network through distinct microRNA-target interactions[END_REF] but

Medicago truncatula miR396 represses MtGRF together with two MtbHLH79 affecting root growth and mycorrhizal colonization [START_REF] Bazin | MiR396 affects mycorrhization and root meristem activity in the legume Medicago truncatula[END_REF].

Mutant plants impaired in the miRNA biogenesis pathway present strong developmental defects suggesting miRNA are implicated in a wide range of biological processes [START_REF] Budak | Plant miRNAs: biogenesis, organization and origins[END_REF]. For example, Arabidopsis dcl1 knock-down mutants exhibit pleiotropic developmental defects, and the dcl1 knock-out is embryo-lethal [START_REF] Chen | microRNA biogenesis and function in plants[END_REF]. Similarly, disruption of the HYL1, HEN1, DCL1, HST and AGO1 genes impaired fertility [START_REF] Cubillos | Lessons from eQTL mapping studies: Non-coding regions and their role behind natural phenotypic variation in plants[END_REF][START_REF] Oliver | Loss of function of Arabidopsis microRNA-machinery genes impairs fertility, and has effects on homologous recombination and meiotic chromatin dynamics[END_REF]. Also in rice, proper function of the MEIOSIS ARRESTED AT LEPTOTENE 1 (MEL1), component of the RISC, is necessary for proper pollen grain development [START_REF] Nonomura | A germ cell-specific gene of the ARGONAUTE family is essential for the progression of premeiotic mitosis and meiosis during sporogenesis in rice[END_REF][START_REF] Komiya | Rice germline-specific Argonaute MEL1 protein binds to phasiRNAs generated from more than 700 lincRNAs[END_REF]. Similarly, the maize AGO104, also functioning through RdDM , is directly involved in meiosis [START_REF] Singh | Production of viable gametes without meiosis in maize deficient for an ARGONAUTE protein[END_REF]. MiRNAs activity also participates in the plant response to environmental cues. For example, in Gossypium hirsutum, miRNVL5 expression decreases upon salt stress, positively participating in the plant salt stress response through the increase of expression of GhCHR, a positive regulator of salt stress tolerance [START_REF] Gao | A cotton miRNA is involved in regulation of plant response to salt stress[END_REF]. Similarly, the Arabidopsis miR156 is implicated in heat stress resilience and memory through the regulation of SPL genes [START_REF] Stief | Arabidopsis miR156 regulates tolerance to recurring environmental stress through SPL transcription factors[END_REF], whereas the miR398 participates in heat tolerance via csd-mRNA cleavage [START_REF] Guan | Heat stress induction of miR398 triggers a regulatory loop that is critical for thermotolerance in Arabidopsis[END_REF]. Finally, miRNA assists the assimilation of key nutrients. For example, the Arabidopsis miR395 targets the ATP sulfurylase (APS) gene transcript involved in sulfate assimilation, participating in the plant sulfate metabolization (Jones-Rhoades and Bartel, 2004). Also, the miR399 generated from 6 loci participates in the plant Pi homeostasis through the targeting of PHO2 mRNA (Pant et al., 2008). PHO2 encodes for an ubiquitin-conjugating E2 enzyme that mediates the degradation of the PHO1 protein, implicated in Pi loading to the Xylem (Liu et al., 2012a). Logically, overexpression of the miR399 increased PHO1 protein level leading to Pi hyperaccumulation (Chiou et al., 2006). Given the direct implication of miRNA for plant development and response to environmental stresses, their manipulation has the potential to enhance some agronomic traits.

Figure 2: The miRNA pathway (Adapted from Li et al., 2018). Pol II-dependent miRNA precursor transcripts generate a natural hairpin structure through base pair complementarity forming the pri-miRNA. Polyadenylated tail and 5' cap are removed through DCL1-mediated cleavage generating the pre-miRNA. The pre-miRNA is further cleaved by DCL1 resulting in mature miRNA/miRNA* duplex in which the duplex extremity is methylated by HEN1. HASTY translocate the miRNA/miRNA* duplex from the nucleus to the cytoplasm. Finally, one strand of the miRNA/miRNA* duplex is recruited by AGO1 forming the mature RNA-induced silencing complex (RISC), which interacts with the target mRNA through base complementarity. Binding of the miRNA-RISC complex to the mRNA can generate cleavage of the mRNA or inhibition of the translation.

The phasiRNA and tasiRNAs

Phased secondary siRNAs (phasiRNAs) are generated after miRNA-mediated RNA cleavage of specific transcripts and leads to the production of regulatory sRNAs acting likely as miRNAs [START_REF] Allen | microRNA-directed phasing during trans-acting siRNA biogenesis in plants[END_REF]. Indeed, the miRNA-mediated RNA cleavage does not always lead to direct degradation of the cleaved transcript. In certain cases, part of the cleaved RNAs, named as phased transcripts, is processed into phasiRNAs through the RDR/DCL machinery [START_REF] Chen | 22-Nucleotide RNAs trigger secondary siRNA biogenesis in plants[END_REF]. More precisely, SUPPRESSOR OF GENE SILENCING (SGS3) binds to the phased transcripts to avoid their degradation and facilitates their dsRNA transformation through RDR6. Once in double strand form, DCL4 processed them regularly into 21nt long phasiRNA duplex which are methylated by HEN1 and processed into the AGO1-RISC as for miRNA [START_REF] Brodersen | The diversity of RNA silencing pathways in plants[END_REF]. Consequently, phasiRNA are likely to function like miRNA to repress gene expression via transcript cleavage or repression of the translation [START_REF] Chen | Small RNAs and their roles in plant development[END_REF][START_REF] Yu | The 'how' and 'where' of plant microRNAs[END_REF] (Figure 3).

PhasiRNA were initially status as tasiRNA for Trans Acting Secondary siRNA since they were initiated from the non-coding TAS gene transcripts [START_REF] Vazquez | Endogenous trans-acting siRNAs regulate the accumulation of arabidopsis mRNAs[END_REF][START_REF] Felippes | Triggering the formation of tasiRNAs in Arabidopsis thaliana: The role of microRNA miR173[END_REF]. As for miRNA they are involved in various aspects of plant development and its response to the environment. For example, the miR390-TAS3-ARF pathway is implicated in lateral root growth, leaf formation, embryogenesis and is conserved in plants. Notably, in ago7

or dcl4 tomato mutants, unable to generate phasiRNAs, the increased level of ARFs transcript leads to abnormal leaf shape [START_REF] Yifhar | Failure of the tomato trans-acting short interfering RNA program to regulate AUXIN response FACTOR3 and ARF4 underlies the wiry leaf syndrome[END_REF]. Similarly, Medicago truncatula and maize ago7 mutants present cylindrical leaves with irregular polarity [START_REF] Douglas | Ragged seedling2 encodes an ARGONAUTE7-like protein required for mediolateral expansion, but not dorsiventrality, of maize leaves[END_REF][START_REF] Zhou | The trans-acting short interfering RNA3 pathway and no apical meristem antagonistically regulate leaf margin development and lateral organ separation, as revealed by analysis of an argonaute7/lobed leaflet1 mutant in Medicago truncatula[END_REF]. Also, overexpression of miR3954 triggers phasiRNAs production from NAC genes in citrus, resulting in early flowering (Liu et al., 2017a). Interestingly, a wheat lncRNA WSGAR is targeted by miR9678 and is involved in seed development and germination [START_REF] Guo | Wheat miR9678 affects seed germination by generating phased siRNAs and modulating abscisic acid/gibberellin signaling[END_REF].

PhasiRNAs are also involved in abiotic stresses. For example, in Arabidopsis, miR173 targets the HEAT-INDUCED TAS1 TARGET1 (HTT1) and HTT2, where increasing the miR173 activity and subsequent phasiRNAs production increase the heat plant sensitivity (Li et al., 2014a). Also, drought-stress resilience of Populus plants involved miR482, miR828, and miR6445 activity.

More precisely, miR6445 targets NAC genes resulting in phasiRNAs productions targeting other NAC genes (Xie et al., 2017). Closely, in legume, drought stress triggers the production of NAC700-phasiRNA through the miR1514a activity, affecting plant drought tolerance [START_REF] Sosa-Valencia | The legume miR1514a modulates a NAC transcription factor transcript to trigger phasiRNA formation in response to drought[END_REF]. Finally, the sweet potato miR828 accumulates in response to wound and target IbMYB and IbTLD implicated in lignin and H2O2 content, affecting plant damage recovery [START_REF] Lin | MicroR828 regulates lignin and h2O2 accumulation in sweet potato on wounding[END_REF]. Wound is a mechanical stress induced under abiotic and biotic stresses. Interestingly, the plant biotic interaction, either beneficial or detrimental, is also governed by phasiRNAs. Among them, the immune receptor NUCLEOTIDE-BINDING LEUCINE-RICH REPEAT (NLR) and PENTATRICOPEPTIDE REPEAT (PPR) genes produced phasiRNA after miRNA-mediated cutting and are implicated in plant-microbe interactions, symbiosis and defense [START_REF] Fei | Phased, secondary, small interfering RNAs in posttranscriptional regulatory networks[END_REF]. Other resistance genes are targeted by phasiRNAs, supporting that phasiRNAs are important factors of plant immunity. For example, miR9863 from barley and wheat targets an MLA gene encoding an NLR protein (Liu et al., 2014a). Similarly, NLR genes from the norway spruce are targeted by the miR482/2118 family and generate phasiRNAs [START_REF] Xia | Extensive families of miRNAs and PHAS loci in Norway spruce demonstrate the origins of complex phasiRNA networks in seed plants[END_REF].

Altogether, miRNAs, siRNAs and phasiRNAs, demonstrate the complexity of sRNAmediated gene regulation, and the importance that they have in all aspects of plant life from organogenesis, to perception of the environment. However non-coding transcripts are not only small or precursors of small RNAs.

Figure 3: The pha-siRNA pathway (Adapted from [START_REF] Allen | microRNA-directed phasing during trans-acting siRNA biogenesis in plants[END_REF]. Certain mRNAs cleaved by miRNAs can be processed into phasiRNAs. After miRNA-RISC mediated mRNA cleavage, part of the cleaved transcript, named the pre-pha-siRNA, is converted into a dsRNA through RDR6 and SUPPRESSOR OF GENE SILENCING3 (SGS3). DCL4 cleaves processively in phase the ds-pre-pha-siRNA every 21nt forming phasiRNA duplexes which are methylated by HEN1. Finally, one of the strands of the phasiRNA/phasiRNA* duplex is incorporated into the RISC through AGO1 interaction and mediates translational inhibition or transcript degradation of their target transcripts.

Discovery of the Long Non-Coding RNAs world

Among the plethora of transcripts produced within the cells, the long non-coding RNAs (lncRNAs) emerged recently as important regulators of gene expression. Their low expression level and their cell-type specific transcriptional activity could explain why they were discovered later than other classes of transcripts, with the increased sensitivity of next-generation sequencing methods [START_REF] Jha | Long non-coding RNAs: Emerging players regulating plant abiotic stress response and adaptation[END_REF]. The intensive progress in bioinformatic for genome annotation, mainly through the use of RNA sequencing (RNA-seq) method, participates also in the in-silico finding of novel lncRNAs. The increasing number of pipelines able to decipher if a transcript is coding or non-coding based on multiple parameters including, the transcript sequence, secondary structure and the RNA conservation significantly improve the genomic annotations in all studied organisms [START_REF] Li | The computational approaches of lncRNA identification based on coding potential: Status quo and challenges[END_REF]. For example, the CPC uses both sequence alignment and Open Reading Frame (ORF) length and coverage (Kong et al., 2007), whereas the phyloCSF uses known protein databases together with sequence alignment to classify a transcript as coding or non-coding [START_REF] Lin | PhyloCSF: A comparative genomics method to distinguish protein coding and non-coding regions[END_REF]. Then, CNCI and PLEK use nucleotide composition such as GC content and k-mer occurrence [START_REF] Sun | Coordinated regulation of Arabidopsis microRNA biogenesis and red light signaling through Dicer-like 1 and phytochrome-interacting factor 4[END_REF][START_REF] Li | PLEK: A tool for predicting long non-coding RNAs and messenger RNAs based on an improved k-mer scheme[END_REF], whereas the COME uses structural features of the transcript and epigenetic information to decipher its coding ability (Hu et al., 2017). These tools are based on a machine learning approach, where the software is trained on a set of known non-coding and coding transcripts [START_REF] Li | The computational approaches of lncRNA identification based on coding potential: Status quo and challenges[END_REF]. More recently, the newly developed tools use a whole set of alignment and non-alignment methods to analyze multiple transcripts features, such as the ORF, RNA secondary structure, peptide isoelectric point and Ficket score. Among these tools, CPC2, emerged as a widely used coding prediction tool for a large range of species (Kang et al., 2017).

Thus, even though lncRNAs were initially described as transcriptional noise within the cell, they are now intensively studied and considered as important regulators of cell life from animals to plants (Ariel et al., 2015). Intergenic ncRNAs (lincRNAs) are transcribed without overlapping any gene or promoter. Promoter lncRNAs are transcribed within the promoter region of a gene without overlapping the core gene region. LncRNA genes overlapping with an exon of another gene in its opposite direction are named Natural Antisense Transcript lncRNA (NAT-lncRNA), whereas it is named sense lncRNA when it overlaps an exon in the same direction as the transcript. Finally, lncRNA can be transcribed within an intron, generating intronic lncRNAs.

LncRNAs features

LncRNAs are transcripts superior to 200nt in length presenting a low ability to encode for proteins. They can be classified according to their genomic position in relation to other genes (Figure 4). First, the intronic lncRNAs are lncRNAs transcribed within the intron of another gene that does not overlap with any exons, independently of its orientation (Ariel et al., 2015), while they are classified as exonic or sense lncRNAs if they overlap with the exon of a gene and on the same strand. Indeed, lncRNAs overlapping with exonic genomic regions but on the other strand of the DNA are classified as natural antisense lncRNA (NAT-lncRNA).

LncRNAs transcribed from the promoter of a gene constitute the promoter-lncRNA. Finally, the intergenic lncRNAs (lincRNAs) are lncRNAs transcriptional units localized in-between two genes, thus constituting an individual isolated transcriptional unit (Ariel et al., 2015) (Figure 4).

The number of discovered lncRNAs increased together with the development of sequencing and bioinformatic prediction tools. For example, in 2017 the FANTOM5 project identified around 27 919 lncRNAs in humans, whereas in 2019 it has been estimated that the human genome contains around 270 044 lncRNAs [START_REF] Ma | Disrupted genome methylation in response to high temperature has distinct affects on microspore abortion and anther indehiscence[END_REF]. Similarly in plants, the number of annotated lncRNAs significantly increased over time. For example, the study of Amor (2009) identified only 76 lncRNAs in Arabidopsis from detailed expertise of full-length cDNA databases, whereas a more recent study using RNAseq identified 6 510 lncRNAs, among which 4 050 NAT-lncRNAs and 2 460 lincRNAs (Zhao et al., 2018a). Now, more than thousands of lncRNAs have been identified within more than 40 plant species. For example, the Green Non-Coding Database (GreeNC) annotated more than 120 000 lncRNAs within 37 plant species and six algae [START_REF] Gallart | GREENC: A Wiki-based database of plant IncRNAs[END_REF].

Even though lncRNAs share common features with coding RNAs, such as addition of 5' cap and poly(A) tail, base editing and splicing to give rise to a mature RNA, they also have distinct characteristics (Figure 5). Indeed, they tend to be shorter in length, contain less exons and produce a lower number of isoforms as compared to coding RNAs (Golicz et al., 2018;Sarropoulos et al., 2019;Zhao et al., 2020a). Moreover, they are often less abundant than coding RNAs and frequently retained in the nucleus whereas the coding RNA are rapidly translocated to the cytoplasm to be translated. In mammals, increasing studies shed light on subtle differences between mRNA and lncRNAs at the transcriptional level. For example, Pol II pausing is less frequent in lncRNAs promoter compared to promoters of coding RNAs and could explain the imprecise transcription of some lncRNAs (Schlackow et al., 2017a). Secondly, promoters of lncRNAs tend to have less TF binding sites and fewer histone modifications that could explain their lower abundance as compared to coding transcripts [START_REF] Mattioli | High-throughput functional analysis of lncRNA core promoters elucidates rules governing tissue specificity[END_REF] (Figure 5). Indeed, a massively parallel reporter assay, which compared the activity of 2 078 coding and lncRNAs genes promoter, shows that coding gene promoters have higher activity than lncRNA promoters [START_REF] Mattioli | High-throughput functional analysis of lncRNA core promoters elucidates rules governing tissue specificity[END_REF]. The low stability of lncRNA transcript could also participate in their poor abundance. Indeed, a study using metabolic labeling (4-thiouracil pulse chase) shows that lncRNAs are 10-fold less stable than coding RNAs (Mukherjee et al., 2017a). Conversely, two studies using ActD to stop transcription shows that their lifespan is similar to coding RNAs [START_REF] Clark | Genome-wide analysis of long noncoding RNA stability[END_REF][START_REF] Melé | Chromatin environment, transcriptional regulation, and splicing distinguish lincRNAs and mRNAs[END_REF] hinting that more investigation is needed to understand why lncRNA are less abundant than coding mRNAs (Figure 5).

Taking together, lncRNAs constitute an important part of the transcriptome sharing commonality with coding RNAs such as processing and splicing but also unique particularities such as lower abundance, higher nuclear localization, shorter length and higher expression specificity.

Are lncRNAs really non-coding?

As lncRNA molecules are more than 200nt in length, it is likely that they can contain an ORF that could be recognized and decoded by ribosomal units [START_REF] Li | Coding or noncoding, the converging concepts of RNAs[END_REF], even though classification generally states that lncRNA have no discernable coding potential [START_REF] Jin | PLncDB: Plant long non-coding RNA database[END_REF][START_REF] Jégu | A SWI/SNF chromatin remodelling protein controls cytokinin production through the regulation of chromatin architecture[END_REF]. More importantly, a long transcript is considered as lncRNA if it is biologically functional in its RNA state, even if it produces some peptides (Ariel et al., 2015). Conversely, some transcripts can have a function as RNA and as protein, making their classification more difficult [START_REF] Crespi | enod40, a gene expressed during nodule organogenesis, codes for a non-translatable RNA involved in plant growth[END_REF]. For example, the ENOD40 transcript is involved in nucleocytoplasmic trafficking of MtRBP1, but can also generate small peptides participating in root symbiotic nodule organogenesis (Bardou et al., 2011). Similarly, Steroid receptor RNA activator (SRA), regulates steroid receptor-dependent gene expression in its RNA state but can also produce a peptide that modulates the transcriptional activity of SRA1 gene within peptide state [START_REF] Emberley | Identification of new human coding steroid receptor RNA activator isoforms[END_REF][START_REF] Hubé | Steroid receptor RNA activator protein binds to and counteracts SRA RNA-mediated activation of MyoD and muscle differentiation[END_REF]. Interestingly, the status as lncRNA or peptide of SRA gene depends on the isoform which is produced, strengthening the relevance of AS for the production of bifunctional RNA molecules [START_REF] Colley | Steroid Receptor RNA Activator -A nuclear receptor coregulator with multiple partners: Insights and challenges[END_REF]. Furthermore, even if the RNA molecule contains an ORF, it does not ensure its ability to produce a protein. In addition to its sequence, the secondary structure harbored by the RNA can modulate its ability to interact with the ribosomes and yield translation products (Xie and Chen, 2017). Indeed, RNA secondary structure can influence the splicing process leading to alternatively spliced mRNA that will have less affinity with ribosomes or lacked a start/stop codon, avoiding their translation into protein [START_REF] Buratti | Influence of RNA Secondary Structure on the Pre-mRNA Splicing Process[END_REF][START_REF] Foley | RNA structure, binding, and coordination in Arabidopsis[END_REF].

More generally, increasing evidence from bioinformatic analyses and ribosome profiling, shows that some lncRNA associates with ribosomes strengthening their putative coding abilities [START_REF] Kageyama | Coding vs non-coding: Translatability of short ORFs found in putative non-coding transcripts[END_REF][START_REF] Nam | Incredible RNA: Dual functions of coding and noncoding[END_REF][START_REF] Yeasmin | Micropeptides encoded in transcripts previously identified as long noncoding RNAs: A new chapter in transcriptomics and proteomics[END_REF]. Despite the thousands of small ORF discovered in human lncRNAs, very few seem to produce functional peptides such as MLN [START_REF] Anderson | A micropeptide encoded by a putative long noncoding RNA regulates muscle performance[END_REF] and HOXB-AS3 [START_REF] Huang | A Peptide Encoded by a Putative lncRNA HOXB-AS3 Suppresses Colon Cancer Growth[END_REF]. As the proportion of coding lncRNA varies greatly between ribosomal-profiling studies [START_REF] Guttman | Ribosome profiling provides evidence that large noncoding RNAs do not encode proteins[END_REF][START_REF] Ingolia | Ribosome Profiling Reveals Pervasive Translation Outside of Annotated Protein-Coding Genes[END_REF], the Mass spectrometry (MS) emerged as a complementary method to decipher a more precise coding ability of lncRNAs. Nonetheless, MS is more accurate but less sensitive than the ribosomal-profiling studies, leading to a very small proportion of coding-lncRNAs detected through MS approaches only [START_REF] Verheggen | Noncoding after All: Biases in Proteomics Data Do Not Explain Observed Absence of lncRNA Translation Products[END_REF].

Thus, the implication of peptidomic approaches together with enrichment protocols may overcome the weak sensitivity of MS [START_REF] Mustafa | Targeted proteomics for biomarker discovery and validation of hepatocellular carcinoma in hepatitis C infected patients[END_REF].

Taken together, the frontier between coding and non-coding is subtle for the lncRNAs molecule and also dual lncRNA/peptide genes exist which can play different roles. )) and less TFs binding sites, decreasing the Pol II pausing and transcripts abundance, respectively. Also, the chromatin from lncRNA promoters is more frequently condensed. It is likely that lncRNA half-life is shorter than that of coding mRNAs. Finally, lncRNA are less frequently spliced than coding mRNAs.

Conservation of lncRNAs

The increasing availability of whole genome sequences from different species allows studying the evolutionary relationships between genes [START_REF] Koonin | Orthologs, paralogs, and evolutionary genomics[END_REF]. Interestingly, some genomic regions are highly conserved between all living organisms, such as the ribosomal related genes and the homeobox-genes. On the other hand, many genes, mainly non-coding, or intergenic regions are lowly conserved between species and even specific to some clades or individuals [START_REF] Bürglin | Homeodomain proteins: an update[END_REF]. Thanks to the human genome project, it has been observed that structural genetic variants are less frequent in Coding DNA Sequences (CDSs) and introns as compared to other DNA regions such as non-coding genes and intergenic regions [START_REF] Sudmant | An integrated map of structural variation in 2,504 human genomes[END_REF]. Similarly, in Arabidopsis thaliana, the 1001 genome project shows that coding regions accumulate less Single Nucleotide Polymorphism (SNP) than non-coding or intergenic regions (1001 genome consortium, [START_REF] Alonso-Blanco | 1,135 Genomes Reveal the Global Pattern of Polymorphism in Arabidopsis thaliana[END_REF]. Likewise, in 66 rice accessions, less than 5%

of SNPs and Insertion/deletions (indels) were located in coding regions while representing around 10% of the genome (Zhao et al., 2018b;[START_REF] Sun | Global Quantitative Mapping of Enhancers in Rice by STARR-seq[END_REF]. Nevertheless, the promoters of noncoding and coding genes are similarly conserved [START_REF] Ponjavic | Functionality or transcriptional noise? Evidence for selection within long noncoding RNAs[END_REF][START_REF] Melé | Chromatin environment, transcriptional regulation, and splicing distinguish lincRNAs and mRNAs[END_REF]. It has been estimated that only less than 2% of the Arabidopsis thaliana lncRNAs are conserved at the sequence level across the plant kingdom (Liu et al., 2012a). Concomitantly, it has been observed that highly expressed genes tend to be more conserved than lowly or specifically expressed genes, such as lncRNAs (Contreras-Moreira et al., 2017) (Figure 5). Logically, the 18% of sequence-level conserved Brassicaceae lncRNA present a higher level of expression as compared to the nonconserved lncRNA [START_REF] Nelson | A genomic analysis of factors driving lincRNA diversification: Lessons from plants. G3 Genes, Genomes[END_REF]. Nevertheless, genome analysis of five monocotyledon and five dicotyledon species shows that lncRNA conservation remains high within the same species but strongly decreases at the inter-species level (Deng et al., 2018a). Even though, in all organisms, lncRNAs present similar characteristics, such as a lower size, less exon and number of isoforms per gene, they are less conserved than coding genes at the sequence level. Thus, it is tempting to assume that the non-conserved genome reflects the specific adaptation of an accession or an individual to its environment (Vernikos et al., 2015). It is noteworthy that the sequence of the lncRNA gene may not be conserved although their relative position (synteny) within the genome may be (Mohammadin et al., 2015;[START_REF] Jha | Long non-coding RNAs: Emerging players regulating plant abiotic stress response and adaptation[END_REF], suggesting that the interaction of the lncRNA with its syntenic neighboring coding genes may have biological relevance.

Regulation of gene transcription

Gene activity is a dynamic process non-uniformly distributed along the genome and directly dependent on the proteins interacting with the DNA. More precisely, ~147bp of DNA molecule is wrapped along histone octamer, which together form the so-called chromatin critically important for the gene activity [START_REF] Yamaguchi | Removal of H3K27me3 by JMJ Proteins Controls Plant Development and Environmental Responses in Arabidopsis[END_REF]. Broadly, the genome can be divided in two different states: heterochromatin, which contains un-transcribed genes and euchromatin, which generally contains actively transcribed genes [START_REF] Rosa | Insights into chromatin structure and dynamics in plants[END_REF].

The heterochromatin is characterized by a condensed packaged structure due to nucleosome colocalization, limiting gene transcription [START_REF] Kireeva | Nucleosome remodeling induced by RNA polymerase II: Loss of the H2A/H2B dimer during transcription[END_REF]. It is generally rich in repeated sequences and TE and mainly localized in the centromeric and telomeric regions of the chromosome [START_REF] Lippman | Role of transposable elements in heterochromatin and epigenetic control[END_REF]. Whereas, euchromatin is located in chromosomal arms and mostly contains actively transcribed genes [START_REF] De Nooijer | Non-specific interactions are sufficient to explain the position of heterochromatic chromocenters and nucleoli in interphase nuclei[END_REF]. Euchromatin is characterized by an important plasticity of chromatin condensation, which is controlled by various mechanisms, or "dimensions", including the promoter and cis regulatory element sequences, the epigenetic modifications and the chromatin conformation. Interestingly, part of the heterochromatin, named as facultative, can become euchromatin through particular stimuli, triggering a chromatin decondensation and subsequent gene activity [START_REF] Grewal | Heterochromatin revisited[END_REF]. In this second part, I will describe the dimension affecting gene activity at the transcriptional level including DNA cis-regulatory sequences (first dimension), epigenetic modifications (second dimension) and genome conformation (third dimension).

First dimension: Cis-regulatory motifs

In eukaryotes, DNA binding proteins, named Transcription Factors (TFs), bind the 5' outside of the genes called promoters to facilitate the recruitment of the RNA pol complex and the subsequent gene transcription [START_REF] Allen | The Mediator complex: A central integrator of transcription[END_REF]. More precisely, they recruit cotranscriptional factors that together change the chromatin condensation facilitating gene transcription and participate in the formation of the Pre-Initiation Complex (PIC), critical for the initiation of gene transcription [START_REF] Venters | How eukaryotic genes are transcribed regulation of eukaroytic gene transcription B. J. Venters and B. F. Pugh[END_REF] (Figure 6). For example, pioneer factors are a particular class of TFs able to bind to condensate chromatin and loosen it, facilitating the interaction of the DNA with other regulatory TFs [START_REF] Magnani | Pioneer factors: Directing transcriptional regulators within the chromatin environment[END_REF].

TFs bind to the DNA through the recognition of specific DNA sequences named cisregulatory elements (CREs) [START_REF] Wilkinson | Mammalian Transcription Factor Networks: Recent Advances in Interrogating Biological Complexity[END_REF]. As compared to prokaryotic TFs that recognize long DNA sequences, eukaryotic TFs recognize short DNA motifs of ~10nt [START_REF] Stewart | Why transcription factor binding sites are ten nucleotides long[END_REF]. Thus, genes which participate in the same cellular processes will often share these short similar sequences to be bound together by the same TF [START_REF] Haberle | Eukaryotic core promoters and the functional basis of transcription initiation[END_REF].

The specificity of CREs recognition is provided by the DNA binding domain that greatly varies between TF families and can be encoded as a homeodomain, helix-turn-helix and high-mobility group box or other motif, increasing the diversity of recognizable CREs [START_REF] Mitsis | Transcription factors and evolution: An integral part of gene expression (Review)[END_REF]. As gene transcription constitutes the most important and basal mechanism for cell life, it is noteworthy that TFs and CREs evolved in a tightly manner. Notably, bioinformatic evolutionary analysis shows a correlated evolution between TFs and CREs within eukaryotes [START_REF] Yang | Correlated evolution of transcription factors and their binding sites[END_REF]. However, SNPs or indels occurring in these CREs could drastically change the responsiveness of a gene to a particular stimulus, by modifying the ability of the TFs to interact with the promoter, likely participating in the plant adaptation to its local environmental stimuli.

Even though TFs are well-known to positively influence gene transcription, through the recruitment of coactivator protein increasing the transcriptional rate or the chromatin decondensation [START_REF] Cramer | Organization and regulation of gene transcription[END_REF]. TFs can also inhibit transcription when recruiting cotranscriptional repressors which increase the condensation of the chromatin, disturbing the ability of RNA Pol to progress along the gene [START_REF] Cramer | Organization and regulation of gene transcription[END_REF]. For example, the ERF-associated amphiphilic repression (EAR) is a plant repressive TFs [START_REF] Ohta | Repression domains of class II ERF transcriptional repressors share an essential motif for active repression[END_REF], which when associated with transcriptional activators, convert them into repressors [START_REF] Hiratsu | Dominant repression of target genes by chimeric repressors that include the EAR motif, a repression domain, in Arabidopsis[END_REF][START_REF] Hiratsu | Identification of the minimal repression domain of SUPERMAN shows that the DLELRL hexapeptide is both necessary and sufficient for repression of transcription in Arabidopsis[END_REF]. Interestingly, this motif is not specific to a suburb of TFs, as it exists in AUX-IAA, BZR1, class II ERF and C 2 H 2 zinc-finger containing proteins, such as SUPERMAN [START_REF] Ohta | Repression domains of class II ERF transcriptional repressors share an essential motif for active repression[END_REF][START_REF] Hiratsu | The SUPERMAN protein is an active repressor whose carboxy-terminal repression domain is required for the development of normal flowers[END_REF][START_REF] Tiwari | Aux/IAA Proteins Contain a Potent Transcriptional Repression Domain[END_REF][START_REF] He | BZR1 is a transcriptional repressor with dual roles in brassinosteroid homeostasis and growth responses[END_REF]. More generally, in Arabidopsis it has been estimated that 10% of all the TFs act as transcriptional repressors [START_REF] Ikeda | A novel group of transcriptional repressors in arabidopsis[END_REF]. As gene proximal promoters estimated at around ~500bp in length [START_REF] Lenhard | Metazoan promoters: Emerging characteristics and insights into transcriptional regulation[END_REF], it is likely that multiple TFs cooperate or compete to modulate the transcriptional status of the downstream gene [START_REF] Morgunova | Structural perspective of cooperative transcription factor binding[END_REF]. For example, a large proportion of eukaryotic TFs are functional in a dimer state, interacting either with an identical TF or with another TF from a closely related family [START_REF] Morgunova | Structural perspective of cooperative transcription factor binding[END_REF]. Interestingly, TF dimerization is significantly enriched in complex eukaryotic organisms potentially providing an additional regulatory layer on the control of gene expression in these organisms [START_REF] Amoutzias | Choose your partners: dimerization in eukaryotic transcription factors[END_REF]. Strikingly, TFs cooperation can also involve indirect interactions, where multiple TFs collaboratively compete with the same histone to access the DNA [START_REF] Long | Ever-Changing Landscapes: Transcriptional Enhancers in Development and Evolution[END_REF]. In addition to associates within complexes, some TFs are subjected to post-translational modifications affecting their localization, stability or ability to interact or dimerize [START_REF] Everett | Regulating the Regulators: Modulators of Transcription Factor Activity. Methods Mo[END_REF]. For example, phosphorylation of the TF CCAAT/enhancer-binding protein beta (CEBPB) during mammal pregnancy is necessary for its activation [START_REF] Lynch | Regulatory evolution through divergence of a phosphoswitch in the transcription factor CEBPB[END_REF]. Similarly, plethora of plant bZIP TFs are subjected to phosphorylation upon activation, such as the ABI5-ABF-AREB implicated in the ABA signaling pathway [START_REF] Schütze | Post-translational regulation of plant bZIP factors[END_REF]. Also, the phosphorylation of the rice bZIP TF TRAB1 at the Ser102 residue is necessary for its activity, ensuring a proper maturation and dormancy of plant embryos [START_REF] Hobo | A bZIP factor, TRAB1, interacts with VP1 and mediates abscisic acid-induced transcription[END_REF][START_REF] Kagaya | Abscisic acid-induced transcription is mediated by phosphorylation of an abscisic acid response element binding factor, TRAB1[END_REF].

With the development of highly sensitive TF profiling methods, such as Chip-seq, it is now well established that gene expression is not only controlled by interaction between TFs and promoter but also through the binding of TFs to cis-regulatory sequences located far away from the gene they regulate, that were named enhancer [START_REF] Arnone | The hardwiring of development: Organization and function of genomic regulatory systems[END_REF][START_REF] Johnson | Protein-DNA Interactions Advance Access[END_REF]. Enhancers are intergenic regulatory sequences bound by TFs that interact with other promoters to strengthen their activity. Enhancer-mediated regulation of gene expression is detailed further in section 2.3.3.2.

Collectively, the physical interaction between TFs and their corresponding genomic cisregulatory sequences, promoter and enhancers, constitute a highly complex layer of regulation of gene activity within the cells.

Second dimension: Epigenetic marks

The DNA molecule is very long and could fit inside the nucleus only thanks to an organized packing that is the result of a tight organization of a complex comprising the DNA molecule, RNA and proteins, altogether forming the chromatin. This DNA packing is controlled by chromatin modifications that alters locally its level of condensation without altering the sequence of the DNA [START_REF] Prakash | Evidence for the implication of the histone code in building the genome structure[END_REF]. Namely, epigenetic study the changes of gene function that are mitotically or meiotically heritable without involving a shift in DNA sequence [START_REF] Dupont | Epigenetics: Definition, mechanisms and clinical perspective[END_REF]. Among these epigenetic modifications, DNA methylation is often associated with gene silencing (Matzke and Mosher, 2014), whereas histone modifications can be associated with active or silenced gene activity [START_REF] Ueda | Histone modifications form epigenetic regulatory networks to regulate abiotic stress response1[OPEN][END_REF].

DNA methylation

DNA methylation occurs on cytosine bases and corresponds to the addition of a methyl group to form 5-methylcytosine. In plants, DNA methylation can occur on different context: CG, CHG and CHH (where H represents A, T or C) [START_REF] Lister | Highly Integrated Single-Base Resolution Maps of the Epigenome in Arabidopsis[END_REF], even though the majority of DNA methylation is observed in a non-CG context [START_REF] Kenchanmane Raju | Establishment, maintenance, and biological roles of non-CG methylation in plants[END_REF]. In agreement, loss of CG methylation in met1 mutant triggers strong developmental defects [START_REF] Kankel | Arabidopsis MET1 cytosine methyltransferase mutants[END_REF][START_REF] Saze | Maintenance of CpG methylation is essential for epigenetic inheritance during plant gametogenesis[END_REF][START_REF] Reinders | Compromised stability of DNA methylation and transposon immobilization in mosaic Arabidopsis epigenomes[END_REF], whereas disruption of the non-CG methylation does not result in strong physiological phenotype [START_REF] Chan | RNAi, DRD1, and histone methylation actively target developmentally important Non-CG DNA methylation in Arabidopsis[END_REF]. In Arabidopsis, all methylation contexts are significantly enriched within repeated sequences, such as TE. Nevertheless, some genes are methylated through CG or CHH contexts (Zhang et al., 2006). DNA methylation strongly correlates with low transcriptional activity.

Concomitantly, in the majority of eukaryotic cells methylation is highly concentrated in the heterochromatin [START_REF] Lister | Highly Integrated Single-Base Resolution Maps of the Epigenome in Arabidopsis[END_REF]. Strikingly, whereas DNA methylation within gene promoter correlates with a low transcriptional activity, methylation within the gene body may be involved in the transcriptional activation [START_REF] Elhamamsy | DNA methylation dynamics in plants and mammals: overview of regulation and dysregulation[END_REF]. For example, the active X chromosome within female mammals is significantly richer in methylation within the gene body than the inactive X chromosome [START_REF] Hellman | Gene body-specific methylation on the active X chromosome[END_REF]. Nonetheless in plants DNA methylation within the gene body may disturb Pol II elongation inhibiting gene transcription [START_REF] Zilberman | Genome-wide analysis of Arabidopsis thaliana DNA methylation uncovers an interdependence between methylation and transcription[END_REF].

In plants siRNA can direct de-novo methylation through RdDM, described in section 1.1.2.1. Interestingly, phasiRNA, described in 1.1.2.3, can also be recruited into the RdDM pathway, through AGO4 or AGO6, to mediate de-novo methylation (Matzke and Mosher, 2014). Despite that RdDM is often associated with CHH context it can methylate C in all contexts [START_REF] Kenchanmane Raju | Establishment, maintenance, and biological roles of non-CG methylation in plants[END_REF]. Although RdDM predominantly occurs in TE rich regions, mutants impaired in the RdDM pathway doesn't present a strong increase of TEs activity suggesting RdDM is not the only factor silencing TE [START_REF] Wendte | Specifications of Targeting Heterochromatin Modifications in Plants[END_REF].

Nevertheless, under heat stress in maize the RdDM prevents the TE activity, resulting in heritable transposition [START_REF] Guo | RNA-directed DNA methylation prevents rapid and heritable reversal of transposon silencing under heat stress in Zea mays[END_REF].

RdDM and more generally, DNA methylation are implicated in a wide-range of biological processes in plants, from development to stress signaling pathway. For example, RdDM demonstrated its importance for proper plant reproduction in the model plant Arabidopsis, tomato, rice and Brassica rapa [START_REF] Chow | RNA-directed DNA Methylation and sexual reproduction: expanding beyond the seed[END_REF]. Heat stress decreases the level of DNA methylation in multiple plant species, such as Gossypium hirsutum [START_REF] Ma | Disrupted genome methylation in response to high temperature has distinct affects on microspore abortion and anther indehiscence[END_REF], Glycine max L. [START_REF] Hossain | Divergent cytosine DNA methylation patterns in single-cell, soybean root hairs[END_REF], Brassica napus cv. Topas [START_REF] Li | Global DNA methylation variations after short-term heat shock treatment in cultured microspores of Brassica napus cv[END_REF], Oryza sativa [START_REF] Folsom | Rice fertilizationindependent Endosperm1 regulates seed size under heat stress by controlling early endosperm development[END_REF] and Arabidopsis [START_REF] Naydenov | High-temperature effect on genes engaged in DNA methylation and affected by DNA methylation in Arabidopsis[END_REF]. Concomitantly, Arabidopsis

RdDM defective mutants are hypersensitive to heat stress [START_REF] Popova | The RdDM pathway is required for basal heat tolerance in arabidopsis[END_REF]. Similarly, DNA methylation is reduced under cold stress within Arabidopsis, Castanea sativa Mill., Populus tremula, and Cucumis sativus L. [START_REF] Conde | Chilling-responsive DEMETER-LIKE DNA demethylase mediates in poplar bud break[END_REF][START_REF] Lai | The association of changes in DNA methylation with temperature-dependent sex determination in cucumber[END_REF][START_REF] Xie | Variation in ICE1 Methylation Primarily Determines Phenotypic Variation in Freezing Tolerance in Arabidopsis thaliana[END_REF].

Interestingly, chemical mediated inhibition of DNA methylation increased freezing tolerance in Arabidopsis plants [START_REF] Xie | Variation in ICE1 Methylation Primarily Determines Phenotypic Variation in Freezing Tolerance in Arabidopsis thaliana[END_REF]. Finally, various mutants impaired in the RdDM pathway (nrpd2a-1, dcl3-1, ago4-3, clsy1-1 and drd1-6) present a constitutive expression of defense related genes, among them the PATHOGENESIS-RELATED 1 (PR1), important for the systemic acquired resistance, illustrating the importance of RdDM for the plant biotic stress response [START_REF] Mccue | Gene expression and stress response mediated by the epigenetic regulation of a transposable element small RNA[END_REF].

Altogether, DNA methylation constitutes an important additional layer for the control of gene expression within eukaryotic cells. In humans more than 70% of CG-rich regions are methylated [START_REF] Strichman-Almashanu | A Genome-Wide Screen for Normally Methylated Human CpG Islands That Can Identify Novel Imprinted Genes[END_REF] and are directly implicated in many diseases such as cancer development [START_REF] Wang | Metabolic recoding of epigenetics in cancer[END_REF]. Similarly in Arabidopsis, 14% of all the cytosines are methylated [START_REF] Zhang | Genome-wide High-Resolution Mapping and Functional Analysis of DNA Methylation in Arabidopsis[END_REF] and mutant plants impaired in the DNA methylation pathway harbor strong developmental defects (Zhang et al., 2018a).

Chemical modifications of histones

DNA folding around histone proteins is made by two turns of DNA around an octamer of two subunits of each of the core subunits of histones H2A, H2B, H3, and H4 forming the socalled nucleosomes, also corresponding to the unit of the chromatin [START_REF] Tatchell | Reconstitution of chromatin core particles[END_REF]. Post-translational modifications of histones influence their co-localization and the way DNA is wrapped around them [START_REF] Tatchell | Reconstitution of chromatin core particles[END_REF] and directly influence the condensation level of the chromatin. Rationally, histone co-localization positively correlates with high chromatin condensation and low transcriptional activity (Xu et al., 2017a). Several protein types are involved in these histone modifications. Writer enzymes mediate chemical modifications of histones, such as acetylation, methylation, phosphorylation or ubiquitination;

whereas eraser enzymes remove them (Xu et al., 2017a). Reader proteins physically interact with histone epigenetic marks to strengthen their stability (Liu et al., 2010a). Among them, the Polycomb group (PcG) mediates gene repression mainly through trimethylation of the Lysine 27 on Histone 3 (H3K27me3). PcG proteins encompass two complexes: Polycomb Repressive Complex1 (PRC1) and PRC2, containing both writers, readers and erasers proteins, conserved in plants and animals (Figure 7). PRC2 is involved in the spreading and de-novo trimethylation of H3K27 [START_REF] Kim | Current understanding of flowering pathways in plants: focusing on the vernalization pathway in Arabidopsis and several vegetable crop plants[END_REF]. Whereas, the PRC1 cannot perform de-novo trimethylation but can facilitate the spreading of H3K27me3 (Veluchamy et al., 2016) and maintain its occupancy during cell replication [START_REF] Derkacheva | Arabidopsis MSI1 connects LHP1 to PRC2 complexes[END_REF]. Additionally, PRC1 is involved in ubiquitination of H2AK119 and H2AK121, also participating in the chromatin condensation [START_REF] Kim | Current understanding of flowering pathways in plants: focusing on the vernalization pathway in Arabidopsis and several vegetable crop plants[END_REF].

Regarding the vast diversity of chromatin modifying protein, other than PcG, affecting the chemical state of histone, I will focus on the PcG and the H3K27me3 epigenetic mark, a complex widely studied in my thesis. The histone methyltransferase activity of PRC2 mediates H3K27me3 deposition within thousands of genomic loci encompassing many developmental and stress-related genes [START_REF] Turck | Arabidopsis TFL2/LHP1 specifically associates with genes marked by trimethylation of histone H3 lysine 27[END_REF][START_REF] Zhang | Whole-genome analysis of histone H3 lysine 27 trimethylation in Arabidopsis[END_REF][START_REF] Lafos | Dynamic regulation of H3K27 trimethylation during arabidopsis differentiation[END_REF]. Specifically, the FIE subunit binds to the H3 tail [START_REF] Kim | Polycomb-mediated gene silencing in Arabidopsis thaliana[END_REF], and the MSI1-5 subunits directly binds to the nucleosome, both facilitating the PRC2-chromatin interaction. Finally, the PRC2-core subunits FIS, EMF2, VRN2, CLF, SWN and MEA, with the help of additional cofactors, mediates trimethylation of H3K27 (Figure 7). Interestingly, the trimethylation of H3K27 solely is not sufficient to maintain the chromatin in a condensed structure. Indeed, H3K27me3 serves as a signal to recruit the PRC1, which prevent the recruitment of remodeling factors, such as SWI/SNF [START_REF] Shao | Stabilization of chromatin structure by PRC1, a polycomb complex[END_REF][START_REF] Francis | Francis and Kingston[END_REF], through ubiquitination of H2A [START_REF] Cao | Role of histone H3 lysine 27 methylation in polycomb-group silencing[END_REF][START_REF] Eskeland | Ring1B Compacts Chromatin Structure and Represses Gene Expression Independent of Histone Ubiquitination[END_REF][START_REF] Wani | Chromatin topology is coupled to Polycomb group protein subnuclear organization[END_REF][START_REF] Yin | H2AK121ub in Arabidopsis associates with a less accessible chromatin state at transcriptional regulation hotspots[END_REF], maintaining the chromatin in a silenced state [START_REF] Cao | Role of histone H3 lysine 27 methylation in polycomb-group silencing[END_REF]. In agreement, co-localization of PRC2 and PRC1 in many H3K27me3-rich sites is observed from animals to plants [START_REF] Tolhuis | Genome-wide profiling of PRC1 and PRC2 Polycomb chromatin binding in Drosophila melanogaster[END_REF][START_REF] Turck | Arabidopsis TFL2/LHP1 specifically associates with genes marked by trimethylation of histone H3 lysine 27[END_REF][START_REF] Ku | Genomewide analysis of PRC1 and PRC2 occupancy identifies two classes of bivalent domains[END_REF]Kim et al., 2012;[START_REF] Zhou | H2A monoubiquitination in Arabidopsis thaliana is generally independent of LHP1 and PRC2 activity[END_REF][START_REF] Yin | H2AK121ub in Arabidopsis associates with a less accessible chromatin state at transcriptional regulation hotspots[END_REF]. Nevertheless, some H2AUb are detected in non-H3K27me3 loci, suggesting PRC1-H2Aub may act independently of H3K27me3 deposition [START_REF] Kim | EMF1 and PRC2 cooperate to repress key regulators of arabidopsis development[END_REF]. Adversely, the non-PcG proteins eukaryotic-conserved Jumonji C (JmjC) [START_REF] Crevillén | Histone Demethylases as Counterbalance to H3K27me3 Silencing in Plants[END_REF], removed methylation on H3K27 through its histone demethylase activity [START_REF] Klose | JmjC-domain-containing proteins and histone demethylation[END_REF][START_REF] Black | Histone Lysine Methylation Dynamics: Establishment, Regulation, and Biological Impact[END_REF], thus participating in the H3K27me3 equilibrium [START_REF] Shen | Dynamics of h3k27me3 modification on plant adaptation to environmental cues[END_REF].

Arabidopsis mutants affected in PRC2 [START_REF] Ueda | Histone modifications form epigenetic regulatory networks to regulate abiotic stress response1[OPEN][END_REF]. In agreement, PRC2 mutants exhibit lipids accumulation in root tips, characteristic of an abnormal seed to seedling transition [START_REF] Suzuki | Repression of the Leafy Cotyledon 1/B3 regulatory network in plant embryo development by VP1/Abscisic Acid Insensitive 3-Like B3 genes[END_REF]. Consistently, PRCs mediates the transition from seed to seedling through the targeting of genes positively regulated by ABA signaling and negatively regulated by GA, two hormones implicated in seed development and germination [START_REF] Bouyer | Polycomb repressive complex 2 controls the embryo-to-seedling phase transition[END_REF]Kim et al., 2012), respectively [START_REF] Finkelstein | Molecular aspects of seed dormancy[END_REF]. Later in the plant life, vegetative to flowering transition is also tightly regulated by PRC2. Indeed, key regulator of floral transition such as:

floral MADS box genes AGAMOUS (AG), APETALA1 (AP1), AP3, SEPATALLA1 (SEP1), PETALLOSS (PTL), and FRUITFUL/AGAMOUS-LIKE8 (FUL/AGL8) are bound by PRC2 [START_REF] Ueda | Histone modifications form epigenetic regulatory networks to regulate abiotic stress response1[OPEN][END_REF].

These floral-related genes have been found highly up-regulated in PRC1 and PRC2 mutants, in agreement with the rapid transition from vegetative to flowering in these mutants [START_REF] Pu | PcG and trxG in plants -friends or foes[END_REF]. In addition, the flowering genes FLC and LFY are also targeted by the PRCs [START_REF] Kim | EMF1 and PRC2 cooperate to repress key regulators of arabidopsis development[END_REF]. Indeed, when temperature decreases, the FLC locus is subjected to a PRCsmediated dynamic epigenetic switch from H3K4me3 to H3K27me3 and H3K9me3 which decreases the transcription of FLC. Moreover, major genes involved in cell differentiation, including Shoot Meristemless (STM), Wuschel (WUS), Knotted 1-Like Homeobox (KNATs), Cup-Shaped Cotyledon 2 (CUC2), Wuschel Re-lated Homeobox 1 (WOX1), Plethora 1 (PLT1), and PLT2, are targeted by PRCs and could explain the abnormal leaf and flower shapes [START_REF] Kim | EMF1 and PRC2 cooperate to repress key regulators of arabidopsis development[END_REF][START_REF] Deng | Arabidopsis Polycomb Repressive Complex 2 binding sites contain putative GAGA factor binding motifs within coding regions of genes[END_REF] observed in PRCs mutants.

For the plant sessile organism, the epigenetic-mediated regulation of gene expression is a very convenient mechanism to quickly adapt their transcriptome to the current environmental condition. For example, the MSI1 PRC2 subunits fine-tune ABA signaling, modulating the plant sensitivity to drought and salt stresses [START_REF] Alexandre | Arabidopsis MSI1 is required for negative regulation of the response to drought stress[END_REF][START_REF] Mehdi | The WD40 domain protein MSI1 functions in a histone deacetylase complex to fine-tune abscisic acid signaling[END_REF]. Concomitantly, the level of expression of the barley CLF homolog HvE(Z) (Enhancer of zeste) significantly increased in response to ABA application [START_REF] Kapazoglou | Epigenetic chromatin modifiers in barley: IV. The study of barley Polycomb group (PcG) genes during seed development and in response to external ABA[END_REF]. In response to drought stress, the PRC1 subunits BMI1a/DRIP2 and BMI1b/DRIP1, targets the TF DREB2A to proteasome-mediated degradation, negatively influencing the plant drought-stress response [START_REF] Qin | Arabidopsis DREB2A-interacting proteins function as Ring E3 ligases and negatively regulate plant drought stressresponsive gene expression[END_REF]. Also, loss of CLF function significantly reduced the plant tolerance to drought stress [START_REF] Liu | H3K27me3 and H3K4me3 chromatin environment at super-induced dehydration stress memory genes of arabidopsis thaliana[END_REF]. Interestingly, CLF also interacts with the plant-specific coiled-coil protein BLISTER (BLI) promoting the plant cold stress resistance [START_REF] Schatlowski | The CURLY LEAF interacting protein BLISTER controls expression of polycomb-group target genes and cellular differentiation of Arabidopsis thaliana[END_REF][START_REF] Purdy | A nuclearlocalized protein, KOLD SENSITIV-1, affects the expression of cold-responsive genes during prolonged chilling in Arabidopsis[END_REF]. Following, EMF1 and EMF2 represses the cold stress-induced gene COR15A [START_REF] Kim | Epigenetic regulation of gene programs by EMF1 and EMF2 in Arabidopsis[END_REF]. More precisely, the decrease in temperature reduces H3K27me3 occupancy at COR15A and GALACTINOL SYNTHASE3 (GOLS3) genes, logically increasing their transcriptional activity [START_REF] Kwon | Histone occupancy-dependent andindependent removal of H3K27 trimethylation at cold-responsive genes in Arabidopsis[END_REF]. Interestingly, the return to normal temperature does not recover the initial level of H3K27me3 even though COR15A and GOLS3 gene expression decreases [START_REF] Kwon | Histone occupancy-dependent andindependent removal of H3K27 trimethylation at cold-responsive genes in Arabidopsis[END_REF].

Together, PcG proteins constitute key regulators of gene expression, through modulation of H3K27me3 and H2AUb occupancy, influencing the plant stress signaling pathways and critical developmental processes such as seed development and flowering [START_REF] Kleinmanns | Polycomb and Trithorax group protein-mediated control of stress responses in plants[END_REF].

Third dimension: chromatin conformation

The DNA packing within the nucleus is not randomly organized [START_REF] Tiang | Chromosome organization and Dynamics during interphase, mitosis, and meiosis in plants[END_REF]. Even more, spatial organization of the chromatin participates in the regulation of gene expression (Diesinger et al., 2010). Microscopic studies first addressed chromosome architecture within the nucleus: electron microscopy was used to assess the basic profile of nuclear architecture, whereas fluorescence microscopy shed light on the nuclear compartmentalization and colocalization of chromosomes [START_REF] Solovei | Spatial preservation of nuclear chromatin architecture during three-dimensional fluorescence in situ hybridization (3D-FISH)[END_REF][START_REF] Dehghani | Organization of chromatin in the interphase mammalian cell[END_REF][START_REF] Belton | Hi-C: A comprehensive technique to capture the conformation of genomes[END_REF].

Later, 3C-related techniques, based on digestion and ligation of cross-linked DNA, showed the importance of chromosome architecture for the regulation of gene activity [START_REF] Dekker | Capturing chromosome conformation[END_REF][START_REF] Belton | Hi-C: A comprehensive technique to capture the conformation of genomes[END_REF].

General configuration of genome within the nucleus

Eukaryotic genomes, from animals to plants, occupy well defined territories within the nucleus [START_REF] Schubert | Interphase chromatin organisation in Arabidopsis nuclei: Constraints versus randomness[END_REF]. As the general genome characteristics, such as genome length, TE abundance and epigenetic marks, the genome spatial organization also vary greatly between organisms and can be classified into three major conformations: Rabl, Rosette, and Bouquet structure (Figure 8). The Rabl genome configuration is found in many large plant genome species and is characterized by a spatial separation of telomeres and centromeres. Interestingly, the Bouquet genome conformation is also found in large plant genomes, such as maize. In this configuration, the telomeres interact with each other close to the nuclear periphery whereas the centromeres and chromosome arms are located within the nucleoplasm region. Finally, small plant genomes, such as Arabidopsis, showed a Rosette-like configuration, in which the heterochromatin (centromeres and telomeres) tends to interact with itself whereas the euchromatin (chromosome arms) does not.

For example, plants with large genomes, such as wheat [START_REF] Abranches | Transcription sites are not correlated with chromosome territories in wheat nuclei[END_REF] and barley [START_REF] Noguchi | Chromatin arrangements in intact interphase nuclei examined by laser confocal microscopy[END_REF], preferentially harbored the Rabl configuration (Figure 8; Rabl). In this configuration the chromosomes bend in their centromere, mainly during interphase and meiotic prophase, leading to spatial separation of centromere and telomere [START_REF] Rosa | Insights into chromatin structure and dynamics in plants[END_REF]. Consequently, telomeres clustered together in the nuclear periphery.

This configuration is also found in the animal kingdom such as mice [START_REF] Van Driel | Nuclear architecture and genome functioning in plants and animals: What can we learn from both?[END_REF] and yeast [START_REF] Mizuguchi | Chromosome domain architecture and dynamic organization of the fission yeast genome[END_REF]. Maize genome presents a Bouquet conformation where telomeres are close to each other's and localized in the nuclear periphery in opposition to chromosome arms and centromeres located in the nucleoplasm region [START_REF] Bass | Evidence for the coincident initiation of homolog pairing and synapsis during the telomere-clustering (bouquet) stage of meiotic prophase[END_REF][START_REF] Cowan | The polar arrangement of telomeres in interphase and meiosis. Rabl organization and the bouquet[END_REF] (Figure 8; Telomere bouquet). The strong similarities between the Rabl and Bouquet configurations make this classification controversial among the scientific community (Rodriguez-Granados et al., 2016). Finally, plants with small genomes, such as Arabidopsis, present a Rosette-like configuration (Figure 8; Rosette-like). In this state, heterochromatin is clustered together and forms chromocenters from which euchromatin evades [START_REF] Fransz | Interphase chromosomes in Arabidopsis are organized as well defined chromocenters from which euchromatin loops emanate[END_REF]. The important packing of the genome within the nucleus ultimately leads to intra and inter chromosomal interactions. In Arabidopsis, intra-chromosomal interactions have been detected in centromeric and pericentromeric segments, in agreement with the Rosette-like conformation, but also to a lesser extent within the chromosomal arms.

Whereas, inter-chromosomal interactions are mainly detected within the pericentromeric and telomeric regions [START_REF] Moissiard | MORC family ATPases required for heterochromatin condensation and gene silencing[END_REF][START_REF] Grob | Characterization of chromosomal architecture in Arabidopsis by chromosome conformation capture[END_REF][START_REF] Feng | Genome-wide Hi-C Analyses in Wild-Type and Mutants Reveal High-Resolution Chromatin Interactions in Arabidopsis[END_REF][START_REF] Grob | Hi-C Analysis in Arabidopsis Identifies the KNOT, a Structure with Similarities to the flamenco Locus of Drosophila[END_REF]Liu et al., 2016). Interestingly regions close to the centromere are less subjected to interchromosomal interactions as compared to segments far away from it, i.e. telomere. This indicates that chromosome arms are potentially more flexible, in terms of spatial localization, than the anchor-like centromere [START_REF] Grob | Characterization of chromosomal architecture in Arabidopsis by chromosome conformation capture[END_REF].

Long range interactions

In animals and large plants genome long-range interactions relate to the AB compartments, where A corresponds to active transcription and B corresponds to gene silencing [START_REF] Lieberman-Aiden | Comprehensive mapping of long-range interactions reveals folding principles of the human genome[END_REF][START_REF] Dong | 3D Chromatin Architecture of Large Plant Genomes Determined by Local A/B Compartments[END_REF]. These regions are dynamically regulated under stress or tissue specificity in agreement with gene expression changes [START_REF] Roy | A predictive modeling approach for cell line-specific long-range regulatory interactions[END_REF].

Curiously, such genome compartmentalization is absent from the small genome of Arabidopsis. Instead, within chromosome arms, some heterochromatic regions interact with each other forming heterochromatic islands (IHIs). These regions are marked with the silenced H3K9me2 epigenetic mark and enriched in TE. IHIs includes few transcribed genes, most of them implicated in transposon silencing [START_REF] Grob | Hi-C Analysis in Arabidopsis Identifies the KNOT, a Structure with Similarities to the flamenco Locus of Drosophila[END_REF]. In harmony, IHIs also interact with telomeric regions. These interactions are altered in RdDM-related mutants such as morc6, met1, and ddm1 [START_REF] Feng | Genome-wide Hi-C Analyses in Wild-Type and Mutants Reveal High-Resolution Chromatin Interactions in Arabidopsis[END_REF][START_REF] Liu | Chromatin in 3D: Progress and prospects for plants[END_REF], strengthening the importance of DNA methylation in IHI-telomeres interactions and more generally the maintenance of a proper nuclear architecture. Also, the LHP1 protein from the PRC1, in addition to its role as recruiter of PRC2 and spreader of H3K27me3 marks, is involved in shaping the conformation of the chromatin. Indeed, genome-wide conformation analysis of the lhp1 mutant shed light on important chromatin conformation changes both at the long and local-range scale. This modification of chromatin conformation together with the impairment of H3K27me3 deposition, can explain changes of expression of multiple genes in the lhp1 mutant (Veluchamy et al., 2016).

Short-range interaction

The development of high-resolution chromatin conformation methods in both animals and plants, allows the identification of multiple chromatin loops at the local or gene scale.

These interactions can regulate gene expression in a wide variety of way involving looping within a gene, two genes or between a gene and a cis-regulatory intergenic region [START_REF] Miele | Long-range chromosomal interactions and gene regulation[END_REF]Cavalli and Misteli, 2013;[START_REF] Gibcus | The Hierarchy of the 3D Genome[END_REF]. Interestingly, in animals and large plant genomes, these short-range interactions are not randomly positioned within the genomes. Indeed, some genomic regions from the same chromosome present high interaction frequency. These regions are named Topologically Associated Domains (TADs) in which the DNA sequence localized within the TAD more frequently interacts with each other than with the genomic region outside of the TAD. Thus, it has been proposed that these topological units restrict the chromatin looping interactions between promoter and enhancers with genes (Rodriguez-Granados et al., 2016). Nevertheless, the small Arabidopsis genome does not contain such well-defined independently regulated regions [START_REF] Gibcus | The Hierarchy of the 3D Genome[END_REF]Liu et al., 2016).

Looping within a gene locus

Gene looping is when DNA-DNA interaction occurs between the Transcription Start Site (TSS) and the Transcription End Site (TES) of a gene. In yeast and mammal cells these types of interaction have been linked to Pol II recycling, in which the Pol II processing is renewed several times to extend the production of RNA from the concerning gene (Figure 9; 5'-3' gene looping). For example, the transcription initiation factor TFIIB participates in the collocation of promoter and TES at actively transcribed genes enabling quick and efficient Pol II recycling Sullivan et al., 2004;[START_REF] Singh | A Transcription-Independent Role for TFIIB in Gene Looping[END_REF]. In Arabidopsis, this type of chromatin looping configuration has also been described in IPT3 and IPT7 genes, both involved in cytokinin biosynthesis. Interestingly, the chromatin remodeler BAF60 can disrupt the chromatin loop under certain stimuli decreasing the expression of IPT3 and IPT7 [START_REF] Jégu | A SWI/SNF chromatin remodelling protein controls cytokinin production through the regulation of chromatin architecture[END_REF].

(O'
Likewise, the epigenetically regulated FLC gene also presents an additional layer of regulation involving changes of chromatin conformation. Strikingly, the FLC TSS-TES interactions negatively correlate with gene expression. Indeed, in this case, the disruption of the gene looping configuration facilitates the epigenetic switch which is occurring during vernalization from inactive to active transcriptional state [START_REF] Crevillén | A gene loop containing the floral repressor FLC is disrupted in the early phase of vernalization[END_REF]. Similarly, a gene looping within the Arabidopsis WUS locus is negatively associated with gene transcription. Excluding the promoter region, this gene loop blocks the recruitment of Pol II to the core gene, limiting gene transcription [START_REF] Guo | Wheat miR9678 affects seed germination by generating phased siRNAs and modulating abscisic acid/gibberellin signaling[END_REF]. Lastly, when actively transcribed the flowering-repressing gene TFL1 harbored a chromatin loop putting in close proximity its promoter with its TSS. In floral meristem the binding of AP1 together with MADS-box proteins (SOC1, AGL24, SVP, and SEP4) to the TFL1 promoter disrupts the chromatin loop, repressing TFL1 expression, allowing flower development [START_REF] Liu | A Conserved Genetic Pathway Determines Inflorescence Architecture in Arabidopsis and Rice[END_REF]

Enhancer loop

The development of technologies assessing the chromatin-condensation at the genome scale allow the discovery of multiple intergenic CREs, named enhancer [START_REF] Kyrchanova | Mechanisms of enhancer-promoter interactions in higher eukaryotes[END_REF]. For example, it has been estimated that the human genome contains 300 000 enhancers (Li et al., 2018a). Similarly, more than thousands of enhancers have been discovered in Arabidopsis [START_REF] Zhu | Genome-wide prediction and validation of intergenic enhancers in arabidopsis using open chromatin signatures[END_REF]Yan et al., 2019), maize [START_REF] Oka | Genome-wide mapping of transcriptional enhancer candidates using DNA and chromatin features in maize[END_REF][START_REF] Ricci | Widespread long-range cisregulatory elements in the maize genome[END_REF] and rice [START_REF] Sun | Global Quantitative Mapping of Enhancers in Rice by STARR-seq[END_REF]. Enhancers are generally 100-1000bp regions rich in TFs binding sites. Their chromatin feature is quite similar to promoter region, where both can bind TFs which correlates with chromatin decondensation [START_REF] Kyrchanova | Mechanisms of enhancer-promoter interactions in higher eukaryotes[END_REF].

Nevertheless, enhancers are not able to recruit the Pol machinery which initiates the gene transcription. Interestingly, enhancers can generate non-poly adenylated transcripts, referred as enhancers RNA, most of them unstable and staying in the nucleus [START_REF] Andersson | Promoter or enhancer, what's the difference? Deconstruction of established distinctions and presentation of a unifying model[END_REF].

Constitutively, or under certain stimuli, enhancer regions physically interact with gene promoters. The physical interaction between enhancer and gene promoter together with their activation (binding of TFs) positively affects gene transcription (Figure 9; Enhancer-promoter looping). Multiple enhancers can be clustered within the same genomic loci forming "superenhancers" which can reach more than thousands of bp [START_REF] Whyte | Master transcription factors and mediator establish super-enhancers at key cell identity genes[END_REF]. Interestingly, subdomain of these super-enhancers can act independently or together to activate a large number of gene promoters [START_REF] Hay | Genetic dissection of the α-globin super-enhancer in vivo[END_REF][START_REF] Shin | Hierarchy within the mammary STAT5-driven Wap super-enhancer[END_REF][START_REF] Huang | Dissecting super-enhancer hierarchy based on chromatin interactions[END_REF].

Many enhancers are closely related to their promoter target. For example, in Drosophila it has been estimated that 20% of the enhancer' promoter targets are located within 100kb

from the enhancer [START_REF] Ghavi-Helm | Enhancer loops appear stable during development and are associated with paused polymerase[END_REF][START_REF] Kvon | Genome-scale functional characterization of Drosophila developmental enhancers in vivo[END_REF]. Similarly, during Arabidopsis floral development, enhancer activity significantly positively correlates with the expression of the closely located genes (Yan et al., 2019). Illustratively, a chromatin loop positively regulates the VERDANDI (VDD) gene involved in female gametophyte identity [START_REF] Matias-Hernandez | VERDANDI is a direct target of the MADS domain ovule identity complex and affects embryo sac differentiation in Arabidopsis[END_REF]. Also, the floral organ identity is controlled by a dimer formed with SEEDSTICK (STK) and SEPALLATA3 (SEP3), which directly mediate the formation of two enhancer-chromatin loops directly implicated in the transcriptional activity of the female gametophyte identity gene VERDANDI (VDD) [START_REF] Mendes | MADS domain transcription factors mediate short-range DNA looping that is essential for target gene expression in Arabidopsis[END_REF]. In maize, many enhancer-promoter interactions occur within long-distance [START_REF] Salvi | Conserved noncoding genomic sequences associated with a flowering-time quantitative trait locus in maize[END_REF][START_REF] Studer | Identification of a functional transposon insertion in the maize domestication gene tb1[END_REF][START_REF] Huang | Dissecting super-enhancer hierarchy based on chromatin interactions[END_REF][START_REF] Ricci | Widespread long-range cisregulatory elements in the maize genome[END_REF].

For example, the two maize b1 epialleles involved in flavonoid pigmentation present a significantly different transcription rate. Notably, the promoter of the most expressed one is interacting with an enhancer located 100kb upstream from B1 gene (Louwers et al., 2009).

Taking together, the 3D nuclear organization of the chromatin constitutes, in addition to cisregulatory sequences and epigenetic modifications, a third complex regulatory layers for the regulation of gene transcriptional activity. Notably, the epigenetic landscape and chromatin condensation is directly implicated in the 3D genome organization from the chromosome to the gene scale.

Regulation of gene expression by lncRNAs

In the last decade, next generation sequencing methods unveiled that the large majority of the eukaryotic genome is transcribed, whereas only a few transcripts are translated into proteins. For example, 95% of the human genome is transcribed and only 2% encodes for protein. Similarly in Arabidopsis 71% of the genome is transcribed, whereas less than 2% of the transcripts are coding (Lucero et al., 2020). This large diversity of non-coding transcripts includes cell housekeeping transcripts (rRNA, tRNA, snRNA and snorRNA), small regulatory transcripts and long regulatory transcripts, all of which are described in section 1.1. NcRNAs recently emerged as important regulators of gene expression acting both at the transcriptional and post-transcriptional level in multiple biological processes. This third part will present more in detail, the miscellaneous mechanisms that lncRNAs molecules use to fine-tune gene expression, and includes a revision about the importance of lncRNAs for plant root growth and development.

Modulation of the transcriptional activity by lncRNAs

Transcription is realized by a remarkable cooperation between the Pol machinery, TFs and other complexes [START_REF] Woychik | The RNA polymerase II machinery: Structure illuminates function[END_REF]. Among them, the MEDIATOR complex mediates the communication between TFs and Pol II, influencing the transcriptional rate [START_REF] Allen | The Mediator complex: A central integrator of transcription[END_REF]. Interestingly, in Arabidopsis, the lncRNA ELF18-INDUCED LONG-NONCODING RNA1 (ELENA1) is able to bind to MED19a, a subunit of the MEDIATOR complex, and modulate its recruitment to specific promoters (Seo et al., 2017) (Figure 10A). Indeed, upon pathogen attack ELENA1 transcript abundance increases and binds to the promoter region of PATHOGENESIS-RELATED1 (PR1) gene facilitating the recruitment of MED19a in this region, enhancing the expression of PR1 (Seo et al., 2017). Likewise, in humans PANDA and DHFR lncRNAs are able to directly interact with TFs modulating their binding to gene promoters, directly influencing their target gene activity. LncRNAs can also act negatively on gene transcription. For example, in Arabidopsis, HIDDEN TREASURE1 (HID1) lncRNA decreases PHYTOCHROME-INTERACTING FACTOR 3 (PIF3) transcriptional activity by binding to its first intron. Downregulation of HID1 expression increases PIF3 gene activity and the subsequent elongation of the hypocotyl. The mechanism by which HID3 decreases gene expression is still unclear, but one explanation is that HID1 forms a ribonucleoprotein complex interacting with the PIF3 genomic region, thus impairing Pol II progression along the PIF3 gene [START_REF] Wang | Arabidopsis noncoding RNA mediates control of photomorphogenesis by red light[END_REF].

When two genes oriented in the opposite direction overlap, they cannot be transcribed at the same moment, otherwise the two Pol II complexes will collapse and their respective progression on the DNA will be blocked and then degraded. In Arabidopsis, the lncRNA SVALKA takes advantage of this phenomenon to regulate its neighboring reverse-oriented gene CBF1 involved in cold tolerance. Prolonged cold exposure triggers a transcriptional readthrough of the SVALKA genomic region leading to Pol II collision between SVALKA and CBF1

transcriptions, decreasing CBF1 transcription rate [START_REF] Kindgren | Transcriptional read-through of the long non-coding RNA SVALKA governs plant cold acclimation[END_REF] (Figure 10B). In agreement, in yeast, it has been shown that after the Pol collision, both Pol II polymerases are removed from the DNA through proteolysis [START_REF] Hobson | RNA Polymerase II Collision Interrupts Convergent Transcription[END_REF]. In yeast, transcriptional read through of CUT60 lncRNA reached the promoter of its neighboring similarly orientated gene ATP16, repressing its expression [START_REF] Du Mee | Efficient termination of nuclear lncrna transcription promotes mitochondrial genome maintenance[END_REF]. Surprisingly, when two genes are in the same orientation, the transcriptional readthrough can lead to transcripts fusion [START_REF] Kaessmann | Origins, evolution, and phenotypic impact of new genes[END_REF], increasing the transcripts diversity or leading to abnormal transcripts. Indeed, it has been estimated that 65% of human genes can form chimeric RNAs through association of the RNA sequence with another gene [START_REF] Birney | Identification and analysis of functional elements in 1% of the human genome by the ENCODE pilot project[END_REF][START_REF] Gingeras | Implications of chimaeric non-co-linear transcripts[END_REF]. Taken together, lncRNAs can change the transcriptional efficiency of a gene through interaction with transcription-related proteins or through transcriptional readthrough both as sense or antisense leading to different transcriptional outputs.

LncRNAs-mediated modification of the epigenetic landscape

LncRNAs are able to physically interact with histone modifier proteins. For example, the human PANDA lncRNA, in addition to interact with the TF NF-YA, can binds to the scaffoldattachment-factor A (SAFA) and the PRC1-2 to modulate cell senescence (Hung et al., 2011).

Similarly, in Arabidopsis, AGAMOUS INTRONIC RNA 4 (AG-incRNA4) binds to the CLF sub-unit from the PRC2 complex (Figure 11A). AGAMOUS (AG) gene encodes a MADS TF involved in flower development [START_REF] Schubert | Silencing by plant Polycomb-group genes requires dispersed trimethylation of histone H3 at lysine 27[END_REF]. In the absence of AG-incRNA4, there is a reduction of H3K27me3 on AG chromatin and an induction of AG, suggesting a role of AG-incRNA4 in the recruitment of PRC2 to this region (Figure 11A). Interestingly, the intronic AG-incRNA4 lncRNA also mediate the PRC2 recruitment to its promoter mediating its own silencing (Wu et al., 2018). Also, FLOWERING LOCUS C (FLC) gene is a MADS TF involved in flowering transition through a complex chromatin state switch involving the action of three lncRNAs. First, COOLAIR, an antisense transcript of the FLC gene, physically interacts with the 5' region of FLC and mediates, with FLOWERING LOCUS D (FLD) the demethylation of H3K4me2 resulting in FLC transcriptional repression [START_REF] Liu | Targeted 3′Processing of AntisenseTranscripts TriggersArabidopsis FLC Chromatin Silencing[END_REF]. Additionally, the COLD ASSISTED INTRONIC NONCODING RNA (COLDAIR) FLC intronic lncRNA interacts with the CLF PRC2 sub-unit to further regulate epigenetic silencing of FLC gene through H3K27me3 deposition (Heo and Sung, 2011). Similarly, the COLDWRAP lncRNA arising from the promoter of FLC is also able to interact with the PRC2 complex and could modulate FLC silencing in different ecotypes (Kim and Sung, 2017) (Figure 12A). In rice, the expression of the LRKs gene cluster involved in grain yield is modulated by an antisense lncRNA, named LRK ANTISENSE INTERGENIC RNA (LAIR), transcribed from a region inside the LRK1 gene. This lncRNA is able to recruit the OsWDR5

(WD REPEAT DOMAIN5), involved in the H4K16 acetylation, of the LRK genes region, increasing their expression. Concomitantly, the overexpression of LAIR lncRNA drastically increased grain yields in rice (Wang et al., 2018) (Figure 11B).

LncRNAs can also modulate the chromatin state by being processed into siRNA to trigger gene silencing through DNA methylation via the RdDM mechanism. In this case, the lncRNA can be transcribed by Pol IV and Pol V, two Pol II homologs which are specific to the plant kingdom. For example, in Arabidopsis, an auxin stimulus triggers a strong upregulation of both PID, a key regulator of auxin transport, and its neighboring lncRNA AUXIN-REGULATED

50

PROMOTER LOOP (APOLO) through the disruption of a chromatin loop encompassing the APOLO region and the PID promoter likely via an active DNA demethylation. In this linear chromatin configuration, both APOLO and PID are expressed. Gradually, Pol II-dependent APOLO transcripts recruit LHP1 protein to reform the chromatin loop while PRC2 complex deposits H3K27me3 to strengthen the compaction of the chromatin loop (see details in 3.3).

Interestingly, APOLO is also transcribed by Pol IV-V and generates siRNA that triggers DNA methylation through the RdDM pathway, maintaining the silencing of the APOLO-PID regions (Figure 12B). As the epigenetic status and chromatin condensation sometimes directly influences the genome topology, it was proposed that lncRNA-mediated modification of the epigenetic landscape can influence the 3D configuration of the genome.

Chromatin architecture changes through lncRNAs activity

In the last decade increasing examples show the ability of lncRNAs to modulate the chromatin conformation (Quinodoz and Guttman, 2014). The addition of RNase-A into the nucleus has proven that euchromatin is maintained in a decondensed configuration through nuclear-located RNAs (Caudron-Herger et al., 2011;Caudron-Herger and Rippe, 2012). In addition, genome-wide conformation analyses in mammalian cells show that TAD boundaries [MC5] [tr6] are regulated through RNA transcription (Barutcu et al., 2019). Indeed, introduction of transcriptional inhibitors within chromatin extracts significantly weakens the TAD frontiers (Barutcu et al., 2019). More specifically, the COLDWRAP lncRNA transcribed within the FLCepigenetically regulated flowering gene mediates the formation of a repressive intragenic chromatin loop which blocks Pol II transcription, inhibiting FLC expression allowing plant to flower (Kim and Sung, 2017) (Figure 12A). Strikingly, it has also been shown that the APOLO lncRNA also acts in trans, through the formation of RNA:DNA duplexes named R-loops. Rloops containing the APOLO RNA are able to remove LHP1 away from the chromatin in order to modulate the local chromatin conformation and influence gene transcription (Ariel et al., 2020) (Figure 12B). Interestingly, a significant proportion of genomic loci recognized by the APOLO RNA contain auxin-responsive genes. Among them, the LEUCINE RICH EXTENSIN2 (LRX2), involved in cell wall remodeling upon lateral root emergence [START_REF] Lewis | A kinetic analysis of the auxin transcriptome reveals cell wall remodeling proteins that modulate lateral root development in Arabidopsis[END_REF], and ROOT HAIR DEFECTIVE 6 (RHD6), a key regulator of root hair initiation [START_REF] Lin | GLABRA2 directly suppresses basic helix-loop-helix transcription factor genes with diverse functions in root hair development[END_REF]Moison et al., 2021). to be transcribed later after longer cold exposure and also recruit the PRC2 complex within the FLC upstream region. Interestingly, the COLDWRAP-mediated PRC2 recruitment within FLC region mediates the formation of an intragenic chromatin looping encompassing the first intron of FLC. Consequently, the Pol II processing is disturbed and the FLC gene is silent. B. In Arabidopsis, an intergenic chromatin loop encompassing the APOLO and PID promoters, is disrupted in response to auxin stimulus, triggering APOLO and PID gene transcription. Interestingly, APOLO directly interacts with the DNA forming R-loop in the PID locus and other auxinresponsive genes. APOLO also interacts with the PRC1 component LHP1 chromatin protein, modulating its binding to the chromatin where APOLO forms R-loops. Consequently, APOLO regulates neighboring and distant genes within the Arabidopsis genome, through fine-tuning LHP1 binding to these loci, thus influencing the auxin transport (Ariel et al., 2014), lateral root (Ariel et al., 2020) and root hair development (Moison et al., 2021). C. The sunflower promoter lncRNA ncRNAW6 fine-tunes WRKY6 gene expression in a tissuespecific manner. In the cotyledons, ncRNAW6 mediates DNA methylation within WRKY6 gene through RdDM, together with the formation of a chromatin loop encompassing the whole WRKY6 region. The gene looping allows Pol II recycling, positively correlating with WRKY6 gene transcription but negatively with ncRNAW6. Gradually, the gene looping is disrupted allowing ncRNAW6 expression and the re-formation of the loop. An alternative chromatin loop is formed within the leaves putting in close proximity the ncRNAW6 and the WRKY6 core gene. Here, the ncRNAW6 is still expressed and the chromatin loop is not disrupted, thus repressing WRK6 gene transcription.

In sunflower, a gene loop putting in close proximity the promoter and TES of HaWRKY6 allows an efficient Pol II recycling, increasing HaWRKY6 expression. Interestingly, this active chromatin loop is only found in cotyledons tissues (Figure 12C). Whereas, in the leaves a chromatin loop encompassing the promoter and the middle of the gene (4 th intron) of

HaWRKY6 block the elongation of Pol II, decreasing HaWRKY6 expression. Strikingly, a RdDMfunctioning lncRNA is transcribed from the HaWRKY6 promoter. In cotyledons, the active chromatin loop avoids its transcription which in term maintains a high level of expression of HaWRK6, while its absence in leaves allows the repression of HaWRK6 through RdDM (Gagliardi et al., 2019) (Figure 12C).

LncRNAs mediating post-transcriptional regulation of gene expression

We saw previously that housekeeping ncRNAs participate in the maturation of mRNA through splicing (see section 1.1.1). Interestingly, some lncRNA can also impact splicing. This is the case of the Arabidopsis lncRNA Alternative Splicing COmpetitor (ASCO) which can physically interact with the nuclear speckle RNA-binding proteins (NSRs), involved in AS regulation during developmental processes in Arabidopsis (Bardou et al., 2014). ASCO-NSRs interactions impaired the splicing of auxin responsive genes influencing auxin-driven lateral root formation in Arabidopsis (Figure 13A). More recently the ASCO lncRNA have been found

to interact with other components of the spliceosome (SmD1b and PRP8a), shedding light on the relationship between ASCO action on AS and plant resistance to pathogens (Rigo et al., 2020) (Figure 13B). More precisely, increased levels of ASCO impaired the full splicing of flagellin-related transcripts. Consequently, RNAi-mediated ASCO silencing increases root growth sensitivity to flagellin 22 (Rigo et al., 2020). Other lncRNAs regulate post-transcriptional processes by perturbing miRNA regulation. For example, the gene PHOSPHATE 2 (PHO2), a key regulator of phosphate (Pi) homeostasis, is targeted by miR399 (see section 1.1.2.2.). The AtIPS1 lncRNA shares a highly similar 23nt sequence with PHO2, corresponding to the recognition sequence of miR399. Interestingly, in IPS1, there is a 3 nt sequence mismatches in the middle of the miR399 binding site leading to the formation of a bulge avoiding IPS1 cleavage.

Consequently, IPS1 is acting like a substrate of the miR399 which remains stuck on IPS1 as it cannot cleave this RNA. Therefore, miR399 action on PHO2 is inhibited by the target mimicry lncRNA (Franco-Zorrilla et al., 2007). These miRNA "sponges" can block miRNA-mediated posttranscriptional regulation of specific mRNA targets. Interestingly, the IPS1-mediated miR399

inhibition is also conserved in maize, tomato and Medicago truncatula ((Wang et al., 2017a;[START_REF] Du | The PILNCR1-miR399 regulatory module is important for low phosphate tolerance in maize[END_REF] (Figure 13C). Also, in Arabidopsis, 407 putative lncRNA miRNA-sponge has been identified in response to blue-light; one of which directly implicated in the blue-light-mediated photomorphogenesis and mannitol stress response through the sequestration of miR167, involved in hypocotyl elongation [START_REF] Sun | Coordinated regulation of Arabidopsis microRNA biogenesis and red light signaling through Dicer-like 1 and phytochrome-interacting factor 4[END_REF]Sun et al., 2020a).

As two thirds of the human transcriptome is regulated by miRNA, it is likely that many lncRNAs are involved in this type of post-transcriptional regulation [START_REF] Borah | Long noncoding rnas as endogenous target mimics and exploration of their role in low nutrient stress tolerance in plants[END_REF].

Interestingly, circular RNAs (circRNAs) are a recent class of non-coding RNAs formed through reverse splicing of an exon or cyclization of an intron within a gene, generally coding [START_REF] Tian | Bioinformatics Analysis of Key Genes and circRNA-miRNA-mRNA Regulatory Network in Gastric Cancer[END_REF]. They present a covalent 5'-3' ends linkage [START_REF] Santer | Circular RNAs: A Novel Class of Functional RNA Molecules with a Therapeutic Perspective[END_REF], are nonpolyadenylated and significantly more stable than linear lncRNA [START_REF] Memczak | Circular RNAs are a large class of animal RNAs with regulatory potency[END_REF].

Abundant in the human genome, and expressed in a tissue-specific manner [START_REF] Shao | Global circular RNA expression profile of human gastric cancer and its clinical significance[END_REF], many have been shown to act as miRNA sponges [START_REF] Salzman | Circular RNAs are the predominant transcript isoform from hundreds of human genes in diverse cell types[END_REF] indicating that these circRNAs may also act as IPS1. Thousands of circRNAs have been identified in humans [START_REF] Glažar | CircBase: A database for circular RNAs[END_REF], many of which seem directly implicated in cancer [START_REF] Su | Circular RNAs in Cancer: Emerging functions in hallmarks, stemness, resistance and roles as potential biomarkers[END_REF]. Similarly, thousands of circRNAs have been detected in nearly 30 plant species, among which Arabidopsis and many crops (Wang et al., 2015;[START_REF] Ye | Widespread noncoding circular RNAs in plants[END_REF]Wang et al., 2017b;[START_REF] Dong | Bioactive ingredients in Chinese herbal medicines that target non-coding RNAs: Promising new choices for disease treatment[END_REF]. Despite their responsiveness to abiotic and biotic stresses, their biological functions in plants remain to be elucidated [START_REF] Zhang | Characterization and Function of Circular RNAs in Plants[END_REF]. Now, the only example of a plant functional circRNA in the regulation of gene expression is the circRNAs emerging from the SEP3 gene transcript, a positive regulator of female organ development [START_REF] Favaro | MADS-Box Protein Complexes Control Carpel and Ovule Development in Arabidopsis[END_REF]. Here, the circRNAs formation modulates flower development by impacting the splicing of its cognate mRNA SEP3 [START_REF] Conn | A circRNA from SEPALLATA3 regulates splicing of its cognate mRNA through R-loop formation[END_REF]. Altogether, lncRNA molecules constitute important regulators of gene expression. Physically interacting with the epigenetic modifying complexes, TFs or the DNA itself, they fine-tune transcriptional activity of target genes. Additionally interacting with key modulators of the mRNA splicing and stability, they also influence mRNA abundance at the post-transcriptional level, thus globally constituting new elements of the regulatory networks controlling cell gene expression.

Review: Regulatory long non-coding RNAs in root growth and development

The root is a dynamically regulated organ that constantly adapts its architecture according to the plant surrounding and its stage of development. Notably, some lncRNAs are involved in the control of root gene expression, influencing the root growth and development.

The following part will be presented under the form of a review, under revision in Biochemical Society Transactions, in which I participate as first author. It presents the current involvement of lncRNAs in the quantitative control of the root architecture under physiological and environmentally adverse conditions. This review also discusses how lncRNAs participate in the initiation of new root organs such as lateral roots or nodules.

Introduction

The development of next generation sequencing methods to analyze transcription in multiple cell types and conditions revealed that more than 90% of the eukaryotic genome is transcribed (Chen et al., 2021). Strikingly, the large majority of these transcripts remain in their RNA state and generally do not produce peptides constituting the so-called "dark matter" of the genome. From mammals to plants, this tremendous quantity of enigmatic non-coding transcripts is intensively studied, showing remarkable regulatory ways to fine-tune gene expression. Regulatory non-coding transcripts are divided into two main groups based on their size. First, the small RNAs (sRNAs), which are inferior to 200nt, counting the small-interfering RNAs (siRNAs), the micro-RNAs (miRNAs) and the phasing-siRNAs (phasiRNAs), regulating gene expression at the transcriptional or post-transcriptional levels through RNA-dependent DNA methylation or transcript cleavage/inhibition of translation, respectively. Second, the long non-coding RNAs (lncRNAs) are non-coding transcriptional units generally superior to 200nt.

Similarly, to coding RNAs, the majority are transcribed by Pol II, poly-adenylated and subjected to splicing events (Chen et al., 2021). Other classes of lncRNAs involved in RNA dependent DNA methylation (RdDM) can also be transcribed by Pol IV and V leading to the production of 21-24 nt siRNAs. When compared to coding transcripts, lncRNAs are more specifically expressed: at certain state of developmental processes, only in certain tissues or in response to environmental stimuli (Rinn and Chang, 2020). In addition, they are generally shorter in length, contain less introns, thus generating a lower number of isoforms compared to coding transcripts (Golicz et al., 2018;Sarropoulos et al., 2019;Zhao et al., 2020a). Those lncRNA influencing gene expression at the transcriptional level are often enriched in the nuclear fraction of the cells. However, many lncRNAs can also be translocated to the cytoplasm, similar to the coding transcripts, to post-transcriptionally regulate gene expression (Fok et al., 2017).

More than thousands of lncRNAs are transcribed within eukaryotic cells, manifesting the likely participation in a wide range of biological processes (Statello et al., 2021). For example in mammals, the Xist lncRNA mediates the X chromosome epigenetic inactivation, critical for normal embryonic development (Zhao et al., 2008), whereas COOLAIR, COLDAIR and COLDWRAP lncRNAs mediates the epigenetic silencing of the Flowering Locus C (FLC) gene ensuring the end of vernalization and flowering in Arabidopsis (Swiezewski et al., 2007;[START_REF] Liu | Targeted 3′Processing of AntisenseTranscripts TriggersArabidopsis FLC Chromatin Silencing[END_REF]. It has been shown that plant lncRNAs are involved also in stress signaling, photomorphogenesis, fertility and plant growth and development (Chen et al., 2021). This review will focus on the influence of lncRNAs on root growth and development, during normal development as well as in response to stresses and their relevance for key root-mediated assimilation of soil nutrients.

Influence of lncRNAs on root growth and development

Phytohormones constitute key regulators of plant growth and development. Among them, auxin, regulates cell division and elongation and is described as a positive regulator of root growth (Hu et al., 2021). Nuclear Speckle RNA-binding Proteins (NSR) proteins are a class of protein involved in Alternative Splicing (AS) and auxin-related developmental processes, among which the development of lateral roots (LR) (Bardou et al., 2014) (Figure 1A). Indeed, nsra/nsrb double mutant is less sensitive to auxin and present a lower number of LR in response to auxin application (Bardou et al., 2014). Interestingly, the lncRNA Alternative Splicing COmpetitor (ASCO) is able to physically interact with the NSR proteins regulating AS during auxin signaling, directly modifying auxin-related root growth and development (Bardou et al., 2014) (Figure 1A). As a result, plants overexpressing ASCO exhibit an increased sensitivity to auxin. More recently, immunoprecipitation of the NSRa protein followed by a transcriptomic analysis has served to identify multiple NSR-dependent spliced mRNAs. Strikingly, many lncRNAs, other than ASCO, were shown to also interact with NSRa, suggesting that they could also direct NSR-dependent AS and subsequently impact the root architecture (Bazin et al., 2018).

Auxin transport within the root also designs the root architecture by modifying the distribution of auxin inside root tissues. For example, mutants impaired in auxin transport exhibit a significant reduction of root length and meristem size (Billou et al., 2005). The PINOID (PID) kinase is a key regulator of auxin transport as it seems to phosphorylate PIN proteins directly involved in this process, and its repression leads to a reduced sensitivity to root gravitropism (Sukumar et al., 2009). In normal conditions, the PID gene is lowly expressed.

Auxin signaling enables the disruption of a chromatin loop encompassing the PID promoter and its neighboring gene, the lncRNA AUXIN-REGULATED PROMOTER LOOP (APOLO), and induces the transcription of both PID and APOLO genes through ARF TFs (Ariel et al., 2014).

Later on, APOLO physically interacts with the LHP1 chromatin looping protein, reforming the chromatin loop, subsequently repressing PID gene activity. In addition, APOLO is able to direct DNA methylation to this region and the deposition of the H3K27me3 repressive histone mark, which strengthen the repressive state of the PID-APOLO region (Figure 1B). As expected, APOLO downregulated lines exhibit a reduced sensitivity to root gravitropism as the pid mutant (Ariel et al., 2014). More recently, it has been observed that APOLO promoter is active during LR development, a process controlled by auxin (Ariel et al., 2020). In the same study, authors showed that the APOLO RNA is able to recognize multiple individual loci in trans through the formation of R-loops, directly influencing the local chromatin conformation and gene activity of several auxin-responsive genes (Ariel et al., 2020). Interestingly, a significant proportion of APOLO-target genes, in addition to being responsive to auxin, were also implicated in LR formation (Figure 1B). For example, the LEUCINE-RICH REPEAT/EXTENSIN2 gene which is involved in cell wall remodeling during LR emergence [START_REF] Lewis | A kinetic analysis of the auxin transcriptome reveals cell wall remodeling proteins that modulate lateral root development in Arabidopsis[END_REF], is targeted by APOLO.

In agreement, seedlings overexpressing APOLO present a significant increase in LR density in response to auxin (Ariel et al., 2020) Cytokinins (CK) are another crucial class of plant hormone described as an inhibitor of root growth (Amasino, 2005), widely used for plant regeneration to inhibit adventitious root formation (Bishopp et al., 2009). Essential for shoot meristem activity, leaf growth and senescence (Mok et al., 2000), CK are mainly produced in the root tip, but are also synthesized locally in the plant aerial parts (Nordström et al., 2004). Interestingly, the Sho gene from Petunia hybrida, implicated in CK biogenesis contains an antisense non-coding transcript (Zubko and Meyer, 2007). The expression of Sho and of its antisense seems to be correlated and leads to the production of 24nt small RNA in most tissues. This suggests a role of Sho antisense transcript to fine tune the accumulation of its sense transcript (Figure 1C). Strikingly, roots were the only tissue, where the 24nt were absent suggesting that Sho is not degraded in this compartment. This would allow the maintenance of a high level of CK synthesis in the root as compared to other organs. Thus, the Sho antisense lncRNA could be implicated in the organdependent control of CK biogenesis within Petunia hybrida (Zubko and Meyer, 2007).

The SCARECROW (SCR) gene is required for the asymmetric cell division occurring during Arabidopsis root development (Di Laurenzio et al., 1996). Similarly, the SCR-like transcription factors are a family of proteins found in rice and critical for proper root development (Cho and Paszkowski, 2017). More precisely, in non-root organs, such as shoots and flowers, OsmiR171 is acting as an inhibitor of SCR-like genes. Interestingly, a non-coding transcriptional unit, named MIKKI ('decoy' in Korean), derived from a retrotransposable element indirectly controls SCL-like gene expression through the inhibition of the miR171 activity (Figure 1D). This inhibition is realized through target mimicry or miRNA sponge (Franco-Zorrilla et al., 2007), as the MIKKI non-coding transcripts bind to the miR171 but showed a loss of base pair complementarity at the cleavage site. This resulted in avoiding both MIKKI cleavage and led to the sequestration of miR171. Notably, the root length was significantly reduced in plants overexpressing the MIKKI transcripts (Cho and Paszkowski, 2017) demonstrating a functional role of this miR171-MIKKI-SCR regulatory node. Altogether, lncRNAs constitute emerging regulators of root growth and development through their participation in plant hormone signaling pathway through the regulation of key root-related genes.

LncRNAs as root-stress signaling molecules

The increasing global population and concomitant climate change has resulted in an unsustainable path for global food security. In order to address these challenges new strategies are required that may allow us to prepare our global crops against environmental problems linked to climate change. Interestingly, many lncRNAs are activated upon biotic and abiotic stresses underpinning that certain lncRNAs may be implicated in plant stress resilience.

Although stresses act at various levels of the plant, the root compartment is particularly sensitive to adverse environmental conditions such as osmotic-related stresses, cold or pathogen attacks. Impacting root growth directly affects the proper development of agriculturally valuable plants. Large variation in lncRNA expression has been linked to environmental stresses in multiple model and crop plants. For example, transcriptomic analyses revealed that more than hundred lncRNAs are differentially expressed upon drought stress in Arabidopsis (Chen et al., 2021), poplar (Shuai et al., 2014), millet (Qi et al., 2013), cassava (Li et al., 2017a) and rice (Chung et al., 2016). Notably, two rice Natural Antisense Transcripts (NATs), Os02g0250700 and Os02g0180800, may be implicated in the transcriptional regulation of a late embryogenesis abundant (LEA) and cinnamoyl-CoA reductase (CCR) genes, both participating in plant drought tolerance (Chung et al., 2016) (Figure 1E). In Arabidopsis, the DRought-Induced lncRNA (DRIR) regulates a subset of genes implicated in ABA signaling and water translocation, of particular interest for plant drought stress resilience. Interestingly, DRIR also regulates the transcriptional activity of FUCOSYLTRANSFERASE4 and the transcription factor NAM/ATAF/CUC3 potentially modulating the plant tolerance to drought and salt stresses through these target pathways (Qin et al., 2017) (Figure 1F). Interestingly, salt stress also disturbs transcript accumulation of miscellaneous lncRNAs in many plant species, including Arabidopsis (Di et al., 2014), soybean (Chen et al., 2019), Medicago (Wang et al., 2015), tea bush (Wan et al., 2020), cotton (Zhang et al., 2019), sorghum (Sun et al., 2020b) and poplar (Ma et al., 2019). The cotton lncRNA973 regulates a subset of genes of the salt-stress signaling pathway, mainly participating in scavenging ROS, directly fine-tuning the plant salt stress tolerance (Zhang et al., 2019) (Figure 1G). Remarkably, ectopic overexpression of the cotton lncRNA973 within Arabidopsis significantly increased the plant salt tolerance from germination to seedling establishment, whereas reducing the lncRNA973 through virusinduced gene silencing (VIGS) in cotton significantly reduced the plant tolerance to salt stress.

Additionally, the lncRNA973 may act as target mimicry for the miR399 (Deng et al., 2018b), regulating the root phosphate (Pi) assimilation and homeostasis (Kim et al 2011;Bari et al 2006;(Bari et al., 2006;Chiou et al., 2006;Pant et al., 2008;Kim et al., 2011a). Similarly, the root crop cassava regulates drought and cold stress responses through the lncRNA340 acting as a target mimicry for miR169 (Li et al., 2017a) (Figure 1H).

Low temperature also affects plant growth and development. For example, it has been noticed that prolonged cold exposure increased root hair growth, presumably participating in the plant cold acclimation. Strikingly, plantlets with reduced or increased levels of APOLO lncRNA transcripts constitutively produced more root hairs compared to WT (Moison et al., 2021). In agreement, plants with a modified level of APOLO present an increased level of RHD6 transcripts, a key gene critically involved in the regulation of root hair fomation. Notably, the authors showed that APOLO controls RHD6 transcriptional activity during cold stress through the recruitment of WRKY42 to its promoter region (Moison et al., 2021) (Figure 1B).

Roots can also be subjected to deleterious biotic stresses which induce significant changes of the plant coding and non-coding transcriptome. For example, the Root-knot nematodes (RKNs) is one of the most destructive pests for agriculture, due to its large spectrum of plant hosts and the serious decrease in plant yield provoked upon infection (Giannakou and Panopoulou, 2019)Interestingly, more than 500 lncRNAs are differentially expressed upon RKNs infection of roots and several may be involved in the nematode stress response [START_REF] Li | Genome-wide identification and functional prediction of tobacco lncRNAs responsive to root-knot nematode stress[END_REF]. Furthermore, deregulation of the ASCO lncRNA significantly deregulates activity of genes implicated in flagellin response in addition to the auxin-related genes (Rigo et al., 2020) (Figure 1A). Curiously, most of these "biotic stress" genes affected by ASCO knock-down in root tissues do not overlap with the deregulated genes detected in nsra/nsrb mutants suggesting an alternative role of ASCO for plant biotic stress response. Indeed, recently ASCO has been shown to also interact with PRP8a and SmD1b, in addition to NSRs. These proteins are central spliceosome components implicated in the splicing of several flagellin-responsive genes. Accordingly, plants with a reduced level of ASCO transcripts are hypersensitive to flagellin treatments as indicated by a significant reduction of primary root growth and root apical meristem size in flagellin-treated plants (Rigo et al., 2020). Collectively these results support that lncRNA activity is highly sensitive to environmental cues, and they are emerging as novel components in the plant response to biotic and abiotic environmental stresses.

LncRNAs-mediating nutrient homeostasis

With the increasing worldwide population, it has been estimated that the global agriculture production needs to be increased by 70% to meet the food demand of 2050, making critical the finding of new alternatives to increase crop yield. Substantial application of nitrogen (N)-containing fertilizers significantly participates in increasing cereal yield and has a strong impact on root architecture, the tissues involved in nutrient uptake. Notably, Ndependent changes in root architecture are found in many plant species (Forde and Walch-Liu, 2009;Forde, 2014) and this has consequences on other root-dependent traits such as water acquisition. Legume plants are able to develop a unique organ on their roots: nodules, in which symbiotic N²-fixing rhizobia capture the atmospheric N (Kistner and Parniske, 2002). Hence, they can have a major impact on soil nitrogen content and are key components of environmentally friendly agricultural practices (Zhao et al., 2020b). Interestingly, the ENOD40 lncRNA participates in legume nodulation (Figure 1I) and certain sequences of this gene are also conserved in non-legume plants (Gultyaev and Roussis, 2007). RNAi-mediated silencing of ENOD40 in Lotus japonicus suppresses nodule formation (Kumagai et al., 2006). Strikingly, it has been proposed that the legume ENOD40 lncRNA presents a dual function either as a lncRNA involved in nucleocytoplasmic trafficking of MtRBP1, a key regulator of gene expression (Campalans et al., 2004), or through the production of two small peptides interacting with the sucrose synthase protein and participating in sucrose mobilization during nodulation (Bardou et al., 2011). In Medicago, another lncRNA consisting of a short variant of the TAS3 lncRNA participates in nodule formation through target mimicry of miR390, preventing the fabrication of trans-acting small interfering RNA targeting TF from the ARFs family (Traubenik et al., 2020) (Figure 1J). Interestingly, the TAS3-miR390-ARFs module is also involved in the lateral root initiation. Notably, the miR390 is specifically expressed at the sites of lateral root initiation, triggering the processing of TAS3-tasiRNAs that mediates inhibition of ARF2/3 and ARF4 promoting the lateral root expansion (Marin et al., 2010) (Figure 1J).

Apart from legumes, it has been observed that N starvation changes the expression of multiple lncRNAs in Arabidopsis (Fukuda et al., 2020), poplar (Chen et al., 2016), barley (Chen et al., 2020), maize (Lv et al., 2016) and rice (Shin et al., 2018). For example, two antisense lncRNAs are significantly induced in rice roots upon N starvation (Shin et al., 2018), likely regulating the AMT1 gene participating in N assimilation (Ishiyama et al., 2004). Interestingly, the T5120 lncRNA is directly regulated by NLP7, a master regulator of the N signaling pathway, prompting that the T5120 may participate in NRT1.1 gene activation (Liu et al., 2019) (Figure 1K). As the root constitutes the main compartment for N uptake in land, plants elaborate many strategies to enhance N acquisition efficiency, mainly through changes of root architecture (Walch-Liu et al., 2006). Thus, the implication of lncRNA for the regulation of N signaling pathway, assimilation and nitrogen-fixing symbiosis strengthen the relevance of the noncoding transcriptome for a proper plant N homeostasis in different plants.

In addition to N, phosphate (Pi), is another mineral essential for plant growth and development. Interestingly, Pi starvation also triggers strong changes of root architecture together with changes of gene expression within the root to increase P-nutrient acquisition from the soil environment. Notably, the primary root growth of the Arabidopsis Col-0 ecotype is immediately interrupted upon Pi starvation through a Fe-dependent mechanism (Gutiérrez-Alanís et al., 2017). Interestingly, this response is not conserved across all Arabidopsis accessions, such as the Ler ecotype (Reymond et al., 2006). Notably, it has been observed that the non-coding transcriptomes between Col-0 and Ler ecotypes among a Pi starvation is strongly different when compared to the coding transcriptomes (Blein et al., 2020). Strikingly, in only one hour of Pi starvation, many lncRNAs are differentially regulated within Arabidopsis root tips (Blein et al., 2020). In the same study, authors showed that two lncRNAs enriched in a particular Arabidopsis ecotype, NPC48 and NPC72, significantly reduced the main root growth when overexpressed. Furthermore, one of this lncRNAs disturbs the transcriptional activity of key root growth regulator such as RGF7, BIG, RPK2 and CASP5 (Blein et al., 2020) (Figure 1L)

further supporting that lncRNA are emerging regulators of root growth.

Plants have evolved many adaptive mechanisms to maintain a homeostatic level of Pi within their cells in soil environments with variable levels of combined Pi. For example, upon Pi starvation the ZAT6 TF is induced and regulates a subset of Pi-responsive genes in Arabidopsis thaliana. Interestingly, RNAi-mediated silencing of ZAT6 is lethal, whereas overexpression limits the expression of Pi-related genes, impairing Pi acquisition and root growth upon seedling development. Indeed, the ZAT6-mediated repression of primary root growth directly regulates the Pi homeostasis through changes of root architecture (Devaiah et al., 2007). In addition, PHO2 is another gene involved in the Pi-starvation response, directly regulating the plant Pi homeostasis by controlling the degradation of PHO1, implicated in Pi loading onto the xylem (Poirier and Bucher, 2002). Notably, pho2 mutant accumulates toxic amounts of Pi onto the shoot, because of an uncontrolled Pi uptake and translocation into this tissue (Delhaize and Randall, 1993;Dong et al., 1998). Interestingly, miR399 is induced upon Pi starvation and target PHO2 transcript limiting Pi assimilation (Liu et al., 2012b). Strikingly, two Arabidopsis lncRNAs, At4 and IPS1, conserved in Medicago, maize and tomato, are also induced within low Pi condition (Burleigh and Harrison, 1999;Shin et al., 2006;Wang et al., 2017a) and share a partial complementary sequence with miR399. Concomitantly, the Pi-induced lncRNAs act as miRNA sponge for miR399 directly influencing PHO2 transcript level, thus the Pi homeostasis (Burleigh and Harrison, 1999;Shin et al., 2006;Franco-Zorrilla et al., 2007;Wang et al., 2017a) (Figure 1M). In agreement, at4 mutants present a Pi homeostasis defect between the root and shoot compartment under Pi starvation (Burleigh and Harrison, 1999;Shin et al., 2006). Other regulations may appear in other plant species that impinge on this regulatory node. For example, in rice, an antisense lncRNA of OsPHO1;2 (cis-NATPHO1;2) participate in Pi homeostasis promoting the translation of OsPHO2 transcript (Jabnoune et al., 2013) (Figure 1N). Notably, RNAi-mediated silencing of cis-NATPHO1;2 decrease the quantity of OsPHO2 protein, reducing Pi content and grain yield. Whereas, cis-NATPHO1;2 ectopic overexpression increased the PHO1;2 protein level (Jabnoune et al., 2013). Hence antisense lncRNA regulation regulates PHO2 but this type of regulation is not conserved in Arabidopsis or other species.

Altogether, there is growing evidence that lncRNAs are emerging regulators of various biological processes and stress responses. Many Genome Wide Association Studies (GWAS) for diverse agriculturally relevant biological traits mapped to intergenic regions where lncRNAs

may exist hinting at a potential function of lncRNA in QTLs for root growth, development and responses to adverse constraints (Liu et al., 2017b;Stagnati et al., 2019;Alseekh et al., 2021;Ma et al., 2021); Liu et al 2017;Stagnati et al 2019;Ma et al 2021). Also, RNA molecules might be more attractive than proteins to modulate gene regulation as their effects may be more transient (Song et al., 2019). Finally, as root growth and development is highly described at the protein coding genes, co-regulatory networks involving lncRNAs may offer new mechanisms for root growth regulation. With less than 1% of the annotated plant lncRNAs functionally characterized, they constitute an immense reservoir for discovering new gene regulatory mechanisms and may offer attractive targets for future plant breeding programs.

Perspectives

• Climate change-related environmental constraints directly or indirectly disturbs the root compartment, impacting water uptake, nutrient acquisition and plant anchoring, ultimately decreasing crop yield. In this context, lncRNAs emerged as interesting molecules for the quantitative control of root-related traits, notably in response to the environment, and could be of interest for plant breeding programs. • In all transcriptomically investigated crop species lncRNAs have been detected within the root compartment. However, few lncRNAs have been functionally characterized.

Nevertheless, current studies show they are majoritarily involved in the quantitative regulation of plant root architecture allowing notably to adapt to root-related environmental stresses, strengthening their importance as key regulators of agronomically relevant traits. • Despite the important quantity of lncRNAs detected in root, few have been functionally characterized, especially in crop species. Nevertheless, we anticipate that the intensive emerging characterization of these molecules will be used in plant breeding programs to subtly acclimate the crops to the coming environmental changes. Bardou et al., 2014) and SmD1b-RPR8 (Rigo et al., 2020), respectively. B. The lncRNA APOLO fine-tunes the expression of several auxin-responsive genes (Ariel et al., 2020), regulating lateral root development and gravitropism (Ariel et al., 2014). In addition, it modulates the binding of the WRKY42 TF to the RHD6 promoter, promoting root hair initiation under cold stress (Moison et al., 2020). C. In Petunia hybrida, the cytokinin-mediated root growth inhibition is modulated by an antisense lncRNA of Sho gene, a repressor of cytokinin signaling (Zubko and Meyer, 2007). D. The rice MIKKI lncRNA shapes the root architecture through target mimicry of the miR171-SCR module (Jungnam Cho and Jerzy Paszkowski, 2017). E. Os02g0250700 and Os02g0180800 are two antisense rice lncRNAs regulating the plant drought tolerance through the regulation of LEA and CCR genes, respectively (Chung et al., 2016). F. DRIR is an Arabidopsis lncRNA regulating a subset of ABA-related genes, including FUT4 and NAC3 (Qin et al., 2017). G.

LncRNA973 is a cotton lncRNA participating in the plant salt stress responses (Zhang et al 2019). Also, it acts as target mimicry of the miR399-PHO2 node for phosphate (Pi) homeostasis (Kim et al., 2011;Bari et al., 2006;Chiou et al., 2006;Pant et al., 2008). H. The lncRNA340 from the crop cassava roots acts as target mimicry of the miR169-NF-Y regulatory interaction, participating in the plant cold and drought stress tolerance (Li et al., 2017;Fang et al., 2014). I. The Medicago ENOD40 lncRNA promotes nodule development (Bardou et al., 2011). J. A short variant from the Medicago TAS3 lncRNA acts as target mimicry with the miR390-ARFs module, positively influencing nodule formation (Traubenik et al., 2020) and lateral root growth (Marin et al., 2010). K. The master regulator of nitrogen signaling NLP7 in Arabidopsis promote the T5120 lncRNA transcriptional activity, likely participating in NRT1.1 gene activation known to be implicated in the nitrogen assimilation (Liu et al., 2019). L. NPC48 and NPC72 are two Arabidopsis lncRNAs whose overexpression inhibits primary root growth (Blein et al., 2020). M. The IPS1 lncRNA act as target mimicry for the miR399-PHO2 module in Arabidopsis, Medicago, maize and tomato, participating in the plant Pi homeostasis (Burleigh and Harrison, 1999;Shin et al., 2006;Wang et al., 2017;Franco Zorrilla et al., 2007;Wang et al., 2017). N. In rice, an antisense lncRNA of PHO1;2 (cis-NATPHO1;2), promote PHO2 transcript translation participating in the plant phosphate homeostasis (Burleigh and Harrison, 1999;Shin et al., 2006;Wang et al., 2017).

Aim of the thesis

Long non-coding RNAs are implicated in a wide-range of plant-developmental and stress-related processes, rapidly emerging as novel regulators of gene expression (Chen et al., 2021). At the start of my PhD, the host team was interested in exploring the diversity of noncoding transcripts for the plant adaptation to environmental constraints.

In this context, I started by studying the conservation of the non-coding transcriptome using two Arabidopsis accessions, under a Pi starvation time-course experiment. These two accessions present a striking difference in root growth architecture in response to Pideprivation. Whereas the Col-0 accession immediately stops its primary root growth upon Pi starvation, the Ler accession does not. The first chapter describes our efforts to compare the features of the non-coding genes as compared to coding genes along these differential responses, shedding light on the unique specificity of the non-coding transcriptome among accessions. I also contributed to the identification of two lncRNAs able to modulate root growth. In the second chapter, I investigate the transcriptomics data emerging from our accession Pi starvation analyses to identify new lncRNAs implicated in root growth and development. Finally, the third chapter constitutes the major part of my thesis work and describes the molecular characterization of an unknown lncRNA implicated in the epigeneticmediated regulation of a group of genes organized in clusters.

Overall, my thesis work aimed to uncover the implication of the non-coding transcriptomes in the local plant adaptation to its environment, together with finding and characterizing new lncRNAs implicated in the regulation of plant-root-related genes.

The non-coding transcriptome from two

Arabidopsis ecotypes

Genome conservation is not uniformly distributed along eukaryotic genomes. In humans and plants, it has been shown that coding genes are more frequently conserved than non-coding genes at the sequence level [START_REF] Sudmant | An integrated map of structural variation in 2,504 human genomes[END_REF][START_REF] Alonso-Blanco | 1,135 Genomes Reveal the Global Pattern of Polymorphism in Arabidopsis thaliana[END_REF]. Even within the same species, the conservation of non-coding genes, such as lncRNAs, can vary drastically. For example, among the Arabidopsis accessions, very few Single Nucleotide Polymorphisms (SNPs) are found in coding mRNAs compared to non-coding genes [START_REF] Alonso-Blanco | 1,135 Genomes Reveal the Global Pattern of Polymorphism in Arabidopsis thaliana[END_REF]. Similarly, in rice less than 4% of all the SNPs and insertion/deletions (indels)

were found in coding regions (Zhao et al., 2018b). Even though non-coding genes are poorly conserved at sequence level, their position in relation to neighboring genes within the genome (synteny) is more conserved suggesting that their position more than their sequence could participate in their regulatory function (Mohammadin et al., 2015). Indeed, the act of transcription could be involved in their regulatory activity (Kopp and Mendell, 2018). The specificity of the non-coding genome between organisms of the same species could also reflect the presence of regulatory mechanisms involved in the local adaptation of an individual to its environment. For example, plant response to the lack of phosphate (Pi) drastically changed between ecotypes. Indeed, Col-0 limits rapidly its main root growth through a Fedependent pathway involving LOW PHOSPHATE ROOT (LPR1), PHOSPHATE DEFICIENCY RESPONSE2 (PDR2), SENSITIVE TO PROTON RHIZOTOXICITY1 (STOP1), and ALUMINUM-ACTIVATED MALATE TRANS-PORTER1 (ALMT1); whereas Ler root growth continues under low Pi condition and is similar to the length observed in a phosphate-containing medium (Reymond et al., 2006). In this context, we thought of interest to characterize the noncoding transcriptomes of Col-0 and Ler ecotypes under early Pi starvation. Using RNAseq and bioinformatics analysis, we identified many new non-coding genes conserved, enriched, or even specific of one ecotype, in addition to being responsive, or not, to the lack of Pi. We also analyze their links with different classes of small RNAs. Finally, we functionally characterized several of the new intergenic lncRNAs and found that two ecotype-enriched lncRNAs were involved in primary root growth regulation. These results strengthened that lncRNAs participate in the transcriptomic response to environmental cues and suggest that they may play roles in ecotype adaptation to the soil environment.

In this first chapter, I will present the publication emerging from this study where I contributed by performing qPCR to validate the RNA-seq results, investigate trans regulatory effects of lncRNA expression between Col-0 and Ler genomes and better understand the function of some ecotype-enriched lncRNAs for the root growth and Pi starvation response.

Finally, I participate in the writing process and exchange ideas with the reviewers.

Publication: Landscape of the Noncoding Transcriptome Response of Two Arabidopsis Ecotypes to Phosphate Starvation

Root architecture varies widely between species; it even varies between ecotypes of the same species, despite strong conservation of the coding portion of their genomes. By contrast, noncoding RNAs evolve rapidly between ecotypes and may control their differential responses to the environment, since several long noncoding RNAs (lncRNAs) are known to quantitatively regulate gene expression. Roots from ecotypes Columbia and Landsberg erecta of Arabidopsis (Arabidopsis thaliana) respond differently to phosphate starvation. Here, we compared transcriptomes (mRNAs, lncRNAs, and small RNAs) of root tips from these two ecotypes during early phosphate starvation. We identified thousands of lncRNAs that were largely conserved at the DNA level in these ecotypes. In contrast to coding genes, many lncRNAs were specifically transcribed in one ecotype and/or differentially expressed between ecotypes independent of phosphate availability. We further characterized these ecotype-related lncRNAs and studied their link with small interfering RNAs. Our analysis identified 675 lncRNAs differentially expressed between the two ecotypes, including antisense RNAs targeting key regulators of root-growth responses. Misregulation of several lincRNAs showed that at least two ecotype-related lncRNAs regulate primary root growth in ecotype Columbia. RNA-sequencing analysis following deregulation of lncRNA NPC48 revealed a potential link with root growth and transport functions. This exploration of the noncoding transcriptome identified ecotype-specific lncRNA-mediated regulation in root apexes. The noncoding genome may harbor further mechanisms involved in ecotype adaptation of roots to different soil environments.

Over the last decade, genome-wide transcriptomics has revealed that a large intergenic part of eukaryotic genomes is transcribed. These transcripts, globally known as noncoding RNAs (Ariel et al., 2015), can regulate genome expression at transcriptional, posttranscriptional, and epigenetic levels, and are generally classified as small (21-24 nucleotides [nt]), long (.200 nt, ,100 kb), and circular noncoding RNAs. Plant small RNAs (sRNAs) are produced by processing longer noncoding transcripts that generally contain a hairpin structure or lead to double-strand RNA formation. Plant sRNAs include microRNAs (miRNAs), endogenous small interfering RNAs (siRNAs; generally 21-22 nt long), and, most abundantly, heterochromatin siRNA (24 nt long; [START_REF] Borges | The expanding world of small RNAs in plants[END_REF]. On the other hand, long noncoding RNAs (lncRNAs) are a heterogeneous group of RNA molecules with a coding capacity ,50 amino acids [START_REF] Chekanova | Long non-coding RNAs and their functions in plants[END_REF]. lncRNA transcripts are generally polyadenylated and can be intergenic (lincRNAs), intronic, or natural antisense (NATs) with respect to protein-coding genes (Ariel et al., 2015). When compared to mRNAs, lncRNAs are expressed at low levels in a tissue-specific manner or in response to environmental stresses (Liu et al., 2012) and are more frequently accumulated in the nucleus [START_REF] Derrien | The GENCODE v7 catalog of human long noncoding RNAs: Analysis of their gene structure, evolution, and expression[END_REF], where they can regulate nuclear organization or function (Ariel et al., 2015).

lncRNAs utilize both cis-and trans-modalities of action to regulate gene expression through interactions with ribonucleoproteins and can form scaffolds and/or sequester proteins or RNA molecules as decoys or sponges. However, molecular functions have only been identified for a few lncRNAs in plants. As lncRNA genes lack regions with high primary sequence constraints [START_REF] Derrien | The GENCODE v7 catalog of human long noncoding RNAs: Analysis of their gene structure, evolution, and expression[END_REF], it is difficult to use sequence conservation to identify potential functions. Even though the sequences of lncRNAs are not particularly conserved between plant species, they may show relative positional conservation in genomes (Mohammadin et al., 2015).

Finally, lncRNAs could be simply transcriptional byproducts; in this framework, the sole act of their transcription rather than their sequence per se would be the source of the regulatory activity (Kopp and Mendell, 2018).

Resequencing approaches in model species have allowed the determination of whole-genome variations and evolution, from which it has been possible to provide the characterization of pan-genomes composed of "core" genomes (present in all accessions) and "dispensable" genomes (those specific to two or more accessions or even unique sequences specific to only one accession). Core genes are frequently highly expressed whereas dispensable genes are variably expressed, and generally in a tissue-specific manner (Contreras-Moreira et al., 2017). The dispensable genomes may play important roles in the capacity of individual organisms to cope with environmental conditions (Vernikos et al., 2015). Indeed, identification of natural variations in large worldwide populations (accessions) of Arabidopsis (Arabidopsis thaliana) showed an average of one SNP per 10 bp more frequently located in intergenic regions than in coding mRNAs (The 1001 Genomes Consortium, 2016). This has also been observed recently in rice (Oryza sativa), for which only 3.5% of SNPs and 2.5% of small insertion-deletions (InDels) were located in coding regions (Zhao et al., 2018). This latter observation would explain why lncRNAs differ even between closely related plant species (Nelson et al., 2017). In plants, three mechanisms have been proposed for the origin of lncRNAs: evolution from transposable element (TE) sequences, pseudogenization of protein-coding gene sequences, or duplication of preexisting lncRNAs [START_REF] Kapusta | Volatile evolution of long noncoding RNA repertoires: Mechanisms and biological implications[END_REF] The inorganic phosphate (Pi) accumulated in the upper soil layer is perceived by plants at the root apex [START_REF] Svistoonoff | Root tip contact with low-phosphate media reprograms plant root architecture[END_REF]. Accessions of ecotypes Columbia (Col) and Landsberg erecta (Ler) of Arabidopsis display different primary root growth and architecture in response to Pi starvation (Reymond et al., 2006). The identification of LOW PHOSPHATE ROOT1 (LPR1), a major quantitative trait locus, has been done in recombinant inbred lines obtained by crosses of accessions presenting this opposite root response to low Pi and it has been linked to differential expression of LPR1 in root apexes (Reymond et al., 2006;[START_REF] Svistoonoff | Root tip contact with low-phosphate media reprograms plant root architecture[END_REF]. When the primary root tip of a Col seedling encounters a low-Pi medium, cell elongation in the transition zone rapidly decreases and cell proliferation in the root apical meristem (RAM) progressively ceases as callose deposition occurs in RAM plasmodesmata [START_REF] Müller | Iron-dependent callose deposition adjusts root meristem maintenance to phosphate availability[END_REF][START_REF] Abel | Phosphate scouting by root tips[END_REF][START_REF] Gutiérrez-Alanís | Adaptation to phosphate scarcity: Tips from Arabidopsis roots[END_REF]. Root growth inhibition in low Pi depends on iron (Fe) availability in soil or media [START_REF] Svistoonoff | Root tip contact with low-phosphate media reprograms plant root architecture[END_REF][START_REF] Ward | The effect of iron on the primary root elongation of Arabidopsis during phosphate deficiency[END_REF], as Fe concentrations clearly increase in Col plants during Pi starvation [START_REF] Misson | A genome-wide transcriptional analysis using Arabidopsis thaliana Affymetrix gene chips determined plant responses to phosphate deprivation[END_REF][START_REF] Hirsch | Phosphate deficiency promotes modification of iron distribution in Arabidopsis plants[END_REF][START_REF] Baxter | The leaf ionome as a multivariable system to detect a plant's physiological status[END_REF]. Indeed, inhibition of cell elongation and the RAM arrest are Fe-dependent [START_REF] Svistoonoff | Root tip contact with low-phosphate media reprograms plant root architecture[END_REF][START_REF] Ward | The effect of iron on the primary root elongation of Arabidopsis during phosphate deficiency[END_REF][START_REF] Müller | Iron-dependent callose deposition adjusts root meristem maintenance to phosphate availability[END_REF][START_REF] Abel | Phosphate scouting by root tips[END_REF]Balzergue et al., 2017;[START_REF] Mora-Macías | Malatedependent Fe accumulation is a critical checkpoint in the root developmental response to low phosphate[END_REF][START_REF] Gutiérrez-Alanís | Adaptation to phosphate scarcity: Tips from Arabidopsis roots[END_REF]. Interestingly, in low-Pi conditions, Fe accumulates in the elongation zone, but not in the RAM, and more generally, in Col plants, Fe is redistributed among tissues [START_REF] Mora-Macías | Malatedependent Fe accumulation is a critical checkpoint in the root developmental response to low phosphate[END_REF][START_REF] Gutiérrez-Alanís | Adaptation to phosphate scarcity: Tips from Arabidopsis roots[END_REF]. By contrast, in Ler seedlings that are subject to low Pi, elongation and proliferation of root cells in the root apex continue, thereby sustaining root growth (Reymond et al., 2006). The corresponding regulatory system controlling root inhibition involves LPR1, PHOSPHATE DEFICIENCY RESPONSE2 (PDR2), SENSITIVE TO PROTON RHIZOTOXICITY1(STOP1), and ALUMINUM-ACTIVATED MALATE TRANS-PORTER1 (ALMT1). The LPR1-PDR2 and STOP1-ALMT1 modules allow Fe accumulation in roots under low Pi [START_REF] Ticconi | ER-resident proteins PDR2 and LPR1 mediate the developmental response of root meristems to phosphate availability[END_REF][START_REF] Abel | Phosphate scouting by root tips[END_REF]Balzergue et al., 2017;[START_REF] Gutiérrez-Alanís | Adaptation to phosphate scarcity: Tips from Arabidopsis roots[END_REF]. From these results, one concludes that interactions between Pi and Fe determine the differential growth response of Col and Ler ecotypes. In this work, we identified and characterized the noncoding transcriptomes of Col and Ler root apexes during early Pi starvation responses. Thousands of Arabidopsis lncRNAs, notably in the Ler accession, were identified, with only a minor fraction linked to sRNA production. Several "ecotype-specific" or "ecotype-enriched" variants were highly conserved at the DNA level and showed expression variation correlated with changes in the expression of key regulators of the Pi-starvation response. Functional analysis of five lncRNAs in Col revealed two further regulators of primary root growth, allowing us to hypothesize that lncRNA expression patterns contribute to the modulation of environmental responses in different ecotypes.

RESULTS

Col and Ler Root Tip Transcriptome Assemblies

We characterized the root-tip transcriptome of Col and Ler ecotypes, which present contrasting root phenotypes in response to Pi deficiency (Reymond et al., 2006). We performed comparative whole-genome transcriptomic analyses using paired-end sequencing of three biological replicates of root tips during a short kinetics (0, 1, and 2 h) of low (10 mM) Pi treatment (Supplemental Table S1). To avoid possible differences related to the erecta mutation present in the Ler ecotype, we used the Col er105 mutant here. For each ecotype, the reads were independently mapped to their reference genome (Supplemental Fig. S1A): TAIR10 for Col (Lamesch et al., 2012) and Ler v7 for Ler [START_REF] Gan | Multiple reference genomes and transcriptomes for Arabidopsis thaliana[END_REF] that shared the same TAIR10 annotation (unlike Ler v8; Zapata et al. [2016]). We predicted previously unannotated transcripts by comparing our data to TAIR10. The homology of these predicted transcripts in Col and Ler was determined by mapping them onto the other genome. We retained as transcripts only RNA molecules of at least 200 nt. When these previously uncharacterized transcripts overlapped with preexisting annotations, fusions were generated. Transcripts identified by this pipeline (Supplemental Fig. S1A) were compared with those from different Arabidopsis databases: Araport 11 (Cheng et al., 2017), RepTas (Liu et al., 2012), CANTATAdb [START_REF] Szcze Sniak | 1,135 Genomes reveal the global pattern of polymorphism in Arabidopsis thaliana[END_REF], miRBase v21 (Kozomara and Griffiths-Jones, 2014), and with those from two previous studies concerning lncRNAs (Ben [START_REF] Amor | Novel long non-protein coding RNAs involved in Arabidopsis differentiation and stress responses[END_REF]Li et al., 2016). Finally, we used COME software to determine the potential coding capacity of identified transcripts (Hu et al., 2017). On the basis of both database information and COME predictions we classified the corresponding genes as coding or noncoding.

In total, we identified 5,313 and 6,408 previously uncharacterized putative genes in Col and Ler ecotypes, Figure 1. Identification of the transcripts and their occurrence across the two ecotypes. A, Number of predicted coding and noncoding transcripts in the two ecotypes, classified by type. New transcripts refers to genes not characterized in previously published studies. B to E, Predicted transcripts in each ecotype were classified as coding (B) or noncoding (C). For the latter case, two subclasses are defined: antisense of another annotation (NAT; D) and intergenic (lincRNA; E). In contrast to coding genes, many noncoding RNAs, notably lincRNAs, were detected only in one ecotype despite the high DNA sequence similarity in both ecotypes.

respectively (Fig. 1A; Supplemental Table S2; Supplemental Files S1 and S2). In root apexes, these identified genes were predominantly noncoding RNAs: 76% and 77% of the total previously uncharacterized genes in the case of Col and Ler, respectively (Fig. 1A; Supplemental Fig. S1, B andC). As expected, noncoding genes were globally less expressed than coding genes (Supplemental Fig. S2, A andB). Genes specifically detected in one ecotype belong much more often to the noncoding (.40% of the total noncoding genes) than to the coding class (,8% of the total coding genes; Fig. 1, B andC), notably true for lincRNA genes (52% of the total lncRNAs) as compared to NATs (34% of the total NATs; Fig. 1, D andE). Overall, expression of noncoding genes is more ecotype specific than that of coding genes.

We detected a greater number of previously uncharacterized genes in the Ler ecotype (Fig. 1, A andC). Such differences do not result from library sequencing saturation. Indeed, in the last 2% of sequencing reads, ,10 additional genes of this type were detected (Supplemental Fig. S2C). Thus, sequencing was deep enough to detect expressed genes, and the difference in gene detection between Col and Ler does not result from a sequencing bias.

We next sought to determine whether these previously uncharacterized detected genes expressed in Ler (coding or noncoding) could correspond to specific parts of the Ler genome missed or rearranged in the Col genome. Out of these 7,357 genes, only 41 and 53 genes in Col and Ler, respectively, coincided with missing DNA sequences in the other ecotype (Fig. 2A), showing that the DNA sequence of the different previously uncharacterized genes is largely conserved apart from a few SNPs. Thus, the ecotype differences in transcript accumulation came from a shift in transcription that could be due to the deregulation of gene regulators, the accumulation of small sequence differences in promoters, or to specific differences in epigenetic status in the lncRNA-producing region due to TE insertions or other rearrangements possibly at large distance from the differentially expressed loci.

Evolutionary Analysis of lncRNA Genes Expressed in Root Tips

Mechanisms modifying root architecture result from local signaling that occurs at the root tip [START_REF] Svistoonoff | Root tip contact with low-phosphate media reprograms plant root architecture[END_REF][START_REF] Thibaud | Dissection of local and systemic transcriptional responses to phosphate starvation in Arabidopsis[END_REF][START_REF] Müller | Iron-dependent callose deposition adjusts root meristem maintenance to phosphate availability[END_REF]Balzergue et al., 2017). We therefore characterized the Arabidopsis genes expressed in the root apex, taking advantage of the extensive sequence information in Arabidopsis accessions (The 1001 Genomes Consortium, 2016). According to current annotations, genes were considered as non-NAT (no gene on the other strand) or NAT (presence of a gene on the other strand). For Arabidopsis species, we calculated the rate of SNPs accumulated in the different types of genes among all accessions (Fig. 2B). As expected, TEs accumulated many more SNPs than coding genes, whereas non-NAT lncRNAs and structural RNA genes showed an intermediate level of SNPs between TEs and coding genes. By contrast, the amount of SNPs was generally similar for NAT lncRNA and coding genes, as can be justified by the fact that the coding regions are under strong selection pressure.

To investigate sequence evolution at a larger scale, we used the PhastCons score that represents an interspecies level of nt conservation (normalized between 0 and 1) according to the alignment of 20 angiosperm genomes (Fig. 2C; [START_REF] Hupalo | Conservation and functional element discovery in 20 angiosperm plant genomes[END_REF]. As expected, structural RNA genes were strongly conserved (median score of 1), whereas TEs were not (median score of 0). Coding genes presented a score between these two extremes (;0.5). Interestingly, non-NAT lncRNA genes showed an intermediate score between those of coding and transposable genes (median score of ;0.3), whereas NAT lncRNA genes again showed the same degree of conservation as coding genes. These observations further suggest that NAT lncRNA genes are strongly constrained, whereas intergenic noncoding genes allow more variability even though they are more constrained than TEs.

Few lncRNA Transcripts Colocalize with Small RNA-Generating Loci

In animals and plants, some lncRNA loci colocalize with regions producing sRNA molecules (Matzke and Mosher, 2014). Therefore, we asked whether the lncRNA loci identified could generate sRNAs. Using similar samples previously prepared for the lncRNA studies, we prepared sRNA libraries for each ecotype and sequenced sRNAs to obtain a full description of the sRNAome mapped on each ecotype genome. Only a minority of the lncRNAs accumulate sRNAs, but of those, most contained sequences capable of generating nonphased RNA molecules of 21/22 or 24 nt (Supplemental Fig. S3, A andB) and only a small fraction of lncRNAs overlapped with phased siRNAs or were miRNA precursors.

We then analyzed the potential link between siRNAs and lncRNAs in each ecotype. The majority of lncRNAs (6,452 genes of 7,850 detected) did not lead to accumulation of siRNAs. This is also true for the lncRNAs specifically detected only in one ecotype (2,688 genes out of 3,110 ecotype-specific genes), since many of them did not generate any siRNA in either ecotype (long in Col and not detected [ND] in Ler, or vice versa; Fig. 3A). Thus, the differential detection of lncRNA between ecotypes could not be linked to a change in the processing of siRNA by the encoding lncRNA loci.

We then wondered whether different sRNA processing by lncRNAs could occur between the two ecotypes. We first looked at lncRNAs that accumulated sRNAs in only one accession: (1) lncRNAs that could generate siRNAs in only one ecotype while being detected as lncRNAs in both ecotypes (long in Ler and long and small in Col [113 genes], and vice versa [240 genes]); and (2) loci that produce siRNAs only in one ecotype yet are detected as lncRNAs only in the other (long in Ler and small in Col [57 genes], or vice versa [47 genes]). It is known that 21/22-nt siRNAs act on gene transcripts, whereas 24-nt siRNAs mediate chromatin modifications (Matzke and Mosher, 2014). Thus, a difference in the size of accumulated siRNAs for a given gene in one ecotype could indicate a modification of posttranscriptional (21/22 nt) or epigenetic (24 nt) regulation in the other ecotype. Among the lncRNA genes accumulating siRNA, a large portion accumulated the same size in both ecotypes (591 for 21/22-nt siRNA and 391 for 24-nt siRNA) or produced siRNA in just one ecotype (367 for 21/22-nt and 316 for 24-nt; Fig. 3B). Among the 1,694 lncRNA genes accumulating siRNAs, only 29 accumulated a different size of siRNAs between the two ecotypes. Therefore, no major change of reciprocal posttranscriptional or transcriptional regulation of lncRNA by sRNAs could be established between ecotypes.

Finally, we investigated the specificity of detection of sRNAs between the two ecotypes. First, we studied lncRNA genes predicted to produce phased 21/22-nt siRNAs. Among the seven predicted lncRNAs, only two were specific to Ler (Supplemental Fig. S3C). Second, searching for miRNA loci, we found that 23 and 12 of the 191 detected miRNAs were specifically detected in Col and Ler, respectively (Supplemental Fig. S3D), a proportion related to the variation detected for proteincoding genes. Third, we analyzed the proportion of specific expression for the vast majority of 21/22-nt and 24-nt siRNAs located in coding or noncoding genes. Altogether, the Ler ecotype produces a larger number of 21/22-nt siRNAs specifically linked to this ecotype (Fig. 3, C andE), whereas Col is more enriched in ecotype-specific 24-nt siRNAs (Fig. 3, D and F), suggesting that in these loci, links with differential posttranscriptional and epigenetic regulations among ecotypes occurred.

Overall, the major difference in the noncoding transcriptome of the two ecotypes was linked to lncRNAs and not associated with small RNAs, even though in certain cases sRNAs may be involved in ecotypespecific regulation.

Differential Accumulation of Transcripts between Ecotypes in Early Response to Pi Deficiency

Root growth arrest in the Col ecotype occurs in the first hours of low-Pi sensing by the root tip (Balzergue et al., 2017), whereas root growth continues in Ler. To determine the effects of short kinetics in Pi deficiency, we examined gene expression patterns in the two ecotypes in response to this stress. Principal component analysis (PCA) showed a data dispersion that allowed a clear distinction between effects of the ecotype (first axis; Supplemental Fig. S4A) and of the kinetics (second axis; Supplemental Fig. S4A). Thus, we used a multifactor analysis that takes into account the ecotype, the kinetics, and their interaction to investigate differential gene expression independent of coding classification, as coding and noncoding genes had comparable dispersion in our experiments. For each comparison, we confirmed the distribution of P-values as a criterion of statistical robustness [START_REF] Rigaill | Synthetic data sets for the identification of key ingredients for RNA-seq differential analysis[END_REF]. After processing the differential analyses, we interpreted the results by separating the genes as "coding" or "noncoding" as defined above.

For coding genes, we observed 3,315 genes differentially expressed between the two ecotypes over the kinetics, with 2,504 differentially expressed between at least two kinetics points over the two ecotypes (Fig. 4A; Supplemental Fig. S4B; Supplemental Table S3). The number of differentially expressed coding genes between ecotypes or along the stress kinetics was similar. However, the response to phosphate starvation was significantly impacted by ecotype in only 55 genes ("interaction" of the two factors; Fig. 4A). Upregulation was observed in 1,566 and 1,749 coding genes along the Figure 4. Differentially expressed genes according to ecotype and kinetic effects. Statistical analysis revealed differentially expressed genes between ecotypes and kinetics during phosphate starvation treatments for coding and noncoding genes. The differentially expressed genes can be grouped according to their significant link with genotype effect (different level between the two ecotypes), kinetic effect (differential between any pair of time points in the phosphate starvation kinetics), and the interaction of the two effects (showing differential expression in response to phosphate stress according to genotype). After determining the global distribution, genes were partitioned between coding (A) and noncoding (B) transcripts. Among noncoding genes, we sorted transcripts according to their being antisense to another annotation (C) or intergenic (D).

kinetics in Col and Ler, respectively (Supplemental Fig. S4B). Interestingly, a clear bias of expression between ecotypes could be observed for noncoding genes (Fig. 4B). Indeed, 675 (666 1 6 1 2 11) noncoding genes were differentially expressed between the two ecotypes, whereas only 70 (61 1 6 1 2) were differentially expressed along at least one point of the kinetics. Comparable biases were observed for both classes of noncoding genes, lincRNAs and NATs (Fig. 4, C andD). Globally, 146 lincRNAs and 236 NATs were significantly upregulated in Col compared to Ler and 106 lincRNAs and 187 NATs in Ler compared to Col (Supplemental Fig. S4C).

We used reverse transcription quantitative PCR (RT-qPCR) analysis of independent replicates of Col, Col er105 , and Ler to confirm the differential expression of 14 lincRNA genes (seven in Col and seven in Ler) previously identified in the RNA-sequencing (RNAseq) analysis. We were thereby able to confirm the differential expression of 12 lncRNAs (Fig. 5). Globally, Col and Col er105 showed similar expression levels despite minor differences. To investigate any dominant expression effect from one ecotype, we investigated the level of expression of these lncRNAs in the F1 offspring of Col and Ler crosses. Among the 12 differentially expressed genes, an intermediate level of expression was always detected (of which eight were statistically significant; Fig. 5). This suggests independent regulation of lncRNAs between the parental genomes and discards major dominant "trans" regulatory effects of lncRNA expression between genomes.

One interesting possibility is that specific lncRNAs may be expressed in the Col and Ler genomes in relation to known regulators of the Pi-starvation response. As a first such case, we were able to identify two specific Ler antisense lncRNAs to the Pie transporter AT5G43370/PHT1.2 gene [START_REF] Mudge | Expression analysis suggests novel roles for members of the Pht1 family of phosphate transporters in Arabidopsis[END_REF], which is expressed at a higher level in Ler compared to Col (Supplemental Fig. S5A). The increase of Pi transporter expression in Ler might impart an increased Pi uptake. As a second case, we found that a Col-expressed NAT RNA is complementary to SPX4, a critical regulator of phosphate responses [START_REF] Duan | Characterization of a sub-family of Arabidopsis genes with the SPX domain reveals their diverse functions in plant tolerance to phosphorus starvation[END_REF]; in our analysis, it shows reduced expression in Col compared to Ler (Supplemental Fig. S5B). In other cases, we observed that two consecutive coding transcripts showing differential levels of expression among ecotypes flank a lincRNA with an ecotype-specific expression pattern (Supplemental Fig. S5C), suggesting that various cis effects may be involved in these differential ecotype-linked expression patterns. S5). Results were analyzed by one-way ANOVA followed by Tukey's honestly significant difference (HSD) mean-separation test.

Lowercase letters indicate statistical difference among groups (P # 0.05).

Differential Accumulation of sRNAs between Ecotypes

The differential accumulation of sRNAs of 21/22and 24 nt was also examined in each ecotype and during the Pi starvation response. PCA of these sequencing data again clearly separated sRNA abundance between ecotypes but not at the level of the kinetics response (Supplemental Fig. S6, A andB; Supplemental Table S3). We identified 416 coding and 211 noncoding genes that accumulated 21/22-nt siRNAs differentially between ecotypes, with generally more siRNAs in Ler (298 coding genes, 83 lincRNAs, and 40 NATs) than in Col (118 coding genes, 49 lincRNAs, and 39 NATs; Supplemental Fig. S6D; Supplemental Table S3). A greater number of genes accumulated 24-nt siRNAs between ecotypes differentially, showing upregulated siRNAs in Ler (758 coding genes, 189 lincRNAs, and 67 NATs) compared to Col (391 coding genes, 104 lincR-NAs, and 69 NATs; Supplemental Fig. S6E).

Concerning miRNA, as for other small RNAs, the PCA analysis showed only differences between ecotypes (Supplemental Fig. S6C). Indeed, 38 miRNAs were differentially expressed between the two ecotypes (15 and 23 for Col and Ler, respectively, Supplemental Fig. S6F; Supplemental Table S3). Interestingly, the families of miR399 and miR397 specifically accumulated in the Ler ecotype. These miRNAs target the PHOSPHATE2 (PHO2) and NITROGEN LIMITATION ADAPTATION (NLA) transcripts, the encoded proteins of which are known to act together to allow degradation of the Pi transporter PHT1;4 [START_REF] Park | NITROGEN LIMITATION ADAPTA-TION recruits PHOSPHATE2 to target the phosphate transporter PT2 for degradation during the regulation of Arabidopsis phosphate homeostasis[END_REF]. In Ler, the higher amount of miR399 and miR397 might be expected to lead to a lower level of PHO2 and NLA and therefore a higher level of PHT1;4 protein; upregulation could increase Pi uptake if there were no counteracting posttranslational regulations affecting Pi transporters [START_REF] Bayle | Arabidopsis thaliana highaffinity phosphate transporters exhibit multiple levels of posttranslational regulation[END_REF][START_REF] Nussaume | Phosphate import in plants: Focus on the PHT1 transporters[END_REF][START_REF] Ayadi | Reducing the genetic redundancy of Arabidopsis PHOSPHATE TRANSPORTER1 transporters to study phosphate uptake and signaling[END_REF]. However, we observed no difference between ecotypes in accumulation of transcripts of those targets, namely PHO2 and NLA, nor of PHT1;4 (as also previously reported; [START_REF] Shin | Phosphate transport in Arabidopsis: Pht1;1 and Pht1;4 play a major role in phosphate acquisition from both low-and high-phosphate environments[END_REF][START_REF] Ayadi | Reducing the genetic redundancy of Arabidopsis PHOSPHATE TRANSPORTER1 transporters to study phosphate uptake and signaling[END_REF]. This result suggests that the promoter activity of PHO2 and NLA may compensate for the increased accumulation of these miRNAs in Ler.

Misregulation of lncRNA Expression Affects Primary Root Growth in Col

The different patterns of lncRNA expression between ecotypes may induce regulation of root-growth responses. We selected five lncRNA genes, NPC15, NPC34, NPC43, NPC48, and NPC72, that showed differential expression among ecotypes to study the impact of their expression on Col primary root growth. In RNA-seq data, three of these lncRNA genes were more highly expressed in Col (NPC15, NPC43, and NPC72) and two in Ler (NPC34 and NPC48; Fig. 6A). The potential dominant expression patterns of these lncRNA genes were evaluated in two F1s of Col 3 Ler reciprocal crosses. Expression analysis by RT-qPCR confirmed the RNA-seq results for NPC15, NPC34, and NPC72 genes (Fig. 6B), whereas for NPC48, the differential expression was only detected in the Col er105 mutant, suggesting that the erecta mutation affects the expression of this gene. No differential expression could be detected for the NPC43 gene. This latter result might be due to the concomitant accumulation of antisense transcripts (NPC504) in this locus. The differential expression of lncRNA genes between ecotypes could be linked to S5). Results were analyzed by one-way ANOVA followed by Tukey's HSD mean-separation test. Lowercase letters indicate statistical difference among groups (P # 0.05).

genetic changes at their loci. No significant modifications (except a few SNPs) were detected between Col and Ler for the NPC34, NPC43, and NPC48 loci (Supplemental Fig. S7). By contrast, the NPC15 locus contains an insertion of 2,417 nt in Ler v8 and NPC72 is completely missing in Ler v7 and v8 genomes (Supplemental Fig. S7, A-E). Therefore, genome modifications at the locus level could explain the specific expression pattern of NPC72 and NPC15 genes in Ler.

To support the potential actions of lncRNAs at a phenotypic level, we used overexpressing (35S promoter) and T-DNA insertion lines for the same five genes (NPC15, NPC34, NPC43, NPC48, and NPC72) in Col to monitor the effects on root growth in control and low-Pi conditions. In control conditions, only NPC48 and NPC72 overexpression lines led to significant root growth reduction compared to Col (Fig. 7, A andB; Supplemental Fig. S8, A-E). The T-DNA insertions have been mapped in the 59 region of the NPC48 and NPC72 loci. In these lines, the lncRNAs were overexpressed (Fig. 7C). Furthermore the npc48 T-DNA line strongly supported the phenotype observed with the 35S:NPC48 lines. Repeating the analysis in low-Pi conditions known to inhibit root growth in Col but not in Ler, we observed minor differences in root length across the different lines. A priori, the ratio of root growth in control and low-Pi conditions should highlight potential differences in Pi sensitivity of transgenic lines. This ratio was significantly increased for NPC48 and NPC72 lines compared to Col (Supplemental Fig. S8F).

NPC48 and NPC72 deregulated lines presented a significant decrease in root length in control conditions, but not in low-Pi conditions. Perhaps even in control conditions the mutants act as if they are partially limited in Pi. Hence, we asked whether these phenotypes could be linked to a root growth arrest due to oversensitive perception of Pi starvation under control conditions or an alteration of Pi systemic sensing (which would affect Pi uptake). This does not seem to be the case, since (1) known Pi-starvation markers were not deregulated in roots under control conditions; and

(2) these markers were induced in these lines to the same extent as in Col (Supplemental Fig. S9, A andB). Then, we investigated the local Pi signaling response, exploiting the genes LOW PHOSPHATE ROOT1 (LPR1) and LPR2 and the transcription factor STOP1, which are known to be locally involved in primary root growth arrest under low Pi [START_REF] Svistoonoff | Root tip contact with low-phosphate media reprograms plant root architecture[END_REF][START_REF] Ticconi | ER-resident proteins PDR2 and LPR1 mediate the developmental response of root meristems to phosphate availability[END_REF][START_REF] Müller | Iron-dependent callose deposition adjusts root meristem maintenance to phosphate availability[END_REF]Balzergue et al., 2017;[START_REF] Mora-Macías | Malatedependent Fe accumulation is a critical checkpoint in the root developmental response to low phosphate[END_REF]. Expression analysis of NPC48 and NPC72 genes in lpr1/lpr2 and stop1 mutant lines revealed no significant variation of these lncRNA expression patterns (Fig. 7C). Reciprocally, no significant expression variation was detected for the LPR1/LPR2 pathway in NPC48 or NPC72 lines (LPR1, LPR2, STOP1, ALMT1, and MATE genes; Supplemental Fig. S9,C to G).

To gain further insight into the function of NPC48, we performed an RNA-seq analysis of Col ecotype overexpressing NPC48 (35S:NPC48-1) or not under control conditions (where the phenotype was observed) in order to assess the impact of NPC48 deregulation on the genome-wide transcriptome (Fig. 8). Among the 158 differentially expressed genes, 140 were coding genes, and the majority of these were upregulated in correlation with increased NPC48 expression (Fig. 8, A andB). In contrast, the great majority of noncoding genes were downregulated in 35S:NPC48-1, including two lincR-NAs and 15 NATs, but none of their antisense coding genes. Since NPC48 was upregulated in the Ler ecotype, we asked whether, among the deregulated genes, several could be linked to ecotype-or phosphate kineticsrelated variations (Fig. 8, C andD). However no such direct link could be made, as the large majority of deregulated genes are not linked to any of these categories. To further confirm the deregulation of specific targets, we also tested another 35S:NPC48 line showing an intermediate level of expression compared to 35S:NPC48-1 (Supplemental Fig. S10). Several genes could not be confirmed in this second line, notably the strongly downregulated gene in 35S:NPC48-1 encoding the iron-regulated gene AT3G01260 (Rodríguez-Celma et al., 2013). Among the genes differentially expressed, we could identify several linked to nutrient transport and root hair growth, such as ABCB3 [START_REF] Shibata | GTL1 and DF1 regulate root hair growth through transcriptional repression of ROOT HAIR DEFECTIVE 6-LIKE 4 in Arabidopsis[END_REF], CPL1 [START_REF] Zhang | C-terminal domain (CTD) phosphatase links Rho GTPase signaling to Pol II CTD phosphorylation in Arabidopsis and yeast[END_REF], and JAL22 [START_REF] Diet | The Arabidopsis root hair cell wall formation mutant lrx1 is suppressed by mutations in the RHM1 gene encoding a UDP-L-rhamnose synthase[END_REF]. ABCB3 and CPL1 were repressed by overexpression of NPC48, in contrast to JAL22 (Supplemental Fig. S10). There were also several genes encoding known growth regulators of primary root growth that might be linked to the NPC48 overexpression phenotype (Supplemental Table S8). These include RGF7, BIG, RPK2, and CASP5, which were upregulated. Differentially expressed genes are significantly enriched in genes regulated during iron starvation (25 of 138 genes; P 5 1.62 3 10 23 ; [START_REF] Rodríguez-Celma | Mutually exclusive alterations in secondary metabolism are critical for the uptake of insoluble iron compounds by Arabidopsis and Medicago truncatula[END_REF]. Hence, we tested, in the two different NPC48 overexpressing lines, whether overexpression of NPC48 might lead to iron-related phenotypes such as (1) modification of the expression of genes related to iron responses (Supplemental Fig. S11, A andB), (2) significant root growth in response to changes in the iron concentration of the growth medium (Supplemental Fig. S11, C and D), or (3) modification of iron accumulation in root tips (Supplemental Fig. S11, E andF). However, no clear link could be made with any alteration of Fe-related responses dependent on NPC48 misregulation.

Altogether, the identification of ecotype-related lncRNAs allowed us to characterize further regulators of primary root growth. However, overexpression of NPC48 did not affect the Col transcriptome in a manner that could be linked to Ler expression patterns. Nevertheless, several genes related to transport, growth regulation, and root hair function are deregulated by misregulation of this ecotype-specific lncRNA.

DISCUSSION

Until recently, transcriptome studies were mainly focused on protein-coding gene transcripts and ignored lncRNAs. Variation in the nt sequences or expression patterns of the noncoding genome can have less pleiotropic effects than changes in the protein sequence of critical regulators. However, it is now commonly accepted that lncRNAs can play central roles in development and response to environmental conditions by their expression in a particular cell-type (Ariel et al., 2015). In the current study, using strand-specific RNA-seq analysis in root tips, we identified thousands of previously uncharacterized lncRNAs (lincR-NAs and NATs) expressed at low levels from two Arabidopsis accessions. We focused on root growth, as it is a complex trait that is responsive to the soil environment [START_REF] Petricka | Control of Arabidopsis root development[END_REF] and impacts a large number of loci spread across the genome. Interestingly, in our study, we observed many lncRNA genes that were differentially or specifically expressed in Col or Ler, in contrast to the number of protein-coding genes. It had already been shown in other systems that intraspecies variation is strongly linked to the noncoding part of the genome. For example, ;45% of diseaseassociated human SNPs mapped to noncoding regions of the genome [START_REF] Ning | LincSNP 2.0: An updated database for linking disease-associated SNPs to human long non-coding RNAs and their TFBSs[END_REF]. In chicken (Gallus gallus), domestication traits governing body morphology or behavior are under selection and often associated with lncRNA genes (Wang et al., 2017). Similarly, in plants, the comparison of SNPs associated with fruit phenotypes in two tomato (Solanum lycopersicum) cultivars also corresponded to noncoding genomic regions [START_REF] Scarano | In silico identification and annotation of non-coding RNAs by RNA-seq and de novo assembly of the transcriptome of tomato fruits[END_REF]. The SNPs could act directly at the level of lncRNA expression or affect the expression of lncRNA-neighboring genes (Kopp and Mendell, 2018). lncRNAs are thus elements to be considered in genetic association studies.

Surprisingly, very few ecotype-specific lncRNAs coincided with the deletion of specific DNA sequences in one particular ecotype (Fig. 3). Hence, we propose that the lncRNA differential expression of a relatively similar DNA molecule results from shifts in transcription rate or stability of lncRNAs that could be connected to SNP or InDel polymorphisms in promoters and/or rearrangements distant from lncRNA loci in the two ecotypes (e.g. transposon insertions). As lncRNAs can repress or activate the transcription of other genes, the expression polymorphisms observed between the two ecotypes could also result in a cascade of cis-local or trans-distal action on target genes (Ariel et al., 2015;[START_REF] Marchese | The multidimensional mechanisms of long noncoding RNA function[END_REF]. It is noteworthy that the majority of ecotype-specific lncRNAs identified did not colocalize with siRNAs and thus could not reflect putative gene silencing differences between ecotypes (either transcriptional or posttranscriptional processes; Matzke and Mosher, 2014). This points to the lncRNA itself, or its transcription, being linked to the quantitative regulation of target gene expression [START_REF] Marchese | The multidimensional mechanisms of long noncoding RNA function[END_REF].

We were able to confirm, by RT-qPCR, the expression levels of 12 lincRNAs among the 14 chosen for validation, supporting the expression variation identified by RNA-seq (Fig. 5). Allele-specific expression is known to affect productivity in plants [START_REF] Springer | Allele-specific expression patterns reveal biases and embryo-specific parent-of-origin effects in hybrid maize[END_REF]. Moreover, in Arabidopsis, heterosis has been reported for different traits, such as flowering time [START_REF] Seymour | Genetic architecture of nonadditive inheritance in Arabidopsis thaliana hybrids[END_REF] or phosphate acquisition [START_REF] Narang | Phosphate acquisition heterosis in Arabidopsis thaliana: A morphological and physiological analysis[END_REF]. As lncRNAs are able to modify chromatin and thus alter gene expression, we added, in our expression analysis, the study of the F1 resulting from reciprocal crosses between Col and Ler. In the F1 hybrid, the 12 confirmed differentially expressed lncRNA genes chosen for validation globally exhibited an additive expression pattern compared to their parents. This is consistent with results obtained in maize (Zea mays) F1 hybrids, where additivity is frequently observed for lncRNAs (Li et al., 2014).

Analysis of the pan-genome (restricted to coding genes) of 19 Arabidopsis ecotypes showed that at least 70 accessory genes could be identified in each ecotype (Contreras-Moreira et al., 2017). In response to stress, accessory genes can explain at least part of the phenotypic difference of behavior observed among ecotypes [START_REF] Gan | Multiple reference genomes and transcriptomes for Arabidopsis thaliana[END_REF]. Since we find that lncRNAs have a lower selection pressure (Fig. 2), our results give further evidence that they might play a similar role as accessory coding genes in response to environmental changes.

Root apexes play an important role in sensing external stimuli. We examined the gene expression profile soon after stress application (at 1 and 2 h) in two ecotypes that present differences in response to Pi starvation. In the two ecotypes, the number of differentially expressed coding genes during stress kinetics was similar to that between ecotypes. By contrast, a clear bias of specific expression of lncRNAs and siRNAs was identified. However, among the coding genes that are differentially expressed along the Pi kinetics, one-quarter are also differentially expressed between the two ecotypes, whereas for the noncoding genes, this fraction is one-eighth (Fig. 4). This suggests that the main part of the ecotype-specific phosphate response comes from the coding portion of the genome. However, despite their lack of response to early stress application, lncRNAs may still influence the plant response to stress by priming the chromatin conformation for a fast response of the coding part of the genome.

The general adaptation of root architecture in response to low Pi consists of an arrest of primary root growth after the perception of Pi limitation. In Arabidopsis, studies concerning plant Pi homeostasis during Pi deficiency characterized the IPS1/AT4 lncRNA controlling the distribution of Pi from root to shoot. It acts as a target mimic for miR399, which regulates PHO2 mRNAs [START_REF] Lin | Regulatory network of microRNA399 and PHO2 by systemic signaling[END_REF]. Moreover, [START_REF] Yuan | Systematic characterization of novel lncRNAs responding to phosphate starvation in Arabidopsis thaliana[END_REF] identified lncRNAs differentially expressed in roots and shoots of plants grown in the presence or absence of Pi for 10 d. Those authors suggested that a coexpression between lncRNAs and adjacent coding genes may be linked to cis-regulation by lncRNAs of target genes involved in Pi-starvation processes. Interestingly, in fission yeast (Schizosaccharomyces pombe), two of the three genes of the Pi regulon are repressed in Pi-rich medium by the transcription of lncRNA genes [START_REF] Shah | lncRNA recruits RNAi and the exosome to dynamically regulate pho1 expression in response to phosphate levels in fission yeast[END_REF] that are present in the 59 region (cisregulation). The molecular mechanisms that govern root growth modification by Pi have been mostly elucidated in Col plants. For the local impact of Pi (restricted to root architecture), Pi deficiency is sensed by the root tips and primary root growth inhibition is induced by both the reduction of cell elongation (STOP1 and LPR1/LPR2 pathways) and the progressive arrest of meristem division (LPR1/LPR2 pathway), notably linked to the presence of iron in the medium. Expression analysis in response to Pi deficiency in our mutant lines NPC48 and NPC72 did not link these two lncRNAs to Pi starvation root growth arrest mediated by LPR1/ LPR2 and STOP1 pathways.

The overexpression of NPC48 leads to reduction of the main root growth. Transcriptome analysis of the strongest deregulated line (35S:NPC48-1) shows enrichment in genes that are deregulated during iron starvation [START_REF] Rodríguez-Celma | Mutually exclusive alterations in secondary metabolism are critical for the uptake of insoluble iron compounds by Arabidopsis and Medicago truncatula[END_REF]. However, no visible relation to iron homeostasis could be confirmed in the lines overexpressing NPC48. Apart from the link to iron starvation, no enrichment of any pathway could be demonstrated. Few identified deregulated genes could be confirmed in the second line, possibly because of the lower level of expression of NPC48 in this line or because of the specificity of the T-DNA insertions of both lines. Notably, the iron-regulated gene AT3G01260 was only deregulated in the 35S:NPC48-1 line. Few potential regulators of root growth or genes related to root hair growth and nutrient transport are modified. Nevertheless, we confirmed the presence of these latter genes in the second overexpressing line, supporting their link with NPC48 overexpression. Overexpression of NPC48 may decrease the absorption of essential nutrients, leading to a restriction of root growth by global nutrition deficiency. This lncRNA is a quantitative regulator of primary root growth, but its overexpression did not show any major alteration in the transcriptome. However, the overexpression did modify expression of several genes dealing with root growth or nutrient assimilation, and this is likely linked to its quantitative phenotype. Only a few core regulators of root growth rate have been identified up to now [START_REF] Satbhai | Underground tuning: Quantitative regulation of root growth[END_REF][START_REF] Motte | Molecular and environmental regulation of root development[END_REF]. Clearly, one can expect more subtle regulators to exist, and NPC48 might be one of them.

In Arabidopsis, using grafts between ecotypes presenting a high frequency of SNPs, [START_REF] Thieme | Endogenous Arabidopsis messenger RNAs transported to distant tissues[END_REF] showed that about 2,000 mRNAs, among which 9,300 contain SNPs, could move in plants that were subjected to Pi deficiency for 2 weeks. These mRNAs were transported from root to shoot or shoot to root. The authors suggested that these mobile mRNAs might function widely as specific signaling molecules coordinating growth, cell differentiation, and stress adaptation of distant plant organs. As the lncRNAs described here have 39 polyadenyylated tails and are probably 59 capped, it is tempting to assume that at least some of them can be transported through the xylem and/or the phloem and may contribute to systemic signaling responses.

Globally, the in-depth exploration of the noncoding transcriptome of two ecotypes presented in this work identified thousands of previously uncharacterized lncRNAs with ecotype-specific expression. Statistical analysis among ecotypes identified several coregulations between coding and noncoding genes (including sRNAs). These coregulations are likely linked to the evolution of different regulatory mechanisms among ecotypes grown in diverse soil environments, and our detailed study of specific cases has provided two ecotype-related lncRNAs that are potentially involved in regulating primary root growth.

MATERIALS AND METHODS

Plant Growth

Seeds were surface-sterilized and sown on a horizontal line in plates vertically disposed in a growing chamber (16-h photoperiod; intensity 90 mE; 21°C). The growth medium was previously described in Balzergue et al. (2017). The 2 Pi and 1Pi agar medium contained 10 and 500 mM Pi, respectively.

For the root apex isolation, seeds were sown on 1-cm bands of nylon membrane (Nitex 100 mm). After 1 week on 1Pi agar medium, the membranes were transferred to 2Pi agar medium. Plants were sampled at time points 0, 1, and 2 h after transfer. Each biological replicate is a pool of .100 root apexes cut at 0.5 cm from the root tip.

Arabidopsis Lines

The stop1 (SALK_114108, Nottingham Arabidopsis Stock Center [NASC] reference N666684), lpr1;lpr2 [START_REF] Svistoonoff | Root tip contact with low-phosphate media reprograms plant root architecture[END_REF]

Library Construction and Sequencing

For each time point (0, 1, and 2 h), total RNA of three biological replicates of the Col er105 and Ler pool of root apexes were extracted following the RNeasy micro kit (Qiagen) protocol. One microgram of total RNA of each sample was used for mRNA library preparation using the Illumina TruSeq Stranded mRNA library preparation kit according to the manufacturer's instructions. Libraries were sequenced on a HiSeq 2000 Sequencing System (Illumina) using 100-nt paired-end reads.

For 35S:NPC48-1 RNA-seq analysis, total RNA of three biological replicates of whole roots from Col and 35S:NPC48-1 were extracted using a Quick-RNA Miniprep kit (Zymo Research). One microgram of total RNA of each sample was used for mRNA library preparation using an Illumina Truseq Stranded mRNA library preparation kit according to the manufacturer's instructions. Libraries were sequenced on a NextSeq 500 Sequencing System (Illumina) using 75-nt single-end reads.

sRNAs of root apexes were extracted using the mirVana miRNA Isolation Kit (Ambion). sRNA libraries were constructed using the Ion Total RNA-Seq Kit v2 (Ion Torrent, Life Technologies) according to the manufacturer's instructions. Libraries were then sequenced using IonProton and the adapters.

Previously Uncharacterized Transcript Identification

According to their ecotype of origin, transcript reads were aligned to the TAIR10 (Lamesch et al., 2012) or Ler v7 [START_REF] Gan | Multiple reference genomes and transcriptomes for Arabidopsis thaliana[END_REF] genome. The previously uncharacterized transcripts were predicted in each ecotype independently and then cross-positioned on the other genome to analyze homology. Using the information available on the Col genome from databases, the different transcripts were classified as coding and noncoding (lncRNAs), lncRNAs included lincRNAs and NATs. See the detailed protocol in Supplemental Materials and Methods.

sRNA Analysis

The cleaned sRNA reads were aligned on the TAIR10 or Ler v7 genome. For Araport11 annotations and previously uncharacterized genes predicted here, the accumulation of sRNA was analyzed to classify them as miRNA, phased siRNA cluster, or siRNA cluster and determine the main siRNA size. See detailed protocol in Supplemental Materials and Methods.

Expression Analysis

For each annotation, mRNA reads were counted to estimate the level of expression of each gene. These counts were used for differential gene expression analysis. Using siRNA accumulation on each annotation, differential accumulation of 21/22-nt and 24-nt sRNAs on coding and noncoding genes was computed independently. Bonferroni correction of the P-value was used for each analysis. Differentially expressed genes or siRNA accumulations were defined as having an adjusted P-value inferior to 0.01. See the detailed protocol in Supplemental Materials and Methods.

Measurement of the Primary Root Length

Images were taken with a flat scanner and root lengths were measured using RootNav software (Pound et al., 2013).

RT-qPCR

Total RNA was extracted from whole roots using the Quick-RNA MiniPrep kit (Zymo Research, USA) according to the manufacturer's instructions. Reverse transcription was performed on 500 ng total RNA using the Maxima Reverse Transcriptase (Thermo Scientific). RT-qPCR was performed on a 480 LightCycler thermocycler (Roche) according to the manufacturer's instructions with Light cycler 480 SYBR Green I Master (Roche) and the primers listed in Supplemental Table S4. We used PP2A subunit PD (AT1G13320) as a reference gene for normalization.

Statistics and Reproducibility of Experiments

For each measure (root length, qPCR expression level), the least-squares means were computed. This approach makes it possible to correct for interrepetition variation. Data are presented as least-squares means 6 SE. The statistical significance tests are included in the legend of each figure. See detailed protocol in Supplemental Materials and Methods.

Accession Numbers

Sequence files generated during this study have been deposited in the NCBI GEO database under the accessions GSE128250, GSE128256 and GSE151005. Names and accession numbers of genes mentioned are listed in Supplemental Table S9. A, Flowchart of identification of lncRNAs responsive to Pi starvation in two Arabidopsis ecotypes. Plants of the two ecotypes were grown in control condition for seven days before transfer to low phosphate condition. Root tips were then sampled at time point 0 h, 1 h and 2 h after transfer. After RNA extraction, PolyA transcripts were sequenced. The retrieved reads were then mapped independently for the two ecotypes onto their respective genomes (TAIR10 for Col and Ler v7 for Ler). Based on this mapping we predicted new transcriptional units on each genome compared to TAIR10 annotation available on both genomes. We then aligned the resulting transcriptional data onto the opposite genome to compute a homology and fused the overlapping transcripts. Only transcripts with a length superior to 200 nt were kept for further analysis. Each transcript was then categorized in one of the 4 following classes: coding, structural RNA (rRNA, tRNA, snRNA, snoRNA, ...), transposable element and non-coding RNA. The classification was estimated based first the overlap with already annotated transcripts (by order of importance in Araport11, RepTAS database, CANTATA database, BenAmor et al. 2009, Li et al. 2016 andmiRBase v21). For the transcripts that were not found in any of these databases, their coding potential was predicted using COME. B and C, occurrence of the different detected genes in the different Arabidopsis databases for genes predicted as coding (B) or as non-coding (C). A and B, distribution of coding genes, non-coding genes, structural RNA and transposable elements according to their level of expression in Col (A) or Ler (B). C, Number of newly detected genes per percent of additional sequencing reads of the library. Line corresponds to the median of 100 bootstraps, grey shadow corresponds to the standard deviation. A, PCA analysis showing the effect of ecotype and phosphate kinetics on the variance of gene expression. For the 3315 coding genes B, and 675 non-coding genes C, that are ecotype differentially expressed, we give the number of genes that accumulated more in Col and in Ler. Ler_NEW_RNA_R_34181 A to C, PCA analysis showing the effect of genotype and phosphate kinetics on the variance between samples for 21/22nt (A), 24nt (B) and miRNAs (C). The samples can be well separated according to genotype (Col or Ler) but not according to the phosphate kinetics time point. D to F, For ecotype differentially accumulated siRNAs, the number of differentially accumulated siRNAs in either ecotype for 21/22nt siRNA precursors (D), 24nt siRNA precursors (E) and miRNAs (F). A to E, genome alignment around NPC15 (A), NPC34 (B), NPC43 (C), NPC48 (D) and NPC72 (E) between the reference sequence of Arabidopsis Col and two sequences from Ler (Ler v7 and Ler v8). The identity line is color coded according to conservation: green is for full conservation, yellow is for mismatches. For each sequence, dashes represent missing sequences, plain grey represents identical sequences and plain black represents specific insertion or mismatches.

Col Ler

• • • • • • • • • • • • • • • • • • 0 1 2 3 4 t0 t1 t2 time average reads Ler_NEW_RNA_R_34180 • • • • • • • • • • • • • • • • • •
• • • • • • • • • • • • • • • • • • 0 3 6 9 12 t0 t1 t2 time average reads AT5G43370/PHT1;2 • • • • • • • • • • • • • • • • • • 2000 4000 6000 t0 t1 t2 time average reads A C • • • • • • • • • • • • • • • • • • 200 400 600 t0 t1 t2 time average reads AT1G31885/NIP3;1 • • • • • • • • • • • • • • • • • • 3000 5000 7000 t0 t1 t2 time average reads AT1G31910 • • • • • • • • • • • • • • • • • • 0 5 10 15 t0 t1 t2 time average reads AT1G31920 • • • • • • • • • • • • • • • • • • 0 200 400 600 t0 t1 t2 time average reads At1NC041650 • • • • • • • • • • • • • • • • • • 100
Supplemental Figure S7 -Genome homology at selected ncRNA loci

Supplemental Figure S8 -Deregulation of selected non-coding RNAs

A to E, Representative pictures of a root of each genotype 11 days after sowing on control or low phosphate condition. The scale represents 1 cm. F, Main root length ratio between root of 11-day-old plants grown under low phosphate and that grown under control condition. Measures represent corrected means of all possible ratios (n ≥ 14, details in Supplemental Table S6). G to I, Expression levels of NPC15, NPC34 and NPC43 in root of 11-day-old plants grown under control or low phosphate conditions of lines deregulated in the corresponding non-coding gene. Measures represent corrected means of log 2 fold changes compared to Col (B and F) or WS (D). Error bars represent standard error (n ≥ 2, details in Supplemental Table S5). F to I, results were analyzed by two-way analysis of variance (ANOVA) followed by Tukey's HSD posthoc test: groups with different letters are statistically different (p ≤ 0.05). S5). Results were analyzed by two-way ANOVA followed by Tukey's HSD posthoc test: groups with different letters are statistically different (p ≤ 0.05). S5). Results were analyzed by one-way ANOVA followed by Tukey's HSD posthoc test: groups with different letters are statistically different (p ≤ 0.05). S9. Measures represent log 2 fold changes compared to Col measured by RT-qPCR. No statistically significant differences in the expression of these genes were found (n ≥ 2, details in Table S5). C, mean primary root length according to genotype and iron condition 11 days after sowing (n ≥ 26, details in Table S6). D, Representative pictures of roots for each genotype 11 days after sowing in control or low phosphate conditions, the scale represents 0.5cm. E and F, Fe distribution in primary root tips. Four-days old seedlings of the indicated genotypes, grown on high phosphate, were transferred to high phosphate (E) or low phosphate (F) plates for 48 h prior to Perls/DAB staining. Images are representative of two independent experiments. the scale represent 100µm. Measures represent corrected means of the FC (A, B) or length of the primary root (C). Error bars represent standard error. Results were analyzed by one-way ANOVA followed by Tukey's HSD posthoc test: groups with different letters are statistically different (p ≤ 0.05).

Col

Expression of gene down-regulated in 35S:NPC48-1 RNASeq

Expression of gene related to iron homeostasis in low iron condition

Supplemental Materials and Methods

New transcript identification

All reads were quality trimmed using Trimmomatic and remaining ribosomal sequences were removed using sortMeRNA (Kopylova et al., 2012). According to their ecotype of origin, mRNA cleaned reads were aligned on TAIR10 (Lamesch et al., 2012) or Ler v7 [START_REF] Gan | Multiple reference genomes and transcriptomes for Arabidopsis thaliana[END_REF] genome using TopHat2 (version 2.0.13, (Kim et al., 2013)) with the following arguments: --max-multihits 1 --num-threads 8 -i 20 --minsegment-intron 20 --min-coverage-intron 20 --library-type fr-firststrand -microexon-search -I 1000 --max-segment-intron 1000 --max-coverage-intron 1000 --b2-very-sensitive. Independently of ecotype, new transcripts were predicted using GFFprof included in RNAprof [START_REF] Tran | Detection of generic differential RNA processing events from RNA-seq data[END_REF] The transcripts predicted on each genome, TAIR10 and Ler v7, were positioned on the other genome, respectively Ler v7 and TAIR10, and on Ler v8 (Zapata et al., 2016) using blastn (from BLAST suite 2.2.29+) using a maximum e-value of 10 -4 . For each transcript, the different blast hits fragments were fused together if the distance between two fragments was less than 5000 nucleotides and placed on the same strand of the chromosome. Only hits with at least 90 % of sequence identity and where the length was conserved (at least 90 % and less than 110 % of length outside of insertion) were kept.

For each transcript only the best hit was conserved according first to the conservation of the sequence length and then identity. In case of hits of the same strength, a higher priority was given when the chromosome and then the strand were conserved. Each transcript was therefore placed on each of the three genomes.

For Ler-predicted transcripts only those positioned on Ler v8 were kept. On each genome independently, transcripts coming from the same ecotype (GFFprof prediction)

or the other one (blastn positioning) were fused using cuffmerge (version 1.0.0) with default parameters. Only transcripts longer than 200nt in either ecotype were kept for further processing.

Based on the position of the transcripts on the TAIR10 genome, new transcripts were annotated according to already known transcripts in the following databases:

Araport 11 (Cheng et al., 2017), RepTas (Liu et al., 2012), CANTATAdb [START_REF] Szcześniak | CANTATAdb: A collection of plant long non-coding RNAs[END_REF], miRBase v21 (Kozomara and Griffiths-Jones, 2014) lncRNAs predicted from Ben [START_REF] Amor | Novel long non-protein coding RNAs involved in Arabidopsis differentiation and stress responses[END_REF] and root predicted lncRNAs from Li et al. (2016). GffCompare (version 0.10.4, https://ccb.jhu.edu/software/stringtie/gffcompare.shtml) was used for the comparison. In case of overlap with a known transcript (=, c, k, j, e, o codes of GffCompare), the closest transcript was used to determine the identification and the coding potential of the transcript. For previously non-discovered transcripts we used the COME software (Hu et al., 2017) to predict their coding potential.

Library saturation analysis

The saturation of libraries was computed using the RPKM_saturation.py script included in RSeQC v3.0.0 [START_REF] Wang | RSeQC: quality control of RNA-seq experiments[END_REF][START_REF] Wang | Measure transcript integrity using RNA-seq data[END_REF]. Raw counts were quantified at gene level from resampled bam for each additional 2% of the reads from 2% to 100% of the reads (-l 2 -u 100 -s 2). The saturation was computed 100 times.

For each library and each sampling the number of detected gene (known or new and coding or non-coding) was defined as the number of genes having at least one read.

Small RNA analysis

The cleaned small RNA reads were aligned on TAIR10 or Ler v7 genome using ShortStack (version 3.8.5, Johnson et al. ( 2016)) without mismatch (--mismatches 0), keeping all primary multi-mapping (--bowtie_m all) and correcting for multi-mapped reads according to the uniquely mapped reads (--mmap u).

For each annotation in Araport11 (mRNA coding and non-coding and TE) and each new annotation predicted in this study according to mRNA sequencing the accumulation of small RNA was analyzed using ShortStack with default parameters. The counts for 21nt and 22nt were summed for each sample. Using these new counts, the DicerCall, defined as the size of the majority of the reads of a cluster, was recomputed.

The other description and counting are according to ShortStack prediction.

Expression analysis

For each annotation, coding and non-coding, mRNA reads were counted with htseq-count (Anders et al., 2015) using strand specific and intersection strict mode (--stranded=reverse -t gene --mode=intersection-strict). These counts were used for differential gene expression analysis with DEseq2 (v1.16.1 (Love et al., 2014)) using a linear model and as factors the ecotype (two levels), the kinetic time (3 levels), the interaction between the two and the replicate (3 levels). Differential comparisons between the two ecotypes were computed in average over the 3 time points of the kinetic. Differential comparisons between each point of the kinetic were computed on average over the 2 ecotypes. Low counts were discarded using DESeq2 independent filtering with default parameters and raw p-values were adjusted with the Bonferroni method. Differentially expressed genes were defined as having an adjusted p-value lower than 0.01.

Differential siRNA accumulation was computed using DESeq2 with a model taking into account only the genotype (two levels) as factor, and using the counts of ShortStack. Differential accumulation was computed independently for the 21/22nt on one side and the 24nt on the other side and limited to coding and non-coding genes.

Bonferroni correction of the p-value was used and differential siRNAs were defined as having an adjusted p-value inferior to 0.01.

Statistics and reproducibility of experiments

Statistical analyses were performed using R (v3.4.2 (R Core Team, 2017)) with the help of the tidyverse (v1.2.1 [START_REF] Wickham | tidyverse: Easily Install and Load the[END_REF]) and emmeans packages [START_REF] Lenth | emmeans: Estimated Marginal Means, aka Least-Squares Means[END_REF]. For each measure (root length, qPCR expression level, experimental repetition), the least-squares means were computed taking into account all the factors (genotype and condition) in a linear model. This allows correcting for inter-repetition variation.

Data are presented as least-squares means±SEM.

The results for statistical significance tests are included in the legend of each figure. 'n' values represent the number of independent samples in a repetition, i.e. the number of roots or pools of root per condition. The number of independent experiments is denoted as "repetition". For each analysis, the detail number of repetition and number of sample per repetition are available in Supplemental Table S5 and Supplemental Table S6.

PERLs-DAB staining

PERLs-DAB-staining was adapted from [START_REF] Roschzttardtz | Identification of the endodermal vacuole as the iron storage compartment in the Arabidopsis embryo[END_REF]. Plants were incubated 15 minutes in 0.5% (v/v) HCl, 2% (w/v) K-ferrocyanide, washed twice with dH2O and incubated 15 minutes in methanol containing 10 mM Na-azide and 0.3% (v/v) H2O2. After washing with 100 mM Na-phosphate buffer (pH 7.4), plants were incubated 5 min in the same buffer containing 0.0125% (w/v) DAB (Sigma-Aldrich) and 0.005% (v/v) H2O2. The reaction was stopped by washing twice with 100 mM Na-phosphate buffer (pH 7.4) and plants were optically cleared with 8:2:1 hydrochlorate:water:glycerol (w/v). Roots were observed and documented with the Axiozoom-V16 macroscope Zeiss (microscopy equipment from the ZoOM, CEA Cadarache) using the 2.3x objective.

Supplemental Tables

Supplemental Table S1 Long non-coding RNAs (lncRNAs) with their low abundance and poor level of conservation were initially thought to be non-functional or "junk" DNA. Nowadays, it is clear that many lncRNAs are functional, and constitute important regulators of cell life. For example, the Xist lincRNA is the major actor of the X-inactivation process in mammal, disturbing its functionality leads to a plethora of medical significance such as cancer predisposition [START_REF] Brown | Skewed X-Chromosome Inactivation : Cause or Consequence[END_REF], Rett syndrome [START_REF] Huppke | Very mild cases of Rett syndrome with skewed X inactivation[END_REF], autoimmunity [START_REF] Simmonds | Skewed X chromosome inactivation and female preponderance in autoimmune thyroid disease: An association study and metaanalysis[END_REF], autism [START_REF] Talebizadeh | Brief report: Non-Random X chromosome inactivation in females with autism[END_REF] and recurrent miscarriage [START_REF] Sullivan | Pregnancy outcome in recurrent miscarriage patients with skewed X chromosome inactivation[END_REF]. In plants, several lncRNAs rising from the FLC locus are necessary for a normal flowering, ensuring the transition to the next generation (Jarroux et al., 2017). Outside of FLC regulation, bioinformatic analyses revealed nearly fifty lncRNA with increased abundance in roots [START_REF] Amor | Novel long non-protein coding RNAs involved in Arabidopsis differentiation and stress responses[END_REF]. Interestingly, ectopic overexpression of two of these lncRNAs affects root growth in Arabidopsis (Amor et al., 2009). One of them, namely APOLO, has been intensively studied and is now known as an important regulator of the plant auxin and cold stress response (Ariel et al., 2014;Ariel et al., 2020;Moison et al., 2021). Also, two ecotype-enriched lincRNAs, namely NPC48 and NPC72 in Ler and Col-0, respectively, disturbs primary root growth when overexpressed in Col-0 (Blein et al., 2020). LncRNAs can regulate gene activity in trans or in cis by modifying the expression of a distant or a neighboring gene, respectively. In plants, several cis-regulatory lncRNAs have been described and are involved in flowering control (FLC related lncRNAs), auxin transport (APOLO), grain yield (LAIR) and freezing tolerance (SVALKA) (Chen et al., 2021). To date, apart from APOLO (Ariel et al., 2014), no cis-RdDM-acting lncRNA has been described as root growth regulator. Thus, we decided to look for novel root-related lncRNAs using the transcriptomics data from Blein et al (2020) as template for the analysis. Taking advantage of the data on long and small transcriptomes, we focused our search on lincRNAs able to generate 24nt siRNA, thus, likely functioning through RdDM regulation. [START_REF] Hruz | Genevestigator V3: A Reference Expression Database for the Meta-Analysis of Transcriptomes[END_REF]. C. Schematic illustration of the different isoforms of AT4G14548/LATERALINC genomic region. First line corresponds to the genomic region whereas the other lines present the various isoforms of AT4G14548/LATERALINC and AT4G14550/IAA14. For each isoform, exons are indicated with rectangles and introns with solid lines. LATERALINC has a single exon.

In this second chapter, I investigated the transcriptomic analyses from the first chapter (Blein et al., 2020) to search for an RdDM-cis-acting lincRNA neighbor to a critical root growth modulator gene on a list derived from the literature [START_REF] Berardini | Functional annotation of the Arabidopsis genome using controlled vocabularies[END_REF]. We expect to identify in this way, lncRNAs modulating the root growth through an eventual regulation of the neighboring gene. Among many others, I found that the deregulation of an intergenic lncRNA (lincRNA) neighbor to the gene IAA14, a main regulator of the auxin-mediated Lateral Root (LR) development, disturbs the root system architecture. We named it LATERAL ROOT LINCRNA (LATERALINC). Despite this genomic position, the LATERALINC is not responsive to auxin and its silencing does not change IAA14 transcript abundance. Altogether, the LATERALINC may regulate LR growth or development through a IAA14-independent pathway.

Results and discussion

LATERALINC is positively correlated with IAA14 during plant development

Among the 1 671 lincRNAs detected in Arabidopsis thaliana root tips (Blein et al., 2020), 456 co-localize with 24 nt siRNAs, potentially acting through RdDM. Interestingly, five lincRNAs/siRNA precursors are neighbors to root-related coding genes: XLOC_005697, XLOC_002421, AT1G48625, AT2G34655 (APOLO lncRNA), and AT4G14548 (Figure 1A). Using the Genevestigator database [START_REF] Hruz | Genevestigator V3: A Reference Expression Database for the Meta-Analysis of Transcriptomes[END_REF], and to strengthen the relationship between the lincRNA and its putative neighbor target gene in the control of root architecture, we look for uncharacterized lincRNAs and target genes expressed in the root compartment and positively correlated during plant development. XLOC_005697 and XLOC_002421 do not have public identifiers and thus cannot be searched in this data. The AT1G48625 lincRNA was only detected in inflorescences and poorly correlated with its putative target AT1G48630/RACK1B

during plant development (Figure 1B). Interestingly, both AT4G14548 lincRNA and its putative target AT5G14550/IAA14 are detected in root and positively correlated during plant development (Figure 1B). Notably, their expression seems to decrease throughout plant life, reaching their lower level of expression in flower-related organs (Figure 1B). Intriguingly, AT4G14548 lincRNA is separated from the IAA14 gene 3' ends by only 59bp (Figure 1C).

Twenty-nine Aux/IAA genes are annotated in Arabidopsis and encode short-nuclear proteins involved in the inactivation of the Auxin Response Factors (ARF) proteins, consequently repressing the auxin transcriptional responses. Auxin stimulus increases the interaction between Aux/IAA proteins and the Skp1-Cullin-F-box/Transport Inhibitor Response 1 (SCF-TIR1) complex, promoting the proteasome mediated degradation of the Aux/IAA proteins.

Notably, gain-of-function mutations of 10 IAA proteins affect plant development. Among them, iaa14/solitary root (slr) gain of function mutation stops the auxin-mediated cell division in the pericycle, blocking the development of LR [START_REF] Fukaki | Tissue-specific expression of stabilized SOLITARY-ROOT/IAA14 alters lateral root development in Arabidopsis[END_REF][START_REF] Vanneste | Cell cycle progression in the pericycle is not sufficient for SOLITARY ROOT/IAA14-mediated lateral root initiation in Arabidopsis thaliana[END_REF].

Hence, AT4G14548 lincRNA could influence the LR development through the regulation of IAA14 gene expression. Thus, we decided to name it LATERALINC for LATERAL root LINCrna.

Lateral root growth is impaired in LATERALINC downregulated lines

To assess the physiological role of LATERALINC, we decided to test the physiological impact of LATERALINC downregulation on root architecture. To this end, we modified LATERALINC expression without affecting the encoding DNA region using an RNAi construct triggering post-transcriptional lincRNA degradation, and isolated three independent lines (RNAi LATERALINC 1, RNA LATERALINC 2 and RNAi LATERALINC 3). As expected, the downregulation of LATERALINC does not affect the primary root length (Figure 2A) but triggers a significant reduction of LR length compared to Col (Figure 2B). Notably, the LR inhibition was stronger in the RNAi line 2 and line 3 as compared to the line 1 (Figure 2B).

Taken together, the deregulation of LATERALINC through RNAi inhibition influences the LR length, hinting the possibility that LATERALINC modulates the transcriptional activity of IAA14

or other LR-development related genes.

LATERALINC expression is correlated with the one of IAA1 but its downregulation does not affect IAA14 and IAA1 genes expression

Thus, we assessed the transcriptional activity of IAA14, key regulator of LR development [START_REF] Fukaki | Tissue-specific expression of stabilized SOLITARY-ROOT/IAA14 alters lateral root development in Arabidopsis[END_REF][START_REF] Vanneste | Cell cycle progression in the pericycle is not sufficient for SOLITARY ROOT/IAA14-mediated lateral root initiation in Arabidopsis thaliana[END_REF] within the LATERALINC RNAi lines. Transcript abundance of LATERALINC was significantly decreased in the RNAi lines, as expected.

Remarkably, the level of downregulation of LATERALINC correlates with the LR phenotype (Figure 3A and2B), strengthening the relationship between LR length and LATERALINC expression. However, LATERALINC downregulation does not affect the IAA14 gene expression (Figure 3A), pointing that the LATERALINC-mediated LR growth may occur independently of this gene. Nevertheless, as IAA14 gene activity increased in response to auxin [START_REF] Fukaki | Tissue-specific expression of stabilized SOLITARY-ROOT/IAA14 alters lateral root development in Arabidopsis[END_REF][START_REF] Vanneste | Cell cycle progression in the pericycle is not sufficient for SOLITARY ROOT/IAA14-mediated lateral root initiation in Arabidopsis thaliana[END_REF], we wondered whether the IAA14 auxin-mediated transcript accumulation is disturbed upon LATERALINC downregulation. Nonetheless, the increased transcript abundance of IAA14 in response to auxin were comparable between the RNAi lines and Col strengthening that LATERALINC acted independently of IAA14 to modulate the LR length (Figure 3B). Thus, we look at other closely located genes (using available transcriptomics data from Araport11) in case LATERALINC act in cis on other neighbors (Figure 3C). Strikingly, we found that LATERALINC transcriptional accumulation positively correlates with AT4G14560/IAA1 located at 10.5 kp from the 5' end of the IAA14 gene. IAA1 is also a substrate of SCF-TIR1 like IAA14 but it is involved in cell elongation and differentiation in the aerial parts of Arabidopsis plants [START_REF] Yang | The IAA1 protein is encoded by AXR5 and is a substrate of SCF TIR1[END_REF][START_REF] Ku | Overexpression of IAA1 with domain II mutation impairs cell elongation and cell division in inflorescences and leaves of Arabidopsis[END_REF] and not in roots. Despite this positive correlation observed in databases (Figure 3C), the RNAi-mediated LATERALINC downregulation does not affect the IAA1 transcript abundance in control condition or in response to auxin (Figure 3A and3B). Surprisingly, the AT4G14540 auxin-mediated transcript accumulation is disturbed upon LATERALINC downregulation. More precisely, the AT4G14540 gene transcripts start to accumulate 48 hours after the auxin application in Col whereas it accumulates sooner, as early at 8 hours for the RNAi lines 1-2 and 24 hours for the RNAi line 3, and return to a basal level at 24 and 48 hours for RNAi lines 1-2 and RNAi line 3, respectively (Figure 3B). Interestingly, AT4G14540 encodes for NF-YB3 TF, implicated in heat stress tolerance [START_REF] Sato | NF-YB2 and NF-YB3 have functionally diverged and differentially induce drought and heat stress-specific genes[END_REF]. Notably, plants overexpressing NF-YB3 showed an enhanced tolerance to a lethal heat stress [START_REF] Sato | NF-YB2 and NF-YB3 have functionally diverged and differentially induce drought and heat stress-specific genes[END_REF]. Taken together, LATERALINC does not regulate IAA14 and IAA1 gene expression but may be involved in the transcriptional regulation of NF-YB3 in response to auxin.

RT-qPCR

Total RNA was extracted from roots using TRI Reagent (Sigma-Aldrich) and treated with DNase (Fermentas) as indicated by the manufacturers. Reverse transcription was performed using 1µg total RNA and the Maxima Reverse Transcriptase (Thermo Scientific). qPCR was performed on a Light Cycler 480 with SYBR Green master I (Roche) in standard protocol (40 cycles, 60°C annealing). Primers used in this study are listed in Table 2. Data were analyzed using the ΔΔCt method using PROTEIN PHOSPHATASE 2A SUBUNIT A3 (AT1G13320) for gene normalization (Czechowski et al., 2005) and time 0 for time-course experiments.

Conclusion and perspectives

IAA14 is a key auxin-dependent regulator of LR development [START_REF] Fukaki | Tissue-specific expression of stabilized SOLITARY-ROOT/IAA14 alters lateral root development in Arabidopsis[END_REF][START_REF] Vanneste | Cell cycle progression in the pericycle is not sufficient for SOLITARY ROOT/IAA14-mediated lateral root initiation in Arabidopsis thaliana[END_REF]. Even though LATERALINC is a lincRNA involved in the quantitative control of LR length, LATERALINC downregulation does not seem to affect IAA14 transcripts' abundance, hinting that the LATERALINC-mediated LR development is occurring independently of the IAA14 neighboring gene. In addition, LATERALINC is not responsive to exogenous auxin application whereas IAA14 gene expression increased (Figure 3B).

Interestingly, the auxin kinetics pinpoints that the RNAi-mediated LATERALINC downregulation influences the expression of its neighboring gene AT4G14540/NF-YB3. The NF-Ys are a family of TF widely conserved within eukaryotes [START_REF] Dolfini | NF-Y and the transcriptional activation of CCAAT promoters[END_REF]. In Arabidopsis, there are 3 main families of NF-Ys (NF-YA, NF-YB and NF-YC) which include around 10 genes in each class [START_REF] Petroni | The promiscuous life of plant NUCLEAR FACTOR Y transcription factors[END_REF]. NF-Ys are involved in a wide-range of biological processes, among which, root nodule formation [START_REF] Combier | MtHAP2-1 is a key transcriptional regulator of symbiotic nodule development regulated by microRNA169 in Medicago truncatula[END_REF][START_REF] Zanetti | A C subunit of the plant nuclear factor NF-Y required for rhizobial infection and nodule development affects partner selection in the common bean-Rhizobium etli Symbiosis[END_REF], flower development [START_REF] Nakashima | Three arabidopsis SnRK2 protein kinases, SRK2D/SnRK2.2, SRK2E/SnRK2.6/OST1 and SRK2I/SnRK2.3, involved in ABA signaling are essential for the control of seed development and dormancy[END_REF], seed maturation [START_REF] Nambara | Characterization of an Arabidopsis thaliana mutant that has a defect in ABA accumulation: ABA-dependent and ABA-independent accumulation of free amino acids during dehydration[END_REF] and abiotic stress responses [START_REF] Yoshida | Arabidopsis HsfA1 transcription factors function as the main positive regulators in heat shock-responsive gene expression[END_REF]. The rapid and transient upregulation of NF-YB3 in response to auxin observed in the RNAi LATERALINC lines as compared to Col hints at a potential relationship between LATERALINC and NF-YB3. NF-YB3 is induced under a mild heat stress and its ectopic overexpression in-plants increased the plant resistance to lethal heat stress [START_REF] Sato | NF-YB2 and NF-YB3 have functionally diverged and differentially induce drought and heat stress-specific genes[END_REF] making noteworthy to investigate the influence of LATERALINC downregulation on the NF-YB3

transcriptional responsiveness and plant resistance to heat stress, even though it is not directly linked to LR development. Furthermore, the NF-YA have been shown to regulate root architecture [START_REF] Sorin | A miR169 isoform regulates specific NF-YA targets and root architecture in Arabidopsis[END_REF], making of interest to test whether NF-YB3 deregulated lines display subtle root phenotypes and whether it regulates LATERALINC expression. Early termination of LR growth?). LR emergence and growth are tightly regulated by a plethora of genes, involving auxin biosynthesis, transport, signaling and degradation, but also auxin unrelated genes such as cell wall remodeler, water transporter, other hormones such as ABA and/or ROS-related genes [START_REF] Banda | Lateral Root Formation in Arabidopsis: A Well-Ordered LRexit[END_REF]. Transcriptomic analyses of the LATERALINC RNAi lines may be informative to find the putative target genes of LATERALINC, helping to understand their LR length phenotype. Also, phenotypic and transcriptomic analyses of plants overexpressing LATERALINC may help to strengthen the relationship between LATERALINC and root architecture (i.e. do the LATERALINC-overexpressing plants present a reverted phenotype, a higher LR length? Does LATERALINC overexpression disturbs the transcriptional activity of key LR regulators?). Finally, as LATERALINC produces 24nt-length siRNA, its potential function through the RdDM pathway to regulate its target gene activity merits to be explored. Thus, the finding of differentially expressed genes within LATERALINC deregulated lines sharing a 24nt length sequence complementarity with LATERALINC and involved in root growth could constitute another interesting bioinformatics analysis to search for putative target genes linked to RdDM dependent-LATERALINC regulation. Indeed, all DNA sequences complementary to siRNAs are subjected to be targeted for methylation, as shown through genome wide methylation and transcript profiling of RdDM mutants [START_REF] Herr | RNA polymerase IV directs silencing of endogenous DNA[END_REF][START_REF] Onodera | Plant nuclear RNA polymerase IV mediates siRNA and DNA methylation-dependent heterochromatin formation[END_REF][START_REF] Pontier | Reinforcement of silencing at transposons and highly repeated sequences requires the concerted action of two distinct RNA polymerases IV in Arabidopsis[END_REF].

Hence, LATERALINC opens new perspectives to understand how this root growth regulator impinges on the regulatory networks involved in LR length determination.

MARS, a lncRNA implicated in the transcriptional

regulation of an embedded gene cluster

Introduction

Bacteria, fungi and plants can synthesize numerous bioactive secondary metabolic (SM) compounds principally to cope with their environments or to interact with other organisms. For many of these SMs, the metabolic pathways that govern their synthesis are currently unknown.

Curiously, some of these organic molecules are generated by enzymes whose corresponding genes are co-localized at a specific chromosome locus. These latter genes are referred to as biosynthetic gene clusters (BGC) or metabolic gene clusters (Yu et al., 2016).

In plants, BGCs have been identified in both monocots and dicots; the size of these clusters varies from approximately 35 kb to several hundred kb and include three to ten genes (Nützmann and Osbourn, 2015). These clusters, arising by the recruitment of duplicated genes and neofunctionalization (Nützmann and Osbourn, 2015), are generally present in chromosome regions under strong selection pressure (Boutanaev et al., 2015). It has been proposed that gene clustering could be a strategy to allow co-inheritance of genes involved in the pathway, conferring a selective advantage to plants. Indeed, plant BGCs are either species specific or limited to a taxonomic subgroup (Field et al., 2011;Castillo et al., 2013). Additionally, the genes of plant BGCs are generally co-expressed in particular organs or under specific conditions [START_REF] Nützmann | Metabolic Gene Clusters in Eukaryotes[END_REF]. For example, in Arabidopsis, the better characterized BGCs are the ones involved in the synthesis of triterpenes [START_REF] Nützmann | Metabolic Gene Clusters in Eukaryotes[END_REF]. These pathways are specifically expressed in roots where they can produce more than 50 metabolites. Recently, the important role of these SMs in the establishment of root microbiota has been clearly demonstrated [START_REF] Huang | A specialized metabolic network selectively modulates Arabidopsis root microbiota[END_REF]. Intriguingly, as BCGs, lncRNA are also mainly expressed in a tissue specific manner and rarely conserved between and within the same plant species. For example, the comparative transcriptomic Arabidopsis ecotypes study from the chapter one of my thesis highlighted that the non-coding transcriptome shows a more ecotype-specific expression than the coding transcriptome, hinting at the importance of the non-coding genomes for the plant local adaptation (Blein et al., 2020). Interestingly, a LRK (leucine-rich repeat receptor kinase) genes cluster is important for grain yield in rice and the coregulation of the genes cluster is mediated by an antisense lncRNA transcribed from LRK1 genomic region, supporting a link between noncoding transcriptional units and the transcriptional activity of gene clusters (Wang et al., 2018).

In this chapter, I will investigate the function of a lncRNA embedded in the marneral genes cluster. After briefly introducing how this cluster-related lncRNA was identified, I will present the preprint emerging from this study where I contributed by designing and performing the large majority of the experiments and participating in the writing process. This preprint will be followed by additional results and perspectives to thoroughly characterize the lncRNA function.

Identification of the MARS lncRNA

Using the transcriptomics data from Blein et al (2020), I wanted to identify lincRNAs that regulate their neighboring genes in cis. In the screen presented in the 2 nd chapter, I used the Blein et al. (2020) lincRNA-siRNA precursor annotation, together with the genomic positioning and public gene expression database (Genevestigator [START_REF] Hruz | Genevestigator V3: A Reference Expression Database for the Meta-Analysis of Transcriptomes[END_REF]) to find putative RdDM-cis-acting lincRNAs implicated in the regulation of root growth and/or development.

Differently, I decided this time to explore the transcriptomic analysis of both Col-0 and Ler, together with the genomic positioning to find putative lncRNAs that may be enriched in a specific ecotype and have potential action as cis-acting lincRNAs. Between Col-0 and Ler ecotypes, the variability of the transcriptional activity within the non-coding genome may contribute to the modification of expression observed for the coding genome. Thus, after filtering the genes significantly enriched in Col-0 ecotypes compared to Ler, I isolated the Colenriched lincRNA their neighboring Col-enriched coding. To strengthen the functional relationship between the lincRNA and the neighboring coding gene, I performed a Pearson correlation analysis among the 128 lincRNA-coding gene pairs using the normalized counts from the Pi starvation kinetics from Blein et al (2020) (Figure 1). Focusing on the lincRNA-coding gene pairs presenting a Pearson correlation score superior to 0.5, I isolated 35 putative cis-acting lincRNA candidates (see Table1). From them, I isolated the AT5G00580 lincRNA localized in-between the genes CYP71A16 (AT5G42590) and MARNERAL SYNTHASE 1 (MRN1, AT5G42600), both belonging to the marneral cluster together with CYP705A12 (AT4G42580) (Figure 1). Indeed, taking into account that the repression or activation of this cluster of genes seems to be in part governed by the repressive histone mark H3K27me3 [START_REF] Nützmann | Metabolic Gene Clusters in Eukaryotes[END_REF] and that lncRNA can modulate the deposition of this mark (Chen et al., 2021), we decided to focus on this intriguing candidate and wondered whether a link between the epigenetic landscape of the marneral cluster and the lncRNA that is transcribed from it could exist. Therefore, I investigated a potential function of this lincRNA in the epigenetic regulation of gene expression of the marneral cluster.

Preprint: The lncRNA MARS modulates the epigenetic reprogramming of the marneral cluster in response to ABA

Under favorable revision in Molecular Plant, our characterization of the MARS lncRNA for its implication in marneral cluster genes regulation is available in BioRxiv as a preprint (10.1101/2020.08.10.236562).

INTRODUCTION

In eukaryotes, functionally related genes are usually scattered across the genome.

However, a growing number of operon-like clustered organization of non-homologous genes participating in common metabolic pathways point at an emerging feature of animal, fungi and plant genomes (Nützmann et al., 2016). In plants, synthesis of numerous secondary metabolic compounds is important for the dynamic interaction with their environment, affecting their life and survival (Go et al., 2012). Terpenoids are bioactive molecules of diverse chemical structure (Yasumoto et al., 2016). In Arabidopsis thaliana, the biosynthesis of four triterpenes, namely thalianol (Field and Osbourn, 2008), tirucalla-7,24-dien-3b-ol (Boutanaev et al., 2015), arabidiol (Castillo et al., 2013) and marneral (Field et al., 2011), is governed by enzymes encoded by genes organized in clusters (Nützmann et al., 2016). The thalianol and marneral related genes are located in the smallest metabolic clusters identified in plants to date, each being less than 40kb in size (Nützmann et al., 2016). Both compounds are derived from 2,3-oxidosqualene and the corresponding gene clusters contain the oxidosqualene cyclases (OSCs), thalianol synthase (THAS) and marneral synthase (MRN1), respectively. The marneral cluster includes two additional protein-coding genes, CYP705A12 and CYP71A16, participating in marneral oxidation (Field et al., 2011).

Growing evidence indicates that the co-regulation of clustered genes relies on epigenetic mechanisms. It has been shown that the deposition of the histone variant H2A.Z positively correlates with transcriptionally active clusters. Accordingly, nucleosome stability precluding gene expression is dependent on ARP6, a component of the SWR1 chromatin remodeling complex required for the deposition of H2A.Z into nucleosomes (Nützmann and Osbourn, 2015). Additionally, it was shown that the thalianol and marneral clusters exhibit increased expression in the Polycomb mutant curly leaf (clf) with compromised H3K27me3 deposition, and reduced expression in the trithorax-group protein mutant pickle (pkl), a positive regulator that counteracts H3K27me3 silencing (Yu et al., 2016). Strikingly, it has been recently shown that biosynthetic gene clusters are embedded in local hot spots of threedimensional (3D) contacts that segregate cluster regions from the surrounding chromosome environment in a tissue-dependent manner. Notably, H3K27me3 appeared as a central feature of the 3D domains at silenced clusters (Nützmann et al., 2020).

Long noncoding RNAs (lncRNAs) have emerged as important regulators of eukaryotic gene expression at different levels (Rinn and Chang, 2020). In plants, several lncRNAs have been shown to interact with the Polycomb Repressive Complex 1 and 2 components LIKE HETEROCHROMATIN PROTEIN 1 (LHP1) and CLF, respectively, which are related to H3K27me3 distribution (Berry et al., 2017;Lucero et al., 2020). Furthermore, it has been proposed that lncRNAs can modulate the transcriptional activity of neighboring genes by shaping local 3D chromatin conformation (Ariel et al., 2014;Kim and Sung, 2017;Gagliardi et al., 2019). Here we show that the marneral cluster in Arabidopsis includes three noncoding transcriptional units.

Among them, the lncRNA MARS influences the expression of marneral cluster genes in response to ABA through modification of the epigenetic landscape. MARS deregulation affects H3K27me3 distribution, LHP1 deposition and chromatin condensation throughout the cluster.

Furthermore, an ABA responsive chromatin loop dynamically regulates MRN1 transcriptional activation by bringing together the MRN1 proximal promoter and an enhancer element enriched in ABA-related transcription factors (TF) binding sites. MARS-mediated control of the marneral cluster affects seed germination in response to ABA. The general co-regulation of genes located within lncRNA-containing clusters in Arabidopsis points to noncoding transcription as an important feature in coordinated transcriptional activity of clustered loci.

a growth chamber at 37°C under the same lightning conditions. For nitrate and phosphate starvation assays, seedlings were transferred at day 12 after sowing (DAS) from respectively nitrate and phosphate containing medium to nitrate and phosphate free medium. For root phenotype characterization, seedlings were sown in control media and transferred at day 6 in control medium or medium containing 2µM ABA, 200mM mannitol or 100mM NaCl, respectively. After 3 days of growth, the root length was measured using RootNav software (Pound et al., 2013) from images taken with a flat scanner. Finally, for seed germination assay, 0.5µM ABA was supplemented or not to the medium. Germination rate was evaluated twice a day. Seeds were considered germinated when the seed coat was perforated by elongating radicle. For all the experiments, samples were taken from 12 DAS starting two hours after light illumination, at different time-points, after cross-linking or not, depending on the experiment.

RT-qPCR

Total RNA was extracted from whole seedlings using TRI Reagent (Sigma-Aldrich) and treated with DNase (Fermentas) as indicated by the manufacturers. Reverse transcription was performed using 1µg total RNA and the Maxima Reverse Transcriptase (Thermo Scientific).

qPCR was performed on a Light Cycler 480 with SYBR Green master I (Roche) in standard protocol (40 cycles, 60°C annealing). Primers used in this study are listed in Supplementary Table 1. Data were analyzed using the ΔΔCt method using PROTEIN PHOSPHATASE 2A SUBUNIT A3 (AT1G13320) for gene normalization (Czechowski et al., 2005) and time 0 for timecourse experiments.

Chromatin Immunoprecipitation (ChIP)

ChIP was performed using anti-IgG (Millipore,Cat#12-370), anti-H3K27me3 (Millipore, Cat#07-449) and anti-LHP1 (Covalab, Pab0923-P), as previously described (Ariel et al., 2014), starting from two grams of seedlings crosslinked in 1% (v/v) formaldehyde. Chromatin was sonicated in a water bath Bioruptor Plus (Diagenode; 60 cycles of 30s ON and 30s OFF pulses at high intensity). ChIP was performed in an SX-8G IP-Star Compact Automated System (Diagenode). Antibody-coated Protein A Dynabeads (Invitrogen) were incubated 12 hours at 4 °C with the samples. Immunoprecipitated DNA was recovered using Phenol:Chloroform:Isoamilic Acid (25:24:1, Sigma) followed by ethanol precipitation and quantified by qPCR. For input samples, non-immunoprecipitated sonicated chromatin was processed in parallel.

In-vitro transcribed MARS and GFP RNA were obtained from a PCR product amplified from wild-type genomic cDNA and pB7FWG2 plasmid harbouring the GFP gene, respectively, using the T7 promoter included in the forward primer used for amplification (Supplementary Table 1). PCR products were purified by agarose electrophoresis and NucleoSpin kit (Macherey-Nagel). 1µg of purified DNA was used for in-vitro transcription following the manufacturer instructions (HiScribe T7 High Yield RNA Synthesis Kit, NEB). Purified noncrosslinked chromatin obtained from five grams of MARS RNAi line 1 seedlings were resuspended in 1 mL of nuclei lysis buffer and split into five tubes. An increasing amount of MARS RNA was added to each tube from 0 to 10 µg RNA and incubated under soft rotation during 3 h at 4 °C. Chromatin samples were then cross-linked using 1% (v/v) of formaldehyde for five minutes. Sonication and the following ChIP steps were performed as described above.

Formaldehyde-Assisted Isolation of Regulatory Elements (FAIRE)

FAIRE was performed as described by (Simon et al., 2012). After chromatin purification following the ChIP protocol, only 50 µl from the 500 µl of purified chromatin were used (diluted to 500 µl in 10 mM Tris-HCl pH 8). Quantification was performed by qPCR using the same set of primers as for ChIP.

Immunoprecipitation of methylated DNA (meDIP)

MeDIP was performed as described by (Nagymihály et al., 2017). For genomic DNA purification, 100mg of non-cross-linked seedlings were incubated 30min at 65°C in 600uL of cetyltrimethylammonium bromide (CTAB) buffer (2% CTAB, 1.4M NaCl, 100mM Tris pH8, 20mM

EDTA and 0.2% B-mercaptoethanol). A Chloroform:Isoamyl Alcohol (24:1) wash was performed prior to precipitation with isopropanol. After RNAse A treatment, 1µg of pure DNA was sonicated in a water bath Bioruptor Plus (Diagenode; 10 cycles of 30s ON and 30s OFF pulses at low intensity). The IP of the methylated DNA was performed overnight at 4°C using Protein A Dynabeads coated with anti-5mC (Diagenode, C15200081) or anti-IgG (Diagenode, C15400001). Immunoprecipitated DNA was recovered using Phenol:Chloroform:Isoamyl Alcohol (25:24:1, Sigma) followed by ethanol precipitation and quantified by qPCR. For input samples, non-immunoprecipitated sonicated chromatin was processed in parallel.

Nuclear purification

Non-cross-linked seedlings were used to assess the sub-cellular localization of RNAs.

To obtain the nuclear fraction, chromatin was purified as for ChIP and resuspended, after the sucrose gradient, into 1mL of TRI Reagent (Sigma-Aldrich). For the total fraction, 200 µL of cell suspension from the first step of the ChIP protocol, were treated with 800 µL of TRI Reagent to follow with the RNA extraction. RNA samples were treated with DNase, and RT was performed using random hexamers prior to qPCR analysis.

RNA immunoprecipitation (RIP)

For RIP, the lhp1 mutants complemented with the ProLHP1:LHP1:GFP (Nakahigashi et al., 2005) were treated for 4h with ABA. After crosslinking and chromatin extraction as for ChIP, ten percent of resuspended chromatin was kept at -20 °C as the input. Chromatin was sonicated in a water bath Bioruptor Plus (Diagenode; 5 cycles of 30 s ON and 30 s OFF pulses at high intensity). Anti-LHP1 RIP was performed using the anti-GFP antibody (Abcam ab290), as previously described (Ariel et al., 2014). The enrichment was determined as the percentage of cDNA detected after IP taking the input value as 100%.

Chromosome conformation capture (3C)

3C was performed as previously described (Louwers et al., 2009). Briefly, chromatin was extracted from two grams of cross-linked seedlings as for ChIP. Overnight digestion at 37 °C was performed using 400U of Hind III enzyme (NEB). Digested DNA was ligated during 5 h incubation at 16 °C with 100 U of T4 DNA ligase (NEB). DNA was recovered after reverse crosslinking and Proteinase K treatment (Invitrogen) by Phenol:Chloroform:Isoamyl Acid (25:24:1; Sigma) extraction and ethanol precipitation. Interaction frequency was determined by qPCR using a DNA region uncut by Hind III to normalize the amount of DNA across samples.

RESULTS

The marneral gene cluster contains three noncoding transcriptional units

The small marneral cluster includes three genes: marneral synthase (MRN1), CYP705A12

and CYP71A16 that are two P450 cytochrome-encoding genes (Figure 1A), all participating in the biosynthesis and metabolism of the triterpene marneral (Field et al., 2011).

The advent of novel sequencing technologies has allowed the identification of an increasing number of lncRNAs throughout the Arabidopsis genome. According to the latest annotation (Araport 11 (Cheng et al., 2017)), three additional transcriptional units are located within the marneral cluster, between the CYP71A16 and the MRN1 loci. The AT5G00580 and the pair of antisense genes AT5G06325 and AT5G06335 are located upstream of the MRN1 gene at 6kpb and 3kbp, respectively (Figure 1A). The 1,941bp-long AT5G00580 locus generates four transcript isoforms ranging from 636 nt to 1,877 nt in length (Figure 1B). In contrast, each of the antisense genes AT5G06325 and AT5G06335 are transcribed into only one RNA molecule of 509 nt and 367 nt, respectively (Figure 1A). All these transcripts were classified as lncRNAs when using two coding prediction tools, CPC (Kong et al., 2007) and CPC2 (Kang et al., 2017) because of their low coding potential and their length (over 200 nt), similarly to previously characterized lncRNAs (COLDAIR (Heo and Sung, 2011); APOLO (Ariel et al., 2014);

and ASCO (Bardou et al., 2014)) (Figure 1C).

According to available transcriptomic datasets (Araport11), AT5G00580 transcriptional accumulation positively correlates with that of marneral genes, whereas AT5G06325 and AT5G06335 RNAs do not (Supplementary Figure S1). Notably, our analysis of the transcriptional dynamics of the noncoding gene AT5G00580 and the marneral cluster proteincoding genes revealed a correlated expression in response to phosphate and nitrate starvation, heat stress, as well as to exogenous auxin and ABA (Figure 1D). Interestingly, the AT5G00580 lncRNA exhibited the strongest transcriptional induction in response to heat stress and exogenous ABA, in comparison with MRN1 and the two CYP genes (Figure 1D). Altogether, our observations uncovered that the marneral cluster includes three noncoding transcriptional units, one of which is actively transcribed and co-regulated with its neighboring protein-coding genes.

The lncRNA MARS shapes the transcriptional response of the marneral gene cluster to

ABA

It has been shown that lncRNAs can regulate the expression of their neighboring genes through epigenetic mechanisms (Jarroux et al., 2017). Thus, we wondered if the lncRNA derived from the AT5G00580 locus may regulate the transcriptional activity of the protein-coding genes included in the marneral cluster. To this end, we modified the lncRNA expression without affecting the cluster DNA region, i.e. constitutively expressing AT5G00580 under the control of the UBI10 promoter or knocking down by RNAi. Over-expression lines did not affect the accumulation of protein-coding transcripts in the marneral gene cluster, regardless if the plants were treated or not with ABA (Figure S2). On the other hand, AT5G00580 knock-down by RNAi displayed only in a slight basal induction of MRN1 (Figure 2A). Strikingly, the response of the three protein-coding genes of the marneral cluster to exogenous ABA was significantly deregulated in the RNAi lines (Figure 2B and S3A) compared to mock treatment (Figure S5), in contrast to the expression of two AT5G00580-unrelated ABA marker genes taken as a control of ABA treatment (Figure S3B and S3C). Therefore, we named the AT5G00580-derived noncoding transcript MARneral Silencing (MARS) lncRNA. Transcriptional levels of MRN1 and the two CYP genes increased earlier in RNAi-MARS seedlings (15 min) than in the wild-type (Col-0, 30 min) (Figure 2B bottom panel). In addition, the transcriptional accumulation of these genes later reached two-fold higher levels in the RNAi-MARS lines compared to Col-0 (Figure 2B top panel and S3A). The same behavior was observed using a higher concentration of ABA (Figure S4). Notably, none of the marneral cluster genes exhibit any transcriptional oscillation during the day (Covington et al., 2008;Hsu and Harmer, 2012;Romanowski et al., 2020), indicating that the transcriptional modulation of MARS and the marneral gene cluster linked to an ABA-mediated pathway.

To further support our observations, we isolated a transgene insertional mutant (SALK_133089) located 200 bp upstream the transcription start site (TSS) of MARS gene that we named mrs1-1. We found that mrs1-1 partially impairs the transcriptional accumulation of MARS and protein-coding genes in the marneral cluster, mainly CYP71A16 (Figure 2A). In agreement with the RNAi-MARS lines, compared to wild-type plants MRN1 and CYP705A12

genes responded earlier to ABA and reached higher levels in mrs1-1 (Figure 2B). In contrast, CYP71A16, whose promoter region may be locally affected by the T-DNA insertion, was no longer responsive to ABA. In addition, we characterized a transgene insertional mutant in CYP71A16 gene (mro1-2; (Field et al., 2011)), which did not influence the expression of the other marneral cluster genes (Figure S6A,B) or known ABA-responsive genes (Figure S6C,D), suggesting that the transcriptional misregulation observed in mrs1-1 mutant could be caused by the down-regulation of this lncRNA. In agreement, the deregulation of MRN1 in overexpression and in knock-out mrn1 mutant did not affect the expression of the CYP genes of the marneral cluster, nor ABA-responsive genes (Figure S7), but resulted in a decrease in MARS transcripts abundance under control condition (Figure S7A,B). Collectively, our results indicate that the noncoding transcriptional activity of MARS, represses the dynamic expression of the marneral cluster genes, mainly MRN1, in response to ABA.

MARS affects seed germination and root growth under osmotic stress

The phytohormone ABA has been implicated in the perception and transduction of environmental signals participating in a wide range of growth and developmental events such as seed development, germination and root growth response to environmental stimuli (Vishwakarma et al., 2017).

Considering that the marneral cluster exhibited a strong MARS-dependent response to ABA, we wondered what was the physiological impact of MARS deregulation during seed germination. We assessed seed germination in Col-0 and MARS down-regulated lines with or without exogenous ABA. Notably, MARS silencing resulted in a delayed germination compared to wild type seeds, both in response to ABA and in control conditions as revealed by an increase in T50 (time for 50% of germination; Figure S8A,B,C,D). Accordingly, 35S:MRN1 and mro1-2 seeds also exhibit a delayed germination phenotype regardless of the treatment with ABA, whereas the germination speed rate of mrn1 was only impaired in response to ABA (Figure S8A,B). The physiological behavior of the cluster-related mutants suggests that the misregulation of marneral genes in the MARS down-regulated lines could be linked to an increased sensitivity to ABA during germination (Figure 2B).

Considering the influence of the marneral cluster in the regulation of seed germination, we decided to assess root growth response to ABA and ABA-related environmental stimuli such as osmotic stress and salt. Except for mrs1-1 which showed a significantly reduced root growth, all the other knock-down and mutant lines tested were not affected in root growth under normal growth conditions. However, all the genotypes presented a tendency to reduce root growth in response to ABA or hyperosmotic salt stress (Figure S8E). Notably, in response to osmotic stress mediated by mannitol, MARS knock-down lines and the lines with modified MRN1 expression exhibited a weaker impact on lateral root development with increased lateral root density and length (Figure S8E; Lateral root length and Lateral root density). The similar behavior between MRN1 deregulated lines and RNAi-MARS lines suggest that the decreased sensitivity to osmotic stress observed in the RNAi-MARS lines could be linked to MRN1

MARS controls the epigenetic status of the marneral locus

It has been shown that gene clusters in plants are tightly regulated by epigenetic modifications, including the repressive mark H3K27me3 (Yu et al., 2016). ChIP-Seq datasets (Veluchamy et al., 2016) reveals that the marneral cluster region is highly enriched in H3K27me3 in shoots and overlaps with the deposition of LHP1 (Figure S9). ATAC-Seq data (Sijacic et al., 2018) also revealed that the marneral cluster exhibits a high chromatin condensation in shoots (Figure S9). These data suggest that the marneral cluster is in a epigenetically silent state in aerial organs, thus correlating with its low expression level in leaves (Yu et al., 2016).

We wondered if the transcriptional activation of the marneral cluster in response to exogenous ABA was associated with a dynamic epigenetic reprogramming. We first assessed H3K27me3 deposition across the marneral cluster, including the gene body of MRN1, MARS and the two CYP loci (Figure 3A andS10). Interestingly, exogenous ABA triggered a strong reduction of H3K27me3 deposition throughout the marneral cluster (Figure 3A andS10).

Markedly, H3K27me3 basal levels were also significantly lower in RNAi-MARS seedlings.

Remarkably, H3K27me3 deposition was even lower across the body of all genes of the cluster in response to ABA in the RNAi-MARS lines when compared with Col-0, in agreement with the stronger induction by ABA of this subset of genes upon MARS silencing (Figure 3A, S10 and Figure 2B). Furthermore, we assessed the deposition of LHP1 on different regions of the marneral cluster and found that LHP1 was enriched at the MRN1 promoter and more weakly across MARS gene body and the intergenic region between CYP71A16 and MARS (Figure 3B and S11). Remarkably, LHP1 recognition was strongly impaired in response to ABA as well as in RNAi-MARS seedlings (Figure 3B andS11). Therefore, our results indicate that ABA triggers an epigenetic reprogramming of the marneral cluster, likely in a process involving the lncRNA MARS.

MARS is directly recognized by LHP1 and modulates local chromatin condensation

It has been shown that the deposition of the repressive mark H3K27me3 and the concomitant recognition of the plant PRC1 component LHP1 are correlated with high chromatin condensation (Yang et al., 2017). Therefore, we determined the chromatin condensation of the whole marneral cluster by Formaldehyde-Assisted Isolation of Regulatory Elements (FAIRE). In contrast to Col-0 showing a highly condensed chromatin, RNAi-MARS seedlings exhibit a lower chromatin condensation in control conditions, including the MARS locus (Figure S12A). In agreement, RNAi-MARS plants did not exhibit altered levels of DNA methylation across the MARS gene body, indicating that MARS silencing occurs at posttranscriptional level, without affecting the epigenetic state of the endogenous locus (Figure S12B). Notably, the global chromatin status of the cluster was less condensed in RNAi-MARS seedlings in response to ABA (Figure 4A and S12A), in agreement with a decrease of both H3K27me3 deposition and LHP1 binding (Figure 4A,B, S10 and S11) and the concomitant transcriptional activation of the clustered genes (Figure 2B).

Consistently, lhp1 mutant seedlings also showed a global chromatin decondensation in control conditions, comparable to Col-0 in response to ABA. Notably, chromatin decondensation triggered by ABA was completely impaired in lhp1 (Figure 4B and S13),

supporting the role of LHP1 in the dynamic epigenetic silencing of the marneral cluster.

Concomitantly, the increased chromatin decondensation of lhp1 mutant seedlings correlates with increased abundance of marneral genes transcripts (Figure S14A), as observed in the RNAi-MARS seedlings (Figure 2B) with decondensed chromatin (Figure 4A andS12A).

It has been shown that LHP1 can recognize RNAs in vitro (Berry et al., 2017) and the lncRNA APOLO in vivo (Ariel et al., 2014). Moreover, it has been proposed that APOLO overaccumulation can decoy LHP1 away from target chromatin (Ariel et al., 2020). Therefore, we wondered whether MARS lncRNA was able to interact with the chromatin-related protein LHP1

participating in the modulation of the local epigenetic environment. Thus, we first determined that MARS was enriched in the nucleus, compared with total RNA, as the previously characterized lncRNAs APOLO and ASCO that interact respectively with nuclear epigenetic and splicing machineries, and the nuclear structural ncRNA U6 (Figure S14B), involved in the spliceosome. Then, we confirmed by RNA immunoprecipitation (RIP) that LHP1 can interact with MARS in vivo, in contrast to MRN1 or a randomly selected housekeeping gene (PP2A) taken as negative controls (Figure 4C).

LHP1 binding to the marneral cluster was impaired both in response to exogenous ABA (concomitantly inducing MARS, Figure 3B, S11 and 2B) and in RNAi-MARS seedlings, hinting at a stoichiometry-dependent action of MARS on LHP1 recognition of the marneral cluster.

Therefore, we used chromatin extracts from RNAi-MARS line 1 seedling, that contains very low MARS transcript levels (Figure 2A) to assess LHP1 recognition of the marneral cluster upon the addition of increasing concentrations of in vitro-transcribed MARS RNA. Strikingly, we found that low MARS RNA concentrations (between 0.01 and 0.1 µg of RNA; Figure 4D andS14C)

successfully promoted LHP1 binding to the cluster, in contrast to higher concentrations (between 1 and 10 µg of RNA). Moreover, the in vitro-transcribed GFP RNA was not able to promote LHP1 binding, supporting the relevance of the specific MARS-LHP1 stoichiometric interaction for LHP1-target recognition (Figure 4D andS14C). Altogether, our results suggest that the physical interaction of the nuclear-enriched lncRNA MARS to LHP1 modulates its binding to proximal chromatin in a dual manner likely participating in the modulation of the dynamic chromatin condensation of the marneral cluster.

MARS expression modulates an LHP1-dependent chromatin loop bringing together the MRN1 locus and an ABA enhancer element

It has been reported that the spatial conformation of cluster-associated domains differs between transcriptionally active and silenced clusters. In Arabidopsis, segregating 3D contacts are distinguished among organs, in agreement with the corresponding transcriptional activity of clustered genes (Nützmann et al., 2020). Therefore, we explored whether MARS could participate in the dynamic regulation of the local 3D chromatin conformation modulating the transcription of the marneral cluster. According to available HiC datasets (Liu et al., 2016;Veluchamy et al., 2016) there is a significant interaction linking the intergenic region between CYP71A16 and MARS and the MRN1 locus (indicated as "Chromatin loop" in Figure 5A). We used Chromatin Conformation Capture (3C) to monitor the formation of this chromatin loop and found that it increased drastically after 30 min exposure of seedlings to exogenous ABA and that this chromatin loop remained for at least 4 hours after the treatment (Figure 5B).

These data indicate that the formation of this chromatin loop positively correlates with the transcriptional accumulation of the marneral cluster genes in response to ABA (Figure 2B).

The MARS locus is encompassed in the ABA-dependent chromatin loop (Figure 5A). In order to determine the role of MARS in the modulation of local 3D chromatin conformation, we assessed the formation of the chromatin loop in RNAi-MARS lines. Notably, RNAi-MARS seedlings exhibit enhanced chromatin loop formation, which remained unchanged in response to exogenous ABA (Figure 5B). Interestingly, LHP1 has been implicated in shaping local 3D conformation of target regions (Veluchamy et al., 2016), suggesting that the LHP1-MARS module may dynamically switch the epigenetic status of the marneral cluster from a condensed-linear to a decondensed-3D structured chromatin conformation. Supporting this hypothesis, lhp1 mutant seedlings exhibited enhanced chromatin loop formation compared to Col-0 (Figure 5C). Overall, our results suggest that the formation of a chromatin loop within the marneral cluster is regulated by LHP1 through the interaction with MARS lncRNA transcripts.

To better understand the role of the MARS-dependent chromatin loop in response to ABA we looked for ABA-related cis regulatory sequences throughout the marneral cluster. We analyzed the distribution of binding sites for 13 ABA-related transcription factors (TFs) determined experimentally (Song et al., 2016). Interestingly, we found a high enrichment for ABA TF binding sites at the MARS locus, as well as in the intergenic region between the CYP71A16 and MARS loci, in particular at regions surrounding the contact point brought into close spatial proximity with the MRN1 locus by the ABA-dependent 3D chromatin loop (Figure 5A). We thus assessed the capacity of these genomic regions to enhance the transcriptional activity of MRN1. To this end, we generated transcriptional reporter line combining the candidate distant enhancer elements to a minimal 35S promoter and ß-glucuronidase (GUS) gene (Yan et al., 2019). We also included as controls two genomic regions nearby the putative enhancers, one between CYP705A12 and CYP71A16 and the other at the 3' end of AT5G42620 locus (Figure 5A). Among the two putative distal enhancers tested, one was able to activate GUS expression (Intergenic region 2, Figure 5D and Figure S15), coinciding with the region showing a high enrichment of ABA-related TF binding sites close to the chromatin loop anchor point (Figure 5A). Collectively, our results indicate that an ABA-driven chromatin loop brings into close spatial proximity the MRN1 locus and a transcriptional activation site likely acting as an ABA enhancer element and that this chromatin reorganization process depends on the LHP1-MARS module.

Long noncoding RNAs as emerging regulators of gene clusters

Physically linked genes organized in clusters are generally coregulated (Nützmann et al., 2016). Considering that the lncRNA MARS is implicated in the regulation of the marneral cluster, we wondered whether the presence of noncoding transcriptional units may constitute a relevant feature of gene cluster organization. Therefore, we looked for the presence of lncRNAs in other gene clusters using two different datasets, one of gene clusters for coexpressed neighboring genes (Yu et al., 2016) and one for metabolic gene clusters (PlantiSMASH (Kautsar et al., 2017)). Among the 390 clusters of co-expressed neighboring genes, 189 (48%) contained at least one lncRNA embedded within the cluster. Most importantly, among the 45 metabolic clusters, 28 (62%) include lncRNAs inside the cluster (Figure 6A). Furthermore, among the clusters containing a lncRNA, a correlation analysis based on the maximum strength of co-expression between a lncRNA and any clustered gene revealed that the metabolic clusters exhibit a significantly higher correlation than co-expressed clusters (Figure 6B). Altogether, our analyses suggest that lncRNA-mediated local epigenetic remodeling may constitute an emerging feature of non-homologous genes metabolic clusters in plants.

DISCUSSION

The cell nucleus is a dynamic arrangement of DNA, RNAs and proteins (Cavalli and Misteli, 2013). Genome topology has emerged as an important feature in the complex network of mechanisms regulating gene activity and genome connectivity, leading to regionalized chromosomal spatial distribution and the clustering of diverse genomic regions with similar expression patterns (Rodriguez-Granados et al., 2016).

In the last few years, noncoding transcription has been implicated in shaping 3D nuclear organization (Quinodoz and Guttman, 2014). Notably, RNase-A micro-injection into the nucleus revealed that long nuclear-retained RNAs maintained euchromatin in a biologically active decondensed state, whereas heterochromatin domains exhibited an RNA-independent structure (Caudron-Herger et al., 2011;Caudron-Herger and Rippe, 2012). More recently, HiC analyses were performed in mammalian cells exposed or not to RNase, before and after crosslinking, or upon transcriptional inhibition (Barutcu et al., 2019). As a result, it was observed that topologically associated domains (TAD) boundaries remained mostly unaffected by RNase treatment, whereas compartmental interactions suffered a subtle disruption. In contrast, transcriptional inhibition led to weaker TAD boundaries, hinting at different roles of steadystate RNA vs. active transcription in nuclear organization (Barutcu et al., 2019).

In plants, several lncRNAs have been implicated in local chromatin conformation dynamics affecting the transcriptional activity of neighboring genes (Gagliardi and Manavella, 2020;Lucero et al., 2020). Notably, the lncRNA COLDWRAP participates in the formation of an intragenic chromatin loop blocking the transcription of the flowering time regulator FLOWERING LOCUS C (FLC (Kim and Sung, 2017)) in response to cold, in a process involving the recruitment of PRC2 by direct interaction with the component CLF. The lncRNA APOLO also controls the transcriptional activity of its neighboring gene PINOID (PID) by dynamically modulating the formation of an intergenic chromatin loop encompassing the divergent promoter of PID and APOLO (Ariel et al., 2014), in a process involving the PRC1 component LHP1. More recently, it was proposed that high levels of APOLO can decoy LHP1 away from multiple loci in trans, modulating the 3D conformation of distal target genes (Ariel et al., 2020).

In rice, the expression of the leucine-rich repeat receptor kinase clustered genes RLKs is modulated by the locally-encoded lncRNA LRK ANTISENSE INTERGENIC RNA (LAIR). It was proposed that LAIR may directly recruit OsMOF (MALES ABSENT ON THE FIRST) and OsWDR5

(WD REPEAT DOMAIN 5), involved in H4K16 acetylation and chromatin remodeling (Wang et al., 2018). Here, we showed that the lncRNA MARS contributes to the co-regulation of a set of physically linked genes in cis in Arabidopsis. We demonstrated that the relative abundance of in vitro-transcribed MARS fine-tunes LHP1 binding to the cluster region in a stoichiometrydependent manner, thus explaining how MARS levels affect H3K27me3 deposition and chromatin condensation. It has been shown in yeast that histone depletion boosts chromatin flexibility and facilitates chromatin loop formation on the kilobase pair scale (Diesinger et al., 2010). In agreement thereof, we uncovered here the dynamic role of the LHP1-MARS module affecting nucleosome distribution across the marneral cluster in response to ABA, thus promoting the formation of an intra-cluster chromatin loop.

It has been recently observed that biosynthetic gene clusters are embedded in local three-dimensionally organized hot spots that segregate the region from the surrounding chromosome environment (Nützmann et al., 2020). Here, we showed that active noncoding transcriptional units within the cluster may contribute to 3D conformation dynamics switching from silent to active states. Our results indicated that a MARS-dependent chromatin loop may bring the MRN1 locus and a distal ABA-responsive element into close spatial proximity, likely acting as an enhancer. Notably, MARS-dependent LHP1 and H3K27me3 removal in Col-0, RNAi-MARS and the lhp1 mutant correlated with chromatin decondensation, loop formation and increased marneral genes transcriptional activity in response to ABA. According to this model, chromatin loop conformation is related to LHP1 binding and is modulated by MARS in a dual manner. LHP1 recognition at basal MARS levels maintains a possibly linear conformation of the region, precluding the enhancer-MRN1 locus interaction, whereas the positively activating chromatin loop is formed in the absence of LHP1. MARS transcriptional accumulation in response to ABA directly modulates LHP1 binding to the marneral cluster (Figure 6C; in response to ABA).

In vitro, high level of MARS RNA can titrate LHP1 binding even though a minimal amount of MARS RNA is required to recruit LHP1 to the marneral locus (Figure 4D andS14C). In vivo, the overexpression of MARS does not affect the transcriptional activity of the marneral cluster, suggesting that additional ABA-related factors are required to trigger the activation of the cluster upon LHP1 titration (Figure S2). Also, when MARS levels are too low compared to basal levels (in vitro (Figure 4D and S14C) or in vivo with the RNAi lines (Figure 3B and S11)), recruitment of LHP1 to the cluster is also impaired (Figure 6C; MARS repression). Thus, the concentrationdependent dual effect of MARS on LHP1 binding (Figure 4D and S14C) appears as a key factor determining the dynamics of the marneral cluster epigenetic landscape.

In mammals, growing evidence supports the role of lncRNAs in chromatin conformation determination (Gil and Ulitsky, 2020) and enhancer activity (e.g. PVT1 (Cho et al., 2018) and CCAT1-L (Xiang et al., 2014)). Here, we showed that the nuclear-enriched lncRNA MARS brings together the MRN1 proximal promoter and a putative enhancer element enriched in ABAresponsive TF binding sites. Interestingly, it has been shown that human lncRNAs can modulate the binding of TFs to their target chromatin (DHFR (Martianov et al., 2007)) and PANDA (Hung et al., 2011), whereas TFs have been implicated in chromatin loop formation in plants (Rodriguez-Granados et al., 2016). Furthermore, it was shown that in addition to the TF NF-YA, the lncRNA PANDA interacts with the scaffold-attachment-factor A (SAFA) as well as with PRC1 and PRC2 to modulate cell senescence (Puvvula et al., 2014). Therefore, further research will be needed to determine what ABA-responsive TFs are in control of the marneral cluster and to elucidate how they participate in chromatin loop formation along the area, in relation with the PRC1-interacting lncRNA MARS.

Plants are a tremendous source of diverse chemicals which are important for their life and survival (Yu et al., 2016). Marneral biosynthesis has been linked to root and leaf development, flowering time and embryogenesis (Go et al., 2012). Here we found that the Arabidopsis marneral cluster is activated by the phytohormone ABA, in a lncRNA-dependent epigenetic reprogramming. MARS deregulation affects the cluster response to ABA, impacting seed germination and root sensitivity to osmotic stress. Interestingly, lncRNAs had already been associated with seed germination and environmental stress. For example, the overexpression of the cotton lncRNA973 resulted in an increased seed germination rate and salt-tolerance in Arabidopsis (Zhang et al., 2019). Concomitantly, the decrease in lncRNA973 transcript abundance in cotton was associated with hypersensitivity to salt stress. In addition, the Arabidopsis lncRNA DRIR regulates plant response to drought and salt stress by altering the expression of stressresponsive genes (Qin et al., 2017).

It was proposed that the marneral cluster was founded by the duplication of ancestral genes, independent events of gene rearrangement and the recruitment of additional genes (Field et al., 2011). The exploration of the noncoding transcriptome in Arabidopsis recently served to identify ecotype-specific lncRNA-mediated responses to the environment (Blein et al., 2020). It was suggested that the noncoding genome may participate in multiple mechanisms involved in ecotype adaptation. Collectively, our results indicate that the acquisition of novel noncoding transcriptional units within biosynthetic gene clusters may constitute an additional regulatory layer behind their natural variation in plant evolution. (C) Coding potential of the transcripts located in the marneral cluster genomic region. Scores were determined using CPC1 (left) and CPC2 (right) algorithms (Kong et al., 2007;Kang et al., 2017). For each, the threshold between coding and noncoding genes is displayed with a horizontal solid black line. Coding genes are situated above the threshold, whereas non coding genes are situated under. COLDAIR, APOLO and ASCO are used as positive controls of noncoding transcripts.

TABLE AND FIGURES LEGENDS

(D) Dynamic transcriptional levels of co-regulated genes of the marneral cluster in response to phosphate and nitrate starvation, heat stress, and exogenous ABA and auxin. Gene expression data are shown as the mean ± standard error (n = 3) of the log2 fold change compared to time 0h. (C) LHP1-MARS interaction was assessed by RNA immunoprecipitation (RIP) using LHP1-GFP seedlings. Negative controls include a housekeeping gene (PP2A) and MRN1 mRNA. MRN1 transcript levels in nuclei samples are comparable to MARS. The interaction between APOLO and LHP1 was taken as a positive control (Ariel et al., 2014). Results are shown as the mean ± standard error (n = 4) of the percentage of input (signal measured before immunoprecipitation). The orange track shows the number of different ABA-related transcription factor binding sites (HB6, HB7, GBF2, GBF3, MYB3, MYB44, NF-YC2, NF-YB2, ANAC102, ANAC032, ABF1, ABF3, ABF4, RD26, ZAT6, FBH3, DREBA2A, AT5G04760, HAT22 and HSFA6A) found on the marneral cluster (Song et al., 2016). Green and red rectangles indicate the putative enhancer region and the negative controls, respectively, tested for the GUS-based reporter system in (D).

(B) Relative chromatin loop formation in response to ABA in Col-0 and RNAi-MARS seedlings.

Results are shown as the mean ± standard error (n = 2) from 3C-qPCR using primer F and R shown on (A), compared to time 0h.

(C) Relative chromatin loop formation in response to ABA treatment in Col-0 and lhp1 mutant.

Data are shown as the mean ± standard error (n = 3) from 3C-qPCR using primer F and R shown on (A), compared to time 0h.

(D) Constructs used for the GUS-based reporter system are illustrated on the left.

Corresponding transformed tobacco leaf discs are on the right (n = 4). First line represents the positive control in which the 35S sub-unit controls GUS expression. The second and third lines show two independent negative controls in which the GUS gene is driven by a genomic region that does not contain ABA-related binding sites indicated in (A). In the remaining lines, the transcriptional activity is assessed for the two intergenic regions indicated in (A).

Figure 6. Regulation of metabolic clusters in plants by lncRNA

(A) The proportion of metabolic clusters including lncRNA loci is higher than for other clusters.

The co-expressed clusters were predicted in (Yu et al., 2016) and correspond to co-expressed neighboring genes. The metabolic clusters are co-expressed neighboring genes involved in the biosynthesis of a particular secondary metabolite predicted by plantiSMASH (Kautsar et al., 2017).

(B) Maximum level of correlation between a lncRNA and any clustered gene determined in each cluster. The number shown above indicates the p-value of the difference between the two type of clusters determined by Student t-test.

(C) The lncRNA MARS regulates the expression of the marneral cluster genes through epigenetic reprogramming and chromatin conformation. In control conditions (upper panel) the chromatin of the marneral cluster is enriched in H3K27me3 and LHP1, which results in a condensed and linear chromatin conformation. In response to ABA (bottom left panel) MARS over-accumulated transcripts titrate LHP1 away from the cluster. The decrease of LHP1 deposition diminishes H3K27me3 distribution, relaxes the chromatin and as a consequence allows the formation of a chromatin loop that approaches the enhancer element and MRN1 proximal promoter, leading to a transcriptional activation. When MARS is repressed, LHP1 recruitment to the cluster is impaired, thus leading to a similar chromatin state: decrease in H3K27me3 mark, chromatin decondensation and increase in chromatin loop conformation. Under this chromatin state, the clustered genes become highly responsive to the ABA treatment.

C

C o l -0 R N A i M A R S 1 R N A i M A R S 2 R N A i M A R S 3 m r s 1 -1 C o l -0 R N A i M A R S 1 R N A i M A R S 2 R N A i M A R S 3 m r s 1 -1 C o l -0 R N A i M A R S 1 R N A i M A R S 2 R N A i M A R S 3 m r s 1 -1 C o l -0 R N A i M A R S 1 R N A i M A R S 2 R N A i M A R S 3 m r
FigureS2 CYP705A12 CYP71A16 MARS MRN1 RAB18 RD29B WT WT_1 UBQ_1 WT_2 UBQ_2 WT_3 UBQ_3 WT WT_1 UBQ_1 WT_2 UBQ_2 WT_3 UBQ_3 WT WT_1 UBQ_1 WT_2 UBQ_2 WT_3 UBQ_3 WT WT_1 UBQ_1 WT_2 UBQ_2 WT_3 UBQ_3 WT WT_1 UBQ_1 WT_2 UBQ_2 WT_3 UBQ_3 WT WT_1 UBQ_1 WT_2 UBQ_2 WT_3 UBQ_3
Col-0 RNAi MARS 1 RNAi MARS 2 RNAi MARS 3 mrs1-1 35S:MRN1 mrn1 mro Col-0 RNAi MARS 1 RNAi MARS 2 RNAi MARS 3 mrs1-1 35S:MRN1 mrn1 mro Col-0 RNAi MARS 1 RNAi MARS 2 RNAi MARS 3 mrs1-1 35S:MRN1 mrn1 mro Col-0 RNAi MARS 1 RNAi MARS 2 RNAi MARS 3 mrs1-1 35S:MRN1 mrn1 mro Col-0 RNAi MARS 1 RNAi MARS 2 RNAi MARS 3 mrs1-1 35S:MRN1 mrn1 mro Col-0 RNAi MARS 1 RNAi MARS 2 RNAi MARS 3 mrs1-1 35S:MRN1 mrn1 mro Col-0 RNAi MARS 1 RNAi MARS 2 RNAi MARS 3 mrs1-1 35S:MRN1 mrn1 mro Col-0 RNAi MARS 1 RNAi MARS 2 RNAi MARS 3 mrs1-1 35S:MRN1 mrn1 mro Col-0 RNAi MARS 1 RNAi MARS 2 RNAi MARS 3 mrs1-1 35S:MRN1 mrn1 mro Col-0 RNAi MARS 1 RNAi MARS 2 RNAi MARS 3 mrs1-1 35S:MRN1 mrn1 mro Col-0 RNAi MARS 1 RNAi MARS 2 RNAi MARS 3 mrs1-1 35S:MRN1 mrn1 mro Col-0 RNAi MARS 1 RNAi MARS 2 mrs1-1 35S:MRN1 mrn1 Col-0 RNAi MARS 3 mro AT5G42330 AT5G42370 AT5G42400 AT5G42420 AT5G42460 AT5G42500 AT5G42530 AT5G42567 AT5G42590 AT5G00580 AT5G42610 AT5G42630 AT5G42640 AT5G42655 AT5G42680 AT5G42710 AT5G42740 AT5G42760 AT5G42790 AT5G06395 AT5G42490 AT5G42560 AT5G06325 AT5G06365 AT5G06385

Additional results and discussion

Deregulation of genes involved in the Carbon/Nitrogen equilibrium and cell oxidation status in the RNAi MARS line

MARS deregulation significantly affects seed germination and root growth under osmotic stress (Roulé et al. preprint). Thus, we wondered whether other genes outside from the marneral cluster, are also mis-regulated in the RNAi lines further supporting the phenotypes observed. To this end, we analyzed the complete transcriptome of MARS RNAi line 1 in response to an ABA stimulus by RNA-seq. While ABA treatment induces a strong gene deregulation in wild type, we found many more differentially expressed genes in response to ABA in the RNAi line compared to Col (3 238 versus 2 849, respectively), without any bias toward induced or repressed genes by ABA treatment (Figure 2A). However, under control condition, only 29 genes were differentially regulated between the two genotypes, none of them implicated in seed germination, ABA or stress-related mechanism. To further investigate the impact of the MARS-deregulation on the plant ABA response I conducted a hierarchical clustering of all the differentially expressed genes. This allowed us to determine two main clusters (cluster 1 and 2) that grouped the great majority of the differentially expressed genes (5 313 among 5 343 genes) that behave the same way in the two genotypes. Two smaller clusters can be isolated that present opposite expression profiles between the genotypes (Figure 2C and3A). The cluster 4 contains three genes always significantly more expressed in the RNAi line, whereas the cluster 3 contains 27 genes always significantly less expressed in the RNAi line (Figure 3A). Notably, QUA-QUINE STARCH (QQS) from cluster 4 is a gene that when overexpressed, decreases the seed starch content [START_REF] Li | Identification of the novel protein QQS as a component of the starch metabolic network in Arabidopsis leaves[END_REF][START_REF] Li | The QQS orphan gene of Arabidopsis modulates carbon and nitrogen allocation in soybean[END_REF]Li et al., 2015b). As high starch content positively correlates with better germination rate (Zhao et al., 2018c), it supports the delay in germination we observed in the MARS deregulated lines (Figure 3B). Additionally, two genes, namely GLUTAMATE:GLYOXYLATE AMINOTRANSFERASE 2 (GGT2) and ABC1-LIKE KINASE 3 (ABC1K3) from cluster 3, are involved in the maintenance of a normal cell oxidation status [START_REF] Ohkama-Ohtsu | Characterization of the extracellular γ-glutamyl transpeptidases, GGT1 and GGT2, in Arabidopsis[END_REF][START_REF] Martinis | A chloroplast ABC1-like kinase regulates vitamin E metabolism in arabidopsis[END_REF] and are significantly less abundant in the RNAi line as compared to the Col (Figure 3B). As germination and osmotic stress are known to generate oxidative stresses (Li et al., 2017b) it is likely that the reduced activity of these genes participates in the reduced seed germination and root growth under osmotic stresses. Finally, two genes involved in nitrate assimilation and metabolization, NITRITE REDUCTASE1 (NIR1) and PHOSPHOENOLPYRUVATE CARBOXYLASE2 (PPC2), respectively, are also significantly less abundant in the RNAi line which could participate in the lower germination efficiency observed in the MARS downregulated lines as nitrate promote seed germination at low concentration in many plant species [START_REF] Shi | Phosphoenolpyruvate carboxylase in arabidopsis leaves plays a crucial role in carbon and nitrogen metabolism[END_REF][START_REF] Duermeyer | Regulation of seed dormancy and germination by nitrate[END_REF] (Figure 3B). Taken together, the disturbance of genes involved in the C/N balance and cell oxidation status may participate in the lower seed germination and root growth under osmotic stress in the MARS deregulated lines.

MARS physically interact with the marneral cluster genomic region to titrate LHP1 binding

In Roulé et al (preprint) we showed that MARS physically interacts with LHP1 and that it modulates its binding to the chromatin in a dose-dependent manner. To increase our understanding on how MARS recruit LHP1 to the marneral cluster we decided to look if the MARS RNA itself is able to physically interact with the marneral genomic region. Indeed, the APOLO lncRNA, which also physically interacts with LHP1, modulates LHP1 binding throughout the genome via the formation of R-loops. More precisely, the RNA-DNA APOLO duplex decoys LHP1 away from the chromatin, affecting chromatin configuration and gene expression (Ariel et al., 2020). Hence, we decided to use a ChIRP-qPCR approach (Ariel et al., 2020) to characterize the interaction of the MARS RNA with the chromatin DNA. To this end, we designed biotinylated probes complementary to the MARS RNA. After confirming the specificity of our probes through biotin immunoprecipitation followed by RNA extraction (Figure 4A), we extracted and quantified the chromatin genomic DNA copurified together with the MARS RNA. Strikingly, we found that MARS is significantly enriched in the MRN1 genomic region and the intergenic region 2 of the DNA (Figure 4B) as compared to control oligos.

Interestingly, the MARS enrichment corresponds to the region where LHP1 binding was detected (Figure 4C) with higher LHP1 binding and MARS RNA-DNA interaction in the intergenic region 2 and the MRN1 5' and promoter region. Taking together, MARS physical interaction with the marneral cluster genomic region may facilitate the recruitment and binding of LHP1 to the chromatin within this genomic region, as it has already been shown with the APOLO lincRNA and LHP1 binding to auxin-responsive genes (Ariel et al., 2020).

Conclusion and perspectives of complementary results

The marneral is a key secondary metabolite for Arabidopsis thaliana growth and development and it is produced by the enzymes encoded in three genes organized in one cluster (Field et al., 2011;Go et al., 2012). Interestingly, we noticed that a lincRNA embedded in this cluster is positively co-regulated with the coding genes of the marneral cluster and it is involved in their ABA-mediated gene expression. The downregulation of MARS transcript abundance results in ABA-dependent transcriptomic changes. Notably, thirty genes showed a contrasted expression pattern between Col and the RNAi MARS 1 line. Among them, genes involved in the C/N balance and the antioxydant regulatory system (Figure 3B), that could participate in the reduced seed germination efficiency and enhance osmotic stress sensitivity observed within the MARS and marneral cluster genes deregulated lines (Li et al., 2017;Zhao et al., 2018;Roulé et al preprint).

As an additional analysis, we showed that the MARS transcript physically interacts with the genomic region of the marneral cluster (Figure 4B). Remarkably, the MARS-DNA interaction corresponds to the chromatin regions showing binding to LHP1 (Figure 4C), strengthening the dependency between MARS and LHP1 binding. Interestingly, the APOLO lncRNA fine-tunes the chromatin topology and epigenetic landscape of independent genomic regions all along the Arabidopsis genome through the formation of R-loop (Ariel et al., 2020).

APOLO-R-loop formation mediates the binding of LHP1 onto the chromatin, resulting in transcriptional activity changes of more than 150 genes, involved in a wide-range of physiological processes, including root gravitropism (Ariel et al., 2014), root hair formation (Moison et al., 2020), LR development and more generally auxin signaling pathway (Ariel et al., 2020). Thus, the transcriptional activity changes observed within the MARS downregulated line could be directly controlled by the MARS RNA itself through the formation of R-loop, or be a consequence of the marneral genes' increased transcriptional activity. To confirm this assumption, it is necessary to profile genome-wide the DNA-RNA hybrid R-loops as well as the MARS transcript DNA localization under the ABA stimulus inducing the accumulation of MARS. Indeed, even though the marneral epigenetic landscape is changing upon MARS downregulation (decrease of H3K27me3 mark and LHP1 deposition), we could not correlate this with changes in marneral genes transcription, indicating that an additional ABA-requiring factor is needed for the MARS-mediated transcriptional activity changes of marneral cluster genes. Likely, the chromatin is affected without affecting transcription but predisposing this locus to a modified response against the ABA stimulus. This resembles a priming response as sown for many environmental stresses [START_REF] Hilker | Priming and memory of stress responses in organisms lacking a nervous system[END_REF].

Hence, MARS is a lncRNA able to regulate the marneral cluster epigenetic landscape and chromatin conformation, which changes the marneral cluster genes transcriptional responsiveness to ABA stimulus. MARS downregulation affects the plant sensitivity to osmotic stress and ability of the seeds to germinate, through the deregulation of marneral cluster genes and/or other ABA-related genes. With nearly five hundred clusters within Arabidopsis (Yu et al., 2016;Kautsar et al., 2017) together with more than thousands of lncRNAs (Blein et al., 2020), it is likely that other lncRNAs could be implicated in the co-regulation of clustered genes.

Particularly, we found that lncRNAs within clusters are significantly more co-regulated with metabolic clustered genes than non-metabolic clustered genes, hinting at an intriguing putative co-evolutionary relationship between BGCs and lncRNAs (Roulé et al., preprint).

We observed a lower level of conservation for non-coding genes as compared to coding genes (Blein et al., 2020). In agreement, the 1000 human genome [START_REF] Sudmant | An integrated map of structural variation in 2,504 human genomes[END_REF] and 1001 Arabidopsis genome (2016) consortiums show that the gene sequence conservation between individuals is higher in coding than in non-coding portions of the genome. Nevertheless, this does not explain the significant gene expression differences observed between the two ecotypes. Clearly, it will be also relevant to compare the level of conservation of gene promoters instead of the gene core sequences. Interestingly, two studies in mammalian organisms revealed that the level of conservation of the non-coding gene promoter is as comparable as that of coding genes [START_REF] Ponjavic | Functionality or transcriptional noise? Evidence for selection within long noncoding RNAs[END_REF][START_REF] Melé | Chromatin environment, transcriptional regulation, and splicing distinguish lincRNAs and mRNAs[END_REF]. Notably, some lncRNA promoters contain cis-regulatory motifs such as transcription factor (TF) binding sites which are conserved within 11 tetrapods species [START_REF] Necsulea | The evolution of lncRNA repertoires and expression patterns in tetrapods[END_REF]. Thus, the survey of promoter sequence conservation between Col-0 and Ler, together with the transcriptomic information we produced could increase our understanding of the basis for the non-coding genes' accession-specific expression. In our context, it is conceivable that nondifferentially expressed genes will have conserved promoter sequences in-between Col-0 and Ler, whereas the ecotype-specifically expressed genes will not. This difference may be more marked for non-coding genes as compared to coding genes since the formers are much more expressed in an ecotype-specific way (Blein et al., 2020). Finally, it is likely that a higher proportion of ecotype specific-expressed genes will be involved in the plant environmental cues perception as compared to the generally expressed genes. Indeed, as ecotype specific genes are enriched in lowly conserved non-coding genes (Blein et al., 2020), it is unlikely that they present a constitutive expression.

Rather, they should be specifically expressed under specific environmental constraints, constituting a transcriptomic signature of their respective ecotype environment.

Another point to consider is that the positioning of non-coding genes throughout the genome may be conserved independently of their sequences. For example, in zebrafish it has been estimated that only 5% of lncRNAs are conserved at the sequence level within the animal kingdom [START_REF] Ulitsky | Conserved function of lincRNAs in vertebrate embryonic development despite rapid sequence evolution[END_REF][START_REF] Hezroni | Principles of Long Noncoding RNA Evolution Derived from Direct Comparison of Transcriptomes in 17 Species[END_REF]. Nevertheless, lncRNAs appear more often in syntenic positions as opposed to protein-coding genes [START_REF] Ulitsky | Conserved function of lincRNAs in vertebrate embryonic development despite rapid sequence evolution[END_REF]. The enriched appearance of lncRNAs in conserved genomic positions indicates that there is an evolutionary pressure to preserve lncRNAs in these positions while the rest of the genome is rearranged. In agreement, the SyntDB lncRNAs database has defined positionally conserved lncRNAs across primates [START_REF] Bryzghalov | SyntDB: Defining orthologues of human long noncoding RNAs across primates[END_REF]. Altogether, it could be noteworthy to look for syntenic conservation of non-coding genes, together with their transcriptional activity, to find syntenic-conserved non-coding genes between plant species, potentially participating in the regulation of their closely located proteincoding genes.

The lncRNA features: an advantage for a quick adaptation to environmental changes?

The comparison of lncRNA promoter versus the ones of coding genes has been intensively studied in mammals. It has been revealed that lncRNA promoters contain less TF binding site, are more subjected to Pol II pausing, and present a higher level of chromatin condensation as compared to the coding genes promoter (Schlackow et al., 2017b;Golicz et al., 2018;Sarropoulos et al., 2019). Also, lncRNA often demonstrate a strong cell-specific or tissue-specific expression patterns as compare to protein coding mRNAs [START_REF] Cabili | Integrative annotation of human large intergenic noncoding RNAs reveals global properties and specific subclasses[END_REF][START_REF] Wang | Arabidopsis noncoding RNA mediates control of photomorphogenesis by red light[END_REF]. All these characteristics may participate in the lower abundance of noncoding transcripts as compared to coding transcripts [START_REF] Mattioli | High-throughput functional analysis of lncRNA core promoters elucidates rules governing tissue specificity[END_REF]. Indeed, one of the particularities of non-coding genes is that they are expressed, or detected, in a lower extent compared to coding genes. This assessment has also been confirmed in our studies, where non-coding transcripts in both Col-0 and Ler were significantly less abundant than the coding transcripts (Blein et al., 2020). This could explain why lncRNA molecules were discovered later as the sensitivity of the transcriptomic tools increased. Another explanation of their lower abundance could be that long non-coding transcripts are less stable than coding transcripts.

For this latter hypothesis, several studies in mammals have used metabolic labelling (Mukherjee et al., 2017b) or drugs inhibiting gene transcription [START_REF] Clark | Genome-wide analysis of long noncoding RNA stability[END_REF][START_REF] Melé | Chromatin environment, transcriptional regulation, and splicing distinguish lincRNAs and mRNAs[END_REF] to assess global transcript stability. However, results vary according to the method used, making it difficult to conclude whether transcript stability participates in the lower transcript abundance of lncRNAs. Thus, it could be interesting to repeat these experiments within Arabidopsis or other plant species, using both methods, to know if lncRNAs are less stable than coding mRNAs in the plant kingdom. Anyhow, the lower stability of non-coding transcripts, together with their high responsiveness and specificity of expression to environmental cues (Di et al., 2014), could participate in the plant's rapid adaptation to its environment. Indeed, with low transcript abundance and high responsiveness, a plant's transcriptome can rapidly adapt to changes in its environment, increasing the rapid adaptability of the cell to reach an equilibrium with all the environmental signals it perceives.

Two ecotype-associated lncRNAs in the environmental control of plant growth and development

Exploring the Col-0 and Ler transcriptomic landscapes allowed us to discover new rootexpressed lncRNAs involved in the quantitative control of root architecture. Notably, the overexpression of two ecotype-enriched lincRNAs, namely NPC48 and NPC72 from Ler and Col-0, respectively, resulted in a significant limitation of primary root growth under normal conditions (Blein et al., 2020). As ectopic overexpression correlates with root-architecture changes, it is likely that these two lincRNAs act in trans to regulate their target genes. Transcriptomic analyses of NPC48-overexpressing plants have been conducted. Strikingly, despite the strong phenotypic impact of the NPC48 overexpression, the transcriptomes were not dramatically different from Col plants. Thus, it is likely that NPC48 is a subtle root growth regulator. Interestingly, these two rootgrowth-modulators lincRNAs, NPC48 and NPC72, were initially identified in a genome-wide bioinformatic analyses of cDNA libraries that identified 76 Arabidopsis lncRNAs [START_REF] Amor | Novel long non-protein coding RNAs involved in Arabidopsis differentiation and stress responses[END_REF].

In the same study, plants overexpressing the NPC48 present strong developmental defects, including late flowering and abnormal leaf shape [START_REF] Amor | Novel long non-protein coding RNAs involved in Arabidopsis differentiation and stress responses[END_REF]. Regarding the strong influence of NPC48 overexpression on plant development it is surprising that the transcriptome of the NPC48-overexpressing plants is not dramatically affected (Blein et al., 2020). As the NPC48 transcriptome analyses have been realized on the root compartment, one explanation for the poor root transcriptomic changes detected, together with the striking aerial phenotypes observed could be that the NPC48 lncRNA is more implicated in the control of gene expression in late stages of plant development, notably in the aerial part of the plant. Additionally, the root growth limitation observed in the NPC48-overexpressing line could simply be an indirect phenotype, triggered by strong plant aerial defects. Hence, NPC48 could be an important regulator of the leaf and flower establishment. On the other hand, the influence of NPC72 overexpression on the transcriptome is unknown, but it has been shown that NPC72 is responsive to salt stress [START_REF] Amor | Novel long non-protein coding RNAs involved in Arabidopsis differentiation and stress responses[END_REF]), hinting at a potential relationship between NPC72 and the plant salt-stress response. To go further, it could be interesting to grow NPC72-overexpressing lines under salt stress conditions and perform phenotypic and transcriptomic analyses. Also, it could be noteworthy to generate and analyze at the transcriptomic and phenotypic level, knock-down (RNAi) or knock-out (CRISPR deletion) mutant lines for both NPC48 and NPC72 to better understand their function as plant, direct or indirect, root-growth regulators.

An RdDM-acting lincRNA regulates the root system architecture

The extensive characterization of the small and long non-coding transcriptome of Col-0 and Ler ecotypes in the early response of Pi shortage constitutes a great resource to look for functional lncRNAs. In addition to NPC48 and NPC72, the LATERALINC lincRNA (described in chapter 2) also participates in the quantitative control of the root architecture, likely through RdDM. Notably, LATERALINC downregulation significantly reduced the Lateral Root (LR) length in control conditions. Initially, LATERALINC was selected as a putative regulator of its downstream neighboring gene IAA14. However, the absence of impact on IAA14 gene expression between Col and LATERALINC-downregulated lines reinforces that the LR-related phenotype is occurring independently of IAA14. However, its upstream neighboring gene NF-YB3 may be regulated by LATERALINC in an auxin-dependent context. Indeed, low levels of LATERALINC transcripts significantly delayed the NF-YB3 induction in response to auxin, strengthening the potential regulatory relationship between these genes. As NF-YB3 has been described as a positive regulator of heat stress tolerance [START_REF] Sato | NF-YB2 and NF-YB3 have functionally diverged and differentially induce drought and heat stress-specific genes[END_REF], it could be noteworthy to investigate the LATERALINC deregulated line's sensitivity to heat stress. Notably, the soil temperature can influence many root-related developmental processes such as LR initiation, root branching, and more generally root growth [START_REF] Pregitzer | Responses of tree fine roots to temperature[END_REF]. More than thousands of genes are differentially regulated within root hair upon heat stress in soybean [START_REF] Valdés-López | Soybean roots grown under heat stress show global changes in their transcriptional and proteomic profiles[END_REF], all of which hinting at a potential relationship between the root architecture and the ability of plants to face heat stress episodes. Hence, LATERALINC may be involved in fine-tuning the plant heat stress tolerance through modulation of the root system architecture via regulation of its neighboring gene NF-YB3. Nevertheless, LATERALINC may regulate other genes in trans involved in LR development or growth. To confirm this hypothesis, we could investigate whether overexpressing LATERALINC in trans modifies LR architecture. Also, transcriptomic analyses of LATERALINC-deregulated lines could be used to identify new direct or indirect LATERALINC' targets. Finally, the LATERALINC-mediated auxin-dependent regulation of NF-YB3 may also directly participate in the auxin-dependent LR initiation and growth as this trait has not been studied within the NF-YB3 overexpressing plants [START_REF] Sato | NF-YB2 and NF-YB3 have functionally diverged and differentially induce drought and heat stress-specific genes[END_REF].

Taken altogether, the intensive characterization of the coding and non-coding transcriptome within two Arabidopsis ecotypes in the early Pi starvation response constituted a nice opportunity to discover new modulators of root-growth. Indeed, the deep-investigation of these transcriptomics data allowed us to identify several lincRNAs that modify root architecture: NPC48 and NPC72, subtle regulators of the primary root growth, and LATERALINC, a regulator of LR development. In addition, the screening used to find the MARS lincRNA also identified more than thirty lincRNAs potentially involved in the regulation of expression of their neighboring genes, lying ahead of deeper investigation. The roots, in addition to anchoring them to the soil, allow water uptake and mineral nutrition required for plant growth and development. Climate change, together with the unsustainable use of mineral-enriched fertilizer, makes critical the deep understanding of the regulatory mechanisms behind the establishment of the root system architecture. Since lncRNAs regulate gene expression in a quantitatively-manner and are mainly expressed only under certain constraints (specific development stage or environment condition), they could be good candidates for targeting in plant breeding programs to subtly acclimate the emerging crops to the coming environmental changes without strongly affecting plant growth. This may also serve to improve the plant nutrition under adverse environmental conditions and in nutrientpoor soils, interesting agricultural traits for the near future.

8. The MARS lncRNA, a novel actor in the plant response to environment 8.1 MARS-mediated marneral genes expression changes is involved in the plant response to its environment

The marneral cluster is a 30kb region within Arabidopsis thaliana, containing three genes: MARNERAL SYNTHASE1 (MRN1), CYP705A12 and CYP71A16, directly involved in the biosynthesis and metabolism of the marneral (Field et al., 2011). The marneral is a triterpene critical for proper plant development (Go et al., 2012). Indeed, knock-out of MRN1 triggers general plant developmental defects such as a delay in flowering, abnormal leaf shape and less grain per siliques (Go et al., 2012). Interestingly, three additional transcriptional units are embedded in the marneral cluster. According to their features and the use of bioinformatic tools, we classified these transcripts as lncRNAs. Notably one of these transcriptional units positively correlates with the expression of the marneral genes cluster. Strikingly, RNAimediated decrease of this transcriptional unit, significantly enhance the marneral genes transcriptional response to an ABA stimulus. Therefore, we named this lncRNA MARS for MARneral Silencing. High responsiveness to environmental cues is characteristic of lncRNAs.

Many lncRNAs are differentially regulated under abiotic or biotic stresses such as drought, heat, osmotic, hypoxic, cold and nutrient deficiency stresses or Sclerotinia sclerotiorum and Fusarium oxysporum infection, tomato yellow leaf curl virus (TYLCV) and stripe rust [START_REF] Zhang | Long non-coding genes implicated in response to stripe rust pathogen stress in wheat (Triticum aestivum L.)[END_REF]. Furthermore, a systematic comparison of lncRNAs in Miscanthus lutarioriparius revealed that, when these plants face a new environment, the expression of their lncRNAs is modified with much larger amplitude than for coding mRNAs (Xu et al., 2017b). Similarly in Arabidopsis, lncRNAs show an enhanced transcriptional stress-response specificity to cold, heat, drought, salt and high light stresses as compared to coding mRNAs (Di et al., 2014). In agreement, MARS was significantly more upregulated in response to heat stress and ABA stimulus as compared to the protein-coding genes of the marneral cluster.

The phytohormone ABA is involved in a wide range of biological processes, such as seed development and germination, but also in the perception and signal transduction of many environmental cues (Vishwakarma et al., 2017). Even though we showed that the marneral cluster misregulation is associated with an increased sensitivity to osmotic stress and a seed germination delay, we have not looked at other ABA-related physiological responses such as seed maturation. Indeed, in rice almost five hundred lncRNAs exhibit different expression patterns during seed development, highlighting that lncRNAs might be important in this process (Zhao et al., 2020). In Arabidopsis, the non-coding transcriptome upon silique development has not been explored yet, even though time-course transcriptomic approaches during Arabidopsis fruit development and maturation have been realized [START_REF] Mizzotti | Time-course transcriptome analysis of arabidopsis siliques discloses genes essential for fruit development and maturation[END_REF].

Similarly, the transcriptome of Arabidopsis dormant seeds have been investigated without pointing out the non-coding part of it [START_REF] Dekkers | Dormant and after-ripened Arabidopsis thaliana seeds are distinguished by early transcriptional differences in the imbibed state[END_REF]Nelson et al., 2017). Thus, it could be noteworthy to re-investigate all these transcriptomic analyses to test if the marneral cluster genes present a peculiar expression pattern upon seed establishment. This may guide us for the associated phenotypic analysis, such as the number of grain per silique, weight of grains and seed dormancy maintenance in MARS and the marneral protein-coding gene mutants.

Finally, in addition to ABA, the marneral cluster responds to auxin stimulus, N and Pi starvation indicating that the marneral could be involved in a wide-range of non-ABA related physiological processes. As the marneral was initially described as a critical triterpene for the normal development of plants (Go et al., 2012), including the aerial organs, it is likely that MARS may regulate such developmental processes in a marneral-dependent manner. In agreement, the APOLO lncRNA is involved in both developmental and stress related mechanisms, such as LR development (Ariel et al., 2020) and plant cold-stress response (Moison et al., 2020). Thus, it could be noteworthy to look at other plant developmental phenotypes in MARS deregulated lines compared to Col.

ABA as precursor or signaling molecule for the marneral biosynthesis and metabolization

The strong developmental defects of mrn1 knock-out mutant are associated with cell elongation defects in both the root and the shoot apical meristem (Go et al., 2012). In agreement, the MARS-mediated deregulation of marneral cluster genes significantly affects many ABA-related plant physiological processes including, root growth under osmotic stress or seed germination. It is tempting to assume that the marneral triterpene may participate in the ABA signaling pathway or that ABA is responsible for the biosynthesis of this secondary metabolite. Indeed, the squalene synthase (BfSS1) gene, involved in the biosynthesis of phytosterol and triterpene in Bupleurum falcatum, is induced after ABA treatment indicating that this hormone may participate in the regulation of biosynthesis of the plant phytosterol and triterpene (Kim et al., 2011b). Furthermore, ABA has been reported to regulate some of the secondary metabolites biosynthesis within plant cell cultures (Zhao et al 2000;Luo et al 2001).

In addition, many ABA-related genes are differentially expressed upon the apple russet development, a process characterized by a significant triterpenoid composition shift [START_REF] Falginella | Differential regulation of triterpene biosynthesis induced by an early failure in cuticle formation in apple[END_REF]. Finally in grapevine, ABA and GA increased the content of mono and sesquiterpenes in leaves and berries [START_REF] Murcia | ABA and GA3 regulate the synthesis of primary and secondary metabolites related to alleviation from biotic and abiotic stresses in grapevine[END_REF]. Marneral triterpene quantification through Mass-Spectrometry (MS), together with transcriptional activity measurements of marneral gene within Arabidopsis mutant impaired in ABA biosynthesis or signaling may provide additional evidence about the importance or direct relationship between the ABA signaling and the marneral synthesis.

MARS: an enhancer lncRNA located within a Super

Enhancer region?

MARS is a lncRNA able to modulate the binding of LHP1, a protein linked to epigenetic modifications on chromatin (Chen et al., 2021) and a member of PRC1 who also impacts chromatin looping [START_REF] Kim | Current understanding of flowering pathways in plants: focusing on the vernalization pathway in Arabidopsis and several vegetable crop plants[END_REF]. Notably, knock-out of LHP1 results in strong modifications of genome topology together with a decrease in H3K27me3 mark (Veluchamy et al., 2016). In agreement, we observed that LHP1 binding on the marneral cluster genomic region positively correlates with a condensed "linear" chromatin organization and poor gene transcription. Using RNA immunoprecipitation (RIP) assays we confirmed in-vivo that MARS interacts with LHP1.

Nevertheless, RIP is a cross-linked based method that, cannot decipher whether MARS directly interacts with LHP1, or interacts with another member of PRC1 or PRC2. To test this, the use of direct RNA-protein interaction methods such as Tri-FC (Seo and Chua, 2017), electrophoretic mobility shift assay [START_REF] Rio | Electrophoretic mobility shift assays for RNA-protein complexes[END_REF] or co-transfection of LHP1 and MARS-RNA binary vectors in a heterologous transient system will be required. Anyhow, we demonstrated that the MARSmediated LHP1 binding fine-tunes the activity of an MRN1-enhancer located 18kb from MARS.

In agreement, Nützman et al (2020) demonstrated that the marneral cluster harbors a compact topology in roots where the cluster is expressed as compared to the shoot where it is less active (Field et al., 2011;Nutzman et al., 2018). As the MRN1-enhancer activity (i.e. leading to chromatin decondensation and relaxation for transcription to occur) increases upon MRN1 and CYPs gene activation, it is conceivable that the MRN1-enhancer can also target other genes within the cluster. Furthermore, the over-represented TF binding sites upstream and downstream to the MRN1-enhancer points that this intergenic region could be a Super Enhancer (SE). Indeed, SEs are genomic regions containing multiple closely located enhancers [START_REF] Huang | Dissecting super-enhancer hierarchy based on chromatin interactions[END_REF]. Finally, the MARS genomic region itself could be an enhancer region for the marneral cluster even though the large majority of transcribed enhancers generates small short, bi-directional, unspliced and non-polyadenylated transcripts. Recently, certain enhancers were shown to also generate long, unidirectional, spliced and polyadenylated transcripts such as the MARS RNA [START_REF] Sartorelli | Enhancer RNAs are an important regulatory layer of the epigenome[END_REF]. An integrative analysis of chromatin configuration and condensation upon an ABA stimulus could help to gain a complete view of the activity of the marneral cluster enhancers. For example, the use of capture Hi-C (i.e. probes enriching the marneral cluster genomic region followed by topological analyzes) together with ATAC-seq (method allowing the identification of decondensed regions of the chromatin susceptible to in vitro transposition) upon ABA stimulus would help to precisely localized the marneral enhancers, and the spatial arrangements in relation to their relative targets in chromatin linked to ABA action.

MARS act in cis or in trans for the control of marneral cluster gene expression?

RNAi-mediated approach, insertional mutant and ectopic overexpression were used to change MARS transcripts abundance. Unfortunately, none of these methods could clearly conclude whether the MARS lncRNA act in cis (MARS transcription) or trans (MARS RNA molecule acting at a distance) to regulate the marneral cluster expression. Interestingly, we showed that addition of in-vitro transcribed MARS RNA to the MRN1 chromatin region is able to titrate LHP1 to the chromatin in a dose-dependent manner suggesting a trans-regulatory function. However, as the ectopic in vivo overexpression did not result in gene expression changes, it is likely that other ABA-related factors are needed for this trans activity to titer LHP1 in the frame of the MARS-dependent marneral cluster regulation. CRISPR-based deletion approaches may be used to remove the MARS locus from the marneral cluster to further propose that the MARS-dependent regulation occurs in cis on its embedded cluster (i.e. that MARS transcription is linked to its regulatory function). Nevertheless, deletion of a 2kb region within a three-dimensionally regulated locus may result in unrelated side-effects on the topological status of the locus. An alternative will be the use of a dead-Cas9 (dCas9) based approach, together with a transcriptional inhibitor or activator (dCas9a) of the targeted loci. By activating and inactivating MARS in situ without changing the DNA sequence, this may help to conclude whether the MARS lncRNA transcription is required for its function. For example, a comparable molecular phenotype between plants overexpressing MARS in trans (ectopic overexpression) or in cis (dCas9a), will reinforce that MARS act in trans (i.e. MARS transcription not involved in its regulatory function) for the marneral cluster regulation. DCas9 systems start to be widely used in plants [START_REF] Lowder | Robust Transcriptional Activation in Plants Using Multiplexed CRISPR-Act2.0 and mTALE-Act Systems[END_REF][START_REF] Lee | CRISPR-based tools for targeted transcriptional and epigenetic regulation in plants[END_REF][START_REF] Moradpour | CRISPR/dCas9 platforms in plants: strategies and applications beyond genome editing[END_REF].

Notably, Flowering Locus T (FT) gene expression and epigenetic status have been modified in Les lncRNAs peuvent réguler l'activité des gènes en cis ou en trans, en modifiant l'expression d'un gène voisin ou distant, respectivement. Chez les plantes, quelques lncRNAs agissant en cis sont impliqué dans le contrôle de la floraison (lncRNAs dérivé du locus FLC), transport de l'auxine (APOLO), rendement en grain chez le riz (LAIR) et tolérance au froid (SVALKA) (Chen et al., 2021). A ce jour, hormis APOLO (Ariel et al., 2014 ;Ariel et al., 2020), aucun lncRNA module l'architecture de la racine via le contrôle de l'expression de son gène voisin. Ainsi, nous avons exploré l'analyse transcriptomique réalisé chez les écotypes Col-0 et Ler au cours d'une cinétique de manque de Pi, afin de détecter des corrélations d'expression positives entre un lncRNA et son gène voisin, impliqué dans le développement ou la croissance de la racine. Sortie de ces analyses, nous avons isolé LATERALINC, un lncRNA modulant la croissance des racines latérales et potentiellement impliqué dans la régulation de son gène voisin NF-YB3, décrit comme impliqué dans la réponse des plantes aux fortes températures [START_REF] Sato | NF-YB2 and NF-YB3 have functionally diverged and differentially induce drought and heat stress-specific genes[END_REF]. De plus, nous avons identifié MARS, un lncRNA régulant l'expression d'un groupe de gènes organisé en cluster et impliqué dans la biosynthèse et la métabolisation du marneral, un métabolite secondaire important pour le développement de la plante (Field et al., 2011 ;Go et al., 2012). La majeure partie de mon travail de thèse à consister à une caractérisation moléculaire approfondie du lncRNA MARS, et des mécanismes épigénétiques qu'il utilise pour moduler l'environnement chromatinien du cluster marneral. Brièvement, nous avons démontré que le lncRNA MARS module la compaction et la conformation tridimensionnelle de la chromatine du cluster marneral, afin de moduler la sensibilité des gènes à un stimulus ABA. Nous avons montré que la dérégulation d'expression de MARS perturbe la germination de la graine et la croissance des racines en réponse à des stress osmotiques. Enfin, nous avons observé que de nombreux clusters de gènes, autre que le marneral, contiennent des unités de transcription non-codantes, également corrélé avec les gènes du cluster, suggérant que le contrôle de la co-régulation des gènes en cluster par des lncRNA pourrait être un mécanisme conservé chez les plantes.

Ensemble, ma thèse souligne l'importance des lncRNAs pour l'adaptation des plantes à leur environnement, et illustre un complexe mécanisme de régulation génique chez les plantes impliquant lncRNA, statut épigénétique et conformation tridimensionnelle de la chromatine. Title: Plant non-coding RNAs: important regulator of root growth and gene activity Keywords: non-coding RNA, root growth, chromatin Abstract: The development of high throughput sequencing methods in the early 2000's revealed that the eukaryotic genomes are tremendously transcribed even though a small part of transcripts give rise to proteins. For example, it has been estimated that 95% of the human genome is transcribed whereas only 2% of the transcripts will produce protein. This surprising observation strengthens the idea that non-coding transcripts are an important regulator of cell life. Interestingly, the non-coding genes show lower conservation than the coding genes between species and even within a species. For example, in plants, the sequencing of Arabidopsis accessions during the 1001 genome project demonstrated more frequent sequence variation in non-coding regions as compared to coding regions. Non-coding genes play regulatory roles and it has been proposed that their modification reflect the adaptation of an individual to its environment. In this context, my thesis aims to better understand the function and importance of noncoding transcriptional units for plant adaptation to their environment. Phosphate (Pi) constitutes one of the most important minerals for plants and is absolutely needed for a proper plant development. To investigate the function of non-coding transcript during a shortage of Pi we analyzed Col-0 and Ler ecotype that present a strikingly contrasted root growth response when subjected to Pi starvation: in medium with low amount of Pi, Col-0 immediately stops its main root growth whereas Ler ecotype does not. Compared to the similar protein coding genes activity during the Pi shortage, the non-coding genes transcriptional activity shows strong differences between ecotypes. Interestingly, we found that the deregulation of two long non-coding RNAs (lncRNAs) alters root architecture, strengthening that lncRNAs molecules constitute important regulators of root growth. Our comparison of the transcriptomes of these two Arabidopsis accessions reinforces the relevance of non-coding transcripts in the plant local adaptation to its environment. Taking advantage of our transcriptomic survey, we look for other functional lncRNAs molecules participating in the control of root architecture. Taking into account that many lncRNAs are involved in the regulation of their neighboring genes, we look for positive correlation of expression between lncRNA and their neighboring coding genes, focusing on coding genes directly involved in root growth and development. Among our putative candidates we found a lncRNA, which we named MARneral Silencing (MARS), involved in the regulation of marneral cluster genes. The marneral is a triterpene produced by three genes organized in cluster directly involved in plant growth and development. We show that this lncRNA is involved in the epigenetic reprogramming of the marneral cluster in response to an ABA cue, directly influencing marneral genes response to ABA stimulus. Together, my thesis strengthens the importance of lncRNAs on the plant adaptation to its environment, and illustrates a complex chromatin-based mechanism involving lncRNA, epigenetic state and 3D chromatin conformation.

Figure 2 MARS

 2 Figure 2

Figure 3 MARS

 3 Figure 3

Figure 4

 4 Figure 4

Figure 4 :

 4 Figure 4: The different classes of lncRNAs. LncRNAs are classified according to their position relative to other genes. Long Intergenic ncRNAs (lincRNAs) are transcribed without overlapping any gene or promoter.Promoter lncRNAs are transcribed within the promoter region of a gene without overlapping the core gene region. LncRNA genes overlapping with an exon of another gene in its opposite direction are named Natural Antisense Transcript lncRNA (NAT-lncRNA), whereas it is named sense lncRNA when it overlaps an exon in the same direction as the transcript. Finally, lncRNA can be transcribed within an intron, generating intronic lncRNAs.

Figure 5 .

 5 Figure 5. LncRNA specificities compared to coding mRNAs (adapted fromRinn et al., 2020). LncRNAs promoter contains more polymerase C-terminal domain modifications (i.e. threonine 4 phosphorylation (CTD-T4P)) and less TFs binding sites, decreasing the Pol II pausing and transcripts abundance, respectively. Also, the chromatin from lncRNA promoters is more frequently condensed. It is likely that lncRNA half-life is shorter than that of coding mRNAs. Finally, lncRNA are less frequently spliced than coding mRNAs.

Figure 6 .

 6 Figure 6. Control of gene expression by promoter' cis-regulatory motif. The promoter region is located upstream of genes and harbor multiple cis-regulatory motifs, recognizable by Transcription Factors (TFs). TFs bind to the promoter region and facilitate the recruitment of the Pol II complex. This TF/Pol II complex associates with co-activators to trigger gene transcription.

Figure 7 .

 7 Figure 7. Polycomb-mediated chromatin condensation (Adapted from[START_REF] Kim | Current understanding of flowering pathways in plants: focusing on the vernalization pathway in Arabidopsis and several vegetable crop plants[END_REF]. The polycomb complexes, PRC2 and PRC1, harbor several core components, highly conserved within eukaryotes. Also, both sub-complexes are associated with several other regulators. On one hand, PRC2 core associates with two PHD-finger domain proteins (VIN3 and VIL1), two B3 domain proteins (VAL1 and VAL2), and some HDAC proteins, among which HDA19. On the other hand, PRC1 core associates with LHP1, VRN1 and EMF1. PRCs complexes interact with histone proteins and mediates trimethylation of H3K27 and ubiquitination of H2AK119/121 maintaining the chromatin in a condensed and transcriptionally silenced state.

Figure 8 .

 8 Figure 8. Chromosome arrangements within the nucleus. (Adapted from[START_REF] Tiang | Chromosome organization and Dynamics during interphase, mitosis, and meiosis in plants[END_REF] The Rabl genome configuration is found in many large plant genome species and is characterized by a spatial separation of telomeres and centromeres. Interestingly, the Bouquet genome conformation is also found in large plant genomes, such as maize. In this configuration, the telomeres interact with each other close to the nuclear periphery whereas the centromeres and

Figure 9 .

 9 Figure 9. Short range interactions. (Adapted from Cavalli and Misteli, 2013). The intragenic chromatin looping (5'-3-gene looping) puts in close proximity the TSS and the TES of a gene, generally facilitating the transcriptional activity through Pol II recycling. The enhancer-promoter looping joins enhancer regions with target promoters, positively affecting gene transcription at distant loci.

Figure 10 .

 10 Figure 10. Pol-mediated regulation of gene transcription by lncRNAs (Adapted from Lucero et al., 2020). A. The Arabidopsis ELENA1 lncRNA physically interacts with the Med19a subunit of the MEDIATOR complex and facilitates its recruitment to the PR1 gene promoter, activating its transcription in response to ELF18. B. A transcriptional Pol II readthrough within the Arabidopsis lncRNA SVALKA locus is occurring during a prolonged cold exposure, triggering the transcription of the exosome-sensitive lncRNA asCBF1 which is cleaved at its polyadenylated sites. The transcription of asCF1 causes Pol II collision with the CBF1 locus, directly stopping CBF1 transcription.

Figure 11 .

 11 Figure 11. Epigenetic-mediated regulation of gene transcription by lncRNAs. A.The Arabidopsis intronic lncRNA AG-incRNA4 recruits PRC2 to the second intron of the AGAMOUS gene through interaction with the CLF subunit, directly influencing H3K27me3 deposition. Interestingly, the AG-incRNA4 is repressed in flowers but not in leaves, impairing AGAMOUS transcription in a tissue-specific manner (Adapted fromLucero et al., 2020). B. The rice lncRNA LAIR recruits the epigenetic modifier OsMOF and OsWDR5a, increasing H3K4me3 and H3K16ac deposition within the LRK1 gene, enhancing its transcription (Adapted fromWang et al., 2018).

Figure 12 .

 12 Figure 12. Chromatin loop-mediated regulation of gene expression by lncRNAs (Adapted from Lucero et al., 2020). A. The floral repressing gene FLOWERING LOCUS C (FLC) is regulated by three lncRNAs. COOLAIR distal isoform lncRNA transcripts accumulation correlates with a gene looping joining the promoter and TES of the FLC locus, likely participating in Pol II recycling. Cold exposure triggers the disruption of the chromatin looping, allowing the production of a COOLAIR proximal isoform and the intronic lncRNA COLDAIR. COLDAIR lncRNA recruits PRC2 to the FLC locus, increasing H3K27me3 deposition. The COLDWRAP lncRNA promoter starts

Figure 13 .

 13 Figure 13. Post-transcriptional regulation of gene expression by lncRNAs. A. The Arabidopsis lncRNA ASCO physically interacts with the NSRs protein modulating the NSR-dependent alternative splicing of auxin-responsive gene transcripts (Adapted from Zhang et al 2019). B. In addition to hijack NSR proteins, the Arabidopsis ASCO lncRNA interacts with PRP8a and SmD1b, core subunits of the spliceosome, modulating the alternative splicing of flagellin-responsive genes (Adapted from Rigo et al., 2020). C. Phosphate starvation triggers the expression of the Arabidopsis IPS1 lncRNA and miR399 and their orthologs in multiple plant species. The IPS1 lncRNA sequester miR399, preventing it to cleave the miR399-target PHO2 transcript, thus participating in modulating PHO2 transcript abundance upon Pi deprivation (Adapted from Zhang et al., 2019).

Figure 1 .

 1 Figure 1. LncRNAs involved root growth and development and its response to environmental stresses. A. The Arabidopsis ASCO lncRNA modulates lateral root growth and development modulated by auxin and in response to biotic stress through its interaction with NSR (Bardou et al., 2014) and SmD1b-RPR8 (Rigo et al., 2020), respectively. B. The lncRNA APOLO fine-tunes the

Figure 2 .

 2 Figure 2. Characterization of transcripts at the DNA level. A, Detection of the DNA sequences of previously uncharacterized predicted transcripts in the two ecotypes (minimum of 90% sequence identity along 90% of the RNA length). The large majority of RNAs come from common DNA regions from both ecotypes. B, SNP accumulation per 100 bp of transcript length for each type of transcript according to data from The 1001 Genomes Project (The 1001 Genomes Consortium, 2016). C, Conservation among plant species (average PhasCons score) of each type of transcript according to genomic position in relation to other annotations. In B and C, Non-NAT refers to transcripts which do not overlap with annotations on the other DNA strands, independent of annotation type (coding, noncoding, and structural RNA or TE).

Figure 3 .

 3 Figure 3. LncRNAs as sRNA precursors. The major specificity difference between Col and Ler is the lncRNA component of the transcriptome. A, Identification of noncoding transcripts as siRNAs or long RNAs. B, Distribution of the major siRNA sizes for noncoding transcripts detected as long in the two ecotypes. There is no major change of siRNA size between the two accessions. C, Detection of noncoding RNAs as 21-nt and 22-nt siRNA precursors. D, Detection of noncoding RNA as 24-nt siRNA precursors. E, Detection of coding RNA as 21-nt and 22-nt siRNAs precursors. F, Detection of coding RNA as 24-nt siRNAs precursors. Detection threshold for small RNA set at 1 read per million. ND, Not detected; ncRNA, noncoding RNA.

Figure 5 .

 5 Figure 5. Expression of strongly deregulated lncRNAs between the two ecotypes. The level of expression of strongly deregulated lncRNAs between ecotypes was investigated by RT-qPCR in roots of 11-d-old plants of Col, Ler, Col er105 , and hybrids between Col and Ler grown under control conditions. A, LincRNAs upregulated in Col relative to Ler. B, LincRNAs upregulated in Ler relative to Col. Measurements represent the log 2-fold change (FC) compared to Col (A) or Ler (B) grown in the same high-phosphate conditions. Error bars represent the SE (n 5 8; for details, see Supplemental TableS5). Results were analyzed by one-way ANOVA followed by Tukey's honestly significant difference (HSD) mean-separation test. Lowercase letters indicate statistical difference among groups (P # 0.05).

Figure 6 .

 6 Figure 6. Expression of selected lncRNAs in Col and Ler. A, Expression profiles of selected lincRNAs in Col and Ler for early Pi starvation kinetics (RNA-seq data, average expression 6 SD, and three replicates). Five selected lincRNAs showed differential expression between Col and Ler at each time point. B, Level of expression of selected lincRNAs in roots of 11-d-old plants grown under high-Pi conditions in Col, Ler, Col er105 , and hybrids between Col and Ler. Measurements represent corrected means of log 2-fold changes (FC) compared to Col measured by RT-qPCR. Error bars represent the SE (n $ 7; for details, see Supplemental TableS5). Results were analyzed by one-way ANOVA followed by Tukey's HSD mean-separation test. Lowercase letters indicate statistical difference among groups (P # 0.05).

Figure 7 .

 7 Figure 7. Overexpression of the lincRNAs NPC48 and NPC72 affects primary root growth. A, Mean primary root length according to genotype and Pi condition at the age of 11 d after sowing (n $ 23; for details, see Supplemental Table S6). B, Representative pictures of roots of each genotype 11 d after sowing under high-Pi conditions. Scale bar 5 1 cm. C, Expression levels of NPC48 and NPC72 in roots of 11-d-old plants grown under high-Pi conditions in lines deregulated in NPC48 or NPC72 and mutants affected in Pi-related root arrest. Measurements represent the log 2-fold changes (FC) compared to Col (n $ 4; for details, see Supplemental Table S5). Measurements represent corrected means of primary root growth (A) or of the fold change compared to Col (C). Error bars represent the SE. Results were analyzed by two-way ANOVA (A) or one-way ANOVA (C) followed by Tukey's HSD mean-separation test. Lowercase letters indicate statistical difference among groups (P # 0.05).

Figure 8 .

 8 Figure 8. Differentially expressed genes in plants overexpressing NPC48. A and B, Numbers of coding (A) and noncoding (B) genes showing statistically different expression levels in RNA-seq data between Col and 35S:NPC48-1. C and D, Venn diagrams showing how differentially expressed coding (C) and noncoding (D) genes in 35S:NPC48-1 correlate with those between Col and Ler or those found during the phosphate starvation kinetics.

  , npc15 T-DNA1 (SALK_027817; NASC reference N527817), npc15 T-DNA2 (SALK_090867; NASC reference N590867), npc43 T-DNA (SALK_007967; NASC reference N507967), npc48 T-DNA (SAIL_1165_H01; NASC reference N843057), and npc72 T-DNA (SAIL_571_C12; NASC reference N824316) lines are in the Plant Physiol. Vol. 183, 2020 1069 Ecotype-Related Long Noncoding RNA Control of Root Growth Downloaded from https://academic.oup.com/plphys/article/183/3/1058/6116367 by guest on 13 August 2021 ecotype Columbia (Col) of Arabidopsis (Arabidopsis thaliana) background. Overexpressing lines 35S:NPC43, 35S:NPC48-1, 35S:NPC48-2, and 35S:NPC48-3 were retrieved from Ben Amor et al. (2009) and are in the Col-0 background. npc34 T-DNA (FLAG_223D08 or FLAG_228A07) is in the Wassilewskija background. Col er105 is in the Columbia background (Col-0) with the null allele erecta-105.

D

  Detection of miRBase miRNAsSupplemental FigureS3-Ecotype specific classification of lncRNAs as siRNA precursors A and B, ShortStack classification of all non-coding genes as siRNA precursors expressed at more than 1 RPM of major siRNA in Col (A) or Ler (B). C, Detection specificity of non-coding RNA as phased 21 nt and 22 nt siRNAs precursors at the level of 1 RPM in each ecotype. D, Detection specificity of miRBase miRNAs at the level of 1 RPM in each ecotype. The majority of the miRNAs are detected in both ecotypes.

  organization and correlation of expression at selected loci A, Increased expression of PHT1;2 in Ler correlated with the specific expression of the two NATs Ler_NEW_R_34181 and Ler_NEW_R_34180. B, Decreased expression of SPX4 gene in Col correlated to the expression of the NAT Col_NEW_RNA_R_29088. C, Differential expression of NIP3;1 and AT1G1910 between Col and Ler correlated with the expression of lincRNA At1NC041650. Error bars represent standard deviation, (n = 3).

  of NPC48 and NPC72 do not change the expression of phosphate starvation related genes A to G, level of expression in root of 11-day-old plants grown under high phosphate condition or low phosphate condition of genes involved in phosphate sensing, IPS1 (A) and SPX3 (B), or in the response to phosphate related growth arrest, LPR1 (C), LPR2 (D), STOP1 (D), AMLT1 (F) and MATE (G). Measures represent corrected means of log 2 fold changes compared to Col measured by RT-qPCR. Error bars represent standard error (n ≥ 3, details in Table

  up-regulated in 35S:NPC48-1 RNASeq Supplemental Figure S10 -Expression analysis of genes regulated in 35S:NPC48-1 line in a different independent transgenic line A and B, Expression levels in root of 11-day-old plants grown under control condition of genes differentially expressed in the RNA-seq between Col and 35S:NPC48-1. Note that AT1G2160 is only de-regulated in one transgenic line. Measures represent corrected mean log 2 fold changes compared to Col measured by RT-qPCR. Error bars represent standard error (n ≥ 3, details in Table

  LerExpression of gene related to iron homeostasis in control conditionA Supplemental FigureS11-The over-expression of NPC48 does not affect iron homeostasis and/or phenotypic responses Expression levels in root of 11-day-old plants grown under control conditions (A) or low phosphate conditions (B) of genes related to iron homeostasis. Description of gene names is shown in Supplementary Table

Figure 1 .

 1 Figure 1. Identification of the LATERALINC lincRNA. A. Flowchart of identification of lncRNA-sRNA neighbors to root-related genes. LncRNAs co-localizing with 24nt length sRNA and the neighbor coding gene with a Gene Ontology related to root growth and development were collected using the Blein et al (2020) transcriptomic analyses and the GO consortium (Berardini et al 2004), respectively. The lncRNAs-sRNA potential precursor neighbors to a root-related gene were isolated. The resulting five lncRNAs and the name of the neighboring gene are indicated on the right. B. Transcript abundance of lncRNA-sRNA and neighbors' genes throughout plant life for two lncRNA/coding gene couples. Genevestigator snapshot from the Development condition search tool

Figure 2 .

 2 Figure 2. LATERALINC shaped the root architecture. For Col and RNAi LATERALINC lines; A. mean primary root length and B. mean lateral root length. Letters indicate a statistical group determined by one-way analysis of variance (ANOVA) followed by Tukey's post-hoc test. For each condition, letters indicate statistical differences between genotypes (p ≤ 0.05).

Figure 3 .

 3 Figure 3. LATERALINC does not regulate IAA14 but its expression correlates with the one of IAA1. Transcript levels of LATERALINC and its neighboring genes for Col and RNAi LATERLINC lines; A. in control condition and B. in response to auxin stimulus. Gene expression data are expressed as the mean ± standard error (n = 3) of the log2 fold change compared to the Col or Col at time 0h for A or B, respectively. For each gene, letters indicate statistical differences between genotypes (p ≤ 0.05). C. Pearson correlation analysis derived from transcriptomics data from Araport11. Correlations between two genes are indicated with scores ranging from -1 to +1 where -1 corresponds to a negative correlation and +1 a positive correlation. A color scale indicates the Pearson correlation score. Each correlation was tested for significant differences (*** for p ≤ 0.001).

Figure 1 .

 1 Figure 1. Identification of the MARS lincRNA. Flowchart of identification of the MARS lincRNA. LincRNAs and coding genes were collected using the Blein et al (2020) transcriptomic analysis and those significantly enriched in Col-0 ecotype as compared to Ler were retrieved. Pearson correlation scores were calculated for a lncRNA and its upstream and downstream neighboring genes.

Figure 1 .

 1 Figure 1. AT5G00580 is a lncRNA transcribed from the marneral cluster locus and its expression correlates with its neighboring genes

Figure 2 .

 2 Figure 2. MARS transcriptional activity modulates the response to ABA of the marneral cluster

Figure 3 .

 3 Figure 3. MARS modulates the epigenetic landscape of MRN1 locus

Figure 4 .

 4 Figure 4. MARS influences chromatin condensation of MRN1 gene through its interaction with LHP1 protein

(

  D) LHP1 binding to the MRN1 promoter region in chromatin from RNAi-MARS 1 seedlings upon increasing amounts of in-vitro transcribed MARS or GFP RNAs. After incubation (see Methods), the samples were crosslinked for LHP1 ChIP-qPCR. Higher values indicate more LHP1-DNA interaction. Results are shown as the mean ± standard error (n = 2) of the LHP1/Igg ratio.In (C) and (D) numbers are p-value of the difference between the different corresponding genes determined by Student t-test.

Figure 5 .

 5 Figure 5. An LHP1-dependent chromatin loop approaches the MRN1 locus with a putative enhancer element in response to ABA

  Figure1

  Figure2

  Figure3

  FigureS3

  FigureS5

  FigureS6

  FigureS9

Figure 2 .

 2 Figure 2. Differentially expressed genes in RNAi MARS 1. A. Number of differentially expressed (DE) genes after an ABA treatment to Col and RNAi MARS 1. B. Hierarchical clustering of all DE genes between Col and RNAi MARS 1 based on the DEseq2 normcounts values. Square colors correspond to the standardized normalized counts values by row as shown in the legends on the left.

Figure 3 .

 3 Figure 3. MARS downregulation impaired gene implicated in carbon/nitrogen equilibrium and cell oxidation status. A. Cluster profiles of k-means clusters based on the DEseq2 normcounts values from Col and RNAi MARS 1. Each grey line corresponds to a gene. The red line corresponds to the mean of all the lines in the cluster. B. Transcript abundance of selected indicated genes from cluster 3 and 4 implicated in the Carbon/Nitrogen equilibrium or cell oxidation status. Gene expression data are expressed as the mean ± standard error (n = 3) of the normalized counts.

Figure 4 .

 4 Figure 4. The MARS lncRNA physically interacts with the marneral cluster genomic region. A. The biotinylated probes against the MARS sequence significantly capture the MARS RNA (ChIRP RNA) Results are shown as the mean ± standard error (n = 3) of the MARS/LacZ probes ratio, used as non-specific oligos. The PP2A gene transcripts are used as a negative control. B. MARS RNA associates with the marneral genomic region (ChIRP-DNA). Enrichment of MARS RNA on marneral cluster genomic region is shown as the mean ± standard error (n = 3) of the MARS/LacZ probes ratio. Numbers are the p-value of the difference between the H3.3 (AT4G00430) taken as negative control and the different marneral genomic position determined by Student t-test. C. LHP1 binding to the marneral genomic region in Col-0 (ChIP). Higher values of ChIP-qPCR indicate more LHP1 deposition. Deposition of LHP1 is found on chromatin DNA using qPCR amplification and oligos from the MRN1 5' region, intergenic 2 and MRN1 promoter. Results are shown as the mean ± standard error (n = 2) of the LHP1/IgG ratio, used as IP control.

  Route de l'Orme aux Merisiers RD 128 / 91190 Saint-Aubin, France Titre : ARN-non codants chez les plantes : régulateur important de la croissance racinaire et de l'activité des gènes Mots clés : ARN non-codant, croissance racinaire, chromatine Résumé : Le progrès des méthodes de séquençage à haut-débit au début des années 2000 a révélé que la grande majorité du génome des eucaryotes est transcrit. Étonnamment, seule une infime partie de ces transcrits donnent naissance à des protéines. En effet, il a été estimé que 95% du génome humain est transcrit avec seulement 2% de transcrit traduit en protéine. Cette observation soutient l'hypothèse que les transcrits non-codants constituent d'importants régulateurs de la vie de la cellule. Curieusement, les gènes non-codants présentent une moindre conservation par rapport aux gènes codants, entre et au sein des espèces. Par exemple, chez les plantes, le séquençage d'accessions d'Arabidopsis durant le projet 1001 génomes montre des variations de séquences plus fréquentes dans les régions non codantes par rapport aux régions codantes. Au vu du rôle régulateur des gènes non-codants, il a été proposé que ces changements reflètent l'adaptation d'un individu à son environnement. Dans ce contexte, ma thèse a pour objectif de mieux comprendre les fonctions et l'importance des unités de transcriptions non-codantes pour l'adaptation des plantes à leur environnement. Le phosphate (Pi) constitue un des minéraux les plus importants pour les plantes et est absolument nécessaire à leur bon développement. Pour étudier les fonctions des transcrits non-codants durant une pénurie de Pi, nous avons analysé les écotypes Col-0 et Ler présentant une réponse surprenamment contrastée au manque de Pi (dans un milieu pauvre en Pi, la croissance de la racine principale s'arrête immédiatement chez Col-0, alors qu'elle se poursuit chez l'écotype Ler). Par rapport à l'activité comparable des gènes codants aux cours de la cinétique de carence en Pi, les gènes non-codants présentent une dynamique très différente en fonction de l'écotype. Élément intéressant, la dérégulation d'expression de deux longs ARN noncodants (lncRNAs) altère l'architecture racinaire, soulignant que les lncRNAs constituent d'importants régulateurs de la croissance racinaire. Notre comparaison du transcriptome de ces deux accessions d'Arabidopsis renforce l'importance des transcrits non-codants pour l'adaptation locale des plantes à leur environnement. Profitant de notre étude transcriptomique, nous avons cherché d'autres lncRNAs impliqués dans le contrôle de l'architecture racinaire. Prenant en compte que de nombreux lncRNAs régulent l'activité de leur gène voisin, nous avons recherché des corrélations d'expression positives entre des lncRNAs et leurs gènes voisins, en se focalisant sur les gènes impliqués dans la croissance et le développement de la racine. Parmi, les candidats sélectionnés, nous avons trouvé un lncRNA, que nous avons surnommé MARneral Silencing (MARS), régulant l'expressiond'un groupe de gènes organisés en cluster et impliqué dans la biosynthèse du marneral. Le marneral est un triterpène directement impliqué dans la croissance et le développement de la plante. Nous avons montré que ce lncRNA module la reprogrammation épigénétique du cluster marneral en réponse à l'ABA, influençant directement la réponse des gènes du cluster à ce stimulus. Ainsi, ma thèse souligne l'importance des lncRNAs pour l'adaptation des plantes à leur environnement, et illustre un complexe mécanisme de régulation génique chez les plantes impliquant lncRNA, statut épigénétique et conformation tridimensionnelle de la chromatine.

-Mapping efficiency for each sequence sample

	Supplemental
					mapped	reads
	Ecotype	Time	replicates	(mate1 + mate2) original reads	(mate1 + mate2)
					number	from original
			col_t0_A	48886542		97.67%
		0	col_t0_B	49805886		97.44%
			col_t0_C	49359434		98.03%
			col_1_A	53743064		97.43%
	Col	1h	col_1_B	58599074		97.77%
			col_1_C	61443288		98.28%
			col_2_A	57988404		97.79%
		2h	col_2_B	51030526		97.78%
			col_2_C	54236002		98.23%
			ler_t0_A	54211518		96.4%
		0	ler_t0_B	58151120		97.5%
			ler_t0_C	55034548		97.6%
			ler_t15_A	56236294		96.9%
	Ler	1h	ler_t15_B	64959674		96.9%
			ler_t15_C	51056408		96.6%
			ler_t1_A	57013228		96.7%
		2h	ler_t1_B	51457646		97.6%
			ler_t1_C	58824614		97.6%
	6

Table S4 -Sequence of primers used in this study

 S4

	Supplemental
		BET10	GCTTGACGACGTCCACAGGGG	AGGGTGCTCCCCTTCACAGCA
		Gene JAL22	Forward primer GGTGGTGGTGATGGGCACGA	Reverse primer CGCTTGGAGAGGAAGGCCACA
		Col_NEW_RNA_F_21743 MRN1	CATGAAATCGGGAGAGCAACCACA ACTTTTTCACTGGTCGGTACCGAAGAC	ACCACAAGATCCGGTTTGAAGTTGAT TGCATAAAGTGTCGCACTCATTCGACTA
		Col_NEW_RNA_F_7035 SRG3	TAGCGGTCATGGCCGGTTTATGT CTGCATGTAAACCGCCTCGGCT	CGGCTTAAGGAATCGTGATAAACGACG CCGCAACCCCGCTCTTGTCA
		Col_NEW_RNA_R_17895 ABCB3	AAGTCCTCCGCCGACGATGGG GCCTCGGATGCTTCATCACCGC	CGACGTCGCAGCAATTGGGCT GGGGCTTGTAGGGCAAGAGCCT
		Col_NEW_RNA_R_19102 AT1G04247	AGGGATAAGCTCTTCTTTTTCCCCTCA ACCTACAAAATAAGCCTGGCACAACC	GTGAATCTAGCCGTGAGTTTGGTGAT GGTGGGGAGGTTGGGACTTTTCT
		Col_NEW_RNA_R_19128 AT3G01260	AAACGAAACTTCTTGTCGGAGAACAGC CGGAGTTGGCGAAAATCGAAGGGT	GGGATATTGTTGGAGCAAAACAAGCGA TCGGTTCCGGTTATACTCATCTCGACG
		NPC29 CPL1	TCGAGCCAGCCATCAAACACAGG TGTAGTGGCGAATACCATGCGCTC	GCGTTTGCGCCGTTCACCAT GGCAAGGCGTTGAGGGTCCA
		NPC51 SCPL31	CTCCGTCGACGATACCAAAGGCG TCACTCGCATGAAGAGACTTCTGAACA	CGTCGATCATCATTCCGGCGGT CACCGAGGCTCATGGGTGGA
		Ler_NEW_RNA_F_10801 BTS	TCCTAGATCCCGTCGCTCAAAAATGC ACCATGTCGATCTCCGGCTG	GCGGCTGCGACGATGGAGAAA CAAGAGAATATGTTTGCGCTACATT
		Ler_NEW_RNA_F_11139 BTSL1	ATCGCCACAAACACCACCACCA TGTCCCTTGGCTTCCAATGCTGG	CGGTTCTCGCGACGATGGCT TCGCGGAGTTTCAGCAACGGT
		Ler_NEW_RNA_F_14485 BTSL2	ACATGGTACACGACCCAACCCATCT CCAGCATGTTCAATTCTGCCAACTCA	TGATCGCCACTGATTTTGAGAGCCG TCCAGCTTTGGAGGCAAAGGGT
		Ler_NEW_RNA_F_19778 FIT	ACCCGGAAAAAGACAGAACAAACATGC TCGGTCTAGGACTTTGATCTCTG	CTTCCCTCCCATCTCCTTCCTCTGT TCTTGAACATACAACACTGCATCT
		Ler_NEW_RNA_F_23887 FRO2	TGAGTCACCGCCTCGGGAGC TTCACCGTTCATGGTCTTTGTT	CGTCCTAGGTCGGGTCGGGG GAGCTATCTCTCCGGCCAAATT
		Ler_NEW_RNA_R_24296 IRT1	GGAGCTTGCCTTCAAGTACCGC CTCTTTGCTTCCATCAAATGTTC	AACGAGATCTTTCCGAGCCGTGA CCTAACGCTATTCCGAATGG
		Ler_NEW_RNA_R_4778 NAS4	TGCATACGTCTTTTCAACTCATCCACG TGTTCTTGGCTGCTCTTGTAGG	ATAGCTGCGGAGGCACAAGAGAA CAAGGCTCAACGATTGGATAGA
		NPC15 PYE	TCCAATGCTCAAATAATCTCTTG CAGGACTTCCCATTTTCCAA	AACTCGTCAAAAGACCCAATTT CTTGTGTCTGGGGATCAGGT
		NPC34	CTAGCAACAGAGACCAACCC	GTGGCTTCCATAGCGCCGGA
		NPC43	GAACAGTCCGACTCCAAGCC	CAAAAGTCACGATTTGGCGAGT
		NPC72	ACCGGAATCAGCTCAACG	CGACAACCGGGATATACCAC
		NPC48	TGCCTGTTCTTCAAATCAACAC	ACCAACAATTGGACGAAGAATC
	8	IPS1	ATGGCGAAATGGTTCTGCTA	CCTCCTCTTGTTCGCTTGTC
		SPX3	TGAAGACTGCAGAAGGCTGA	CGAAGCTTGCCAAAGGATAG
		LPR1	ACTGGCGGCAGGATTGGAGGA	CCAACCCCTCTCGTGTGCCG
		LPR2	GCCTGGCTCTTGAGTTGCGTCA	CGGTTACGGTACACGAGCGGG
		STOP1	ACCCACGAGAAGCACTGCGG	GCAGGCGTGTGTCCCTGGAA
		ALMT1	GGCAGTGTGCCTACAGGATT	TGAGTTTCCCGATTCCGAGC
		MATE	GCATAGGACTTCCGTTTGTGGCA	CGAACACAAACGCTAAGGCA
		AT3G01520	GCGGTGAACGCTTCGACGAT	TCGACGACTTGTACGTGGAGCA
		AT3G01690	CCCTTTCCCCCACCGCGAAA	GCGGCGTTGCCATGGGAGTA
		AT3G02360	ACCGGCATGAAAGGCACAGGA	AGCCCGCTAAGGAACCTCGC
		AT3G02620	TGCCGCAGAGGATCAGGAGGT	TCTCTCCCGTAAATCCAGCTGAACGA
		AT3G02750	TCCGGTTGGGTTTCTGTGTTCTCCTC	TGCTGCTGCTGCTGCTGCTT

Table S7 -Software used for the bioinformatic analyses 1

 S7

	2	Supplemental Table S9 -Gene name abbreviation
	Name	Version 6. Exploring Reference Name	Short Name	Non default arguments --max-multihits 1 --num-threads 8 -i 20 AGI
	INDUCED BY PHOSPHATE STARVATION1	IPS1	--min-segment-intron 20 --min-coverage-intron 20 AT3G09922
	TopHat2	2.0.13	Kim et al. (2013)		--library-type fr-firststrand --microexon-search -I 1000
	SPX DOMAIN GENE 3		SPX3	--max-segment-intron 1000 --max-coverage-intron 1000 AT2G45130
	LOW PHOSPHATE ROOT1		LPR1	--b2-very-sensitive AT1G23010
	GFFprof LOW PHOSPHATE ROOT2 1.2.6	Tran et al. (2016)	LPR2	AT1G71040
	blastn SENSITIVE TO PROTON RHIZOTOXICITY 1 2.2.29+ https://www.ncbi.nlm.nih.gov/books/NBK279690/ STOP1	AT1G34370
	cuffmerge ALUMINUM-ACTIVATED MALATE TRANSPORTER 1 1.0.0 http://cole-trapnell-lab.github.io/cufflinks/cuffmerge/ ALMT1	AT1G08430
	GffCompare MATE	0.10.4	https://ccb.jhu.edu/software/stringtie/gffcompare.shtml MATE	MATE
	COME BROMODOMAIN AND EXTRATERMINAL DOMAIN PROTEIN 10 1.0 Hu et al. (2017)	BET10	AT3G01770
	RSeQC JACALIN-RELATED LECTIN 22 3.0.0 Wang et al. (2012) Wang et al. (2016) JAL22	-l 2 -u 100 -s 2 AT2G39310
	ShortStack MARNERAL SYNTHASE 1 3.8.5	Johnson et al. (2016)	MRN1	--mismatches 0 --bowtie_m all --mmap u AT5G42600
	htseq-count SENESCENCE-RELATED GENE 3 0.6.1 Anders et al. (2015)	SRG3	--stranded=reverse -t gene --mode=intersection-strict AT3G02040
	R ATP-BINDING CASSETTE B3 3.4.2	R Core Team (2017)	ABCB3	AT4G01820
	DESeq2 C-TERMINAL DOMAIN PHOSPHATASE-LIKE 1 1.16.1 Love et al. (2014)	CPL1	AT4G21670
	tidyverse SERINE CARBOXYPEPTIDASE-LIKE 31 1.2.1 Wickham (2017)	SCPL31	AT1G11080
	emmeans BRUTUS	1.3.3	Lenth (2019)	BTS	AT3G18290
	BTS LIKE1			BTSL1	AT1G74770
	BTS LIKE2			BTSL2	AT1G18910
	FER-LIKE IRON DEFICIENCY INDUCED TRANSCRIPTION
					FIT	AT2G28160
	FACTOR
	FERRIC REDUCTION OXIDASE 2	FRO2	AT1G01580
	IRON-REGULATED TRANSPORTER 1	IRT1	AT4G19690
	NICOTIANAMINE SYNTHASE 4	NAS4	AT1G56430
	POPEYE			PYE	AT3G47640
		9

transcriptomes to find new cis- regulatory root-related lncRNAs 6.1 LATERALINC, new regulator of lateral root growth in Arabidopsis 6.1.1 Introduction

Table 1 . List of Gene Ontology related to the root growth and development used for LATERALINC identification.

 1

		GO
	GO term	Identifier
	root hair cell development	GO:0080147
	root development	GO:0048364
	primary root development	GO:0080022
	regulation of root development	GO:2000280
	post-embryonic root morphogenesis	GO:0010101
	maintenance of root meristem identity	GO:0010078
	root hair elongation	GO:0048767
	root epidermal cell differentiation	GO:0010053
	lateral root formation	GO:0010311
	regulation of root meristem growth	GO:0010082
	lateral root development	GO:0048527
	embryonic root morphogenesis	GO:0010086
	root meristem specification	GO:0010071
	root meristem growth	GO:0010449
	root hair cell differentiation	GO:0048765
	regulation of root morphogenesis	GO:2000067
	root radial pattern formation	GO:0090057
	root cap development	GO:0048829
	root hair initiation	GO:0048766
	lateral root morphogenesis	GO:0010102
	adventitious root development	GO:0048830
	root hair cell tip growth	GO:0048768
	root morphogenesis	GO:0010015
	post-embryonic root development	GO:0048528
	negative regulation of lateral root development	GO:1901332
	regulation of lateral root development	GO:2000023
	root system development	GO:0022622
	lateral root branching	GO:0080181
	root hair tip	GO:0035619
	regulation of post-embryonic root development	GO:2000069
	root hair	GO:0035618

Table 2 . List of primer used in LATERALINC study. Primers used to generate the RNAi lines Forward primer Reverse primer

 2 is a positive regulator of LR extension. Highly resolutive temporal phenotypic analyses of LR emergence and growth are needed to understand the reason behind the LATERALINC-mediated LR length decrease (i.e. Delay in the LR initiation? Lower LR growth rate?

	LATERALINC	CACCTCCTTTCATCTACGGGATTTGCTGC CCGTAAACGGCGGTGAGACA
	Primers used for	Forward primer	Reverse primer
	transcripts abundance
	analysis from cDNA
	AT4G14540	ACCGGTGAGGCTTCTGACAAGTG	GGCTCCACGTAGTCCTCAAACCC
	LATERALINC	TGGTCGTTCCAACCATGCCAGAG	ACGGCGGTGAGACAAACCAACA
	IAA14	AAGGCGACGGTTCCTCCACCA	CCACCGGTGAGGAACTACCGGAAA
	IAA1	CGTTTGGGATTACCCGGAGCACA	TGTTGAGTCGTTGTTCTTGCGCTTG
	AT1G13320	GAGCTGAAGTGGCTTCCATGAC	GGTCCGACATACCCATGATCC
	(housekeeping gene;
	Czechowski et al., 2005)

LATERALINC

  misregulation. Collectively, our results indicate that MARS can modulate various ABA-related physiological responses, through the regulation of MRN1 expression.

TableS1 List of primers used in the study Primers used for SALK_133089 homozygous selection

		Forward primer	Reverse primer
		AGTCCAGGTTTGGTTTGGTTC	ACATGTTCTTTTGGCAAGCAG
	Primers used to generate the	Forward primer	Reverse primer
	RNAi lines
	MARS	TCAATGATAGGACTACTACTCATGGCCCAGG	GATAGGACTACTACTCATGGCCCAGGTAAAC
	Primers used for transcripts	Forward primer	Reverse primer
	abundance analysis from cDNA
	CYP705A12	CAACAAGTGTTGTTTTCTCCGGTACG	GGGCTAAGATTGCATCCCTCGG
	CYP71A16	CTGGTGTTTCTTTGTCGGCAGTTGT	CGGTCGGGGAATACATTCCGAGTT
	MARS	ACTTTTTCACTGGTCGGTACCGAAGAC	TGCATAAAGTGTCGCACTCATTCGACTA
	MRN1	GGGAGAAAGTGCTCTCTCTTGCCCTAA	GCGGCGCGATGAACAGGAGA
	AT1G13320 (housekeeping gene;	GAGCTGAAGTGGCTTCCATGAC	GGTCCGACATACCCATGATCC
	Czechowski et al., 2005)
	Primers

used for ChIP, meDIP and FAIRE experiments Forward primer Reverse primer

	MARS.5'	TTCCTTCAGTGAGACCAGACGCTTTCA	ACGGTTTCGCGTCCCGACC
	MARS.3' Primers used for the GUS	ACTTTTTCACTGGTCGGTACCGAAGAC Forward primer	TGCATAAAGTGTCGCACTCATTCGACTA Reverse primer
	MRN1.5' transient assay	TCGAGCAGAAGATCCCGAGAGTGAGA	CGTTAGCCTGACATGCCGCGT
	MRN1.3' Negative control 1	GGGAGAAAGTGCTCTCTCTTGCCCTAA AACAGGTCTCAACCTAACTAAGTGTTACTAAATCATCTCACC	GCGGCGCGATGAACAGGAGA AACAGGTCTCTTGTTCATTTGTAAATCTTTTAAAGCCCTTG
	APOLO Negative control 2	GTGGCTTCCATAGCGCCGGA AACAGGTCTCAACCTTGCTCAGAATGCCCCTACCT	CTAGCAACAGAGACCAACCC AACAGGTCTCTTGTTCCTTGGTAACCCAAGCAACCA
	Intergenic 1	AACAGGTCTCAACCTGATTTTGGAGTTCCTGGTAAATGT	AACAGGTCTCTTGTTATGTCGCACTCATTCGACTA
	Intergenic 2	AACAGGTCTCAACCTGCATGTTGCCTATGATTAGAAGGA	AACAGGTCTCTTGTTACTCGATTTGGAGAAGTGTTTCAA
	Primers used for in-vitro RNA minimal 35S promoter element transcription MARS RNA sub-unit B3 from 35S promoter element	Forward primer AACAGGTCTCAAACAGCAAGACCCTTCCTCTATATAAGGAAG TTCATTTCATTTGGAGAGGACACGCTG TAATACGACTCACTATAGGGGGCGACATTCACAAAACGTCGT TAAATA AACAGGTCTCAACCTCATCGTTGAAGATGCCTCTGCCGACAG TGGTCCCAAAGATGGACCCCCACCCA	AACAGGTCTCTAGCCCAGCGTGTCCTCTCCAAATGAAATGAA Reverse primer CTTCCTTATATAGAGGAAGGGTCTTGC AACAGGTCTCTTGTTTGGGTGGGGGTCCATCTTTGGGACCAC GTTGGCAGCACCCATGTTTAGATGTCC TGTCGGCAGAGGCATCTTCAACGATG
	GFP RNA	GTAATACGACTCACTATAGGATGGTGAGCAAGGGCGAG	GGCTTGTACAGCTCGTCCATGC
	Primers used for transcript	Forward primer	Reverse primer
	abundance analysis from sub-
	cellular localization and RIP
	experiments
	AT1G13320 (housekeeping gene;	GAGCTGAAGTGGCTTCCATGAC	GGTCCGACATACCCATGATCC
	Czechowski et al., 2005)
	APOLO	GTGGCTTCCATAGCGCCGGA	CTAGCAACAGAGACCAACCC
	ASCO	CCCATCGCACTGATCGGCGG	TCGAGCGCTGCCGTCTTCAC
	MARS	ACTTTTTCACTGGTCGGTACCGAAGAC	TGCATAAAGTGTCGCACTCATTCGACTA
	U6	CGGGGACATCCGATAAAATTGG	CGATTTGTGCGTGTCATCCTTG
	MRN1	GGGAGAAAGTGCTCTCTCTTGCCCTAA	GCGGCGCGATGAACAGGAGA
	CYP705A12.3'	GGAGTGCACTTAAGCGGATGAGCC	GCGATTGGAGCGATGGTGCAGT
	CYP705A212.5'	GATGCGGAGATGGAGAAGAGGTCCA	CCTGCCTCCGAGCCCTCCTT
	CYP71A16.3' Primers used for 3C experiment Forward primer TGTGAGGTCGTTAAGCTCATGGAGAGG	GGAGCTGGAATCGATGTTTGCCGA Reverse primer
	CYP71A16.5' Chromatin loop	ATTGTGCCTTCCTCGCCGCT GGCTTTTGTTGACTACTATTACAGGCGGG	TGGAGACGCTGGAAGAAGCAAGT CCAGACCAGTCATACACTCCTAGAACCTG
	intergenic1 Control	GGCTTTTGTTGACTACTATTACAGGCGGG GTCCGAATCTTACGGACCGGATTGTC	GGGTTTAGGGTTTATGGTTTAGGGTTTAGGG ACTGATAAACCCATCACCGGTGTTTCC
	intergenic2	ACACCTATTAGTGACATCCACAAAGCGT	ACATTTTTGTGGTGTTAGGTGTGTGAAGC

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Acknowledgement

ACKNOWLEDGMENTS

We thank Ambre Miassod and Janina Lüders (Institute of Plant Sciences Paris-Saclay, Centre Nationale de la Recherche Scientifique, Institut National 1070

Plant Physiol. Vol. 183, 2020

Blein et al.

ACKNOWLEDGEMENT

IPS2 benefits from the support of Saclay Plant Sciences-SPS (ANR-17-EUR-0007). We thank Jeremie Bazin and Aurélie Christ from IPS2 for the helpful discussion about results interpretation and design of the experiments. We thank Olivier Martin for critical reading of the manuscript.

IPS2 benefits 1 This work was supported by the Agence Nationale pour la Recherche (grant nos. ANR-12-ADAP-0019 [RNAdapt], ANR-16-CE12-0032 [SPLISIL], and ANR-17-EUR-0007 [Saclay Plant Sciences Graduate School of Research] managed under an Investments for the Future program [grant no. ANR-11-IDEX-0003-02]) and The King Abdulla University of Science and Technology (KAUST) International Program (grant no. OCRF-2014-CRG4).

Funding

This work was supported by Saclay Plant Sciences-SPS (ANR-17-EUR-0007).

FUNDING

This work was supported by BBSRC grant (BB/L016966/1) to J.G-M and Saclay Plant Sciences-SPS (ANR-17-EUR-0007) and CNRS (Laboratoire International Associé NOCOSYM) to MC and FA.

AVAILABILITY

Further information and requests for resources and reagents should be directed to and will be fulfilled by the Lead Contact, Martin Crespi (martin.crespi@ips2.universite-parissaclay.fr). Plant lines generated in this study are available from the Lead Contact with a completed Materials Transfer Agreement.

Competing Interests

The authors declare that there are no competing interests associated with the manuscript.

Author Contributions

The manuscript was drafted by T.R. and revised by M.C. and T.B..

Supplemental Data

The following supplemental materials are available.

Supplemental Figure S1. Characteristics of identified transcripts.

Supplemental Figure S2. Expression level and detection of coding and noncoding genes.

Supplemental Figure S3. Ecotype-specific classification of lncRNAs as siRNA precursors.

Supplemental Figure S4. Ecotype effect on gene expression.

Supplemental Figure S5. Genome organization and correlation of expression at selected loci.

Supplemental Figure S6. Ecotype effect on siRNA accumulation.

Supplemental Figure S7. Genome homology at selected ncRNA loci Supplemental Figure S8. Deregulation of selected noncoding RNAs.

Supplemental Figure S9. Deregulation of NPC48 and NPC72 does not change the expression of phosphate starvation-related genes.

Supplemental Figure S10. Expression analysis of genes regulated in the 35S:NPC48-1 line in a different independent transgenic line.

Supplemental Figure S11. Overexpression of NPC48 does not affect iron homeostasis and/or phenotypic responses.

Supplemental Table S1.Mapping efficiency for each sequence sample.

Supplemental Table S2. Genomic information of previously uncharacterized transcripts compared to TAIR10.

Supplemental Table S3. Differential gene expression analysis.

Supplemental Table S4. Sequence of primers used in this study.

Supplemental Table S5. Number of samples used for each genotype and condition in the RT-qPCR experiments.

Supplemental Table S6. Number of samples used for each genotype and condition in root-length measurements.

Supplemental Table S7. Software used for the bioinformatics analyses.

Supplemental Table S8. Differentially expressed genes in 35S:NPC48-1.

Supplemental Table S9. Gene name abbreviations.

Supplemental File S1. Identified Col transcripts.

Supplemental File S2. Identified Ler transcripts.

Supplemental Materials and Methods. Detailed bioinformatic and extra materials and methods.

Supplemental references

Amor

Data processing and statistical analysis

Except if stated otherwise, all data processing and statistical analyses were realized in R v3.4.2 (R Core Team, 2017) with the help of the tidyverse (v1.2.1; (Wickham et al., 2019)).

Identification of LATERALINC

The coding gene, lncRNA and siRNA annotations were collected using the transcriptomics data from Blein et al (2020). The root-related Gene Ontology (GO) was collected from the GO consortium [START_REF] Berardini | Functional annotation of the Arabidopsis genome using controlled vocabularies[END_REF] and included all the GO terms related to root growth and development (see GO term and identifier in Table 1). The lncRNA-sRNA precursor neighboring genes were collected with bedtools closest using the default parameter [START_REF] Quinlan | BEDTools: A flexible suite of utilities for comparing genomic features[END_REF].

Lines generation

All plants used in this study are in Columbia-0 background. RNAi-LATERALINC were obtained using the pFRN binary vector (Ariel et al., 2012) bearing 250bp of the 5' exon of LATERALINC gene (see primers in Table 2), previously sub-cloned into the pENTR/D-TOPO vector. Arabidopsis plants were transformed using Agrobacterium tumefaciens Agl-0 (Clough and Bent, 1998a).

Growth conditions and phenotypic analyses

Seeds were sown in plates vertically placed in a growing chamber in long day conditions (16 h in light 150uE; 8 h in dark; 21°C). Plants were grown on solid half-strength MS medium (MS/2) supplemented with 0.7% sucrose and supplemented with 0.8g/L agar (Sigma-Aldrich, A1296 #BCBL6182V), buffered at pH 5.6 with 3.4mM 2-(N-morpholino) ethane sulfonic acid.

For root phenotype characterization, the root length was measured at 12 day after sawing (DAS) using RootNav software (Pound et al., 2013) from images taken with a flat scanner. For the treatment with auxin, seedlings were sprayed with 10µM 1-Naphthaleneacetic acid (NAA) at 12 DAS.

The lncRNA MARS modulates the epigenetic reprogramming of the marneral cluster in response to ABA

MATERIAL AND METHODS

Lines selection and generation

All plants used in this study are in Columbia-0 background. RNAi-MARS were obtained using the pFRN binary vector (Ariel et al., 2012) bearing 250bp of the first exon of MARS gene (see primers in Supplementary Table 1), previously sub-cloned into the pENTR/D-TOPO vector. UBQ:MARS were obtained using the GreenGate Cloning system (Lampropoulos et al., 2013) including the UBQ10 promoter (pGGA006). Arabidopsis plants were transformed using Agrobacterium tumefaciens Agl-0 (Clough and Bent, 1998b). The T-DNA inserted line SALK_133089 was ordered to NASC (N633089). Homozygous mutants were identified by PCR (see primers in Supplementary Table 1).

Seeds of rnm1 (Go et al., 2012), 35S:MRN1 (Field et al., 2011), and mro1-2 (cyp71a16, (Field et al., 2011)) mutants were kindly provided by Dr. Ben Field (BIAM, CEA Cadarache, France) and Pr. Suh (Chonnam National University, Department of Bioenergy Science and Technology, Korea), respectively.

Growth conditions and phenotypic analyses

Seeds were sown in plates vertically placed in a growing chamber in long day conditions

Transcriptional activation assay in tobacco leaves

The GUS reporter system for validating the activity of the putative enhancer element was adapted from (Yan et al., 2019). Different DNA fragments were cloned in the GreenGate system (Lampropoulos et al., 2013) A. tumefaciens-mediated transient transformation was performed on 5-week-old tobacco plants using a needle-less syringe. Together with enhancer constructs, another vector containing mCherry driven by 35S promoter was co-transfected to control the transformation efficiency. Two leaf discs were collected near the infiltration site. One, to determine the transfection efficiency by mCherry fluorescence observation under epifluorescent microscope.

The second was used for GUS staining, as previously described (Jefferson et al., 1987). In addition, a single leaf was also stained with the different constructs to compare their relative activity. Samples were incubated 4 h in the dark at 37 °C before observation.

Identification of lncRNA loci in Arabidopsis gene clusters

The genes of co-expressed clusters were retrieved from (Yu et al., 2016). The boundaries of the gene clusters were extracted using Araport11 annotations. The boundaries of the metabolic clusters were extracted from the plantiSMASH predicted clusters on Arabidopsis (Kautsar et al., 2017). Using Araport11 GFF, the lncRNAs (genes with a locus type annotated as "long_noncoding_rna", "novel_transcribed_region" or "other_rna") present within the boundaries of the cluster were retrieved.

Gene expression correlation analyses

To compute the correlation of expression in different organ of Arabidopsis we used the 113 RNA-seq datasets previously considered for the Araport11 annotations (Cheng et al., 2017).

These datasets were generated from untreated or mock-treated wild-type Col-0 plants. After removing the adaptors with Trim Galore with default parameters, the reads were mapped on 104 TAIR10 with STAR v2.7.2a (Dobin et al., 2013) and the parameters '--alignIntronMin 20 --alignIntronMax 3000'. Gene expression was then quantified with featureCounts v2.0.0 (Liao et al., 2014) with the parameters "-B -C -p -s 0" using the GFF of Araport11. Raw counts were then normalized by median of ratios using the DESeq2 R package (Love et al., 2014).

For the correlation of expression inside the marneral cluster, the transcript levels of the genes included in the cluster and 25kb around it (four genes upstream and two downstream)

were considered for the correlation analysis. Pearson's correlations for each pair of genes were computed after log 2 transformation of the normalized counts. The correlation value and associated p-value were plotted with the corrplot R package (Wei et al., 2017).

Inside each co-expressed and metabolic clusters of genes, Pearson's correlation was computed between every possible pair of lncRNA and coding gene as well as for the genes inside the marneral cluster. The maximum correlation value was kept as an indication of lncRNAs correlation with the genes of the cluster.

Quantification and statistical analyses

For all the experiments, at least two independent biological samples were considered.

For RT-qPCR, each sample was prepared from a pool of 5 to 10 individual seedlings. For biochemistry assays (ChIP, FAIRE, nuclear purification, RIP and 3C) two to five grams of seedlings were prepared for each independent biological sample. For validation of enhancer function, the four leaf discs were taken from four independent tobacco plants. An additional replicate was performed on three independent tobacco plants upon the agroinfiltration of all the different constructs on the same leaf. The tests used for statistical analyses are indicated in the respective figure legends. Statistical tests and associated plots have been generated using R software (v3.6.3(R Core, 2004)) with the help of the tidyverse package (Wickham et al., 2019).

CONFLICT OF INTEREST

The authors declare no competing financial interests.

LEGENDS TO SUPPLEMENTAL FIGURES

Figure S1. MARS is coregulated with the genes of the marneral cluster

Pearson correlation analysis derived from transcriptomics data from Araport11. Correlations between two genes are indicated with scores ranging from -1 to +1 where -1 corresponds to a negative correlation and +1 a positive correlation. A color scale indicates the Pearson correlation score. Each correlation was tested for significant differences (* for p ≤ 0.05, ** for p ≤ 0.01, *** for p ≤ 0.001).

Figure S2. MARS cannot regulate the marneral cluster in trans

Transcript abundance of the marneral cluster genes in response to 10µM ABA treatment in lines overexpressing MARS from the UBIQUITIN 10 promoter (UBQ). RNA was extracted from individual T2 plants from 3 independent lines. The genotypes have been classified as wild-type or overexpressor (UBQ) according to the level of expression of MARS taking a Col-0 line as the MARS wild-type background. Results are expressed as the mean ± standard error (n≥4) of the log2 fold change compared to the Col-0 genotype at time 0h. (E) Mean primary root length, lateral root length and lateral root density according to the genotype and the condition of 11-day-old seedlings. Letters indicate a statistical group determined by one-way analysis of variance (ANOVA) followed by Tukey's post-hoc test. For each condition, letters indicate statistical difference between genotypes (p ≤ 0.05).

Figure S9. The epigenetic landscape of the marneral cluster and surrounding genomic region

First track represents DNA accessibility determined by the ATAC-seq (44). ATAC-peaks are indicated with a blue rectangle and correspond to relaxed chromatin. Second and third tracks show H3K27me3 and LHP1 ChIP-Seq, respectively (43). The three experiments shown here have been performed using Arabidopsis shoot. Gene annotation is shown at the top.

Figure S15. The intergenic region between CYP71A16 and MARS is able to activate gene transcription

Agroinfiltration of the different constructs from Figure 5D in the same tobacco leaf. Points of agroinfiltration for each construct are indicated with a star (*), and each letter indicates the corresponding constructs. Same result has been observed in two other independent tobacco leaves.

Methods

Data processing and statistical analysis

Except if stated otherwise, all data processing and statistical analyses were realized in R v3.4.2 (R Core Team, 2017) with the help of the tidyverse (v1.2.1; Wickham, 2019).

Identification of MARS

The coding gene and lncRNA annotations were collected using the transcriptomics data from Blein et al (2020). Similarly, the Col-enriched genes, together with their respective normalized counts all along the Pi shortage were retrieved from the Blein et al ( 2020) study.

The lncRNA neighboring genes were picked up with bedtools closest using the default parameter [START_REF] Quinlan | BEDTools: A flexible suite of utilities for comparing genomic features[END_REF]. Pearson correlation analyses were realized with the help of corrplot (v0.9; [START_REF] Wei | Package ' corrplot[END_REF])) R packages.

Plant materials and growth condition

All plants used in this study are in the Columbia-0 (Col-0) background. The RNAi-MARS were obtained as described (Roulé et al preprint). Plants were grown in plates containing solid half-strength MS medium (MS/2) supplemented with 0.7% sucrose, vertically positioned within a growing chamber. Plants were grown for 11 days after sowing in long day conditions (16 h in light 150uE; 8 h in dark; 21°C). ABA treatments were conducted by spraying plants with water containing 10μM ABA for four hours.

Library construction and sequencing

Total RNA of whole seedlings of three biological replicates from control and ABAtreated plants were extracted using TRI Reagent (Sigma-Aldrich) and treated with DNase (Fermentas) as indicated by the manufacturers. For each sample, libraries were processed with one microgram of total RNA using an Illumina Truseq Stranded mRNA library preparation kit following the manufacturer's instructions. Libraries were sequenced on a NextSeq 500

Sequencing System (Illumina) using 75-nt single-end reads.

Differential expression analysis and clustering

All reads were trimmed using Trimmomatic and ribosomal sequences were removed using sortMeRNA (Kopylova et al., 2012). Cleaned mRNA reads were aligned on the TAIR10 genome (Lamesch et al., 2012) using STAR (version 2.7.2a, Dobin et al 2012) with the following arguments: --runThreadN 2 --sjdbGTFfile Araport11.gtf --readFilesCommand zcat --alignIntronMin 20 --alignIntronMax 3000 --outSAMtype BAM SortedByCoordinate --outReadsUnmapped Fastx --outBAMsortingBinsN 100. FeatureCounts from the subread package (v1.6.5, (Liao et al., 2014)) were used for read counting using strand specific mode (-s 2 -O -M --fraction). Differential gene expression analysis was performed with DEseq2 (v1.16.1; (Love et al., 2014)) using a linear model and as factors the genotypes and the treatment (two levels each). Low counts were discarded using DESeq2 independent filtering with default parameters and raw p-values were adjusted with the Bonferroni method. Differentially expressed genes were defined as having an adjusted p-value lower than 0.01.

To generate the heatmap, the DEseq2 normalized count of all the differentially expressed genes were processed into a maximum parsimony phylogenetic tree with four factors: the two genotypes (Col-0 and RNAi-MARS) and the two conditions (control and ABAtreated) with the help of the pheatmap (v1.0.12; Kolde et al 2019) R packages. The optimal number of clusters were defined through the Elbow method with pkgs (v1.8.0; [START_REF] Zhang | Package ' ELBOW ' Advance Access[END_REF]). Clustering profiles were determined from the clustered heatmap.

ChIRP followed by qPCR

The ChIRP protocol has been adapted from Chu et al [START_REF] Chu | Chromatin isolation by RNA purification (ChIRP)[END_REF] and already described in Ariel et al (2014;2020). Briefly, two grams of ABA-treated seedlings of 11 DAS were cross-linked in 1% (v/v) formaldehyde. Chromatin was sonicated in a water bath Bioruptor Plus (Diagenode; 60 cycles of 30s ON and 30s OFF pulses at high intensity). After a brief centrifugation, the supernatants were collected and incubated with 100 pmol of complementary biotinylated probes for the MARS transcript. Biotinylated probes targeting the LacZ RNA were used as negative control. Magnetic streptavidin beads were used to recover the probes and RNA/chromatin bind to it. For ChIRP-RNA, the co-purified ribonucleoprotein complexes were eluted and RNA were extracted with 1mL of TRI Reagent (Sigma-Aldrich)

following the instructions of the manufacturer. Reverse transcription was performed using the total RNA and the Maxima Reverse Transcriptase (Thermo Scientific). For ChIRP-DNA, the DNA from the co-purified ribonucleoprotein complexes were extracted using Phenol:Chloroform:Isoamyl Acid (25:24:1, Sigma) followed by ethanol precipitation. For both ChIRP assays, qPCR was performed on a Light Cycler 480 with SYBR Green master I (Roche) in standard protocol (40 cycles, 60°C annealing). For ChIRP-RNA data were analyzed using the ΔΔCt method using PROTEIN PHOSPHATASE 2A SUBUNIT A3 (AT1G13320) for gene normalization. For ChIRP-DNA the enrichment was calculated as the ratio of MARS/LacZ probes taking the H3.3 (AT4G40030) genomic region as a negative control for statistical enrichment analysis.

ChIP followed by qPCR

ChIP was performed using anti-IgG (Millipore,Cat#12-370) and anti-LHP1 (Covalab, Pab0923-P), as previously described (Ariel et al., 2014), starting from two grams of seedlings crosslinked in 1% (v/v) formaldehyde. Briefly, chromatin was sonicated in a water bath Bioruptor Plus (Diagenode; 60 cycles of 30s ON and 30s OFF pulses at high intensity). ChIP was performed in an SX-8G IP-Star Compact Automated System (Diagenode). Antibody-coated Protein A Dynabeads (Invitrogen) were incubated 12 hours at 4 °C with the samples.

Immunoprecipitated DNA was recovered using Phenol:Chloroform:Isoamilic Acid (25:24:1, Sigma) followed by ethanol precipitation and quantified by qPCR. For input samples, nonimmunoprecipitated sonicated chromatin was processed in parallel.

III Conclusions and perspectives

7. The non-coding transcriptome, signature of the plant local environment

Conservation of non-coding genes

The extensive characterization of the root transcriptomes of two Arabidopsis accessions presenting a contrasted response to phosphate (Pi) shortage, likely revealed a co-evolutionary link, in-between non-coding and coding genes, potentially linked to different root growth regulatory mechanisms. The QTL associated with the Col-0 and Ler differential root architecture under Pi shortage was mapped to the LOW PHOSPHATE ROOT1 (LPR1) quantitative trait locus (Reymond et al., 2006). Notably, the Col-specific root growth arrest under low Pi condition depends on iron (Fe) availability in the soil or media. During a Pi shortage in the Col-0 accession, a Fe-dependent callose deposition is occurring within the root elongation zone, avoiding cell elongation and consequently stopping cell division within the Root Apical Meristem (RAM) (Balzergue et al., 2017;Gutiérrez-Alanís et al., 2017). This mechanism is governed by LPR1-PDR2 and STOP1-ALMT1 modules which control Fe accumulation within the root under low Pi conditions. Interestingly, the LPR1 gene is absent in the Ler ecotype, participating to its continuous root growth even in Pi starvation (Reymond et al., 2006). We detected a surprising bias of expression for non-coding genes upon the early Pi starvation plant response. Indeed, as compared to the protein-coding genes, the non-coding genes were significantly more differentially regulated in-between the ecotypes, suggesting they could also participate in the divergent Pi-starvation-dependent root growth response observed in Col-0 and Ler accessions. Notably, we could identify ecotypeenriched expression of the root growth regulators PHT1;2, SPX4 and NIP3;1 correlating with the expression of closely located ecotype-enriched lncRNAs (Blein et al., 2020).

Arabidopsis remarkably resulting in flowering time changes [START_REF] Lee | CRISPR-based tools for targeted transcriptional and epigenetic regulation in plants[END_REF]. Nevertheless, as the addition of in-vitro transcribed MARS RNA is sufficient to change the epigenetic landscape of the marneral cluster purified chromatin, it is likely that MARS act, at least partially, in trans.

While prokaryotic organisms use operon configuration to ensure gene co-expression [START_REF] Lawrence | Shared strategies in gene organization among prokaryotes and eukaryotes[END_REF], the localization of eukaryotic genes within the genome is quite random, even for genes involved in the same biological function (Nutzman et al., 2018). However, Biosynthetic Gene Clusters (BGCs) are a eukaryotic exception in which genes involved in the same biological pathway are embedded in a local hotspot [START_REF] Hurst | The evolutionary dynamics of eukaryotic gene order[END_REF][START_REF] Michalak | Coexpression, coregulation, and cofunctionality of neighboring genes in eukaryotic genomes[END_REF].

In Arabidopsis thaliana, the local chromatin conformation of three secondary metabolite clusters, including the marneral cluster, exhibit a compact configuration when active, suggesting a conserved topologically-based regulatory mechanism (Nutzman et al., 2020). As we noticed that a significant proportion of gene cluster contain a co-regulated lncRNA, notably for the metabolic clusters (Roulé et al., preprint), it is conceivable that lncRNA-mediated local epigenetic remodeling may exists in other cluster than the marneral and constitute a conserved mechanism to ensure cluster genes co-regulation.

Also, regarding the high quantity of lncRNAs and enhancers, yet uncharacterized it is likely that other lncRNAs sharing similar molecular mechanisms as MARS lncRNA will be found elsewhere in Arabidopsis thaliana genome, or more broadly in other plant species. Taken all together the discovery of the MARS lincRNA, together with its mechanistic characterization, led us to think that lncRNAs could constitute general regulators of the activity of enhancer in plants.

Synthèse en français

La grandissante disponibilité des séquences génomiques de divers organismes vivants à changer notre vision du génome. Jusque dans les années 1940, il était proposé que la longueur du génome soit positivement corrélée avec la complexité de l'organisme (Elliot and [START_REF] Elliott | What's in a genome? The C-value enigma and the evolution of eukaryotic genome content[END_REF]. Cependant les différences de taille de génome entre des organismes proches, ainsi que l'existence de très long génome chez des organismes simple, noté au début des années 1950, donna naissance au dilemme du « C-value paradox » [START_REF] Thomas | The genetic organization of chromosomes[END_REF]. Après, la découverte de large région génomique ne contenant pas de gène codant résoudra le manque de corrélation entre longueur du génome et complexité de l'organisme. Cette observation soulèvera de nombreuses questions quant à l'intérêt de ces régions non codantes : comment ces séquences sont-elles apparues ? Sont-elles fonctionnelles ? Pourquoi n'existe-t-elle pas chez tous les organismes ?

Plus tard, au début des années 2000, les méthodes de séquençage à haut-débit ont révélé que la grande majorité du génome des eucaryotes est transcrit avec étonnamment, seule une infime partie de ces transcrits donnant naissance à des protéines. En effet, il a été estimé que 95% du génome humain est transcrit avec seulement 2% de transcrit traduit en protéine.

Similairement, chez Arabidopsis, 71% du génome est transcrit, avec seulement 2% de transcrits codants (Lucero et al., 2020). Parmi ces transcrits non-codants, nous trouvons, des transcrits structuraux (rRNA, tRNA, snRNA et snoRNA) assurant les fonctions de base de la cellule, tel que les mécanismes de traduction. Mais aussi des petits et longs transcrits régulateurs, inférieure et supérieure à 200nt, respectivement (Ariel et al., 2015). Ces transcrits non-codants régulateurs sont impliqués dans la régulation de l'activité génique au niveau transcriptionnelle et post-transcriptionnelle.