Sandie Tuduri

Laure Crabbé

Chiara Conti

Hélène Tourrière

Heidi Holtgreve-Grez

Anna Jauch

Véronique Pantesco

John De Vos

Aubin Thomas

Charles Theillet

Yves Pommier

Jamal Tazi

Arnaud Coquelle
email: arnaud.coquelle@inserm.fr

Philippe Pasero
email: philippe.pasero@igh.cnrs.fr

Topoisomerase 1 suppresses replication

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Introduction

DNA replication is a complex process that involves the coordinated activation of thousands of replication origins distributed along the chromosomes [START_REF] Aladjem | Replication in context: dynamic regulation of DNA replication patterns in metazoans[END_REF] . Replication forks progressing from these origins frequently stall when they encounter obstacles such as DNA lesions or tightlybound protein complexes [START_REF] Ivessa | The Saccharomyces cerevisiae helicase Rrm3p facilitates replication past nonhistone protein-DNA complexes[END_REF][START_REF] Tourriere | Maintenance of fork integrity at damaged DNA and natural pause sites[END_REF] . Arrested forks are unstable structures, which must be readily processed to avoid inappropriate recombination and genomic instability [START_REF] Tourriere | Maintenance of fork integrity at damaged DNA and natural pause sites[END_REF][START_REF] Aguilera | Genome instability: a mechanistic view of its causes and consequences[END_REF] . It has been recently proposed that activated oncogenes increase the rate of replication fork stalling and chromosome rearrangement at common fragile sites (CFS) in precancerous lesions [START_REF] Bartkova | Oncogene-induced senescence is part of the tumorigenesis barrier imposed by DNA damage checkpoints[END_REF][START_REF] Di Micco | Oncogene-induced senescence is a DNA damage response triggered by DNA hyper-replication[END_REF] . This chronic replication stress promotes the bypass of anticancer barriers such as checkpoints and senescence [START_REF] Halazonetis | An Oncogene-Induced DNA Damage Model for Cancer Development[END_REF] . However, the origin of this replication stress and the nature of sites that impede fork progression in the human genome are currently unknown.

Approximately 1400 natural replication pause sites have been identified in the yeast genome, which include centromeres, telomeres, inactive replication origins and highly-expressed genes 2, 8 . Replication through active genes has been implicated in genomic instability through a process called transcription-associated recombination (TAR) [START_REF] Aguilera | Genome instability: a mechanistic view of its causes and consequences[END_REF] . TAR does not result from frontal collisions between RNA and DNA polymerases but is rather due to RNA-DNA hybrids (R-loops) that form when the assembly of mRNA-particle complexes (mRNPs) is perturbed [START_REF] Aguilera | Genome instability: a mechanistic view of its causes and consequences[END_REF] . In agreement with this model, several mutations affecting the maturation of mRNPs increase TAR in budding yeast [START_REF] Aguilera | Genome instability: a mechanistic view of its causes and consequences[END_REF] . TAR-related events have also been reported in human cells [START_REF] Nickoloff | Transcription stimulates homologous recombination in mammalian cells[END_REF][START_REF] Gottipati | Transcription-Associated Recombination Is Dependent on Replication in Mammalian Cells[END_REF] . It is therefore likely that transcription represents a potential source of replication stress and genomic instability in human cells, as it is the case in budding yeast.

DNA topoisomerase I (Top1) is a ubiquitous enzyme that plays multiple biological functions at the crossroads between replication, transcription and mRNA maturation [START_REF] Pommier | Topoisomerase I inhibitors: camptothecins and beyond[END_REF] . Top1 relaxes DNA supercoiling generated by transcription, replication and chromatin remodelling [START_REF] Champoux | DNA TOPOISOMERASES: Structure, Function, and Mechanism[END_REF] . It is essential for viability in metazoan [START_REF] Thrash | Cloning, characterization, and sequence of the yeast DNA topoisomerase I gene[END_REF][START_REF] Zhang | Essential functions of DNA topoisomerase I in Drosophila melanogaster[END_REF] and Top1 depletion by RNA interference induces DNA damage in human cells [START_REF] Miao | Nonclassic Functions of Human Topoisomerase I: Genome-Wide and Pharmacologic Analyses[END_REF] . Besides its DNA relaxation activity, Top1 is also implicated in the regulation of mRNA splicing in higher eukaryotes, presumably through the direct phosphorylation of splicing factors of the Serine/Arginine (SR)-rich family [START_REF] Rossi | Specific phosphorylation of SR proteins by mammalian DNA topoisomerase I[END_REF][START_REF] Soret | Altered serine/arginine-rich protein phosphorylation and exonic enhancer-dependent splicing in Mammalian cells lacking topoisomerase I[END_REF][START_REF] Malanga | Poly(ADP-ribose) Binds to the Splicing Factor ASF/SF2 and Regulates Its Phosphorylation by DNA Topoisomerase I[END_REF] . Since one of these factors, ASF/SF2, prevents DNA breaks by avoiding the formation of R-loops [START_REF] Li | Inactivation of the SR Protein Splicing Factor ASF/SF2 Results in Genomic Instability[END_REF] , we asked whether Top1 suppresses genomic instability in higher eukaryotes by coordinating replication and transcription.

Results

Top1-deficient cells accumulate chromosome breaks in S phase

We first monitored genomic instability in murine B lymphoma-derived cells (P388), Top1deficient subclone (45/R) and 45/R cells complemented with human Top1-GFP (21/P; Fig. 1a). Using comet assay, a 4.4-fold increase of DNA breaks was detected in 45/R cells relative to control cells (Fig. 1b,c, Table S1). We also observed a sharp increase of histone H2AX phosphorylation (γ-H2AX) [START_REF] Rogakou | Megabase chromatin domains involved in DNA double-strand breaks in vivo[END_REF] in BrdU-positive Top1-deficient cells, indicating that chromosome breaks occur in S phase (Fig. 1d, e, Table S1). Importantly, both DNA breaks and γ-H2AX foci were suppressed upon complementation of 45/R cells with Top1-GFP (Fig. 1c-e). Moreover, M-FISH analysis revealed a 6-fold increase of chromosome rearrangements in Top1-cells (Fig. 1f,g). This increased genomic instability is reminiscent of the phenotype of human HCT116 cells expressing Top1 shRNA (shTop1), which accumulate chromosome breaks (Table S1) and display activation of S-phase checkpoints [START_REF] Miao | Nonclassic Functions of Human Topoisomerase I: Genome-Wide and Pharmacologic Analyses[END_REF] . Interestingly, Top1 depletion also induced a 2-to 3-fold increase of the frequency of chromosome breaks at the common fragile sites (CFSs) FRA3B, FRA16D and FRAXD (Fig. 1h,i), which are frequently rearranged in cancer cells [START_REF] Coquelle | Expression of fragile sites triggers intrachromosomal mammalian gene amplification and sets boundaries to early amplicons[END_REF][START_REF] Durkin | Chromosome Fragile Sites[END_REF] . These data indicate that Top1 plays an important role in S phase by preventing DNA breaks and chromosomal aberrations.

Replication forks are slower in the absence of Top1

Since CFSs break more frequently upon replication stress, we asked whether the chronic genomic instability in Top1-cells results from replication defects. Murine cells were pulselabelled with BrdU to identify newly-replicated regions and the length of BrdU tracks was monitored along individual DNA fibres by DNA combing [START_REF] Michalet | Dynamic molecular combing: stretching the whole human genome for high-resolution studies[END_REF][START_REF] Tourriere | Mrc1 and tof1 promote replication fork progression and recovery independently of Rad53[END_REF] . This analysis revealed that BrdU tracks are shorter in Top1-deficient cells (17.9 kb) than in control and complemented cells (28.5 and 28.2 kb; Fig. 2a,b,S1). Forks are therefore ~50% slower in the absence of Top1. Interestingly, we also observed a concomitant increase of the initiation rate in Top1deficient cells, as centre-to-centre distances between BrdU tracks were significantly shorter in Top1-deficient cells (54.7 kb) than in complemented and control cells (94.5 kb and 111.6 kb; Fig. 2c,S1).

In human shTop1 cells, BrdU tracks length and centre-to-centre distances were also found to be shorter than in control cells (Fig. S2). This effect was slightly lower in than in Top1deficient murine cells, probably because of differences in residual levels of Top1. In order to determine elongation rates, human cells were pulse-labelled with IdU and CldU and the distance covered by individual forks during the pulse was determined. This analysis revealed that forks move at 0.7 kb/min in shTop1 cells versus 1.1 kb/min in control HCT116 cells (Fig. S2a). Similar results were obtained upon transient depletion of Top1 with siRNA (Fig. 2d, 2e, S3). We therefore conclude that Top1 is required for normal fork progression in mammalian cells and that backup origins fire in Top1-deficient cells to compensate for slower forks. Shorter BrdU tracks observed in Top1-deficient cells could be due to slower forks or to increased fork stalling. To discriminate between these two possibilities, progression of sister replication forks was analysed by DNA combing (Fig. 3). In control cells, sister forks progress at a similar rate from a given origin [START_REF] Conti | Replication Fork Velocities at Adjacent Replication Origins Are Coordinately Modified during DNA Replication in Human Cells[END_REF] and generate symmetrical patterns of IdU/CldU incorporation (Fig. 3a). In contrast, more than 50% of asymmetrical patterns were detected in murine Top1-cells (Fig. 3b). Analysis of the ratio of the longest to the shortest IdU signals for each pair of sister replication forks also revealed a 3-fold increase in fork asymmetry in Top1-deficient cells (42%) relative to complemented and control cells (17.2 and 16.4 %; Fig. 3c,S4a). Similarly, a significant increase of sister fork asymmetry was detected in Top1-depleted human cells (Fig. 3d-f, S4b-c). Together, these results indicate that replication forks are not only slower, but also pause or stall more frequently in the absence of Top1.

Top1 modulates the function of ASF/SF2 to prevent genomic instability in S phase

Next, we asked whether the replication stress observed in Top1-deficient cells results from the accumulation of supercoiled DNA or whether it reflects defects in the regulation of RNA splicing. Indeed, Top1 has been implicated in the regulation of mRNP assembly, presumably through the binding and the phosphorylation of splicing factors of the SR family [START_REF] Rossi | Specific phosphorylation of SR proteins by mammalian DNA topoisomerase I[END_REF][START_REF] Soret | Altered serine/arginine-rich protein phosphorylation and exonic enhancer-dependent splicing in Mammalian cells lacking topoisomerase I[END_REF][START_REF] Malanga | Poly(ADP-ribose) Binds to the Splicing Factor ASF/SF2 and Regulates Its Phosphorylation by DNA Topoisomerase I[END_REF] . Since the THO/TREX complex prevents fork collapse in yeast by promoting mRNP assembly and preventing the formation of R-loops [START_REF] Aguilera | Genome instability: a mechanistic view of its causes and consequences[END_REF] , we reasoned that Top1 could play a similar role in metazoan by regulating RNA splicing. In order to address this possibility, we have complemented Top1-deficient human cells with the S. cerevisiae Top1 enzyme (ScTop1), which is required for normal fork progression but does not have a kinase domain and does not regulate fork pausing (Fig. S6). The relaxation activity of Top1 is highly conserved between yeast and human cells [START_REF] Bjornsti | Expression of human DNA topoisomerase I in yeast cells lacking yeast DNA topoisomerase I: restoration of sensitivity of the cells to the antitumor drug camptothecin[END_REF] . Moreover, it has been reported that ScTop1 is targeted to the nucleus in mammalian cells and is fully able to relax supercoiled DNA [START_REF] Hann | Increased Camptothecin Toxicity Induced in Mammalian Cells Expressing Saccharomyces cerevisiae DNA Topoisomerase I[END_REF] . Here, we found that although transfection with ScTop1 significantly reduced DNA breaks in human Top1-cells (Fig. 4a, S5), this suppression was much less efficient than complementation with human Top1-GFP (Fig. 1c). These data indicate that accumulation of supercoiled DNA is not the only cause of DNA breaks in Top1-cells.

To test whether RNA splicing is altered in Top1-cells, we next monitored the subnuclear localization of SR proteins by indirect immunofluorescence. Indeed, it has been reported that SR proteins form speckles in the nucleus and are recruited to transcription sites by phosphorylation [START_REF] Misteli | Serine phosphorylation of SR proteins is required for their recruitment to sites of transcription in vivo[END_REF] . Interestingly, we found that the distribution of phospho-SR proteins was severely perturbed in Top1-deficient cells, the absence of speckles correlating with the formation of γ-H2AX foci (Fig. 4b). We therefore depleted the SR protein ASF/SF2 using RNA interference (Fig. S7a) and monitored the effect of this depletion on genomic instability.

A sharp increase of both DNA damage (Fig. 4c) and chromosome breaks (Fig. 4d,e) was detected in ASF/SF2-depleted cells, which is reminiscent of Top1-deficient cells. Importantly, depletion of both Top1 and ASF/SF2 did not further increase DNA breaks, which indicates that both proteins work in the same pathway to prevent genomic instability in S phase. This view is also supported by the fact that Diospyrin D1, an inhibitor of Top1 kinase [START_REF] Tazi | Selective inhibition of topoisomerase I and various steps of spliceosome assembly by diospyrin derivatives[END_REF] , induced a 10-fold increase of DNA breaks in Top1-proficient cells (Fig. 4f, S7b, Table S2), in a replication-dependent manner (Fig. 4g, Table S2). Finally, we found that both Diospyrin and ASF/SF2 depletion induced a sharp increase of sister fork asymmetry in human and murine cells (Fig. 4h,i,S7c,d), to a level comparable to Top1 depletion (Fig. 3). We therefore propose that mammalian Top1 suppresses replication stress in S phase both by relaxing DNA supercoiling and by regulating mRNA splicing.

RNaseH1 suppresses fork asymmetry and chromosome breaks in Top1-cells

The above results suggest that transcription interferes with replication fork progression in Top1-deficient cells through the accumulation of R-loops. To test this possibility, Top1-cells were treated with Cordycepin, a potent inhibitor of RNA chain elongation, and sister forks progression was monitored by DNA combing. Cordycepin suppressed sister-fork asymmetry in murine and human Top1-cells (Fig. 5a, b, Table S3), supporting the view that transcription interferes with fork progression in Top1-cells. To confirm the implication of R-loops in this process, human cells were transfected with a vector expressing RNaseH1 to degrade DNA-RNA hybrids. Remarkably, RNaseH1 induced a reduction of both sister fork asymmetry and DNA breaks (Fig. 5c-e, Table S3) in shTop1 cells. In contrast, control cells were not affected by this treatment.

Since Cordycepin and RNaseH1 suppress sister fork asymmetry in Top1-deficient cells, we next asked whether these treatments restore normal fork progression in the absence of Top1.

Conversely, we checked whether increased fork stalling induced by Diospyrin or ASF/SF2 depletion also reduced fork rates in control cells. To this end, fork speed was measured in cells exposed to Cordycepin, RNaseH1, Diospyrin and siASF (Fig. 5f,S8, Table 1). We found that Diospyrin and siASF induced a 0.33 kb/min reduction of fork rate in control cells (1.2 kb/min), which represents ~70% of the delay measured in Top1-deficient cells (-0.45 kb/min relative to control cells). Conversely, 46% of the delay measured in Top1-deficient cells was suppressed upon treatment with Cordycepin or RNaseH1 (+0.21 kb/min; Fig. 5f,S8, Table 1).

Together, these data indicate that the slow fork phenotype of Top1-deficient cells is largely caused by transcription-dependent replication fork pausing.

γ-H2AX is preferentially detected at transcribed regions in Top1-cells

To test whether DNA breaks observed in Top1-deficient cells occur preferentially at transcribed loci, we monitored the distribution of γ-H2AX by ChIP-on-chip on highresolution tiling arrays encompassing human chromosomes 1 and 6 (Fig. 6a). This analysis identified 435 loci in shTop1 and 149 loci in shCtrl cells (Fig. 6b) averaging ~1 kb in length (Fig. 6c). Interestingly, 50% of γ-H2AX loci were found within 2 kb of the 5'-or 3'-boundary of annotated genes in shTop1 cells, a frequency that is significantly higher than expected for a random distribution (11.2%; Fig. 6d). In contrast, no significant enrichment was detected in shCtrl cells (8.3%; Fig. 6d). This is best illustrated for SFRS3 (Fig. 6e), one of the most highly expressed genes on chromosome 6. Significant γ-H2AX enrichment near promoters, termination sites and at converging genes was also detected by calculating average γ-H2AX profiles for the complete set of protein-coding genes on human chromosomes 1 and 6 (Fig. 6f).

We next asked whether γ-H2AX enrichment at annotated genes correlates with transcriptional activity in S phase. To this end, we focused on replication-dependent histone genes, whose expression is linked to DNA replication by transcriptional and posttranscriptional mechanisms [START_REF] Holmes | Coordinate Control and Selective Expression of the Full Complement of Replication-dependent Histone H4 Genes in Normal and Cancer Cells[END_REF] . We found that about 70% of the replication-dependent histone genes were enriched in γ-H2AX specifically in shTop1 cells (Fig. 6g ,S9). Moreover, the five major histone H4 genes (representing more than 80% of histone H4 mRNAs 30 ) were highly enriched in γ-H2AX in shTop1 cells (Fig. S8). We found a positive correlation between the level of expression of H4 genes and their probability to overlap with γ-H2AX domains (r 2 =0.71; Fig. 6h). Collectively, these data indicate that, in the absence of Top1, DNA breaks form at highly-expressed genes during S phase and that the rate of DNA damage correlates with the level of gene expression.

Discussion

It is generally believed that Top1 relaxes DNA supercoiling that accumulates ahead of replication forks. However, this view has been recently challenged by reports showing that Top2 relaxes chromatin templates more efficiently than Top1 31 and that Top1 is dispensable for normal DNA replication in budding yeast [START_REF] Bermejo | Top1-and Top2-mediated topological transitions at replication forks ensure fork progression and stability and prevent DNA damage checkpoint activation[END_REF] . Here, we have used DNA combing to monitor fork progression in top1Δ yeast mutants and in mammalian cells presenting low to undetectable levels of Top1. We found that forks are ~50% slower in these cells, confirming thereby that Top1 plays a major role in replication elongation. We also found that Top1-cells compensate for slow forks by increasing the rate of initiation. These data are consistent with other studies showing that dormant replication origins fire in response to various types of fork impediments [START_REF] Anglana | Dynamics of DNA replication in mammalian somatic cells: nucleotide pool modulates origin choice and interorigin spacing[END_REF][START_REF] Courbet | Replication fork movement drives remodeling of chromatin loops and origin choice in mammalian cells[END_REF][START_REF] Gilbert | Replication origin plasticity, Taylor-made: inhibition vs recruitment of origins under conditions of replication stress[END_REF] . They also explain why yeast top1Δ mutants have a normal S phase [START_REF] Bermejo | Top1-and Top2-mediated topological transitions at replication forks ensure fork progression and stability and prevent DNA damage checkpoint activation[END_REF] although their forks are 50% slower.

We also show that, besides slow replication forks, Top1-deficient mammalian cells display an increased rate of fork stalling. Our data indicate that fork arrest in Top1-deficient cells is largely due to the accumulation of R-loops during transcription. Since DNA supercoiling accumulates at transcription sites in the absence of Top1, an attractive possibility could be that nascent RNA chains reanneal with the DNA template in Top1-deficient cells and interfere with fork progression. This view is supported by studies in E. coli showing that Top1 prevents the formation of abnormal RNA-DNA hybrids by relieving negative supercoiling behind RNA polymerases [START_REF] Drolet | Growth inhibition mediated by excess negative supercoiling: the interplay between transcription elongation, R-loop formation and DNA topology[END_REF] . This model is also consistent with the fact that DNA breaks in Top1human cells are at least partially suppressed by S. cerevisiae Top1. Alternatively, Top1 could prevent R-loop formation by promoting the ASF/SF2-dependent assembly of mRNPs [START_REF] Rossi | Specific phosphorylation of SR proteins by mammalian DNA topoisomerase I[END_REF][START_REF] Soret | Altered serine/arginine-rich protein phosphorylation and exonic enhancer-dependent splicing in Mammalian cells lacking topoisomerase I[END_REF][START_REF] Lin | The splicing factor SC35 has an active role in transcriptional elongation[END_REF] .

In agreement with this model, we found that the subnuclear organization of ASF/SF2 speckles is profoundly altered in Top1-deficient cells. Moreover, inhibition of Top1 kinase with Diospyrin or depletion of its target ASF/SF2 induces fork arrest and chromosome breaks to a similar extent as in Top1-cells. Importantly, no additive effect of ASF/SF2-and Top1depletion was detected, which indicates that both proteins act in the same pathway to prevent replication stress. We therefore conclude that Top1 avoids conflicts between replication and transcription both by relaxing DNA supercoiling and by promoting mRNP assembly (Fig. 7).

A simple prediction of the model described above is that chromosome breaks occur preferentially at gene-rich regions in the absence of Top1. To test this possibility, we mapped γ-H2AX enriched loci by ChIP-on-chip. This analysis identified a significant overlap between γ-H2AX-enriched regions and annotated genes, with a preferential localization at 5'-and 3'boundaries. We also observed a strong accumulation of γ-H2AX at replication-dependent histone genes, which is proportional to their level of expression. γ-H2AX-enriched regions encompass ~1 kb on average which is strikingly smaller than the megabase-length domains that form at DNA double-strand breaks induced by ionizing radiation [START_REF] Rogakou | Megabase chromatin domains involved in DNA double-strand breaks in vivo[END_REF] . They are reminiscent of the γ-H2AX tracks that form at natural replication pause sites in budding yeast (our unpublished results and D. Durocher, personal communication) and probably correspond to checkpoint signalling of stalled forks. These observations support the view that transient breaks accumulate in Top1-deficient cells as the consequence of conflicts between replication and transcription.

It is now well established that TAR represents a prominent source of genomic instability in budding yeast [START_REF] Aguilera | Genome instability: a mechanistic view of its causes and consequences[END_REF] . Together with earlier studies [START_REF] Nickoloff | Transcription stimulates homologous recombination in mammalian cells[END_REF][START_REF] Gottipati | Transcription-Associated Recombination Is Dependent on Replication in Mammalian Cells[END_REF][START_REF] Li | Inactivation of the SR Protein Splicing Factor ASF/SF2 Results in Genomic Instability[END_REF] our results indicate that TAR-related events also occur in mammals and are suppressed in a Top1-and ASF/SF2-dependent manner.

Intron-containing genes represent only a small fraction of the human genome, which may question the significance of TAR as a general source of genomic instability. However, other types of transcriptional activity could promote TAR in human cells. Indeed, stalled RNA polymerases have been detected at the promoter of most inactive genes [START_REF] Guenther | A Chromatin Landmark and Transcription Initiation at Most Promoters in Human Cells[END_REF] and small nuclear RNAs complementary to the 5'-and 3'-ends of genes have also been identified [START_REF] Kapranov | RNA maps reveal new RNA classes and a possible function for pervasive transcription[END_REF] . Moreover, unbiased transcriptome analyses have recently shown that most of the human genome is transcribed [START_REF] Kapranov | Genome-wide transcription and the implications for genomic organization[END_REF] . Since ASF/SF2 is permanently associated with RNA pol II and is required for optimal elongation [START_REF] Lin | The splicing factor SC35 has an active role in transcriptional elongation[END_REF][START_REF] Das | SR Proteins Function in Coupling RNAP II Transcription to Pre-mRNA Splicing[END_REF] , it is tempting to speculate that pervasive transcription interferes with fork progression and is regulated in a similar Top1-dependent manner. Since it is believed that R-loops affect DNA replication by generating marks that persist after transcription [START_REF] Aguilera | Genome instability: a mechanistic view of its causes and consequences[END_REF] , gene expression could therefore interfere with replication even if these processes do not occur simultaneously [START_REF] Wei | Segregation of Transcription and Replication Sites Into Higher Order Domains[END_REF][START_REF] Vieira | Recruitment of Transcription Complexes to the {beta}-Globin Gene Locus in Vivo and in Vitro[END_REF] .

Recent evidence indicates that the DNA damage checkpoint is constitutively induced in precancerous lesions due to persistent replication defects [START_REF] Bartkova | Oncogene-induced senescence is part of the tumorigenesis barrier imposed by DNA damage checkpoints[END_REF][START_REF] Di Micco | Oncogene-induced senescence is a DNA damage response triggered by DNA hyper-replication[END_REF] and acts as a selective pressure to inactivate p53 [START_REF] Halazonetis | An Oncogene-Induced DNA Damage Model for Cancer Development[END_REF] . However, the origin of replication stress in precancerous cells is currently unknown. Here, we show that transcription represents a source of replication fork stalling in mammalian cells. Since transcription units act as polar replication barriers, alterations of origin usage in oncogene-activated cells [START_REF] Lengronne | The yeast CDK inhibitor Sic1 prevents genomic instability by promoting replication origin licensing in late G(1)[END_REF][START_REF] Ekholm-Reed | Deregulation of cyclin E in human cells interferes with prereplication complex assembly[END_REF] could modify the direction of fork progression and increase conflicts with transcription. It was recently proposed that transcription at highlyexpressed genes is co-oriented with the replication fork in a large fraction of the human genome, presumably to minimise these conflicts [START_REF] Huvet | Human gene organization driven by the coordination of replication and transcription[END_REF] . This view is supported by our ChIP-onchip data, which show that γ-H2AX is preferentially detected outside of these organised domains in Top1-cells (data not shown).

The human genome contains a large excess of replication origins that can be used as backup initiation sites when fork progression is impeded [START_REF] Ge | Dormant origins licensed by excess Mcm2 7 are required for human cells to survive replicative stress[END_REF][START_REF] Ibarra | Excess MCM proteins protect human cells from replicative stress by licensing backup origins of replication[END_REF] . Likewise, we found here that Top1depleted cells activate new origins to rescue stalled forks (Fig. 7). Common fragile sites (CFSs) are specific regions of the genome that are prone to breakage upon replication stress and induce chromosomal rearrangements from the early stages of tumorigenesis [START_REF] Halazonetis | An Oncogene-Induced DNA Damage Model for Cancer Development[END_REF][START_REF] Coquelle | Expression of fragile sites triggers intrachromosomal mammalian gene amplification and sets boundaries to early amplicons[END_REF][START_REF] Durkin | Chromosome Fragile Sites[END_REF] . These loci end their replication very late in S phase and are frequently associated with very large genes [START_REF] Helmrich | Common fragile sites are conserved features of human and mouse chromosomes and relate to large active genes[END_REF][START_REF] Widrow | Very late DNA replication in the human cell cycle[END_REF] . We observed a significant increase of chromosome breaks at CFSs in Top1-cells.

It is therefore tempting to speculate that regions of the genome that are either devoid of dormant origins or that replicate very late in S phase are hypersensitive to fork stalling induced by transcription, this sensitivity further increasing in cancer cells due to perturbations of the replication program. In Top1-proficient cells (Top1+), replication forks progress at a normal rate and dormant origins are passively replicated by ongoing forks. In absence of Top1 (Top1-), defective RNA processing leads to the formation of R-loops, which block fork progression, generate DNA breaks and induce H2AX phosphorylation. In most cases, stalled forks are rescued by replisomes progressing from dormant origins, which fire more frequently in Top1-cells.

Regions of the genome that contain fewer backup origins or that replicate very late in S phase could be more prone to irreversible fork stalling and to chromosomal rearrangements.

Table 1 Analysis of fork rates in

Methods

Cell lines and culture conditions. Cells lines and culture conditions for P388 murine leukaemia cell line (control), 45/R cells (CPT-resistant subclone which do not display Top1 activity) and 21/P cells (45/R cells complemented with human Top1-GFP) were described previously [START_REF] Soret | Altered serine/arginine-rich protein phosphorylation and exonic enhancer-dependent splicing in Mammalian cells lacking topoisomerase I[END_REF] . HCT116 and derived HCT116-shTop1 cells were grown in DMEM, with 500 µg/ml Hygromycin B as described [START_REF] Miao | Nonclassic Functions of Human Topoisomerase I: Genome-Wide and Pharmacologic Analyses[END_REF] .

RNAi, transient transfections and drugs.

The following siRNA duplexes designed to repress ASF/SF2: siASF 1 (5'-GUAUUGACCUUAUACUAAAdTdT -3') and siASF 2 (5'-GGGUAGCAAUGCCAGUAAAdTdT-3') were transfected using INTERFERin (Polyplustransfection) at 1 nM and 10 nM, respectively. Depletion was verified by western blot with antibodies against SF2/ASF (Zymed Laboratories) 24 to 48 hrs after transfection. For Top1 transient downregulation, cells were transfected with the following siRNA (5'-GGACTCCATCAGATACTAT-3') and were analysed 72 hrs after transfection as described [START_REF] Miao | Nonclassic Functions of Human Topoisomerase I: Genome-Wide and Pharmacologic Analyses[END_REF] . For RNaseH1 expression studies, 5x10 4 HCT116 cells were seeded 24 hrs before transfection and were transfected with 1 µg of pCMV6-XL5-RNaseH1 vector (Origene, Rockville, MD) and 1 µg of pHygEGFP reporter vector (Clontech) using jetPEI reagents (Polyplus-transfection). Cells were analysed 24 hrs after transfection. Cells were analysed 48 Comet Assay. DNA breaks were monitored using the CometAssay Reagent Kit for Single Cell Gel Electrophoresis Assay (Trevigen, Inc., Gaithersburg, MD) according to the manufacturer's instructions with minor modifications. Briefly, 75 µl of cells resuspended in LMP agarose (3x10 5 cells/ml) were pipetted onto Trevigen CometSlides. After electrophoresis with a horizontal apparatus (30 min at 1 V/cm in 1xTBE), slides were stained with 50 µl SYBR green dye (Trevigen; 1/ 10,000 in TE) and viewed using a Leica epifluorescence microscope. Imaging was performed using the Comet Imager Software V 2.0.105 (Metasystem, Germany). Tail moment (TM) considers both the tail length (TL) and the fraction of DNA in the comet tail (TM = %DNA in tail x TL/100). A total of 100 cells were analysed per slide.

Analysis of metaphase spreads, FISH and M-FISH. Cells were incubated for 2 hrs with 10 µM nocodazole, harvested then spread on slides and analysed as described [START_REF] Coquelle | Expression of fragile sites triggers intrachromosomal mammalian gene amplification and sets boundaries to early amplicons[END_REF] . FISH with BACs RP11-147N7 (FRA3B), RP11-22N7 (FRA16D) RP11-323J20 (FRAXB) was performed as described [START_REF] Coquelle | Expression of fragile sites triggers intrachromosomal mammalian gene amplification and sets boundaries to early amplicons[END_REF] For each cell line, the frequency of chromosome breaks were scored on more than 100 metaphases. Multiplex-Fluorescence In Situ Hybridization (M-FISH) analysis of metaphase spreads was performed as described [START_REF] Smogorzewska | DNA Ligase IV-Dependent NHEJ of Deprotected Mammalian Telomeres in G1 and G2[END_REF] . For each experiment, 20 metaphase spreads were acquired by using a Leica DM RXA epifluorescence microscope equipped with a Sensys CCD camera (Photometrics) and the Q-FISH software (Leica).

Images were processed using MCK software (Leica Microsystems Imaging Solutions). Indirect immunofluorescence. Cells were labelled for 10 min with 10 µM BrdU (Sigma).

Murine leukaemia cells were resuspended in PBS (10 6 cells/ml) and cytospined (Shandon Cytospin 3, Thermo Scientific). Cells were fixed for 15 min with 4% formaldehyde at room temperature, washed twice with PBS and incubated with 4N HCl for 30 min. Cells were permeabilised with 0.5% Triton X100 in PBS for 10 min and then incubated in PBS, 3% BSA (Sigma-Aldrich) for 30 min at 37°C. BrdU was detected with a rat monoclonal antibody (BU1/75, AbCys) and a secondary antibody-coupled Alexa 488 (Molecular Probes). γ-H2AX (Ser139) was detected with a rabbit antibody (2577, Cell Signalling) and an anti-rabbit IgG conjugated with TRITC (Jackson ImmunoResearch). Phospho-SR proteins were detected with the monoclonal antibody Mab104 17 and an anti-mouse IgG conjugated with TRITC (Jackson ImmunoResearch).Slides were mounted with Vectashield containing 1.5 µg/ml DAPI. DNA combing. DNA combing was performed as described [START_REF] Michalet | Dynamic molecular combing: stretching the whole human genome for high-resolution studies[END_REF][START_REF] Versini | The yeast Sgs1 helicase is differentially required for genomic and ribosomal DNA replication[END_REF] . Briefly, cells DNA fibres were extracted in agarose plugs immediately after BrdU labelling and were stretched on silanized coverslips. BrdU was detected with a rat monoclonal antibody (BU1/75, AbCys; 1/20) and a secondary antibody coupled to Alexa 488 (A11006, Molecular Probes; 1/50).

DNA molecules were counterstained with an anti-ssDNA antibody (MAB3034, Chemicon; 1/500) and an anti-mouse IgG coupled to Alexa 546 (A11030, Molecular Probes, 1/50). CldU and IdU were detected with BU1/75 (AbCys, 1/20) and BD44 (Becton Dickinson, 1/20) anti-BrdU antibodies, respectively. DNA fibres were analysed on a Leica DM6000B microscope equipped with a CoolSNAP HQ CCD camera (Roper Scientifics). Data acquisition was performed with MetaMorph (Roper Scientifics). Representative images of DNA fibres were assembled from different fields of view and were processed as described [START_REF] Pasero | Single-molecule analysis reveals clustering and epigenetic regulation of replication origins at the yeast rDNA locus[END_REF] .

Graphs and statistical analysis. Box-and-whiskers graphs were plotted with Prism v5.0 (GraphPad Software). For all graphs, top and bottom of the box correspond to the 25th and 75th percentile (the lower and upper quartiles, respectively) and the line near the middle of the box marks the median (50th percentile). Whiskers correspond to the 10-90 percentiles.

Data not included between the whiskers are plotted as outliers (dots). Statistical analysis was performed in Prism v5.0 (GraphPad Software) using the non-parametric Mann-Whitney rank sum test. Normality of distributions was assessed with the Kolmogorov-Smirnov test. Linear regressions and r 2 were computed with Prism v5.0 (GraphPad Software).

Cloning and expression of S. cerevisiae Topoisomerase I. cDNA of scTop1 cDNA was amplified by PCR with primers 5'-GGGAGCTCATGACTATTGCTGATGCTTCC-3' (introducing a SacI site) and 5'-GGCCCGGGTTAAAACCTCCAATTTTCATCTACC-3' (introducing a SmaI site). PCR products were cloned into pIRES2-AcGFP1 (Clontech Laboratories) using SacI and SmaI cloning sites. Sequence of ScTop1 was verified with primers 5'-TTTCTCGTTGCGATTTCAC-3', 5'-AAACTACATGCCGGGATT-3' and ('-CGCAAATGGGCGGTAGGCGTG-3' (CMV promoter). 3x10 5 HCT116 cells were seeded 24 hrs before transfection with 6 µg of pIRES2-AcScTop1-GFP1 vector and 6 µg of pIRES2-AcGFP1 reporter vector using jetPEI reagents (Polyplus-transfection). Expression of ScTop1 was checked by RT-PCR with a Light Cycler LC480 (Roche) and cells were analysed 48 hrs after transfection.

ChIP-on-chip. Asynchronous cultures of HCT116 cells were harvested with Trypsin, washed with PBS, and fixed with 1% formaldehyde in PBS during 10 minutes at room temperature.

The ChIP experiments were performed as previously described [START_REF] Verdun | Functional Human Telomeres Are Recognized as DNA Damage in G2 of the Cell Cycle[END_REF] with the following modifications. Briefly, 5 μg of anti γ-H2AX antibody (Abcam ab2893) was first bound to 20 μL of Dynabeads Protein A (Dynal, Invitrogen) then added to 200 μL of lysate, normalised to 2.10 6 cells. After purification, the immunoprecipitated DNA was amplified using the WGA2 GenomePlex Complete Whole Genome Amplification kit (SIGMA) with addition of 0.1 mM dUTP. Seven μg of amplified DNA was fragmented and labelled using the GeneChip WT Double-Stranded DNA Terminal Labelling kit (Affymetrix, PN 900812) following manufacturer's recommendations. DNA was hybridised to Affymetrix GeneChip Human Tiling 2.0R A Array Set (Chromosome 1, 6), using the GeneChip Hybridization, Wash, and Stain Kit (Affymetrix, PN 900720). Tiling arrays were scanned with the GeneChip scanner 3000 7G. Enrichment was calculated with the Tiling Analysis Software v1.1 (Affymetrix) and profiles were generated with the Affymetrix Integrated Genome Browser (v5.01). Data from two biological replicates were combined and loci enriched in γ-H2AX with a P-value lower than 0.01 were scored. Enrichment analyses were performed with the Affymetrix Tiling Analysis Software (v. Supplementary Table S3

Figure 1

 1 Figure 1 Top1-deficient murine cells form DNA breaks in S phase and accumulate

Figure 2

 2 Figure 2 Replication fork progression is impaired in the absence of Top1. (a) Single-

Figure 3

 3 Figure 3 Analysis of sister replication forks progression in Top1-depleted cells. (a)

Figure 4

 4 Figure 4 Inhibition of ASF/SF2 function induces fork asymmetry and chromosome breaks.

Figure 5

 5 Figure 5 RNaseH1 suppresses fork asymmetry and DNA damage in Top1-cells. (a) Murine

Figure 6 γ

 6 Figure 6 γ-H2AX is enriched at active genes in Top1-HCT116 cells. (a) Chromatin from

Figure 7

 7 Figure 7 Model for the role of Top1 in the coordination of DNA replication and gene

  Top1 proficient (P388, HCT116) and Top1-deficient (45/R, shTop1 HCT116) cells exposed to Diospyrin (D1), Cordycepin (Cord.), RNaseH1 or siASF. Statistical analyses were performed with the Mann-Whitney rank sum test. ***: P<0.0001; **: P<0.001. Differences between cells treated with Diospyrin and siASF or Cordycepin and RNaseH1 are not statistically significant.

  hrs after transfection. Transcription was inhibited by exposing murine cells for 6 hrs to 100 µM Cordycepin and human cells for 3 hrs to 50 µM Cordycepin. Inhibition of Top1 kinase activity was performed by treating cells with the following concentrations Diospyrin (IC50): shCtrl HCT116 (35 µM), shTop1 HCT116 (10 µM), P388 (10 µM), 45R (5 µM), 21P (7.5 µM).

  Figure1

Table 1 Pasero

 1 1.1.02).

	Fork rate		P388		45/R			HCT116			shTop1
	(kb/min)	Ctrl	D1	siASF	Ctrl	Cord.	Ctrl	D1	siASF	Ctrl	Cord.	RNaseH
	Nb. values	137	146	103	141	112	122	133	108	149	118	103
	Median	1.22	0.96	0.78	0.73	0.90	1.18	0.85	0.91	0.77	1.01	0.95
	Mean	1.25	0.94	0.88	0.79	0.90	1.16	0.89	0.92	0.83	1.02	1.01
	St. Dev.	0.41	0.34	0.36	0.29	0.29	0.31	0.28	0.27	0.34	0.32	0.39
		-	***	***	-	**	-	***	***	-	***	***
	P-value
		-	0.06		-	-	-	0.44		-	0.34

Acknowledgements

We thank Michelle Debatisse and Marcel Méchali for discussions and for critical comments on the manuscript. We are also grateful to D. Durocher for communicating data prior to publication. We thank Jacques Piette for his initial contribution to this project. We also thank Etienne Schwob and the DNA combing facility of Montpellier for providing silanized coverslips. Work in the PP lab is supported by FRM (Equipe FRM), ANR, INCa and the EMBO Young Investigator Programme. AC is supported by the Ligue contre le cancer (Comité de l'Hérault), ARC (Association pour la Recherche contre le Cancer) and INCa. ST, LC and HT were recipients of fellowships from ARC, EMBO and CNRS, respectively.

Accession Numbers

Microarray data presented in this article can be obtained from Gene Expression Omnibus (http://www.ncbi.nlm.nih.gov/geo/) with accession number GSE17552.

Author Contributions

AC and PP designed the project and wrote the paper; ST, LC, CC and HT performed most of the experiments; MFISH studies were done by HHG and AJ; VP and JDV performed microarray experiments. Bioinformatic analyses were done by AT; CT, YP and JT provided important experimental materials and advices. All authors contributed to the interpretation of the results and edited the manuscript.

Supplementary Figure S1

Single-molecule analysis of DNA replication in Top1-deficient murine leukemia cells. P388 (control), 45/R (Top1-) and 21/P (Top1-GFP) cells were pulse-labelled for 15

Supplementary Figure S2

Single-molecule analysis of DNA replication in shCtrl and shTop1 HCT116 cells. (a) Box plots of fork velocity, BrdU tracks length and centre-to-centre distances between BrdU tracks in shCtrl and shTop1 HCT116 cells. (b) Frequency distribution of data displayed in above panels. (c) Statistics of fork velocity, BrdU tracks length and centre-to-centre distances. Fork velocity was determined as described in Fig. 2d. Analysis of BrdU tracks length and centreto-centre distances between BrdU tracks was performed as described for P388 cells (Fig. 2ac).

Supplementary Figure S3

Single-molecule analysis of DNA replication in human HCT116 cells transfected with siRNA against Top1. (a) Frequency distribution of BrdU tracks length and centre-to-centre distances in siCtrl and siTop1 HCT116 cells. (b) Statistical analysis of DNA combing data. See Fig. 2d,e for experimental details. (c) Western blot analysis of Top1 levels in HCT116 cells transfected with control or Top1 siRNAs.

Supplementary Figure S4

DNA combing analysis of replication fork stalling in Top1-depleted cells. Cells were pulselabelled for 15 min with IdU and 15 min with CldU and processed as described (Fig. 3) (a) Statistical analysis of fork asymmetry in P388, 45/R and 21/P cells. See Fig. 3b,c for experimental details. (b) Statistical analysis of fork asymmetry in shCtrl and shTop1 HCT116 cells. (c) Frequency of fork arrest in cells transiently transfected with siTop1 and siCtrl, calculated as described previously (Conti et al., 2007).

Supplementary Figure S5

Complementation of Top1-deficient human cells with S. cerevisiae TOP1. (a) Frequency distribution of comet tail moments in shCtrl and shTop1 HCT116 cells, transfected (ScTop1) or not (empty vector) with a vector expressing the S. cerevisiae TOP1 gene. Note that ScTop1 overexpression is not toxic in HCT116 cells as it did not significantly increase the incidence of DNA breaks. (b) Statistical analysis of comet tail moment in shCtrl and shTop1 HCT116 cells complemented or not with yeast TOP1. (c) RT-PCR analysis of ScTop1 mRNA levels in shCtrl and shTop1 cells transfected with an empty vector (-) or with a vector expressing the S. cerevisiae TOP1 gene (+ ScTop1). ScTop1 mRNA levels were normalized to human HPRT and 18S RNAs.

Supplementary Figure S6

Single-molecule analysis of DNA replication in S. cerevisiae wild type and top1∆ cells.

Supplementary Figure S8

Frequency distribution of replication fork rates in P388, 45/R, HCT116 shCtrl and shTop1 cells exposed or not to Dyospyrin (D1), siASF1, Cordycepin (Cord.) and RNaseH. Median fork rate is indicated in red. See Fig. 5 for experimental details.

Supplementary Figure S9

ChIP-on-chip analysis of γ-H2AX enrichment at the three major histone gene clusters on human chromosome 1 and 6. Chromatin immunoprecipitation and microarray analysis were performed as described in Fig. 6 for two biological replicates. The position of histone genes on (+) and (-) strands is shown in red. Non-histone genes are indicated in black. γ-H2AXenriched loci (p<0.01) are indicated. Red: shTop1. Gray: shCtrl. The five nonallelic histone H4 genes contributing to more than 80% of histone H4 mRNAs are indicated with an asterisk.

Supplementary Table S1

Accumulation of chromosome breaks in Top1-deficient cells. Numerical values for the experiments shown in Fig. 1c and 1e are indicated, together with statistical differences between Top1-deficient and control cells. See Fig. 1 for experimental details. Comet tail moment in shTop1 and shCtrl HCT116 cells was measured as described for P388 cells (see Methods section).

Supplementary Table S2

Analysis of chromosome breaks in HCT116 and P388 cells exposed to Diospyrin or to an siRNA against ASF/SF2. See Fig. 4 for experimental details.

Supplementary Table S3

Analysis of fork asymmetry and DNA breaks in Top1-deficient cells exposed to Cordycepin and to RNaseH. See Fig. 5


Fanny Kerninon
email: fanny.kerninon@univ-brest.fr

Olivier Monnier
email: olivier.monnier@ofb.gouv.fr

Note de présentation des indicateurs candidats pour la surveillance DCE des herbiers aux Antilles

pour le suivi et l'évaluation des herbiers d'outre-mer développés dans le cadre de la Directive Cadre sur l'Eau et de l'Initiative française pour les récifs coralliens ».

Eléments de contexte

C'est dans un cadre global de proposition d'outils harmonisés de suivi et d'évaluation de l'état des herbiers tropicaux français (action nationale herbiers mutualisée Ifrecor-DCE) que s'inscrit le développement de l'indicateur DCE « herbiers » pour les Antilles françaises mené par l'UBO (conventions UBO-OFB et UBO-MTECT). La démarche s'est appuyée sur la mobilisation en 2017 d'un groupe national thématique (atelier national herbiers, séminaire science-gestion en Martinique ; GTN herbiers Ifrecor-DCE, 2017) dont les objectifs concernant la DCE aux Antilles étaient notamment de faire le point sur les actions déjà mises en oeuvre, d'ajuster le protocole de surveillance DCE et de valider la stratégie d'actions de la première phase de R&D (2017-2020) dédiée au pré-développement de l'indicateur DCE « herbiers ». Une seconde phase de R&D est actuellement en cours pour ajuster et valider l 'indicateur (2020-2023) :

Phase 1 : pré-développement de l'indicateur (2017)(2018)(2019)(2020) Du fait de l'évolution des protocoles de surveillance DCE en place dans les Antilles françaises au cours de ces dernières années, du positionnement des stations sous influence anthropiques modérées et de la nécessité d'évaluer la réponse de paramètres (ou métriques) d'intérêt (notamment ceux répondant spécifiquement aux dégradation de la qualité de l'eau), un jeu de données a été spécifiquement acquis dans le cadre d'expérimentations menées en conditions environnementales contrastées sur les territoires de Guadeloupe, Saint-Martin et de Saint-Barthélemy. Lors de cette première phase de R&D, la réponse d'une soixantaine de paramètres aux pressions anthropiques présumées, intégrant des informations sur la physiologie des phanérogames marines, des individus, des populations, des communautés associées et du sédiment a été étudiée à l'échelle des différents territoires pilotes en fin de saison sèche en 2017 et 2018. Les données collectées sur les 17 stations expérimentales (Figure 1, Tableau 1) ont permis d'apporter de nouvelles connaissances sur ces milieux, d'amorcer l'évaluation des relations pressions-état et de sélectionner les descripteurs les plus pertinents selon les principaux objectifs de gestion, notamment pour la DCE [START_REF] Kerninon | Développement d'outils méthodologiques pour le suivi et l'évaluation de l'état de santé des herbiers d'outre-mer français et de leur environnement, dans un contexte de perturbations multiples[END_REF][START_REF] Kerninon | Selection of parameters for seagrass management: Towards the development of integrated indicators for French Antilles[END_REF]. Une première version d'un indicateur multimétriques intégré, adapté aux herbiers des Antilles françaises et aux échelles de temps de la gestion a été développé en 2020 [START_REF] Kerninon | Développement d'outils méthodologiques pour le suivi et l'évaluation de l'état de santé des herbiers d'outre-mer français et de leur environnement, dans un contexte de perturbations multiples[END_REF]. Basé principalement sur la phanérogame marine Thalassia testudinum, espèce native à forte capacité de bioindication, cet indicateur de complexité intermédiaire combine des paramètres d'alerte précoce et de diagnostic de la qualité de l'eau auxquels les herbiers sont sensibles (nutriments et certains métaux traces) ainsi que des paramètres de réponse à long terme (densité et recouvrement des phanérogames), reflétant leur intégrité structurelle.

Phase 2 : adaptation, validation et transfert (2020)(2021)(2022)(2023) Une nouvelle campagne de terrain réalisée en fin de saison sèche en 2021 en Martinique a permis de compléter le jeu de données utilisé pour le développement de cette première version de l'indicateur par l'ajout de 14 stations expérimentales (Figure 1, Tableau 1). C'est ainsi sur un total de 39 séries d'acquisition de données, réparties sur 30 stations intégrant l'ensemble des territoires des Antilles françaises (Fig. 1) que cet indicateur de niveau de complexité intermédiaire développé durant la thèse (niveau 2, voir ci-après) a été une deuxième fois implémenté. Il s'accompagne de deux nouveaux modèles d'indicateurs (niveau 1 et niveau 3, voir ci-après). A ce stade de la phase de développement, ces modèles intègrent la totalité du jeu de données disponible afin de tester leur fonctionnement, de réaliser leur calibration et d'apporter les éléments nécessaires d'analyse critique concernant leur robustesse. Une première partie de celui -ci (stations profondes, herbiers dominés par Halophila stipulacea) sera retiré pour la calibration de l'indicateur final et une autre pour la phase de test sur un jeu de données indépendant (une série temporelle des stations échantillonnées à deux reprises), étape méthodologique cadrant le développement des indicateurs DCE (OFB, MTES, 2020).

Une analyse des pressions anthropiques a été réalisée à l'échelle de l'ensemble des stations expérimentales. La principale difficulté de ce travail a été de faire face à l'absence de données homogènes sur ces pressions pour répondre aux besoins particuliers du développement de l'indicateur (ex : données physico-chimiques fines, localisées et à fréquence suffisante pour l'ensemble des stations de l'étude). C'est pourquoi un indice cumulatif des pressions anthropiques se basant une analyse des données spatiales de l'occupation du sol et des usages a été développé puis calculé de manière identique pour l'ensemble des stations (Tableau 1 ; [START_REF] Cariou | Développement d'indices de pressions anthropiques pour la caractérisation environnementale des eaux côtières ultramarines dans le cadre de la validation d'indicateurs écologiques « Herbiers[END_REF]Kerninon et al. in prep). Une mise en corrélation des différents modèles d'indicateurs à cet indice de pressions a ensuite été réalisée (voir ci-après). Tableau 1. Caractérisation des stations échantillonnées. Les stations sont présentées selon le niveau de pressions anthropiques présumé tel que défini en amont de l'échantillonnage (bleu : faible, jaune : modéré, rouge : important). Le score de l'indice de pression a été réalisé à postériori sur la base d'une analyse spatiale dans un rayon de 3 Km autour des stations.

Etapes et méthodes de calcul de l'indicateur

Rappel des objectifs attendus

Un bioindicateur devant refléter l'état de santé d'un milieu en se basant sur les caractéristiques des communautés et rendre compte des causes responsables de son altération, l'objectif de l'indicateur « herbiers » développé pour la surveillance DCE aux Antilles est de résumer en une valeur normée des informations sur l'intégrité structurelle et le degré d'anthropisation de l'écosystème.

Sélection des paramètres

La réalisation d'analyses bibliographiques a permis de sélectionner les paramètres les plus pertinents à tester in situ pour étudier leurs réponses le long de gradients de pressions anthropiques présumés. Ces informations issues de la littérature, couplées aux données obtenues sur les sites d'expérimentation aux Antilles ont également permis d'écarter certains paramètres ne répondant pas aux objectifs et aux contraintes de robustesse attendues de l'outil pour une application dans la région (p. ex. données morphologique, faune benthique, données du sédiment). Les paramètres montrant des différences significatives entre les stations ont ensuite été intégrés dans une première série d'analyses en composantes principales (ACP) afin d'explorer leur comportement de manière continue.

Sur la base de cette analyse, et pour limiter les redondances, un second ensemble de paramètres a été sélectionné, incluant les paramètres les plus corrélés aux facteurs anthropiques (résultant de l'interprétation de l'ACP et des connaissances sur les pressions). Certains paramètres structuraux associés à l'intégrité du système (densité et recouvrement), à réponse plus longue et généraliste, ont également été inclus à cette sélection pour la mise en oeuvre d'une seconde ACP. Cette dernière permet de proposer une classification des stations à travers un score normé sur la base des critères retenus (ci-dessous).

Calcul du ratio de qualité écologique (EQR)

L'état écologique est ensuite calculé sur la base d'une ACP intégrant les paramètres retenus lors de l'étape de sélection des paramètres, incluant les stations d'études, complété par deux stations virtuelles (dénommées ici « optimal » et « worst »), représentatives des situations extrêmes. Elles sont obtenues par le calcul de la moyenne des cinq valeurs extrêmes pour chaque paramètre. Le score obtenu sur le premier axe de l'ACP est considéré comme une estimation de son statut écologique. Ainsi, l'Ecological Quality Ratio (EQR) a été calculé en deux étapes :

EQR'X = (CIx -CIworst) / CIoptimal -CIworst) EQR = (EQR' + 0,11) / (1 + 0,11)

EQR'x représente le ratio de qualité écologique du site, CIx le score du site x sur la première composante de l'ACP, CIoptimal le score du site "optimal" (site de référence) pour la première composante et CIworst le score du site "pire cas". La deuxième étape permet de rapporter l'EQR sur une échelle de 0 à 1 et selon cinq classes, tel qu'attendu dans le cadre de la DCE (Tableau 2). Deux modes de calcul de classe, de types basiques, sont ici testés.

Tableau 2. Limites quantitatives de l'Ecological Quality Ratio (EQR) selon deux types de classes fréquemment utilisées et codes couleurs associés.

EQR (classe1) EQR (classe 2)

Etat écologique

Code couleur 0,775 -1 0,80 -1 très bon bleu 0,550 -0,774 0,6 -0,79 bon vert 0,325 -0,549 0,4 -0,59 moyen jaune 0,1 -0,324 0,2 -0,39 médiocre orange 0 -0,1 0 -0,19 mauvais rouge

Synthèse des étapes de la construction des indicateurs

Les étapes de construction des indicateurs sont résumées sur la Figure 2.

Présentation des modèles d'indicateurs candidats pour la surveillance DCE des herbiers aux Antilles

Présentation générale

Les trois modèles présentés ci-dessous sont adaptés aux herbiers peu profonds (inférieurs à 5-7 mètres) des Antilles françaises de types monospécifiques et mixtes à Thalassia testudinum, correspondant aux types d'herbiers natifs dominants sur lesquelles les stations de surveillance DCE sont actuellement positionnées. Ces modèles, plus ou moins complexes selon le nombre de paramètres intégrés, se basent tous sur une combinaison de paramètres de diagnostic et d'alerte précoce ciblant les pressions anthropiques, et de paramètres structuraux, reflets de l'intégrité structurelle du milieu. Ces données représentent des informations majeures pour la gestion de cet écosystème et de son environnement. Ce choix résulte de l'étape de sélection des paramètres les plus pertinents pour l'utilisation des herbiers des Antilles françaises comme bioindicateur de la qualité de l'eau [START_REF] Kerninon | Développement d'outils méthodologiques pour le suivi et l'évaluation de l'état de santé des herbiers d'outre-mer français et de leur environnement, dans un contexte de perturbations multiples[END_REF][START_REF] Kerninon | Selection of parameters for seagrass management: Towards the development of integrated indicators for French Antilles[END_REF]voir plus haut). Pour l'analyse des paramètres de diagnostic et indicateurs d'alerte, le compartiment des feuilles a été retenu.

Ces indicateurs ne se distinguent pas sur le temps de collecte sur le terrain, mais d'expertise d'analyse en laboratoire et de traitement des données.

L'objectif de présentation de ces différents modèles, de complexité variée selon le nombre de paramètres intégrés, est de définir collégialement un compromis acceptable entre la robustesse attendue concernant les objectifs de l'indicateur DCE, notamment en termes de réponse aux pressions anthropiques, tout en tenant compte des contraintes opérationnelles de mise en oeuvre. Bien que la réponse de l'azote dans les phanérogames aux pressions anthropiques soit positive, la faible amplitude des teneurs en azote rend potentiellement l'indicateur insuffisamment discriminant. Cela pourrait contribuer à une surestimation de l'état de dégradation de certains herbiers se traduisant par la grande proportion de stations en situation « modérées » et l'absence de station en très bon état. Au niveau analytique, la détermination des concentrations en azote s'effectue directement sur des poudres brutes de feuilles de phanérogames nettoyées, séchées, broyées et encapsulées pour être passées au sein d'un analyseur élémentaire (EA). La biomasse nécessaire est faible (1 à 2 mg par échantillon). Cet indicateur multimétriques peut être couplé à l'utilisation de l'isotope de l'azote (ẟ 15 N) dans les feuilles de T. testudinum pouvant apporter des indications sur l'origine des sources azotées (p.ex. au-delà d'un certain seuil estimé autour de 4‰). Cette analyse, plus onéreuse, peut se faire en même temps que l'analyse de l'azote si l'analyseur élémentaire est couplé un spectromètre de masse (EA-IRMS). Comme pour le modèle 1, cet indicateur multimétriques peut être couplé à l'utilisation de l'isotope de l'azote (ẟ 15 N) dans les feuilles de T. testudinum pouvant apporter des indications sur l'origine des sources azotées (p.ex. au-delà d'un certain seuil estimé autour de 4‰). Cette analyse, plus onéreuse, peut se faire en même temps que l'analyse de l'azote si l'analyseur élémentaire est couplé un spectromètre de masse (EA-IRMS).

Modèle indicateur DCE herbiers

Conclusion et perspectives

La proposition de différents modèles d'indicateurs multimétriques candidats pour la surveillance DCE des herbiers aux Antilles a pour objectif d'aider au choix du modèle final en se basant à la fois sur un compromis acceptable entre la robustesse attendue de l'outil (objectif de l'évaluation, réponse aux pressions anthropiques), tout en tenant compte des contraintes opérationnelles de mise en oeuvre.

Les modèles proposés présentent un temps de terrain équivalent mais se distinguent sur le nombre de métriques d'alerte précoce et de diagnostic intégrés. Le modèle n°2 présente un compromis intéressant, de type intermédiaire, avec un nombre de métriques raisonnable. Néanmoins, puisque l'ensemble des éléments traces sont obtenus dans une même analyse (par ICP-MS), une alternative pourrait être d'intégrer à l'indicateur uniquement les trois métaux retenus pour ce modèle mais de maintenir l'acquisition des autres métaux. L'analyse de l'isotope de l'azote (ẟ 15 N) dans les feuilles de T. testudinum, pouvant se faire en même temps que l'analyse de l'azote (par EA-IRMS), elle permettrait d'apporter des indications sur l'origine des sources azotées au-delà d'un certain seuil. Ces informations seraient intéressantes en guise de veille ou dans le cadre de la mise en oeuvre de suivis de contrôle opérationnel (RCO). Puisque les pressions anthropiques sont évaluées principalement sur la base de données d'occupation de sols et non de données de qualité de l'eau fines, nous ne pouvons pas nous attendre à une corrélation avec l'EQR totalement réaliste. En ce sens, les premières corrélations réalisées sont tout de même satisfaisante.

Afin de prendre en compte les conclusions des résultats de ces travaux de R&D en cours dans les actions de surveillance, des préconisations méthodologiques ont été établies lors des rencontres DCE Outre-mer 2022 afin d'ajuster les protocoles de collecte dans l'attente de la validation et de la traduction réglementaire de l'indicateur. L'importance de la compatibilité des données et du respect de procédure méthodologique de collecte in situ et analytique a été soulignée. Les analyses des paramètres d'alerte précoce et de diagnostic nécessitent en effet un cadre analytique rigoureux pour assurer la robustesse des données, ce qui sera par ailleurs particulièrement essentiel pour réaliser les ajustements nécessaires au mode de calcul de l'EQR au cours du temps. Ces analyses devront donc être réalisées par des laboratoires agréés, selon des méthodes normées. Des réflexions sur choix d'un laboratoire d'analyse ou la réalisation d'une étude d'intercalibration entre laboratoires seront ainsi à mener afin d'assurer la qualité des données de surveillance. Enfin, le choix de la saison d'échantillonnage de la surveillance DCE pourrait être rediscutée du fait que les paramètres de diagnostics ciblés sont probablement moins présents en fin de saison sèche qu'en fin de saison humide.

Une fois que le choix des métriques intégrant l'indicateur et son mode de calcul seront finalisés, la démarche scientifique sera validée par la publication de l'indicateur dans une revue de rang A.

La mise en oeuvre opérationnelle nécessitera un travail d'automatisation du calcul de l'indicateur avant son intégration dans le Système d'évaluation de l'état des eaux (SEEE). La traduction réglementaire s'accompagnera ensuite de la réalisation du guide méthodologique associé et de formations.

Considérant l'évolution de la composition spécifique des herbiers à l'échelle de la Caraïbe, le développement d'indicateurs basés sur les espèces Syringodium filiforme et Halophila stipulacea sont en cours de réflexion sur la base des données déjà acquises.

Figure 1 .

 1 Figure 1. Localisation et années d'échantillonnage des stations expérimentales en Guadeloupe, Saint-Martin, Saint-Barthélemy et en Martinique.

Figure 2 .

 2 Figure 2. Synthèse de la stratégie mise en oeuvre pour le développement de l'indicateur DCE herbiers en cinq grandes étapes.

  Antilles de niveau 1 (v.1) Indicateur herbiers DCE Antilles (niveau 1) Nombre de jeu de données utilisé : 39 (30 stations, 3 années, 4 territoires) Nombre de paramètres : 4 -2 paramètres structuraux : recouvrement total phanérogames et densité T. testudinum -2 paramètres d'alerte précoce et de diagnostic: N et C/N feuilles T. testudinum Nombre de plongeurs : 2 à 3 Temps de collecte sur le terrain : 45'-1h30 Objectif ciblé : Evaluation intégrée de l'intégrité structurelle des herbiers peu profonds monospécifiques et mixtes à Thalassia testudinum et du niveau d'enrichissement nutritif du milieu en azote Cet indicateur multimétriques agrégeant 4 paramètres répond à un objectif d'évaluation de l'état structurel et du niveau d'enrichissement en azote dans un contexte de perturbations multiples. En intégrant uniquement la teneur en azote et le ratio carbone sur azote dans les feuilles au niveau des métriques d'alerte, ce modèle reste peu spécifique au regard des objectifs de la DCE. L'accumulation en azote dans les tissus pouvant résulter d'une origine naturelle comme anthropique, ces paramètres de diagnostic et d'alerte précoce sont donc insuffisamment précis pour identifier les causes de changement du milieu, tout comme les paramètres structuraux qui répondent sur un temps long et de manière non spécifique. Cet indicateur ne prend pas en compte la pollution métallique des eaux, caractéristique clé de l'état écologique des eaux côtières.

  de données utilisé : 39 (30 stations, 3 années, 4 territoires) Nombre de paramètres : 7 -2 paramètres structuraux : recouvrement total phanérogames et densité T. testudinum -5 paramètres d'alerte précoce et de diagnostic : N, C/N, Pb, Zn, Cd feuilles T. testudinum Nombre de plongeurs : 2 à 3 Temps de collecte sur le terrain : 45'-1h30 Objectif : Evaluation intégrée de l'intégrité structurelle des herbiers peu profonds monospécifiques et mixtes à Thalassia testudinum et du niveau d'anthropisation (enrichissement en azote et pollution en éléments traces) Cet indicateur multimétriques agrégeant 7 paramètres répond à un objectif d'évaluation de l'état structurel des herbiers, du niveau d'enrichissement en azote et de la pollution en éléments traces dans l'environnement par analyse dans les tissus de T. testudinum. Ce modèle, initialement présenté dans Kerninon, 2020 et Kerninon et al., 2021 représente un compromis intéressant par une approche intégrée en accord avec les objectifs de la DCE, permettant d'identifier les principales causes de changement du milieu. Par rapport au modèle de niveau 1, cet indicateur prend en compte la pollution métallique des eaux. Ces éléments permettent par ailleurs de discriminer davantage les stations entre elles. Au niveau analytique, la détermination des concentrations en azote s'effectue directement sur des poudres brutes de feuilles de phanérogames nettoyées, séchées, broyées et encapsulées pour être analysés au sein d'un analyseur élémentaire (EA). La biomasse nécessaire est faible (1 à 2 mg par échantillon). En revanche les analyses des éléments traces par spectrométrie de masse à plasma à couplage inductif (ICP-MS) nécessitent un broyage des échantillons dans des contenants en agate pour éviter les contaminations. La biomasse des échantillons, selon le mode d'attaque, est plus importante (pour les analyses réalisées ici : 100 à 200 mg).

  de données utilisé : 39 (30 stations, 3 années, 4 territoires) Nombre de paramètres : 12 -2 paramètres structuraux : recouvrement total phanérogames et densité T. testudinum -10 paramètres d'alerte précoce et de diagnostic : N, C/N, Pb, Zn, Cd, Ni, Cu, Fe, Cr, Mn feuilles T. testudinum Nombre de plongeurs : 2 à 3 Temps de collecte sur le terrain : 45'-1h30 Objectif : Evaluation intégrée de l'intégrité structurelle des herbiers peu profonds monospécifiques et mixtes à Thalassia testudinum et du niveau d'anthropisation (enrichissement en azote et pollution en éléments traces) Cet indicateur multimétriques agrégeant 12 paramètres répond à un objectif d'évaluation de l'état structurel des herbiers, du niveau d'enrichissement en azote et de la pollution en éléments traces dans l'environnement par leur analyse dans les tissus de T. testudinum. Ce modèle est le plus complet et reflète davantage l'état et les causes de changement du milieu en accord avec les objectifs de la DCE. Par rapport au modèle de niveau 1, cet indicateur prend en compte la pollution métallique des eaux et intègre plus de métaux que celui de niveau 2. Les différentes influences anthropiques du milieu sont davantage reflétées. Cependant, ce modèle est peu discriminant (majorité de stations en très bon et bon état) en raison probablement de concentrations élevées de certains métaux sur quelques séries de données.Au niveau analytique, la détermination des concentrations en azote s'effectue directement sur des poudres brutes de feuilles de phanérogames nettoyées, séchées, broyées et encapsulées pour être analysées au sein d'un analyseur élémentaire (EA). La biomasse nécessaire est faible (1 à 2 mg par échantillon). En revanche les analyses des éléments traces par spectrométrie de masse à plasma à couplage inductif (ICP-MS) nécessitent un broyage des échantillons dans des contenants en agate pour éviter les contaminations. La biomasse des échantillons, selon le mode d'attaque, est plus importante (pour les analyses réalisées ici : 100 à 200 mg). Comme pour le modèle 1, cet indicateur multimétriques peut être couplé à l'utilisation de l'isotope de l'azote (ẟ 15 N) dans les feuilles de T. testudinum pouvant apporter des indications sur l'origine des sources azotées (p.ex. au-delà d'un certain seuil situé autour de 4‰). Cette analyse, plus onéreuse, peut se faire en même temps que l'analyse de l'azote si l'analyseur élémentaire est couplé un spectromètre de masse (EA-IRMS).

Territoires Nombre de stations Nom et code des stations Profondeur (m) Année échantillonnage Surveillance DCE Score indice pression

			Passe à colas (PCO)	3	2017-2018	X	1
			Petite Terre (PTT)	2	2018		1
			Bouée verte (BVE)	3	2017-2018		0
	Guadeloupe		Morne rouge (MOR) Ilet Gosier (IGO)	2 1	2017 2017		5 6
			Ilet Fortune (FOR)	2	2018	X	5
			Ilet Christophe (ICH)	2	2017-2018		4
			Petit Bourg (PTB)	1	2018		10
			Ilet du François (ILF)	1,5	2021		4
			Baie du Trésor (BDT)	1,3	2014	X	1,25
			Pointe Faula (FAU)	2,5	2021		6
			Caye à vache (CVA)	1	2021	X	8
			Grande anse d'Arlet (GDA)	5,3	2021	X	7,5
			Corps de Garde (CGA)	2,4	2021	X	8
			Petite anse d'Arlet (PTA)	5,2	2021		6,25
	Martinique	14	Pointe de Borgnesse (PBO)	2,5	2021	X	8
			Baie des Mulets (BMU)	1,5	2021	X	8
			Anse du Bourg (ABG)	4,5	2021		8,75
			Pointe des Sables (PTS)	0,8	2021		13,75
			Fort Saint-Louis (FSL)	2,5	2021		15
			Baie du Marin (MRN)	0,7	2021		17,5
			Robert Lynch (ROL)	1	2021		19,25
			Tintamarre (TIT)	2	2017-2018		1
	Saint-Martin		Rocher Créole (ROC) Grande Case (GCA)	6 4	2017-2018 2018	X	7,5 6,25
			Galion (GAL)	2	2017-2018		12
			Petit Cul-de-sac (PCS)	2	2017-2018		5
	Saint-		Grand Cul-de-sac (GCS)	1	2017		5
	Barthélemy		Colombier (COL)	9	2017-2018		2,5
			Marigot (MAR)	0,5	2017-2018		8

Note réalisée dans le cadre du contrat de coopération OFB-UBO (2020-2024) « validation et transfert des outils méthodologiques