Johanna Cormenier

Nathalie Martin

Julie Deslé

Clara Salazar-Cardozo

Albin Pourtier

Corinne Abbadie

Olivier Pluquet

The ATF6α arm of the Unfolded Protein Response mediates replicative senescence in human fibroblasts through a COX2/prostaglandin E 2 intracrine pathway

Keywords:

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

The ATF6α arm of the Unfolded Protein Response mediates replicative senescence in human fibroblasts through a COX2/prostaglandin E 2 intracrine pathway

Introduction

Normal cells have a limited proliferation potential; after a finite number of population doublings, they stop dividing while remaining viable and metabolically active, a process called senescence [START_REF] Campisi | Cellular senescence as a tumor-suppressor mechanism[END_REF]. Senescent cells are accumulated in the body with aging and directly influence the lifespan and the occurrence of several dysfunctions and pathologies associated with aging [START_REF] Baker | Clearance of p16Ink4a-positive senescent cells delays ageing-associated disorders[END_REF][START_REF] Baker | Naturally occurring p16(Ink4a)-positive cells shorten healthy lifespan[END_REF]. Senescence may result from various mechanisms, including decrease in telomere length (d'Adda di Fagagna et al., 2003), DNA damage [START_REF] Sedelnikova | Senescing human cells and ageing mice accumulate DNA lesions with unrepairable double-strand breaks[END_REF], oxidative stress [START_REF] Dumont | Induction of replicative senescence biomarkers by sublethal oxidative stresses in normal human fibroblast[END_REF] or activation of oncogenes [START_REF] Campisi | Senescent cells, tumor suppression, and organismal aging: good citizens, bad neighbors[END_REF][START_REF] Bernard | Involvement of Rel/nuclear factor-kappaB transcription factors in keratinocyte senescence[END_REF]. Interestingly, all types of senescence inducers lead to common cellular and molecular changes, resulting in a senescent phenotype characterized by deep morphological changes, such as enlargement [START_REF] Zwerschke | Metabolic analysis of senescent human fibroblasts reveals a role for AMP in cellular senescence[END_REF][START_REF] Cristofalo | Replicative senescence: a critical review[END_REF], epigenetic and transcriptomic changes [START_REF] Narita | Rb-mediated heterochromatin formation and silencing of E2F target genes during cellular senescence[END_REF][START_REF] Wennmalm | The expression signature of in vitro senescence resembles mouse but not human aging[END_REF], increase in autophagic activity [START_REF] Gosselin | Senescent keratinocytes die by autophagic programmed cell death[END_REF][START_REF] Young | Autophagy mediates the mitotic senescence transition[END_REF], expression of Senescence-Associated β-galactosidase (SA-β-Gal) activity [START_REF] Dimri | A biomarker that identifies senescent human cells in culture and in aging skin in vivo[END_REF], and a pro-inflammatory secretome [START_REF] Coppé | Senescence-associated secretory phenotypes reveal cell-nonautonomous functions of oncogenic RAS and the p53 tumor suppressor[END_REF][START_REF] Malaquin | Senescent fibroblasts enhance early skin carcinogenic events via a paracrine MMP-PAR-1 axis[END_REF].

Several complex signaling pathways related to stress are involved in the induction and/or maintenance of senescence. The most investigated one is the DNA Damage Response pathway that leads to the activation of the p53/p21

WAF1 axis and the irreversible cell cycle arrest in G1 phase of senescent cells [START_REF] Campisi | Senescent cells, tumor suppression, and organismal aging: good citizens, bad neighbors[END_REF]. The cell-cycle inhibitor p16

INK4 is also a key mediator of senescence by eliciting growth arrest

Abbreviations: ER stress, endoplasmic reticulum stress; ATF6α, activating transcription factor 6 alpha; UPR, Unfolded Protein Response; COX2, cyclooxygenase 2; PGE 2 , prostaglandin E 2 ; EP3, prostaglandin receptor 3; PTGS2, prostaglandin-endoperoxide synthase 2.

⁎ Corresponding author at: Institut de Biologie de Lille, UMR8161, 1 rue Calmette, CS50447, 59021 Lille Cedex, France.

Email address: olivier.pluquet@ibl.cnrs.fr (O. Pluquet) upon inhibition of pRB phosphorylation, in part in response to the oxidative stress that increases at senescence [START_REF] Chen | Loss of proliferative capacity and induction of senescence in oxidatively stressed human fibroblasts[END_REF][START_REF] Nassour | Defective DNA single-strand break repair is responsible for senescence and neoplastic escape of epithelial cells[END_REF]. The mTOR regulator of cell metabolism and cell growth, and the p38MAPK cascade were also shown to participate in senescence [START_REF] Dulic | Senescence regulation by mTOR[END_REF][START_REF] Debacq-Chainiaux | p38(MAPK) in the senescence of human and murine fibroblasts[END_REF][START_REF] Maruyama | Stress-activated MAP kinase cascades in cellular senescence[END_REF]. We and others pointed out the Cyclooxygenase 2 (COX2/PT-GS2)-prostaglandin E 2 (PGE 2 ) pathway as taking part both in the induction and in the maintenance of the senescent phenotype. COX2 is the limiting and inducible enzyme in the PGE 2 biosynthesis pathway from arachidonic acid. COX2 expression is increased during replicative and oxidative stress-induced senescence in fibroblasts. In consequence, the senescent secretome becomes enriched in PGE 2 [START_REF] Zdanov | Normal or stress-induced fibroblast senescence involves COX-2 activity[END_REF][START_REF] Han | Selective COX-2 inhibitor, NS-398, inhibits the replicative senescence of cultured dermal fibroblasts[END_REF][START_REF] Dagouassat | The cyclooxygenase-2-prostaglandin E2 pathway maintains senescence of chronic obstructive pulmonary disease fibroblasts[END_REF]. PGE 2 being a pro-inflammatory mediator, it could be responsible, together with the pro-inflammatory cytokines that are also up-secreted by senescent cells, of the specific properties of the senescent secretome, i.e. stimulating the growth of pretumoral cells, stimulating the removing of senescent cells through immune-mediated clearance or inducing and reinforcing the senescent phenotype in a paracrine or autocrine manner. Arguing against this last putative function, it was shown that even high concentrations of extracellular PGE 2 (10-50 μM) have only modest effects on the onset of senescence [START_REF] Yang | Involvement of IGF binding protein 5 in prostaglandin E(2)-induced cellular senescence in human fibroblasts[END_REF][START_REF] Martien | Cellular senescence involves an intracrine prostaglandin E2 pathway in human fibroblasts[END_REF]. However, PGE 2 can act by binding to plasma membrane EP receptors, but it can also act in an intracrine manner by binding to intracellular EP receptors. An intracellular role of PGE 2 was especially demonstrated at the senescent stage. Indeed, only 1-10 nM PGE 2 were shown to be enough to induce premature senescence when applied in the presence of a high glucose concentration which favors the uptake of PGE 2 through the enhancement of the functioning of the lactate/prostaglandin exchanger. This induction of senescence involves the binding of PGE 2 mainly to the intracellular EP3 receptor [START_REF] Martien | Cellular senescence involves an intracrine prostaglandin E2 pathway in human fibroblasts[END_REF]. This indicated that COX2 participates in the induction and maintenance of the senescent phenotype through a PGE 2 /EP3-dependent intracrine pathway. The endoplasmic reticulum (ER) is a multifunctional intracellular organelle required for protein folding, post-translational modifications, control of the secretory pathway, and calcium storage [START_REF] Görlach | The endoplasmic reticulum: folding, calcium homeostasis, signaling, and redox control[END_REF]. Disruption of ER functions following oxidative stress or inflammation can lead to accumulation of misfolded proteins or aggregates that are detrimental to cell survival [START_REF] Schröder | The mammalian unfolded protein response[END_REF]. This in turn triggers an adaptive response called the Unfolded Protein Response (UPR). The UPR is mediated by three ER stress sensors (PERK, ATF6α and IRE1α) and their downstream signalling pathways. The PERK kinase phosphorylates its substrate eIF2α to reduce the overall synthesis of proteins, but also induces the transcription of genes encoding specific chaperones containing micrORF or IRES. Once activated, IRE1α induces the unconventional splicing of XBP1 mRNA, allowing the translation of an active transcription factor, whose main targets are the genes involved in quality control of proteins in the ER. Other mRNAs are cleaved by IRE1α, however, the physiological consequences associated with this mechanism are not yet fully known. The protein ATF6α, upon activation, exits the ER and migrates to integrate the Golgi apparatus membrane, where it is cleaved to give its active form and acts as a transcription factor targeting genes encoding chaperones [START_REF] Schröder | The mammalian unfolded protein response[END_REF]. As such, the UPR limits the accumulation of misfolded proteins and restore ER homeostasis [START_REF] Ron | Signal integration in the endoplasmic reticulum unfolded protein response[END_REF][START_REF] Hetz | The unfolded protein response: controlling cell fate decisions under ER stress and beyond[END_REF] or, if the cells are unable to cope with stress, induces cell death by apoptosis [START_REF] Schröder | The mammalian unfolded protein response[END_REF] or autophagy [START_REF] Verfaillie | Linking ER stress to autophagy: potential implications for cancer therapy[END_REF].

ER stress and UPR activation have been found to be components of the replicative senescent phenotype [START_REF] Pluquet | The unfolded protein response and cellular senescence: a review in the theme: cellular mechanisms of endoplasmic reticulum stress signaling in health and disease[END_REF][START_REF] Matos | ER Stress Response in Human Cellular Models of Senescence[END_REF][START_REF] Druelle | ATF6α regulates morphological changes associated with senescence in human fibroblasts[END_REF]. An ER expansion and UPR activation following HRas-driven senescence in melanocytes or in a model of adriamycin-induced senescence in lymphoma cells were reported [START_REF] Denoyelle | Anti-oncogenic role of the endoplasmic reticulum differentially activated by mutations in the MAPK pathway[END_REF][START_REF] Dörr | Synthetic lethal metabolic targeting of cellular senescence in cancer therapy[END_REF]. Recently, we demonstrated the key role of ATF6α in the control of the morphological features characteristic of senescence [START_REF] Druelle | ATF6α regulates morphological changes associated with senescence in human fibroblasts[END_REF]. As COX2 can localize on the luminal surface of the endoplasmic reticulum (ER) [START_REF] Spencer | Subcellular localization of prostaglandin endoperoxide H synthases-1 and -2 by immunoelectron microscopy[END_REF], COX2 is thought to be sensitive to ER changes and be part of ER stress [START_REF] Rasheed | Endoplasmic reticulum stress induces the expression of COX-2 through activation of eIF2α, p38-MAPK and NF-κB in advanced glycation end products stimulated human chondrocytes[END_REF]. Therefore, we wondered whether there is a link between COX2 and the UPR in the context of senescence.

In the present study, we provide evidence that the ATF6α arm of the UPR mainly induces some features of senescence through the activation of the COX2/PGE 2 /EPs pathway in NHDFs.

Material and methods

Compounds and cell culture

Normal human dermal fibroblasts (NHDFs) were purchased from Clonetics or Promocell. According to the experiment, three different skin donors were used (referred as 1F1853, a 38 years-old Caucasian female, 2F1966, a 37 years-old Caucasian female, and F1MC, a 1 year-old Caucasian male). Skin donors are anonymous and informed consent of each of them was obtained by the supplier. NHDFs were grown at 37 °C in an atmosphere of 5% CO 2 in the ad hoc medium supplied by Clonetics (FGM-2 bulletKit system). In all experiments, cells were seeded as recommended by the supplier and always split at 70% confluence. The number of population doublings (PDs) was calculated at each passage by using the following equation: PD = ln(number of collected cells/number of plated cells)/ln2. Depending on the donor, the senescent stage was reached after a slightly different number of population doublings. NHDFs designated "exponentially growing" actively divide, do not display the enlarged and spread morphology nor express the SA-β-Gal marker. Those designated "senescent" were taken at the senescence plateau, a phase during which the growth of the culture is stopped, with 70-100% of cells displaying the enlarged and spread phenotype and the SA-β-Gal activity. The SA-β-Gal activity was revealed as described by [START_REF] Dimri | A biomarker that identifies senescent human cells in culture and in aging skin in vivo[END_REF]. Thapsigargin (TG), and Dithiothreitol (DTT), were purchased from Sigma and diluted in DMSO. NS398 (COX-2 inhibitor) and L-798,106 (EP3 antagonist) were from Tocris and diluted in DMSO.

Western blotting

Cells were lysed in the following solution: Hepes 27.5 mM pH 7.6, urea 1.1 M, NaCl 0.33 M, EGTA 0.1 M, EDTA 2 mM, KCl 60 mM, DTT 1 mM and NP40 1.1%. The total protein concentration was measured with the Bio-Rad protein assay. Proteins were resolved by SDS-PAGE and transferred to nitrocellulose membranes (Hybond-C extra, Amersham). Equal loading was verified after a Ponceau Red coloration of the membranes. Primary antibodies used were an anti-human COX2 (Cayman), anti-human PERK (sc-13073, Santa Cruz Biotechnology), anti-human ATF6α (Bioacademia, Japan), anti-human IRE1α (sc-20790, Santa Cruz Biotechnology), and anti human GAPDH antibody (Chemicon International). Secondary antibodies used were peroxidase-conjugated (Jackson ImmunoResearch Laboratories). Peroxidase activity was revealed using an ECL (enhanced chemiluminescence) or ECL advanced kit (Amersham Biosciences). The band densities were analyzed using Image J software. The density of each band was divided by the density of the GAPDH band, and the obtained value was normalized with respect to that obtained under control conditions.

RNA isolation, reverse transcription and quantitative Real-time PCRs (qRT-PCR)

Total RNA was prepared using the Trizol reagent (Invitrogen). 1 μg of RNA was reverse-transcribed using random hexamers, Superscript III and dNTPs (Invitrogen) in a final volume of 20 μl according to manufacturer's instructions. Quantitative Real-time PCR reactions were performed using the Mx3005P Real-time PCR system (Stratagene). Primers used were designed with the qPrimerDepot software (http:// primerdepot.nci.nih.gov/) and are listed in Table S1. PCR products were measured by SYBR Green fluorescence (SYBR Green Master Mix, Applied Biosystems). Experiments were performed in triplicates for each data point. Results were analyzed with the MxPro software (Agilent). The expression of each gene was normalized to RPLP0 or EAR gene, and fold expression relative to the control is shown.

Small interference RNA

Invalidation experiments for PERK, ATF6α, IRE1α and COX2 were performed using pools of four siRNAs from Dharmacon (ON TARGET Plus Smart pool, Dharmacon, USA). A non-targeting siRNA pool (Dharmacon) was used as control. Senescent NHDFs were seeded at a density of 5 × 10 4 cells per well in 6-well plates and were transfected by using the RNAiMAX Lipofectamine reagent (Invitrogen Corp., Carlsbad, CA, USA) according to manufacturer's instructions.

PGE 2 immunoassay

Exponentially growing and senescent NHDFs were seeded at 20,000 cells per well in 6-well plates. PGE 2 levels in the supernatant medium were assayed at day 4 after seeding or transfection by using a prostaglandin E 2 Kit (GE Healthcare) according to manufacturer's instructions.

Immunofluorescence

NHDFs were seeded onto coverglasses and fixed with 4% paraformaldehyde in PBS and permeabilized with 0.2% Triton-X-100. Coverglasses were incubated with the primary antibody diluted in presence of 5% BSA. They were then washed three times with PBS and incubated with the secondary antibody diluted in PBS with 0.5% casein. After three washes, nuclei were stained with Hoechst 33258 at 1 μg/mL for 3 min and the coverglasses mounted in Glycergel ® . Primary antibodies used were rabbit anti-calnexin from Abcam (dilution 1/100), mouse anti-53BP1 from Santa Cruz (dilution 1/100) and rabbit anti-vimentin from Santa Cruz (dilution 1/100). Secondary antibodies were IgG conjugated with Alexa Fluor 488 or Alexa Fluor 555 purchased from Life Technologies. Images were taken with either an AxioImager Z1-apotome or a confocal Zeiss LSM780 microscope (Zeiss, Germany). The ZenLite ® (Zeiss, Germany) and ImageJ softwares were used for microscope image analysis.

Statistical analyses

Student's one-or two-tailed t-tests, as appropriate, were used for statistical analyses. The p values are indicated in the diagrams with * for p values <0.05, ** for p values <0.01 or *** for p values <0.001.

Results

The COX2/PGE 2 /EP3 pathway mediates senescence induced by ER stressors

To establish the potential relationship between the UPR and COX2 at senescence, we first used the model of premature senescence induced by the ER stressor Dithiothreitol (DTT) that we previously established in Normal Human Dermal Fibroblasts (NHDFs) [START_REF] Druelle | ATF6α regulates morphological changes associated with senescence in human fibroblasts[END_REF]. We first verified that DTT induced as expected an ER stress when applied to the exponentially growing NHDFs from the donor used in these experiments. For that, we measured the expression of the UPR components CHOP (DDIT3), GRP78 (HSPA5), HERPUD1, ERO1Lβ by qRT-PCR. The results indicate that the mRNA levels of these genes were all statistically significantly induced by the DTT treatment (Fig. 1A). Next, we measured the COX2 mRNA levels in presence of DTT using qRT-PCR and showed that mRNA levels of COX2 were increased (Fig. 1B). In accordance, ELISA assays show that in exponentially growing NHDFs treated with DTT, PGE 2 synthesis and secretion increased (Fig. 1C). In the same experiment, we verified that exposure to NS398, a specific COX2 inhibitor, significantly inhibited PGE 2 production either in basal or DTT conditions (Fig. 1C). We also verified that NS398 does not alter the induction of ER stress by DTT. For that, we measured the GRP78 relative expression level by qRT-PCR. It was not significantly altered by NS398 (Fig. 1D). We then tested whether inhibiting the COX2 activity with NS398 could affect the establishment of the senescence markers induced by DTT. The results show that NS398 partially restored the proliferation arrest (Fig. 1E) and the increase in SA-β-Gal positive cells induced by DTT (Fig. 1F). Similar effects of NS398 on SA-β-Gal staining were observed on exponentially growing NHDFs undergoing premature senescence induced by another ER stressor, thapsigargin (TG) (Fig. 1G). The EP3 receptor was shown to be involved in mediating the effects of PGE 2 on the induction and maintenance of senescence in NHDFs [START_REF] Martien | Cellular senescence involves an intracrine prostaglandin E2 pathway in human fibroblasts[END_REF]. Treatment with L-798,106, a potent and specific antagonist of EP3 decreased by about half the number of SA-β-Gal-positive cells induced by DTT (Fig. 1H). Together, these data show that premature senescence induced by ER stressors in NHDFs is mediated in part through the COX2/PGE 2 /EP3 pathway.

The PGE 2 biosynthesis pathway is activated by ATF6α during DTTinduced premature senescence

We then wanted to determine which UPR branch relays the induction of the COX2/PGE 2 pathway during DTT-induced premature senescence. To this goal, we first performed SA-β-Gal assays in exponentially growing NHDFs knocked-down for each ER sensor (PERK, ATF6α, or IRE1α) by RNA interference and then treated them or not with DTT. The efficacy of each siRNAs was verified at the protein level four days post-transfection (Fig. 2A). Only ATF6α knock-down significantly reduced the number of SA-β-Gal positive-cells upon DTT treatment (Fig. 2B). We next determined whether ATF6α relays the DTT-induced PGE 2 production. ELISA assays showed that the increase in PGE 2 synthesis and secretion induced by DTT was totally abolished upon ATF6α knocked-down cells (Fig. 2C). These results suggest that DTT induces premature senescence in exponentially growing NHDFs through the activation of ATF6α which controls downstream an increase in COX2 activity.

The COX2/PGE 2 biosynthesis pathway is activated during senescence as a downstream effector of ATF6α and IRE1α

We wondered here whether the activation of the COX2/PGE 2 pathway could result from UPR activation during replicative senescence as it does during DTT-induced premature senescence. We first verified that the COX2/PGE 2 pathway was indeed up-regulated at replicative senescence in the NHDF donor used herein. For that, we measured PGE 2 levels in the culture medium of young and senescent NHDFs. The results show that the synthesis and secretion of PGE 2 increases about 3-fold at senescence (Fig. 3A). We also examined the expression of COX2 by qRT-PCR and Western blot. The results show that the expression of COX2 is up-regulated at senescence both at the mRNA and protein level (Fig. 3B andC). We then addressed the role of the three ER stress sensors in the regulation of the expression of COX2 at senescence. Knockdown of ATF6α and IRE1α, but not PERK, reduced COX2 mRNA and protein levels at senescence, as well as the production and secretion of PGE 2 (Fig. 3D-F). These data indicate that the COX2/PGE 2 pathway is induced at senescence in response to ER stress and UPR activation and is a direct or indirect downstream effector of ATF6α and IRE1α.

U N C O R R E C T E D P R O O F

U N C O R R E C T E D P R O O F

The overexpression of COX2 at senescence does not induce the activation of the UPR

Since part of the COX2 pool is associated to the luminal surface of the ER membrane [START_REF] Spencer | Subcellular localization of prostaglandin endoperoxide H synthases-1 and -2 by immunoelectron microscopy[END_REF], we wondered whether its overexpression at senescence could cause ER stress and activate the UPR. To answer to this question, we invalidated COX2 by siRNA in senescent NHDFs and examined the activation of the UPR. qRT-PCR experiments showed that CHOP, GRP78, HERPUD1, and ERO1Lβ mRNA levels were all up-regulated in senescent cells but did not significantly change in senescent NHDFs treated with siRNA against COX2 compared to senescent control cell (Fig. 4). Taken together with the results of the previous paragraph, these results suggest that the up-regulation of COX2 at senescence is a consequence of the senescent-associated ER stress, not a cause. COX2 is up-regulated by the ATF6α and IRE1α branches of the UPR to induce some senescence characteristics, in parallel of the up-regulation of chaperones aiming at restoring proteostasis.

The ATF6α/COX2/intracellular PGE 2 axis is involved in the control of morphological changes associated to senescence

We have previously shown that COX2 and the intracellular PGE 2 it produces are involved in the establishment and maintenance of the main senescence markers including proliferation arrest, increase in SA-β-Gal activity and expression of some secretome components [START_REF] Zdanov | Normal or stress-induced fibroblast senescence involves COX-2 activity[END_REF][START_REF] Martien | Cellular senescence involves an intracrine prostaglandin E2 pathway in human fibroblasts[END_REF]. Recently, we reported the functions of ATF6α and IRE1α at senescence. As COX2, they are involved in the increase of the SA-β-Gal activity, in the expression of some secretome components, but they are little involved in the proliferation arrest. More originally, we reported that the morphological changes associated to replicative senescence, including ER expansion, cell shape and cell size are ATF6α (and IRE1α to a lesser extent) dependent [START_REF] Druelle | ATF6α regulates morphological changes associated with senescence in human fibroblasts[END_REF]. Therefore, we focused here, in determining whether the expression of COX2 and the production of PGE 2 could be responsible for these functions of ATF6α and IRE1α. To this end, we measured the ER surface after immunostaining of Calnexin (CNX), an ER-resident protein. As previously shown [START_REF] Druelle | ATF6α regulates morphological changes associated with senescence in human fibroblasts[END_REF], the ER occupied a large surface in enlarged senescent cells and this surface was significantly reduced in cells silenced for ATF6α during four days. The silencing of COX2 reduced as efficiently as the silencing of ATF6α the ER surface (Fig. 5A andB). We similarly investigated the role of COX2 in cell shape and cell size changes associated to senescence. After invalidation of COX2 in senescent NHDFs during 4 days, we marked the vimentin network by immunofluorescence to measure the cell surface and appreciate the cell shape. COX2 silencing decreased the cell surface as efficiently as ATF6α silencing but did not restore a fusiform shape as ATF6α knock-down did (Fig. 5A andC). We noticed that silencing of COX2 led to an altered cytoskeleton with accumulation of punctuate vimentin aggregates compared to long, dense bundles of vimentin found in senescent control cells. Co-staining of vimentin and 53BP1 indicates that cells invalidated for ATF6α and COX2 still displayed 53BP1 foci. This indicates that these cells initially derived from a population of senescent NHDFs, characterized by DNA damage (Fig. 5A). These results indicate that COX2 mediates the ER expansion and the increase in cell size induced by ATF6α at senescence.

We then reasoned, in further support of this conclusion, that supplementing senescent NHDFs with PGE 2 should prevent the effects of the ATF6α silencing. Given that PGE 2 may only enter cells only via the PGT lactate/prostaglandin exchanger, and given that the activity of this exchanger is increased in the presence of a high glucose concentration (that favors glycolysis and thereby lactate production), we silenced ATF6α in senescent NHDFs and added low physiological concentration of PGE 2 (20 nM) in the culture medium enriched or not in glucose for 4 days. As expected, the invalidation of ATF6α in the absence of PGE 2 whatever the glucose concentration led to a significant decrease in cell size (Fig. 6A andB). In contrast, adding PGE 2 only under high glucose concentration prevented in part this cell size decrease (Fig. 6A andB). These results indicate that COX2 acts downstream of ATF6α to control some morphological ATF6α features associated to senescence through the production and direct intracrine action of PGE 2.

Discussion

COX2 expression and ensuing PGE

2 production are highly up-regulated during replicative and oxidative stress-induced senescence of human fibroblasts and are involved both in the occurrence and maintenance of the senescent fibroblast phenotype through an intracrine signalization involving mainly intracellular EP3 receptors [START_REF] Zdanov | Normal or stress-induced fibroblast senescence involves COX-2 activity[END_REF][START_REF] Martien | Cellular senescence involves an intracrine prostaglandin E2 pathway in human fibroblasts[END_REF][START_REF] Dagouassat | The cyclooxygenase-2-prostaglandin E2 pathway maintains senescence of chronic obstructive pulmonary disease fibroblasts[END_REF]. Here, we show that this intracrine COX2/PGE 2 pathway is activated in response to the senescence-associated ER stress by the ATF6α arm of the UPR and to a lesser extent by IRE1α (Fig. 7).

We initially suspected that the overexpression of COX2 at the ER membrane at senescence could be the inducer of the ER stress associated with senescence. Our results show that it is not the case: the invalidation of COX2 by siRNA did not revert the activation of the UPR pathway. A contrario, invalidating ATF6α and IRE1α decreased the expression of COX2, at the messenger level. Therefore, the activation of the COX2/ PGE 2 pathway is the consequencenot the causeof the ER stress and ensuing activation of the UPR occurring at senescence, and it seems that there is no amplifying positive loop between COX2/PGE 2 and the UPR branches. We can also conclude that COX2/PGE 2 are not intermediates in the pathway leading to the expression of the canonical effectors of the UPR, COX2/PGE 2 can be considered as UPR effectors per se, activated in parallel to the canonical ones.

ATF6α is a transmembrane protein of the ER. Upon activation in a context of ER stress, it leaves the ER membrane and integrates that of the Golgi apparatus. There, it is cleaved by the S1P and S2P proteases to generate a soluble fragment of 50 kDa which then translocates to the nucleus to act as a transcription factor. As such, it could directly control COX2 transcription by binding to specific sequences in its promoter. However, by using a ChiPSeq database from UCSC genome browser and SABiosciences for predicting transcription factor binding sites in DNA sequences, we did not identify any putative ATF6α bind ing site in the COX2 promoter sequences. Therefore, the control of COX2 expression by ATF6α could be indirect. IRE1α is also a transmembrane protein of the ER. It gains endoribonuclease activity once activated. Amongst its targets is the XBP1 RNA it splices to generate XP-B1s. The XBP1s mRNA is then translated in an active transcription factor that might as well directly control the expression of COX2. By using ChiPSeq database from UCSC genome browser and SABiosciences, we identified one XBP1s binding site in the COX2 promoter. However, the knock-down of XBP1 by siRNA in senescent cells did not affect the COX2 protein levels (data not shown) as did the knock-down of IRE1α. Therefore, the control of COX2 expression by IRE1α would not be mediated by XBP1. A few studies have investigated the control of COX2 expression in a context of ER stress. It was shown that the ER stress inducer HBx protein (encoded by the Hepatitis Virus B) promoted the binding of ATF4 to COX-2 promoter [START_REF] Cho | Endoplasmic reticulum stress induced by hepatitis B virus X protein enhances cyclo-oxygenase 2 expression via activating transcription factor 4[END_REF]. ATF4 is a transcription factor activated by ER stress downstream of the PERK kinase. Our experiments in senescent fibroblasts did not reveal any alteration of the mRNA or protein COX2 levels upon PERK invalidation, indicating that this pathway is not involved in the context of the ER stress occurring at senescence. Two studies have shown that ER stress stimulates the expression of COX2 through the activation of p38MAPK and NF-κB transcription factors [START_REF] Rasheed | Endoplasmic reticulum stress induces the expression of COX-2 through activation of eIF2α, p38-MAPK and NF-κB in advanced glycation end products stimulated human chondrocytes[END_REF][START_REF] Hung | Endoplasmic reticulum stress stimulates the expression of cyclooxygenase-2 through activation of NF-kappaB and pp38 mitogen-activated protein kinase[END_REF]. Independently, it was shown that NF-κB can be activated by IRE1α and ATF6α [START_REF] Hu | Autocrine tumor necrosis factor alpha links endoplasmic reticulum stress to the membrane death receptor pathway through IRE1alpha-mediated NF-kappaB activation and down-regulation of TRAF2 expression[END_REF][START_REF] Yamazaki | Activation of the Akt-NF-kappaB pathway by subtilase cytotoxin through the ATF6 branch of the unfolded protein response[END_REF]. Since NF-κB and p38MAPK are both activated at senescence [START_REF] Gosselin | Involvement of Rel/NF-kappa B transcription factors in senescence[END_REF][START_REF] Debacq-Chainiaux | p38(MAPK) in the senescence of human and murine fibroblasts[END_REF], this opens the possibility that COX2 expression could be positively controled during senescence by the ATF6α and IRE1α branches of the UPR through a pathway involving NF-κB and/or p38MAPK.

U N C O R R E C T E D P R O O F
Our previous studies [START_REF] Zdanov | Normal or stress-induced fibroblast senescence involves COX-2 activity[END_REF][START_REF] Martien | Cellular senescence involves an intracrine prostaglandin E2 pathway in human fibroblasts[END_REF] together with the present one demonstrate that the UPR/COX2 pathway contributes to the establishment and maintenance of the main senescence markers. This includes proliferation arrest, increase in SA-β-Gal activity, expression of some secretome components and, more originally, increase in cell size, a major marker of senescence whose molecular bases have not yet been established. Importantly, these pro-senescence functions are not mediated by the secreted PGE 2 , but directly by the intracellular PGE 2 which binds to intracellular EP receptors, preferentially EP3 (Fig. 7). EP3 is a G-coupled receptor. When localized at the plasma membrane, it is coupled to Gi and signals by inhibiting the adenylate cyclase. The intracellular pool of EP3 is localized at the nuclear membrane, linked to G 0 , and signals through a BKCa 2+ channel which modulates nuclear Ca 2+ , the kinases AKT and ERK1/2, and some transcription factors such as NF-κB. Hence, plasma membrane EP3 receptors are assumed to elicit rather immediate responses, whereas intracellular EP3 receptors control gene transcription, potentially chromatin remodeling, with more long-term effects [START_REF] Zhu | Intracrine signaling through lipid mediators and their cognate nuclear G-protein-coupled receptors: a paradigm based on PGE2, PAF, and LPA1 receptors[END_REF] consistent with their role in senescence. A huge increase in cell size, also called cell hypertrophy, in addition to be a characteristic of senescence, is associated to diverse physiological and pathological situations such as endochondral ossification, osteoarthritis, myocardial injury and obesity in which it affects chondrocytes, cardiomyocytes and adipocytes respectively. NS-398 and celecoxib COX2 inhibitors were shown to inhibit the hypertrophy of cardiomyocytes induced by en dothelin-1 [START_REF] Li | COX-2 is involved in ET-1-induced hypertrophy of neonatal rat cardiomyocytes: role of NFATc3[END_REF] and, conversely, mice overexpressing COX2 specifically in the heart developed a mild cardiomyocyte hypertrophy [START_REF] Streicher | Compensatory hypertrophy induced by ventricular cardiomyocyte-specific COX-2 expression in mice[END_REF]. The inhibition of cell hypertrophy by NS-398 was also shown in a model of human periosteum differentiation [START_REF] Welting | Inhibition of cyclooxygenase-2 impacts chondrocyte hypertrophic differentiation during endochondral ossification[END_REF]. EP3 overexpression in mouse was shown to lead to an increase of the surface of isolated cardiomyocytes and to an enlargement of the heart. The activities of the phosphatase calcineurin and of the transcription factor NFAT were increased correlatively [START_REF] Meyer-Kirchrath | Overexpression of prostaglandin EP3 receptors activates calcineurin and promotes hypertrophy in the murine heart[END_REF]. Therefore, the role of the COX2/ PGE 2 /EP3 intracrine pathway in the cell size control would not be restricted to senescence. Our experiments also show that ATF6α/COX2 participates in the ER expansion occurring at senescence. ATF6α and IRE1α have already been shown to participate in ER expansion and biogenesis by inducing biosynthesis of phospholipids, in order to adapt the ER capacity to the protein folding demand [START_REF] Bommiasamy | ATF6alpha induces XBP1-independent expansion of the endoplasmic reticulum[END_REF][START_REF] Sriburi | Coordinate regulation of phospholipid biosynthesis and secretory pathway gene expression in XBP-1(S)-induced endoplasmic reticulum biogenesis[END_REF][START_REF] Maiuolo | Selective activation of the transcription factor ATF6 mediates endoplasmic reticulum proliferation triggered by a membrane protein[END_REF]. We previously speculated that the ATF6α-controlled ER expansion at senescence would be to adapt to the increase in synthesis and folding of the proteins of the senescent pro-inflammatory secretome, since the invalidation of ATF6α impacts the production of IL6 together with the ER surface [START_REF] Druelle | ATF6α regulates morphological changes associated with senescence in human fibroblasts[END_REF]. The ATF6α/COX2 pathway could also promote the biosynthesis of phospholipids in order to ensure the supply in arachidonic acid, the substrate of COX2 which is produced from phospholipids by the activities of phospholipases A2 [START_REF] Murakami | Recent advances in molecular biology and physiology of the prostaglandin E2-biosynthetic pathway[END_REF]. However, the evidence of a direct role of COX2 in phospholipids metabolism is very few. We found a study showing that the COX2 silencing in MDA-MB-231 breast cancer cells results in reduced levels of phosphocholine, a membrane phospholipid precursor, and of choline kinase (CHK), the enzyme which synthetises it from free choline [START_REF] Shah | Noninvasive imaging identifies new roles for cyclooxygenase-2 in choline and lipid metabolism of human breast cancer cells[END_REF].

U N C O R R E C T E D P R O O F
Remarkably, in our present and previous experiments [START_REF] Druelle | ATF6α regulates morphological changes associated with senescence in human fibroblasts[END_REF], the ATF6α/COX2-induced ER expansion always occurs contemporarily with increase in cell size or changes in cell shape, together with some remodelling of the cytoskeleton. Since the ER and the plasma membrane are anchored to the cytoskeleton, it is possible that their expansion is consecutive to modification of the cytoskeleton dynamics. Studies in astrocytes where the COX2 activity was inhibited by NSAIDs revealed such a link between COX2 and cytoskeleton dynamics via Rho-GTPases, PAK and ERK1/2 [START_REF] Lichtenstein | Secretase-independent and RhoGTPase/PAK/ERK-dependent regulation of cytoskeleton dynamics in astrocytes by NSAIDs and derivatives[END_REF]. The effects of the ATF6α/COX2 pathway on the cell size and shape could also result from an effect on cell adherence to the extracellular matrix (ECM). And indeed, the inhibition of COX2 was shown to decrease the ability of colon cancer cells to adhere on ECM through the down-regulation of beta1-integrin [START_REF] Yazawa | Selective inhibition of cyclooxygenase-2 inhibits colon cancer cell adhesion to extracellular matrix by decreased expression of beta1 integrin[END_REF]. Similarly, a deficiency in COX2 was shown to inhibit the adhesion of macrophages to ICAM-1 or fibronectin in in vitro assays, as well as to the peritoneal membrane in in vivo assays. The COX2-deficient macrophages showed an altered actin network organisation. All these effects were rescued by supplying PGE 2 [START_REF] Díaz-Muñoz | Cyclooxygenase-2 deficiency in macrophages leads to defective p110γ PI3K signaling and impairs cell adhesion and migration[END_REF].

In conclusion, our data provide the first evidence of the involvement of ER stress in COX2-mediated induction and maintenance of several changes associated with senescence in NHDFs, mainly through ATF6α activation (Fig. 7). Therefore, targeting specific components of the ATF6α pathway may have potential anti-aging effects. Additional studies are needed to fully understand all the consequences of ATF6α activation at senescence.

Fig. 1 .

 1 Fig. 1. Senescence induced by ER stressors goes through the COX2/PGE 2 /EP3 pathway in NHDFs. (A) Exponentially growing NHDFs were treated with DTT (200 μM) for 4 days and the UPR markers GRP78, CHOP, ERO1Lβ, HERPUD1 were measured at the mRNA levels by qRT-PCR and were normalized to EAR levels. The measures were performed in triplicate. The bar chart represents the mean ± SD of three independent experiments. (B) Exponentially growing NHDFs were treated with DTT (200 μM) for 4 days and COX-2 mRNA levels were measured by qRT-PCR and were normalized to RPLP0 levels. The bar chart represents the mean ± SD of three independent experiments. (C) Exponentially growing NHDFs were subjected to DTT (200 μM) in presence or absence of the COX2 inhibitor NS398 (5 μM). The amount of PGE 2 in the culture media (secreted) were measured by an ELISA assay. The bar chart represents the mean ± SD of three independent experiments. (D) Exponentially growing NHDFs were subjected to DTT (200 μM) in presence or absence of the COX2 inhibitor NS398 (5 μM) and GRP78 mRNA levels in NHDFs were measured by qRT-PCR and were normalized to RPLP0 levels. The bar chart represents the mean ± SD of three independent experiments (E) Cells were counted and the cumulative doubling numbers were calculated. The bar chart represents the mean ± SD of the count of three independent culture dishes. (F) In parallel, the percentage of SA-β-Gal-positive cells was determined 4 days after the beginning of the treatment. SA-β-Gal positive cells were counted in 3 different microscopic fields. The bar chart represents the mean ± SD of the 3 counts. (G) Exponentially growing NHDFs were treated with TG (0.1 μM) for 4 days. The percentage of SA-β-Gal-positive cells was determined 4 days after the beginning of the treatment. SA-β-Gal positive cells were counted in 3 different microscopic fields. The bar chart represents the mean ± SD of the 3 counts. (H) Percentage of SA-β-Gal-positive cells 4 days after the treatment with DTT (200 μM) in presence or absence of the prostaglandin receptor EP3 antagonist. SA-β-Gal positive cells were counted in 3 different microscopic fields. The bar chart represents the mean ± SD of the 3 counts. This experiment is representative of 3 independent ones.

Fig. 2 .

 2 Fig. 2. DTT causes a premature senescence in NHDFs through an ATF6α dependent pathway. (A) Analysis of PERK, ATF6α, IRE1α, COX-2 protein levels in senescent NHDFs subjected to individual PERK, ATF6α or IRE1α silencing compared to a non-target siRNA as control, 96 h after transfection. GAPDH was used as loading control. Bands were quantified using Image J software. The results are given as the ratio to GAPDH. (B) Exponentially growing NHDFs were subjected to PERK, ATF6α, IRE1α siRNAs or to non-target control siRNAs. Twenty four hours later, NHDFs were treated with DTT 200 μM and the percentage of SA-β-Gal-positive cells 4 days after the drug treatment is indicated. SA-β-Gal positive cells were counted in 3 different microscopic fields. The bar chart represents the mean ± SD of the 3 counts. This experiment is representative of 2 independent ones. (C) Exponentially growing NHDFs were subjected to ATF6α siRNAs or to non-target control siRNAs. Twenty four hours later, NHDFs were treated with DTT 200 μM and the amount of PGE 2 in the culture media was measured by an ELISA assay. The bar chart represents the mean ± SD of three independent experiments.

Fig. 3 .

 3 Fig. 3. UPRsilencing inhibits the PGE 2 biosynthesis pathway in senescent NHDFs. (A) The amount of PGE 2 in the culture media from exponentially growing or senescent NHDFs was measured by an ELISA assay. Measures were performed in triplicate. The bar chart represents… (B) COX2 mRNA levels were analyzed by qRT-PCR in exponentially growing or senescent NHDFs. Results were normalized to RPLP0 levels. The measures were performed in triplicate. The bar chart represents the mean ± SD of three independent experiments. (C) Total protein extracts from equivalent number of exponentially growing and senescent NHDFs were analyzed by immunoblotting with an anti-COX2 antibody. (D) Analysis of COX2 mRNA levels by qRT-PCR in senescent NHDFs subjected to PERK, ATF6α or IRE1α siRNAs or to non-target control siRNAs for 96 h. COX2 mRNA levels were normalized to RPLP0 levels and are shown as fold induction ± SD considering 100% the induction obtained upon senescence. (E) Total protein extracts from senescent NHDFs upon PERK, ATF6α, IRE1α silencing by siRNA were analyzed by immunoblotting with anti-COX2 antibody. Expression of GAPDH was detected to serve as loading control. (F) The amount of released PGE 2 in the culture medium at different stages was measured by an ELISA assay 4 days after transfection siRNAs against PERK, ATF6α, IRE1α or non-target control siRNAs. The given results are representative of 3 independent experiments.

Fig. 4 .

 4 Fig. 4. Silencing of COX2 does not prevent UPR activation in senescent NHDFs. Senescent NHDFs were subjected to PTGS2/COX2 siRNAs or non-target control siRNAs for 96 h. Exponentially growing NHDFs transfected by non-target control siRNAs were used as control. The UPR markers GRP78, CHOP, ERO1Lβ, HERPUD1 were measured at the mRNA levels by qRT-PCR and were normalized to EAR levels. The measures were performed in triplicate. The bar chart represents the mean ± SD of three independent experiments.

Fig. 5 .

 5 Fig. 5. The senescence-associated morphological cell changes are COX2 dependent. (A) Senescent NHDFs were transfected with ATF6α or COX2 siRNAs or with non-target control siR-NAs as for 96 h. Exponentially growing NHDFs transfected with non-target control siRNAs were used as control. NHDFs were then seeded onto coverglasses, fixed, and processed for immunofluorescence detection of: endogenous calnexin (CANX) (green), in upper panel; endogenous vimentin (red) and 53BP1 foci (green) in lower panel. Cell nuclei were detected by DAPI staining (blue). Each condition was tested in triplicate; representative confocal photomicrographs of CANX or Vimentin/53BP1 are shown. Bar represents 50 μm. (B) Quantification of the ER surface occupied by fluorescence signal coupled to anti-CANX antibody. Surface were calculated with Image J in five independent microscopic fields for a total of at least 30 cells. The bar chart represents the mean ± SD of each 5 counts. (C) Quantification of the cell surface occupied by fluorescence signal coupled to anti-Vimentin antibody. Surface were calculated with Image J in five independent microscopic fields for a total of at least 30 cells. The bar chart represents the mean ± SD of each 5 counts. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)

Fig. 6 .

 6 Fig. 6. The ATF6α-dependent increase in cell size is mediated by intracellular PGE 2 . (A) Senescent NHDFs were transfected by ATF6α siRNAs or non-target control siRNAs. PGE 2 (20 ng/ mL) was added alone to the culture medium (Normal Glc) or in the presence of glucose (High Glc). Representative confocal photomicrographs of endogenous vimentin (red) inmmunostaining are shown. Cell nuclei were detected by DAPI staining (blue). Bar represents 50 μm. (B) Quantification of the cell surface occupied by fluorescence signal coupled to anti-Vimentin antibody. Surface were calculated with Image J in five independent microscopic fields for a total of at least 30 cells. The bar chart represents the mean ± SD of each 5 counts. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)

Fig. 7 .

 7 Fig. 7. The UPR/COX2/PGE 2 /EP3 intracrine signalling pathway contributes to the increase in cell size characteristic of the senescent phenotype. The UPR mainly transduces signals to senescence through activation of the COX2/PGE2 /EP3 intracrine pathway. NS398 (COX2 inhibitor) and L-798,106 (EP3 antagonist) are pharmacological agents that delay or prevent some senescent features, notably the characteristic increase in cell size. Abbreviations: ER stress: endoplasmic reticulum stress; ATF6α: Activating Transcription Factor 6 alpha; UPR: Unfolded Protein Response; COX2: cyclooxygenase 2; PGE 2 : Prostaglandin E 2 ; EP3: Prostaglandin receptor 3.

Acknowledgements

This work was supported by grants from the Ligue contre le Cancer (Comité de la Somme, Comité du Nord et Comité du Pas-De-Calais), the Conseil Régional Nord/Pas-de-Calais and the European Regional Development Fund, the Association pour la Recherche sur le Cancer, the SIRIC OncoLille (grant INCa-DGOS-Inserm 6041), the SFR Cancer Lille, and the GEFLUC. OP was supported by a "chaire d'excellence" from the University of Lille 2 and the Centre National de la Recherche Scientifique (CNRS). JD was supported by a fellowship from "Relations Internationales" of the University of Lille 1.

Conflicts of interest

The authors declare that they have no competing interests.


Jean-Louis Milan

Maxime Vassaux

Laurent Pieuchot

Karine Anselme

Ian Manifacier

Developing in silico avatars of cells to predict and drive cell migration on travelling waves

come   L'archive ouverte pluridisciplinaire

Introduction

We showed previously that cell migration is guided towards concave areas by a curvature gradient [1]. We called this phenomenon curvotaxis that is not induced by gravity. We showed that curvotaxis is a mechanobiological process involving the cytoskeleton and nucleus. The nucleus, is pushed by apical cortex pressure and is consequently displaced toward concave areas where the stress is lower [2,3]. Influenced by the repositioning of the nucleus, the cell migrates to concave areas until a more relaxed mechanical state is reached. Nonetheless static curved surfaces guide cell migration only locally. We hypothesize that changing curvature dynamically by generating travelling waves at the surface of the substrate should stimulate and drive cell migration over large distances. Verifying our hypothesis directly by in vitro methods would require a long-time to develop the working prototype of a dynamic wavy substrate able to stimulate and drive cell migration. We proposed to verify it first using in silico with cell avatars.

Methods

We have developed in silico cell avatars that reproduce the mechanisms by which the cells sense the curvature of the substrate dynamically during cell migration [2,3]. Cell model is a network of mechanical interactions that reproduce the contractile and dynamic structure of the cytoskeleton bound to the extracellular environment via focal adhesions. The cell model, in open access [START_REF] Vassaux | Zenodo available in Github[END_REF] is developed using LMGC90, a computational framework dedicated to nonsmooth contact dynamics and multi interaction systems. Waves of 10µm of amplitude and 160µm of wave-length were simulated at the surface of a virtual substrate at a speed of 4µm/iteration. Migration of the cellular model was then initiated and driven by curvotaxis.

Results

The cell model follows the progression of the wave and migrates indefinitely as long as the wave exists. Influenced by the travelling wave, the nucleus moves towards the concavity and the cell migrates in that direction. When retaining the same wavelength and velocity parameters, the speed of the cellular avatar decreased by a third when we lowered the amplitude of the wave 5 µm. But, with a wave amplitude of 20µm, cell model migrated 1.5 times faster.

Discussion & Conclusion

Using the model, concerning the impact of travelling waves on cell migration, we answer a question that would otherwise be hard to directly address by experimental means. We propose the first in-silico proof-of-concept to guide cell migration by generating travelling waves. This will help in the designing of dynamic substrates to stimulate cell migration in vitro. Our in-silico avatar of migrating cells can later be implemented for cell engineering and lab-on-chip technology, as a live augmented reality tool to drive smart substrate actuation and control cells in vitro.


Thomas Palmeira Ferraz ♦♣

Marcely Zanon

Caroline Brun

Vassilina Nikoulina

MULTILINGUAL DISTILWHISPER: EFFICIENT DISTILLATION OF MULTI-TASK SPEECH MODELS VIA LANGUAGE-SPECIFIC EXPERTS

Keywords: knowledge distillation, multitask speech processing, automatic speech recognition, multilingual speech processing, language experts

Whisper is a multitask and multilingual speech model covering 99 languages. It yields commendable automatic speech recognition (ASR) results in a subset of its covered languages, but the model still underperforms on a non-negligible number of under-represented languages, a problem exacerbated in smaller model versions. In this work, we propose DistilWhisper, an approach able to bridge the performance gap in ASR for these languages while retaining the advantages of multitask and multilingual capabilities. Our approach involves two key strategies: lightweight modular ASR fine-tuning of whisper-small using language-specific experts, and knowledge distillation from whisper-large-v2. This dual approach allows us to effectively boost ASR performance while keeping the robustness inherited from the multitask and multilingual pre-training. Results demonstrate that our approach is more effective than standard fine-tuning or LoRA adapters, boosting performance in the targeted languages for both in-and out-of-domain test sets, while introducing only a negligible parameter overhead at inference.

INTRODUCTION

Whisper [START_REF] Radford | Robust speech recognition via largescale weak supervision[END_REF] is a popular multilingual and multitask speech model that is known for its robustness (i.e. invariant performance over different out-of-domain data) for automatic speech recognition (ASR) [START_REF] Gandhi | Esb: A benchmark for multidomain end-to-end speech recognition[END_REF]. This model covers 99 languages, and jointly trains on ASR, speech translation (manyto-English), language identification, and voice activity detection tasks. The original paper points this multitask training as a reason for the observed robustness of the model to out-of-domain data: compared to the English wav2vec 2.0 model [START_REF] Baevski | wav2vec 2.0: A framework for self-supervised learning of speech representations[END_REF], Whisper performance seems to generalize better to unseen domains. Presented in many sizes (from tiny to large-v2), we note that there is an important gap in ASR performance between whisper-large-v2 (largest model) and whisper-small (second smallest model) on a large set of languages, including low-resource languages, but also many high-and mid-resource ones. Such phenomenon in NLP is often referred as curse of multilinguality [START_REF] Arivazhagan | Massively multilingual neural machine translation in the wild: Findings and challenges[END_REF]5,[START_REF] Goyal | Larger-scale transformers for multilingual masked language modeling[END_REF], where the performance drop due to the growing amount of covered languages can only be recovered via extensive model scaling. Such scaling comes with an important inference cost increase: for instance, whisper-large-v2 is 2-3 times slower than whisper-small.

A common approach to efficient inference is distilling knowledge from a large multilingual teacher model into a smaller model [7,[START_REF] Mohammadshahi | SMaLL-100: Introducing shallow multilingual machine translation model for low-resource languages[END_REF]. However, to apply such knowledge distillation (KD) to whisper-large-v2, the best and largest Whisper model, we would need to access unavailable information such as the training data across all the tasks and languages, in order to preserve the robustness of the original model. Recent works [START_REF] Pfeiffer | Lifting the curse of multilinguality by pre-training modular transformers[END_REF][START_REF] Vineel | Scaling speech technology to 1,000+ languages[END_REF] have demonstrated that the curse of multilinguality can also be solved by equipping a moderately sized model with language-specific (LS) modules. Such architecture allows to extend model parameters via extra modules when more languages are added into the model, thus maintaining consistent performance across languages, with no (or very low) extra computations at inference.

Inspired by those findings we propose DistilWhisper, which extends whisper-small with LS feed-forward layers, that are used in parallel with the original feed-forward layers of the model. In order to preserve the robustness of the original model, DistilWhisper introduces the following extensions of previous works: (1) Following [START_REF] Zhang | Share or not? learning to schedule language-specific capacity for multilingual translation[END_REF] we extend conditional language-specific routing (CLSR) modules with the gating mechanism that can route input representation either through the original feed-forward layer or through newly learned LS feed-forward layer; (2) When learning LS layers, we use whisper-large-v2 as a teacher model with the hypothesis that the KD loss should help reproducing the robustness of the larger Whisper model.

Through extensive experiments on a diverse set of languages we demonstrate the effectiveness of DistilWhisper compared to standard fine-tuning or LoRA [START_REF] Edward | Lora: Low-rank adaptation of large language models[END_REF] adapters. Our lightweight ASR fine-tuning approach based on CLSR modules generalizes better than LoRA, and the introduction of KD further boosts results in both in-and out-of-domain test sets. We perform additional ablation studies showing our approach can cope with different amounts of training data. Finally, we demonstrate that the flexibility introduced by the gating mechanism equips DistilWhisper with an efficient adaptation approach, leveraging the LS modules only when those are relevant. We make available the models' weights 1 and code developed in this work.

BACKGROUND

State of the art for ASR: Current approaches for ASR mainly rely on the adaptation of pre-trained Transformer stacks learned through self-supervision (i.e. SSL models) on unlabeled audio data. Such pre-trained models vary on the usage of pretext tasks [START_REF] Baevski | wav2vec 2.0: A framework for self-supervised learning of speech representations[END_REF][START_REF] Hsu | Hubert: Self-supervised speech representation learning by masked prediction of hidden units[END_REF][START_REF] Chen | Wavlm: Large-scale self-supervised pre-training for full stack speech processing[END_REF] and language coverage [START_REF] Conneau | Unsupervised Cross-Lingual Representation Learning for Speech Recognition[END_REF][START_REF] Babu | XLS-R: Self-supervised Cross-lingual Speech Representation Learning at Scale[END_REF][START_REF] Vineel | Scaling speech technology to 1,000+ languages[END_REF][START_REF] Zhang | Google usm: Scaling automatic speech recognition beyond 100 languages[END_REF]. In contrast to this branch of research, the Whisper model relies on weak supervision, which means that the architecture is trained on weakly labeled data only (no self-supervision). Nonetheless, they show that with sufficient amounts of data, the model reaches competitive results compared to mono [START_REF] Radford | Robust speech recognition via largescale weak supervision[END_REF][START_REF] Gandhi | Esb: A benchmark for multidomain end-to-end speech recognition[END_REF] and multilingual SSL models [START_REF] Vineel | Scaling speech technology to 1,000+ languages[END_REF]. Knowledge distillation (KD) has been initially proposed by [START_REF] Hinton | Distilling the knowledge in a neural network[END_REF] to distill knowledge from ensemble of models into a single model for ASR. It has further been used to distill knowledge from a large teacher model into smaller student models [7,[START_REF] Mohammadshahi | SMaLL-100: Introducing shallow multilingual machine translation model for low-resource languages[END_REF][START_REF] Shen | Language-universal adapter learning with knowledge distillation for end-to-end multilingual speech recognition[END_REF]. While original KD methods relied on minimization of KL-divergence between a teacher model and a student model, recent research [START_REF] Wen | f-divergence minimization for sequence-level knowledge distillation[END_REF][START_REF] Go | Aligning language models with preferences through f -divergence minimization[END_REF] have shown that symmetric divergences, such as Jensen-Shannon (JS) divergence, suffer less from borderline behaviors and lead to better results on sequence level distillation. Adapters are small lightweight modules which are commonly used to adapt pre-trained models to new tasks or domains. In speech-related tasks, adapter-based fine-tuning has been utilized for speech translation [START_REF] Le | Lightweight adapter tuning for multilingual speech translation[END_REF][START_REF] Gow-Smith | Naver labs europe's multilingual speech translation systems for the iwslt 2023 lowresource track[END_REF][START_REF] Antonios | Findings of the iwslt 2022 evaluation campaign[END_REF], and domain adaptation [START_REF] Thomas | Efficient adapter transfer of selfsupervised speech models for automatic speech recognition[END_REF][START_REF] Tomanek | Residual adapters for parameterefficient asr adaptation to atypical and accented speech[END_REF], for which they exhibit a similar performance to standard fine-tuning, but with only a fraction of trainable parameters. We also find work on task-adaptation of Whisper [START_REF] Srijith Radhakrishnan | A parameter-efficient learning approach to arabic dialect identification with pretrained general-purpose speech model[END_REF][START_REF] Wang | WhiSLU: End-to-End Spoken Language Understanding with Whisper[END_REF][START_REF] Feng | Peft-ser: On the use of parameter efficient transfer learning approaches for speech emotion recognition using pre-trained speech models[END_REF] using LoRA adapters. In contrast to adapters, in this work we introduce gated LS layers into Whisper, and propose a parameter-efficient KD approach that allows us to increase robustness to out-of-domain data.

DISTILWHISPER

With the goal of increasing performance for different languages in models of limited capacity, we propose the Dis-tilWhisper approach: we plug conditional language-specific routing (CLSR) modules [START_REF] Zhang | Share or not? learning to schedule language-specific capacity for multilingual translation[END_REF] into a small Whisper (whispersmall), and optimize these modules jointly on ASR finetuning and KD from a larger Whisper (whisper-large- 1 Weights available at: https:// huggingface.co/collections/naver/ multilingual-distilwhisper-6576ecae8d209fc6a767d9e7. v2). Figure 1 presents our architecture, below we detail its key components. CLSR module. We extend CLSR modules for the first time to the speech domain. This module learns a hard binary gate g(•) for each input token by using its hidden embedding z l .

These decisions enable a layer to selectively guide information through either a LS path denoted as h lang or a shared path referred to as h shared , as in Eq 1. In contrast to the original CLSR, in this work we use LS gates as shown in Figure 1, instead of sharing them across languages. This allows us to train LS components individually (i.e. in parallel), and then only load the relevant modules at inference. Moreover, our approach also differs from the original CLSR by the positioning: supported by previous work [START_REF] Zhang | Share or not? learning to schedule language-specific capacity for multilingual translation[END_REF][START_REF] Pfeiffer | Lifting the curse of multilinguality by pre-training modular transformers[END_REF], we limit CLSR to the feed-forward, which we also replace entirely by the CLSR module, reducing further the number of parameters.

Gating follows [START_REF] Zhang | Share or not? learning to schedule language-specific capacity for multilingual translation[END_REF]: each gate g(.) is made by a two-layer bottleneck network, which is summed to an increasing zeromean Gaussian noise during training in order to discretize it. At inference time, we adopt hard gating.

DistilWhisper approach is detailed at Figure 1. Our student is enriched with CLSR modules at each feed-forward for each language. These CLSR layers are initialized from the frozen weights of the corresponding feed-forward layer. At training time, for each language the model updates only the corresponding LS layers and gates. At inference time, the model loads the shared layers (multilingual) and the LS modules and gates for the languages of interest, resulting in a limited parameter overhead. We highlight that the use of CLSR modules brings more flexibility to our architecture when compared to adapters, as it allows for routing at the token-level. This makes this approach more capable of leveraging pre-existing knowledge (shared frozen module) via LS gating activation. DistilWhisper optimization. Following [START_REF] Zhang | Share or not? learning to schedule language-specific capacity for multilingual translation[END_REF], when learning CLSR module parameters, in addition to standard crossentropy loss L CE , we employ a gate budget loss L g (Eq 2) to balance models' usage of LS and language-shared modules. It relies on the gate g(.) activation values for a pair (audio, text)

(X, Y ) in a batch B, which is expressed by G (X,Y ) = ∑ x∈X ∑ m∈M enc g m (x)+∑ y∈Y ∑ m∈M dec g m (y)
where M enc and M dec are respectively the encoders and decoders layers, and g m (.) = 1 when LS layer is selected, or 0 otherwise. The average of this gate usage is constrained to a budget b (Eq 2).

For KD, following recent research [START_REF] Wen | f-divergence minimization for sequence-level knowledge distillation[END_REF][START_REF] Go | Aligning language models with preferences through f -divergence minimization[END_REF], we use JS divergence, whose loss is detailed in Eq 3, where p is the teacher distribution, q θ is the student distribution, Y and Y ′ are sampled from the teacher's and student's distributions and compared with their average m(⋅) = 1 2 p(⋅) + 1 2 q θ (⋅). Thus, CLSR modules parameters are learned to minimize final loss expressed as

L = L CE + L g + αL KD . CLSR(z l ) = g(z l )⋅h lang (z l )+(1-g(z l ))⋅h shared (z l ). (1)
L g = ∑ (X,Y )∈B G (X,Y ) ∑ (X,Y )∈B (|X||M enc | + |Y ||M dec |) -b (2)
L KD = 1 2 E Y∼p [log p(Y) m(Y) ] + 1 2 E Y ′ ∼q θ [log q θ (Y ′ ) m(Y ′ ) ] (3)

EXPERIMENTAL SETUP

Datasets: We downsample the train and validation sets of the CommonVoice 13.0 (CV-13) dataset [START_REF] Ardila | Common voice: A massivelymultilingual speech corpus[END_REF], using equal amounts of training data for each selected language: 10k utterances for training (approx. 14 h), 1k for validation. Data selection depends on the amount of up-votes utterances received by annotators. We do not downsample the test set. The FLEURS [START_REF] Conneau | Fleurs: Few-shot learning evaluation of universal representations of speech[END_REF] dataset is used for out-of-domain evaluation, as it provides both a good language overlap with CV-13, and an effective out-of-domain setting for ASR evaluation. For instance, average number of tokens per sample for CV-13 is 36, and 97 for FLEURS.

Language Selection: We consider all Whisper languages with a WER gap of more than 11 between large and small models on CV-13. We then narrow this list considering: 1) minimum amount of utterances (10k); 2) overlap with the FLEURS dataset. The final list of languages is: Catalan (ca), Czech (cs), Galician (gl), Hungarian (hu), Polish (pl), Thai (th), Tamil (ta) and Ukranian (uk). 2 These languages encompass 5 language sub-families and vary widely in terms of coverage in the Whisper training set, spanning from 4,300 h (pl) to just 9 h (gl). Models: We compare our approach to both whispersmall (pre-trained student) and whisper-large-v2 (teacher) models, as well as two approaches of fine-tuning (FT) for the student: standard fine-tuning (all weights are updated), and LoRA adaptation on top of the feed-forward layer. Finally, we also investigate the impact of the CLSR layer without the use of KD (CLSR-FT), decoupling the effect of KD from the flexibility offered by the routing mechanism on the consequent robustness of the model. Implementation: We train all models using the Transformers library [START_REF] Wolf | Transformers: State-of-the-art natural language processing[END_REF], and make use of whisper-small and whisper-large-v2 pre-trained weights from Hugging- 2 Although Arabic would also qualify considering our criteria, we find that the dialect from FLEURS differs from the ones present on CV-13.

Face. 3 All models are trained for 10 epochs using learning rate 10 -4 with linear decay, one epoch warm-up, batch size 16, and label smoothing factor 0.1. For LoRA, we use the hyperparameters proposed by [START_REF] Wang | WhiSLU: End-to-End Spoken Language Understanding with Whisper[END_REF]. For CLSR training we set gate budget b = 0.5 and skip-gate probability s = 0.2. For KD we employ JS divergence with temperature τ = 1, weighted such as the learning objective is L = L CE + L g + 2L KD . We report normalized WER using the Whisper normalization with a slight modification to avoid splitting numbers and latin-scripted text into individual characters in languages that do not use space delimitation (th). In all cases, the best model is chosen based on WER on the downsampled CV-13 validation set.

RESULTS

We conduct training for each setting using three distinct seeds and present the average scores. Table 1 presents our results. The top portion presents whisper-large-v2 (upper bound) and whisper-small (lower bound) pre-trained scores. The middle portion presents standard fine-tuning (FT) and LoRA adaptation at the feed-forward layers (LoRA-FT). Our results are presented in the bottom: CLSR-FT corresponds to the setting without L KD , while DistilWhisper is the complete setting in which both CLSR and KD losses are leveraged. DistilWhisper versus other adaptation approaches. For whisper-small, we observe that both FT and LoRA-FT approaches (middle portion of Table 1) are able to improve performance on both in-and out-of-domain test sets. However, for FT this boost in performance comes with the cost of language specialization. In contrast to that, LoRA-FT is a light adaptation technique that does not modify the pretrained representation. This method increases performance on both in-domain (avg -13.1) and out-of-domain (avg -3.5) test sets compared to whisper-small. DistilWhisper further improves performance over whisper-small (avg -15.3) and LoRA-FT (avg -2.2) for in-domain data. It also presents better out-of-domain adaptation capabilities compared to LoRA-FT (avg -2.1). Impact of knowledge distillation. We observe that Distil-Whisper on average outperforms all other adaptation ap- proaches (FT, LoRA-FT) for in-and out-of-domain test sets (bottom portion of Table 1). Comparing our models (CLSR-FT and DistilWhisper), we observe that the version with KD (DistilWhisper) exhibits a slight increase in average in-domain performance (-0.3). In out-of-domain settings, this model consistently outperforms CLSR-FT across all languages (avg -0.6), which confirms our initial hypothesis that the KD loss leverages the robustness from the teacher into the final model. Overall, these results highlight the effectiveness of our proposed architecture: we were able to reduce the out-of-domain performance gap between whisperlarge-v2 and whisper-small by 35.2% (avg -5.5) with a parameter overhead at inference time of only 10% (25 M). Effect of training data size. We now show the effectiveness of our approach on lower and higher data resource settings. For this, we select a subset of languages for which we find more training data available on CV-13 (ca, th, ta). Table 2 presents results for our approach in low (3k utterances; ∼4 h), and higher-resource settings (28k utterances; ∼40 h), compared to the 10k results from Table 1. We observe that, as expected, increasing the amount of trainable examples leads to superior ASR performance for both approaches, with the leveraging of KD (DistilWhisper) being consistently superior to CLSR-FT. For the 28k setup (ca, th, ta), we are able to reduce the out-of-domain WER gap between whisperlarge-v2 and whisper-small by 75% (from 12 to 3 WER). 4 For the 3k setup, we reduce the WER gap by 35.8% using only 4 h of training data. This implies that our approach has the potential to improve ASR performance across low-resource languages for which less training data is 4 whisper-large-v2 and whisper-small avg FLEURS scores for ca, th, ta are respectively 12.5 and 24.5. available. Gate Activation Analysis. To better understand how the model uses routing mechanism, we plot gate activation statistics for both CLSR-FT and DistilWhisper on Figure 2. We observe that the models tend to rely more on the new LS modules in out-of-domain settings (FLEURS vs CV-13), which could be attributed to the greater complexity and larger size of sentences in FLEURS. Also, as expected, increasing the training data size leads to more reliable LS modules, and therefore higher LS usage. The only exception for this is Thai at the 28k setup, and this might be due to dataset quality and requires further investigation. When comparing the 3 languages, we observe that Catalan exhibits a higher reliance on LS routes, which could also be related to the data quality for this language in CV-13. Finally, we observe that for languages with a weaker teacher (Thai, Tamil) the model may receive contradictory signals at lower-resource settings (3k, 10k), leading to less LS routing usage with KD. However, in the higher resource setting (28k), KD usage leads systematically to more reliable LS module and therefore higher LS routing.

CONCLUSION

We presented DistilWhisper, a parameter-efficient distillation approach that boosts performance of whisper-small by leveraging the robustness from the whisper-large-v2 into a smaller model, while preserving its multilingual capabilities. This is done by adding language-specific gated modules, and by jointly optimizing ASR fine-tuning and KD losses. Compared to LoRA adapters, and across eight languages, we are able to consistently improve performance in both in-and out-of-domain test sets, while adding only a neg-ligible number of parameters at inference time. We believe that such architecture would make usage of Whisper models accessible to a larger amount of researchers and practitioners since it allows to boost the performance of a low-inference cost model by 35.2% using only 14 h of training data.

Stability of the Method

We investigate the stability of the different proposed methods, CLSR-FT, DistilWhisper using JS with τ = 1 and DistilWhisper using KL with τ = 1, by running three different seeds using the 28k setup. Results are presented in Table 5. Considering a confidence interval of 95%, we confirm DistilWhisper with JS loss is statistically superior than its version with only CLSR FT.

Fig. 1 .

 1 Fig.1. The DistilWhisper optimization approach (left), and its architecture (right). The feed-forward is replaced by a CLSR module, where the LS gates (g) learn to alternate between the pre-trained frozen multilingual representation and the LS layer.

Fig. 2 .

 2 Fig. 2. Ratio of LS layers chosen by the models (CLSR-FT and DistilWhisper) depending on (1) amount of training data; (2) in (CV-13) or out-of-domain (FLEURS); (3) language.

Table 1 .

 1 WER (↓) with dataset averages (avg) for baselines (top), adaptation approaches (middle), and our method (bottom) for in-domain (CV-13, FT only) and out-of-domain (FLEURS, all) test sets. Best results for whisper-small in bold.

				FLEURS	CV-13			FLEURS (out-of-domain)					CV-13 (in-domain for FT only)
			#params	avg		avg	ca	cs	gl		hu	pl	ta	th	uk	ca	cs	gl	hu	pl	ta	th	uk
	whisper-large-v2		1.5B	12.5		14.9	5.6	14.3	16.6	17.9	5.9	19.3	12.2	8.1	16.9	14.4	18.9	18.7	8.0	17.3	9.2	15.5
	whisper-small		244M	28.3		31.4	14.6	40.4	32.7	43.0	16.7	36.0	22.8	20.5	30.1	38.4	35.5	45.6	18.6	30.0	20.3	32.3
	whisper-small+FT		244M	23.3±0.06	16.3±0.09	15.5	31.0	16.9	36.7	22.0	22.7	15.6	25.9	13.7	20.5	11.3	24.1	16.3	13.6	7.4	23.4
	whisper-small+LoRA-FT	379M	24.9±0.07	18.2±0.02	17.6	36.9	18.2	41.6	25.9	15.2	11.7	31.8	14.0	23.7	12.7	28.0	21.2	12.0	7.9	26.4
	whisper-small+CLSR-FT	369M	23.4±0.19	16.3±0.08	15.7	30.5	17.2	36.9	22.8	22.7	15.6	25.8	14.1	20.3	11.6	24.3	16.1	13.3	7.4	23.4
	DistilWhisper		369M	22.8±0.21	16.0±0.04	15.3	30.2	16.7	36.9	21.4	21.8	15.1	24.9	13.8	20.0	11.8	24.0	15.9	12.6	7.2	23.1
		Train	FLEURS	CV-13		FLEURS		CV-13
		size	avg	avg	ca	ta	th	ca	ta	th
	whisper-small+CLSR-FT	3k	20.5±0.17	15.0±0.07	17.9	25.6	18.0	19.0	16.4	9.8
	DistilWhisper	3k	20.2±0.13	14.6±0.08	17.4	25.5	17.7	18.7	15.7	9.6
	whisper-small+CLSR-FT	10k	18.0±0.25	11.6±0.01	15.7	22.7	15.6	14.1	13.3	7.4
	DistilWhisper	10k	17.4±0.13	11.2±0.08	15.3	21.8	15.1	13.8	12.6	7.2
	whisper-small+CLSR-FT	28k	15.7±0.15	9.5±0.13	13.5	19.8	13.9	11.3	11.3	6.0
	DistilWhisper	28k	15.5±0.03	9.3±0.06	13.3	19.3	13.7	11.3	11.0	5.7

3 https://huggingface.co/openai/

Table 2 .

 2 Average WER (↓) for different training data sizes (3k, 10k, and 28k utterances) for in-domain (CV-13) and out-of-domain (FLEURS) test sets. Best results in bold.

Table 3 .

 3 WER (↓) for in-and out-of-domain validation sets for DistilWhisper equipped with JS and KL losses, with different temperatures (τ ). Best results in bold.

		FLEURS CV-13		FLEURS			CV-13
		avg	avg	ca	ta	th	ca	ta	th
	JS w/ τ = 1	14.8	8.8	12.7 10.7 12.9 5.9 18.7 9.9
	JS w/ τ = 3	15.4	8.5	14.5 10.0 12.7 6.0 18.9 9.4
	KL w/ τ = 1	15.6	10.2	15.1 13.8 12.8 6.3 18.8 10.5
	KL w/ τ = 3	15.7	8.6	15.2 10.5 13.0 5.9 18.8 9.5
		FLEURS CV-13		FLEURS			CV-13
		avg	avg	ca	ta	th	ca	ta	th
	JS w/ τ = 1	15.4	9.3	13.1 19.2 14.0 11.3 10.9 5.7
	JS w/ τ = 3	16.3	9.7	14.8 20.1 14.1 11.8 11.3 5.9
	KL w/ τ = 1	15.6	10.8	14.6 18.7 13.3 14.9 11.3 6.2
	KL w/ τ = 3	16.5	9.7	15.8 19.8 14.0 12.2 11.1 5.9

Table 4 .

 4 WER (↓) for in-and out-of-domain test sets for DistilWhisper equipped with JS and KL losses, with different temperatures (τ ). Best results in bold.

Acknowledgements

This work was partially funded by the ANR project Diké (grant number ANR-21-CE23-0026-02). It was also partially funded by the European Horizon 2022 project UTTER (Unified Transcription and Translation for Extended Reality), under grant agreement No 101070631.


C Chan Yone

J L Milan

J M Rossi

J F Witz

M Brieu

P Chabrand

Model of cancellous bone adaptation considering hypermineralized bone tissue

Keywords: bone, remodeling, hypermineralization

Introduction

Aging and a low physical activity often involve a decrease in bone mass and a mechanical weakening of bone at global scale, increasing fracture risk. In particular, cancellous bone tissue which plays a mechanical role on the overall rigidity and strength of bone organs, is importantly degraded. Numerous computational models have been developed to simulate bone remodelling process and predict over decades the evolution of microstructure of cancellous bone and its mechanical behaviour [START_REF] Liu | Dynamic simulation of three dimensional architectural and mechanical alterations in human trabecular bone during menopause[END_REF][START_REF] Van Der Linden | A simulation model at trabecular level to predict effects of antiresportive treatment after menopause[END_REF]. These models were based on µCT reconstruction of bone samples and consider generally the tissue as a homogeneous material. However bone remodelling is spatially heterogeneous with concomitance of aged and new bone tissue. Moreover mineralization is a process occurring with time leading to heterogeneous mineral concentration and so mechanical properties. To determine the influence of a heterogeneous material on remodelling of cancellous bone, a computational study was performed here on a bone sample reconstructed in 3D from a µ-CT image stack. Heterogeneous material properties were defined depending on mineral density revealed by µCT gray value. The results show that hypermineralization of cancellous bone influences considerably local mechanical behaviour although overall mechanical behaviour was not affected. Besides, bone cells are mechano-sensitive and adapt bone microstructure by resorbing aged or damaged tissue and aposing new one. So, a iterative computation of bone degradation was then performed in case of a low physical activity. Sites which were underloaded were considered as possible sites of remodelling process. This study shows that heterogeneous material properties may influence localization of bone remodelling This model which analyzes alterations of cancellous bone microstructure may help in the prediction of fracture risk. It can be also implemented so as to predict effect of drug treatment on bone cell activity and the balance between apostition and resorption.

Methods

Trabecular bone sample and -CT imaging

We considered a bone sample from human femoral head (woman of 87 year-old). A cubic specimen of 10x10x10mm 3 was scanned with a -CT system of 20 m resolution. A phantom representing the mineral content of a normal mineralized bone tissue was placed under the sample. This latter gave a gray-value reference in order to identify locally material components and the related mechanical properties.

Finite element model

A first F.E. heterogeneous model (4x4x4 mm 3 ) extracted from cubic sample consisted of 2344910 elements converting voxels into equally sized eight-node brick. All elements were assumed to be linearly elastic and isotropic with Poisson's ratio set to 0.3 and the following power law relationship between Young's modulus and bone grey value :

Eelt=E0 . GVelt γ
where GVelt is the normalized gray-value element, γ a constant exponent set to 1.5 [START_REF] Homminga | Introduction and evaluation of a gray-value voxel conversion technique[END_REF], and E0 was taken to be equal to 19 GPa [START_REF] Hoc | Effect of microstructure on the mechanical properties of haversian cortical bone[END_REF]. In order to compare the influence of material heterogeneities, an homogeneous model with constant material properties was also considered. Young's Modulus and Poisson's ratio were equal to 19 GPa and 0.3 respectively. Uniaxial compressive loading of 0.1% was applied to the the cubic sample

Bone adaptation model

The finite element of cancellous bone is implemented in a algorithm modifying bone structure iteratively and under compressive loading by apposition or resorption. Some authors considered that when strain stimulus is lower than ε res =100µε bone matrix is resorbed. On the contrary, when strain stimulus exceeds εapp=3000µε new bone is apposed [START_REF] Frost | On our age-related bone loss: insights from a new paradigm[END_REF]. Here we considered that if strain energy is lower than wres, bone is resorbed and if it is greater than wapp new bone is apposed : wres =½ Ebone. εres² w app = ½ E bone . ε app ² with Ebone = 19GPa. When strain energy in a element of the model is outside [wres;wapp], elements are either deleted or created at bone surface to represent respectively resorption or apposition. New bone tissue was given Young modulus which increased iteratively from 0.5 GPa to 35Gpa to simulate mineralization.

Influence of hypermineralized bone tissue

Numerical compression tests were performed and Apparent Young's modulus and mean strain energy density were calculated for both previous models. Difference of 7.5% was noted between model with homogeneous material and model with heterogeneous material, which was in agreement with the literature [START_REF] Follet | The degree of mineralization is a determinant of bone strength: a study on human calcanei[END_REF]. Nevertheless a difference of 20% of over-constrained bone volume was identified (where strain exceed 3000µε). From a biological point of view beyond this threshold new bone tissue is apposed, or in contrary when strains are under 100µε bone tissue is removed [START_REF] Frost | On our age-related bone loss: insights from a new paradigm[END_REF].

Hypermineralized bone precursor of bone remodeling

Iterative compression tests were simulated on the cancellous bone model. Remodelling sites where identified: elements were removed or added respectively where strains are under 100 µε or exceed 3000 µε. Strain distribution in bone sample is due to a structure effect, but also a material heterogeneities distribution.

Conclusions

Geometrical bone degradation was simulated and overall stiffness of the sample was computed. The developed model took into account the hypermineralization of bone tissue. As alendronate treatment increased bone mineral density, but also degree of mineralization of bone, this model can simulate medical treatment on bone stiffness, and quantify fracture risk in particular in osteoporosis case.

Figure 1 :

 1 Figure 1 : Young's modulus heterogeneous distribution. Hypermineralized bone tissue in red

Figure 1 :

 1 Figure 1: Degradation of cancellous bone from initial to remodelled (after 5 year)