PhD Mira Maalouf

BSc Adeline Granzotto

Clément Devic

PhD,* ,z Me ´lanie Larry Bodgi

Mélanie Ferlazzo

PhD Christophe Peaucelle

PhD Marcel Bajard

PhD Jean-Yves Giraud

MSc Cle ´ment Devic

PhD Ferlazzo

MD Jacques Balosso

PhD, { Joe ¨l He ´rault

PhD, # Marie-Claude Biston

PhD, # Claude Malet

PhD Nicolas Foray
email: nicolas.foray@inserm.fr

Influence of Linear Energy Transfer on the Nucleo-shuttling of the ATM Protein: A Novel Biological Interpretation Relevant for Particles and Radiation

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

Introduction

There is a large body of evidence that each type of ionizing radiation (IR) and particles induces a specific spectrum of energy microdepositions differing by their frequency, energy density, and spatial distribution. [START_REF] Goodhead | The initial physical damage produced by ionizing radiations[END_REF][START_REF] Goodhead | Spatial and temporal distribution of energy[END_REF][START_REF] Goodhead | Track structure analysis of ultrasoft x-rays compared with high-and low-let radiations[END_REF][START_REF] Goodhead | Effects of radiations of different qualities on cells: Molecular mechanisms of damage and repair[END_REF][START_REF] Ward | DNA damage produced by ionizing radiation in mammalian cells: Identities, mechanisms of formation, and reparability[END_REF] IR and particles produce 3 major types of DNA damage: base damage (BD), DNA single-strand breaks (SSB), and DNA double-strand breaks (DSB). Because energy microdepositions of more than 1, 10, and 100 eV/nm 3 are required for the occurrence of BD, SSB, and DSB, respectively, the physical features of IR and particles condition the induction rates of BD, SSB, and DSB. For example, 1-Gy g-rays simultaneously produces approximately 10,000 BD, 1000 SSB and 40 DSB per human diploid cells, whereas the same dose of a particles produces less BD and more DSB. [START_REF] Goodhead | Effects of radiations of different qualities on cells: Molecular mechanisms of damage and repair[END_REF][START_REF] Foray | The repair rate of radiationinduced DNA damage: A stochastic interpretation based on the gamma function[END_REF][START_REF] Foray | Repair of radiation-induced DNA double-strand breaks in human fibroblasts is consistent with a continuous spectrum of repair probability[END_REF][START_REF] Steel | Basic Clinical Radiobiology[END_REF] The energy microdepositions also condition the DNA damage reparability: the denser the energy microdeposition, the less repairable the resulting DNA damage. For example, in human radioresistant cells, 50% of BD are repaired in 5 to 10 minutes, whereas the same proportion of DSB requires 50 to 60 minutes. [START_REF] Goodhead | Effects of radiations of different qualities on cells: Molecular mechanisms of damage and repair[END_REF][START_REF] Foray | The repair rate of radiationinduced DNA damage: A stochastic interpretation based on the gamma function[END_REF][START_REF] Foray | Repair of radiation-induced DNA double-strand breaks in human fibroblasts is consistent with a continuous spectrum of repair probability[END_REF] Furthermore, DSB induced by high-linear energy transfer (LET) particles may be less frequent but also less repairable than those induced by low-LET ones. [START_REF] Goodhead | Effects of radiations of different qualities on cells: Molecular mechanisms of damage and repair[END_REF][START_REF] Foray | The repair rate of radiationinduced DNA damage: A stochastic interpretation based on the gamma function[END_REF][START_REF] Foray | Repair of radiation-induced DNA double-strand breaks in human fibroblasts is consistent with a continuous spectrum of repair probability[END_REF] The current paradigm to explain the relative biological efficiency (RBE) of high-LET radiation and particles is that dense energy microdepositions produce more irreparable DNA damage. [START_REF] Goodhead | Effects of radiations of different qualities on cells: Molecular mechanisms of damage and repair[END_REF][START_REF] Foray | The repair rate of radiationinduced DNA damage: A stochastic interpretation based on the gamma function[END_REF][START_REF] Foray | Repair of radiation-induced DNA double-strand breaks in human fibroblasts is consistent with a continuous spectrum of repair probability[END_REF][START_REF] Li | A stochastic model of DNA fragments rejoining[END_REF] Although there is a large body of evidence that unrepaired DSBs are correlated with radiosensitivity, [START_REF] Foray | Individual response to ionizing radiation[END_REF][START_REF] Jeggo | DNA double-strand breaks: Their cellular and clinical impact?[END_REF] the DSB endpoints predictive of radiosensitivity remain to be clarified.

Since 2003, hundreds of skin fibroblasts deriving from patients suffering from genetic diseases linked to radiosensitivity or showing abnormal tissue reactions after radiation therapy were collected. [START_REF] Granzotto | Influence of nucleoshuttling of the ATM protein in the healthy tissues response to radiation therapy: Toward a molecular classification of human radiosensitivity[END_REF] From this collection, the ATM protein, a major actor of the IR response has been suggested to be a cytoplasmic protein that translocates in the nucleus after irradiation. The presence of ATM monomers in the nucleus has been shown to ensure the phosphorylation of H2AX histone variants (gH2AX), an early sensor of the recognition of DSB by the nonhomologous end-joining (NHEJ) pathway. The ATM nucleoshuttling, quantified by the number of nuclear foci formed by the auto-transphosphorylation forms of ATM (pATM), was shown to be the basis of powerful predictive assays. [START_REF] Granzotto | Influence of nucleoshuttling of the ATM protein in the healthy tissues response to radiation therapy: Toward a molecular classification of human radiosensitivity[END_REF][START_REF] Belkacemi | The Henri Mondor procedure of morbidity and mortality review meetings: Prospective registration of clinical, dosimetric, and individual radiosensitivity data of patients with severe radiation toxicity[END_REF][START_REF] Bodgi | The nucleo-shuttling of the ATM protein as a basis for a novel theory of radiation response: Resolution of the linearquadratic model[END_REF][START_REF] Ferlazzo | Radiobiological characterization of tuberous sclerosis: A delay in the nucleo-shuttling of ATM may be responsible for radiosensitivity[END_REF][START_REF] Ferlazzo | Mutations of the Huntington's disease protein impact on the ATM-dependent signaling and repair pathways of the radiation-induced DNA double-strand breaks: Corrective effect of statins and bisphosphonates[END_REF][START_REF] Granzotto | DNA double-strand breaks repair and signaling of human gliomas and normal brain cells in response to radiation: Potential impact of the ATM-and BRCA1-dependent pathways[END_REF][START_REF] Pereira | Fast and binary assay for predicting radiosensitivity based on the nucleoshuttling of ATM protein: Development, validation and performances[END_REF] This model is consistent with the findings that ATM monomers are more active than ATM dimers. [START_REF] Baretic | Structures of closed and open conformations of dimeric human ATM[END_REF][START_REF] Paull | Mechanisms of ATM activation[END_REF] Furthermore, the ATM nucleo-shuttling model provides a novel biological interpretation of the linear-quadratic formula that is also consistent with the hypersensitivity to the low-dose phenomenon. [START_REF] Bodgi | The nucleo-shuttling of the ATM protein as a basis for a novel theory of radiation response: Resolution of the linearquadratic model[END_REF] Here, by applying gH2AX and pATM immunofluorescence assays to some human quiescent fibroblasts from different origin and exposed to 6-MV X-rays, g-rays, 9-and 12-MeV electrons, 3-and 64-MeV protons, and 75-MeV/u carbon ions, we verified whether the model of the ATM nucleo-shuttling is relevant whatever the LET and the radiation or particle type.

Methods and Materials

Cell lines

The 149BR, 1BR3, and AG1521 untransformed human fibroblast cell lines were chosen for their genomic stability and the abundant literature associated with their radioresistance. [START_REF] Granzotto | Influence of nucleoshuttling of the ATM protein in the healthy tissues response to radiation therapy: Toward a molecular classification of human radiosensitivity[END_REF][START_REF] Foray | Hypersensitivity of ataxia telangiectasia fibroblasts to ionizing radiation is associated with a repair deficiency of DNA double-strand breaks[END_REF][START_REF] Joubert | DNA double-strand break repair defects in syndromes associated with acute radiation response: At least 2 different assays to predict intrinsic radiosensitivity?[END_REF] The radiobiological characteristics of the ATM-mutated AT4BI and AT5BI and the LIG4-mutated 180BR hyper-radiosensitive fibroblast cell lines were published elsewhere. [START_REF] Foray | Hypersensitivity of ataxia telangiectasia fibroblasts to ionizing radiation is associated with a repair deficiency of DNA double-strand breaks[END_REF][START_REF] Joubert | DNA double-strand break repair defects in syndromes associated with acute radiation response: At least 2 different assays to predict intrinsic radiosensitivity?[END_REF] The radiobiological characteristics of the 01HNG and 06CLB fibroblast cell lines derived from patients showing severe tissue reactions after radiation therapy were published elsewhere. [START_REF] Granzotto | Influence of nucleoshuttling of the ATM protein in the healthy tissues response to radiation therapy: Toward a molecular classification of human radiosensitivity[END_REF] All the experiments were performed with quiescent fibroblasts. Cell culture conditions are detailed in supplementary data (SD). [START_REF] Foray | Hypersensitivity of ataxia telangiectasia fibroblasts to ionizing radiation is associated with a repair deficiency of DNA double-strand breaks[END_REF]

Statin and bisphosphonate treatment

The zoledronate plus pravastatin (ZOPRA) treatment was applied as published previously. [START_REF] Varela | Combined treatment with statins and aminobisphosphonates extends longevity in a mouse model of human premature aging[END_REF] Cells were incubated with 1 mM pravastatin (Sigma-Aldrich France, Saint-Quentin-Fallavier, France) for 24 hours. Thereafter, 1 mM zoledronate (Sigma-Aldrich) was added into the culture medium and cells were incubated for 12 hours. The culture medium was renewed immediately before irradiation.

Immunofluorescence

Immunofluorescence protocol and foci scoring were described elsewhere [START_REF] Granzotto | Influence of nucleoshuttling of the ATM protein in the healthy tissues response to radiation therapy: Toward a molecular classification of human radiosensitivity[END_REF][START_REF] Foray | A subset of ATM-and ATRdependent phosphorylation events requires the brca1 protein[END_REF] and detailed in SD. Anti-gH2AX ser139 (#JBW301) and -pATM ser1981 (#10H11.E12) antibodies were applied at 1:800 and 1:100, respectively (Millipore, Darmstadt, Germany).

Low-LET irradiation

Dosimetry certifications and the irradiations were performed with a 6 MV X-rays clinical irradiator (SL 15 Phillips; doserate, 4 Gy/min) at anti-cancer Centre Le ´on-Be ´rard, (Lyon, France) [START_REF] Ferlazzo | Mutations of the Huntington's disease protein impact on the ATM-dependent signaling and repair pathways of the radiation-induced DNA double-strand breaks: Corrective effect of statins and bisphosphonates[END_REF] with 60 Co (1.25 MeV) and 137 Cs (0.662 MeV) g-rays sources (dose-rate, 2 and 4 Gy/min, respectively) at anti-cancer Centre Gustave-Roussy (Villejuif, France) [START_REF] Foray | Hypersensitivity of ataxia telangiectasia fibroblasts to ionizing radiation is associated with a repair deficiency of DNA double-strand breaks[END_REF][START_REF] Foray | Dose-rate effect on radiationinduced DNA double-strand breaks in the human fibroblast hf19 cell line[END_REF] and with an orthovoltage 250 kV X-rays Phillips irradiator (dose-rate, 4 Gy/min) at the European Synchrotron Radiation Facility (Grenoble, France) [START_REF] Joubert | Irradiation in presence of iodinated contrast agent results in radiosensitization of endothelial cells: Consequences for computed tomography therapy[END_REF] (Table E1; available online at https://doi.org/10.1016/j.ijrobp.2018.10.011).

Electrons irradiation

Dosimetry certifications and electrons irradiations were performed at 9 and 12 MeV (dose-rate, 4 Gy/min) with a Clinac Varian accelerator at Grenoble University Hospital (Grenoble, France) (Table E1).

Protons irradiation

Irradiation with the 3 MeV protons was performed with the 4-MV Van de Graaff accelerator of University of Lyon (Lyon, France) (dose-rate > 10 Gy/min) (Table E1). The 65-MeV protons irradiations were performed with the MEDICYC medical proton therapy cyclotron of the anticancer Center of Nice (France) (dose-rate: 2 Gy/min) and the physical features and dosimetry were published elsewhere [START_REF] Foray | Dose-rate effect on radiationinduced DNA double-strand breaks in the human fibroblast hf19 cell line[END_REF][START_REF] Joubert | Irradiation in presence of iodinated contrast agent results in radiosensitization of endothelial cells: Consequences for computed tomography therapy[END_REF][START_REF] Angellier | Radiochromic ebt2 film dosimetry for low-energy protontherapy[END_REF] (Table E1). [START_REF] Angellier | Radiochromic ebt2 film dosimetry for low-energy protontherapy[END_REF][START_REF] Bettega | Radiobiological studies on the 65 MeV therapeutic proton beam at nice using human tumour cells[END_REF][START_REF] Courdi | Results of proton therapy of uveal melanomas treated in nice[END_REF]

Carbon ions irradiation

The carbon ions irradiations were performed at the Grand Accelerateur National d'Ions lourds (GANIL, Caen, France) (dose-rate, 2 Gy/min). The carbon ions were delivered at 75 MeV per nucleon (72 MeV/u in cells). The physical features and dosimetry were published elsewhere (Table E1). [START_REF] Beuve | Radiobiologic parameters and local effect model predictions for head and neck squamous cell carcinomas exposed to high linear energy transfer ions[END_REF][START_REF] Hanot | Glutathione depletion and carbon ion radiation potentiate clustered DNA lesions, cell death and prevent chromosomal changes in cancer cells progeny[END_REF][START_REF] Maalouf | Different mechanisms of cell death in radiosensitive and radioresistant p53 mutated head and neck squamous cell carcinoma cell lines exposed to carbon ions and xrays[END_REF]

Statistical analysis

For practical reasons, the maximal dose used with carbon ions was 1 Gy. For all the other particle and radiation types, the dose applied was 2 Gy. To permit relevant data comparisons, foci data were expressed, when indicated, as a percentage of the g-rays reference data obtained at 10 minutes after irradiation (ie, 80 DSB/Gy/cell). This data transformation does not generate biases because the DSB repair rate of radioresistant cells when expressed as percentage of unrepaired DSB was shown to be independent of the dose and the technique used. [START_REF] Foray | Radiation-induced DNA doublestrand breaks and the radiosensitivity of human cells: A closer look[END_REF] The fitting formula for DSB repair kinetics [START_REF] Bodgi | A single formula to describe radiation-induced protein relocalization: Towards a mathematical definition of individual radiosensitivity[END_REF] and the RBE calculations are described in SD.

Results

The assessment of gH2AX foci in cells exposed to

low-LET radiation

The kinetics of the appearance and disappearance of gH2AX foci are characterized by 2 phases: the foci appearance phase, which reflects the DSB recognition step, and the foci disappearance phase, which is interpreted as the DSB repair step. [START_REF] Bodgi | A single formula to describe radiation-induced protein relocalization: Towards a mathematical definition of individual radiosensitivity[END_REF] By using pulsed-field gel electrophoresis or gH2AX immunofluorescence, exposure of the 149BR, 1BR3, and AG1521 radioresistant fibroblasts to 250-kV or 6-MV X-rays and 60 Co or 137 Cs g-rays leads to a DSB induction rate of 37 AE 4 DSB per gray per cell, whatever the precited radiation types, in agreement with other published data. [START_REF] Goodhead | Effects of radiations of different qualities on cells: Molecular mechanisms of damage and repair[END_REF][START_REF] Foray | Hypersensitivity of ataxia telangiectasia fibroblasts to ionizing radiation is associated with a repair deficiency of DNA double-strand breaks[END_REF][START_REF] Foray | Dose-rate effect on radiationinduced DNA double-strand breaks in the human fibroblast hf19 cell line[END_REF] The maximal number of gH2AX foci was reached at 10 minutes after irradiation after 2 Gy. At more than 24 hours after, negligible yields of residual gH2AX foci were observed in the 3 cell lines, which was in agreement with previously published data (Fig. 1A; Table E2; available online at https://doi.org/10. 1016/j.ijrobp.2018.10.011). [START_REF] Foray | The repair rate of radiationinduced DNA damage: A stochastic interpretation based on the gamma function[END_REF][START_REF] Granzotto | Influence of nucleoshuttling of the ATM protein in the healthy tissues response to radiation therapy: Toward a molecular classification of human radiosensitivity[END_REF][START_REF] Foray | Hypersensitivity of ataxia telangiectasia fibroblasts to ionizing radiation is associated with a repair deficiency of DNA double-strand breaks[END_REF][START_REF] Joubert | DNA double-strand break repair defects in syndromes associated with acute radiation response: At least 2 different assays to predict intrinsic radiosensitivity?[END_REF][START_REF] Foray | Dose-rate effect on radiationinduced DNA double-strand breaks in the human fibroblast hf19 cell line[END_REF][START_REF] Foray | A new model describing the curves for repair of both DNA double-strand breaks and chromosome damage[END_REF] Assessment of gH2AX foci in cells exposed to electrons Ten minutes after irradiation with 9-MeV electrons, the percentages of gH2AX foci assessed in the 149BR, 1BR3, and AG1521 radioresistant cell lines were found to be similar (85% AE 4%, 82% AE 4%, and 88% AE 2%, respectively; P > .8) but significantly different from the corresponding X-rays data (P < .01). Twenty-four hours after irradiation, the numbers of residual gH2AX foci were found significantly higher with electrons (4% AE 1%, 4% AE 1%, and 3% AE 1%, respectively; P < .05) than with X-rays, suggesting that DSB produced by 9 MeV electrons are less repairable than those induced by X-rays (Fig. 1A; Table E2). Similar conclusions were reached at 12 MeV (4% AE 1%, 5% AE 1%, and 5% AE 1%, respectively; P < .05).

Assessment of gH2AX foci in cells exposed to protons

Ten minutes after irradiation with 3 MeV protons, the percentages of gH2AX foci assessed in the 149BR and 1BR3 fibroblast cell lines (100% AE 0% for both) were found to be similar to that obtained with X-rays. By contrast, 10 minutes after irradiation with 65-MeV protons, the percentages of gH2AX foci assessed in the 149BR, 1BR3, and AG1521 fibroblast cell lines were found negligible (0 AE 0% for all the cell lines).

These findings suggest that DSB recognition is impaired with 65 MeV protons but normal with 3-MeV protons. In the same cell lines tested, the percentages of gH2AX foci assessed with 3-MeV protons (45% AE 3% and 42% AE 3%, respectively) were found to be clearly higher than with X-rays. A similar conclusion was reached with 65-MeV protons (25% AE 5%, 23% AE 5%, and 19% AE 2%, respectively), although the 3-MeV protons data were higher than the 65-MeV protons ones. Altogether, these data suggest that DSB produced by protons are less repairable than those produced by X-rays, in agreement with the literature (Fig. 1A; Table E2). [START_REF] Sorensen | In vitro RBE-LET dependence for multiple particle types[END_REF][START_REF] Tommasino | Proton radiobiology[END_REF] Assessment of gH2AX foci in cells exposed to carbon ions Ten minutes after irradiation, the percentages of gH2AX foci in the 149BR and 1BR3 fibroblast cell lines were found to be significantly lower with carbon ions (72% AE 3% and 66% AE 6%, respectively; P > .8) than with X-rays (P < .001). Furthermore, these percentages reached its maximum at 1 hour (90% AE 3% and 81% AE 5%, respectively). The percentages of residual gH2AX foci were found to be clearly higher than with X-rays (50% AE 5% and 48% AE 5%, respectively). The DSB induced by carbon ions therefore appeared weakly recognized by NHEJ and slowly repairable, as suggested elsewhere (Fig. 1A; Table E2). [START_REF] Hanot | Glutathione depletion and carbon ion radiation potentiate clustered DNA lesions, cell death and prevent chromosomal changes in cancer cells progeny[END_REF]

Assessment of pATM foci in cells exposed to various types of radiation

With 65-MeV protons and carbon ions, the percentage of gH2AX foci at 10 minutes after irradiation was found lower than that assessed with X-rays and the maximal percentage of gH2AX foci was not reached at 10 minutes but at 1 hour. The pattern of this kinetics is not unusual, because it was already observed in some radiosensitive cells, [START_REF] Kuhne | A double-strand break repair defect in ATM-deficient cells contributes to radiosensitivity[END_REF][START_REF] Stiff | ATM and DNA-pk function redundantly to phosphorylate h2ax after exposure to ionizing radiation[END_REF] suggesting a lack of an early DSB recognition and a delayed ATM nucleoshuttling. We therefore applied antipATM immunofluorescence in the same conditions as described earlier. With X-rays, the radioresistant 149BR, 1BR3, and AG1521 cells showed similar numbers of pATM foci per gray per cell at 10 minutes after irradiation (37% AE 3%, 38% AE 5%, and 36% AE 6%, respectively; P > .8). The percentages of pATM foci decreased to zero at 24 hours after irradiation for the 3 cell lines tested (Fig. 1B). These findings fully agreed with other data published elsewhere. [START_REF] Granzotto | Influence of nucleoshuttling of the ATM protein in the healthy tissues response to radiation therapy: Toward a molecular classification of human radiosensitivity[END_REF][START_REF] Ferlazzo | Mutations of the Huntington's disease protein impact on the ATM-dependent signaling and repair pathways of the radiation-induced DNA double-strand breaks: Corrective effect of statins and bisphosphonates[END_REF] With electrons, the percentages of pATM foci were not found different from that assessed with X-rays. With 3-MeV protons, the pATM data of the 2 cell lines tested (149BR and 1BR3) are in agreement with the corresponding gH2AX data with a normal pATM foci value for the first hour following irradiation and a slower disappearance rate of pATM foci after this time. With carbon ions, for both cell lines, the percentage of pATM foci past 10 minutes after irradiation was significantly lower than that observed after Xray irradiation. However, the percentages of pATM foci induced by carbon ions past 10 minutes after irradiation were found clearly lower that those obtained with X-rays. Past 24 hours after irradiation, the percentages of the residual pATM foci with 3-and 65-MeV protons and with carbon ions were found systematically higher than those observed after Xrays in the indicated cell lines (Fig. 1B).

As specified earlier, the phosphorylation of gH2AX histone triggers DSB repair via NHEJ pathway. The DNAend-joining results in the reduction of the distance between ATM monomers situated on each end, which facilitates the redimerization of ATM and, consequently, the formation of nuclear pATM foci. [START_REF] Granzotto | Influence of nucleoshuttling of the ATM protein in the healthy tissues response to radiation therapy: Toward a molecular classification of human radiosensitivity[END_REF] Hence, the number of gH2AX should be 2-fold larger than that of pATM foci. By plotting the number of gH2AX foci assessed in the 149BR, 1BR3, and AG1521 cell lines for a given time after irradiation against the corresponding number of pATM foci, we observed a ratio that was not statistically different from 2, in agreement with the prior assumption (Fig. 2).

Effect of statins and bisphosphonates on cells irradiated with carbon ions

The ZOPRA treatment was previously shown to accelerate the nucleo-shuttling of ATM. [START_REF] Varela | Combined treatment with statins and aminobisphosphonates extends longevity in a mouse model of human premature aging[END_REF] Because the percentages of gH2AX and pATM foci per cell past 10 minutes after irradiation were found to be significantly lower with carbon ions than with X-rays, the effect of the ZOPRA treatment was investigated in the 149BR and 1BR3 radioresistant cell lines exposed to carbon ions. Interestingly, the ZOPRAtreated cells elicited a larger number of gH2AX foci than untreated cells, suggesting that the low number of ATM monomers and their weak diffusion in the nucleus observed with carbon ions can be partially corrected by the ZOPRA treatment via the acceleration of the nucleo-shuttling of ATM (Fig. 3A). This hypothesis was supported by pATM data (Fig. 3B). It is noteworthy that ZOPRA-treated 149BR and 1BR3 cells exposed to g-rays did show similar kinetics of gH2AX foci to untreated cells. [START_REF] Ferlazzo | Mutations of the Huntington's disease protein impact on the ATM-dependent signaling and repair pathways of the radiation-induced DNA double-strand breaks: Corrective effect of statins and bisphosphonates[END_REF]

Discussion

Model of the ATM nucleo-shuttling is relevant for high-LET particles and radiation

According to the recent resolution of the linear-quadratic model published elsewhere, [START_REF] Bodgi | The nucleo-shuttling of the ATM protein as a basis for a novel theory of radiation response: Resolution of the linearquadratic model[END_REF] 2 categories of unrepairable DSB can be lethal:

-The a-type DSB, recognized by NHEJ but remaining unrepaired after 24 hours. These DSBs are reflected by early pATM foci but persistent residual g-H2AX foci. -The b-type DSB not recognized by any DSB repair pathways and therefore never repaired. These DSB are not directly visible by pATM and g-H2AX immunofluorescence, and they correspond to a low number of pATM foci.

ATM dimers CYTOPLASM

NUCLEUS ATM monomers

Low-LET radiation

In the case of 3 MeV protons, the energy microdepositions distribution is likely to be quantitatively and qualitatively sufficient to produce a minimal flux of ATM monomers to recognize all the DSBs produced in the nucleus. Conversely, the energy microdeposition might be so dense that the DSBs created are unrepairable; therefore, the a-type DSB contribution might be predominant for 3 MeV protons. Hence, exposure to 3-MeV protons can result in severe DSB but fully recognized by NHEJ, in agreement with our data (Fig. 4).

In the case of 65-MeV protons, the energy microdepositions can be so scattered that the production of ATM monomers would not be sufficiently high to recognize all the induced DSBs. Conversely, their energy density might be sufficiently high to produce unrepairable DSB like with 3-MeV protons; therefore, the b-type DSB contribution might be predominant for 65-MeV proton. Hence, exposure to 65-MeV protons can result in severe DSB but poorly recognized by NHEJ, in agreement with our data (Fig. 4).

In the case of carbon ions, the scenario is intermediate between that of 65-and 3-MeV protons: the flux of ATM monomers might be significant but still not sufficiently high to recognize all the DSBs at 10 minutes after irradiation.

Hence, both aand b-type DSBs contribute to the RBE of carbon ions (Fig. 4).

In the case of 9-to 12-MeV electrons, X-rays, and g-rays, the LET is low and the energy microdepositions are distributed more homogeneously in cells. Consequently, the flux of ATM monomers might be higher than with high-LET particles. As a result, all the DSBs are generally recognized, and their reparability is high, probably because of the energy microdepositions that are less dense than with high-LET particles (Fig. 4).

The effect of the nucleo-shuttling of ATM after exposure to high-LET particles was also illustrated by the radioprotective effect of the ZOPRA treatment shown in Fig. facilitatedeaccelerated diffusion of ATM monomers can be sufficient for improving the recognition of DSB by NHEJ and therefore for stimulating DSB repair (Fig. 4). Further study is needed to investigate whether ZOPRA can serve as a radioprotection approach for healthy tissues without modulating the antitumor efficiency.

RBE versus LET

Because the number of unrepaired DSBs was shown to be correlated with cell survival, we plotted the RBE calculated from the numbers of residual g-H2AX foci shown in Fig. 1A against the LET values corresponding to each radiation type (Table E1; Fig. 5A). Using previously published formulas, [START_REF] Bodgi | The nucleo-shuttling of the ATM protein as a basis for a novel theory of radiation response: Resolution of the linearquadratic model[END_REF][START_REF] Joubert | DNA double-strand break repair defects in syndromes associated with acute radiation response: At least 2 different assays to predict intrinsic radiosensitivity?[END_REF] we also calculated the corresponding cell surviving fraction at 2 Gy (SF2) and the dose reached when cell surviving fraction was equal to 10% (D 10% ) from the gH2AX and pATM data, respectively. The RBE values were deduced thereafter (Fig. 5B, 5C; SD). The resulting RBE-LET curves of 2 radioresistant fibroblast cell lines tested (149BR and 1BR3) were in good agreement with the literature data, even if the RBE values strongly depend on the calculation method. [START_REF] Steel | Basic Clinical Radiobiology[END_REF] However, the RBE value calculated from the 01HNG cells data were not found in agreement with the classical RBE-LET. This discrepancy was not surprising because the yield of residual DSB was found higher than that of radioresistant controls (Fig. 6) and the SF2 of this cell line was found much lower (25% AE 2% vs 68% AE 4%, respectively). These numerical values strongly influence on the RBE calculations. Similarly, the RBE values calculated from the ATM-mutated AT5BI cell line were found invariable because the maximal hypersensitivity level is already reached with X-rays in these cell lines. It is noteworthy that we aimed to elucidate the wellknown RBE-LET relationship that was established from radioresistant cell lines. Hence, we did not apply the high-LET radiation to cells from patients who already showed radiosensitivity with X-rays. Altogether, these data show that the ATM nucleo-shuttling model might help to predict the RBE-LET curves for any human cell line regardless of its radiosensitivity.

Parallel between high/low-LET differences and individual radiation responses

From the ATM nucleo-shuttling model, a classification of radiosensitivity in 3 groups was proposed [START_REF] Joubert | DNA double-strand break repair defects in syndromes associated with acute radiation response: At least 2 different assays to predict intrinsic radiosensitivity?[END_REF][START_REF] Bodgi | A single formula to describe radiation-induced protein relocalization: Towards a mathematical definition of individual radiosensitivity[END_REF] :

- in the text and irradiated with g-rays: group I, the radioresistant 149BR and 1BR3 fibroblasts; group II, the moderately radiosensitive 01HNG and 06CLB fibroblasts [START_REF] Varela | Combined treatment with statins and aminobisphosphonates extends longevity in a mouse model of human premature aging[END_REF] ; group IIIa, the hyper-radiosensitive ATM-mutated AT5BI fibroblasts; group IIIb, the hyper-radiosensitive LIG4-mutated 180BR fibroblasts. All these cell lines were characterized elsewhere. [START_REF] Varela | Combined treatment with statins and aminobisphosphonates extends longevity in a mouse model of human premature aging[END_REF] Each plot represents the mean AE standard error of the mean of triplicates.

shuttling; eg, the LIG4 syndrome [LIG4 e/e mutations]) [START_REF] Granzotto | Influence of nucleoshuttling of the ATM protein in the healthy tissues response to radiation therapy: Toward a molecular classification of human radiosensitivity[END_REF][START_REF] Bodgi | A single formula to describe radiation-induced protein relocalization: Towards a mathematical definition of individual radiosensitivity[END_REF] Interestingly, a number of similarities was observed in the shape of DSB repair kinetics between the exposure of radioresistant cells to high-LET particles and the exposure of radiosensitive cells to low-LET radiation (Fig. 6):

-The gH2AX kinetics of LIG4-mutated cell lines (group IIIb) is similar to that obtained with 3-MeV protons, reflecting a gross NHEJ-dependent DSB repair defect but a normal DSB recognition.

-The gH2AX kinetics of ATM-mutated cell lines (group IIIa) is similar to that obtained with 65-MeV protons, reflecting a lack of DSB recognition combined with a gross NHEJ-dependent DSB repair defect.

-The gH2AX kinetics of group II cell lines (moderate radiosensitivity) is similar to that obtained with electrons, reflecting moderate impairments of DSB recognition and repair.

-The gH2AX kinetics obtained with carbon ions might correspond to intermediate case of group II (upper level) and group III, but we did not encounter such a case in our collection of human radiosensitive fibroblasts (data not shown).

Conclusion

All the repair data kinetics shown here obey the mathematical constraints detailed by Bodgi et al, [START_REF] Bodgi | A single formula to describe radiation-induced protein relocalization: Towards a mathematical definition of individual radiosensitivity[END_REF] the requirements linked to the ATM nucleo-shuttling model [START_REF] Bodgi | The nucleo-shuttling of the ATM protein as a basis for a novel theory of radiation response: Resolution of the linearquadratic model[END_REF] and suggest that our assumptions are consistent with the radiobiological effects linked to high-LET particles and low-LET radiation in quiescent cells. Further theoretical and experimental investigation is needed to better link physical and biological data and to develop reliable algorithms to predict the radiation response with a given LET and a given individual radiosensitivity.

Fig. 1 .Fig. 2 .

 12 Fig. 1. Kinetics of gH2AX and pATM foci with different radiation types. Immunofluorescence against (A) gH2AX and (B) pATM was applied to the radioresistant 149BR (diamonds), 1BR3 (squares), and AG1521 (circles) fibroblast cell lines. Data are represented as a percentage of the number of foci assessed 10 minutes after irradiation with g-rays. Each plot represents the mean AE standard error of the mean of triplicates. The lower panels show representative immunofluorescence images (149BR cells, 3-MeV protons, 0 and 4 hours after irradiation) with the indicated biomarkers. White bars represent 10 mm.

Fig. 3 .

 3 Fig. 3. Kinetics of gH2AX and pATM foci after exposure to carbon ions and ZOPRA treatment. (A) Immunofluorescence against gH2AX foci was applied to the radioresistant 149BR (diamonds) and AG1521 (circles) fibroblast cell lines presubmitted to ZOPRA treatment (open symbols) or not (closed symbols) and irradiated with carbon ions (1 Gy). Dotted lines and corresponding plots represent the data obtained with or without ZOPRA treatment followed by g-ray irradiation. (B) The corresponding pATM data in 149BR cells obtained 10 minutes after irradiation were plotted at the indicated treatments. Each plot represents the mean AE standard error of the mean of duplicates.

Fig. 4 .

 4 Fig.4. Recapitulative scheme of the effect of the ATM nucleo-shuttling of ATM in the response to high-LET particles. Low-LET radiation induces a homogeneous oxidation that permits the formation of ATM monomers. They rapidly diffuse into the nucleus, thus contributing to DSB recognition and repair. After exposure to high-LET particles, the ionization clusters are not sufficiently dispersed to interact with ATM dimers to produce numerous ATM monomers. DSB recognition is impaired. If ZOPRA treatment is applied, the permeability of the nuclear membranes is increased, and some additional ATM monomers can diffuse into the nucleus to participate to the DSB recognition and repair.

3 .Fig. 5 .

 35 Fig. 5. RBE-LET relationships. (A) The number of residual g-H2AX foci shown in Fig. 1A (the g-rays data were taken as a reference) were plotted against the LET values corresponding to each radiation type for the radioresistant

Fig. 6 .

 6 Fig. 6. Analogies in the response of radioresistant cells to high-LET particles and the response of radiosensitive cells to low-LET radiation. (A) The gH2AX foci data shown in Fig. 1 and expressed as a percentage are reproduced. (B) gH2AX foci kinetics data, also expressed as percentage, and obtained from cells representative of the 3 groups of radiosensitivity defined

AcknowledgmentsdThe authors are thankful for the technical assistance of Christian Beaufre `re, Jean-Pierre Lopez, Gersende Alphonse and Michael Beuve (Lyon University, Lyon, France), Isabelle Testard and Jean-Louis Lefaix (GANIL, Caen, France), and Jean-Pierre Ge ´rard and Je ´ro ˆme Doyen (Antoine-Lacassagne anticancer Center, Nice, France). They thank the Association Pour la Recherche sur l'Ataxie-Telangiectasie (APRAT), the Electricite ´de France (Comite ´de Radioprotection), the Plan Cancer/ AVIESAN "Micromegas project," the Center National d'Etudes Spatiales (CNES), and the Commissariat Ge ´ne ´ral a `l'Investissement (INDIRA project). This work was supported by the Centre National d'Etudes Spatiales (CNES) and the Commissariat Ge ´ne ´ral a l'Investissement (Programmes d'Investissement d'Avenir e Projet INDIRA)


Philippe Martin
email: philippe.martin@sciencespo.fr

Thierry Mayer
email: thierry.mayer@sciencespo.fr

Florian Mayneris
email: florian.mayneris@uclouvain.be

Are clusters more resilient in crises? Evidence from French exporters in 2008-2009 *

Keywords: F1, R10, R11, R12, R15 Clusters, Competitiveness clusters, Exports, Crisis, Resilience

Clusters have already been extensively shown to favor firm-level economic performance (productivity, exports, innovation etc.). However, little is known about the capacity of firms in clusters to resist economic shocks. In this paper, we analyze whether firms that agglomerate in clusters and firms that have been selected to benefit from the "competitiveness cluster" industrial policy, implemented in France in 2005, have performed better on export markets during the recent economic turmoil. We show that, on average, both agglomeration and the cluster policy are associated with a higher survival probability of firms on export markets, and conditioning on survival, a higher growth rate of their exports. However, these effects are not stronger during the 2008-2009 crisis; if anything, the opposite is true. We then show that this weaker resilience of competitiveness cluster firms is probably due to the fact that firms in clusters are more dependent on the fate of the "leader", i.e. the largest exporter in the cluster.

Introduction

During the 2008-2009 financial crisis, manufacturing has been hit severely in many countries. The collapse of international trade in particular led many firms to exit export markets, or even to cease activities. In this paper we analyze the factors behind the resilience of exporters during the crisis. Using French firms as a case study, we ask the data why were some exporters less severely affected than others during the trade collapse.

Let us first return to the alternative explanations that have been provided to explain the collapse of trade during financial crises. [START_REF] Chor | Off the cliff and back? credit conditions and international trade during the global financial crisis[END_REF] analyze the effect that credit conditions had on international trade during the recent global crisis by examining the evolution of monthly US imports over the November 2006 to October 2009 period, and compare trade patterns before and during the crisis. They find that during the crisis period, countries with tighter credit availability exported less to the US, relative to other countries. [START_REF] Amiti | Exports and financial shocks[END_REF] show that Japanese banks transmitted financial shocks to exporters during the systemic crisis in Japan that took place in the 1990s. [START_REF] Ahn | Trade finance and the great trade collapse[END_REF] review evidence that financial factors may have resulted in a greater decline in exports than were predicted in models without financial frictions. In the same vein, [START_REF] Bricongne | Firms and the global crisis: French exports in the turmoil[END_REF] find that the exports of French firms in more external finance-dependent sectors were more adversely hit during the recent global crisis. Finally, [START_REF] Berman | Time to ship during financial crises[END_REF] found that the fall in trade caused by financial crises is magnified by the time-to-ship goods between the origin and the destination country. Because risk of default increases with time to ship, this aggregate and firm level evidence points to the existence of financial frictions that are exacerbated during a financial crisis.

However, some economists have downplayed the role financial frictions when explaining the drop in international trade. [START_REF] Levchenko | The collapse of international trade during the 2008-2009 crisis: In search of the smoking gun[END_REF] emphasize the disruption of global production lines and the reduction in trade in intermediate goods during the recent financial crisis to explain that the fall in trade has been larger than the fall in output, and therefore conclude that trade finance has played a minor role in the trade collapse of 2008[START_REF] Koenig | Agglomeration and the export decisions of french firms[END_REF][START_REF] Eaton | Trade and the global recession[END_REF] quantify the relative contributions of changes in demand versus changes in trade frictions, using a general equilibrium model of production and trade. They also conclude that the fall in demand was more important.

Whatever the exact mechanisms behind the trade collapse may be, some firms have been more resilient than others, which is our topic of interest. It is well known that larger and more productive firms are more resilient to shocks than others. However, we know little about how the local environment of firms and how public policies affect this resilience. In this paper, we focus on a specific dimension of the local environment of firms and on a specific type of public policy, namely clusters and cluster policies, as determinants of a specific type of resilience, namely resilience on the export markets. To document the resilience of clusters and firms that benefit from cluster policies, we use data on the French exporters from 2004 to 2009.

Since the end of the 1980's, agglomeration economies have been used to justify cluster policies by national and local governments. This has been the case in Germany, Brazil, Japan, Southern Korea, Spanish Basque country or more recently in France (see [START_REF] Duranton | The economics of clusters: Lessons from the French experience[END_REF]. The economic literature on the empirical evaluation of these cluster policies is relatively scarce, when compared in particular to the numerous studies conducted by government agencies and consulting firms. Most of these use qualitative methodologies and are descriptive (for a recent survey of evidence, see [START_REF] Duranton | California dreamin': the feeble case for cluster policies[END_REF].

In two previous papers (Martin et al., 2011a,b), we find mixed evidence on the effect of clusters and cluster policies. In Martin et al. (2011a), we use firm and plant panel data to measure the strength and the shape of agglomeration externalities in France, very closely to the micro theories. The sample of those papers covers the whole manufacturing sector. The estimation relies on GMM, and thus on short-run (yearly) variations of the variables. We find that in the short-run, taking into account several possible biases, localization economies are the only significant agglomeration externalities in the French economy. Hence, the starting point of those who favor cluster policies is right: there are productivity gains associated to clusters. However, the elasticity of firm-level TFP to the size of its own sector at the local level is rather low, equal to 5% (in line with measures obtained in other contexts, see [START_REF] Rosenthal | Evidence on the nature and sources of agglomeration economies[END_REF], not because agglomeration economies are weak, but because those gains seem to be already well internalized by firms in their location decisions. Indeed, we show that localization economies are bell-shaped, and the comparison between an estimated geographical distribution of plants that would maximize productivity and the one that is actually observed suggests no large gap, at least in the French case. It points neither to a situation where geography is too concentrated and specialized, nor to a geography that needs more clustering. In the same vein, note that many papers studying firm location decisions show that the presence of other firms in a region increases significantly the probability that a plant chooses to locate in this region (see, e.g., [START_REF] Head | Attracting foreign manufacturing: Investment promotion and agglomeration[END_REF][START_REF] Crozet | How do firms agglomerate? a study of fdi in france[END_REF][START_REF] Devereux | Firm location decisions, regional grants and agglomeration externalities[END_REF]. Consequently, the gains we can expect from more clustering are, at least in the short-run, relatively small. Of course, this result is "only" about productivity and is not about welfare, which agglomeration could affect through other channels than productivity. However, this suggests that even though the starting point of cluster policy advocates is right, their conclusion advocating costly public intervention to favor agglomeration is dubious, at least in France. Moreover, in Martin et al. (2011b), we use the same dataset to evaluate the first cluster policy implemented in France, the "Systèmes Productifs Locaux" policy. We find that the policy, contrary to its official goal, helped declining firms operating in declining sectors and areas, and had no measurable impact on firm-level productivity, employment or exports.

Another strand of the literature focuses on export spillovers, i.e. on the role of surrounding exporters on firm-level export activities. The underlying idea is that the presence of other exporters might reduce the fixed and/or the variable export costs firms have to pay to serve foreign markets, through the exchange of information or the mutualization of some of these costs. In a pioneer work, [START_REF] Aitken | Spillovers, foreign investment, and export behavior[END_REF] show for example that the probability that Mexican plants export in a given year is positively related to the presence of multinationals. More recently, [START_REF] Koenig | Agglomeration and the export decisions of french firms[END_REF] and [START_REF] Koenig | Local export spillovers in france[END_REF] show on French data that the presence of other exporters (whatever their nationality) increases the probability that French firms start exporting a given product to a given country; however, these spillovers occur at a very fine level in terms of activity, being stronger when specific to the product and the destination country that are considered. Moreover, existing studies suggest that export spillovers mainly affect the firm-level extensive margin of trade (export status or export entry), rather than the intensive margin (value of exports).

While the positive impact of agglomeration on firm-level productivity, exports or innovation is now well documented, little is known about the potential effect of clusters on firm resilience. In the present paper, we deal with this issue and ask whether the probability to remain on an export market and, conditioning on staying, the growth rate of firm-level exports, are correlated to the presence of other exporters, and to the fact that firms benefit from cluster policies. We are specifically interested on how this correlation behaves during the financial crisis of 2008-2009. Such an issue is particularly important for developed economies in a context of structural change, where more and more industrial activities are offshored; indeed, if the presence of other producers and exporters positively affects firm-level resistance to shocks, the "desindustrialization" process could be reinforced in a context of crisis due to weaker spillovers.

In our analysis, we distinguish the effect of surrounding exporters from the specific behavior of French firms that are part of clusters benefiting from public support. We are more specifically interested in a cluster policy, the "poles de competitivité (competitiveness clusters)" policy, which was launched in 2005. The policy is based on calls for tender leading to financial subsidies for innovative projects which are managed collectively by firms, research departments and universities. The map of these clusters shows that they are quite dispersed on the French territory. Most commentators have analyzed this geography (which does not correspond fully to the industrial geography of France) as the result of political constraints that obliged policy-makers to "give" a cluster to each of the French large regions. Regarding the effect of the competitiveness cluster policy, we do not want to interpret the correlations we observe as causal, since many unobservable characteristics of the firms could both make them good candidates to be selected in publicly subsidized clusters and to be more resilient. This is so even though we control for many characteristics of the firms and of their local environment.

Our results show that the agglomeration of exporters positively affects the survival probability of firms on export markets, and conditioning on survival, the growth rate of their exports. These spillover effects are not stronger during the crisis; if anything, the opposite is true. Moreover, we find that on average, exporters that belong to competitiveness clusters are We then show that this weaker resilience of competitiveness cluster firms is probably due to the fact that firms in clusters are more dependent on the fate of the "leader", i.e. the largest exporter in the cluster. Section 2 details our empirical strategy and section 3 presents our results. Section 4 concludes.

Determinants of firm-level resilience on export markets: estimation strategy

We use the French customs dataset that is now well-known and has been extensively used in other studies (see Berman et al., 2012, for example). We exploit the information available on firm-level export values from 2004 to 2009 at the firm-sector (hs2)-destination country level. The information on the municipality where firms are located is taken from the SIRENE database, which records various data for all plants operating in France.

Estimating equation

We study two dimensions of firm-level resistance on export markets: the probability to survive, and conditioning on surviving, the growth rate of exports. We conduct the analysis at the firm-sector (hs2)-destination country level. Firm-level export activities are subject to a lot of annual entries and exits which are linked to experimentations or occasional transactions for firms (see, e.g., [START_REF] Eaton | Export dynamics in colombia: Firm-level evidence[END_REF][START_REF] Albornoz | Sequential exporting[END_REF]. These movements do not necessarily reflect deep patterns of firm-level exports; in order to smooth the possible noise introduced by these multiple entries and exits, we focus on survival and export growth over periods of two years. Since we analyze customs data from 2004 to 2009, we have in the end a sample composed of four waves (from 2004-2006 to 2007-2009).

The main equation we estimate has the following form:

y idsct = αcc i + βcc i × crisis t + δX i(sc)t-2 + γX i(sc)t-2 × crisis t +ηY d(sc)t-2 + νY d(sc)t-2 × crisis t + u sct + idsct , (1)
where, on the left-hand side, we are interested by either the probability to remain on an export market or, conditioning on survival, by the growth rate of exports for firm i, located in département d1 , exporting in hs2 sector s, to country c, at time t.2 cc i is a dummy that equals 1 if firm i is in a competitiveness cluster, whatever the year. Since the competitiveness clusters are labeled in 2005 and the first subsidies are allocated in 2006, there is no before/after analysis for the estimation of α in our regressions. This is why we do not claim to provide an evaluation of the impact of the French competitiveness cluster policy on firm-level resilience on export markets. We rather document a possible gap in terms of survival between firms selected in these clusters and other firms, this gap being possibly different during the crisis, as captured by the coefficient β. Indeed, crisis t is a dummy that equals 1 if the observation is during the crisis period (2008 and 2009). X i(sc)t-2 correspond to firm-level controls at the beginning of the two-year period considered. These are the number of sectors and countries the firm exports to, and the value of its total exports by sector and/or destination. Hence, we take into account the fact that bigger exporters or exporters with a larger portfolio in terms of sectors and/or destinations are probably more resilient to negative shocks. Note that since we use customs data only, and not balance sheet data, these variables proxy for firm-level TFP, more productive firms being also bigger exporters. We also include Y d(sc)t-2 which are départementlevel controls: these are the number of exporters by sector and/or destination, to measure potential externalities from surrounding exporters on firm-level resistance on export markets, and the Balassa index of specialization of exports at département-sector and département-country level, to control for local comparative advantage. 3 Finally, we include u sct which are sector-country-year fixed effects. They control for all time-varying characteristics that are specific to both the sector and the destination country: these fixed effects capture in particular both supply and demand shocks that are sector and destination specific. Firm and département controls are also interacted with the dummy identifying the years of the crisis. Given this specification, our estimation is based on repeated cross-sections: the coefficient on the competitiveness cluster dummy is obtained comparing competitiveness cluster firms to other firms exporting to the same market (sector-destination country) in a given year, while spillovers are estimated comparing firms exporting to the same market but located in different départements. 4 The interaction terms capture the difference in the impact of these variables during crisis as compared to normal times.

Finally, the information on exports is available at the firm-level, and not at the plant-level. For multi-plant firms which are active in different départements, all the département-level variables are thus subject to measurement error. 5 Our baseline results restrict the sample to single-plant firms to minimize those measurement issues. However, we have checked that results are the same when we use all firms, considering in that case that multi-plant firms are located in the département of their headquarters (Tables A-2 and A-3).

Descriptive statistics

We first provide descriptive statistics on the survival rate and the growth rate of exports (conditioning on survival) separately for competitiveness cluster firms and for the other firms. "Normal times" corresponds to the waves 2004-2006 and 2005-2007, and the "Crisis" to the waves 2006-2008 and 2007-2009; indeed, as shown by [START_REF] Bricongne | Firms and the global crisis: French exports in the turmoil[END_REF], the collapse of French exports associated with the crisis starts in September 2008.

Table 1 reveals that competitiveness cluster firms are much bigger than the others: they export more and have a wider export portfolio, both in terms of sectors and destinations, resulting into a higher number of observations at the firm-sector-destination country level. This is in line with the results obtained by [START_REF] Fontagné | Cluster policies and firm selection: Evidence from france[END_REF], who analyze the characteristics of the exporting firms selected in the French competitiveness clusters. Their survival rate on export markets and the growth rate of their exports are also higher. Competitiveness cluster firms are thus bigger and more resistant exporters as compared to other firms, both in normal times and during the crisis. However, given the results of [START_REF] Fontagné | Cluster policies and firm selection: Evidence from france[END_REF] 3 Which might explain both the agglomeration of exporters for certain types of sectors and destinations and firm-level export performance. 4 We do not take into account potential spatial correlation in the explanatory variables or in the residuals. However, other papers studying export spillovers on firm entry, such as [START_REF] Koenig | Local export spillovers in france[END_REF] or [START_REF] Mayneris | Chinese firms'entry to export markets : the role of foreign export spillovers[END_REF], show that taking into account agglomeration in surrounding regions does not affect the results.

5 Since a firm is "located" in the département of its headquarters, which might be different from where its actual production for export is. a clear selection effect may be at work here in the sense that better performing and more resilient exporters may have been selected to be part of competitiveness clusters. Hence, we will be careful not to draw causality interpretations from our regressions relating performance or resilience and the competitiveness cluster status.

However, the evolution of the gap between competitiveness cluster firms and the other exporters provides a slightly different picture. Both the survival rate and the growth rate of total exports decreases during the crisis for competitiveness cluster firms: 63% of their export flows survive during the crisis on average, vs 66% in normal time, and their total exports decrease by 7% during the crisis, while they are increasing by 12% on average in normal times. Hence, the survival rate of their transactions decreases by 3 percentage point, and the growth rate of their overall exports by 19 percentage point. For the other exporters, the survival rate remains the same, equal to 53%, while the growth rate of their exports decreases by 12 percentage points only, from 3% to -9%. Quite surprisingly, when comparing normal to crisis times, it thus seems that competitiveness cluster firms suffered more during the crisis.

These patterns could be due to the fact that big firms were more affected by the crisis. Figure 2 presents the predicted survival probability at the firm-sector (hs2)-destination country level, estimated from a linear probability model6 that controls for the initial size of the export flow and time trends. It is clear that on average, firms in competitiveness clusters are more resistant on export markets. But while non-cluster firms exhibit a very similar survival probability is in normal and in crisis times, those in competitiveness clusters see their survival probability decrease during the crisis. This is true for the entire sample of firms and for single plant firms only. Hence, controlling for the size of the initial export flow does not affect the picture provided by Table 1.

Other firm-level characteristics, such as the sector of activity and the width of the export portfolio, or local characteristics such as the number of surrounding exporters and local comparative advantages, could be correlated to both the presence in competitiveness clusters and survival on export markets during the crisis. We address these issues in the econometric analysis.

3 Empirical results

Baseline results

We first estimate the determinants of the probability that firms exporting in a given sector to a given country at time t -2 remain active on that market in t. We use a linear probability model, which has the decisive advantage of making interpretation of interaction terms much easier. We moreover investigate whether these determinants vary during crisis, as compared to normal times. Regression (1) in Table 2 shows that when we do not control for firm performance indicators, exporters that belong to competitiveness clusters have a much higher probability to stay on a given export market: their survival probability is higher by 10 percentage points. However, this is less the case during the crisis, as the interaction term with the two years of crisis (2008)(2009) turns out to be negative and significant (even though it is small): during the crisis, their survival probability is higher by 8.3 percentage points only. Hence, in line with previous descriptive statistics, firms belonging to competitiveness clusters appear to be less resilient, even when the sector and the destination of exports are controlled for.

In regression (2) we control for some of the characteristics of the environment of the firm. We control in particular for the number of exporters located in the same département. We distinguish four types of surrounding firms: those that export to the exact same market (same sector-same destination) as the one considered on the left-hand side, firms exporting in other sectors but to the same country, firms exporting in the same sector but to other countries, and firms exporting to completely different export markets. These variables can be interpreted as a measure of "natural" clusters. Note first that this measure is also correlated to the survival of firms on export markets. In particular, being surrounded by firms exporting to the exact same market positively affects firm-level survival probability on that market. This is interesting and to our knowledge this is a first time that firm resilience has been shown to be related to clustering. However, these spillovers are not stronger during the crisis; if anything, the opposite is true. Controlling for the size and the composition of the pool of surrounding exporters barely changes the gap between competitiveness cluster firms and the others in terms of survival probability, either in normal time or during the crisis.

In regression (3), we control for some firm-level observable performance characteristics, such as the size and the composition of exports (following the same decomposition as the one adopted for the pool of surrounding exporters) and the number of sectors and countries in the export portfolio of the firm; the "average" premium of competitiveness cluster firms in terms of survival rate falls a lot, and is now equal to 3.1 percentage points only: this suggests that a selection effect into these competitiveness clusters exists. To save space, the coefficients on firm-level characteristics are not detailed, but by and large, bigger firms have higher survival rates. 7 Hence, around two thirds of the survival premium of competitiveness cluster firms in normal times can be attributed to their bigger size. However, the weakening of this correlation during the crisis persists, with a coefficient that remains equal to -0.016. The introduction of local comparative advantage measures in regression (4) does not change the results. Hence, even when sector, destination, firm-level and local characteristics are controlled for, competitiveness clusters appear less resilient, i.e. they suffer more during a crisis. The picture is even reinforced in relative terms, since in the end, their survival premium is reduced by more than half during the crisis, from 3.1 percentage points to 1.5 percentage points.

In unreported regressions, we have conducted several robustness checks. Results remain similar when we focus on the sectors that are most relevant for the exporting firms, i.e. when we eliminate the sectors representing less than 5% of overall firm-level exports, when we distinguish intra-EU and extra-EU exports, when we control for the fact that firms have benefited from the first cluster policy implemented in France at the end of the 1990's 8 , or when we control for the value of exports of surrounding exporters instead of their count.

Table A-1 presents the same regression as in column (4) of Table 2, but at the sectoral level. We see that the survival premium for competitiveness cluster firms is the largest for the transport sector. It is positive and significant for all sectors except for mineral products. Note also that the negative coefficient on the interaction term between competitiveness cluster and times of crisis mainly comes from three sectors: agrifood, machinery and miscellaneous, which represent altogether around 40% of the observations in the entire sample. The weaker resilience of competitiveness cluster firms is thus not a general feature of French exporters; it 7 And this correlation tends to be reinforced during the crisis. 8 The "Systèmes Productifs Locaux" policy hat we evaluate in Martin et al. (2011b). mainly concerns a few sectors, that are still quite important in overall French exports. Finally, Table 3 shows that the results on the survival probability extend to the growth rate of exports, conditioning on survival. Firms in competitiveness clusters experience on average higher growth rates of exports, but not so during the financial crisis, which seems to have hit them more strongly. When firm-level and local characteristics are taken into account, their average premium on a given market in terms of export growth rate decreases by more than one third, from 8.6 percentage points in normal times to 5.6 percentage points.

The role of the leader

After having shown that competitiveness cluster firms suffer more than others during the crisis, even when individual and local characteristics are controlled for, we dig deeper to understand the reasons explaining this weaker resilience. One explanation could be that the survival of competitiveness cluster firms on export markets heavily depends on the export performance of a leader firm. Indeed, clusters are often viewed as a network of firms with strong relations, whether these relations go through market mechanisms (between input and Note: All explanatory variables taken in t -2. All regressions clustered at the country-hs2-dep-year level.

output suppliers for example) or non-market ones (technology spillovers or cooperation on specific projects). These strong relations may generate local dependence, and this local dependence may be hierarchical in that the largest firms/exporters may have more influence than others. This is certainly the case for input suppliers. Think of Airbus in Toulouse for example, and its network of local partners, suppliers and subcontractors. Due to the tight relationships between them, we can imagine that the export performance of Airbus strongly affects the performance of the other local firms active in the aerospace industry. If this the case, part of the weaker resilience of competitiveness cluster firms during the crisis might be related to an amplification of the shock, due to their stronger dependence on one leading exporter in the region, when is itself strongly hit by the crisis. A related literature [START_REF] Crespo | Lock-in or lock-out? how structural properties of knowledge networks affect regional resilience[END_REF], is a recent example) has formulated several hypotheses linking the resilience of the cluster to key statistics of the network structure linking firms. The definition of cluster resilience in this literature is both larger and richer than in our study and is analyzed in an evolutionary economic geography framework. Our test of the impact of the dependence on the leader can be interpreted as a test of whether linkages to the main node of the network matter in resilience to shocks.

In this section, given the focus of our analysis, we restrict the sample to exporters that are active in sectors and départements in which there are also competitiveness cluster firms. We define the leader as the largest competitiveness cluster exporter in a given sector (hs2) and a given département at time t -2.

Since we have restricted our sample, the coefficient on the dummy identifying competitiveness cluster firms is now estimated comparing competitiveness cluster firms to non-cluster firms which are active in the same sector-départements as competitiveness cluster firms. In column (1) of Table 4, we verify that we still measure weaker resilience for competitiveness cluster firms. The "raw" survival premium in normal times is now reduced (from 10 percentage points to 3.8 percentage points), in line with [START_REF] Fontagné | Cluster policies and firm selection: Evidence from france[END_REF] who show that, within sectors, the French competitiveness clusters have been labeled in the best performing départements. However, we still observe a strong reduction of this premium during the crisis. In regression (2), we control for the survival (or not) of the leader. We find that in normal times, the fact that the leader survives on export markets increases the survival probability of firms exporting in the same sector-département. This is indicated by the positive coefficient on the dummy "cluster leader stays".9 Next, we see that this dependence effect is even stronger when the firm belongs to a competitiveness cluster, as indicated by the positive coefficient on the interaction term "cluster leader stays" × "Comp. cluster". These two results remain very robust in the following regressions, where we add firm-level controls or local (département) controls. This shows that the survival on export markets depends more strongly on the performance of the leader for competitiveness cluster firms than for the other firms.

How this dependence behaves during the crisis is less clear and robust. We see that the crisis reduces the role of the leader, but this effect disappears once local controls are added. For firms in competitiveness clusters there is no specific dependence on the leader during the crisis. However, it seems now that once firm-level characteristics are controlled for (regressions (3) and (4) of Table 4), the survival rates of competitiveness firms during the crisis and in normal times are not significantly different. These results consequently suggest that the weaker resilience measured so far was due to a stronger dependence of competitiveness cluster firms on the export activity of the local leading firm, both in normal an in crisis times.

Conclusion

Clusters are popular among policy makers. There are good reasons for this: geographical concentration of firms operating in the same industry has been extensively shown to favor firmlevel economic performance (productivity, exports, innovation etc.). However, the previous literature has also shown that the gains to expect from more agglomeration are relatively modest, since they are already partly internalized by firms in their location choices. Much less is known on whether firms in clusters (whether natural or policy-induced) resist better to economic shocks than others. In this paper, we use French customs data to document how agglomeration of exporters is correlated to export performance of French manufacturing firms during the 2008-2009 crisis. On average, exporters that belong to competitiveness clusters are more resilient in that their probability to continue exporting on a market is higher than for other firms. However, this premium decreases sharply during the 2008-2009 crisis. We show that this can be explained by the fact that firms in competitiveness clusters are more dependent on the fate of the "leader", the largest exporter in the cluster. These "stylized facts" cannot be interpreted in a causal way regarding the effect of the policy itself. However, they suggest that by reinforcing the relationships and the interdependencies between firms, clusters might amplify the transmission of shocks, for the best in case of economic booms, or the worse during crises.

Figure 1 :

 1 Figure 1: Map competitiveness clusters

Figure 2 :

 2 Figure 2: Survival probability (firm-sector-destination country export flow)

Table 1 :

 1 Firm-level descriptive statistics All figures are averages for the considered cell. Export values are in Million euros. Survival rates and export growh rates are calculated from t -2 to t.

		exp. value # hs2 # dest.	# obs.	surviv. rate	exp. growth	exp. growth
		total			Firm×hs2×des. Firm-hs2-dest Firm-hs2-dest	total
					Competitiveness cluster firms
	Normal times	49.12	5.7	16.3	37.4	0.66	0.10	0.12
	Crisis	47.17	5.7	16.4	37.8	0.63	-0.04	-0.07
					Other firms
	Normal times	2.63	2.6	5.2	9.1	0.53	0.06	0.03
	Crisis	2.66	2.6	5.3	9.4	0.53	-0.06	-0.09
	Note:

Table 2 :

 2 Survival probability: linear probability model (Single-plant firms)

	Dep. variable

Note: All explanatory variables taken in t -2. All regressions clustered at the country-hs2-dep-year level.

Table 3 :

 3 Growth-rate between t -2 and t -Firm/hs2/country (Single-plant firms)

	Dep. variable

Table 4 :

 4 The role of the leader (Single-plant firms)

	Dep. variable	Prob. to stay. in t (linear probability model)
		(1)	(2)	(3)	(4)	(5)
	Cluster dummies
	Competitiveness cluster firm	0.038*** -0.116*** -0.107*** -0.022*	-0.031**
		(0.004)	(0.011)	(0.015)	(0.013)	(0.012)
	Competitiveness cluster firm × crisis	-0.021*** -0.026*** -0.047**	-0.000	-0.000
		(0.006)	(0.006)	(0.021)	(0.018)	(0.018)
	Dummy "cluster leader stays"		0.014*** 0.018*** 0.006*** 0.006***
			(0.001)	(0.002)	(0.002)	(0.002)
	Dummy "cluster leader stays" × Comp. cluster firm		0.168*** 0.156*** 0.056*** 0.061***
			(0.011)	(0.016)	(0.013)	(0.013)
	Dummy "cluster leader stays" ×crisis			-0.007*** -0.006**	-0.003
				(0.002)	(0.002)	(0.002)
	Dummy "cluster leader stays" × Comp. cluster firm×crisis			0.026	-0.023	-0.024
				(0.022)	(0.019)	(0.019)
	Cluster leader exp. growth		-0.001	-0.003***	-0.001	-0.000
			(0.000)	(0.001)	(0.001)	(0.001)
	Cluster leader exp. growth× Comp. cluster firm		0.001	-0.001	-0.005	-0.003
			(0.003)	(0.004)	(0.004)	(0.004)
	Cluster leader exp. growth× crisis			0.004***	0.000	-0.001
				(0.001)	(0.001)	(0.001)
	Cluster leader exp. growth× crisis× Comp. cluster firm			0.002	0.010**	0.009*
				(0.005)	(0.005)	(0.005)
	Observations	1172526	1172526	1172526	1172526 1172526
	Avg prob.			0.51
	Country-hs2-year fixed effect	yes	yes	yes	yes	yes
	Firm-level controls	no	no	no	yes	yes
	Local controls (spillovers and comparative advantage)	no	no	no	no	yes

All explanatory variables taken in t -2. All regressions clustered at the country-hs2-dep-year level.

Table A

 A Note: All firm-level and local characteristics taken into account but not reported. All explanatory variables taken in t -2. All regressions clustered at the country-hs2-dep-year level.

			Machin. Transp. Misc.	0.0341*** 0.0727*** 0.00624	(0.00486) (0.0124) (0.00690)	-0.0427*** -0.00235 -0.0255***	(0.00682) (0.0169) (0.00950)	409663 91587 228479	0.1679 0.1651 0.1729	0.50 0.41 0.49	yes yes yes	yes yes yes	yes yes yes
	-1: Survival probability by sector	Prob. to stay. in t (linear probability model)	Agrifood Min. Prod. Chem./Plastics Leath./Text/App. Stone/Glass/Met.	0.0435*** 0.0228 0.0402*** 0.0528*** 0.0155**	(0.00721) (0.0255) (0.00516) (0.00889) (0.00621)	-0.0167* -0.0396 -0.00263 -0.00501 -0.000507	(0.00985) (0.0368) (0.00712) (0.0127) (0.00873)	205486 13710 300026 282229 271977	0.1439 0.1361 0.1433 0.1550 0.1637	0.63 0.55 0.55 0.48 0.51	yes yes yes yes yes	yes yes yes yes yes	yes yes yes yes yes
		Dep. variable	Cluster dummies	Competitiveness cluster firm		Competitiveness cluster firm × crisis		Observations	R2	Avg prob.	Country-hs2-year fixed effect	Firm-level controls	Local controls	(spillovers and comparative advantage)

  Table A-2: Survival probability: linear probability model (All firms) Note: All explanatory variables taken in t -2. All regressions clustered at the country-hs2-dep-year level.

	Dep. variable		Prob. to stay. in t
		(1)	(2)	(3)	(4)
	Cluster dummies
	Competitiveness cluster firm	0.129*** 0.129*** 0.024*** 0.024***
		(0.001)	(0.001)	(0.001)	(0.001)
	Competitiveness cluster firm × crisis	-0.024*** -0.024*** -0.025*** -0.025***
		(0.002)	(0.002)	(0.002)	(0.002)
	Export cluster variables
	Log(# exp., same hs2-country-dep+1)		0.020*** 0.018*** 0.018***
			(0.001)	(0.001)	(0.001)
	Log(# exp., same hs2-country-dep+1) × crisis		-0.002*	-0.001	-0.002*
			(0.001)	(0.001)	(0.001)
	Log (# exp., same hs2-other count., same dep+1)		0.012*** -0.014*** -0.011***
			(0.001)	(0.001)	(0.001)
	Log (# exp., same hs2-other count., same dep+1) × crisis		-0.001	-0.001	-0.002*
			(0.001)	(0.001)	(0.001)
	Log (# exp., other hs2-same country, same dep+1)		-0.007*** 0.005*** -0.004***
			(0.001)	(0.001)	(0.001)
	Log (# exp., other hs2-same country, same dep+1) × crisis		0.003**	0.002	0.006***
			(0.001)	(0.001)	(0.002)
	Log (# exp., other hs2-other country, same dep+1)		-0.027*** -0.014*** -0.010***
			(0.001)	(0.001)	(0.001)
	Log (# exp., other hs2-other country, same dep+1) × crisis		-0.001	-0.000	-0.002
			(0.002)	(0.002)	(0.002)
	Observations	3739953	3739953	3739953	3739953
	Country-hs2-year fixed effect	yes	yes	yes	yes
	Firm-level controls	no	no	yes	yes
	Local comparative advantages	no	no	no	yes

  Table A-3: Growth-rate between t -2 and t -Firm/hs2/country (All firms)Note: All explanatory variables taken in t -2. All regressions clustered at the country-hs2-dep-year level.

	Dep. variable	∆ log(firm-hs2-country exports)
		(1)	(2)	(3)	(4)
	Cluster dummies
	Competitiveness cluster firm	0.034*** 0.034*** 0.081*** 0.077***
		(0.003)	(0.003)	(0.003)	(0.003)
	Competitiveness cluster firm × crisis	-0.024*** -0.024*** -0.023*** -0.023***
		(0.005)	(0.005)	(0.005)	(0.005)
	Export spillover variables
	Log(# exp., same hs2-country-dep+1)		-0.010*** 0.015***	-0.004**
			(0.002)	(0.002)	(0.002)
	Log(# exp., same hs2-country-dep+1) × crisis		0.005*	0.006**	0.008***
			(0.003)	(0.003)	(0.003)
	Log (# exp., same hs2-other count., same dep+1)		0.003	-0.014*** -0.034***
			(0.002)	(0.002)	(0.003)
	Log (# exp., same hs2-other count., same dep+1) × crisis		-0.003	-0.001	-0.001
			(0.004)	(0.003)	(0.004)
	Log (# exp., other hs2-same country, same dep+1)		0.008*** 0.047*** 0.057***
			(0.003)	(0.003)	(0.003)
	Log (# exp., other hs2-same country, same dep+1) × crisis		-0.020*** -0.016*** -0.010**
			(0.004)	(0.004)	(0.004)
	Log (# exp., other hs2-other country, same dep+1)		-0.007** -0.052*** -0.026***
			(0.003)	(0.003)	(0.003)
	Log (# exp., other hs2-other country, same dep+1) × crisis		0.021*** 0.013***	0.006
			(0.004)	(0.004)	(0.005)
	Observations	2041154	2041154	2041154	2041154
	Country-hs2-year fixed effect	yes	yes	yes	yes
	Firm-level controls	no	no	yes	yes
	Local comparative advantages	no	no	no	yes

Départements are administrative entities; there are a bit less than 100 départements in France.

Note that following[START_REF] Davis | Job Creation and Destruction[END_REF], the growth rate of exports is computed taking the average size of exports in t and t -2 as the denominator, so as to reduce noise and regression to the mean issues. As a consequence, the growth rate of exports is bounded by -2 for disappearing flows and 2 for new trade flows.

Graphs are very similar when the predicted probabilities are computed with a logit estimation.

This dummy is equal to 1 for around 72% of the observations.

Maria Bas and an anonymous referee for useful comments. Philippe Martin thanks the Banque de France-Sciences Po partnership for financial assistance. Florian Mayneris thanks the Belgian French-speaking Community

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Appendix