K Sachse

Solmonson

Hsia Rc

Bavoil Pm

Chlamydia buteonis, a new Chlamydia species isolated from a red-shouldered hawk

Keywords: Chlamydiaceae, red-shouldered hawk, MLST, WGS, electron microscopy. Introduction

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

(Threskiornis aethiopicus) were assigned to Candidatus C. ibidis [START_REF] Vorimore | Isolation of a New Chlamydia species from the Feral Sacred Ibis (Threskiornis aethiopicus): Chlamydia ibidis[END_REF]. More recently, a group of C. abortus strains that are distinct from ruminant strains were isolated from wild swans and ducks [START_REF] Szymańska-Czerwińska | Dissemination and genetic diversity of chlamydial agents in Polish wildfowl: Isolation and molecular characterisation of avian Chlamydia abortus strains[END_REF]. Overall, this suggests that the spectrum of chlamydial lineages occurring in wild birds is wider than current knowledge indicates. Avian chlamydiosis has been repeatedly diagnosed in raptors, including red-tailed hawks, Swainson's hawks and ferruginous hawks [START_REF] Blomqvist | Chlamydia psittaci in birds of prey, Sweden[END_REF][START_REF] Johns | What is your diagnosis? Blood smear from an injured red-tailed hawk[END_REF][START_REF] Mirandé | Chlamydiosis in a red-tailed hawk (Buteo jamaicensis)[END_REF][START_REF] Schettler | Newcastle disease virus and Chlamydia psittaci in free-living raptors from eastern Germany[END_REF]. Detailed whole-genome sequence (WGS) characterization of strain RTH, isolated from a red-tailed hawk, putatively identified a new species separate from other members of the Chlamydiaceae, and with C. psittaci as its closest relative [START_REF] Joseph | Chlamydiaceae Genomics Reveals Interspecies Admixture and the Recent Evolution of Chlamydia abortus Infecting Lower Mammalian Species and Humans[END_REF][START_REF] Read | Comparative analysis of Chlamydia psittaci genomes reveals the recent emergence of a pathogenic lineage with a broad host range[END_REF]. Here, we describe the isolation and molecular characterization of a new chlamydial strain from a redshouldered hawk (Buteo lineatus).

Materials and Methods

Case description

In August 2016, a female hatch-year red-shouldered hawk (id 16-066) developed conjunctivitis and died within a week. It was the second fatal case among a group of 12 hawks kept in a flight room. The animal was in a good nutritional state based on significant internal adipose tissue stores. At necropsy, a thick beige exudate covered the conjunctival sac of one eye. The liver was slightly swollen and discolored brown. One small beige nodule, approximately 1 mm in diameter, was present in one lung lobe and the spleen was moderately enlarged. Histopathological analysis revealed ulcerations on conjunctival tissues with numerous coccobacilli and non-specific mononuclear hepatitis and splenitis. Enterococcus faecalis and Pseudomonas aeruginosa were isolated from conjunctival tissue. Following a positive test obtained for a swab collected from the diseased bird using a rapid Chlamydia test, a duplicate swab was used to inoculate Vero cells and a suspected Chlamydia strain, designated RSHA, was isolated.

Sampling, first diagnosis and isolation procedure

Conjunctival tissue and cloaca were swabbed in duplicate from the dead bird. One swab was tested with the QuickVue Chlamydia test (Quidel) whereas the second one was inoculated onto fresh monolayers of Vero cells, with four dilutions of the inoculum tested. After inoculation, vials were centrifuged at 2,000 rpm for 1 h at 30ºC. Monolayers were then washed three times with the Chlamydia Hank's solution and 1 mL of fresh medium was added to each vial. The vials were then incubated at 37ºC for 7 days and examined daily. Each dilution was passed once after 7 days of inoculation. Infected monolayers on coverslips were then fixed with acetone, and monolayers were stained with the 601-RDV C. psittaci fluorescent antibody conjugate (National Veterinary Service Laboratories, USA). When inclusions of typical chlamydial morphology were observed as bright apple-green spots, the sample was considered positive. The isolate was kept frozen at -80°C until further processing.

DNA-based characterization

Species-specific real-time PCR PCR assays targeting C. psittaci, C. abortus, C. avium and C. gallinacea species were conducted as previously described [START_REF] Ménard | Development of a realtime PCR for the detection of Chlamydia psittaci[END_REF][START_REF] Pantchev | New real-time PCR tests for species-specific detection of Chlamydophila psittaci and Chlamydophila abortus from tissue samples[END_REF][START_REF] Zocevic | Molecular characterization of atypical Chlamydia and evidence of their dissemination in different European and Asian chicken flocks by specific real-time PCR[END_REF][START_REF] Zocevic | A real-time PCR assay for the detection of atypical strains of Chlamydiaceae from pigeons[END_REF]. Multi Locus Sequence Typing (MLST) analysis Genotyping by MLST was carried out according to the scheme developed by Pannekoek et al [START_REF] Pannekoek | Multi locus sequence typing of Chlamydia reveals an association between Chlamydia psittaci genotypes and host species[END_REF] targeting seven housekeeping genes: gatA, oppA, hflX, gidA, enoA, hemN and fumC. Target genes were amplified and sequenced using primers and conditions described on the Chlamydiales MLST website (http://pubmlst.org/chlamydiales/). Sequencing of both strands was done by Eurofins (Germany). Sequences for each locus were queried against the online Chlamydiales MLST Databases to determine allelic designations and a subsequent allelic profile was used to determine the sequence type (ST). New allele sequences are accessible via the Chlamydiales MLST web-site. Preparation of genomic DNA for WGS Genomic DNA was prepared from 14 mL of a BGM cell culture infected with the RSHA isolate. The suspension was centrifuged at 20,000 g at 8°C for 45 min, and the supernatant was discarded. The pellet was resuspended in 40 mL of PBS buffer and centrifuged at 500 g for 15 min to remove cell debris. The supernatant was transferred to a fresh tube and high-speed centrifuged as above. The pellet was resuspended in 200 µL of PBS. Prior to DNA extraction, a DNAse I (NEB, France) treatment was performed to remove potential contaminating DNA from cells. Genomic DNA was extracted using the Qiamp DNA minikit (Qiagen, France), with an additional RNase A (Roche) treatment, following the cell protocol. DNA was eluted in 50 µL of water and re-purified using the NEBNext Microbiome DNA enrichment kit (NEB, France). Genome sequencing, assembly and draft annotation Libraries were prepared using the Nextera XT kit (Illumina). Whole-genome sequencing was performed using an Illumina MiSeq platform (Illumina) according to the manufacturer's instructions. The MiSeq run was carried out on the DNA preparation, with paired-end reads of 150 bp using MiSeq V2 reagents. The raw reads were trimmed with a quality value of 20 and a window size of 5 using Trimmomatic [START_REF] Bolger | Trimmomatic: a flexible trimmer for Illumina sequence data[END_REF] and assembled de novo using SPAdes v3.11.1 with the careful parameter and the automatic determination of the k-mer values [3]. To remove residual mammalian sequences from cell culture constituents, scaffolds from SPAdes were subsequently run on the nucleotide database of NCBI. The draft genome was evaluated using Quast v4.6.3 [START_REF] Gurevich | QUAST: quality assessment tool for genome assemblies[END_REF] and annotated using Prokka v1.13 [START_REF] Seamann | Prokka: rapid prokaryotic genome annotation[END_REF]. The genome sequence of strain RSHA has been deposited at the European Nucleotide Archive (ENA) under accession number PRJEB30277.

Phylogenetic and genome analysis

The parameters of the tetranucleotide signature frequency correlation coefficient (tetranucleotide regression) and average nucleotide identity (ANIb) were determined as described previously [START_REF] Richter | Shifting the genomic gold standard for the prokaryotic species definition[END_REF] using the program JSpecies v1.2.1 with default parameters. Nine phylogenetical markers previously described [START_REF] Pillonel | Taxogenomics of the order Chlamydiales[END_REF] were extracted from 12 Chlamydiaceae reference sequences and one outgroup. Amino acid sequences were aligned with mafft v7 [START_REF] Katoh | MAFFT multiple sequence alignment software version 7: improvements in performance and usability[END_REF] and were concatenated to build a reference phylogeny using RAxML v8 [START_REF] Stamatakis | RAxML version 8: a tool for phylogenetic analysis and post-analysis of large phylogenies[END_REF], with the LG+G+I model and 1000 bootstrap replicates. Pairwise amino acid sequence identities were calculated based on the mafft alignment by Geneious v10 (www.geneious.com). Electron microscopy BGM cells were infected with strain RSHA. Infected cells were harvested at 24, 48, 72 and 96 hours post-infection. After washing, cells were fixed in 2% paraformaldehyde and 2.5% glutaraldehyde, scrapped off the culture plate and enrobed in gelatin. Gelatin containing infected cells was trimmed into ~1 mm 3 cubes, post-fixed with 1% osmium tetroxide, washed and en-bloc stained with 1% uranyl acetate in water. Specimens were then washed and dehydrated using 30%, 50%, 70%, 90% and 100% ethanol in series, 10 minutes each. This was followed by two more 100% ethanol washes and infiltration with increasing concentrations of Spurr resin (Electron Microscopy Sciences, PA). After two exchanges of pure resin, specimens were embedded in Spurr resin and polymerized at 60°C overnight. Silver colored ultrathin (~70 nm) sections were cut and collected using a Leica UC6 ultramicrotome (Leica Microsystems, Inc., Bannockburn, IL), counterstained with uranyl acetate and lead, and examined in a transmission electron microscope (Tecnai T12, FEI) operated at 80 kV. Digital images were acquired using an AMT bottom mount CCD camera and AMT600 software (Advanced Microscopy Techniques, MA).

Real-time PCR for detection of the new species

Specific

primers RSHA-F (5'-ATTTCCAACACGCACTGCAT-3') and RSHA-R (5'-TGGGACTAGGTGTTTCTCCCT-3') as well as a specific probe RSHA-P (5'-FAM-GGACAACATGCCTAGATGAAGA -TAMRA 3') were designed using the primer3 software and synthesised by Eurofins MWG Operon. Amplification was performed using the 2X TaqMan Universal PCR Master Mix (Applied Biosystems, Courtaboeuf, France). The final volume of the reaction mixture was 20 μL including 10 μL of master mix, 2 μL of DNA sample, 0.6 μM of each primer, 0.1 μM of the probe and sterile PCR water. Amplification was carried out at a Viia7 thermocycler (Applied Biosystems) using the following cycling parameters: heating at 95 °C for 10 min, 50 cycles of 95 °C for 15 s and 56 °C for 1 min. For all PCR reactions, DNA of the isolate RSHA, arbitrarily defined as the reference strain for C. buteonis, was used as a positive control, while sterile water was used as negative control. The specificity of the new real-time PCR was evaluated on 32 Chlamydiaceae DNA samples (Table S1), diluted to a Ct value around 30 when tested with the 23S rRNA-based Chlamydiaceae-specific real-time PCR (Chlamydiaceae 23S-rtPCR) [START_REF] Ehricht | Optimized DNA microarray assay allows detection and genotyping of single PCR-amplifiable target copies[END_REF].

Results

Molecular characterization of the RSHA isolate DNA from the RSHA isolate tested positive using the C. psittaci incA-based real-time PCR assay, but negative using C. psittaci ompA-based real-time PCR. The same result was obtained with primers targeting ompA of C. abortus and with species-specific C. avium and C. gallinacea real-time PCR systems. MLST genes were successfully amplified using primers designed for C. psittaci [START_REF] Pannekoek | Multi locus sequence typing of Chlamydia reveals an association between Chlamydia psittaci genotypes and host species[END_REF]. Nucleotide sequences were obtained for all 7 MLST genes of the RSHA isolate, and their analysis revealed a new sequence type (sequence type number 196), which was not represented in the Chlamydiales database (http://pubmlst.org/chlamydiales/). Comparative phylogenetic analysis of the concatenated MLST sequences from RHSA and the corresponding MLST sequences of the RTH strain [START_REF] Joseph | Chlamydiaceae Genomics Reveals Interspecies Admixture and the Recent Evolution of Chlamydia abortus Infecting Lower Mammalian Species and Humans[END_REF] places the RSHA isolate on the same clade as the RTH isolate, i.e. at an intermediate phylogenetic position between C. abortus and C. psittaci (Figure 1). In order to ascertain the species identity of the RSHA isolate, its WGS was obtained resulting in two scaffolds with a length of 770,995 and 375,551 bases and an average coverage of 117x and 106x, respectively. The full plasmid sequence of pRSHA was identified with a length of 7681 bases and an average coverage of 82x. The 16S and 23S rRNA gene sequences were extracted and then aligned with representative sequences of all established Chlamydiaceae spp. (Figure 2 and Supplementary Figure S1). Sequence homology was greater than 95.17% for 16S and greater than 93.08% for 23S rRNA genes, thus confirming the taxonomic position of strain RSHA within the genus Chlamydia and its close relatedness to both C. psittaci and C. abortus (Table S2). While the 23S rRNA sequence could not be extracted from the RTH genome sequence (SRA061571), comparison of the 16S rRNA sequences of the RTH and RHSA isolate revealed a single point mutation difference. Basic genomic features of the RSHA isolate were then compared with those of established species of the Chlamydiaceae. The RSHA genome size is 1.14 Mbp, with 977 CDS identified and GC% 38.3. These numbers are in the same range as those for the other chlamydial species (Table 1). A plasmid similar to that of C. psittaci M56 was also identified in RSHA. Pairwise comparisons of the RSHA genome sequence with genomic sequences of the type strains of RHSA's closest relatives were conducted to determine average nucleotide identity (ANIb) and tetranucleotide regressions. The results in Table 2 show maximal values of 0.98725 (tetranucleotide regression) and 93.67% (ANIb) between RSHA and type strain 6BC of C. psittaci. A similar ANI value (93.96%) was previously reported for the RTH isolate [START_REF] Joseph | Chlamydiaceae Genomics Reveals Interspecies Admixture and the Recent Evolution of Chlamydia abortus Infecting Lower Mammalian Species and Humans[END_REF]. Using a classification scheme recently proposed for the order Chlamydiales [START_REF] Pillonel | Taxogenomics of the order Chlamydiales[END_REF], the percentage of sequence identity was calculated for nine conserved taxonomically informative gene products (RpoN, FtsK, PepF, Adk, HemL, DnA, SucA, Hyp325 and Fabl) extracted from the RHSA genome sequence and those of the other 11 Chlamydia species. Although results presented in Figure S2 reveal C. psittaci as the most closely related species, all values obtained for RSHA are below the accepted cut-off for species assignment. Phenotypic characterization of the strain RSHA RSHA can be propagated under conditions routinely used for culture of chlamydiae, such as in BGM cells or embryonated chicken eggs (not shown). The electron micrograph in Figure 3 displays a large inclusion in an infected BGM cell with reticulate bodies (RBs), elementary bodies (EBs) and intermediate bodies (IBs). EBs were small (240-330 nm), round and electron dense. RBs appeared larger (450-1100 nm in diameter), round to oval and more electron lucent. Different stages of division by binary fission were seen in RBs. These observations support a conserved biphasic developmental cycle in RHSA as seen in all chlamydiae. Diagnostic tools to detect the new taxon A specific real-time PCR assay for RSHA was designed based on the oxaA gene locus (see Materials and Methods). This gene has the particularity to be present in the genome of all Chlamydiacea with specific mutations identified for the hawk strain. RHSA, C. abortus, C. avium, C. caviae, C. gallinacea, C. felis, C. pecorum and C. psittaci strains were tested using this protocol. The results confirmed the specificity of the assay (data not shown). Measurements on dilution series of cell culture aliquots with defined numbers of inclusion-forming units (IFUs) indicated detection limits in the range of 10 IFUs. As the target oxaA sequence of strain RTH is identical to that of strain RSHA, it is safe to assume that it would be detected by this assay as well.

Discussion

The present report of a new taxon underpins the widely held notion among chlamydia researchers that wildlife, and particularly wild birds, harbor yet unknown or non-classified chlamydial organisms [6,[START_REF] Zweifel | Prevalence of Chlamydophila psittaci in wild birds-potential risk for domestic poultry, pet birds, and public health?[END_REF]. In the past decade, advances in DNA-based diagnostic technology have enabled the discovery of new members of the family Chlamydiaceae, including C. avium and C. gallinacea [START_REF] Sachse | Evidence for the existence of two new members of the family Chlamydiaceae and proposal of Chlamydia avium sp. nov. and Chlamydia gallinacea sp[END_REF], as well as Candidati C. ibidis [START_REF] Pillonel | Taxogenomics of the order Chlamydiales[END_REF][START_REF] Vorimore | Isolation of a New Chlamydia species from the Feral Sacred Ibis (Threskiornis aethiopicus): Chlamydia ibidis[END_REF], C. sanzinia [START_REF] Taylor-Brown | Culture-independent genomic characterisation of Candidatus Chlamydia sanzinia, a novel uncultivated bacterium infecting snakes[END_REF], C. corallus [START_REF] Taylor-Brown | Culture-independent metagenomics supports discovery of uncultivable bacteria within the genus Chlamydia[END_REF], C. serpentis, and C. poikilothermis [START_REF] Staub | Novel Chlamydia species isolated from snakes are temperature-sensitive and exhibit decreased susceptibility to azithromycin[END_REF]. In this study, an isolate, named RSHA, obtained from a dead red-shouldered hawk (Buteo lineatus) was not detected using ompA-based qPCR assays for C. psittaci and C. abortus, but was detected using an incA-based assay originally developed for C. psittaci, which was already known to identify certain C. abortus strains (unpublished data). Preliminary genotyping of this strain based on 7 MLST target genes identified a new sequence type located at an intermediate position between C. psittaci and C. abortus (Figure 1). Intermediate strains from birds have been previously reported [START_REF] Aaziz | Chlamydiaceae in North Atlantic Seabirds Admitted to a Wildlife Rescue Center in Western France[END_REF][START_REF] Herrmann | Chlamydophila abortus in a Brown skua (Catharacta antarctica lonnbergi) from a subantarctic island[END_REF][START_REF] Van Loock | Missing links in the divergence of Chlamydophila abortus from Chlamydophila psittaci[END_REF]. Recently, some of these strains were assigned to the species of C. abortus [START_REF] Szymańska-Czerwińska | Dissemination and genetic diversity of chlamydial agents in Polish wildfowl: Isolation and molecular characterisation of avian Chlamydia abortus strains[END_REF] based on 16S rRNA, ompA, and MLST analyses. Analysis of WGS data of the RSHA isolate revealed that the criteria for assignment to a new chlamydial species within the family Chlamydiaceae were fulfilled. Indeed, according to the recent classification scheme proposed by Pillonel et al. [START_REF] Pillonel | Taxogenomics of the order Chlamydiales[END_REF], sequence identity values of ≥ 80% for 16S rRNA and 23S rRNA, and of ≥ 92.5% for 16S rRNA, ≥ 91% for 23S rRNA confirm the assignment of strain RHSA to the order Chlamydiales and family Chlamydiaceae, respectively. Furthermore, comparative analysis of nine taxonomically conserved proteins including a representative strain from each member of the Chlamydia genus yields sequence identities (RpoN < 96%, FtsK < 98%, PepF < 96%, Adk < 95%, HemL < 95%, DnA ≥ 70%, SucA ≥ 64%, Hyp325 ≥ 57% and Fabl ≥ 78%) that are consistent with RSHA being assigned to a new species with C. psittaci as closest neighbor. A similar analysis was previously applied a posteriori to C. avium and Candidatus C. ibidis confirming their assignment to new species [START_REF] Pillonel | Taxogenomics of the order Chlamydiales[END_REF]. Recently, the same method was used for the description of Candidati C. serpentis and C. poikilothermis [START_REF] Staub | Novel Chlamydia species isolated from snakes are temperature-sensitive and exhibit decreased susceptibility to azithromycin[END_REF]. The tetra nucleotide regression and ANIb values calculated for RSHA are also below values generally considered for delineating bacterial species [START_REF] Richter | Shifting the genomic gold standard for the prokaryotic species definition[END_REF]. In the 1990s, the C. psittaci-like strain RTH was isolated from a wild red-tailed hawk (Buteo jamaicensis) showing signs of respiratory distress and diarrhea [START_REF] Mirandé | Chlamydiosis in a red-tailed hawk (Buteo jamaicensis)[END_REF]. The genome of this strain was recently sequenced and further analysis suggested that RTH represents a new species in the family Chlamydiaceae [START_REF] Joseph | Chlamydiaceae Genomics Reveals Interspecies Admixture and the Recent Evolution of Chlamydia abortus Infecting Lower Mammalian Species and Humans[END_REF]. Although the 23S rRNA sequence could not be extracted from the RTH genome sequence owing to incomplete assembly and low coverage, comparison of the RTH and RSHA 16S rRNA and MLST sequences revealed complete sequence identity, with the exception of a single nucleotide polymorphism in the 16S rRNA gene sequence, thus strongly suggesting a close evolutionary relationship between these two strains. Unfortunately, none of the nine taxonomically conserved protein sequences could be fully extracted from the published RTH genome sequence data (SRA061571). However, comparison of the incompletely assembled RTH sequences with those of RSHA revealed a strong percentage of identity between these two strains (around 99%; not shown).

In consequence, we propose classifying the two hawk strains as members of a new species to be designated as Chlamydia buteonis sp. nov., with strain RSHA T as the type strain. The epidemiological and clinical importance of the proposed new species has yet to be determined. Respiratory distress and diarrhea were reported in the red-tailed hawk (Buteo jamaicensis) infected by the RTH isolate [START_REF] Mirandé | Chlamydiosis in a red-tailed hawk (Buteo jamaicensis)[END_REF], while conjunctivitis was observed in the red-shouldered hawk (Buteo lineatus) sampled in this study. Few reports have focused on clinical signs associated with chlamydial infections in predatory birds. However, lethargy, weight loss, moderate to severe leukocytosis, and radiographic evidence of splenomegaly were recently reported in a wild bird population [START_REF] Luján-Vega | Atypical Chlamydiaceae in wild populations of hawks (Buteo spp.) in California[END_REF]. Therefore, future studies of clinical cases in these birds should take the present findings into account. C. psittaci-like strains were recently detected in red-tailed and Swainson's hawks from California, with a best sequence alignment of the ompA gene, with the C. psittaci M56 genotype. It will be important to re-analyze these strains with the new PCR assay specific for C. buteonis. Moreover, the assay we have developed for C. buteonis should be applied to identify potential pathogens when diseased raptors belonging to the genus Buteo are sampled. Description of Chlamydia buteonis sp. nov. Chlamydia buteonis (bu.te.o'nis. L. gen. n. buteonis of a buzzard, of a bird of the genus Buteo). C. buteonis strains occur in raptor birds called buzzards in Europe and hawks in North America. The agent can be recovered from tissue of the conjunctiva and contents of the cloaca. From the cases seen so far, it seems likely that C. buteonis can cause conjunctivitis and/or respiratory signs, or at least be a contributing factor to these clinical manifestations. Natural routes of transmission have yet to be investigated, but, analogous to most other chlamydiae, airborne transmission through aerosolized dry feces and dust particles seems possible. The potential for zoonotic infection of humans is unknown. C. buteonis strains can be grown in cell culture or embryonated chicken eggs. Their growth is characterized by a biphasic developmental cycle, which is encountered in the other Chlamydia spp. as well. Electron microscopy is the method of choice to visualize the typical vacuole-like intracellular inclusions harboring the various morphological forms, i.e. elementary, reticulate and intermediate bodies.

Members of the species can be specifically identified using real-time PCR targeting the oxaA gene locus as described in Materials and Methods. In addition, MLST based on 7 housekeeping genes and 16S rRNA gene sequence alignments can be used for identification. Sequence homology of C. buteonis 16S genes to those of other Chlamydia spp. is greater than 95.17 %, with C. psittaci and C. abortus being the closest relatives. The size of the circular C. buteonis genome is 1.14 Mbp, with a G+C value of 38.3 mol-%. The type strain was found to harbor a plasmid sized 7681 bp and similar to that of C. psittaci M56. Strain RSHA T isolated from a red-shouldered hawk (Buteo lineatus) is the designated type strain of C. buteonis.

Tables & Figures

Figure 1 .

 1 Phylogenetic analyses of concatenated sequences of 7 MLST housekeeping gene fragments (enoA, fumC, gatA, gidA, hemN, hlfX, and oppA) for the RSHA isolate and established Chlamydiaceae species, including a larger number of representatives for C. psittaci and C. abortus species. Figure2. Dendrogram based on the analysis of the complete 16S rRNA sequences from the RSHA isolate and from the type strains of twelve Chlamydiaceae, including the candidatus C. ibidis representative. The dendrogram was constructed by UPGMA method from a similarity matrix calculated by pairwise alignment. Branch quality was calculated by cophenetic correlation. Horizontal distances correspond to genetic distances expressed in percentage of sequence similarity. Figure3. Electron microscopic images of BGM cell culture infected with RSHA, after 72h. One inclusion is depicted with a mixture of RBs and EBs.

Table 1 .

 1 Comparison of basic genomic features of RSHA with those of established species of Chlamydiaceae.

Table 2 .

 2 Pairwise comparison of the available genome sequences of the type strains of Chlamydiaceae species based on average nucleotide identity (ANIb; lower triangle) and tetranucleotide signature correlation index (upper triangle).


MD Michel Farnier
email: michelfarnier@nerim.net

PhD Clémence Salignon-Vernay

Hermann Yao

Frédéric Chague

Philippe Brunel

Maud Maza

Msc Damien Brunet

Florence Bichat

Jean-Claude Beer

MD Hermann Yao²

MD Frédéric Chague²

MD Maud Maza²

MD Florence Bichat²

PhD Jean-Claude Beer²

MD Yves Cottin²

PhD Marianne Zeller

Prevalence, risk factor burden, and severity of coronary artery disease in patients with heterozygous familial hypercholesterolemia hospitalized for an acute myocardial infarction: Data from the French RICO survey

Keywords: Familial hypercholesterolemia, Myocardial Infarction, Prevalence, STEMI: ST-segment elevation myocardial infarction, PAD: peripheral artery disease, BMI: body mass index, IQR: interquartile range, LVEF: left ventricular ejection fraction

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

INTRODUCTION

Heterozygous familial hypercholesterolemia (FH) is an autosomal co-dominant genetic disease associated with significantly increased risk of atherosclerotic cardiovascular disease including myocardial infraction (MI) [START_REF] Nordestgaard | for the European Atherosclerosis Society Consensus Panel. Familial hypercholesterolaemia is underdiagnosed and undertreated in the general population: guidance for clinicians to prevent coronary heart disease[END_REF][START_REF] Gencer | Identifying familial hypercholesterolemia in acute coronary syndrome[END_REF]. The prevalence of heterozygous FH has been estimated to be 1:250 individuals in Western populations [START_REF] Benn | Mutations causative of familial hypercholesterolaemia: screening of 98 098 individuals from the Copenhagen General Population Study estimated a prevalence of 1 in 217[END_REF][START_REF] Wald | Child-parent familial hypercholesterolemia screening in primary care[END_REF]. In the general population, FH is underdiagnosed and, for many FH patients, the initial clinical manifestation is the occurrence of an acute coronary syndrome (ACS) [START_REF] Nordestgaard | for the European Atherosclerosis Society Consensus Panel. Familial hypercholesterolaemia is underdiagnosed and undertreated in the general population: guidance for clinicians to prevent coronary heart disease[END_REF][START_REF] Foody | Familial hypercholesterolemia: an under-recognized but significant concern in cardiology practice[END_REF]. As a consequence, the prevalence of FH is considerably higher among patients hospitalized for ACS compared with the general population [START_REF] Nanchen | Prevalence and management of familial hypercholesterolaemia in patients with acute coronary syndromes[END_REF][START_REF] Backer | Prevalence and management of familial hypercholesterolaemia in coronary patients: an analysis of EURO-ASPIRE I, a study of the European Society of Cardiology[END_REF][START_REF] Pang | Frequency of familial hypercholesterolemia in patients with early-onset coronary artery disease admitted to a coronary care unit[END_REF][START_REF] Rerup | The prevalence and prognostic importance of possible familial hypercholesterolemia in patients with myocardial infarction[END_REF][START_REF] Mortensen | Familial hypercholesterolemia among unselected contemporary patients presenting with first myocardial infarction: Prevalence, risk factor burden, and impact on age at presentation[END_REF][START_REF] Li | Identification of familial hypercholesterolemia in patients with myocardial infarction: a Chinese cohort study[END_REF][START_REF] Al-Rasadi | Prevalence, management, and outcomes of familial hypercholesterolemia in patients with acute coronary syndromes in the Arabian Gulf[END_REF][START_REF] Auckle | Familial hypercholesterolemia in Chinese patients with premature ST-segment-elevation myocardial infarction: Prevalence, lipid management and 1-year follow-up[END_REF].

Several recent studies have determined the prognostic impact of FH in patients after a first ACS [START_REF] Rerup | The prevalence and prognostic importance of possible familial hypercholesterolemia in patients with myocardial infarction[END_REF][START_REF] Al-Rasadi | Prevalence, management, and outcomes of familial hypercholesterolemia in patients with acute coronary syndromes in the Arabian Gulf[END_REF][START_REF] Auckle | Familial hypercholesterolemia in Chinese patients with premature ST-segment-elevation myocardial infarction: Prevalence, lipid management and 1-year follow-up[END_REF][START_REF] Nanchen | Prognosis of patients with familial hypercholesterolemia after acute coronary syndromes[END_REF][START_REF] Tscharre | Prognostic impact of familial hypercholesterolemia on long-term outcomes in patients undergoing percutaneous coronary intervention[END_REF]. However, the severity of coronary artery disease (CAD) has been poorly described in patients with FH hospitalized for an acute MI [START_REF] Yasuda | Coronary lesion morphology and prognosis in young males with myocardial infarction with and without familial hypercholesterolemia[END_REF]. Moreover, no data are available on the prevalence and characteristics of French patients with FH and hospitalized for MI. The detection of FH at time of an acute coronary event is not systematically performed in France, although these patients are at high risk of recurrence of cardiovascular events [START_REF] Béliard | High burden of recurrent cardiovascular events in heterozygous familial hypercholesterolemia: The French Familial Hypercholesterolemia Registry[END_REF].

Therefore, in the present study, we aimed to investigate the prevalence of FH, the risk factor burden and the severity of CAD at the time of admission in a coronary care unit (CCU) from a large database of a regional French registry (RICO registry) of patients with acute MI.

METHODS

Study population

This study was performed with the framework of the RICO (obseRvatoire des Infarctus de Côte d'Or) survey designed to include all consecutive ≥ 18 years patients hospitalized for an acute MI since 2001 in a multicenter registry of all the CCU of the region of Côte d'Or in France. Details regarding the methods of the RICO survey were previously reported [START_REF] Zeller | Relation between body mass index, waist circumference, and death after acute myocardial infarction[END_REF].

Briefly, baseline characteristics including demographic data, risk factors, treatments for hypercholesterolemia, family history of CAD (defined as premature CAD in first-degree relative < 55 years for men or 65 years for women), cardiovascular history and chronic treatments were prospectively collected. At admission, the following data were also collected: Killip class, ECG signs and hemodynamic parameters (heart arte (HR), systolic (SBP) and diastolic (DBP) blood pressure). Left ventricular ejection fraction (LVEF) was measured by echocardiography < 12h following admission. Blood sample was taken at admission for lipid profile. Hospital complications were determined and delays in time to admission as well as prolongation of stay in CCU). For each patient, GRACE (Global Registry of Acute Coronary Events) risk score, as a tool to assess mortality risk in acute MI (http://www.outcomesumassmed.org/grace/acs_risk/acs_risk_content.html) and SYNTAX (SYNergy between PCI with TAXus and cardiac surgery trial) score, as an index of CAD severity, were calculated [START_REF] Granger | Global Registry of Acute Coronary Events Investigators. Predictors of hospital mortality in the global registry of acute coronary events[END_REF]20].

Patients were recruited consecutively after obtaining written informed consent, with the exception of patients who died in the first hours after admission or with a severe cardiogenic shock. The study was conducted in compliance with Good Clinical Practice guidelines and the French data protection law. The protocol was approved by the Dijon University Hospital Committee for the Protection of Human Subjects and was declared to the Commission Nationale Informatique et Liberté.

For this study, 11 624 consecutive patients hospitalized between 2001 and 2017 were recruited.

Diagnosis of familial hypercholesterolemia

All patients were assessed for the presence of FH based on the Dutch Lipid Clinic Network (DLCN) criteria [START_REF] Hovingh | Diagnosis and treatment of familial hypercholesterolemia[END_REF], recommended by guidelines to diagnose FH in the general population in central European countries [START_REF] Nordestgaard | for the European Atherosclerosis Society Consensus Panel. Familial hypercholesterolaemia is underdiagnosed and undertreated in the general population: guidance for clinicians to prevent coronary heart disease[END_REF][START_REF] Catapano | ESC/EAS Guidelines for the management of dyslipidaemias[END_REF]. The score was calculated using points from the following criteria: premature family history of CAD for a first-degree relative (at age < 55 years for men and < 60 years for women; 1 point); premature personal history of CAD (2 points) or vascular disease (1 point); and plasma low-density lipoprotein cholesterol (LDL-C > 330 mg/dL (8 points), 250-329 mg/dL (5 points), 190-249 mg/dL (3 points), 155-189 mg/dL(1 point). In individuals on lipid-lowering therapy, LDL-C on admission was corrected according to type of agents: statin (+30%), ezetimibe (+20%), statin and ezetimibe (+40%). The choice for a conservative correction factor for statin therapy (+30%) is driven by the preferential use of moderate intensity statin in France. The presence of tendon xanthomas or corneal arcus, family history of hypercholesterolemia or vascular disease were not recorded in the database. Missing information was counted as zero, as done in previous studies [START_REF] Nanchen | Prevalence and management of familial hypercholesterolaemia in patients with acute coronary syndromes[END_REF][START_REF] Rerup | The prevalence and prognostic importance of possible familial hypercholesterolemia in patients with myocardial infarction[END_REF][START_REF] Tscharre | Prognostic impact of familial hypercholesterolemia on long-term outcomes in patients undergoing percutaneous coronary intervention[END_REF]. A probable/definite FH diagnosis was considered when the DLCN score was ≥ 6, a possible diagnosis when the score was 3-5 and unlikely when the score was < 3 points. The group of patients with probable/definite FH was compared with the group unlikely FH, called "no"FH.

Statistical analysis

Characteristics of patients are presented as median (interquartile range) for continuous variables and number (percentage) for categorical variables. Dichotomous variables were expressed as n(%) and continuous variables as median [interquartile range]. A Kolmogorov-Smirnov test was performed to analyse the normality of continuous variables. Mann-Whitney test was used to compare continuous data, and the Chi 2 test or Fisher's test was used for dichotomous data. The threshold for significance was set at 5%. In addition to the full cohort, a matched subset was built, with one FH patient matched with three patients without FH, based on age (±SD in whole population), sex and diabetes mellitus. For matching data, the Wilcoxon test was used instead of the Mann-Whitney test. SPSS version 12.0.1 (IBM Inc, USA) was used for all of the statistical tests.

RESULTS

Among 11624 patients hospitalized for acute MI, 249 (2.1%) had a probable/definite FH (score ≥ 6), 2405 (20.7%) a possible FH (score 3-5) and 8970 (77.2%) no FH (score < 3).

The baseline characteristics of patients with (probable/definite) FH and without (no) FH are reported on Table 1 for the whole population (called "Full cohort") and for the 2 matched cohorts built after adjustment for age, sex and diabetes (called "Matched subset").

The "probable/definite" FH groups was ≈ 20 years younger (p<.001), had a higher BMI (p=.016), a greater prevalence of smoking (p<.001), premature CAD (p<.001), family history of CAD (p<.001), but a lower prevalence of diabetes (p=.007), hypertension (p<.001) and stroke (p=.016). However after adjustment for age, sex and diabetes, the prevalence of hypertension was higher for patients with probable/definite FH (p=.002), and the history of stroke does not differ between the two groups, whereas patients with probable/definite FH have more history of peripheral artery disease (p=.018). After adjustment, the proportion of smokers was similar in the two groups.

As expected, median LDL-C was about twice as high (207 vs 112 mg/dL) for patients with probable/definite FH and more patients with probable/definite FH were receiving lipidlowering medications. However chronic statin treatment was only used in 48% of probable/definite FH patients and only 8% were receiving ezetimibe. The characteristics of the untreated versus treated patients with probable/definite FH are summarized on Suppl. Table 1: globally, a treatment was more frequent for the categories of patients at the highest risk (higher prevalence of patients with previous CAD, with diabetes and with hypertension for patients on lipid-lowering treatment). Patients with probable/definite FH and previous CAD are more likely to receive either statin therapy (84% in the full cohort and 87% in the matched subset) or ezetimibe treatment (25% in the full cohort and 32% in the matched subset). On the other hand, the coronary angiography data are similar in FH patients with and without treatment.

Table 2 shows data on coronary lesions at coronary angiography and Table 3 data on inhospital outcomes. After adjustment (post-matching data), the use of coronary angiography was similar for patients with and without FH. The GRACE risk score was lower for patients with probable/definite FH in both pre-and post-matched cohorts. However, after adjustment for age, sex and diabetes, patients with probable/definite FH were characterized by increased extent of CAD (SYNTAX score 11 vs 7, p<.001) (Figure 1) and multivessel disease (55% vs 40%, p<.001) (Figure 2) (Table 2). After adjustment, no significant differences appear on in-hospital outcomes (Table 3).

DISCUSSION

FH subjects are at high risk for acute MI, and characterization of CAD burden and FH pre valence in MI population are key issues for management strategies. In this large populationbased study, we show that about 2% of acute MI patients had FH, and these patients were characterized by their young age, severity of CAD disease and dramatic underuse of lipidlowering therapies.

As patients with FH have an increased risk of CAD compared to unaffected individuals, the prevalence of FH in patients hospitalized for an acute MI should be higher than estimated in the general Western populations (ie. 1 to 250 individuals). The prevalence of FH has not yet been reported in French patients hospitalized for ACS and in the present study, we show a prevalence 5-fold higher among patients hospitalized for an acute MI compared with the expected prevalence in the general population. The prevalence of 2.1% of probable/definite FH in this large database is in excellent agreement with data obtained in Switzerland (1.6% in the SPUM-ACS cohort) [START_REF] Nanchen | Prevalence and management of familial hypercholesterolaemia in patients with acute coronary syndromes[END_REF] and in Denmark (2.0% in patients with first MI) [START_REF] Mortensen | Familial hypercholesterolemia among unselected contemporary patients presenting with first myocardial infarction: Prevalence, risk factor burden, and impact on age at presentation[END_REF]. The higher prevalence reported in Australia by Pang et al. [START_REF] Pang | Frequency of familial hypercholesterolemia in patients with early-onset coronary artery disease admitted to a coronary care unit[END_REF] could be due to the larger correction factor used for adjustment of LDL-C in individuals on statins associated with a larger use of statins (68% vs 48%). Indeed the mean correction factor was 2.0 in the study from Pang et al. [START_REF] Pang | Frequency of familial hypercholesterolemia in patients with early-onset coronary artery disease admitted to a coronary care unit[END_REF] and only 1.48 in our study. The high prevalence (8.3%) found in EUROASPIRE IV [START_REF] Backer | Prevalence and management of familial hypercholesterolaemia in coronary patients: an analysis of EURO-ASPIRE I, a study of the European Society of Cardiology[END_REF] could be a reflection of the differences in recruitment methodology. As for the Danish cohort [START_REF] Mortensen | Familial hypercholesterolemia among unselected contemporary patients presenting with first myocardial infarction: Prevalence, risk factor burden, and impact on age at presentation[END_REF], all patients hospitalized for an acute MI were included in our consecutive study.

A study conducted in the Arabian Gulf using the same corrected factor for prior statin use has reported a higher prevalence 3.7% [START_REF] Al-Rasadi | Prevalence, management, and outcomes of familial hypercholesterolemia in patients with acute coronary syndromes in the Arabian Gulf[END_REF], close to Chinese data [START_REF] Li | Identification of familial hypercholesterolemia in patients with myocardial infarction: a Chinese cohort study[END_REF][START_REF] Auckle | Familial hypercholesterolemia in Chinese patients with premature ST-segment-elevation myocardial infarction: Prevalence, lipid management and 1-year follow-up[END_REF]. An elevated rate of consanguinity among Gulf citizens has been suggested as explanation [START_REF] Al-Rasadi | Prevalence, management, and outcomes of familial hypercholesterolemia in patients with acute coronary syndromes in the Arabian Gulf[END_REF]. As already reported in several cohorts [START_REF] Gencer | Identifying familial hypercholesterolemia in acute coronary syndrome[END_REF], the characteristics of patients with FH hospitalized for MI differ from patients without FH: they were younger (20 years younger in our cohort), had a higher prevalence of premature CAD and family history of CAD, and had lower incidence of diabetes. A lower prevalence of diabetes in FH has been well described in the Netherlands by comparing FH patients and their non-affected relatives [START_REF] Besseling | Association between familial hypercholesterolemia and prevalence of type 2 diabetes mellitus[END_REF]. In our study, after adjustment for age, sex and diabetes, patients with FH were more frequently hypertensive and had a higher prevalence of peripheral artery disease, in agreement with an increased risk recently reported for FH patients [START_REF] Emanuelsson | Familial hypercholesterolemia and risk of peripheral arterial disease and chronic kidney disease[END_REF]. The GRACE risk score was found lower for patients with FH, even after adjustment, in agreement with other reports [START_REF] Al-Rasadi | Prevalence, management, and outcomes of familial hypercholesterolemia in patients with acute coronary syndromes in the Arabian Gulf[END_REF][START_REF] Nanchen | Prognosis of patients with familial hypercholesterolemia after acute coronary syndromes[END_REF].

Patients with FH hospitalized for ACS or MI have a worse prognosis as already described in several populations, with an approximate 2-fold increased risk for recurrence of coronary or other cardiovascular events [START_REF] Al-Rasadi | Prevalence, management, and outcomes of familial hypercholesterolemia in patients with acute coronary syndromes in the Arabian Gulf[END_REF][START_REF] Nanchen | Prognosis of patients with familial hypercholesterolemia after acute coronary syndromes[END_REF][START_REF] Tscharre | Prognostic impact of familial hypercholesterolemia on long-term outcomes in patients undergoing percutaneous coronary intervention[END_REF]. By contrast, few data are available on the severity of the disease at the time of admission in a CCU: in a small cohort of 16 patients with FH compared to 27 non-FH patients, Yasuda et al. [START_REF] Yasuda | Coronary lesion morphology and prognosis in young males with myocardial infarction with and without familial hypercholesterolemia[END_REF] reported that the FH group more frequently had multiple-vessel disease while the non-FH group was likely to have singlevessel disease. Multivessel CAD was also found more common in two other studies, however conducted in patients with only possible FH [START_REF] Rerup | The prevalence and prognostic importance of possible familial hypercholesterolemia in patients with myocardial infarction[END_REF][START_REF] Auckle | Familial hypercholesterolemia in Chinese patients with premature ST-segment-elevation myocardial infarction: Prevalence, lipid management and 1-year follow-up[END_REF]. Recently, Li et al. [START_REF] Li | Familial hypercholesterolemia phenotype in Chinese patients undergoing coronary angiography[END_REF] have used the Gensini scoring system to assess the severity of CAD and reported an increase in the severity of CAD in Chinese patients with FH. Our data from a large cohort show a significant increase of multivessel CAD in patients with probable/definite FH versus no FH patients.

Moreover, the severity of CAD for FH patients is confirmed by a significant increase of the SYNTAX Score. These results were observed despite a greater use of statin therapy in the FH group, and reflected the high cumulative LDL-C burden associated with FH.

Several limitations need to be considered in the present study: First, there are missing data regarding the presence of tendon xanthomas or corneal arcus, and high cholesterol levels in the first degree family members; therefore the prevalence can be underestimated. The genetic testing was not performed to confirm the FH diagnosis. Nevertheless, our procedure for identifying FH has been employed in numerous previous publications [START_REF] Nanchen | Prevalence and management of familial hypercholesterolaemia in patients with acute coronary syndromes[END_REF][START_REF] Backer | Prevalence and management of familial hypercholesterolaemia in coronary patients: an analysis of EURO-ASPIRE I, a study of the European Society of Cardiology[END_REF][START_REF] Pang | Frequency of familial hypercholesterolemia in patients with early-onset coronary artery disease admitted to a coronary care unit[END_REF][START_REF] Rerup | The prevalence and prognostic importance of possible familial hypercholesterolemia in patients with myocardial infarction[END_REF][START_REF] Mortensen | Familial hypercholesterolemia among unselected contemporary patients presenting with first myocardial infarction: Prevalence, risk factor burden, and impact on age at presentation[END_REF][START_REF] Li | Identification of familial hypercholesterolemia in patients with myocardial infarction: a Chinese cohort study[END_REF][START_REF] Al-Rasadi | Prevalence, management, and outcomes of familial hypercholesterolemia in patients with acute coronary syndromes in the Arabian Gulf[END_REF][START_REF] Auckle | Familial hypercholesterolemia in Chinese patients with premature ST-segment-elevation myocardial infarction: Prevalence, lipid management and 1-year follow-up[END_REF][START_REF] Nanchen | Prognosis of patients with familial hypercholesterolemia after acute coronary syndromes[END_REF][START_REF] Tscharre | Prognostic impact of familial hypercholesterolemia on long-term outcomes in patients undergoing percutaneous coronary intervention[END_REF]. Second, the statin doses were not reported in this study and the LDL-C levels for FH diagnosis might have a degree of bias. However, we have adjusted LDL-C levels by a relatively low correction factor corresponding to a 30% LDL-C decrease on statin treatment to avoid an overestimation of patients with probable/definite FH. Moreover, we have only compared patients with probable/definite FH with patients unlikely FH. In addition to the size of the cohort, a strength of this study is the recruitment of all consecutive patients hospitalized for an acute MI in a French region.

CONCLUSIONS

In a large cohort from a French region, we found a high prevalence of FH in real world patients hospitalized for an acute MI. Patients with FH were associated with a greater severity of CAD burden. Despite a greater use of lipid-lowering therapy compared with patients without FH, about half of patients with FH were not receiving statin therapy before the acute event. This study confirms that FH is underdiagnosed and undertreated and strategies to optimize the identification and the management of FH patients should be advocated.

Figure 1: Severity of CAD assessed by SYNTAX Score for patients with and without FH

Number of significant coronary lesions for patients with and without FHTable 1 : Characteristics of patients at admission for acute MI with and without FH: data from full cohort and matched subset

 1

	n (%) or median (IQR) Figure 2: Full cohort no FH Probable/definite FH (n=8970) (n=249)		P value	no FH (n=699)	Matched subset Probable/definite FH (n=233)	P value
	Patient characteristics
	Age (years)		71 (61-80)	51 (46-57)			<.001	51 (46-57)	51 (46-57)	.993
	Female 50% BMI (kg/m²) Diabetes 45% Hypertension Current smoking 40% Comorbidities	47.5% 2799 (31%) 26 (24-29) 2215 (25%) 5304 (59%) P < .001 38.0% 2098 (23%)	78 (31%) 27 (24-30) 43 (17%) 117 (47%) 140 (56%)	P < .001	.967 .016 .007 <.001 <.001	216 (31%) 26 (23-30) 117 (17%) 243 (35%) 390 (56%)	72 (31%) 27 (24-30) 39 (17%) 107 (46%) 128 (55%)	1 .092 1 .002 .819
	Previous CAD* 35% Premature CAD
	Family history of CAD
	20% 25% Stroke 30% PAD	SYNTAX Score		23.5%	26.2%	16.6%	28.4%
	15%	12.5%
	10%	7.4%
	5%
	0%
		0	1		2		3 or left main
			Unlikely FH no FH no FH	Probable or definite FH Probable or definite FH Probable or definite FH

Acknowledgments

This work was supported by the University Hospital of Dijon, the Association de Cardiologie de Bourgogne, and by grants from the Agence Régionale de Santé (ARS) de Bourgogne Franche-Comté, Institut National de la Santé et de la Recherche Médicale (INSERM) and from the Regional Council of Bourgogne Franche-Comté.


Eric Fina Kamani
email: eric.fina.kamani01@univ-poitiers.fr

The effect of non-traditional banking activities on systemic risk: does bank size matter?

Keywords: Non-interest income, bank size, systemic risk JEL classification: G21, G28 Non-interest income, bank size, systemic risk. JEL classification: G21, G28

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

Introduction

Owing to the deregulation of banking activities as well as the imperative of value creation for shareholders, western banking systems have undergone profound restructurings characterized by the emergence of universal banks. This adoption of the new universal banking principle years before the 2007/2008 crisis raises the question of their role in the propagation of systemic crises.

The purpose of this paper is to shed light on the debate concerning the implications of universal banks on systemic risk. In that regard, as universal banks are jointly characterized by size and activity diversification, we explore the joint influence of non-traditional banking activities and bank size on banks' exposures to systemic risk. Precisely, we analyse how the effect of non-traditional banking activities on European banks' exposures to systemic risk differ according to bank size.

This study is particularly important because the diversification and bank size were at the core of the 2007/2008 crisis.

Additionally, notwithstanding this latter fact, over the past two years, the European banking authorities have shown a keen interest in bank mergers and therefore the emergence of larger and probably more diversified banks in the European banking industry1 . Yet, if larger banks have more sophisticated risk management techniques [START_REF] Hughes | Bank Capitalization and Cost: Evidence of Scale Economies in Risk Management and Signaling[END_REF] and therefore can benefit more from the advantages of diversification than small banks [START_REF] Cerasi | The optimal size of a bank: Costs and benefits of diversification[END_REF]), being aware of their too big to fail position, they are also inclined to invest in risky activities [START_REF] Mishkin | Financial consolidation: Dangers and opportunities[END_REF]).

Furthermore, analysis of the joint effects of non-traditional banking activities and bank size on bank systemic risk has not drawn the attention of researchers. Indeed, the existing banking literature has focused either on the effects of non-traditional banking activities on systemic risk (see for example the contributions of [START_REF] Wagner | Diversification at financial institutions and systemic crises[END_REF], De Jonghe (2010), [START_REF] Brunnermeier | Banks' Non-Interest Income and Systemic Risk[END_REF], [START_REF] Fahlenbrach | This Time Is the Same: Using Bank Performance in 1998 to Explain Bank Performance during the Recent Financial Crisis[END_REF] Our paper is closely related to the recent literature which empirically gauges the relationship between non-traditional banking activities and systemic risk. We make several contributions to this literature.

Firstly, contrary to previous studies, our measure of banks' systemic risk is the SRISK proposed by [START_REF] Acharya | Capital Shortfall: A New Approach to Ranking and Regulating Systemic Risks[END_REF] and [START_REF] Brownlees | SRISK: A Conditional Capital Shortfall Measure of Systemic Risk[END_REF]. 2 Four factors lead us to prefer SRISK. First, it is widely used in studies on bank systemic risk. Second, unlike other measures (MES or Dcovar for example), it presents the advantage of relying on balance sheet and market data. Consequently, it reflects the market conditions as well as the managerial strategies of banks.

Third, [START_REF] Brownlees | SRISK: A Conditional Capital Shortfall Measure of Systemic Risk[END_REF] as well as Brownlees et al. (2017) indicate that SRISK does not merely identify and rank systemically important financial institutions (SIFIs), but also predicts which institutions may be bailed out in a financial crisis. Finally, as underlined by [START_REF] Acharya | Introducing the "Leverage Ratio" inAssessing the Capital Adequacy of European Banks[END_REF], the way it is defined is conceptually similar to the methodology followed in the European banking stress tests.

Secondly, we take into account the dynamic process of bank systemic risk and control the endogeneity of our explanatory variables by using the generalized-method-of-moments (GMM).

This dynamic framework aims to take into consideration the time persistence of bank systemic risk, and to avoid bias due to omissions of explanatory variables in our econometric specification.

To our knowledge, none of the previous studies have applied the GMM.

Thirdly, our paper is the first to address the unobservable common factors between banks. Taking into account the unobservable common factors between banks is particularly important because the rise in national competition encourages banks to develop activities beyond national borders. This geographical diversification is likely to create common factors between banks. Additionally, any estimate with significant unobservable common factors between individuals in panel data is likely to provide inconsistent results [START_REF] Chudik | Common correlated estimation of heterogeneous dynamic panel data models with weakly exogenous regressors[END_REF]).

Finally, this study is the first to analyse the effects of non-traditional banking activities according to bank size within the framework of the European banking system.

The rest of the article is organized as follows: section 2 presents the empirical methodology, section 3 shows the results, section 4 outlines the robustness checks, and section 5 concludes.

Variables, data and econometric methodology

Variables

Our dependent variable is a bank's systemic risk exposure. It is measured by SRISK, 3 proposed by [START_REF] Acharya | Capital Shortfall: A New Approach to Ranking and Regulating Systemic Risks[END_REF] and [START_REF] Brownlees | SRISK: A Conditional Capital Shortfall Measure of Systemic Risk[END_REF]. SRISK is defined as the expected capital shortfall of a given bank, conditional on a crisis affecting the financial system 2 De Jonghe (2010) uses for example the beta tail to assess a bank's systemic risk; [START_REF] Brunnermeier | Banks' Non-Interest Income and Systemic Risk[END_REF] use the Dcovar and De Jonghe et al. (2015), the MES. 3 For a detailed presentation of SRISK, see [START_REF] Acharya | Capital Shortfall: A New Approach to Ranking and Regulating Systemic Risks[END_REF] or [START_REF] Brownlees | SRISK: A Conditional Capital Shortfall Measure of Systemic Risk[END_REF]. [START_REF] Benoit | Where the Risks Lie: A Survey on Systemic Risk[END_REF]). Formally, [START_REF] Acharya | Capital Shortfall: A New Approach to Ranking and Regulating Systemic Risks[END_REF] and [START_REF] Brownlees | SRISK: A Conditional Capital Shortfall Measure of Systemic Risk[END_REF] define SRISK at a given moment as follows: ( ) ( )

1 1 it it it it SRISK kD k W LRMES = -- -
where k is the prudential ratio,4 it W is the market value of equity, it D is the book value of debt, and it LRMES is the long-run marginal expected shortfall. Its aim is to capture the interconnection between a bank and the rest of the system.

They propose to approximate the expected long-term marginal deficit using the daily MES 5 as follows:

( )

1 exp 18* it it LRMES MES = -
. 6 Thus, a higher SRISK means a larger systemic risk exposure. As [START_REF] Acharya | Capital Shortfall: A New Approach to Ranking and Regulating Systemic Risks[END_REF] are interested in estimating capital shortages that, by definition, cannot take on negative values, they restrict SRISK to zero. In this paper, like [START_REF] Laeven | Bank size, capital, and systemic risk: Some international evidence[END_REF] or [START_REF] Leroy | Is there a competition-stability trade-off in European banking?[END_REF], we do not restrict SRISK to zero, allowing it to assume negative values because they provide information on the relative bank systemic risk.

Our explanatory variables of interest are non-traditional banking activities and bank size.

To evaluate a bank's non-traditional banking activities, as De Jonghe et al. (2015), we consider the share of non-interest income 7 in total operating income. Bank size is computed as the natural logarithm of the bank's total assets.

Concerning control variables, we retain banks' specific and macroeconomic variables.

Among banks' specific variables, we consider loan provisions to loans, deposits to liquid assets, and return on equity. In terms of macroeconomic variables, we take annual gross domestic product (GDP) growth, inflation and market structure (concentration) for 8 UE countries.

Data

To carry out this study, we needed information on the balance sheets and income statements of banks. We obtained such information from the Bankscope and Fitch databases.

SRISK information comes from NYU-Stern Volatility Institute (V-Lab). 9 Macroeconomic variables are been extracted from the World Bank's World Development Indicators (WDI).

Concretely, we have selected only those banks for which financial statement information was available in Bankscope and Fitch, and SRISK 10 on the Volatility Institute website. Moreover, as 5 SRISK is also evaluated from market data, unlisted banks are been excluded from this study. In addition, to ensure that the banks included in our sample have traditional banking activities, we have retained only banks which are subject to deposit insurance.11 Finally, this study uses unbalanced panel data on 82 European listed banks over the period 2002-2016. The total assets of the 82 banks at the end of 2016 were 22.5 trillion Euros, which represents approximately three fifths of all European banking assets. Table 1 presents descriptive statistics of the variables. Table A1 (in appendix) gives more information about the banks included in our sample. [START_REF] Brownlees | SRISK: A Conditional Capital Shortfall Measure of Systemic Risk[END_REF]. It is expressed in thousands of euros. Size is the natural logarithm of bank total assets expressed in thousands of euros. HHI is the measure of concentration evaluated from the Hirschmann-Herfindhal index. Nii is non-interest income to total operating income. Credit risk is the ratio of loan provisions to total loans. Liquidity is deposits to liquid assets. ROE is the ratio of net income to equity.

Econometric methodology

To gauge the relationship between bank size, non-interest income and systemic risk, we consider a dynamic framework. Thus, our econometric model is written as follows: Size is bank size indicator; * Nii Size is the interaction variable between non-traditional banking activities and bank size; X is a matrix of control variables; α and u are respectively the constant and the error term.

Due to the presence of the lag endogenous variable among our explanatory variables, our estimations are made by the two-step generalized-method-of-moments12 in system with the robust estimator of variance. To ensure the robustness of our results, we limit the proliferation of instruments and apply the [START_REF] Windmeijer | A finite sample correction for the variance of linear efficient two-step GMM estimators[END_REF] correction, as suggested by [START_REF] Roodman | How to do xtabond2: An introduction to difference and system GMM in Stata[END_REF] for small samples.

Results

This section presents the cross-sectional dependence test and results of our estimates.

Cross-Sectional Dependence Test

GMM estimations assume that disturbances are independent across panels. Nevertheless, [START_REF] Chudik | Common correlated estimation of heterogeneous dynamic panel data models with weakly exogenous regressors[END_REF] show that any estimate with significant dependence of errors between individuals is likely to provide inconsistent results. We therefore proceed to the cross-sectional dependence test developed by [START_REF] Pesaran | Testing Weak Cross-Sectional Dependence in Large Panels[END_REF] between banks in our panel, whose results are presented in Table 2. Our results show that we cannot accept the null hypothesis of independence of errors between the banks in our panel. So, as suggested by [START_REF] Chudik | Common correlated estimation of heterogeneous dynamic panel data models with weakly exogenous regressors[END_REF], to our explanatory variables we add the time lags of their cross-sectional average.

Non-traditional banking activities, bank size and systemic risk

To deal with the joint effect of bank size and non-traditional banking activities on systemic risk, we proceed in two steps. First, considering the rationale that includes the interaction term between two variables derived from the Taylor approximation, we add the square of the bank size and the non-traditional banking activities indicators to our control variables. Second, we re-estimate the equation without the square of the non-traditional banking activities indicator because of its non-significance in the first step.

Table 3 presents our regression results. We find that the coefficient associated with noninterest incomes and the coefficient of the interaction term between non-interest incomes and bank size are significant at the 1% level. Thus, these results indicate that the effect of non-interest incomes on systemic risk exposures varies according to bank size. The positive sign associated with non-interest incomes indicates that non-interest incomes increase European bank exposure to systemic risk. The explanation for this finding is at least twofold. Banks with multiple activities are more complex, and therefore difficult to manage (too-complex-to-manage hypothesis). Additionally, according to the too-complex-to-fail hypothesis, they are more opaque and less transparent, and are therefore more inclined to engage in riskier activities, which increase their exposure to systemic risk. The negative sign associated with the interaction between non-interest incomes and bank size indicates that the effect of non-interest incomes decreases with bank size and could even be negative.

To deepen the interpretation of our results, we calculate the marginal effects of noninterest incomes on SRISK according to banks' size. These marginal effects are calculated with the results reported in column (2). Figure 1 presents the evolution of the marginal effects, and show that non-interest incomes only reduce small banks' systemic risk exposure.

Marginal effects of non-interest incomes

Note: This figure represents the marginal effect of non-interest income (Nii) on bank systemic risk by size as well as the 95 percent confidence bounds. It is based on results presented in column 2 of Table 4. As a reminder, the estimated coefficient for diversification is 233.5, and its interaction with size is -12.87.

D iv H H I       = - +              
A higher value indicates high incomes diversification, and therefore more non-traditional activities.

Secondly, like [START_REF] Laeven | Is there a diversification discount in financial conglomerates?[END_REF], we define diversification as follows:

Interest income -Non interest income (LL) 1

Total income

Div = -
Here too, a higher value indicates high incomes diversification, and therefore more nontraditional activities. In sum, alternative revenues only reduce large banks' exposure to systemic risk. The explanations for these results are multiple. First of all, large banks are subjected to greater market discipline [START_REF] Freixas | Regulating financial conglomerates[END_REF]), which can prevent them from adopting risky strategies. Furthermore, large banks have more sophisticated risk management techniques [START_REF] Hughes | Bank Capitalization and Cost: Evidence of Scale Economies in Risk Management and Signaling[END_REF]), and more qualified and experienced employees. Consequently, they can benefit more from diversification [START_REF] Cerasi | The optimal size of a bank: Costs and benefits of diversification[END_REF]) than small banks.

To refine this study, like [START_REF] Lepetit | Bank income structure and risk: an empirical examination of European banks[END_REF], we proceed to an analysis by types of activities.

Analysis by type of activities

In this section, non-traditional banking activities are represented by diversification in terms of activities. We use the share of trading income in total operating income as proxy of trading activities, and the share of income from commissions and fees in total operating income as proxy of commissions and fees activities. Table 5 presents our regression results and figures 2 and 3 the evolution of marginal effects. Like the effects of non-interest income on banks' systemic exposure, we find that trading incomes only increases small banks' exposure to systemic risk.

Concerning the relationship between income from commissions and fees, bank size and bank systemic risk, unlike trading income, our estimates show that it is negative. However, this relationship becomes positive beyond a certain bank size. Figure 3 shows that an increase in income from commissions and fees only increases large banks' exposure to systemic risk. This result can be explained by the fact that incomes from commissions and fees are less financial market-sensitive, which can consolidate banks' revenues in times of crisis, and therefore reduce their SRISK. However, for large banks', a large part of their income from commissions and fees comes from corporate finance activities that are market-sensitive, which could therefore increase their exposure to systemic risk. It is based on results presented in column 2 of Table 6. As a reminder, the estimated coefficient for diversification is 145.11, and its interaction with size is -8.24.

Note: This figure represents the marginal effect of income from commissions and fees on bank systemic risk by size as well as the 95 percent confidence bounds. It is based on the results presented in column 4 of Table 6. As a reminder, the estimated coefficient for diversification is -468.19, and its interaction with size is 26.43.

Conclusion

This paper analysed how the effect of the non-traditional banking activities on systemic risk exposure differ according to European bank size. We have used the generalized-method-ofmoments while taking into account the unobservable common factors between banks. We found that non-traditional banking activities only increase small banks' exposure to systemic risk. To refine our paper, we have conducted an analysis by type of activities. We found that trading activities only increase small banks' exposure to systemic risk; commissions and fees activities only increase large banks' exposure to systemic risk. Therefore, these results call for the establishment of a European banking regulation similar to the Glass-Steagall Act for small banks.

  , and Duport et al. (2018)), or on the effects of bank size on systemic risk (see for example the contributions of Demirgüç-Kunt and Huizinga (2013), Bertay et al. (2013), Laeven et al. (2016), Brownlees and Engle (2017) and Cubillas et al. (2017)). To our knowledge, only the article by De Jonghe et al. (2015) analyses how the effects of non-traditional banking activities on bank systemic risk differ according to their size.

Figure 1 :

 1 Figure 1: Marginal effects of non-interest incomes on bank systemic risk according to bank size.

  Non-Interest IncomesTo ensure the reliability of these findings, we make other estimates from other measures of non-traditional banking activities. Firstly, like De Jonghe (2015) or[START_REF] Elsas | The anatomy of bank diversification[END_REF] we consider the Hirschmann-Herfindhal approach, as below:

Figure 2 :

 2 Figure 2: Marginal effects of trading incomes according to bank size.

Figure 3 :

 3 Figure 3: Marginal effects of commissions and fees incomes according to bank size.

  in a l e ffe c t o f C o m m is s io n s a n d F e e s In c o m e s 13 13.5 14 14.5 15 15.5 16 16.5 17 17.5 18 18.5 19 19.5 20 20.5 21 21.5 22 Ln (Total Assets)

Table 1 :

 1 Descriptive statistics This table presents descriptive statistics of the variables used in this article. Srisk is the measure of systemic risk proposed by Acharya et al. (2012) and

	Variable	Mean	Std. dev	Min	Max
	SRISK	10.15	24.02	-10.85	120.64
	Size	17.73	1.99	13.34	21.44
	Nii	0.44	0.21	-0.06	1.19
	Credit risk	0.007	0.01	-0.002	0.06
	Liquidity	5.99	5.63	0.02	29.43
	Roe	0.06	0.14	-0.86	0.34
	HHI	0.13	0.06	0.06	0.27
	Inflation	1.67	1.18	-4.47	4.87
	Gdp growth	0.93	2.48	-9.13	5.79
	Note:

Table 2 :

 2 Cross-Sectional Dependence Test

		SRISK	Size	Nii	HHI	Credit_risk Liquidity	Roe	Inflation	Gdp_growth
	Statistic test	93.3*** 143.8*** 20.02*** 217.32***	47.8***	13.16*** 51.5*** 204.5*** 167.23***

Note:

The null hypothesis of the test is a strong inter-individual independence of errors. *** indicate statistical significance at 1% levels

Table 3 :

 3 Results of GMM estimation. SRISK is dependent variable. Non-interest income (Nii), Bank size (Size), and their interaction are independent variables of interest. This table reports only the results of variables of interest and control variables. *, ** and *** indicate respectively statistical significance at 10%, 5% and 1%. The Student t values are in brackets. The null hypothesis of the Hansen test is that the instruments used are not correlated with the residuals. The null hypothesis of the second serial correlation test is that the errors in the first-difference regression exhibit no second-order serial correlation. The null hypothesis of the Pesaran test is a strong inter-individual independence of errors.

	Dependent variable	SRISK	SRISK
	Regressors	(1)	(2)
	Srisk(-1)	0.47***	0.48***
		(4.8)	(5.03)
	Nii	240.32***	233.5***
		(2.85)	(2.8)
	Size	-107.68**	-108.24**
		(-2.16)	(-2.33)
	Nii*Size	-12.82***	-12.87***
		(-2.81)	(-2.98)
	Nii*Nii	-8.84	-
		(-0.25)	-
	Size*Size	3.23**	3.25***
		(2.41)	(2.57)
	Credit risk	424.24*	420.38*
		(1.76)	(1.79)
	Liquidity	-0.13	-0.06
		(-0.26)	(-0.13)
	Roe	-12.2	-10.83
		(-0.65)	(-0.67)
	HHI	-32.15	-42.99
		(-0.27)	(-0.45)
	Inflation	-2.22	-2.18
		(-0.9)	(-0.89)
	Gdp growth	0.75	0.7
		(0.77)	(0.75)
	second-order serial correlation test (P-value)	0.21	0.22
	Hansen test (P-value)	0.17	0.21
	Pesaran test (P-value)	0.34	0.47
	of lags on cross-sectional average	1	1
	Threshold switches sign	-	18.14
	Observations	887	887
	Note:

Table 4

 4 presents our regression results. Once again, we show that an increase in noninterest income only increases small banks' systemic risk exposures.

Table 4 :

 4 Results of GMM estimation. SRISK is dependent variable. Non-traditional banking activities (Div (HHI) or Div (LL)), Bank size (Size), and their interaction are independent variables of interest. This table reports only the results of variables of interest and control variables. Divers indicates Div (HHI) or Div (LL). *, ** and *** indicate, respectively statistical significance at 10%, 5% and 1%. The Student t values are in brackets. The null hypothesis of the Hansen test is that the instruments used are not correlated with the residuals. The null hypothesis of the second serial correlation test is that the errors in the first-difference regression exhibit no second-order serial correlation. The null hypothesis of the Pesaran test is a strong inter-individual independence of errors.

	Dependent variable	SRISK	SRISK	SRISK	SRISK
		Panel A		Panel B
	Regressors	Div (HHI)		Div (LL)
		(1)	(2)	(1)	(2)
	Srisk(-1)	0.37***	0.39***	0.37***	0.37***
		(2.59)	(2.63)	(2.59)	(2.84)
	Divers	544.46**	616.64**	300.79***	220.87*
		(2.02)	(2.22)	(2.58)	(1.91)
	Size	-143.18***	-141.17**	-166.5***	-117.28
		(-4.01)	(-3.82)	(-4.33)	(-1.46)
	Divers*Size	-30.01**	-33.8**	-17.03***	-12.41**
		(-2.04)	(-2.22)	(-2.58)	(-2)
	Divers*Divers	6.17	-	7.06	-
		(0.46)	-	(0.37)	-
	Size*Size	4.46***	4.44***	5.09***	3.71*
		(4.12)	(4.01)	(4.43)	(1.78)
	Credit risk	161.16*	184.82**	229.27*	498.7
		(1.73)	(1.99)	(1.8)	(0.96)
	Liquidity	-0.21	-0.2	-0.33	0.15
		(-0.88)	(-0.92)	(-1.17)	(0.13)
	Roe	-0.47	-1.03	1.66	11.45
		(-0.1)	(-0.2)	(0.44)	(0.64)
	HHI	-26.07	-21.45	-20.66	-41.19
		(-0.91)	(-0.91)	(-0.72)	(-0.22)
	Inflation	0.13	0.21	-2.23	-3.3
		(0.1)	(0.16)	(-0.74)	(-0.83)
	Gdp growth	-0.02	0.02	0.77	1.18
		(-0.05)	(0.06)	(0.74)	(1.23)
	second-order serial correlation (P-value)	0.27	0.3	0.35	0.25
	Hansen test (P-value)	0.11	0.092	0.071	0.1
	Pesaran test (P-value)	0.17	0.29	0.44	0.7
	Number of lags on cross-sectional average	3	3	3	3
	Threshold switches sign	-	18.24	-	17.79
	Observations	795	795	795	795
	Note:

Table 5 :

 5 Results of GMM estimation. SRISK is dependent variable. Incomes from Trading or Commissions and fees, Bank size, and their interaction are independent variables of interest. This table reports only the results of variables of interest and control variables. Divers indicates Trading incomes or commissions and fees incomes. *, ** and *** indicate, respectively statistical significance at 10%, 5% and 1%. The Student t values are in brackets. The null hypothesis of the Hansen test is that the instruments used are not correlated with the residuals. The null hypothesis of the second serial correlation test is that the errors in the first-difference regression exhibit no second-order serial correlation. The null hypothesis of the Pesaran test is a strong interindividual independence of errors.

	Dependent variable	SRISK	SRISK	SRISK	SRISK
		Panel A		Panel B
	Regressors	Trading		Com and fees
		(1)	(2)	(1)	(2)
	Srisk(-1)	0.48***	0.5***	0.46***	0.47***
		(3.87)	(7.75)	(4.22)	(4.3)
	Divers	155.88**	145.11***	-412.09**	-468.19**
		(2.44)	(3.12)	(-2.08)	(-2.08)
	Size	-2.15***	-178.58***	-150.39***	-146.86**
		(-3.58)	(-3.75)	(-4.37)	(4.28)
	Divers*Size	-10.2***	-8.24***	24.12**	26.43**
		(-2.62)	(-3.23)	(2.23)	(2.24)
	Divers*Divers	63.24**	-	-21.72	-
		(2.25)	-	(-0.4)	-
	Size*Size	5.94***	5.03***	4.07***	3.95***
		(3.7)	(3.95)	(4.35)	(4.35)
	Credit risk	232.77	459.89**	19.79	18.53
		(1.54)	(2.3)	(0.29)	(0.33)
	Liquidity	0.35	0.39	0.1	0.12
		(0.63)	(0.89)	(0.71)	(1)
	Roe	10.16	15.80**	5.5	6.21
		(0.72)	(1.98)	(1.4)	(1.55)
	HHI	-296.36**	35.93	-4.09	-3.41
		(-2.07)	(0.67)	(-0.2)	(-0.2)
	Inflation	-5.28*	-5.54**	-2.61	-2.73*
		(-1.83)	(-2.15)	(-1.42)	(-1.64)
	Gdp growth	-0.57	-0.54	0.31	0.31
		(-0.46)	(-0.72)	(0.45)	(0.48)
	second-order serial correlation (P-value)	0.094	0.22	0.21	0.28
	Hansen test (P-value)	0.16	0.093	0.14	0.21
	Pesaran test (P-value)	0.68	0.59	0.15	0.26
	Number of lags on cross-sectional average	3	2	1	1
	Threshold switches sign	-	17.61	18.8	17.71
	Observations	786	831	874	874
	Note:

On this subject, see the speech of the President of the Supervisory Board of European Banks at Madrid the

June 2017 https://www.bankingsupervision.europa.eu/press/speeches/date/2017/html/ssm.sp170927 .

As suggested by[START_REF] Engle | Systemic Risk in Europe[END_REF] and[START_REF] Brownlees | SRISK: A Conditional Capital Shortfall Measure of Systemic Risk[END_REF], we set the prudential capital ratio at

5.5%.5 For a threshold C equal to 2%. For a detailed presentation of MES, seeAcharya et al. (2010). 6 This approximation represents the firm's expected loss over a six-month horizon, obtained conditionally on the market falling by more than 40% within the next six months.7 This comprises trading income, commissions and fees as well as all other non-interest income.8 The concentration of the banking market of the countries is evaluated with the Hirschmann-Herfindhal index approach. Therefore, it is evaluated as the sum of the square of the market shares of each bank in a given country. 9 http://vlab.stern.nyu.edu/ . 10 Like[START_REF] Leroy | Is there a competition-stability trade-off in European banking?[END_REF], we consider SRISK at the end of each period.

However, to calculate our concentration indicator, we considered all European banks (listed and unlisted) for which Bankscope gives consolidated data.

For a detailed presentation of GMM, see[START_REF] Blundell | Initial conditions and moment restrictions in dynamic panel data models[END_REF] or[START_REF] Roodman | How to do xtabond2: An introduction to difference and system GMM in Stata[END_REF].

13.5

14.5

15.5

16.5

17.5

18.5

19.5

20.5

21.5

Ln (Total Assets)

Acknowledgements

I am grateful to Daniel Goyeau, Noelle Duport, Dominique Pepin and the anonymous referees for their very helpful comments on earlier versions of this paper. All remaining errors are mine. I would also like to thank the participants of the International Conference on Economic and Financial Risks which took place on the 20 and 21 June 2018 in Niort (France), as well as the C.R.I.E.F research group and Nouvelle Aquitaine region in France for their financial support. This research was conducted within a framework of the "Banking Governance, Financial Stability and Macroeconomic Performance" program supported by the Nouvelle-Aquitaine region.

Appendix