Maud Darsonval

Marina Grégoire

Nelly Caccia

Lucas Midoux

Sophie Berland

Sabine Leroy

Florence Dubois- Brissonnet

Mickaël Desvaux
email: mickael.desvaux@inrae.fr

Romain Briandet
email: romain.briandet@inrae.fr

Spatial organisation of Listeria monocytogenes and Escherichia coli O157:H7 cultivated in gelled matrices

Keywords: L. monocytogenes, E. coli O157:H7, food matrix, hydrogel, spatial organisation, Confocal Laser Scanning Microscopy (CLSM)

Spatial organisation of bacterial pathogens in food matrices remains poorly understood but of importance to improve risk assessment and prevent infection of consumers by contaminated foodstuff. Combining confocal laser scanning microscopy with genetic fluorescent labelling of key foodborne bacterial pathogens Listeria monocytogenes and Escherichia coli O157:H7 permitted to investigate the spatial patterns of colonisation of both pathogens in gel matrices, alone or in combination, in various environmental conditions. Increasing low melting point agarose (LMPA) concentrations triggers the transition between motile single cell lifestyle to a sessile population spatially organised as microcolonies. The size, number and morphology of microcolonies were highly affected by supplementations in NaCl or lactic acid, two compounds frequently used in food products. Strikingly, single-cell motility was partially restored at higher LMPA concentration in presence of lactic acid for E. coli O157:H7 and in presence of NaCl for L. monocytogenes. Co-culture of both species in the hydrogel affected pathogen colonisation features. L. monocytogenes presented a better ability to colonise gel matrices containing lactic acid in presence of E. coli O157:H7. Altogether, this investigation provides advanced insights in spatial distribution and structural dynamics of bacterial pathogens in gel matrices. Potential impacts on food safety are further discussed.

1) Introduction

In Europe, Listeria monocytogenes and Escherichia coli O157:H7 ranked as the third and fifth most common foodborne zoonotic agents, respectively, with a number of related cases on the slow rise for over 8 years (EFSA and ECDC;2021). E. coli O157:H7 is generally associated with meat contamination because of its presence in the digestive tract of livestock, but contamination of other products such as raw-milk products or vegetables has been recently reported [START_REF] Luna-Guevara | The Role of Pathogenic E. coli in Fresh Vegetables: Behavior, Contamination Factors, and Preventive Measures[END_REF]. As a food contaminant, L. monocytogenes is mostly found in vegetables, fish and cheese, but being a saprophyte and ubiquitous microorganism, the panel of possible vectors is quite wide including soil, water or animals [START_REF] Kallipolitis | Factors contributing to Listeria monocytogenes transmission and impact on food safety[END_REF]. Detection and control of bacterial load in food matrix is hindered by the fact that current predictive models are derived from planktonic bacterial growth that proved to be poorly accurate when applied to bacterial growth in solid and semi-solid food matrices [START_REF] Pin | Validating predictive models of food spoilage organisms[END_REF][START_REF] Skandamis | Colonial vs. planktonic type of growth: Mathematical modeling of microbial dynamics on surfaces and in liquid, semi-liquid and solid foods[END_REF][START_REF] Smith | Evaluation of a Clostridium perfringens predictive model, developed under isothermal conditions in broth, to predict growth in ground beef during cooling[END_REF]. Actually, food matrices are spatially organised environment with heterogeneous microstructures harbouring multiple micro-gradients that evolve with time and microbial activity, e.g. oxygen concentration, pH, temperature, nutrients consumption or metabolite accumulation [START_REF] Lobete | Recent trends in non-invasive in situ techniques to monitor bacterial colonies in solid (model) food[END_REF]. There is no generic model yet to describe substrates diffusion limitations effects in such food matrices and the one existing can be contradictory [START_REF] Cussler | Diffusion: Mass Transfer in Fluid Systems[END_REF][START_REF] Ribeiro | Binary diffusion coefficients for aqueous solutions of lactic acid[END_REF]. As a general rule, small molecules with less reactivity to the matrix (neutral charge or same charge as the reticulating agent) will diffuse faster, and large polymers with flexible chains diffuse more easily than rigid molecules of the same radius [START_REF] Silva | Transport phenomena in a model cheese: The influence of the charge and shape of solutes on diffusion[END_REF]. Local heterogeneities and their dynamics can lead to differentiation in microbial populations as cells can undergo adaptive responses to a given environment such as genotypic variation and stochastic gene expression [START_REF] Stewart | Physiological heterogeneity in biofilms[END_REF]. This heterogeneity of expression can lead to phenotypic diversity of cell subpopulations, expanding the possibilities of multiple stress tolerance or virulence levels for a same species in the same environment [START_REF] Caniça | Antibiotic resistance in foodborne bacteria[END_REF][START_REF] Nielsen | Comparison of heat stress responses of immobilized and planktonic Salmonella enterica serovar Typhimurium[END_REF]. The repartition and physiology of bacterial cells in a food matrix is also dependent on the texture of the media. Matrix hardness alters bacterial motility both on surfaces and inside the matrix itself [START_REF] Yang | Influence of Physical Effects on the Swarming Motility of Pseudomonas aeruginosa[END_REF]. The decrease in speed is also coupled with the selection of swimming behaviour between "runs'' and "tumbles" as meshes of reticulating agents create traps that limit diffusion through straight trajectories [START_REF] Licata | Diffusion of Bacterial Cells in Porous Media[END_REF]. Studies on the growth of bacteria inside gelled nutritive matrix are still sparse. Most of those concern predictive modelling and population survival under stress exposure [START_REF] Antwi | Influence of a gel microstructure as modified by gelatin concentration on Listeria innocua growth[END_REF][START_REF] Costello | Modelling the microbial dynamics and antimicrobial resistance development of Listeria in viscoelastic food model systems of various structural complexities[END_REF][START_REF] Jeanson | Bacterial Colonies in Solid Media and Foods: A Review on Their Growth and Interactions with the Micro-Environment[END_REF][START_REF] Kabanova | Microcalorimetric study of the growth of bacterial colonies of Lactococcus lactis IL1403 in agar gels[END_REF]. While the behaviour of cheese starters such as Lactococcus lactis has been described [START_REF] Theys | Evaluation of a mathematical model structure describing the effect of (gel) structure on the growth of listeria innocua, lactococcus lactis and salmonella typhimurium[END_REF][START_REF] Theys | Effect of pH, water activity and gel micro-structure, including oxygen profiles and rheological characterization, on the growth kinetics of Salmonella Typhimurium[END_REF], only a few investigations have been dedicated to pathogens such as Listeria monocytogenes [START_REF] Uyttendaele | Prevalence and challenge tests of Listeria monocytogenes in Belgian produced and retailed mayonnaise-based delisalads, cooked meat products and smoked fish between 2005 and 2007[END_REF][START_REF] Verheyen | Effect of microstructure and initial cell conditions on thermal inactivation kinetics and sublethal injury of Listeria monocytogenes in fish-based food model systems[END_REF] and, to our knowledge, none to E. coli O157:H7. In order to explore the behaviour of foodborne pathogens in semi-solid food matrices, L. monocytogenes and E. coli O157:H7 were grown in synthetic gelled media mimicking relevant food matrices such as grounded meat or soft cheese. Using fluorescent bacterial strains, spatial organisation and dynamics of the pathogens was investigated in situ by confocal laser scanning microscopy (CLSM). This experimental setup allowed the extraction and analysis of the microcolonies size and morphological parameters as well as single-cell motility in various environmental conditions. The gelled matrix was tuned with different concentrations of low melting point agarose (LMPA) to change the hydrogel texture and to study the variation of cell motility and spatially organised sessile microcolonies. Besides, different NaCl and lactic acid concentrations were considered respective to conditions encountered by these pathogens in food.

2) Materials and methods

Bacterial strains and culture conditions

From cryotubes conserved at -80°C, bacterial strains were plated on Petri dishes with TSB (Tryptone Soya Broth, Oxoid, reference CM0129) agar and incubated overnight at 37°C. One bacterial colony was picked up and inoculated in TSB broth prior to overnight incubation under orbital shaking. When required, growth media were supplemented either with tetracycline (10 μg.mL -1 ; ALFA AESAR, reference B21408) or chloramphenicol (25 μg.mL -1 ; EUROMEDEX, reference 3886-B). L. monocytogenes EGDe [START_REF] Glaser | Comparative Genomics of Listeria Species[END_REF] was transformed by electroporation with the plasmid pNF8 constitutively expressing the green fluorescent protein GFPmut2 [START_REF] Cormack | FACS-optimized mutants of the green fluorescent protein (GFP)[END_REF][START_REF] Fortineau | Optimization of green fluorescent protein expression vectors for in vitro and in vivo detection of Listeria monocytogenes[END_REF]. Noncytotoxic E. coli O157:H7 CM454 [START_REF] Gobert | Shiga Toxin Produced by Enterohemorrhagic Escherichia coli Inhibits PI3K/NF-κB Signaling Pathway in Globotriaosylceramide-3-Negative Human Intestinal Epithelial Cells[END_REF][START_REF] Renier | Inactivation of the SecA2 protein export pathway in Listeria monocytogenes promotes cell aggregation, impacts biofilm architecture and induces biofilm formation in environmental condition[END_REF] was transformed with the plasmid pVaAg-Red constitutively expressing the red fluorescent protein mRuby2 [START_REF] Ageorges | Differential homotypic and heterotypic interactions of antigen 43 (Ag43) variants in autotransporter-mediated bacterial autoaggregation[END_REF].

Gelled matrices

To mimic semi-solid food matrices, such as minced meat and soft cheese, gelled matrices of different textures were obtained using low melting point agarose (LMPA; UltraPure Agarose, Invitrogen, reference 16500-500) at various concentrations (i.e. 0.25 %, 0.50 % and 1.00 %).

From overnight pre-cultures and following serial dilutions, the bacterial inoculum was adjusted to attain the desired initial concentration. After boiling to ensure complete melting, the TSB-LMPA was kept at 60°C and cooled down below 40°C just before adding the bacterial inoculum to prevent heat stress. Medium could be supplemented with lactic acid (Sigma Aldrich, 27715-1L-R) to ajust media to pH5 (8 10 -4 mM) and/or 25 g/L NaCl (AnalaR NORMAPUR, VWR chemicals 27810.295). After homogenising and gentle stirring to avoid bubble formation, the inoculated gel matrix was immediately distributed in each well of a 96well microtiter plate of microscopic grade (Microclear, Greiner Bio-one, France). The microtiter plates were then incubated at 20°C and observed under microscope at different time points. Bacteria were also enumerated as Colony-Forming Units (CFU) by recovering the gelled matrix from a well with a microstreaker and transferring it in a tube with 10 ml of sterile saline solution (9 g/L NaCl). The hydrogel was disrupted and homogenised by mechanical grinding with an Ika-Ultra-Turrax T25 (8000 t/min -1 ) to fragment the microcolonies into dispersed cells. Following serial dilutions, samples were spread on petri dishes, incubated overnight and results expressed as CFU/ml.

Confocal Laser Scanning Microscopy (CLSM)

All microscopic observations were performed with a Leica HCS-SP8 confocal laser scanning microscope at the INRAE MIMA2 imaging platform (www6.jouy.inrae.fr/mima2). GFPmut2 (λex485; λem508) and mRuby2 (λex559; λem600) were excited respectively with laser bands 488 nm and 561 nm. A 40x water long range objective (numerical aperture = 0.8) was used with a 512 x 512 pixels definition (387 µm x 387 µm). Bidirectional acquisition speed of 600 Hz allows a frame rate of 2.3 images per second. For 3D stack analysis, a 1 µm step between z levels was used. For each condition, a minimum of 2 stacks in 4 different wells were acquired (8 z-stacks), except for co-cultures in 1.00 % LMPA where only 2 wells were analysed (4 z-stacks). For the tracking of swimming cells, 2 minutes time-lapse with one image every 459 ms is realised three times for each condition. Each microscopic image shown in the following results has a side (X/Y) of 387 µm and a height Z of 300.0 µm for a total volume of 45 046 875 µm 3 or 4.5 10 -2 mm 3 . For image analysis, all microscopic pictures were treated on IMARIS 9.64 (Bitplane, Switzerland). The raw fluorescence of each image was first binarised using the automated Imaris isosurface function. From binarised objects, it is possible to extract geometric parameters (e.g. biovolume, position, intensity, sphericity, speed) as raw data for further treatments in a data spreadsheet. For cell tracking in LMPA, observations in control, lactic acid and NaCl conditions were performed for both pathogens during 2 minutes timelapse to evaluate the motility radius. The effective radius of motility corresponds to a circle around a cell at a given time T0, with the periphery of said circle being an estimate of the position of the bacteria at the time T1.

Gels textural measurements

To assay the texture of gelled matrices, 30 ml of each hydrogels were prepared in 40 mL plastic cups (weight about 30 g, internal diameter 28 mm) and a TA.XT2 texture analyser (Stable Micro System) equipped with a 5 Kg load cell. Hydrogels were compressed using a flat disc (25 mm diameter, height 2 mm) pushing at a crosshead speed of 1 mm.s -1 until a depth of 40 mm is reached (1.5 cm gap with bottom of cup). During compression, the normal force (N) increased linearly at a fast pace until it reached a plateau. The value of the average force of the plateau (FP, N) was used to calculate the rupture point (in weight, g) to represent the hardness of the sample.

Statistics

ANOVA variance analysis were realised with Statgraphics centurion (Statgraphics Technologies, Inc.) from 4 to 8 independent replicates. Differences were considered significant when P<0.05 with P being the critical probability associated with the Fisher test.

Graphs and heatmaps were performed with Prism 9 (Graphpad; CA, USA).

3) Results

3.1) Microcolony size and morphology are influenced by the initial load of the inoculum and LMPA concentration of the gelled matrices

To evaluate how initial bacterial load could affect spatial organisation and distribution of bacteria in gelled matrices, the size and morphology of in-gel bacterial microcolonies were assessed for initial concentrations ranging from 10 2 to 10 5 CFU/ml at a fixed LMPA concentration of 0.50 % (Figure 1).

Microcolonies in the gelled matrices generally appeared with spherical/ovoid shapes with a rougher form for E. coli O157:H7 compared to L. monocytogenes (Figures 1A and 1C). At low initial loads, scarce but large microcolonies were observed contrary to higher initial loads where microcolonies got smaller but more numerous (Figure 1A). Quantitative analysis demonstrated similar trends for both pathogens (Figure 1B). For initial load from 10 3 to 10 5 CFU/ml in 0.50 % LMPA, the mean biovolumes of microcolonies were 630 000 µm 3 and 15 000 µm 3 , respectively, and inversely proportional with the initial load. For both pathogens, microcolonies tended to adopt a lentillar form associated with a reduced sphericity below an initial load of 10 3 CFU/ml (Figures 1A and1C).

The effect of the concentration of jellifying agent on bacterial colonisation was further explored using different concentrations of LMPA (ranging from 0.25 % to 1.00 %) with an initial load of 10 5 CFU/ml (Figure 2). The morphology and distribution of bacterial cells changed significantly over the different concentrations tested, but the average number of cells per image was not significantly impacted (supplementary material Figure S1). With increasing LMPA concentrations, a shift from single cells to microcolonies was observed from and above 0.50 % LMPA (Figure 2). Between 0.50 % and 1.00 % LMPA, the biovolumes of microcolonies were significantly different and ranged from 1.0 10 2 to 7.0 10 4 µm 3 (Figure 2A and2B). Investigating the effects on these pathogens in co-culture, the overall size of microcolonies was significantly different compared to mono-culture conditions (Figure 2C). The mean values of 1.4 10 4 and 4.5 10 4 µm 3 for the biovolumes of L. monocytogenes and E. coli O157:H7 in mono-culture became 2.0 10 4 and 6.0 10 4 µm 3 in average for each pathogen in co-culture.

3.2) Bacterial growth in gelled matrices supplemented with lactic acid and NaCl

Considering lactic acid and/or NaCl can be found in food matrices subjected to contamination by L. monocytogenes or E. coli O157:H7, e.g. some meat or cheese products, their effect on bacterial colonisation was further considered by supplementing the gelled matrix with lactic acid and/or NaCl as described in the part "Materials and Methods". Besides CLSM examinations in these different conditions with various concentrations of LMPA, lactic acid and/or NaCl (Figure 3A), microcolonies were further quantitatively analysed regarding their bacterial biovolumes (Figure 3B). In this study, bacterial microcolonies were defined as detected objects larger than 10 3 µm 3 . This threshold volume corresponds to a spherical object with a diameter of 20 µm, which was the lowest measurement for microcolonies in the control conditions.

At all LMPA concentrations with lactic acid, quantitative analysis yielded in a loss in microcolonies formation with a drop of objects mean biovolume from 10 4 µm 3 in gels without lactic acid to below 10 2 µm 3 for both L. monocytogenes and E. coli O157:H7 in presence of the acid (Figure 3). While E. coli O157:H7 was more resilient to lactic acid with a final population density of around 10 7 CFU/ml, it was significantly lower for L. monocytogenes with a decrease from 10 9 CFU/ml (control) to 10 5 CFU/ml (Figure 3, supplementary material Figure S2). Interestingly, in the presence of E. coli O157:H7, L. monocytogenes exhibited a better resilience to the effect of lactic acid by reaching a final density of 10 7 CFU/ml (Figures 3); on the contrary, E. coli O157:H7 appeared more severely impacted when in presence of L. monocytogenes compared to mono-culture conditions both in terms of microcolony formation and final cell density as both were lower then.

In presence of NaCl, the size of the microcolonies of L. monocytogenes was quite similar, i.e. around 10 3 µm 3 , whatever the LMPA concentration tested (Figure 3). Of note, some cell aggregates could be observed on the verge of microcolony only at the lowest LMPA concentration (0.25 %). For E. coli O157:H7, there was no variation in the microcolonies size but the population of single cells could not be observed anymore with salt supplementation (Figure 3). NaCl also appeared to promote adherence and biofilm development on the polystyrene at the bottom of the wells for both pathogens at 0.25 % LMPA (Figure 3, supplementary material Figure S3), as well as at 0.50 % LMPA but to a lesser extent (Figure 3). At 1.00 % LMPA concentration, however, no significant difference regarding microcolonies formation and adherence to the bottom of the wells could be observed compared to the control conditions. In the presence of NaCl in co-culture, the size of the microcolonies were significantly smaller (10 2 µm 3 instead of 10 4 µm 3 ) for E. coli O157:H7 at 48 h compared to mono-culture conditions (Figures 3, supplementary material Figure S4); at 96 h, however, the mean biovolumes were equivalent between mono-and co-culture conditions indicating that the expansion of microcolonies was only slowed down and temporarily delayed.

In gelled matrices supplemented with both lactic acid and NaCl, microcolonies were no longer observed for both L. monocytogenes and E. coli O157:H7 at all LMPA concentrations (Figure 3), with mean biovolumes (10 2 µm 3 ) comparable to what was seen in the lactic acid condition. Adherence and biofilm development at the bottom of the wells was still observed for E. coli O157:H7 with 0.25 % and 0.50 % LMPA, but not for L. monocytogenes (Figure 3, supplementary material Figure S3). However, for co-cultures in this condition containing lactic acid and NaCl, L. monocytogenes had a higher biovolume and formed a biofilm with E. coli O157:H7 at the bottom of the well (Figure 3, supplementary material Figure S3).

3.3) Single-cell motility of L. monocytogenes and E. coli O157:H7 in the gelled matrices

With no addition of lactic acid or NaCl, single cells motility were only observed in gelled matrices at 0.25 % LMPA with mean speeds (Vmean) of 0.53 µm/s and 0.68 µm/s for E. coli O157:H7 and L. monocytogenes respectively (Figure 4, supplementary material Figure S5). At higher LMPA concentrations bacterial cells were sessile and generally part of microcolonies (Figure 3). At 0.25 % LMPA supplemented with lactic acid, motility was observed for both pathogens. When it was 0.25 % LMPA supplemented with NaCl, L. monocytogenes was motile but not E. coli O157:H7. The same results were obtained at 0.50 % LMPA but lower speed rates were recorded. At 1.00 % LMPA, no motility was observed whatever the strain and the supplements (Figure 4). In all tested conditions, the variation of Vmean values were less pronounced than for the maximum speed (Vmax) of the single cells. In the presence of lactic acid for E. coli O157:H7 and in the presence of NaCl for L. monocytogenes , there were clear shifts of the Vmax with significantly higher values than in other conditions (Figure 4, supplementary material Figure S5). For E. coli O157:H7 in presence of lactic acid, the Vmax were 2.7 and 2.4 µm/s at 0.25 % and 0.50 % LMPA, respectively (versus 1.27 and 0 µm/s with no supplementation) (supplementary material Figure S5). This increase in the cell motility upon NaCl or lactic acid supplementation was not observed anymore at 1.00 % LMPA. Those results are in accordance with the microscopic observations and motility tracking (Figure 6). For L. monocytogenes, Vmax reached 4.8 and 2.2 µm/s with NaCl supplementation at 0.25 % and 0.50 % LMPA, respectively (versus 1.6 and 0 µm/s with no supplementation) (supplementary material Figure S5).

Considering the distribution of the cell population, the two pathogens appeared to behave differently in conditions promoting cell motility (Figure 4); as most cells of L. monocytogenes reacted to lactic acid or NaCL supplementation, while only a small portion of E. coli O157:H7 cells did. With NaCl supplementation in gelled matrix, over 80 % of L. monocytogenes cells had Vmax higher than 2 µm/s in media NaCl, whereas 38 % of E. coli O157:H7 still had a Vmax below 1 µm/s with lactic acid (Figure 4). When both lactic acid and NaCl were added to the gelled matrices, the Vmean and Vmax for L. monocytogenes were not significantly different from the results obtained with the sole supplementation in lactic acid (Figure 4, supplementary material Figure S5). For E. coli O157:H7, motility was equivalent to what was observed in the control for 0.25 % and 0.50 % LMPA supplemented with lactic acid and NaCl (Figure 4).

In co-cultures, a slight but significant increase of Vmean for L. monocytogenes upon NaCl supplementation was observed (supplementary material Figure S6). In co-culture with lactic acid, the Vmean for L. monocytogenes was doubled in comparison to mono-culture conditions at 0.25 % and 0.50 % LMPA. For E. coli O157:H7 the opposite was observed with a general decreased in the Vmean values. Co-cultivation of E. coli O157:H7 and L. monocytogenes in gelled matrices thus led to significant change in the motility trends for each strain.

With the addition of lactic acid or NaCl in 0.50 % or 1.00 % LMPA gelled matrices, cell motility was promoted for both L. monocytogenes and E. coli O157:H7 (Figure 4). As this could result from variations in the hardness of the gelled matrices in these conditions, the texture of the gelled matrices was further considered (Figure 5). As expected, a significant increase in the hardness of the gelled matrices was confirmed with increasing LMPA concentrations (Figure 5); the gelled matrix was 5.5 times harder at 0.50 % LMPA and 14.5 times harder at 1.00 % LMPA compared to 0.25 % LMPA. When lactic acid or NaCl were added, the rheology of the gelled matrices was significantly modified as hydrogels composed of 0.50 % LMPA and supplemented with NaCl could support up to 795 g (i.e. a 33 % decrease in strength) and a gelled matrix at 0.50 % LMPA with lactic acid only 173 g (i.e. 89 % decrease in strength) compared to the control condition (gelled matrix with no supplementation). Also, variation in the hydrogel hardness could mainly explain the increase in cell motility for L. monocytogenes in gelled matrices with lactic acid. However, for a 0.50 % LMPA supplemented with NaCl, the Vmean and Vmax obtained for L. monocytogenes were significantly higher than at 0.25 % LMPA when the hydrogel hardness of the control condition was in fact lower (Figures 4 and5, supplementary material Figure S5). Similar observations could apply for the Vmean and Vmax of E. coli O15:H7 at 0.50 % LMPA with lactic acid versus conditions at 0.25 % LMPA, where hydrogel hardness is equivalent but motility is not. In these conditions, variations of the cell motility would thus result from physiological adaptation of bacterial cells rather than from modifications in the texture of the gelled matrices.

In order to further characterise the cell motility of L. monocytogenes and E. coli O157:H7 in the different gelled matrix conditions, the effective radius of motility for each bacterial cell was evaluated. At 0.25 % LMPA, where L. monocytogenes and E. coli O157:H7 were less motile, the same effective radius of ~2.5 µm/m was measured (Figure 6, supplementary material Figure S7 video A andD). With the addition of lactic acid, an increase in the cellular speed was observed for both pathogens (Figure 4) and tumbling was also observed (Figure 6, supplementary material Figure S7 video B andE); the effective radii in this condition were about 7.5 µm/m and 12.5 µm/m for L. monocytogenes and E. coli O157:H7 respectively. In gelled matrices supplemented with NaCl, no motility could be observed for E. coli O157:H7, whereas L. monocytogenes had a mean effective radius of 31.8 µm/m (Figure 6, supplementary material Figure S7 video C andF). This increase in effective radius is observed for L. monocytogenes with or without supplementation with NaCl with no increase in the Vmean (0.68 µm/s) but a significant change in Vmax (from 1.57 to 4.77 µm/s) (supplementary material Figure S5).

4) Discussion

Food matrixes are rich media harbouring hundreds of microbial species [START_REF] Chaillou | Origin and ecological selection of core and food-specific bacterial communities associated with meat and seafood spoilage[END_REF][START_REF] Yu | Assessment of food microbiological indicators applied on poultry carcasses by culture combined MALDI-TOF MS identification and 16S rRNA amplicon sequencing[END_REF] and understanding the behaviour of foodborne pathogens in this environment is one of the key prerequisite to refine risk assessment models and to ultimately improve microbiological food safety [START_REF] Koutsoumanis | Last developments in foodborne pathogens modeling[END_REF][START_REF] Membré | Lastest developments in foodborne pathogen risk assessment[END_REF]. Though, behaviours of bacterial foodborne pathogens are generally assessed in liquid growth media that are poorly mimicking food matrices. At the same time, food matrices are often opaque, preventing direct photonic microscopic observation of these microorganisms to evaluate their physiology and behaviour. To alleviate this limitation, genetically fluorescent L. monocytogenes and E. coli O157:H7 were grown in transparent nutritive gelled matrices for in situ observations by CSLM [START_REF] Bridier | Fluorescence-based tools for single-cell approaches in food microbiology[END_REF][START_REF] Verheyen | Effect of microstructure and initial cell conditions on thermal inactivation kinetics and sublethal injury of Listeria monocytogenes in fish-based food model systems[END_REF]. Besides the visualisation of bacterial cells in the gelled matrix, the use of LMPA presented the advantages of a low solidification point (~25°C) limiting bacterial thermal stress, combined to a low melting point (≤65°C) facilitating the experimental handling compared to other jellifying agents such as agar, guar gum or gelatin [START_REF] Jaeger | Beyond agar: Gel substrates with improved optical clarity and drug efficiency and reduced autofluorescence for microbial growth experiments[END_REF]. Furthermore, LMPA allowed to cover a wide range of textures relevant to various food matrices, e.g. 0.50 % LMPA presented a hardness similar to fried beef patties [START_REF] Uriyapongson | Comparison and improvement of chemical and physical characteristics of low-fat ground beef and buffalo meat patties at frozen storage[END_REF] or 1.00 % LMPA was comparable to the texture of mature cheddar [START_REF] Pollard | Textural Changes of Natural Cheddar Cheese During the Maturation Process[END_REF].

In most conditions tested in the gelled matrices, bacterial colonised the media as microcolonies adopting a spherical morphology. While similar observations where already reported in gelatin gels and/or for other bacterial species [START_REF] Antwi | Influence of a gel microstructure as modified by gelatin concentration on Listeria innocua growth[END_REF][START_REF] Mitchell | The effects of agar concentration on the growth and morphology of submerged colonies of motile and non-motile bacteria[END_REF], in this investigation a clear inverted correlation between the density of founder cells and the radius of the microcolonies was highlighted. While giant microcolonies could be observed at lowest rates of initial inoculation rate (e.g. radius of about 110 µm at 10 2 CFU/ml), the distances between microcolonies decreased as the initial inoculation rate increased (e.g. from one millimeter to around 80 µm). Modification of metabolite gradient in the microcolonies can be expected between those conditions [START_REF] Jeanson | Bacterial Colonies in Solid Media and Foods: A Review on Their Growth and Interactions with the Micro-Environment[END_REF]. In addition, the sphericity of the microcolonies increased with the initial concentration of the inoculum, whereas more oblate/lentillar shapes were observed at the lowest initial load. Such lentillar microcolonies morphology were previously reported but as a result of increasing concentrations of gelatin [START_REF] Antwi | Influence of a gel microstructure as modified by gelatin concentration on Listeria innocua growth[END_REF] or agarose [START_REF] Zhang | Morphogenesis and cell ordering in confined bacterial biofilms[END_REF].

The formation of microcolonies is a food safety issue due to emerging properties caused by heterogeneity and diversification of bacterial phenotypes as colonies age [START_REF] Saint-Ruf | Massive diversification in aging colonies of Escherichia coli[END_REF]; quorum sensing [START_REF] Garmyn | Evidence of autoinduction heterogeneity via expression of the agr system of Listeria monocytogenes at the single-cell level[END_REF][START_REF] Ji | Cell density control of staphylococcal virulence mediated by an octapeptide pheromone[END_REF][START_REF] Kanamaru | SdiA, an Escherichia coil homologue of quorum-sensing regulators, controls the expression of virulence factors in enterohaemorrhagic Escherichia coli O157:H7[END_REF]; or long term consequences on virulence in host [START_REF] Kortebi | Listeria monocytogenes switches from dissemination to persistence by adopting a vacuolar lifestyle in epithelial cells[END_REF][START_REF] Travier | ActA Promotes Listeria monocytogenes Aggregation, Intestinal Colonization and Carriage[END_REF]. Supplementation of the gelled matrices in lactic acid and/or NaCl had profound effects on microcolonies development of both pathogens. The final cell density of E. coli O157:H7 and L. monocytogenes as well as microcolony formation was significantly reduced in the presence of lactic acid and most certainly result from cumulative inhibitory effects of pH and diffusion of undissociated weak acid form across the cellular membranes [START_REF] Arcari | Comparative Review of the Responses of Listeria monocytogenes and Escherichia coli to Low pH Stress[END_REF][START_REF] Booth | Regulation of cytoplasmic pH in bacteria[END_REF][START_REF] Lambert | Weak-acid preservatives: modelling microbial inhibition and response[END_REF][START_REF] Russell | Energetics of bacterial growth: balance of anabolic and catabolic reactions[END_REF]. The supplementation with NaCl in tested hydrogels did not significantly impacted the final cell density but a modification of the spatial distribution in the gelled matrix was observed, especially development at the bottom of the wells. Addition of NaCl was previously shown to influence the physiology, including bacterial adhesion and/or biofilm formation abilities of E. coli [START_REF] Cerca | Effect of growth conditions on poly-N-acetylglucosamine expression and biofilm formation in Escherichia coli[END_REF][START_REF] Hou | TolC Promotes ExPEC Biofilm Formation and Curli Production in Response to Medium Osmolarity[END_REF][START_REF] Olesen | Relative gene transcription and pathogenicity of enterohemorrhagic Escherichia coli after long-term adaptation to acid and salt stress[END_REF] or L. monocytogenes [START_REF] Lee | Biofilm Formation of Listeria monocytogenes Strains Under Food Processing Environments and Pan-Genome-Wide Association Study[END_REF][START_REF] Olesen | Gene transcription and virulence potential of Listeria monocytogenes strains after exposure to acidic and NaCl stress[END_REF][START_REF] Silva | Interference of sanitizers, NaCl and curing salts on Listeria monocytogenes adhesion and subsequent biofilm formation[END_REF][START_REF] Xu | Effect of NaCl on the Biofilm Formation by Foodborne Pathogens[END_REF] in various growth conditions.

Interestingly, the behaviours of L. monocytogenes and E. coli O157:H7 appeared to be influenced when they were co-cultured. While generally considered in discrete manner whenever in food safety (from quality control to risk modelling) or in medical microbiology (in case of human infections), these zoonotic foodborne bacterial pathogens can co-occur and contaminate the same foodstuffs, especially some types of cheese and meat as well as some vegetables products [START_REF] Fantelli | Prevalence and characteristics of Shigatoxin-producing Escherichia coli and Listeria monocytogenes strains isolated from minced meat in Switzerland[END_REF][START_REF] Kallipolitis | Factors contributing to Listeria monocytogenes transmission and impact on food safety[END_REF][START_REF] Lin | Occurrence of Listeria monocytogenes. Salmonella spp., Escherichia coli and E. coli O157:H7 in vegetable salads[END_REF][START_REF] Luna-Guevara | The Role of Pathogenic E. coli in Fresh Vegetables: Behavior, Contamination Factors, and Preventive Measures[END_REF] and possibility of co-infection should not be overlooked [START_REF] Opperman | Co-infection with Streptococcus pneumoniae and Listeria monocytogenes in an immunocompromised patient[END_REF].

Cell motility appeared to be dependent on the texture of gelled matrix but also on the presence of lactic acid and/or NaCl. As expected, the speed of swimming in gelled matrices was slower than the one reported in liquid broth [START_REF] Swiecicki | From swimming to swarming: Escherichia coli cell motility in two-dimensions[END_REF] and would rather correspond to speed rate measured at the edge of swarming colonies [START_REF] Damton | Dynamics of bacterial swarming[END_REF]. Besides the speed rate, lactic acid is known to have an impact on the rotation of the flagella in E. coli as it induces inversion of flagellar rotation from counter-clockwise (CCW) to clockwise (CW) [START_REF] Xu | Effects of fermentation products of the commensal bacterium Clostridium ramosum on motility, intracellular pH, and flagellar synthesis of enterohemorrhagic Escherichia coli[END_REF]; CCW rotation is generally associated with straight swimming whereas CW rotation of the flagella results in tumbling behaviour in E. coli [START_REF] Larsen | Change in direction of flagellar rotation is the basis of the chemotactic response in Escherichia coli[END_REF]. E. coli generally do not express motility at low pH but a bacteriophage of E. coli O157:H7 has been demonstrated to induce a swarming phenotype in acidic media [START_REF] Su | Lysogenic infection of a Shiga toxin 2-converting bacteriophage changes host gene expression, enhances host acid resistance and motility[END_REF]. In L. monocytogenes, the expression of flagella is known to be repressed in the presence of lactic acid [START_REF] Cortes | Transcriptome Sequencing of Listeria monocytogenes Reveals Major Gene Expression Changes in Response to Lactic Acid Stress Exposure but a Less Pronounced Response to Oxidative Stress[END_REF] as well as by high concentration of NaCl [START_REF] Caly | Sodium chloride affects Listeria monocytogenes adhesion to polystyrene and stainless steel by regulating flagella expression[END_REF]. In both E. coli and L. monocytogenes, "hopping" and "trapping" behaviour could also contribute to cell motility and occur as observed in porous matrices, where short bursts of free movement are responsible for most of the apparent motility [START_REF] Bhattacharjee | Bacterial hopping and trapping in porous media[END_REF].

Investigation of bacterial colonisation in a model of gelled food matrices by bacterial foodborne pathogens demonstrated a wide range of colonisation patterns depending on environmental conditions. Different strains of a same species, including E. coli O157:H7 or L. monocytogenes, can exhibit different behaviours and phenotypes [START_REF] Biscola | Determination of adhesin gene sequences in, and biofilm formation by, O157 and non-O157 Shiga toxin-producing Escherichia coli strains isolated from different sources[END_REF][START_REF] Borucki | Variation in Biofilm Formation among Strains of Listeria monocytogenes[END_REF][START_REF] Orsi | Listeria monocytogenes lineages: Genomics, evolution, ecology, and phenotypic characteristics[END_REF][START_REF] Renter | Diversity, frequency, and persistence of Escherichia coli O157 strains from range cattle environments[END_REF], but the diversification of cell types in spatially organised communities can triggers emerging community properties. Further layers of complexity can be added by biocoenoses forming discrete microecosystems where foodborne pathogens interact with other species of the food microbiota; as well as various regulatory and molecular mechanisms including phase variation (e.g. sequences inversions, different methylation sites and levels of methylase expression) or bistability of gene expression [START_REF] Ackermann | A functional perspective on phenotypic heterogeneity in microorganisms[END_REF][START_REF] Veening | Bistability, Epigenetics, and Bet-Hedging in Bacteria[END_REF]. While the present investigation highlighted some differences in the spatial organisation of some key foodborne pathogens in gelled matrices, the integration of the different levels of heterogeneity in food matrices is clearly the next frontiers in the improvement of risk assessment of microbial food safety and necessitate to consider the foodborne pathogen at a single cell level besides the population level classically considered in food matrices [START_REF] Lianou | Strain variability of the behavior of foodborne bacterial pathogens: A review[END_REF]. Besides description of growth parameters, reaching this aim will require to acquire in-depth knowledge of molecular mechanisms at play in the phenotypic heterogeneity and combine it to mathematical modelling of the behaviour of foodborne pathogens.

Figure legends

Figure 1 :

 1 Figure 1: Effect of the bacterial initial load on the spatial distribution, biovolume and morphology of microcolonies of L. monocytogenes and E. coli O157:H7 in gelled matrices. A: CLSM observations of L. monocytogenes (green) and E. coli O157:H7 (red) at 0.50 % LMPA for different bacterial initial loads after 96h of growth at 20°C. Representative pictures correspond to microcolonies isosurfaces after fluorescence segmentation. B: Mean biovolume in log10 (µm 3 ) of microcolonies at 96 h at different bacterial initial loads. C: Mean sphericity of microcolonies at different bacterial initial loads. Error bars indicate the mean standard deviation.

Figure 2 :

 2 Figure 2: Effect of gelled matrices LMPA concentration on microcolonies formation by L. monocytogenes and E. coli O157:H7. CLSM observations of mono-cultures of L. monocytogenes (A, green) and E. coli O157:H7 (B, red), as well as co-cultures (C-C) of both pathogens at 20°C and 96 h. Representative hydrogel sections correspond to 387x387x300 µm.

Figure 3 :

 3 Figure 3: Impact of NaCl and/or lactic acid supplementation on microcolonies of L. monocytogenes and E. coli O157:H7 in gelled matrices. A: CLSM observations of genetically fluorescent L. monocytogenes (green) and E. coli O157:H7 (red) cells in gelled matrices with different LMPA concentrations (0.25, 0.50 or 1.00 %) (i) without the addition of lactic acid or NaCl (control condition), (ii) with the addition of lactic acid, (iii) withsupplementation NaCl, or (iv) with both lactic acid and NaCl, in mono-and co-cultures at 96 h. B: Heatmap of the mean biovolume of microcolonies in the different gelled matrix conditions. The colour scale is expressed in log10 (µm 3 ) with cooler tones indicating small microcolonies and warmer colours are attributed to large microcolonies. Data originate from CSLM observations performed at 48 h and 96 h from mono-culture (M-C) and co-culture (C-C) conditions. A threshold of 10 3 µm 3 is set as the limit between single cells or small aggregates and microcolonies.

Figure 4 :

 4 Figure 4: Quantitative analysis of individual cell motility of L. monocytogenes and E. coli O157:H7 in different gelled matrix conditions. The mean speed (Vmean in red) and the max speed (Vmax in blue) were calculated from bacterial cell observations in time-lapse microscopy as described in the Material and Methods section. From there, the percentage of population for the different speed values was represented as a density bar. The averages for

Figure 5 :

 5 Figure 5: Texture of the gelled matrices supplemented with lactic acid or NaCl. Mean of hydrogel hardness (g) at the different LMPA concentrations considered: 1.00 %, 0.50 % and 0.25 %, without supplementation (control condition in black) or supplemented with Lactic acid (green) or with NaCl (orange). Error bars indicate the standard deviation (calculated from 10 measurements).

Figure 6 :

 6 Figure 6: Time-tracking of single cells motility of L. monocytogenes (A, B, C) and E. coli O157:H7 (D, E, F) in gelled media. Data acquisition was performed at 0.25 % LMPA; (A, D) without the addition of lactic acid or NaCl (control condition); (B, E) with the addition of lactic acid, or (C, F) with supplementation of NaCl. Tracks were acquired and represented over the full duration of the timelapse (120 s). All images are 101 µm by 82 µm and correspond to close up of images found in the 6 supplementary material movies V7A to V7F.

Figure S1 :

 S1 Figure S1: Quantitative analysis of the cell population per image (Material and Method)

Figure S3 : 20 Figure S4 :

 S320S4 Figure S3: Impact of lactic acid and/or NaCl supplementation on biofilm of

Table of final CFU/ml achieved in either TSB or 0.50 % LMPA in conditions:

  Media are inoculated at 10 2 CFU/ml and cultivated at 20°C for 9 96 h. Supplementation by lactic acid or NaCl is as described in the Materials and Methods.

	Log(CFU/ml)	TSB	TSB	TSB pH5	TSB pH5	TSB NaCl	TSB NaCl
			0.25 LMPA		0.25% LMPA		0.25% LMPA
	L. monocytogenes	9.36±0.19	9.48±0.13	9.00±0.43	4.87±0.25	9.39±0.20	8.02±0.31
	E. coli O157:H7	9.25±0.11	9.30±0.10	9.00±0.29	7.00±0.24	9.08±0.18	8.62±0.19
	Figure S2:

8

control, lactic acid, NaCl.

and NaCl. (B). Table recapitulating the average Vmean values and standard deviations for

Acknowledgments

This work received funding from the French government through the grant "ANR 17-CE21-0002". The partners of the ANR "PathoFood" are acknowledged for fruitful discussions about L. monocytogenes and E. coli O157:H7 behaviour in food matrices. Julien Deschamps (INRAE) is warmly acknowledged for assistance with Confocal Laser Scanning Microscopy.

Abbreviations

Declaration of competitive interest

None

Supplementary material


Pierre Hausfater
email: pierre.hausfater@aphp.fr

David Boutolleau

Karine Lacombe

Alexandra Beurton

Margaux Dumont

Jean-Michel Constantin

Jade Ghosn

Alain Combes

Nicolas Cury

Romain Guedj

Michel Djibré

Rudy Bompard

Sandie Mazerand

Valérie Pourcher

Linda Gimeno

Clémence Marois

Elisa Teyssou

Anne-Geneviève Marcelin

David Hajage

Florence Tubach

2 infection and associated risk factors among frontline health care workers in Paris: the SEROCOV cohort study

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

The dynamics of the COVID-19 pandemic has deeply affected health services in organizations and potentially exposed healthcare workers (HCWs) to increased risk of SARS-CoV-2 infection. Since the emergence of this new human coronavirus in December 2019 [START_REF] Zhu | A novel coronavirus from patients with pneumonia in China[END_REF] , knowledge of the modes of transmission has improved weekly, leading to the adaptation of HCWs' personal protective equipment (PPE). Hence, the risk of SARS-CoV-2 infection among HCWs has been of major concern for frontline institutions, to prevent COVID-19-associated work disruption (at a time when care resources were critical) and HCW-to-patient contamination. In France, SARS-CoV-2-positive RT-PCR tests were first reported in imported cases on January 24, 2020; the generalized lockdown began on March 17 and emergency room visits for possible COVID-19 peaked on week 13, decreasing thereafter, which led the French government to ease lockdown restrictions on May 11. Although some cross-sectional studies have reported SARS-CoV-2 seroprevalence among HCWs being close to that of the general population [START_REF] Steensels | Hospital-wide SARS-CoV-2 antibody screening in 3056 staff in a Tertiary Center in Belgium[END_REF][START_REF] Moscola | Prevalence of SARS-CoV-2 antibodies in health care personnel in the New York City Area[END_REF][START_REF] Jeremias | Prevalence of SARS-CoV-2 infection among health care workers in a Tertiary Community Hospital[END_REF][START_REF] Self | Seroprevalence of SARS-CoV-2 among frontline health care personnel in a multistate hospital network-13 Academic Medical Centers[END_REF][START_REF] Stubblefield | Seroprevalence of SARS-CoV-2 among frontline healthcare personnel during the first month of caring for patients with COVID-19-Nashville, Tennessee[END_REF][START_REF] Jespersen | Severe acute respiratory syndrome coronavirus 2 seroprevalence survey among 17 971 healthcare and administrative personnel at hospitals, prehospital services, and specialist practitioners in the Central Denmark Region[END_REF] , little is known about the dynamics of SARS-CoV-2 contamination related to the epidemic waves and the risk factors in this population.

In the ascendant phase of the first COVID-19 wave in France, we initiated a multicenter prospective cohort study to estimate the cumulative incidence of laboratory-confirmed SARS-CoV-2 infection over the early phase of this outbreak among frontline public hospital HCWs in Paris and identify the risk factors (SEROCOV, Clini-calTrials.gov NCT04304690, first registered on 11/03/2020).

Patients and methods

Setting, design and participants. The SEROCOV multicenter prospective cohort study was conducted in 4 adult hospitals (Bichat, Pitié-Salpêtrière, Saint-Antoine, Tenon) and 1 pediatric hospital (Trousseau) of the Assistance Publique-Hôpitaux de Paris (AP-HP) network. Participants were included from March 16 to April 24 2020, with a 3-month follow-up. Bichat and Pitié-Salpêtrière hospitals are dedicated referent hospitals for emerging biological risk at AP-HP and primarily received all COVID-19 confirmed cases, from the epidemic onset in Paris until mid-March 2020, when all AP-HP hospitals were positioned to take care of COVID-19 patients.

The SEROCOV study was approved by the ethics committee (CPP Sud-Ouest et Outre-Mer I, approval no. 2-20-023 id7257) and all participants signed informed consent before inclusion. We also confirm that all experiments were performed in accordance with relevant guidelines and regulations.

All HCW staff (doctors-either senior physicians or residents-nurses, care assistants, laboratory technicians and other personals in contact with patients) who worked in COVID-19 frontline departments (emergency, infectious diseases, intensive care units and virology laboratory) of the selected hospitals were informed about the study and asked to participate. Staff who were not active during the inclusion period were not eligible. After signing informed consent, each participant underwent venous blood sampling for SARS-CoV-2 serology and was asked to complete a self-reported paper-based questionnaire on baseline characteristics and pre-inclusion symptoms suggestive of COVID-19. Thereafter and up to 3 months after inclusion, the HCW participants received a weekly reminder to repeat and fill the survey on symptoms and, in case of symptoms, asked for a nasopharyngeal swab for SARS-CoV-2 RT-PCR. The survey was collected on a paper form which was stored in each department and made available all along the follow-up period for the participants. At month 1 (M1) and M3 after inclusion, venous blood sampling was again systematically proposed for serology testing.

Anti-SARS-CoV-2 IgG assay for serology testing. Blood samples were sent immediately to the virology department of each participating hospital and centrifuged, and EDTA plasma was frozen at -20 °C until tested by batch in the Pitié-Salpêtrière virology laboratory. Plasma samples were analyzed on the Abbott Architect platform with the Abbott SARS-CoV-2 IgG assay, targeting the viral nucleoprotein [START_REF] Bryan | Performance characteristics of the abbott architect SARS-CoV-2 IgG assay and seroprevalence in Boise, Idaho[END_REF] . Results were interpreted as recommended by the manufacturer: an index value < 0.5 was considered negative, 0.5-1.4 weak positive, and ≥ 1.4 positive. When SARS-CoV-2 infection was suspected in an HCW during the study period, the COVID-19 diagnosis was established by RT-PCR on a nasopharyngeal swab with the Cobas SARS-CoV-2 kit (Roche Diagnostics) or RealStar SARS-CoV-2 RT-PCR kit (Altona), as previously reported [START_REF] Wirden | Multicenter comparison of the Cobas 6800 system with the RealStar RT-PCR kit for the detection of SARS-CoV-2[END_REF] .

Outcomes. The primary outcome was laboratory-confirmed SARS-CoV-2 infection at M3 (i.e., positive serology results [positive or weak positive result on SARS-CoV-2 IgG assay] and/or a positive RT-PCR result on an unplanned nasopharyngeal swab at any time during the survey). Secondary outcomes were positive serology for SARS-CoV-2 (considering weak positive serology as positive) at M0, M1 and M3.

Covariates. Baseline characteristics collected were demographics, smoking status (current, former or nonsmoker), public transportation use, and job-related data (hospital, type of ward [emergency, infectious diseases, intensive care unit and virology laboratory]), professional category, number of years of experience in the job or department, and night or day shift. Physicians (senior or medical students, including residents), nurses and care assistants who had direct close contacts with COVID-19 patients were considered high-risk HCWs (in contrast to nurses managers for example).

At baseline and up to 3 months after inclusion, participants completed a self-reported questionnaire weekly on symptoms suggestive of COVID-19 and as appropriate, SARS-CoV-2 RT-PCR results, the diagnosis retained, and COVID-related sick days and/or hospitalization.

At M3, adherence to PPE recommendations during the study period was reported on a 5-point Likert scale (from "never" to "systematically").

Statistical analysis.

The number of eligible HCWs in participating services was estimated at approximately 1,000 at the time this study was designed. This planned sample size allowed for estimating a cumulative incidence of laboratory-confirmed SARS-CoV-2 infection at M3 of 5% (95% confidence interval [CI] [3.7; 6.5]), a cumulative incidence of 10% (8.2; 12), and a cumulative incidence of 20% (17.6; 22.6).

Staff characteristics are reported as percentages for categorical variables and median (interquartile range [IQR]) for continuous variables.

Multiple imputation was used to replace missing serology testing at M0, M1 or M3. Briefly, 10 copies of the dataset were created with the missing values replaced by imputed values based on observed data including observed serology results and characteristics of participants (including age, sex, job-related data, smoking status, public transportation use, adherence to PPE recommendations and occurrence of ageusia and/or anosmia over the study period). Each dataset was then analyzed by using standard statistical methods, and the results from each dataset using the statistical methods described below were pooled into a final result by using Rubin's rule [START_REF] Sterne | Multiple imputation for missing data in epidemiological and clinical research: Potential and pitfalls[END_REF] .

For binomial proportions, confidence intervals were estimated by the Wilson method for multiple imputation [START_REF] Lott | Wilson confidence intervals for binomial proportions with multiple imputation for missing data[END_REF] .

Risk factors of laboratory-confirmed SARS-CoV-2 infection at M3 were assessed in the whole cohort by univariate and multivariate logistic regression. Baseline variables included in the multivariate model were defined a priori (age, sex, job-related data, public transportation use and smoking status), and no variable selection was performed. A second logistic model was developed for the same outcome among high-risk HCWs only (i.e., excluding virology staff and jobs rated as "other") and accounting for adherence to PPE recommendations. Finally, risk factors of laboratory-confirmed SARS-CoV-2 infection at inclusion (M0) were evaluated by the same approach.

All the analyses were computed at a two-sided α level of 5% with R software, version 4.0.0.

Ethics approval.

The SEROCOV study was approved by the ethics committee (CPP Sud-Ouest et Outre-Mer I, approval no. 2-20-023 id7257).

Results

From March 16 to April 24, 2020, 1,177 HCWs were included; 115 were excluded because they did not work in the participating departments, so 1062 HCWs remained for analysis. The participation rate was 77% for medical staff and 59.5% for nurses/care assistants. The main characteristics of the study population are in Table 1. Overall, 1060 (99.8%) participants underwent baseline serology testing at inclusion, 1004 (94.5%) at M1, and 938 (88.3%) at M3; 903 (85%) had a SARS-CoV-2 serology result available for the 3 samples (Fig. 1). At M3, during the whole study period, 93.0% of HCWs reported that they wore a surgical mask, 77.8% wore an N95 mask when performing high-risk tasks (nasopharyngeal swab), and 98.5% washed hands and/or used hydro-alcoholic hand friction before and after each contact with a patient (Table 1). The dates of inclusion and follow-up serologies according to the epidemic curve are in Fig. 2.

At baseline, 154/1062 (15.4%) HCWs with available data reported having symptoms suggestive of COVID-19 before inclusion, mainly fever (n = 42), myalgia (n = 34), headache (n = 54), ageusia and/or anosmia (n = 24), cough (n = 55), respiratory signs (n = 69), and gastrointestinal signs (n = 27) (Table 1).

At inclusion, 61 (5.8%) HCWs had positive SARS-CoV-2 serology results: 23 (7.2%) physicians and 38 (5.1%) nurses/care assistants staff. Reports of previous symptoms suggestive of COVID-19 were more frequent for HCWs with positive than negative serology at inclusion (50% vs 13.3%; p < 0.0001), particularly symptoms very suggestive of COVID-19 such as ageusia/anosmia (48.3% vs 8%). Conversely, 50% of participants with positive serology at inclusion reported no previous symptoms.

Because of symptoms suggestive of COVID-19, 33 participants submitted an intermediate nasopharyngeal swab for SARS-CoV-2 RT-PCR between inclusion and M3: 14 (42.4%) had a positive result, 2 with negative results at all serology points. Among the 134 participants with seropositive results at inclusion or M1, 11 (8.2%) had negative serology results at M3, including 8 with positive results at inclusion.

Outcomes.

The estimated cumulative incidence of laboratory-confirmed SARS-CoV-2 infection at M3 after imputation of missing data was 14.6% (95% CI [12.5; 16.9]), and the difference across professional categories was not statistically significant, although the incidence was highest for medical students (23.5% [20.8; 26.4]) and higher in nurses than in care assistants (Table 2 and Fig. 3).

The estimated seroprevalence after imputation of missing data at M0, M1, and M3 was 5.9% [4.7; 7.5], 12.9% [10.9; 15.1] and 13.0% [11.1; 15.2], with the same trend across professional categories but not statistically significant.

Of the 147 participants with laboratory-confirmed SARS-CoV-2 infection at M3, only 88 (59.9%, 95% CI [51.47; 67.85]) reported symptoms suggestive of COVID-19 before or during the study period. Among the 416 participants with negative serology at M0 and reporting symptoms suggestive of COVID-19 during the study period, in those with and without documented SARS-CoV-2 infection, 58.7% vs 13.3% reported sick leave related to these symptoms (p < 0.0001). Among the 147 participants with laboratory-confirmed infection by SARS-CoV-2 at M3, 3 were hospitalized for COVID-19 and none died. Table 1. Baseline characteristics of the study population according to SARS-CoV-2 serology at inclusion. Data are n (%) for qualitative variables and median [interquartile range] for quantitative variables. a HCWs with positive, negative or missing (n = 2) serology at inclusion, thus 2 more individuals than the sum of the 2 other columns. b Others include (but not limited to): secretaries, nurse's managers, social workers. c Collected at the end of the study on a 5-point Likert scale (0, never, to 5, systematically), reported as the proportion of HCWs rating 4 or 5; PPE: personal protective equipment. Risk factors for SARS-CoV-2 positive serology at inclusion. After multiple imputation for missing data, on multivariate analysis (Table 3), risk factors for SARS-CoV-2 positive serology at inclusion (i.e., during the increasing phase of first epidemic wave) were the department where the participant worked, with increased risk for intensive care units (OR 1.29, 95% CI [0.47; 3.51]) and particularly infectious disease units (6.61 [2.64; 16.54]) versus emergency departments (virology laboratory was not included because no virology staff were positive at inclusion). Being a current smoker reduced the risk (0.28 [0.10; 0.82]). Age, sex, using public transportation, professional category, working in a referent hospital for emerging biological risk, number of years of experience in the job or department, and night shift were not significantly associated with baseline positive serology.

Risk factors for laboratory-confirmed SARS-CoV-2 infection at M3. After multiple imputation for

missing data, on multivariate analysis (Table 4), risk factors for laboratory-confirmed SARS-CoV-2 infection at M3 (i.e., at the end of the first epidemic wave) were the department where the participant worked, with increased risk for intensive care units (OR In the subgroup of high-risk HCWs (i.e., physicians, nurses and care assistants in clinical wards), adjustment on adherence to PPE recommendations did not change these results (Table 5).

Discussion

We conducted this multicenter prospective cohort study among frontline HCWs to investigate SARS-CoV-2 infection in HCWs during the first COVID-19 epidemic wave in Paris, France. We report a 5.8% baseline (early phase) SARS-CoV-2 seroprevalence rate together with a 3-month 14.6% rate of laboratory-confirmed SARS-CoV-2 infection (end of first wave). Most infections occurred very early, that is, diagnosed at M1. Among the identified risk factors was the working department, infectious disease units being at highest risk at the very early phase, then emergency departments during the epidemic peak, with the same level of risk for both at the end of the wave and intensive care units with an intermediate risk, with the virology laboratory staff as reference. Current smokers were at reduced risk. Demographics, other job-related characteristics, adherence to PPE, and use of public transportation were not significantly associated with SARS-CoV-2 infection. To our knowledge, our study is the largest reporting the longitudinal evolution of anti-SARS-CoV-2 antibodies in HCWs over a 3-month period.

The seroprevalence in the general population estimated in the SAPRIS study [START_REF] Carrat | Antibody status and cumulative incidence of SARS-CoV-2 infection among adults in three regions of France following the first lockdown and associated risk factors: A multicohort study[END_REF] in the same geographical area (Greater Paris) in May 2020, corresponding to M1 in the SEROCOV study, was significantly lower than in the SEROCOV first-line HCWs: 6.4% in the 20-to 59-year-old population of the SAPRIS study (using a similar Table 3. Risk factors of laboratory-confirmed SARS-CoV-2 infection at inclusion (after imputation of missing data; N = 1017). HCWs from virology department were excluded because none had a positive serology result at inclusion. Others include (but not limited to): secretaries, nurse's managers, social workers. OR odds ratio, CI confidence interval. anti-nucleoprotein assay and considering weak positive as positive as in SEROCOV) versus 13.3% in SEROCOV (p < 0.0001). Previous published HCW surveys reported seroprevalence ranging from 3.4 to 38% [START_REF] Malagón-Rojas | Seroprevalence of the SARS-CoV-2 antibody in healthcare workers: A multicentre cross-sectional study in 10 Colombian cities[END_REF][START_REF] Pérez-García | SARS-CoV-2 seroprevalence among workers in a hospital in Madrid[END_REF] . However, the heterogeneity in type of HCW tested, type of assay used, period of testing and countries with incomparable SARS-CoV-2 epidemic burden precludes a face-to-face comparison. In a multicenter cross-sectional study in the New York city area, Moscola et al. reported a 13.7% prevalence (95% CI [13.4; 14.0]) of SARS-CoV-2 antibodies in 40,329 HCWs, a rate similar to that among adults randomly tested in New York state (14.0%) [START_REF] Moscola | Prevalence of SARS-CoV-2 antibodies in health care personnel in the New York City Area[END_REF] . However, in another cross-sectional study in Roslyn, New York, in one hospital, employees had a significantly lower serology-positive rate than the general population (9.9% vs 16.7%, P < 0.001) [START_REF] Jeremias | Prevalence of SARS-CoV-2 infection among health care workers in a Tertiary Community Hospital[END_REF] [START_REF] Stubblefield | Seroprevalence of SARS-CoV-2 among frontline healthcare personnel during the first month of caring for patients with COVID-19-Nashville, Tennessee[END_REF][START_REF] Jespersen | Severe acute respiratory syndrome coronavirus 2 seroprevalence survey among 17 971 healthcare and administrative personnel at hospitals, prehospital services, and specialist practitioners in the Central Denmark Region[END_REF] . In the United States, in a Multistate Hospital Network, Self et al. reported 6.0% positive serology in front-line HCWs 5 . Among the 194 participants with positive results, 56 (29%) reported no symptoms in the previous weeks. In our study, this asymptomatic proportion was 50% among participants with positive serology at inclusion. Conversely, we confirm that anosmia and/or ageusia during the epidemic wave was a discriminative clinical sign, although with low sensitivity. This result confirms the Belgian monocentric study findings reporting of anosmia associated with an OR for positive serology of 7.78 ([95% CI [5.22; 11.53]) [START_REF] Steensels | Hospital-wide SARS-CoV-2 antibody screening in 3056 staff in a Tertiary Center in Belgium[END_REF] .

Most HCW contaminations occurred very early during the epidemic wave, almost half documented at baseline (positive serology: 5.9%, 95% CI [4.7; 7.5]) and 12.9% at M1 (corresponds to infections during the ascendant phase of the epidemic wave as shown in Fig. 2). The kinetics of these infections probably reflects both infections in the private sphere, following the epidemic curve of the general population, but also the effectiveness of better adherence to protective measures in professional activity related to an enforcement of PPE and standard measures as the epidemic progressed. The family and friends or professional origin of HCW contamination is still debated [START_REF] Baker | Quantification of occupational and community risk factors for SARS-CoV-2 seropositivity among health care workers in a large US health care system[END_REF] . In a single center study in Belgium of 3056 HCWs (tested with an IgG/IgM rapid lateral flow assay), the OR for positive serology was 3.15 (95% CI [2.33; 4.25]) when reporting household contact with a suspected or confirmed COVID-19 case 2 . Conversely, a large study in Denmark of 17,971 hospital staff (based on the adjusted seroprevalence according to living and working places) found risk of SARS-CoV-2 positive serology associated with workplace rather than place of living [START_REF] Jespersen | Severe acute respiratory syndrome coronavirus 2 seroprevalence survey among 17 971 healthcare and administrative personnel at hospitals, prehospital services, and specialist practitioners in the Central Denmark Region[END_REF] .

Recently, a large survey of HCWs in the United Kingdom reported anti-spike or anti-nucleocapsid IgG antibodies associated with substantially reduced risk of SARS-CoV-2 reinfection and highlighted that individual Table 5. Risk factors of laboratory-confirmed SARS-CoV-2 infection at month 3 in high-risk healthcare workers, accounting for adherence to personal protective equipment recommendations (after imputation of missing data; N = 916)). Data are n (%) for qualitative variables; median [interquartile range] for quantitative variables; OR: odds ratio; CI: confidence interval. a Collected at the end of the study on a 5-point Likert scale (0 = never to 5 = systematically), reported as the proportion of HCW rating 4 or 5; PPE: personal protective equipment. [START_REF] Patel | Change in antibodies to SARS-CoV-2 over 60 days among health care personnel in Nashville, Tennessee[END_REF][START_REF] Gudbjartsson | Humoral immune response to SARS-CoV-2 in Iceland[END_REF] . Workers in first-line clinical departments (infectious diseases, emergency, intensive care unit) were at higher risk of laboratory-confirmed SARS-CoV-2 infection as compared with virology laboratory staff (Table 3). In a large study in Denmark, Jespersen et al. reported the highest adjusted seroprevalence in emergency departments: 29.7% (95% CI [23.1; 37.6]); departments with no or limited patient contact had the lowest seroprevalence: 1.79% (0.31; 3.90) [START_REF] Jespersen | Severe acute respiratory syndrome coronavirus 2 seroprevalence survey among 17 971 healthcare and administrative personnel at hospitals, prehospital services, and specialist practitioners in the Central Denmark Region[END_REF] . From January to mid-March 2020, infectious disease departments and intensive care units directly admitted patients with suspected COVID-19 from home or the general practitioner's office, bypassing the emergency department, and were therefore initially more exposed to SARS-CoV-2 than emergency department workers. This situation may explain the association of SARS-CoV-2 positive serology at inclusion with working in intensive care units (OR 1.2 [95% CI (0.47; 3.51)]) and especially infectious diseases units (6.61, [2.64; 16.54]) versus emergency departments (Table 3). Virology laboratory staff, although handling numerous SARS-CoV-2-contaminated samples, are probably more concerned and stricter about PPE enforcement to prevent contamination and had no staff with positive serology at inclusion, together with the lowest seroprevalence rate at 3 months. Similarly, as compared with emergency department staff, intensive care unit staff were at intermediate risk, which could be explained by a more usual concern about the risk of pathogen transmission (particularly during highly resistant bacteria outbreaks).

Bivariate analysis

At M0 and M3 and in the analysis restricted to high-risk HCWs and accounting for adherence to PPE measures, being a current smoker reduced the risk of laboratory-confirmed SARS-CoV-2 infection (OR 0.36, 95% CI [0.21; 0.63]). An increasing number of studies have reported this reduced risk of SARS-CoV-2 infection in current smokers in different contexts (cross-sectional studies in the general population; cross-sectional, case-control or control studies in different populations) [START_REF] Paleiron | Impact of tobacco smoking on the risk of COVID-19. A large scale retrospective cohort study[END_REF][START_REF] De Lusignan | Risk factors for SARS-CoV-2 among patients in the Oxford Royal College of General Practitioners Research and Surveillance Centre primary care network: A cross-sectional study[END_REF][START_REF] Hippisley | Risk of severe COVID-19 disease with ACE inhibitors and angiotensin receptor blockers: Cohort study including 8.3 million people[END_REF][START_REF] Meini | The paradox of the low prevalence of current smokers among COVID-19 patients hospitalized in non-intensive care wards: Results from an Italian Multicenter Case-Control Study[END_REF][START_REF] Simons | The association of smoking status with SARS-CoV-2 infection, hospitalization and mortality from COVID-19: A living rapid evidence review with Bayesian meta-analyses (version 7)[END_REF][START_REF] Leung | Smoking, ACE-2 and COVID-19: Ongoing controversies[END_REF] . Our results from a large multicenter prospective study in a young population of HCWs with 26.9% current smokers support the role of use of tobacco substances as protective against SARS-CoV-2 infection, which may act through the nicotine pathway [START_REF] Changeux | A nicotinic hypothesis for COVID-19 with preventive and therapeutic implications[END_REF][START_REF] Gonzalez-Rubio | Cytokine RELEASE SYNDROME (CRS) and nicotine in COVID-19 patients: Trying to calm the storm[END_REF][START_REF] Farsalinos | Systematic review of the prevalence of current smoking among hospitalized COVID-19 patients in China: Could nicotine be a therapeutic option?[END_REF][START_REF] Miyara | Lower rate of daily smokers with symptomatic COVID-19: A monocentric self-report of smoking habit study[END_REF] . This result should not encourage smoking to limit the risk of COVID-19; indeed, 78,000 deaths per year are due to smoking in France [START_REF] Spf | Bulletin de santé publique Ile[END_REF] . However, the nicotinic hypothesis is of interest, even in the era of an anti-SARS-CoV-2 vaccine, and is being investigating for prevention of COVID-19 (NCT04583410).

The professional category was not a risk factor for laboratory-confirmed SARS-CoV-2 infection, even if medical students exhibited high prevalence as compared with senior physicians (23.5%, 95% CI [20.8-26.4] vs 14.2% [12.2-16.5]) (Table 2). This result may be explained by an insufficient practice or experience with PPE recommendations in medical students or confounded by age. Similarly, reporting high compliance with PPE was not protective (Table 4 and Supplementary Table 1). However, reported adherence to PPE was very good in the SEROCOV population. Regardless, several studies highlighted greater prevalence of SARS-CoV-2 positive serology in HCWs reporting not systematically wearing PPEs in general or covering the face during clinical encounters than in HCWs fully compliant (15.8% vs 4.3%, p = 0.07, and 9% vs 6% p = 0.012) [START_REF] Self | Seroprevalence of SARS-CoV-2 among frontline health care personnel in a multistate hospital network-13 Academic Medical Centers[END_REF][START_REF] Stubblefield | Seroprevalence of SARS-CoV-2 among frontline healthcare personnel during the first month of caring for patients with COVID-19-Nashville, Tennessee[END_REF][START_REF] Pérez-García | SARS-CoV-2 seroprevalence among workers in a hospital in Madrid[END_REF] . The questionnaire on PPE compliance completed only at the end of follow-up in our study may explain in part this discrepancy due to a potential memory bias in addition to social desirability bias.

The strengths of the study are the prospective design, the recruitment in the early phase of the first epidemic wave; the multicenter, multi-department and multi-professional recruitment of the study; the large sample size; the 3-point serology testing; the centralization of serology assays; and the criteria retained for laboratoryconfirmed SARS-CoV-2 infection (rather than seroprevalence alone, which is exposed to negativation). As limitations, first, 11.7% HCWs were lost to follow-up at M3, but missing serology results were handled by multiple imputations. Second, national and center-driven recommandations concerning PPE may have evolved along the study period, while compliance with PPE and standard recommendations were queried only at the end of the study and not weekly, as for the clinical signs, with therefore a potential risk of memory bias and social desirability bias. Third, we did not adjust for clustering by site, which was not possible due to the limited number of centers. Fourth, we did not collect personal risk factors (social and environmental data) as we were focused on professional one's. This deliberate choice was important in the context of the first wave, to promote adhesion of HCW's to the study. Finally, the serology assay we used (targeting the viral nucleoprotein) is less sensitive than anti-spike assays [START_REF] Bryan | Performance characteristics of the abbott architect SARS-CoV-2 IgG assay and seroprevalence in Boise, Idaho[END_REF][START_REF] Carrat | Antibody status and cumulative incidence of SARS-CoV-2 infection among adults in three regions of France following the first lockdown and associated risk factors: A multicohort study[END_REF] . Therefore, the reported incidence rate of documented SARS-CoV-2 infection in the SEROCOV study may be slightly underestimated. However, we accounted for this issue in the comparison with seroprevalence data available in the general population.

In summary, in this study of frontline HCWs in Paris, France, we report a 14.6% SARS-CoV-2 cumulative incidence rate at the end of the first COVID-19 wave, with a seroprevalence in May 2020 significantly higher than in the general population. The study of risk factors for laboratory-confirmed SARS-CoV-2 infection argues for a significant part of professional (together with household) contaminations and highlights smoker status as an independent protective factor. At the era of anti-SARS-CoV2 vaccination, our results represent an argument to include HCWs in the first-line target population.

was 14 .

 14 6% (95% confidence interval [CI] [12.5; 16.9]). Risk factors were the working department specialty, with increased risk for intensive care units (odds ratio 1.80, 95% CI [0.38; 8.58]), emergency departments (3.91 [0.83; 18.43]) and infectious diseases departments (4.22 [0.92; 18.28]); current smoking was associated with reduced risk (0.36 [0.21; 0.63]). Age, sex, professional category, number of years of experience in the job or department, and public transportation use were not significantly associated with laboratory-confirmed SARS-CoV-2 infection at M3. The rate of SARS-CoV-2 infection in frontline HCWs was 14.6% at the end of the first COVID-19 wave in Paris and occurred mainly early. The study argues for an origin of professional in addition to private life contamination and therefore including HCWs in the first-line vaccination target population. It also highlights that smokers were at lower risk. Trial registration The study has been registered on ClinicalTrials.gov: NCT04304690 first registered on 11/03/2020.

  Negative SARS-CoV-2 serology (N = 999) Positive SARS-CoV-2 serology (N = 61) Total a (N = 1062) Age 33 [28-42.

Figure 1 .

 1 Figure 1. Flow chart. M, month.

Figure 2 .

 2 Figure 2. Dates of inclusion (recruitment) and serology tests according to the epidemic curve (in patients with documented COVID-19 in AP-HP hospital network).

Figure 3 .

 3 Figure 3. Laboratory-confirmed infection with SARS-CoV-2 at month 3 (M3) and seroprevalence at M0, M1 and M3 according to professional category.

Adherence to PPE recommendations over the study period c

		5]	31 [26-38]	33 [28-42]
	Women	717 (71.8%)	40 (65.6%)	758 (71.4%)
	Professional category
	Senior physicians	208 (20.8%)	12 (19.7%)	221 (20.8%)
	Medical students (including residents)	89 (8.9%)	11 (18%)	100 (9.4%)
	Care assistants	197 (19.7%)	6 (9.8%)	203 (19.1%)
	Nurses	378 (37.8%)	27 (44.7%)	405 (38.1%)
	Others b	127 (12.7%)	5 (8.2%)	133 (12.5%)
	Working department
	Emergency department	372 (37.2%)	7 (11.5%)	380 (35.8%)
	Infectious diseases unit	239 (23.9%)	43 (70.5%)	282 (26.6%)
	Intensive care unit	343 (34.3%)	11 (18%)	355 (33.4%)
	Virology laboratory	45 (4.5%)	0 (0%)	45 (4.2%)
	Working in a COVID-free unit	80 (8%)	2 (3.3%)	82 (7.7%)
	Working in a referent hospital for emerging biological risk	519 (52%)	32 (52.5%)	552 (52%)
	Night shift	123 (12.3%)	5 (8.2%)	129 (12.2%)
	Missing data, No	1	0	1
	Experience in the job (months)	84 [36-180]	72 [30-168]	84 [36-180]
	Missing data, No	5	0	5
	Experience in the department (months)	36 [10-84]	24 [6-72]	36 [9-84]
	Missing data, No	14	0	14
	Public transportation use	369 (40.5%)	18 (40.9%)	387 (40.5%)
	Missing data, No	88	17	105
	Active smoker	246 (27.8%)	4 (8.9%)	250 (26.9%)
	Missing data, No	115	16	132
	Symptoms suggestive of COVID-19 before inclusion	125 (13.3%)	29 (50%)	154 (15.4%)
	Missing data, No	57	3
	If yes: fever	29 (23.2%)	13 (44.8%)	42 (27.3%)
	Anosmia and/or agueusia	10 (8%)	14 (48.3%)	24 (15.6%)
	Respiratory symptoms	56 (44.8%)	13 (44.8%)	69 (44.8%)
	Gastrointestinal symptoms	25 (20%)	2 (6.9%)	27 (17.5%)
	Wear a surgical mask	829 (93%)	40 (85%)	870 (93.1%)
	Wear an FFP2 mask to take nasopharyngeal swabs	521 (78%)	28 (82%)	549 (77.8%)
	Wear an N95 mask to handle a confirmed COVID-19 case	578 (74%)	24 (56%)	603 (73.2%)
	If wearing a mask (surgical or FFP2), change every 4 h	570 (65%)	22 (47%)	592 (63.9%)
	Hydro-alcoholic friction or hand washing before and after each patient contact	831 (99%)	45 (98%)	877 (98.5%)

Table 2 .

 2 Laboratory

-confirmed infection with SARS-CoV-2 at month 3 (M3) and seroprevalence at M0, M1 and M3 after imputation of missing data, according to professional category. Data are % (95% confidence interval). Others include (but not limited to): secretaries, nurse's managers, social workers. *Comparison across professional categories.

All Senior

physicians Medical students Care assistants Nurses Others p value* Serology positive

	At M0	5.9% [4.7; 7.5]	6.3% [5.0; 7.9]	12.9% [11.0; 15.1] 3.9% [2.9; 5.2]	7.1% [5.7; 8.9]	5.3% [4.1; 6.8]	0.06
	At M1	13.0% [11.1; 15.2] 12.1% [10.3; 14.2]	21.1% [18.7; 23.8] 11.6% [9.8; 13.8]	14.9% [12.9; 17.2] 10.5% [8.8; 12.5]	0.13
	At M3	13.0% [11.1; 15.2] 12.4% [10.5; 14.6]	20.4% [17.7; 23.4] 11.6% [9.7%; 13.8] 14.8% [12.8; 17.1] 10.5% [8.8; 12.5%] 0.21
	Laboratory-confirmed SARS-CoV-2 infection at M3	14.

6%

[12.5; 16.9]

14.2% [12.2; 16.5] 23.5% [20.8; 26.4] 12.5% [10.6; 14.7] 16.6% [14.4; 18.9] 11.3% [9.5; 13.4] 0.12

Table 4 .

 4 Risk factors of laboratory-confirmed SARS-CoV-2 infection at M3 (after imputation of missing data; N = 1,062)). OR odds ratio, CI confidence interval. a Others include (but not limited to): secretaries, nurse's managers, social workers.

	Univariate analysis		Multivariate analysis
	OR	[95% CI]	p value	OR	[95% CI]	p value

  . Stubblefield et al. reported a 7.6% seroprevalence among 249 frontline HCWs during the first month of epidemic in Nashville, Tennessee, whereas Jespersen et al. reported a 3.4% seroprevalence (95% CI [2.5; 3.8]) among 17,971 HCWs, administrative personnel, pre-hospital services and specialist practitioner clinics in Denmark, with high geographical seroprevalence variation (from 1.2 to 11.9%)

  HCWs middle-/long-term protection by post-COVID self-immunization is a major concern[START_REF] Lumley | Antibody status and incidence of SARS-CoV-2 infection in health care workers[END_REF] . Our data support the persistence of SARS-CoV-2 antibodies at 3 months: only 11 (8%) participants with positive serology had later negative results. Controversial results were reported by Patel et al. for 249 HCWs with a positive serology rate decreasing from 7.6% at baseline to 3.2% at 60 days, whereasGudbjartsson et al. showed no decline in anti-SARS-CoV-2 antibodies at 4 months after COVID-19 diagnosis

					Multivariable analysis
		OR	[95% CI]	p value OR	[95% CI]	p value
	Age	0.99 [0.97; 1.01] 0.24	0.99 [0.97; 1.016] 0.51
	Women	1.07 [0.70; 1.62] 0.74	0.99 [0.60; 1.65]	0.89
	Working in a referent hospital for emerging biological risk	0.97 [0.67; 1.42] 0.80	1.20 [0.77; 1.86]	0.43
	Working department			0.0001		0.01
	Intensive care unit	1			1
	Emergency department	1.93 [1.17; 3.18]		1.86 [1.05; 3.31]
	Infectious diseases department	3.10 [1.85; 5.22]		2.38 [1.30; 4.35]
	Professional category			0.17		0.25
	Physicians	1			1
	Medical students	1.80 [0.93; 3.49]		1.76 [0.69; 4.51]
	Care assistants	0.82 [0.45; 1.47]		1.21 [0.58; 2.50]
	Nurses	1.19 [0.74; 1.92]		1.75 [0.96; 3.18]
	Experience in the department ≥ 12 months	0.77 [0.51; 1.16] 0.22	1.26 [0.63; 2.52]	0.55
	Experience in the job ≥ 12 months	0.87 [0.48; 1.56] 0.65	0.92 [0.39; 2.18]	0.77
	Night shift	0.93 [0.54; 1.61] 0.80	0.93 [0.47; 1.83]	0.83
	Public transportation use	1.34 [0.91; 1.99] 0.14	1.28 [0.83; 1.95]	0.27
	Active smoker	0.37 [0.21; 0.66] 0.0006	0.36 [0.20; 0.64]	0.0006
	Adherence to PPE recommendations over the study period a
	Wear a surgical mask < 4	0.70 [0.29; 1.67] 0.44	0.57 [0.21; 1.57]	0.28
	Wear an N95 mask to take nasopharyngeal swabs < 4	1.02 [0.61; 1.71] 0.71	1.02 [0.56; 1.87]	0.71
	Wearing an N95 mask to handle a confirmed COVID-19 case < 4	1.58 [0.99; 2.51] 0.06	1.36 [0.78; 2.35]	0.29
	If wearing a mask (surgical or FFP2), change every 4 h < 4	1.35 [0.90; 2.04] 0.16	1.32 [0.82; 2.13]	0.26

Scientific Reports | (2022) 12:7211 | https://doi.org/10.1038/s41598-022-10945-y

Acknowledgements

We sincerely thank all the HCWs for their participation. We specifically thank for their exceptional involvement in data management and collection of blood samples: Ilaria Cherubini, Enfel Houas, Guillaume Payan, Sylvie Le Gac, Odile Fleurot, Elisabeth Bouvet, Christine Jestin, Justine Dorchies, Nathanelle Yeni, Olivia Da Conceicao, Lucie Touchar, Nicolas Mediamolle, Margot Dropy, Aida Zarhrate, Fatiha Bouchama, Marina Vaz, Mariem Ben Cheikh Souguir and Pr Vincent Calvez for virology laboratory staff management. We thank Laura Smales for English editing.

Funding

The sponsor of the study was Assistance Publique-Hôpitaux de Paris (AP-HP), with study management by URC Pitié-Salpêtrière. This study was funded by the French Ministry of Health (Programme Hospitalier de Recherche Clinique) and the French Agency for Research (Fond d'amorçage de l' Agence National pour la Recherche). The sponsor and funders did not participate in the study design, analysis, interpretation of the results or drafting of the manuscript. FT, PH and DH had full access to all data in the study.

Data availability

Data collected for the study, including individual participant data and a data dictionary defining each field in the set, will be made available to others under request. The study protocol and statistical analysis plan will be available on request on an URL of the URC Pitié Salpêtrière Charles Foix.

Author contributions

Study design and writing the protocol (P.H., F.T., L.G.), participants recruitment and data monitoring (K.L., A.B., M.Du., J.M.C., J.G., A.C., N.C., R.G., M.Dj., R.B., S.M., V.P., L.G., C.M., D.H.), virology tests management (D.B., E.T., A.G.M.), statistical analysis (D.H.), analysis of data (all authors) manuscript writing (P.H., F.T.). All authors reviewed the final version of the manuscript.

Competing interests

The authors declare no competing interests.