Maxime Yon

Franck Fayon

Dominique Massiot

Vincent Sarou-Kanian

Iterative baseline correction algorithm for dead time truncated onedimensional solid-state MAS NMR spectra

published or not. The documents may come

Iterative baseline correction algorithm for dead time truncated one-dimensional solid-state MAS NMR spectra

Introduction

Magic angle spinning NMR spectroscopy is a well-established analytical method in solid-state chemistry and material science allowing to study the chemical environment of different nuclei in a wide range of samples. The simple pulse-acquire experiment remains the cornerstone of a large part of NMR analyses as it minimizes the line shape distortion due to transverse relaxation and allows to record quantitative spectra. In the case of observation of quadrupolar nuclei, this sequence is often used with large acquisition spectral width to record both the central transition (CT) and the spinning sidebands of the satellite transitions (ST) which are spread over a large frequency range by the first-order quadrupolar interaction. Indeed acquiring both central and satellite transitions is useful to determine accurately the quadrupole coupling constant (Cq) and the asymmetry parameter (ƞq), especially when they are too small to be measured from the second-order quadrupolar line shape of the central transition. [START_REF] Kunath | 27Al satellite transition spectroscopy (SATRAS) of polycrystalline aluminium borate 9Al2O3• 2B2O3[END_REF][START_REF] Skibsted | Satellite transitions in MAS NMRspectra of quadrupolar nuclei[END_REF] However, the pulse-acquire sequence suffers from one major drawback as the dead time between the end of the radiofrequency pulse and the beginning of the actual acquisition prohibits the recording of the first points of the free induction decay (FID). [START_REF] Heuer | A new method for suppressing baseline distortions in FT NMR[END_REF] The restoration of these first missing points in the time domain is required to obtain, by Fourier transformation (FT), a pure absorption spectrum with a flat baseline used for accurate integration or fitting of the NMR lines. This restoration is usually performed by a first-order phase correction in the frequency domain (analogous to a shift in the time domain) followed by a baseline correction of the resulting rolling sinc convolution [START_REF] Smith | Getting More Out of MAS NMR Studies of Quadrupole Nuclei : Part 2[END_REF][START_REF] Alemany | Observation and accurate quantification of 27Al MAS NMR spectra of some Al2SiO5 polymorphs containing sites with large quadrupole interactions[END_REF] . The amplitude of the baseline distortion is proportional to the number of missing points (NMP) and thus the ratio between dead time (DE) and dwell time (DW). As the minimum DE is fixed by the hardware, the baseline distortion gets stronger and more difficult to correct with the increase of the acquisition spectral width.

Moreover, additional baseline distortions can also arise from various sources such as acoustic ringing [START_REF] Gerothanassis | Methods of avoiding the effects of acoustic ringing in pulsed fourier transform nuclear magnetic resonance spectroscopy[END_REF] , the discrete nature of the Fourier transformation [START_REF] Tang | An Analysis of Baseline Distortion and Offset in NMR Spectra[END_REF] , or low-quality filters [START_REF] Tang | An Analysis of Baseline Distortion and Offset in NMR Spectra[END_REF] . Unlike the dead time truncation, these latter distortions induce signal contamination of the FID points.

Various algorithms either based on frequency domain correction [START_REF] Güntert | FLATT-A new procedure for high-quality baseline correction of multidimensional NMR spectra[END_REF][START_REF] Pearson | A general baseline-recognition and baseline-flattening algorithm[END_REF][START_REF] Brown | Fully Automated Baseline Correction of 1D and 2D NMR Spectra Using Bernstein Polynomials[END_REF][START_REF] Dietrich | Fast and precise automatic baseline correction of one-and two-dimensional nmr spectra[END_REF][START_REF] Friedrichs | A model-free algorithm for the removal of baseline artifacts[END_REF][START_REF] Bao | A new automatic baseline correction method based on iterative method[END_REF] or time-domain backprediction [START_REF] Marion | Baseline correction of 2D FT NMR spectra using a simple linear prediction extrapolation of the time-domain data[END_REF][START_REF] Koehl | Linear prediction spectral analysis of NMR data[END_REF] and software [START_REF] Cobas | A new general-purpose fully automatic baseline-correction procedure for 1D and 2D NMR data[END_REF][START_REF] Chang | Robust baseline correction algorithm for signal dense NMR spectra[END_REF][START_REF] Golotvin | Improved Baseline Recognition and Modeling of FT NMR Spectra[END_REF] have been proposed to perform automatic baseline correction. However, these algorithms often fail in the case of significant time-domain truncation and wide ranges of shapes and intensities of the NMR lines common in solid-state MAS spectra. The most common solution for the baseline correction of solid-state 1D spectra acquired with a large spectral width remains to use a spline correction based on user-defined baseline points, [START_REF] Massiot | Modelling one-and two-dimensional solid-state NMR spectra[END_REF] however this process is strongly user-dependent, lack of reproducibility and can introduce biases.

In this article, we introduce an automatic baseline correction algorithm for 1D NMR spectra affected by dead time truncation. The algorithm relies on an iterative smoothing spline fit constrained by the dead time duration and a histogram filter used to discard the high-intensity points from the baseline fit. This allows obtaining a baseline correction that does not modify the acquired points of the FID which is the main disadvantage of the baseline correction through model-free smooth fitting 3 . The model does not require any user inputs when the baseline deformation is due to time-domain truncation only.

Nevertheless, if the FID is contaminated by other sources of baseline distortion (ringing effects…), the user is free to remove or adjust the time-domain constraint and optimize the smoothing parameter of the fitting function or the NMP to obtain a meaningful baseline correction.

This method does not aim to compute the mathematically exact baseline modulation but to allow an easy, rapid, and reproducible baseline correction. The quality and the interpretation of the baseline correction remain at the judgment of the user.

Baseline correction algorithm

Theoretical considerations

The algorithm is based on both the invertibility and the linearity properties of the Fourier transformation that, in case of 1D NMR acquisition, can be illustrated by the following equation:

!(#) = ! ! (#) + ! " (#) #$ ' ( )(*) = ) ! (*) + ) " (*) %#$ '( !(#) = ! ! (#) + ! " (#) (1)
With !(#) the ideal FID, ! ! (#) the FID experimentally acquired after the dead time duration and ! " (#)

the part of the FID that could not be experimentally recorded (Figure 1, left). The FT of these signals leads to the spectra )(*), ) ! (*) and ) " (*), respectively (Figure 1, right). ) ! (*) resembles an experimental spectrum processed with first-order phase correction. Here, the zero filling to t = 0 of the ! ! signal avoids the need for first-order phase correction. The shape of ) " (*) corresponds to the convolution of the spectrum )(*) and a sinc function whose periodicity is equal to the inverse of the dead time. Finally, the addition of ) ! (*) and ) " (*) allows retrieving )(*) and -) " (*) de facto corresponds to the so-called baseline distortion.

These considerations imply that the correction of a rolling baseline purely induced by a time-domain truncation of the beginning of the FID must originate from a non-zero signal strictly limited in the timedomain by the dead time duration. This baseline correction must not induce any modification of the acquired FID points.

The algorithm flowchart

The flowchart of the baseline correction protocol is presented in Figure 2 exemplifying its different steps, which are detailed in the following subsections.

Preliminary requirements

The baseline correction procedure is dedicated to phased spectra, it is then mandatory to perform zeroand first-order phase corrections before the baseline correction as illustrated in the blue rectangle in Figure 2. The algorithm requires the number of missing points (NMP) at the beginning of the FID. In practice, the interface load only the real part of the spectrum and its inverse Fourier transform resemble a symmetric echo where the NMP need to be even and its default value is calculated by NMP = (123456(78 79)/2 ⁄ )= • 2 with DW following the convention of 79 = 1 (2 • ?@ℎ) ⁄ with swh the acquisition spectral width. In the case of the Bruker data format, the values of DE and DW are retrieved automatically and displayed in μs while for spectrum imported as simple two-column ASCII file, the NMP value needs to be set manually. The application of additional digital filters can also slightly modify the value of NMP. In such cases, the overall dead time duration can be determined from the very low signal intensity region of the inverse Fourier transform of the spectrum as illustrated in figure 2 step 1 and the value of NMP can be suitably adjusted. The NMP value is only used when the truncation limit option is activated which is the default setting. In this case, all the baseline estimations obtained by the successive fits will undergo an inverse Fourier transformation and their time-domain points of coordinate superior to NMP will be set to zero enforcing the rule stated in theoretical considerations.

This step avoids any modification of the acquired FID points and is suitable if the baseline roll is induced by the dead time truncation only. Conversely, when the FID points are contaminated by other sources of baseline distortion, the user is free to deactivate the truncation limit or virtually increase the value of NMP, which will remove or decrease this time-domain constraint. The deactivation or virtual modification of the NMP will make the correction parametric and the user will need to optimize either the smoothing parameter of the smoothing spline fit function or the NMP. The influence of these two options will be discussed in the results.

Baseline detection

The successive fits performed in the frequency domain required to differentiate the data points corresponding to the baseline from those belonging to the NMR resonances. The identification of the baseline points is performed in two steps: a simple local minimum detection and a thresholding operation. These operations do not aim at precisely identifying the baseline points, which would be difficult without making any assumption regarding the shapes, intensities, or number of NMR lines, but only to remove the majority of the positive high-intensity points of the resonances. The local minimum detection is performed on a spectrum smoothed by a moving average lowpass filter (span = number of spectrum points /1000) to reduce the number of baseline points and increase the fitting speed. The thresholding operation is based on the histogram of the real points of the spectrum, which allows it to be adaptive. Indeed, the flatter the baseline, the weaker its effect on the width of the low intensity points distribution. This reduction induces a decrease of the threshold value and thus the elimination of less and less intense resonances points. Figure S1 illustrates this effect. This operation assumes that the spectrum is sparse: the highest number of points per intensity range corresponds to the noise points. The threshold value TV is calculated with:

BC = DEF &'() + G9HD &'() (2)
with G9HD &'() the full width at half maximum of the histogram and DEF &'() the maximum of the histogram. This threshold value was found to be a good compromise between robustness and selectivity on multiple tests with NMR spectra exhibiting quadrupolar line shapes, large CSA, crystalline or disordered environments, and various SNR. This filter is increasingly selective as the baseline gets better (Fig. S1).

Sinc baseline fitting

A sinc function fit (Figure 2 -step 2) can be performed to correct the major deviations of the baseline allowing to increase the selectivity of the threshold filter as well as making the iterative fit converge faster. This approach results from the relation between the experimental spectrum and the convolution of the ideal spectrum with a sinc function for which the periodicity is the inverse of the dead time.

Iterative smoothing spline fit

The main baseline fit (Figure 2 -step 3) is conducted with the Matlab smoothing spline fit function which estimates ! " ($) by minimizing the following equation:

I J @ K L) K -) M (* ' )N " ' + (1 -I) O P Q " ) M Q " * R " 5F (3)
with & the smoothing parameter, ! ! and ' ! the intensity and the weight of the i-th baseline point,

respectively. The smoothing parameter I ranges between 0 and 1, such that the spline function tends to a straight line when I → 0, while it tends to fit perfectly the baseline points when I → 1. The use of a small p value induces an important constraint leading to the slow evolution of the recursive process.

However, the iterative (recursive) loop ensures that when the truncation limit option is activated, the time domain restriction is the main constraint on the baseline fit. The smoothing parameter p is then set to a default value of I = 1/V " with V, the number of spectrum points, allowing to avoid overfitting of the baseline points which would be entirely discarded by the time domain constraint. The value -log !* (p) is displayed in the user interface to allow manual modification of this parameter required when the truncation limit is deactivated. During this fit, the intensity of each baseline point (! ! ) is weighted by ' ! . This weighting is used to overweight the baseline points near the intense resonances that induce strong baseline distortions. The weighting values ' ! are computed by applying an important Gaussian broadening on the spectrum to obtain the spectrum Sg (Fig. S2) and use its intensity to compute ' ! normalized between 1 and 10 according to:

w + = 9 |Sg + | max(Sg) + 1 (4)

End of fit and correction of the imaginary part

The iterative fit automatically ends when the normalized root mean squared error between the iterative fit ! " ($) and a flat line reaches 10 -6 according to the equation:

4cD)8 = ∑ |) ' | , '-! V • max (?Ief#13g) < 10 ./ (5)
With ) ' the fit value of the i-th point. The user can modify the value of this stopping criterion if needed.

The baseline-corrected spectrum can be finally saved in the format of importation. The imaginary part of the spectrum is also corrected by using a Hilbert transform to bring it in absorption mode, it is then corrected with the same baseline and brought back in dispersion mode by a second Hilbert transform.

Methodology

The The use of synthetic spectra allows comparing the baseline obtained by the algorithm to the theoretical one obtained by the subtraction of the truncated and the theoretical (non-truncated) spectra. This comparison allows computing the goodness of fit (GoF), giving a quantitative metric to evaluate the baseline correction accuracy. The goodness of fit is calculated by:

k2G = 1 - 0 ∑ 23 # .3 # $%& 2 ' ( #)* 0∑ 23 # $%& .3 $%& 444444 2 ' •678 (3) ( #)* (6)
With ) ' <=> the theoretical baseline and ) ' the fitted baseline.

The effect of the baseline correction on the quantification of the different chemical environments was also evaluated by reconstructing the spectra using Dmfit and by calculating the relative quantification errors.

These noting that even at low SNR the baseline error remains approximately centered at zero indicating that the baseline remains well defined in the center of the noise distribution. The GoF for all SNR and NMP conditions are summarized in Figure 4 and are always higher than 0.9 and higher than 0.95 for all SNR above 50. The error bars on each value are defined by the standard deviation on the twenty replicates.

Results

Evaluation on synthetic spectra

The span of the error bars indicates the variability of the baseline and decreases when the SNR increases.

Despite these variations induced by the noise distribution, the baseline error remains mainly constant for a defined spectrum, NMP and SNR. For the same NMP, the GoF increases with the SNR due to the decrease in noise and thus in uncertainty in the baseline definition. At constant SNR, the GoF decreases when the NMP increases due to the increase of the baseline distortions as shown in Supplementary figure 3, but also due to the increased freedom of the baseline induced by the less restrictive time-domain constraint. The GoFs can be further decreased in case of inaccurate zero-order phase adjustment before baseline correction, however, this additional error is negligible compared to the error induced by the phase bias itself as illustrated in Supplementary figure 4. Although the goodness of fit allows assessing the accuracy of the baseline correction, the end-point user will be more interested in the impact of the baseline correction on the quantification. For this reason, the mean baseline errors for each NMP and SNR were added to the theoretical spectra and each resulting spectra was modeled with Dmfit. The amplitudes of the lines were optimized to get the proportions of each environment and calculate the percentage of integration error due to the baseline correction errors.

The percentage of integration error are summarized in supplementary tables 1 to 3. For each type of spectra, the integration error increases when the SNR decreases or when the number of missing points increases, which correlates with the evolution of the GoF values. The integration errors for the two 23 Na sites of Cryst-NaH2PO4 remain inferior to 0.5% confirming the high GoF values and allowing relative quantification with a maximum error of 0.25%. For Cryst-ZrF4, the results are similar with a maximum integration error of the 1.32% for a SNR of 25 and 40 missing points leading to a maximum quantification error of 0.25%. Regarding the glass-CaAl2O4 spectra, the error percentage of the integration based on the CT resonances is more important but remains under 4% for the two major environments (AlO4 and AlO5), and reach 58% for the minor one (AlO6) with a SNR of 25 and NMP=40.

This important relative integration error results in two unfavorable conditions. (i) The proportion of this environment is 2.3% making the relative error more sensitive. Indeed, the quantification error on the proportion of AlO6 is only 1.3% which remains satisfying. (ii) The line shape overlaps with the right side long tailing of the two other intense resonances that may make the baseline correction difficult. The baseline correction induces a more important quantification error (1.3%) for the 27 Al spectrum compared to the 23 Na and 71 Ga spectra (0.25%) probably due to the broad, stepless and poorly resolved 27 Al lines characteristic of Czjzek distributions.

Experimental spectra

The baseline correction algorithm was applied to two practical cases to illustrate different baseline distortion problems. In line with the synthetic spectra previously used, the first example is the In some cases when the spectrum contains numerous very broad low-intensity features, the baseline correction algorithm may introduce small distortions of the line shapes. This is exemplified in Figure S5 with the 71 Ga spectrum of an Yttrium Gallium Garnet sample (Y3Ga5O12). The small mismatch between the corrected spectrum (in black) and the total deconvolution (in green) in the center of the spectrum is due to an underestimation of the baseline induced by the consideration of the dip between the two central resonance as a baseline point. However, it is unlikely that a manual correction would have given a better result. These first examples show the great efficiency of the baseline correction algorithm when the truncation limit option is activated which allows an automatic easy, rapid, and reproducible baseline correction. =0.95) displayed in purple and orange respectively. The total correction was performed in a few tens of seconds on a laptop computer (Intel i7-4800MQ-CPU @ 2.7 MHz with 16 Go of RAM) with the default value of the smoothing parameter p of 10.

The second example is a 71 Ga spectrum of b-Ga2O3 (I=3/2) recorded at 20 T with a MAS frequency of 60 kHz (SNR≈500). The DW and the DE are 0.26 µs and 5 µs, respectively, which corresponds to an initial NMP of 20. In this case, the time domain truncation of the signal alone cannot explain the asymmetrical baseline distortions present between 1800 and 3000 ppm in the original spectrum (Figure 6, the blue line in the top left panel). This important distortion results from signal contamination of the first points of the FID probably due to pulse ringing since no Gallium probe signal was found. When trying to correct such spectrum with the truncation limit activated, the user will see that the baseline correction fails to produce a flat baseline meaning that the time domain truncation only cannot explain the baseline distortion. In this case, the user can either deactivate the truncation limit and optimize the smoothing parameter p or increase the NMP value. These settings will either remove or decrease the time domain constraint on the baseline expression allowing the modification of the FID points.

Increasing the number of NMP allows the user to know how many points of the FID need to be modified to obtain a flat baseline and thus to determine the equivalent T2* filter duration required to remove the additional component of the baseline distortion. Both options need to be used with care: the modification of the early FID points will remove any broad component: artifacts of broadly distributed signals.

In the case of the 71 Ga spectrum of b-Ga2O3 a NMP increased to 40 corresponding to an equivalent dead time of 10 µs allowed obtaining a satisfying baseline as shown in figure 6

Conclusion

We presented a baseline correction algorithm suitable for time-domain truncated pulse-acquire solidstate MAS NMR spectra acquired with large spectral width. It is shown to be of great efficiency for a wide range of nuclei ( 23 Na, 27 Al, 71 Ga, [START_REF] Massiot | Modelling one-and two-dimensional solid-state NMR spectra[END_REF] 0,09 0,13 0,31 -0,15 -0,21 -0,42 -2,40 -3,57 -9,01 10000 0,00 0,01 0,06 0,03 -0,05 0,00 0,03 -0,05 -2,25

Table S3: Error percentage of the integration of each [START_REF] Massiot | Modelling one-and two-dimensional solid-state NMR spectra[END_REF] 25 0,06 0,12 0,87 -0,93 -0,93 -1,24 0,22 0,22 0,27 0,00 -0,06 -0,90 -0,13 -0,13 -0,80 -0,30 -0,45 -0,91 0,42 0,48 1,32 50 0,06 0,06 0,87 -0,93 -0,93 -1,24 0,22 0,22 0,27 -0,06 -0,06 -0,90 -0,13 -0,13 -0,80 -0,30 -0,30 -0,91 0,48 0,48 1,32 100 0,06 0,06 0,81 -0,93 -1,04 -1,24 0,22 0,22 0,27 0,00 -0,06 -0,83 -0,13 -0,13 -0,74 -0,30 -0,45 -0,91 0,42 0,48 1,27 200 0,06 0,06 0,87 -0,73 -0,93 -1,14 0,16 0,22 0,33 0,00 -0,06 -0,83 -0,07 -0,13 -0,80 0,00 -0,30 -0,91 0,26 0,42 1,22 10000 0,00 0,00 0,56 -0,21 -0,31 -1,04 0,00 0,05 0,16 0,00 0,00 -0,64 -0,07 -0,07 -0,60 0,45 0,45 -0,45 -0,05 0,00 1,01 site 6 site 7

ZrF 4 site 1 site 2 site 3 site 4 site 5

Figure 1 :

 1 Figure 1: (Left) numeric models of a typical NMR free induction decay signal: in red !(#) the full ideal signal, in blue ! ! (#) the signal acquired experimentally after the dead time and in green ! " (#) the part of the FID that could not be experimentally recorded. (Right) the three corresponding spectra obtained by Fourier transformation of the FID signals.

Figure 2 :

 2 Figure 2: Flowchart of the baseline correction protocol for 1D NMR spectra. The zero and first-order phase corrections in the blue rectangle have to be performed before the baseline correction. The green arrows indicate the baseline protocol when the truncation limit option is activated. The orange arrows are used when the truncation limit option is deactivated. In the restriction steps (2.1 and 3.1), the inverse Fourier transform points of the baseline fit (in red) at time superior to the dead time are set to zero. In panel 4.1, the time domain signal due to the baseline correction is represented in green, the rest of the points are not modified.

  performances of the baseline correction algorithm toward various SNR values and dead time durations were evaluated on three model spectra synthetized with Dmfit 19 and representative of typical cases encountered in solid-state NMR experiments. Their NMR parameters are derived from experimental data.• Cryst-NaH2PO4: a 23 Na (I = 3/2) spectrum of a crystalline sample (NaH2PO4) with two crystallographic sites at diso = -2.1 and -6 ppm with quadrupolar coupling constants Cq of 1.57 and 2.36 MHz respectively. The spectrum was simulated for a magnetic field B0 = 9.4 T, a MAS frequency νMAS = 25 kHz, and a spectral width of 833.333 kHz (DW = 0.6 μs).• Glass-CaAl2O4: a 27 Al (I = 5/2) spectrum of a structurally disordered sample (CaAl2O4) exhibiting AlO4, AlO5, AlO6 structural units with characteristic Czjzek distributions and proportions of 84.8, 12.9, and 2.3 % respectively. The spectrum was simulated for B0 = 17.6 T, νMAS = 33.35 kHz, and a spectral width of 2 MHz (DW = 0.25 μs).• Cryst-ZrF4: a 19 F (I = 1/2) spectrum of a crystalline sample (b-ZrF4) with seven crystallographic sites with relatives proportion of 16.05, 9.64, 18.31, 15.57, 14.95, 6.6 and 18.88 % for site 1 to 7 respectively and exhibiting different chemical shift anisotropies. The spectrum was simulated for a B0 = 17.6 T, νMAS = 35 kHz, and a spectral width of 500 kHz (DW = 1 μs).

  metrics have been calculated for various SNR: 10000, 200, 100, 50, and 25 (obtained by adding white Gaussian noise to the spectra) and various NMP: 10, 20, and 40. Twenty replicates with different Gaussian white noise were generated for each condition (types, numbers of missing points, and SNR)leading to a total number of spectra of 900 ensuring statistical significance. The spectra with a NMP of 20 and the 5 SNR levels are displayed in Figure3(a-c).

Figure 3 :

 3 Figure 3: Synthetic NMR spectra truncated with NMP = 20 used to evaluate the baseline correction algorithm and the corresponding mean baselines obtained by automatic fitting. Red, green, blue, purple, and orange spectra have a signal-to-noise ratio (SNR) of 10000, 200, 100, 50, and 25, respectively. (a) Cryst-NaH2PO4: 23 Na spectra; (b) glass-CaAl2O4 27 Al spectra; (c) Cryst-ZrF4 19 F spectra. (d,e,f) Corresponding mean baselines obtained by fitting the various SNR spectra with 20 replicates for each SNR value and default values of the smoothing parameter p of 9, 8, 8 respectively. The top of each panel shows the mean

Figure 3

 3 Figure 3 panel d, e, and f display the mean fitted baselines of the 20 replicates for the three simulated spectra with various SNR and 20 missing points. This NMP corresponds to dead time durations of 12 μs for Cryst-NaH2PO4, 5 μs for glass-CaAl2O4, and 20 μs for Cryst-ZrF4. The baseline errors displayed at the bottom of the three panels are scaled as a percentage of the maximum spectrum intensity, and are lower than 0.5% for Cryst-NaH2PO4 and glass-CaAl2O4, and reach 1.1% for Cryst-ZrF4 with the lowest SNR of 25. The highest error values are always localized close to the most intense resonances: the central transition for the quadrupolar nuclei and the most intense spinning sidebands for Cryst-ZrF4. The corresponding goodnesses of fit (GoF) are also included in figure 3 and are all above 0.94. It is worth

Figure 4 :

 4 Figure 4: Goodness of fit comparing the fitted baselines with the theoretical ones as a function of the signal-to-noise ratio (SNR) for Cryst-NaH2PO4 (a), glass-CaAl2O4 (b), and Cryst-ZrF4 (c) spectra. The red, green, and blue lines correspond respectively to NMP= 10, 20, and 40. The error bars correspond to the standard deviation calculated over the twenty replicates.

experimental 23

 23 Na spectrum of NaH2PO4 recorded at 9.4 T with a MAS frequency of 25 kHz (SNR≈1000). The DW and the DE are 0.6 µs and 10 µs, respectively, such that the NMP is initialized as 18. Here the baseline distortion is purely due to dead time truncation, and the original spectrum (Figure 5, blue line) exhibits an intense baseline distortion due to the intense CT resonances of the two different environments. The baseline correction (red line) obtained by a sinc function fit, followed by the smoothing spline iterative fitting, is excellent. The spectral modeling of the two chemical environments lineshape performed with DMfit 19 and including all transitions of 23 Na are displayed in purple and orange. The resulting spectrum (in green) reveals an excellent agreement with the corrected one for both the central transition (top right panel) and the satellite transition (top left panel) demonstrating that NMR line shapes are perfectly preserved by the baseline correction.

Figure 5 :

 5 Figure 5: 23 Na spectrum of NaH2PO4 recorded at 9.4 T at a MAS spinning rate of 25 kHz. The spectrum was acquired with a DW of 0.6 µs and a DE of 10 µs. In blue: original spectrum; in red: the fitted baseline; in black: the corrected spectrum. In green: the full DMfit 19 decomposition obtained with the int2QUAD model including all 23 Na transitions and two chemical environments (& +,- (!) = -2.1 ppm, ' 0 (!) =1.57 MHz, ( 0 (!) =0.47 ; & +,- (") = -6.0 ppm, ' 0 (") =2.36 MHz, ( 0 (")

  in red. The spectral modeling including all transitions of the 71 Ga and two chemical environments (in purple and orange lines), as well as the resulting spectrum, show a very good agreement with the corrected one for both the CT (top right panel) and the STs (top left panel) resonances.

Figure 6 :

 6 Figure 6: 71 Ga spectrum of b-Ga2O3 recorded at 20 T at a MAS spinning rate of 60 kHz. The spectrum was acquired with a DW of 0.26 µs and a DE of 5 µs. In blue: original spectrum; in red: the fitted baseline; in black: the corrected spectrum. In green the DMfit 19 decomposition obtained with the int2QUAD model including all 71 Ga transitions and two chemical environments (& +,- (!) = 40.3 ppm, ' 0 =8.36 MHz, ( 0 (!) =0.085; & +,- (") = 210.9 ppm, ' 0 (") =11.23 MHz, ( 0 (")=0.84) and displayed in purple and orange respectively. The total correction was performed in a total time of 25 seconds on a laptop computer (Intel i7-4800MQ-CPU @ 2.7 MHz with 16 Go of RAM) with the default value of the smoothing parameter p of 10.

  F…) and acquisition parameters. It allows accurate quantification of the corrected spectra with quantification errors below 0.25% for 23 Na spectra of NaH2PO4 or 19 F spectra of ZrF4 and below 1.7% for 27 Al spectra of CaAl2O4 with Czjzek distributions even with SNR level as low as 25. It is made available to the community through a user-friendly standalone Matlab® application allowing to open Bruker and ASCII data and can be download at this address: http://nmr.cemhti.cnrs-orleans.fr/baseline_corrector/default.aspx A video tutorial showing how to perform the baseline correction on some of the examples described in this publication is also available on the web page.

Figure S2 :

 S2 Figure S2: (a) Blue line: synthetic 23 Na solid-state NMR spectrum with a signal-to-noise ratio of 20. Red line: same spectrum after a rough Gaussian apodization. (b) Weighting values calculated from the roughly broadened spectrum in (a).

Figure S3 :

 S3 Figure S3: glass-CaAl2O4 27 Al spectra with a SNR of 10 000 and NMP = 10, 20, and 40 for the black, red, and green spectra respectively.

Figure S4 :

 S4 Figure S4: Goodness of fit comparing the NMR lineshape errors due to inaccurate zero-order phase adjustment in red, and inaccurate zero-order phase adjustment plus baseline correction in blue, for the three synthetic spectra and a NMP of 10. The error introduced by the baseline correction ofthe non-perfectly phased spectra (the difference between the blue and red curves) is much smaller than the error due to the phase error itself which corresponds to the red curve. Thus in case of improper zeroorder phase adjustment, the error due to the baseline correction is negligible compared to the error induced by the phase bias itself.

Figure S5: 71

 71 Figure S5: 71 Ga spectrum of an Yttrium Gallium Garnet sample (Y3Ga5O12) recorded at 20 T at a MAS spinning rate of 60 kHz. The spectrum was acquired with a dwell time of 0.3 µs and a dead time of 10 µs. In blue the original spectrum, in red the baseline correction obtained by cardinal sinc fit followed by the smoothing spline algorithm with the truncation limit option activated. In black the corrected spectrum. In green the DMfit modeling obtained with the int2QUAD model including all 71 Ga transitions and two chemically different environments.

F

  site for the various SNR (25, 50, 100, 200 and 10000) and various NMP (10, 20, 40) corresponding to dead time durations of 10, 20 and 40 μs, respectively. The errors whose absolute values are: inferior to 1 are highlighted in green, comprised between 1 and 5 are highlighted in orange.

  It is generally suitable for spectra dominated by few central intense resonances such as the half-integer spin quadrupolar nuclei spectra containing few intense CT resonances and numerous low-intensity ST resonances. This step can be skipped if not relevant for the baseline correction.

Table S1 :

 S1 Error percentage of the integration of each 23 Na sites for the various SNR (25, 50, 100, 200 and 10000) and various NMP(10, 20, 40) corresponding to dead time durations of 6, 12 and 24 μs, respectively. The errors whose absolute values are inferior to 1 are highlighted in green.

				NaH2PO4
		site 1: -2.1 ppm	site 2: -6 ppm
	NMP SNR	10	20	40	10	20	40
	25	0,26 0,36 0,42	-0,26	-0,36	-0,42
	50	0,26 0,32 0,36	-0,26	-0,32	-0,36
	100	0,20 0,24 0,26	-0,20	-0,24	-0,26
	200	0,18 0,22 0,22	-0,18	-0,22	-0,22
	10000	0,16 0,18 0,18	-0,16	-0,18	-0,18

Table S2 :

 S2 Error percentage of the integration of the CT transition of each chemical environment of 27 Al for the various SNR (25, 50, 100, 200 and 10000) and various NMP(10, 20, 40) corresponding to dead time durations of 2.5, 5 and 10 μs, respectively. The errors whose absolute values are: inferior to 1 are highlighted in green, comprised between 1 and 5 are highlighted in orange and superior to 5 are highlighted in red.

							CaAl2O4
				CT AlO4			CT AlO5			CT AlO6
	SNR	NMP	10	20	40	10	20	40	10	20	40
		25	0,38 0,72 2,00 -0,15 -0,74 -2,85	-13,27	-22,54	-58,03
		50	0,22 0,50 1,05 -0,21 -0,67 -1,60	-6,93	-14,67	-29,78
	100	0,16 0,16 0,58 -0,16 -0,27 -0,91	-4,84	-4,37	-16,45
	200

Acknowledgments

The authors wish to express their gratitude towards Dr. Babacar Diallo and Dr. Alexey Novikov for the numerous test and comments of the baseline correction application.


LABÉO Maud Charles
email: maud.charles@outlook.fr

Frank Duncombe

Keywords: mussels, Vibrio splendidus, pathogenicity, annual cycle, sensitivity, recruitment origin

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.

Experimental infection of Mytilus edulis by two

Introduction

The marine mussels, Mytilus sp., are distributed worldwide [START_REF] Gaitán-Espitia | Mitogenomics of southern hemisphere blue mussels (Bivalvia: Pteriomorphia): Insights into the evolutionary characteristics of the Mytilus edulis complex[END_REF] and constitute food source for aquatic and terrestrial animals including human beings, making them a high economic value product (FAO, 2016). The blue mussel industry occupies an important place in Europe, which is one of the main producers of mussels in the world with almost 600,000 tons per year. France is the second major European producer after Spain, with around 80,000 tons per year, most of them from aquaculture (FAO, 2018). Mussel farming in

France is undertaken along the English Channel, the Atlantic, and the Mediterranean coasts and all mussel juveniles are collected -at spring for Mytilus edulis-in the wild. Thus, production is exposed to several external biotic and abiotic risks.

Indeed, filter feeding bivalves such as mussels are continually exposed to microbiota from the surrounding seawater and therefore vulnerable to attack from potential pathogens.

Up to a recent time, adult mussels were considered to be very resistant to bacterial infections [START_REF] Eggermont | Stimulation of heterotrophic bacteria associated with wild-caught blue mussel (Mytilus edulis) adults results in mass mortality[END_REF] as well as to disease because of a well-working immune system [START_REF] Venier | Insights into the innate immunity of the Mediterranean mussel Mytilus galloprovincialis[END_REF][START_REF] Philipp | Massively Parallel RNA Sequencing Identifies a Complex Immune Gene Repertoire in the lophotrochozoan Mytilus edulis[END_REF]. Mytilus edulis was classified as a resilient cultivated species by mussel farmers insofar as they have only experienced very rare episodes of abnormal mortalities contrarily to what happened to the Pacific oysters, Crassostrea gigas [START_REF] Barbosa-Solomieu | Mass mortality in bivalves and the intricate case of the Pacific oyster, Crassostrea gigas[END_REF]. In addition, these rare mass mortality events have not been attributed to pathogenic bacteria, but only to unfavourable environmental conditions or toxic algal blooms [START_REF] Tsuchiya | Mass mortality in a population of the mussel Mytilus edulis L. caused by high temperature on rocky shores[END_REF][START_REF] Peperzak | Mass mussel mortality in The Netherlands after a bloom of Phaeocystis globosa (prymnesiophyceae)[END_REF]. Nevertheless, since 2014, several important production areas on the French west coast have been affected by high and unusual mass mortality, resulting in significant losses and considerable economic consequences [START_REF] Béchemin | Surmortalités de la moule bleue Mytilus edulis dans les Pertuis Charentais (mars 2014)[END_REF][START_REF] Allain | 1 : synthèse sur l'émergence, la propagation et l'installation des mortalités[END_REF]FAO, 2016;[START_REF] Polsenaere | Potential environmental drivers of a regional blue mussel mass mortality event (winter of 2014, Breton Sound, France)[END_REF].

As the pathogenic bacteria Vibrio splendidus has been found in large abundance in French moribund mussels, its involvement was rapidly suggested [START_REF] Béchemin | Episodes de mortalité massive de moules bleues observés en 2014 dans les Pertuis charentais[END_REF][START_REF] François | Bilan 2014 du réseau Repamo -Réseau national de surveillance de la santé des mollusques marins[END_REF][START_REF] Eggermont | Isolation of Vibrionaceae from wild blue mussel (Mytilus edulis) adults and their impact on blue mussel larviculture[END_REF]. Vibrio spp. are indigenous, saprophytic and very abundant in aquatic environments such as estuaries, coastal waters, marine sediments and aquaculture environments all over the world [START_REF] Colwell | Vibrio cholerae, Vibrio parahaemolyticus, and Other Vibrios: Occurrence and Distribution in Chesapeak Bay[END_REF][START_REF] Ortigosa | Vibrio Species in Seawater and Mussels: Abundance and Numerical Taxonomy[END_REF].

Some of these gram-negative bacteria are pathogenic to humans and other animals, including molluscs [START_REF] Barbieri | Occurrence, Diversity, and Pathogenicity of Halophilic Vibrio spp. and Non-O1 Vibrio cholerae from Estuarine Waters along the Italian Adriatic Coast[END_REF][START_REF] Romero | Occurrence, seasonality and infectivity of Vibrio strains in natural populations of mussels Mytilus galloprovincialis[END_REF]. Species belonging to the genus Vibrio are the most frequently isolated bacteria from molluscs and V. splendidus is widely distributed in marine ecosystems [START_REF] Macián | Ribotyping of Vibrio Populations Associated with Cultured Oysters (Ostrea edulis)[END_REF]; they are known to have impacted marine shellfish for many years with infectious diseases and have caused mortalities reported in the history of shellfish aquaculture [START_REF] Beaz-Hidalgo | Diversity and pathogenecity of Vibrio species in cultured bivalve molluscs[END_REF][START_REF] Lemire | Populations, not clones, are the unit of vibrio pathogenesis in naturally infected oysters[END_REF][START_REF] Travers | Bacterial diseases in marine bivalves[END_REF].

However, knowledge on their ecology and pathogenicity is still scarce.

In France, past mortalities of mussels greatly varied from one year to another, from one season to another and from one site to another [START_REF] Lupo | Revue systématique de la littérature relative aux facteurs de risque de mortalité des moules exploitées en France[END_REF]. However, the French cultural practices, of natural larval recruitment make spat transfers to several growing sites on the Atlantic coast and English Channel, very common; the various growing areas in northern

France are almost all supplied with spat from the same locations -roughly between the Loire and Garonne estuaries. Besides, even though mortality events have been reported at different times of the year, the majority of them took place at the end of winter and early spring, when the temperature increased [START_REF] Soletchnik | Eléments de connaissance sur la mortalité et la reproduction de la moule bleue (Mytilus edulis) sur la façade atlantique[END_REF][START_REF] Travers | Mortalités de moules bleues dans les Pertuis Charentais -MORBLEU[END_REF][START_REF] Cochennec-Laureau | Expertise sur le contexte hydro-climatologique de la baie de Pénestin pour décrire les conditions physio-chimiques environnementales et l'impact possible sur les mortalités de moules[END_REF][START_REF] Glize | Suivi de la mortalité de populations de moules en Pays de la Loire : Programme TESCOR 2[END_REF]. It appeared that the etiology of these mortalities is complex; several factors could be involved like environmental conditions, infectious agent, physiology and genetic of the host, as suggested by [START_REF] Goulletquer | Summer mortality of the Pacific cupped oyster Crassostrea gigas in the Bay of Marennes Oléron (France)[END_REF] for mortality observed with C. gigas.

The objective of the present work was precisely to assess the influence of some internal and external factors of the host during a bacterial injection challenge. In this way, in vivo infection assays were made over a year, with adult blue mussels (M. edulis) from five different recruitment areas, by using two V. splendidus-related species described as virulent for mussels from Brittany at high concentrations (Le Roux, pers. comm., unpubl. data): Vibrio crassostreae 7T4_12 and V. splendidus 3G1_6. The aims of this study were to evaluate (i) the pathogenicity and the virulence of these two strains on mussels by testing two different concentrations: one consistent with the general environmental loads of Vibrio spp. in seawater ~ 10 4 /10 5 CFU mL -1 [START_REF] Pfeffer | Ecology of Vibrio vulnificus in Estuarine Waters of Eastern North Carolina[END_REF][START_REF] Eiler | Growth response of Vibrio cholerae and other Vibrio spp. to cyanobacterial dissolved organic matter and temperature in brackish water[END_REF], also corresponding to the bacterial concentrations that can be reached in M. edulis (equal to 2.9 x 10 4 CFU per mL or g according to [START_REF] Garnier | Evidence for the involvement of pathogenic bacteria in summer mortalities of the Pacific oyster Crassostrea gigas[END_REF], and another one much higher ~ 10 8 CFU mL -1 ; (ii) the influence of the first farming method used and original spat recruitment area on the sensitivity to vibriosis; (iii) the role of physiological and morphological conditions of mussels -impacted by the season-in susceptibility to bacterial infections.

Materials and Methods

Bacterial strains and inoculum preparation

The two V. splendidus-related strains used for experimental infection were provided from the French institute, Ifremer. As part of their study (ANR 13-ADAP-0007-01 "OPOPOP"), they isolated many strains from a Vibrio population assembly isolated from oyster tissues (C.

gigas) during and outside mortality events that hit oysters and mussels in the bay of Brest (Finistère, France) in 2014. Among the hundred species isolated, several appeared to be highly pathogenic for oysters [START_REF] Bruto | Vibrio crassostreae, a benign oyster colonizer turned into a pathogen after plasmid acquisition[END_REF] and mussels from Brittany (Le Roux, pers.

comm., unpubl. data) of which V. crassostreae 7T4_12 and V. splendidus 3G1_6, that are used in this study, and respectively named S1 and S2 in the rest of the article. Twenty-four hours before infection, the bacteria were grown in fresh liquid sterile marine Zobell at 22 °C.

The bacterial inocula were prepared using the fresh bacterial suspensions by diluting it in Artificial Sterilised Sea Water (ASSW) from 1:10 to 1:10,000,000 (serial ten-fold dilutions from 10 -1 to 10 -7 ). The two dilutions used as inocula for experimental infections were 1:10 (10 -1 ) and 1:10,000 (10 -4 ). Bacterial load of pure bacterial suspension (BL) was defined by enumeration of Colony Forming Units (CFU) according to the standard NF ISO7218:2007 (AFNOR, 2007): BL (CFU mL -1 ) = ; where Σc is the sum of the colonies counted on the two plates of marine Zobell agar spread with two successive dilutions (10 -6 and 10 -7 ) that incubated for 48 h at 22 °C; V is the volume that has been spread on plate (0.1 mL) and d is the dilution corresponding to the first dilution retained (10 -6 ).

Experimental mussels

Adult mussels used for this experiment were placed on intertidal mussel stakes ('bouchot') in the shellfish growing area off the coast of Bricqueville-sur-Mer, Normandy, France (48°55'05.8"N 1°34'23.9"W) since automn 2015. Spats were initially collected in early spring 2015 in five different recruitment areas: the cove of Port-Mânes (Ile Dumet, Loire-Atlantique, France); the Loire Estuary (La-Plaine-sur-Mer, Loire-Atlantique, France); the bay of Bourgneuf (Noirmoutier, Vendée, France); the bay of Aiguillon (Charron, Charente-Maritime, France); the Antioche Sound (Fouras, Charente-Maritime, France) (Fig. 1). These five origin groups are respectively named ID, LP, No, Ch, Fo in the rest of the article. The mussels from ID were different from the others: first, larval recruitment and first months of farming were made on longlines (while the others were on 'bouchot'). Second, they were 'pelisse' mussels, i.e. mussels from the upper layer of the collection rope that grew faster than the others. To leave space for the lower layer mussels, and to prevent these bundles of mussels from coming off, farmer collects and puts them back into 2.5 m long mesh tubes.

Like initial collection rope, these nets are then coiled around a wooden pole at the growing area.

Experimental procedure of bacterial challenges

Experimental infections were performed monthly during one year for S1 (from April 2017 to April 2018) and during nine months for S2 (from August 2017 to April 2018). Mussels were collected during low tide, transported in cooler bags to the laboratory and once there, immediately put back in tanks filled with oxygenated natural seawater at 15 °C overnight. The next day, they were anesthetised by bathing in a solution containing 40 g L -1 of magnesium chloride during 30 min. Then, 100 µL of inocula or ASSW (for negative controls) per mussel were injected into the posterior adductor muscle. In the rest of the article, the bacterial loads corresponding to the 100 µL of dilutions 10 -1 and 10 -4 have been respectively named C1 and C2. Following the injections, 20 mussels of each group (origin/strain/loads) were placed in separated tanks filled with 1 L of aerated seawater at 15 °C (Fig. 2). Thus, 100 mussels were injected for each condition (S1_C1; S1_C2; S2_C1; S2_C2 or negative controls). Water exchange and tank cleaning were carried daily for seven days and then, mortalities were recorded. All mussels that did not spontaneously close their valves when the seawater was removed were considered as dead. Dead mussels were sampled for biometry (flesh weight and shell length) and microbiological analysis. The objective was to verify whether among the main bacteria found in the tissues of dead mussels, the strain initially injected was present (Koch's fourth postulate).

Microbiological and molecular analyses of dead mussels

Tissues of dead mussels were mashed with scalpels and a volume of 450 µL of ASSW was added to 50 mg of the homogenate. After stirring and a pulse centrifugation to pellet cellular debris, ten-fold dilutions of supernatant were made and 100 µL of 1: 10,000 (10 -4 ) and 1: 100,000 (10 -5 ) dilutions were sown on Zobell marine agar and incubated for 48 h at 22 °C.

For each plate, the three predominant bacterial colonies were re-isolated in order to ensure their purity. Then, DNA extraction of these three predominant colonies was performed by heating the colony placed in 250 µL of purified water (DNA/nuclease free water) for 10 min at 95 °C. The successful extraction was confirmed by DNA quantification with NanoDrop TM 2000c spectrophotometer (ThermoFisher Scientific TM , Waltham, MA USA) and strains were then characterised by molecular analysis. One TaqMan ® real-time PCR targeting the 16S rRNA gene of V. splendidus-related strains (PCR1) [START_REF] Oden | Multilocus sequence analysis of Vibrio splendidus related-strains isolated from blue mussel Mytilus sp. during mortality events[END_REF] was carried out on a Smart Cycler ® (Cepheid, USA); the primers used for PCR1 were SpF1 5'ATCATGGCTCAGATTGAACG3' and SpR1 5'CAATGGTTATCCCCCACATC3' [START_REF] Nasfi | A European epidemiological survey of Vibrio splendidus clade shows unexplored diversity and massive exchange of virulence factors[END_REF] and the probe SpProbe (5'-3') FAM-CCCATTAACGCACCCGAAGGATTG-BHQ1. The reaction volume of 25 µL contained 12.5 µL of Premix Ex Taq ® 2 X Takara ® (Lonza, Verviers, Belgium), 0.5 µL of each primer (20 µM), 0.5 µL of probe (10 µM), 9 µL of purified water and 2 µL of extracted DNA (replaced with 2 µL of purified water in the negative control). The thermal cycling profile was 95 °C for 10 s, followed by 40 cycles at 95 °C for 5 s and 62 °C for 30 s. Then, when PCR1 was positive, the objective was to differentiate strains of V. splendidus-related species from each other and identify the inoculated strain.

Genotyping of V. splendidus-related strains

A conventional PCR targeting the housekeeping genes mreB of V. splendidus-related strains (PCR2) was performed since it is the most discriminant gene for the identification of closely related strains among the Splendidus clade [START_REF] Oden | Multilocus sequence analysis of Vibrio splendidus related-strains isolated from blue mussel Mytilus sp. during mortality events[END_REF]; the primer pair for PCR2 was mreB-F 5'CTGGTGCTCGYGAGGTTTAC3' and mreB-R 5'CCRTTYTCTGAKATATCAGAAGC3′. For the conventional PCR, typical 25 µL reaction mixtures contained 12.5 µL of Premix Ex Taq ® 2 X Takara ® (Lonza, Verviers, Belgium), 0.5 µL of each primer (20 µM), 9.5 µL of purified water and 2 µL of DNA template (replaced with 2 µL of purified water in the negative control). Conventional PCR amplifications were carried out in a T100 TM Thermal Cycler (Bio-Rad, France) and the thermal program was as follows: 10 s at 95 °C; 30 cycles for 10 s at 95 °C, 30 s at 55 °C, 40 s at 72 °C and a final extension of 3 min at 72 °C. PCR products were then analysed with QIAxcel ® Advanced System (Qiagen, Courtaboeuf, France) and sent to Eurofins MWG Operon (Ebersberg, Germany) to be purified and subsequently sequenced.

Phylogenetic analyses

The mreB sequences were aligned using a multiple sequence alignment Multiple Sequence

Comparison by Log-Expectation (MUSCLE); phylogenetic analyses were performed in MEGA7 [START_REF] Kumar | MEGA7: Molecular Evolutionary Genetics Analysis Version 7.0 for Bigger Datasets[END_REF] using the Neighbor Joining method [START_REF] Tamura | MEGA6: Molecular Evolutionary Genetics Analysis Version 6.0[END_REF]

Establishment of periods related to the annual life cycle of mussels

In order to estimate the role of physiological conditions of mussels in susceptibility to bacterial infections, 'periods' have been defined, related to the mussel cycle. The cycle of M. edulis in cold waters has been known for several years [START_REF] Boromthanarat | Reproduction et recrutement de Mytilus edulis L. cultivée sur les bouchots du bassin de Marennes-Oléron[END_REF][START_REF] Gosling | Bivalve Molluscs: Biology, Ecology and Culture, Fishing News Books[END_REF]: gametogenesis begins in late summer and early autumn (Sept./Oct.), gonads are ripe and animals mature in early winter (Dec./Jan.). The emission of gametes in water occurs in late winter and early spring (Feb.-Apr.) and the resting stage begins in late spring and early summer (June). Thus, the following periods have been defined: from February to April = period A; from May to July = period B; from August to October = period C; from November to January = period D.

Statistical analyses

Statistical analyses and graphical representations were performed by using R software, version 3.5.1 and Microsoft Excel software, version 2016. The differences in mortality observed between (i) the conditions tested (negative control, C1 and C2) and (ii) the periods of the year, were tested using a Kruskal-Wallis test (K-W). When significance was obtained, a pairwise Wilcoxon test was done. The difference in virulence between S1 and S2 was tested by comparing two observed distributions with a chi-squared test of independence. To finish, the numbers of dead mussels observed for each origin were compared with a chi-squared goodness-of-fit test. The statistical analyses described above were carried out on the number of dead mussels found positive for the strain initially injected (S1-or S2-positive); except for the first test (which compared dead mussel counts among all conditions), the comparison was first made between all the dead mussels, then only with the S-positive. Regarding biometry data, normality was confirmed with a Shapiro test and an ANOVA was done. When results were significant, a post-hoc test (Tukey test) was performed. Statistical significance was accepted for *p < 0.05 and **p < 0.01.

Results

Bacterial inocula concentrations

The BL of all experiments performed with S1 and S2 are reported in Table 1. Mussels were inoculated with average bacterial loads equal to 3.12 x 10 7 CFU (S1_C1) and 3.12 x 10 4 CFU (S1_C2) for S1, and 2.23 x 10 7 CFU (S2_C1) and 2.23 x 10 4 CFU (S2_C2) for S2.

Overall obtained mortalities and strain-positive mussels

The numbers of infected mussels, dead mussels and strain-positive dead mussels obtained for each experimental infection are reported in Table 1. On all experimental infections made with S1, 1,300 mussels were inoculated with S1_C1 and 189 died (mean = 15; min = 0; max = 65), and 1,300 mussels were infected with S1_C2 and 26 died (mean = 2; min = 0; max = 10). On all bacterial challenges performed with S2, 900 mussels were inoculated with S2_C1 and 326 died (mean = 36; min = 0; max = 86), and 900 mussels were infected with S2_C2 and 19 died (mean = 2; min = 0; max = 11). On average over all experimentations, S1 was found in 48 % (90/189) of dead mussels in condition S1_C1 and in 4 % (1/26) in S1_C2, and S2 was found in 50 % (163/326) of dead mussels in condition S2_C1 and in 5 % (1/19) in S2_C2. None of the dead mussels in the negative controls (n = 15) were positive in S1 or S2. Figure 3 shows one of the phylogenetic analysis; in total, 255 mreB sequences were aligned over all these bacterial challenges.

Comparison of mortality by tested conditions (strains / loads)

The observed mortality between the tested conditions differed significantly (K-W, S1: pvalue** = 2.7.10 -5 ; S2: p-value** = 2.1.10 -6 ). For S1 and S2, the mortality obtained at the highest concentration (C1) is significantly higher from the others (Wilcoxon test, S1: C1/C2 p-value* = 0.02 and C1/controls p-value** = 0.009; S2: C1/C2 p-value** = 0.007 and C1/controls p-value** = 0.0006). There was no difference between C2 and negative controls for both strains (positive to the strain or not). What is also quite remarkable in this figure is the percentage of S-pos dead mussels; over the period A, the percentage of S-pos dead mussels was on average < 50 % for S1 and S2 while for period C, it was ~ 75 % for S2.

Comparison of mortality by period

Comparison of the pathogenicity of the two strains over the common time interval

By comparing the numbers of dead mussels following infection by S1 and S2 over the common time interval, it appears that S2 was globally significantly more pathogenic than S1

(chi-squared = 68.25 > chi-squared 0.01 = 15.08, df = 5; p-value** < 0.01). When the obtained mortalities for each month are compared, it is notable that the mortalities caused by S2 are significantly higher in most months (Fig. 5). Indeed, S2 caused mortality during period C whereas S1 did not; moreover, it caused mortality over the entire period A whereas S1 caused significantly less, with a bell-shaped evolution during the period and, with a peak in March.

However, despite the difference in pathogenicity, S1 and S2 both cause similar mortalities in March (S-positive dead mussels).

Comparison of mortality depending on spat recruitment sites

Among all S1-or S2-positive dead mussels (n=253), 12 % were from ID (30/253), 19 % from LP (48/253), 22.5 % from Ch (57/253), 22.5 % from Fo (57/253) and 24 % from No (61/253) (Fig. 6). The number of dead mussels originating from ID is significantly lower than the others (chi-squared = 13.87 > chi-squared 0.01 = 13.27, df = 4; p-value** < 0.01).

Biometry

Figure 7 shows the mean values (± SEM) of length and flesh weight of dead mussels according to their origin. Several differences appear between the biometric values of the mussels, but the most notable is for the mussels of ID. Overall, they were much larger and fleshier than the others. On the contrary, the mussels from LP were slightly smaller and less fleshy.

Other observations

The parasite, Mytilicola intestinalis, was fortuitously found in the intestinal tract of some dead animals. Even though, the objective of the study was not to search for it, parasites easily visible to the naked eye (> 0.25 mm) were counted still; between one to five individuals were observed in more than a third of dead mussels. Also, less than 10 % of the mussels had their mantle full of macroscopically visible trematode sporocysts or metacercaria.

For experiments done during period A, an interesting phenomenon was observed. Just a few hours after injection, the mussels that have been infected at the highest concentration with S1 and S2, began to lay their gametes in water tank (Fig. 8A and Fig. 8B). When spawning was observed, the water was immediately changed. However, this occurred several times during the first 2-3 days after infection. For dead mussels collected during this period, more than half had a mantle with some remnants of gametes not expelled (un-spawned gametes) (Fig. 8C and Fig. 8D) or totally empty (like the valve on the right in Fig. 8D).

Discussion

Pathogenicity of V. crassostreae 7T4_12 and V. splendidus 3G1_6

Performing an enumeration to obtain the BL of bacterial suspension of each inoculum allowed to verify precisely the tested concentrations. By achieving the objective of testing bacterial concentrations consistent with those found in seawater or in mussel tissues (~ 10 4 CFU animal -1 ; [START_REF] Eiler | Growth response of Vibrio cholerae and other Vibrio spp. to cyanobacterial dissolved organic matter and temperature in brackish water[END_REF][START_REF] Garnier | Evidence for the involvement of pathogenic bacteria in summer mortalities of the Pacific oyster Crassostrea gigas[END_REF][START_REF] Pfeffer | Ecology of Vibrio vulnificus in Estuarine Waters of Eastern North Carolina[END_REF], as well as high concentrations (~ 10 7 CFU animal -1 ), different results was observed. Most of the experimental infections performed on mussels [START_REF] Parisi | Differential involvement of mussel hemocyte sub-populations in the clearance of bacteria[END_REF][START_REF] Tanguy | Functional and molecular responses in Mytilus edulis hemocytes exposed to bacteria, Vibrio splendidus[END_REF][START_REF] Cheikh | First evidence for a Vibrio strain pathogenic to Mytilus edulis altering hemocyte immune capacities[END_REF], 2017) or oysters [START_REF] Gay | Screening of Vibrio isolates to develop an experimental infection model in the Pacific oyster Crassostrea gigas[END_REF][START_REF] Garnier | Evidence for the involvement of pathogenic bacteria in summer mortalities of the Pacific oyster Crassostrea gigas[END_REF][START_REF] Travers | Several strains, one disease: experimental investigation of Vibrio aestuarianus infection parameters in the Pacific oyster, Crassostrea gigas[END_REF] were made by injections with high bacterial strain concentrations around 10 7 /10 8 CFU animal -1 which is quite far from those found in the field. On this point, [START_REF] Travers | Several strains, one disease: experimental investigation of Vibrio aestuarianus infection parameters in the Pacific oyster, Crassostrea gigas[END_REF] mentioned that defining a strain as virulent through high concentration injections was a debatable issue. As for [START_REF] Goudenège | A single regulatory gene is sufficient to alter Vibrio aestuarianus pathogenicity in oysters: Comparative genomics of Vibrio aestuarianus[END_REF], they determined three categories of strains as part of their highthroughput bacterial infections: virulent (> 50 % mortalities at 10 2 CFU animal -1 ), nonvirulent (< 50 % mortalities at 10 7 CFU animal -1 ) and intermediate (i.e. pathogenic; an effect is observed only at 10 7 CFU animal -1 ). They have thereby shown that a real virulent strain induces high mortality rates on oysters, even at very low concentrations (10 2 CFU animal -1 ).

The present study has indicated that even by intramuscular injection of a pure bacterial solution of S1 or S2 at 10 4 CFU animal -1 , there was no significant mortality whatever the time of the year. Thus, it is possible to affirm that these two strains are not virulent. In contrast, it appeared that, with a high concentration (10 7 CFU animal -1 ), both strains were capable to cause significant mortalities (14.5 % in period C for S2, and in period A, 19.5 % and 38.5 % respectively for S1 and S2). According to the classification of [START_REF] Goudenège | A single regulatory gene is sufficient to alter Vibrio aestuarianus pathogenicity in oysters: Comparative genomics of Vibrio aestuarianus[END_REF], these two strains could be defined as, at most, non-virulent or pathogenic; because it entirely depends on the time of year and that is part of what this study highlights. Indeed, these two strains could have wrongly been defined as pathogenic or non-pathogenic, depending on the period during which bacterial challenges were performed. In any case, it appeared that S2 was overall more pathogenic than S1 at 10 7 CFU animal -1 because it caused significantly more mortality. Besides, S2 caused mortality at a time when S1 caused almost none (period C), and the mortalities observed during this period appear to be actually due to S2 insofar as it is found in three out of four mussels. Indeed, to verify the presence of the inoculated strain seems to be also an important detail to check. During period A -more particularly in Marchsimilar mortalities were obtained with S1-and S2-pos mussels and the injected strains were absent in more than half of the total of dead mussels (over the period A, only 58 out of 122 mussels were S1-pos and only 115 out of 254 mussels were S2-pos); however, higher mortalities were observed with S2 during period A. It can be hypothesized that, in order to disrupt the animal at this vulnerable period, the injected bacteria did not necessarily need to get the upper hand, compared to other commensal bacteria, as during less sensitive periods such as period C. Therefore, if the presence of bacteria had not been investigated, and the experiments had only been carried out at that time of the year, the strains would have been wrongly defined as very pathogenic.

Influence of the annual life cycle during a bacterial challenge

In this study, the observation of forced egg laying following bacterial challenges suggests that the physiological condition of mussels plays a decisive role in the observed mortality. Carrying out the same bacterial challenges monthly over several months made it possible to study and compare mussel response to the same bacterial challenges, in the same laboratory conditions, and thus to evaluate the role of the physiological status of the host.

These experimentations showed that there was a seasonality in mortality caused by the bacteria pointing out a link between sensitivity to infection and the annual cycle of mussels.

The annual cycle of cold-water mussels includes several stages: growth, ripening of gametes, spawning, and gonad redevelopment; this cycle is adjusted according to environmental conditions (temperature and food supply mostly; [START_REF] Gosling | Bivalve Molluscs: Biology, Ecology and Culture, Fishing News Books[END_REF]. During summer, gonadal reserves are accumulated and once the minimum level is reached, the gametes development starts -usually in October-, it continues throughout the winter months and, in February, the gonads are ripe. At that point, mussels are ready to spawn waiting for natural exogenous factors (rise in water temperature, salinity change, etc.). Nevertheless, under laboratory conditions, spawn could be induced by a temperature shock or a physical stimulation -like scraping the shell or pulling the byssus threads [START_REF] Gosling | Bivalve Molluscs: Biology, Ecology and Culture, Fishing News Books[END_REF][START_REF] Seed | The mussel Mytilus: ecology, physiology, genetics and culture[END_REF]. This study showed that the inoculation of a high bacterial load constitutes a stress strong enough to induce spawning in mussels that are in an advanced state of maturation or ripe (period A) and

no matter what strain used. [START_REF] Soletchnik | Mortalité de l'huître Crassostrea gigas dans le bassin de Marennes-Oléron : étude de la variabilité spatiale de son environnement et de sa biologie par un système d'informations géographiques (SIG)[END_REF] described this physiological condition as one of the major factors in the numerous mortality episodes observed in oysters and in mass mortality episodes of French mussels in 2014 and 2015 [START_REF] Soletchnik | Eléments de connaissance sur la mortalité et la reproduction de la moule bleue (Mytilus edulis) sur la façade atlantique[END_REF].

Spawning is an energy demanding process during which mussels are in poor condition with low glycogen content (depleted reserves) which makes them particularly vulnerable to stressful environmental conditions (Bayne et al., 1980;[START_REF] Bayne | Growth and Production of Mussels Mytilus edulis from Two Populations[END_REF][START_REF] Najdek | Total and methyl-mercury content in bivalves, Mytilus galloprovincialis Lamarck and Ostrea edulis Linnaeus: Relationship of biochemical composition and body size[END_REF][START_REF] Myrand | Reproduction and summer mortality of blue mussels Mytilus edulis in the Magdalen Islands, southern Gulf of St. Lawrence[END_REF]. The results observed here are consistence with these studies. Indeed, the high mortalities obtained during period A were the outcome of global weakness of mussels -that accompanies this triggering of spawning-and the inoculation of a high concentration of pathogenic bacteria. It is even more obvious because, as for S1, no mortality has been observed during other periods. Inoculation induced significant stress that triggered spawning; it is then possible to wonder: if the nature of the stress had been different (e.g. chemical contaminant spill or toxic algae bloom), would the spawning have been triggered and what would have been the result on the observed mortality rates? Nevertheless, in the case of S2, the strain seems to have increased mortality more strongly during this period than for S1, but as showed above, S2 caused mortality during period C while S1 did not. This is interesting because this post-spawning period corresponds to the sexual rest during which glycogen content is improved and is accumulated as an energetic reserves [START_REF] Bayne | Growth and Production of Mussels Mytilus edulis from Two Populations[END_REF]; it shows that mussels remain sensitive during this period and this is also what [START_REF] Myrand | Reproduction and summer mortality of blue mussels Mytilus edulis in the Magdalen Islands, southern Gulf of St. Lawrence[END_REF] observed in their analysis of the link between reproduction and summer mortality in Québec mussels. On the other hand, period D appears to be the interval when mussels are more robust since no significant mortality has occurred. During this period, energy reserves are usually used to meet the energy needs of gametogenesis and in December, mussels are full -maturewith gonads that occupy all the space, without being ready or able to spawn. The lack of observed mortality suggests that they have accumulated enough energy to fight against and deal with high infection. It seems that this time of the year is the most appropriate time to test a strain for virulence or pathogenicity; indeed, if strain causes mortality at that moment, then it will cause probably more at another time.

Mortality according to original spat recruitment site

Moving forward in the study, it became very clear that the mussels from ID were different from the others. They were larger, fleshier and died significantly less than the others as a result of bacterial challenge. However, all the mussels were the same age and had the same living conditions over three quarters of their lifespan since they had been on 'bouchot' during the same time (~ the last 18 months at the beginning of the study); only the recruitment site and the first five months of farming method varied. Despite these more than 18 months of identical life, the mussels from ID were significantly bigger and more robust. Thus, two hypotheses to explain their robustness can be proposed: it would either come from (i) the morphology of the mussels, influenced by the farming method used during the first months; or from (ii) the genetic inheritance of the mussels, influenced by the genetic characteristics of the natural local population; or perhaps from a combination of both factors.

Mussel growth is mainly influenced by food availability [START_REF] Gosling | The mussel Mytilus: ecology, physiology, genetics and culture[END_REF] and the longlines farming method -with continuous immersion-allows mussels to have access to trophic resources without interruption. Mussels grown on longlines have higher growth performance than mussels grown on 'bouchot' [START_REF] Prou | The French Mussel Industry: Present Status and Perspectives[END_REF]; and this is what was observed for the ID mussels which were larger from the beginning to the end of the study. As described above, the life cycle of mussels is very energy-intensive, so larger individuals have logically more glycogen reserves, which probably allows them to have a negative energy balance later than others.

Several authors have compared the growth rates and the mortality of mussels by transferring samples from different natural populations in the same growing areas [START_REF] Dickie | Influences of Stock and Site on Growth and Mortality in the Blue Mussel (Mytilus edulis)[END_REF][START_REF] Mallet | The genetics of production characters in the blue mussel Mytilus edulis. I. A preliminary analysis[END_REF][START_REF] Mallet | Mortality Variations in Natural Populations of the Blue Mussel, Mytilus edulis[END_REF][START_REF] Fuentes | Production traits of the mussel Mytilus galloprovincialis cultured in Galicia (NW of Spain): relative effects of source of seed and growing environment[END_REF]. These studies have demonstrated that survival capacities of mussels -and so variations in mortality-could be attributed to different origins (source of mussel stocks). [START_REF] Mallet | General and specific combining abilities of larval and juvenile growth and viability estimated from natural oyster populations[END_REF] indicated that mortality variation from one origin to another evidences the differences between populations of M. edulis; this is also what [START_REF] Dickie | Influences of Stock and Site on Growth and Mortality in the Blue Mussel (Mytilus edulis)[END_REF] observed thanks to the significant differences in genetic constitution among the stock used in their experiments. Following this, [START_REF] Mallet | The genetics of production characters in the blue mussel Mytilus edulis. I. A preliminary analysis[END_REF] showed that viability is highly heritable in M. edulis populations and, very recently, Dégremont et al.

(2019) revealed that selected populations of French M. edulis were significantly more resistant to mortality outbreaks than natural unselected populations. Mytilus edulis and M.

galloprovincialis are native species to the French coast, and numerous different natural populations exists and are capable to hybridise with each other. Thus, the Atlantic coasts of

France is very complex -in terms of genetic structure-with several mosaic hybrid zone between M. edulis and M. galloprovincialis [START_REF] Bierne | Introgression patterns in the mosaic hybrid zone between Mytilus edulis and M. galloprovincialis[END_REF][START_REF] Riquet | Weird genotypes? Don't discard them, transmissible cancer could be an explanation[END_REF]. It is therefore possible that some natural French populations could be more resistant than others. [START_REF] Snieszko | The effects of environmental stress on outbreaks of infectious diseases of fishes*[END_REF] wrote 'an overt infectious disease occurs when a susceptible host is exposed to a virulent pathogen under proper environmental conditions' which means that three elements -pathogen, environment, and status of the host-strongly interact in the disease. In some cases, only two unfavourable elements seem sufficient to provoke mortality [START_REF] Garnier | Evidence for the involvement of pathogenic bacteria in summer mortalities of the Pacific oyster Crassostrea gigas[END_REF]. This fact was observed in this study since the experiments were conducted under the same laboratory conditions. What could be added to Snieszo's quote is that within the host itself, many parameters -physiological state, morphology, and probably genetic inheritance-can influence the host's response to the disease.

Overall, the results provide information on the mortalities observed in mussels, M. edulis, over the annual life cycle, in response to bacterial stress. To our knowledge, this is the first study to test the pathogenicity of V. splendidus-related strains at concentrations close to what is found in the wild, over the entire annual cycle of mussels, and considering their origin. The results demonstrate that these two strains are not virulent for mussels and that the mortalities obtained during in vivo bacterial challenges are distinct depending on the physiological state of mussels -reliant on the time of the year-, and on the recruitment origin of the spat. Thus, to test the virulence or the pathogenicity of a bacterial strain on mussels, it seems consistent to consider the origin of the population and the state of maturity of mussels and thereby, avoid bacterial challenges during the spawning period of the mussels because this is not strictly representative of the pathogenicity of the strain.

Table(s)

659 Table 1: Bacterial load of pure bacterial suspension (BL) used to make the dilutions used for each bacterial challenge, and number of infected mussels, total 660 dead mussels and strain-positive dead mussels in each condition tested for every monthly experiment. S1: Vibrio crassostreae 7T4_12 and S2: Vibrio 661 splendidus 3G1_6; C1: dilution 1:10 (10 -1 ) and C2: dilution 1:10,000 (10 -4 ).

BL (CFU mL

Figures

  Vibrio splendidus -related strains: Determination of pathogenicity level of strains and influence of the origin and annual cycle of mussels on their sensitivity Maud Charles, Suzanne Trancart, Elise Oden, Maryline Houssin To cite this version: Maud Charles, Suzanne Trancart, Elise Oden, Maryline Houssin. Experimental infection of Mytilus edulis by two Vibrio splendidus -related strains: Determination of pathogenicity level of strains and influence of the origin and annual cycle of mussels on their sensitivity. Journal of Fish Diseases, 2019, 43 (1), pp.9-21. 10.1111/jfd.13094. hal-02987868Experimental infection of Mytilus edulis by two Vibrio splendidus-related strains: determination of pathogenicity level of strains and influence of the origin and annual cycle of mussels on their sensitivityMaud Charles 1, 2,* , Suzanne Trancart 2 , Elise Oden 2 , Maryline Houssin 1,2 1 Normandie Université, Université de Caen Normandie, UMR BOREA, CNRS-7208, IRD-207, MNHN, UPMC, UCN, Esplanade de la Paix, 14032 Caen Cedex 4, France 2 LABÉO Frank Duncombe, 1 Route de Rosel, 14053 Caen Cedex 4, France

Figure 4

 4 Figure4illustrates the obtained mortality with S1 (Fig.4A) and S2 (Fig.4B) at each month of

Figure 1 :

 1 Figure 1: Location of the five mussel original recruitment areas and the shellfish growing area

Figure 2 :

 2 Figure 2: Schematisation of the post-infection monitoring system. Mussels inoculated with 100 µL of

Figure 3 :Figure 4 :

 34 Figure 3: Phylogenic tree made from mreB sequences of Vibrio spendidus-related strains isolated

Figure 5 :

 5 Figure 5: Comparison of mortalities obtained at each month with Vibrio crassostreae 7T4_12 (white bars) and Vibrio splendidus 3G1_6 (dark grey bars) at the highest concentration (10 8 CFU mL -1 ). Chi-

Figure 6 :

 6 Figure 6: Proportion (%) of dead mussels on all experiments (all strains included) according to the

Figure 7 :Figure 8 :

 78 Figure 7: Comparison of the length (mm) (A) and flesh weight (g) (B) of dead mussels according to

  and the maximum composite likelihood model with a bootstrap of 1000 replications. Thus, the sequences of the mreB gene of V. spendidus-related strains from dead mussels could be

compared with the sequences of the injected strains and with forty-seven different reference bacterial strains (whose forty-two belonging to the Splendidus clade; see 2.1. in Oden et al. 2016). Vibrio aestuarianus 02/041, Vibrio ordalii 12B09 and Vibrio penaeicida AQ115, were provided from Genomic of Vibrio Research Department (CNRS Roscoff, France) and used as Splendidus clade outsiders.

-1 ) number of infected mussels / dead mussels / strain-positive dead mussels

	Months	S1	S2	S1_C1	S1_C2	S2_C1	S2_C2	Negative controls
	Apr 2017	7.22 x 10 8	NA §	100 / 18 / 15	100 / 0 / 0	NA §	NA	100 / 0 / 0
	May 2017	2.50 x 10 9	NA	100 / 9 / 4	100 / 0 / 0	NA	NA	100 / 0 / 0
	Jun 2017	5.64 x 10 8	NA	100 / 0 / 0	100 / 0 / 0	NA	NA	100 / 4 / 0
	Jul 2017	5.85 x 10 9	NA	100 / 1 / 0	100 / 0 / 0	NA	NA	100 / 1 / 0
	Aug 2017	1.19 x 10 9	9.27 x 10 9	100 / 0 / 0	100 / 0 / 0	100 / 16 / 9	100 / 0 / 0	100 / 0 / 0
	Sep 2017	3.09 x 10 9	1.41 x 10 9	100 / 0 / 0	100 / 0 / 0	100 / 12 / 11	100 / 0 / 0	100 / 0 / 0
	Oct 2017	1.13 x 10 9	3.00 x 10 8	100 / 9 / 7	100 / 0 / 0	100 / 31 / 23	100 / 0 / 0	100 / 1 / 0
	Nov 2017	6.27 x 10 9	4.27 x 10 9	100 / 2 / 2	100 / 0 / 0	100 / 4 / 4	100 / 0 / 0	100 / 0 / 0
	Dec 2017	5.18 x 10 8	2.82 x 10 9	100 / 5 / 0	100 / 2 / 0	100 / 0 / 0	100 / 1 / 0	100 / 4 / 0
	Jan 2018	7.91 x 10 9	4.45 x 10 8	100 / 23 / 4	100 / 8 / 0	100 / 9 / 1	100 / 11 / 1	100 / 5 / 0
	Feb 2018	8.73 x 10 9	4.09 x 10 8	100 / 39 / 18	100 / 3 / 0	100 / 86 / 34	100 / 3 / 0	100 / 4 / 0
	Mar 2018	1.49 x 10 9	4.73 x 10 8	100 / 65 / 30	100 / 10 / 0	100 / 86/ 34	100 / 1 / 0	100 / 0 / 0
	Apr 2018	6.36 x 10 8	6.36 x 10 8	100 / 18 / 10	100 / 3 / 1	100 / 82 / 47	100 / 3 / 0	100 / 1 / 0
		m † = 3.12 x 10 9	m † = 2.23 x 10 9 Σ ‡ = 1,300 / 189 / 90 Σ = 1,300 / 26 / 1 Σ = 900 / 326 / 163	Σ = 900 / 19 / 1	Σ = 1,300 / 20 / 0
		(± 3.00 x 10 9 )	(± 2.93 x 10 9 )
	§ no data; † mean; ‡ sum

Acknowledgements

The authors thank Ifremer and Dr. F. Le Roux (Genomic of Vibrio-Station Biologique de Roscoff) for giving us Vibrio spp. strains, Patrick Céron for making of the map and Dr. D'Rego for the proofreading of English. The Regional Shellfish Committee of Normandy/North Sea (CRC Normandie/Mer du Nord) and the laboratory LABÉO financially supported this study. Maud Charles received co-funding from the Normandy region and from the laboratory LABÉO Frank Duncombe. No conflict of interest declared.

The authors confirm that one part of the data supporting the findings of this study is available within the article and the other part is available on request from the corresponding author.