Abstract

A single strain of *Vibrio parahaemolyticus* and a single strain of *Vibrio cholerae* were isolated from shrimps sourced from seafood processing plants. The isolated strains were evaluated for their ability to adhere to stainless steel surfaces, under conditions encountered in seafood industries. In this way, the impact of different environmental factors such as temperature (8 °C or 37 °C) and culture media (tryptic soy broth (TSB) 2% NaCl, artificial sea water (ASW) or industrial brine) were studied. The viability of these strains was analyzed by three different methods: the enumeration of cultivable bacteria on agar media, and two PCR methods, i.e. a qPCR and a PMA-qPCR method in order to distinguish between viable and dead cells. The proportion of viable but non-cultivable (VBNC) cells was evaluated by comparing the results of propidium monoazide real-time PCR (PMA-qPCR) with the enumeration results. Cultivable bacteria were not detected in brine, regardless of the conditions tested. However, *V. cholerae* in the VBNC state was detected in brine at 8 °C until 48 h of incubation. *V. cholerae* exhibited higher viability at 8 °C compared to 37 °C. *V. cholerae* also exhibited higher viability compared to *V. parahaemolyticus*. In contrast, for *V. parahaemolyticus*, temperature and media (except industrial brine) had little influence on cell viability and adhesion behavior.

1. Introduction

Seafood can be contaminated by Gram-negative bacteria of the genus *Vibrio* present in coastal areas. In particular, *Vibrio parahaemolyticus* and *Vibrio cholerae* are two species potentially pathogenic for humans (Robert-Pillot, Copin, Himber, Gay, & Quilici, 2014). *V. cholerae* can be divided into two major groups: the cholera-causing strains of serogroups O1 and O139, and non-O1/non-O139 *V. cholerae*. The pathogenicity of the strains depends on virulence factors, the most important of which is the cholera toxin (CT) encoded by the *ctxA* and *ctxB* genes. The non-O1/non-O139 strains rarely possess these cholera toxin genes but may also cause human illness. *V. parahaemolyticus* has been implicated in more than 45% of all outbreaks of bacterial gastroenteritis worldwide, and this proportion is increasing with the emergence of pandemic strains spreading over large geographic areas (Nair *et al.*, 2007). Typical *V. parahaemolyticus*
illness results in mild to moderate acute gastroenteritis. As foodborne pathogens, *V. parahaemolyticus* and *V. cholerae* are responsible for vibriosis outbreaks and represent a risk to consumer health, especially in case of ingestion of raw, undercooked or mishandled seafood (Bonnin-Jusserand et al., 2017; Mizan, Jahid, & Ha, 2015). The risk associated with consumption of crustaceans, such as shrimps is well-documented for *V. parahaemolyticus* in several studies (Zarei, Borujeni, Jamnejad, & Khezrzadeh, 2012; Abdullah Sani, Ariyawansa, Babji, & Hashim, 2013; Xu et al., 2014) and for both *V. parahaemolyticus* and *V. cholerae* in others (Rosec, Causse, Cruz, Rauzier, & Carnat, 2012; Yano et al., 2014). *Vibrio* spp. can adopt specific survival strategies in harsh environments. They are found either free-living or associated with sediments, suspended particles and plankton (Thomas, Joseph, Raveendran, & Nair, 2006; Vezzulli et al., 2010; Vezzulli, Colwell, & Pruzzo, 2013). Their ability to adhere to the surface of the exoskeleton of animals composed of chitin, some zooplankton organisms, especially copepods (Zidour et al., 2017), and to plastic marine debris (Zettler, Mincer, & Amaral-Zettler, 2013) allows them to survive in seawater longer than in a free-living state. *Vibrio* spp. have the ability to form organized structures called biofilms on various biotic and abiotic surfaces (Watnick & Kolter, 1999). It has been shown that *Vibrio* spp. are able to form biofilms on aquaculture equipment such as larval rearing tanks (Bourne, Høj, Webster, Swan, & Hall, 2006) as well as on several other substrates, including glass, polystyrene, polyethylene and polyvinyl chloride (Snoussi et al., 2009). *Vibrio* spp. biofilms have also been detected on fishing industry equipment (Bagge-Ravn et al., 2003). Furthermore, biofilms represent a well-known hazard in the food industry (Shi & Zhu, 2009; Bridier et al., 2015), and many environmental factors influence biofilm formation. For example, recent studies on the role of additives currently used in seafood processing, like NaCl and glucose, on the biofilm formation of *V. parahaemolyticus* on shrimp surfaces have been carried out (Mizan et al., 2016). Within the biofilm, cells persist in different physiological stages, including in the viable but non cultivable (VBNC) state. The VBNC state is defined as a form of resistance of bacteria to adverse environmental conditions. The cells conserve their metabolic activity but cultivability is lost. Therefore, bacteria cannot be detected on agar Petri dishes (Oliver & Bockian, 1995). *V. parahaemolyticus* (Zhong et al., 2017; Su, Jane, & Wong, 2013; Mizunoe, Wai, Ishikawa, Takade, & Yoshida, 2000) and *V. cholerae* (Rowan, 2004) can enter
the VBNC state. Importantly, the ISO standard 21872-1 applied to detect potentially pathogenic *Vibrio* in seafood cannot detect bacteria in the VBNC state. This could lead to underestimation of the bacterial population.

This study focused on the biofilm-forming ability on stainless steel of two different potentially pathogenic for humans *Vibrio* species (*V. cholerae* and *V. parahaemolyticus*) isolated from shrimps. The processing of shrimps in the seafood industry follows different steps (GBPH, 2016). Considering the possibility of a recontamination risk after cooking, brine could play a role by transporting *Vibrio*. Interestingly, there is no further critical control point (CCP) making it possible to limit the microbial population level after this step. Moreover, brine is recycled. Therefore, pathogen or spoilage flora could be present for instance in the VBNC state if renewal is not sufficiently frequent. VBNC cells could hardly be detected by classical microbiological methods and thus represent a potential risk for consumers. In order to estimate the VBNC proportion of *V. parahaemolyticus* and *V. cholerae* within a biofilm closest to industrial conditions, in our study, the experiments required the comparison of three different techniques: qPCR, PMA-qPCR (propidium monoazide real-time PCR) (Zhong, Tian, Wang, Fang, & Liao, 2018; Zhu, Li, Jia, & Song, 2012; Wu, Liang, & Kan, 2015) and enumeration on agar plates. The influence of environmental parameters such as temperature and media, including industrial brine, has been assessed. The aim of this study consists of assessing the adhesion to stainless steel surfaces and detecting Viable But Non Cultivable cells of *Vibrio parahaemolyticus* and *Vibrio cholerae* isolated from shrimps in seafood processing environments.

## 2. Materials and methods

### 2.1. Bacterial strains and culture conditions

Two environmental strains, isolated by the National Reference Laboratory (NRL: *Vibrio* sp. in fishery products), ANSES Boulogne-sur-mer, from seafood industries, were used in this study. The first one, *Vibrio parahaemolyticus* (ANSES collection 14-B3PA-0046) was isolated from shrimps imported from Nigeria. The second one, *Vibrio cholerae* (ANSES collection 09-B3PA-
2350) was isolated from shrimps imported from Madagascar. The strains were maintained in marine broth with 10% glycerol and stored at -80 °C. Prior to the experiments, bacteria were initially streaked on nutrient agar supplemented with 2% NaCl and incubated overnight at 37 °C. The strains were then transferred into tryptic soy broth (TSB 2% NaCl) and incubated overnight at 37 °C.

Subsequently, cells were centrifuged at 5,000×g for 10 min and resuspended in TSB 2% NaCl or artificial sea water (ASW) or industrial brine (4 m³ brine bath composed of water with a salinity of 1.17 ± 0.02 (> 1.10), pH 4). This step was repeated twice. Bacterial cell concentrations were adjusted to 10⁸ CFU.mL⁻¹ by spectrophotometry (LibraS11, BioChrom Ltd., Cambridge, UK). In order to check the bacterial concentrations, suitable dilutions of suspensions were made and plated on salted nutrient agar using an automatic spiral plater (easySpiral®, Interscience, St-Nom-la-Bretèche, France). The plates were incubated at 37 °C for 24 h. Cell numbers were then quantified using an automatic colony counter (Scan500®, Interscience).

## 2.2. Preparation of 316 2B stainless steel coupons

AISI 316 2B (Fe/Cr18/Ni10/Mo3) stainless steel coupons (37×16×1 mm) were purchased from Goodfellow SARL (Lille, France). First of all, 316 2B stainless steel coupons were rubbed with paper, soaked in a 50% acetone/50% ethanol (v/v) solution. Coupons were then immersed in a 20% alkali detergent solution (v/v) (RBS50, cleaning agent; Chemical Products R. Borghraef, Brussels, Belgium) at 50 °C under agitation for 10 min. Coupons were rinsed in a 50 °C water bath under agitation for 20 min. Five successive rinses of 1 min each were carried out in ultra-pure water, while stirring at room temperature. Coupons were autoclaved for 15 min, at 121 °C.

## 2.3. Biofilm formation on stainless steel coupons

The bacterial suspension (11 mL) was transferred to a small sterile Petri dish (67 mm×15 mm) containing a sterile coupon. In order to create a humid atmosphere, the Petri dishes were then placed in bigger Petri dishes (140 mm×20 mm) containing paper towels soaked with 25 mL of
sterilized distilled water. Sterile medium was used as a negative control. The Petri dishes were inoculated at 8 °C or 37 °C for 3 h, 24 h and 48 h.

## 2.4. Quantification of viable, VBNC and dead cells attached to stainless steel coupons after 3 h, 24 h and 48 h of incubation at either 37 °C or 8 °C

After incubation, cell suspensions were removed and coupons were washed with 25 mL of sterile physiological water (0.9% NaCl) in order to remove non-adhered bacteria. Washed coupons were then transferred to sterile Petri dishes.

Two coupons were used per experiment. Each coupon was swabbed twice with the same swab. This swab was vortexed into a tube containing 2 mL of physiological water. A second swab, after undergoing the same process, was added to the same tube. This tube containing the two swabs was then vortexed for 20 s. The contents of the tube were divided into three fractions. Each fraction underwent one of the following three techniques:

- First fraction: the proportion of viable cultivable bacteria was obtained by plating serial dilutions on salted nutrient agar using an automatic spiral plater (easySpiral®, Interscience). The plates were incubated at 37 °C for 24 h. Cell numbers were then quantified using an automatic colony counter (Scan500®, Interscience).

- Second fraction: qPCR was used to amplify DNA of viable, VBNC and dead bacteria. Genomic DNA from cell suspensions (495 µL) was extracted using the DNeasy® Blood & Tissue Kit 250 (Qiagen, Courtabœuf, France), following the manufacturer's instructions. Then, *V. parahaemolyticus* DNA was quantified by qPCR following the protocol described by Robert-Pillot *et al.* (2014), with modifications. First at all, the LightCycler® 480 (Roche Diagnostics, France) was used for qPCR. The Qiagen Master Mix QuantiTect® Probe PCR Kit was used. This method was optimized for *V. cholerae*. Concentrations of reagents were adjusted to reach 4mM Mg²⁺ (Qiagen) and 6% glycerol using the Master Mix Light Cycler® 480 Probes Master
(Roche). The PCR cycling conditions were adjusted by including initial denaturation at 95 °C for 15 s, followed by annealing at 61 °C for 1 min, extension at 72 °C for 15 s.

- Third fraction: PMA-qPCR was used to amplify DNA of viable and VBNC bacteria. Propidium monoazide, PMA (Biotium Inc., Hayward, CA, USA) dissolved in ultra-pure water was added to 495 µL of cell suspension to reach a final concentration of 50 mM. Following an incubation period of 5 min in the dark, samples were light-exposed using a photo-activation system (PhAst Blue, GenIUL, Barcelona, Spain), with an intensity of 80 for 10 min. DNA from cell suspensions was extracted using the DNeasy® Blood & Tissue Kit 250 (Qiagen), following the manufacturer's instructions. Then, *V. parahaemolyticus* and *V. cholerae* DNA was quantified by qPCR following the protocol described above for the second fraction. The genome equivalents (GEs) of the *V. parahaemolyticus* and *V. cholerae* strains were calculated based on a *V. parahaemolyticus* genome of 5.0.10⁶ bp (Makino et al., 2003) and on a *V. cholerae* genome of 4.0.10⁶ bp (Heidelberg et al., 2000).

**2.5. Microscopy observation**

A coupon was used to image biofilm samples on stainless steel. The biofilm was stained for 15 min in the dark with 200 µL taken from a LIVE/DEAD® BacLight™ bacterial viability kit (Invitrogen, Carlsbad, CA, USA). Residual LIVE/DEAD® BacLight™ solution was removed. Bacteria attached to the coupon were then examined in a wet state under an epifluorescence microscope (Imager.Z1, Zeiss, Marly-le-Roi, France) connected to a CCD camera (Axiocam - MRm, Zeiss) with apotome (×40 magnification). A minimum of five observations were taken for each observed coupon.

**2.6. Statistical analysis**

Biofilm experiments were carried out in triplicate (three independent experiments). Data were analyzed by one-way analysis of variance (ANOVA) using R3.3.3 software (R Core Team, 2017). The ANOVA model was verified using a Shap-iro-Wilk normality test and Bartlett test of
homogeneity of variances. If the normality and homogeneity of variances of distribution were not verified, a Kruskal-Wallis test was carried out, followed by a post hoc test. Post hoc tests were performed with Bonferroni correction and Fisher's least significant difference (LSD) criterion, using the agricolae 1.2-8 package (De Mendibru, 2017). The results were expressed as means ± standard error. Statistical significance was considered at p < 0.05.

## 3. Results

### 3.1. *Vibrio parahaemolyticus* ANSES collection 14-B3PA-0046

#### 3.1.1. Total population (viable cultivable, VBNC and dead bacteria; qPCR)

In TSB 2% NaCl and ASW broths, there was no impact of the incubation time at either 37 °C (Fig. 1) or 8 °C (Fig. 1) on the total population.

#### 3.1.2. Viable cultivable and VBNC bacteria (PMA-qPCR)

In TSB 2% NaCl and ASW broths, the same proportion of bacteria was observed for 3 h (Fig. 1a, 1b), 24 h (Fig. 1c, 1d) and 48 h (Fig. 1e, 1f) at either 37 °C or 8 °C. No additional viable bacteria were detected in brine, regardless of the conditions (Fig. 1).

#### 3.1.3. Viable cultivable bacteria (enumeration on Petri dishes)

A significant decrease in bacteria numbers was observed in TSB 2% NaCl broth depending on biofilm formation kinetics (3 h, 24 h and 48 h), regardless of the temperature (p < 0.05) (Fig. 1). However, in ASW broth, there were more adherent bacteria at time point 24 h (Fig. 1c, 1d) compared to 3 h (Fig. 1a, 1b) and 48 h (Fig. 1e, 1f), regardless of the temperature (p < 0.05). No viable cultivable bacteria were detected in brine at either 37 °C (Fig. 1) or 8 °C (Fig. 1).
*Vibrio parahaemolyticus* (ANSES collection 14-B3PA-0046)

<figure><img src="image_67.png" /></figure>

Figure 1. *Vibrio parahaemolyticus* ANSES collection 14-B3PA-0046 at 37 °C (a,c,e) and 8 °C (b,d,f): enumeration of total bacteria: viable cultivable, VBNC, and dead bacteria (qPCR: black squares); viable cultivable and VBNC bacteria (PMA-qPCR: white squares); viable cultivable bacteria (on Petri dishes: grey squares) for 3 h, 24 h and 48 h of incubation on TSB (2% NaCl), ASW and brine media. Values are expressed by means, ± 95% confidence interval, of three independent experiments.

## 3.4. Vibrio cholerae ANSES collection 09-B3PA-2350

### 3.4.1. Total population (viable cultivable, VBNC and dead bacteria; qPCR)

In TSB 2% NaCl broth, the total population had decreased at 37 °C at time point 48 h (p < 0.05) (Fig. 2e). Otherwise, the total population was almost equivalent at 8 °C at 3 h (Fig. 2b), 24 h (Fig. 2d) and 48 h (Fig. 2f). In ASW broth, there were no differences regardless of the incubation time or the temperature. In brine, equivalent bacteria proportions were detected at 8 °C, regardless of the incubation time (Fig. 2b, 2d, 2f). In contrast, bacterial populations had decreased at time point 48 h compared to 3 h and 24 h at 37 °C (p < 0.05) (Fig. 2a, 2c, 2e).
### 3.4.2. Viable cultivable and VBNC bacteria (PMA-qPCR)

In TSB 2% NaCl and ASW broths, the proportion of viable bacteria was equivalent regardless of the incubation time and temperature (Fig. 2). In brine, bacteria in the VBNC state were detected at 8 °C for each incubation time, including 48 h (Fig. 2b, 2d, 2f). This means that the VBNC *V. cholerae* cells remained in brine at 8 °C even for a long period of time. However, at 37 °C, at time point 48 h, viable bacteria were no longer detected (Fig. 2e). Indeed, results showed no amplification curve of PMA-qPCR for an incubation time of 48 h at 37 °C in brine only. Thus, all *V. cholerae* cells were dead (Fig. 2e).

### 3.4.3. Viable cultivable bacteria (enumeration on Petri dishes)

In TSB 2% NaCl, the proportion of viable cultivable bacteria was not significant regardless of the incubation time and the temperature (Fig. 2). In ASW, cultivable *V. cholerae* were not detected at 37 °C regardless of the incubation time (Fig. 2a, 2c, 2e). Furthermore, cultivable *V. cholerae* were not detected at 8 °C at time point 3 h (Fig. 2b), but the proportion of bacteria was equivalent at 8 °C at 24 h (Fig. 2d) and 48 h (Fig. 2f) of incubation. No additional viable cultivable *V. cholerae* were detected in brine, regardless of the incubation time and temperature (Fig. 2).
Vibrio cholerae (ANSES collection 09-B3PA-2350)

<figure><img src="image_68.png" /><figcaption>Figure 2. Vibrio cholerae ANSES collection 09-B3PA-2350 at 37 °C (a,c,e) and 8 °C (b,d,f): enumeration of total bacteria (qPCR: black squares); viable and VBNC bacteria (PMA-qPCR: white squares); viable cultivable bacteria (on Petri dishes: grey squares) for 3 h, 24 h and 48 h of incubation on TSB (2% NaCl), ASW and brine media. Values are expressed by means, ± 95% confidence interval, of three independent experiments.</figcaption></figure>

### 3.5. Comparisons between *V. cholerae* and *V. parahaemolyticus* in brine for an incubation time of 48 h

In Brine, for an incubation time of 48 h, temperature had significant effect on the adhesion of total bacteria regardless *Vibrio* species. Indeed, more bacteria were detected at 8 °C compared to 37 °C (Fig. 3.1).

VBNC cells of *V. parahaemolyticus* were no longer detected either at 37 °C or 8 °C, while VBNC cells of *V. cholerae* were detected at 8 °C only. According to these results, the adhesion of VBNC cells could be strain-dependent. Moreover, these results demonstrated that temperature has a significant effect on the adhesion of VBNC *V. cholerae* cells in brine (Fig. 3.2).

Viable cultivable bacteria were not detected either at 37 °C or 8 °C on Petri dishes (Fig. 3.3).
<figure><img src="image_69.png" /><figcaption>Figure 3. *Vibrio cholerae* ANSES collection 09-B3PA-2350 (hatched squares) and *Vibrio parahaemolyticus* ANSES collection 14-B3PA-0046 (spotted squares) in brine for 48 h, at 37 °C and 8 °C; Enumeration of (1) total bacteria (qPCR); (2) Viable and VBNC bacteria (PMA-qPCR); (3) Viable cultivable bacteria (on Petri dishes). Values are expressed by means, ±95% confidence interval, of three independent experiments. The letters a and b represent differences between treatments with two temperatures, according to post hoc tests. The “*” represents differences between strains at the same temperature, according to post hoc tests.</figcaption></figure>

**3.6. Observations of adherent cells**

Micro-colonies were observed at 37 °C and 8 °C for *V. parahaemolyticus* at time points 3 h, 24 h and 48 h (Fig. 4). In brine, aggregates were observed from the early stages of adhesion (3 h), regardless of the temperature (Fig. 4g, 4.p).
Vibrio parahaemolyticus (ANSES collection 14-B3PA-0046)

<figure><img src="image_70.png" /><figcaption>Figure 4. Vibrio parahaemolyticus ANSES collection 14-B3PA-0046 at 37 °C (a,b,c,d,e,f,g,h,i) and 8 °C (j,k,l,m,n,o,p,q,r): relevant microscopic photographs of stainless steel coupons after LIVE/DEAD® BacLight™ staining for 3 h, 24 h and 48 h of incubation on TSB (2% NaCl), ASW and brine media. Experiments were repeated three times and a minimum of five images were taken for each observed stainless steel coupon.</figcaption></figure>
These observations clearly showed a mix of live and dead bacteria during the establishment of these aggregates. Regarding *V. cholerae* (Fig. 5), dense cell clusters were observed from 3 h at 37 °C in TSB 2% NaCl (Fig. 5a). These clusters decreased at 48 h, suggesting dispersal of the biofilm (Fig. 5c). However, at 8 °C at time point 48 h (Fig. 5l), clusters especially composed of dead cells were still present. In ASW broth, minor changes in the structure of the biofilm were observed, regardless of the temperature and the incubation time (Fig. 5d, 5e, 5f, 5.m, 5.n, 5.o). In brine, small aggregates were observed regardless of the temperature and time (Fig. 5g, 5h, 5i, 5.p, 5.q, 5.r). No further viable *V. cholerae* were observed in brine at 37 °C at time point 48 h (Fig. 5i). Viable bacteria could be VBNC cells not detected by the molecular methods used or Petri dish enumeration. This could be explained by a detection threshold level of the techniques used that may be too low.
Vibrio cholerae (ANSES collection 09-B3PA-2350)

<figure><img src="image_71.png" /><figcaption>Figure 5. Vibrio cholerae ANSES collection 09-B3PA-2350 at 37 °C (a,b,c,d,e,f,g,h,i) and 8 °C (j,k,l,m,n,o,p,q,r): relevant microscopic photographs of stainless steel coupons after LIVE/DEAD® BacLightTM staining for 3 h, 24 h and 48 h of incubation on TSB (2% NaCl), ASW and brine media. Experiments were repeated three times and a minimum of five images were taken for each observed stainless steel coupon.</figcaption></figure>
4. Discussion

This study provides a deeper understanding of the viability of bacteria inside the biofilm. qPCR and PMA-qPCR methods were developed for both *Vibrio* species, in addition to cell enumeration on agar. Importantly, the combined findings of these three techniques and their comparison provided the proportions of viable cultivable, VBNC and dead bacteria. Additionally, microscopy using a LIVE/DEAD® staining kit coupled with Apotome technology enabled us to supplement the results obtained by the methods mentioned above. In this way, we were able to observe bacterial adhesion on stainless steel coupons. These observations were corroborated by results obtained by enumeration on agar plates and molecular biology (qPCR and PMA-qPCR methods).

Many factors influence biofilm formation: the bacteria themselves, that is to say their own genetics and physiology, the type of attachment surface, and the environment (pH, temperature, nutrient availability) (Whitehead & Verran, 2015). Among these factors, temperature seems to play a key role in *Vibrio* biofilm formation. In this study, small differences were observed regarding *V. parahaemolyticus* and *V. cholerae* biofilms at either 37 °C or 8 °C, regardless of the incubation time (3 h, 24 h, 48 h). Indeed, the only significant influence of temperature determined was in brine but no influence was detected in TSB or ASW broths. Nonetheless, Ahmed et al. (2018) showed that the biofilm formation of *V. parahaemolyticus* and *V. cholerae* was significantly higher at 37 °C and 25 °C than at 4 °C. In the study carried out by Beshiru and Igbinosa (2018), *V. cholerae* showed better abilities to form biofilm at 21 °C and 37 °C, while the highest biofilm production of *V. parahaemolyticus* was observed at 30 °C in TSB under dynamic culture. Similarly, Song et al. (2017) observed better biofilm production of *V. parahaemolyticus* in TSB 3% NaCl at 25 °C compared to 15 °C and 37 °C. Han, Mizan, Jahid, and Ha (2016) showed that *V. parahaemolyticus* biofilm formation was better at 30 °C than 4 °C on stainless steel coupons immersed in fresh water. Furthermore, Wong, Chung, and Yu (2002) showed that bacterial adhesion on stainless steel surfaces decreased when *V. parahaemolyticus* was incubated at 4 °C. All these results showed better biofilm formation for high temperatures
(between 21 °C and 37 °C) compared to 4 °C, contrary to our findings. Adhesion is also media-
dependent. In this study, we did not observe the same behavior on either TSB 2% NaCl or ASW
between *Vibrio* spp. *V. parahaemolyticus* exhibited the same behavior in TSB or ASW, regardless
of the incubation temperature. However, in ASW for 24 h and 48 h, better survival was observed
for *V. cholerae* at 8 °C compared to 37 °C due to the presence of viable cultivable bacteria. The
amount of viable cultivable bacteria at 37 °C in ASW was not higher than in TSB. Wong *et al.*
(2002) showed that *V. parahaemolyticus* exhibited better adhesion on stainless steel when
cultured in chemically defined MM9 medium than TSB. Regarding *V. cholerae*, this behavior
was dependent on the temperature of incubation. At 8 °C for 24 h and 48 h, there were no
differences in adhesion on either TSB 2% NaCl or ASW. The fact that a nutrient-poor medium
promotes adhesion has already been shown especially for *L. monocytogenes* (Kadam *et al.*, 2013).
The composition of ASW medium is close to that of the marine environment, which is the natural
habitat of *Vibrio*. Therefore, better biofilm formation ability could be expected in this medium,
which was not the case in our study. Moreover, Beshiru and Igbinosa (2018) showed that
*V. cholerae* and *V. parahaemolyticus* biofilm formation was not medium-dependent (even when
the medium was rich or poor). Both tested strains also exhibited different behaviors in brine.
Indeed, in this study, only dead bacteria were detected regardless of the temperature and
incubation time for *V. parahaemolyticus*. However, VBNC bacteria of *V. cholerae* were detected
at 37 °C for 3 h and 24 h. VBNC cells were detected for each incubation time at 8 °C. These
results indicate a greater survival capacity of *V. cholerae* at 8 °C than at 37 °C in poor media.
The induction of the VBNC state is triggered by a low temperature, more often 4 °C, and under
starvation conditions (Zhong *et al.*, 2018; Su *et al.*, 2013; Wong, Wang, Chen, & Chiu, 2004).
This could explain the presence of VBNC cells of *V. cholerae* in brine at 8 °C.

As for temperature and media, bacterial adhesion depends on the food contact surface. Stainless
steel coupons were used in this study to mimic the conditions encountered in seafood industries.
Also, Han *et al.* (2016) studied *V. parahaemolyticus* biofilm formation on stainless steel, crab
and shrimp. In addition to surface hydrophoby, cell hydrophoby must also be taken into account:
*Vibrio* are strongly hydrophilic (Beshiru & Igbinosa, 2018). However, it is difficult to compare
studies of *Vibrio* biofilms with one another because, most of the time, biofilm formation was studied via the microtiter plate assay with crystal violet staining. This assay remains a useful screening tool, but does not reflect real biofilm formation conditions; in particular, the surface type is not considered. Moreover, *Vibrio* biofilm formation is strain-dependent (Song *et al.*, 2017; Odeyemi & Ahmad, 2017; Mizan *et al.*, 2016; Wong *et al.*, 2002). To supplement our results, different *V. parahaemolyticus* and *V. cholerae* strains of different origins (isolated from different seafood products or different countries), including pathogenic strains (carrying pathogenicity factors such as *ctxA/ctxB* genes for *V. cholerae* and *tdh* or *trh* genes for *V. parahaemolyticus*) should be assessed. Clearly, biofilm formation may be different depending on the strain's origin. The formation of mixed biofilm, for example *Vibrio* in association with *Listeria monocytogenes*, could also be evaluated. In addition to developing survival strategies (VBNC, biofilm), strains are constantly adapting to their environment. More and more studies point to the extent of antibiotic resistance for *Vibrio* (Yano *et al.*, 2014). This characteristic should therefore be taken into account in the biofilm survival state. Elexson *et al.* (2014) tested several detergents against biofilms of antibioticresistant *V. parahaemolyticus* isolated from seafood and found hygiene issues and cross-contamination hazards in the seafood industries.

**5. Conclusion**

For the first time, the cell viability was taken into account in order to better understand the survival strategies of potentially pathogenic human *Vibrio*. The aim of this study was to get as close as possible to the conditions encountered in seafood industries, even if this test remained in vitro for the standardization and exploitation purposes of the results. This scoping study highlighted the behavior of both *Vibrio* strains under laboratory conditions for different environmental parameters. Despite the fact that there are few data on the risk for consumer health of *V. cholerae* from shrimps, the strain tested in this study showed a greater survival capacity than the *V. parahaemolyticus* strain. Indeed, VBNC *V. cholerae* cells were detected at 8 °C in brine for each incubation time, including 48 h. Therefore, it could be useful to monitor *V. cholerae* contamination in shrimps. Seafood processing companies need to be aware of and
take into account the *Vibrio* risk in their hazard analysis critical control point (HACCP) approach. The possible presence of VBNC cells cannot be ruled out. The use of molecular biology methods like PMA-qPCR is recommended to ensure the safety of seafood products.

## Acknowledgements

Julia Mougin would like to thank the “Hauts-de-France” regional council for its financial support of her PhD studies. This work was funded by the French government and the region Hauts-de-France in the framework of the CPER 2014-2020 MARCO project. This work was supported by a grant from FranceAgriMer (2014-0813) as part of the SURVIB project. The authors also thank the CITPPM (Confédération des industries de traitement des produits des pêches maritimes et de l'aquaculture) as a contributor to the SURVIB project. The authors are also grateful to Thomas Brauge for his scientific assistance, to Cindy Ducrocq and Marie-Ange Huchin for technical assistance and to Alexandre Dehaut for advices in statistical analyses.

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# Annexe 2

## Characterization of the marine aquaculture microbiome: a seasonal survey in a seabass farm

Roxane Roquigny<fnref n="1" />, Julia Mougin<fnref n="1" />, Cédric Le Bris<fnref n="1" />, Maryse Bonnin-Jusserand<fnref n="1" />, Périne Doyen<fnref n="1" /> and Thierry Grard<fnref n="1" />

**Keywords:** seabass farm microbiomes, biofilms, tank water, 16S rRNA gene sequencing, Vibrionaceae.

## Abstract

Vibriosis is a recurrent disease in marine aquaculture environments. Monitoring the dynamics of bacterial populations in several compartments of the aquaculture infrastructure is of paramount importance to understand the occurrence of the disease. In the present study, the fish farm, located in the north of France, is a flow-through system where the tanks are supplied by two water inlets. Water from breeding tanks, and inlets and the biofilms in the tanks were sampled weekly in a seabass farm for 7 months. DNA was extracted from 477 samples to sequence the variable V4-V5 region of the 16S rRNA gene and analyze the sequences using the QIIME2 platform. Proteobacteria (54.52%) and Bacteroidetes (26.85%) composed more than 80% of the phylum diversity. Alpha- and beta-diversity indicators showed that bacterial communities were more diverse in samples from biofilms and tanks than in samples from water inlets. Some families were specific to one compartment, e.g. ammonium-oxidizing bacteria in biofilms, whereas Vibrionaceae family isolates were mostly found in tank water samples. Taxa were determined up to the species level for the Vibrionaceae. Besides, all six determined genera of Vibrionaceae were

<footnote n="1">¹ Univ. Littoral Côte d'Opale, UMR 1158 BioEcoAgro, Institut Charles Viollette, USC ANSES, INRAe, Univ. Lille, Univ. Artois, Univ. Picardie Jules Verne, Univ. Liège, Yncréa, F-62200 Boulogne-sur-Mer, France</footnote>
identified, with mostly *Vibrio* and *Photobacterium* isolates detected in almost all samples.
Vibrionaceae persisted over time in the biofilms. Our results suggest that biofilms may be a
potential reservoir in case of dysbiosis. This study helps better understand microbial interactions,
especially with Vibrionaceae in aquaculture environments.

# 1. Introduction

Over the past decade, aquaculture mass production has increased worldwide (Ahmed et al., 2019).
European seabass (*Dicentrarchus labrax*, L.) is one of the most farmed aquaculture species
produced with a production about 191,000 t in 2016 (FAO, 2005-2020). Aquaculture is a fast-
growing sector whose growth is limited by several challenges. There are geographic factors, such
as the availability of water and land, as well as environmental factors such as the pollution of
coastal zones (Ahmed et al., 2015). Disease outbreaks remain one of the main challenges,
responsible for severe fish losses, leading to the loss of billions of dollars every year (Lafferty et
al., 2015).

Belonging to Gammaproteobacteria, the Vibrionaceae family is composed of six well-identified
genera and more than 140 species (Thompson et al., 2004). It is one of the most predominant
bacterial groups in marine environments with a worldwide distribution (Ina-Salwany et al., 2019,
Thompson et al., 2004). Several Vibrionaceae species have been identified as pathogens of farmed
aquatic animals. Specifically, the *Vibrio*, *Photobacterium* and *Aliivibrio* genera are responsible
for vibriosis (Ina-Salwany et al., 2019). Lethargy, skin lesions and hemorrhagic septicemia
characterize affected fish (Ina-Salwany et al., 2019). On the one hand, global warming stimulates
the occurrence of the repeated vibriosis outbreaks in farming systems (Travers et al., 2009,
Vezzulli et al., 2015). On the other hand, biofilm formation enhances the virulence and the
persistence of Vibrionaceae and some other species, partly due to horizontal gene transfer of
potential virulence genes (Abe et al., 2020, Faruque et al., 2006, Karunasagar et al., 1996).
Michaud et al. (2009) found that most of the detected potential pathogens (e.g. *Vibrio* spp.,
*Erwinia* spp. and *Coxiella* spp.) are associated with the biofilter biofilms in a recirculating
aquaculture system (RAS) for rearing seabass. Multispecies biofilms are therefore considered as
a reservoir for potential pathogenic and antibiotic-resistant bacteria (King et al., 2004). Moreover, Kotob et al. (2016) indicated that the relationships between microorganisms determine whether opportunistic pathogens proliferate and become virulent. Next-generation sequencing (NGS) technologies are one way to consider the importance of all species for a better understanding of the development of disease outbreaks and the fragile equilibrium within microbiomes.

Despite the development of aquaculture, most studies still mainly focus on microbiota of the reared species' organs (Larsen et al., 2014). One study investigating the microbiome of the shrimp gut and tank water samples from two shrimp farms showed that microbial diversity was lower in the shrimp gut than in the tank water (Md Zoqratt et al., 2018). Another recent study investigated the skin and gill microbiomes of seabass and seabream from a fish farm (Rosado et al., 2019) and attested that the diversity of microbiomes was tissue-specific within and between species. Nevertheless, both studies detected potential pathogens such as Vibrio spp., which can lead to disease outbreaks in case of a shift in bacterial abundance (e.g. dysbiosis). Studies dealing with the microbial composition from biofilms and water in the tanks mainly involve RAS (Michaud et al., 2009, Wang et al., 2018, Wietz et al., 2009), but other studies have investigated temporal variations of bacterioplankton and the seasonal dynamics of bacterial and microeukaryotic plankton communities, e.g. in a semi-intensive European seabass aquaculture system (Duarte et al., 2019, Martins et al., 2018). The seasonal studies showed that changes in environmental variables influence the overall microbial communities.

In this context, we conducted a seasonal survey from April to October 2018 in a seabass farm in the North of France. Every year, this farm undergoes economic losses due to vibriosis outbreaks during summer. The aim of this study was to characterize the marine fish farm microbiomes and to determine the dynamics of bacterial populations in the different compartments of the aquaculture infrastructure. The objective was particularly to understand if Vibrio species get into the tanks during summer through the water inlets or if they persist throughout the year as biofilms. Studying the microbial composition can help understand the recurrence of vibriosis during summer. We therefore sampled biofilms and the water from the tanks and the inlets. The
V4-V5 variable region of the 16S rRNA gene was amplified and then sequenced using an Illumina MiSeq platform. To our knowledge, this is the first study to carry out a weekly seasonal survey for 7 months on a fish farm with a comparison of microbial composition between water inlets, tank water and biofilms in the tanks.

# 2. Materials and methods

## 2.1. Sampling campaign

The experiments were conducted from April to October 2018 for 31 weeks, in Aquanord Ichtus sea farm (Gravelines, France). The fish farm installation and process have been described in the year 2018 IFREMER annual report (Antajan et al., 2019). Briefly, the fish farm is a flow-through system where the tanks are supplied by two water inlets. The “cold-water” inlet supplies tanks with water from the surrounding local seawater. The “hot-water” inlet supplies tanks with seawater coming from the hot-water discharge canal of a nuclear power system. Several parameters, such as temperature, salinity, conductivity, pH and dissolved oxygen, were measured in the upper surface of the water of each compartment (Table S1, available online). During the sampling campaign, no acute vibriosis outbreak occurred and fish losses remained low and stable. Fishes had been vaccinated with an autovaccine, as per annual routine, to fight against vibriosis. Fish density fluctuated from 20,000 to 30,000 individuals per breeding tank. The water capacity of each tank was 75 m³, with a water-column of 1.3 m. The tanks had a mean fish load of 48.13 kg of fish.m⁻³. Every week, 1 L of water was collected 20 cm below the water surface, in a sterilized bottle, in triplicate from the three tanks and the two water inlets. The 15 water bottles were kept on ice during the transport to the laboratory and filtered within 2 h. Nitrocellulose filters of 0.45 µm pore size (Sartorius, Goettingen, Germany) were used and then frozen at -80 °C in Luria-Bertoni (LB) broth medium supplemented with NaCl (LBS)/20% glycerol. The filtration stage has been optimized for maximum recovery of the bacterial community using a larger volume of water and larger pore size. The pore size was determined based on the high turbidity of the tank water due to suspended organic material. Moreover, to mimic the biofilm formation occurring on the walls of aquaculture tanks, we used the same concrete material to manufacture
the pieces of concrete that were immersed (20 cm below the surface of the water), collected and brought back to the laboratory. Three pieces of concrete were not immersed and were considered as blanks (hereafter named B0), whereas samples immersed 2 h in the tanks were named 0B. Every two weeks, pieces of concrete were collected in triplicate from the three same tanks as the water samples. The nine pieces of concrete, still immersed in the water, were maintained on ice during the transport and until processing at the laboratory. Each piece of concrete was swabbed on a defined surface (of 2.34 cm²) with two cotton swabs as previously done by Mougin *et al.* (2019) and swabs were stored at -80 °C in an LBS/20% glycerol medium.

## 2.2. DNA extraction

Filter and swab samples were thawed and vortexed for 3 min to retrieve bacteria. Filters and swabs were discarded and the samples were centrifuged at 8000 × g for 10 min. Supernatants were removed and 25 mL or 3 mL of physiological water were added for samples from water or biofilm, respectively. After a brief vortex, the samples were centrifuged again (8000×g for 10 min) and supernatants were removed. The pellets were resuspended in 2 mL of physiological water, and briefly vortexed. Samples were then centrifuged (8000 × g for 10 min) for the third time and the supernatants were discarded.

The DNA from each pellet was extracted using the DNeasy PowerBiofilm Kit (Qiagen, Hilden, Germany) following the manufacturer's protocol. The DNA purity was assessed on a Denovix DS-11 spectrophotometer (Clinisciences, Nanterre, France) and DNA concentration was measured using the QubitTM dsDNA HS Assay Kit on a Qubit 3.0 fluorometer (Invitrogen, Carlsbad, CA, USA). The quantity of extracted DNA was normalized to 5 ng.µL⁻¹ in Tris HCL (10 mM, pH 8.5) for the subsequent PCR assays and stored at -20 °C. The 477 aliquots and the three negative control samples (composed of nuclease-free water) were used for metagenomic 16S rRNA gene sequencing. Hereafter samples were named tank water (W), cold-water inlet (CW), hot-water inlet (HW) and biofilm (B) samples depending on their compartment of origin. In the results, the sampling week is indicated before the compartment letter, e.g. sample named 8B came from swabbed biofilms at week 8.
## 2.3. PCR amplification, library preparation, 16S rRNA gene amplicon sequencing

A two-step PCR was performed according to 16S metagenomic sequencing library preparation protocol from Illumina. The first PCR step was carried out in duplicate. The amplicon primers 515F-Y/926R targeted the V4-V5 variable region of the 16S rRNA gene and were purchased from TIB MOLBIOL (Syntheselabor GmbH, Germany). The primers coupled with Illumina Reads adapters,

515F-Y 5'-TCGTCGGCAGCGTCAGATGTGTATAAGAGACAGGTGYCAGCMGCCGCGGTAA-3' and

926R 5'-GTCTCGTGGGCTCGGAGATGTGTATAAGAGACAGCCGYCAATTYMTTTRAGTTT-3' resulted in an amplicon of around 480 bp (Parada et al., 2016, Quince et al., 2011). PCR reactions were carried out in a final volume of 25 µL using 0.5 µL of each amplicon primer (10 µM), 2.5 µL of DNA (5 ng.µL⁻¹) and 12.5 µL of 2X PlatinumTM Green Hot Start Master Mix (Life Technologies SAS, Thermofisher Scientific, Villepinte, France). The PCR program was as follows: 94 °C for 2 min followed by 30 cycles of a 94 °C denaturation step for 45 s, a 50 °C annealing step for 60 s and a 72 °C elongation step for 90 s (Raut et al., 2018).

Dual indices were attached during the second step PCR, using the Illumina Nextera XT Index Kits v2 (Illumina, Paris, France). PCR reaction was carried out in a final volume of 50 µL using 5 µL of previously purified amplicon, 5 µL of each Nextera XT Index and 25 µL of 2X PlatinumTM Green Hot Start Master Mix. PCR program started with 94 °C for 2 min followed by 8 cycles with a denaturation step of 94 °C for 45 s, an annealing step of 59 °C for 60 s and an elongation step of 72 °C for 90 s. After each PCR step, PCR products were purified using Agencourt AMPure XP beads (Beckman Coulter, Life Sciences, Villepinte, France) and were verified using electrophoresis on a 1% agarose gel. The expected amplicon size was around 500 bp long. Final product purity was assessed with a Denovix Spectrophotometer and final product concentration was measured using the QubitTM dsDNA HS Assay Kit on a Qubit 3.0 fluorometer. The quantity of each sample was normalized to 4 nM in TrisHCL (10 mM, pH 8.5) and then 5 µL of diluted DNA from each library were mixed.
Following the Illumina protocol, pooled library was denatured, diluted to a final concentration of 4 pM, combined with 10% PhiX (Illumina) and loaded using MiSeq reagent v3 kits (600 cycles, 2 × 300 bp) as recommended by the manufacturer. Finally, five sequencing runs of 96 samples each were sequenced as paired-end reads using an Illumina MiSeq platform (Illumina).

## 2.4. Sequence analyses

Sequences were generated in demultiplexed fastq files and analyzed using the quantitative insights into microbial ecology 2 (QIIME2; release 2019.10) open-source platform (Bolyen et al., 2019, Caporaso et al., 2010). In each run, a total of 47,746,767 sequences were processed using DADA2 pipeline (Callahan et al., 2016, Martin, 2011). Briefly, paired-end sequences were trimmed to 240 bp, filtered, denoised, merged and chimeras were discarded, resulting in 20,134,001 dereplicated sequences (42.2%), (Table S2, available online). The DADA2 pipeline gave five amplicon sequence variant (ASV) tables, which listed the number of times each exact ASV was observed in each sample (Callahan et al., 2017). The five tables were merged and technical triplicates grouped using the sum function. The combined table was filtered to exclude singletons and technical negative control samples. Taxonomy was assigned using a naive Bayes classifier trained on the 99_otus SILVA 132-release database (Altschul et al., 1990, Bokulich et al., 2018, Edgar, 2010, Pedregosa et al., 2011, Quast et al., 2012, Wang et al., 2007). The table with taxonomic assignments was filtered a second time to exclude sequences assigned with mitochondria and chloroplast identification. Thus, analyses were subsequently performed on 159 samples, with 17,243,983 sequences clustered into 147,640 ASVs. Then, we chose a rarefaction depth of 29,725 sequences (27.41% of total retained sequences), which was the lowest read number. All samples were included in the beta-diversity analysis, without need to favor the rarest sequences. The values of the alpha-diversity indicators are detailed in Table S3 (available online). Chao1 and Shannon indices were selected to evaluate richness and evenness across sampling weeks and compartments (Chao, 1984, Shannon, 1997). Two phylogenetic measures of beta-diversity were also selected and visualized in EMPeror (Vázquez-Baeza et al., 2013). The unweighted UniFrac distance was used as a qualitative measure based on the presence/absence of different considered taxa. The weighted UniFrac distance was used as a quantitative measure
based on the relative abundance of taxa (Lozupone and Knight, 2005, Lozupone et al., 2011). The phylogeny pipeline was previously applied to the data using MAFFT (Katoh and Standley, 2013), MASK (Lane, 1991) and FastTree (Price et al., 2010) algorithms. Alpha-diversity statistical significances were tested using a Kruskal-Wallis test for pairwise comparisons (Kruskal and Wallis, 1952). Statistical significance of beta-diversity distances was analyzed using a PERMANOVA test (999 permutations).

**2.5. Data availability**

All sequences generated in this study are available in the NCBI sequence read archive under the Bioproject accession number PRJNA613386.

**3. Results**

**3.1. Sequencing and classification**

During the weekly sampling campaign from April to October 2018, total DNA from 477 samples from water or biofilm samples were extracted. Illumina MiSeq sequenced amplicons of the V4-V5 variable region of the 16S rRNA gene using the paired-end method and high quality sequences were obtained and processed. All technical triplicates were combined, leading to 159 samples subsequently analyzed and split into two datasets (Table S4, available online). The water dataset was composed of 110 water samples from hot- and cold-water inlets and from the three breeding tanks for 22 weeks. The second dataset was composed of 48 biofilm samples from the three breeding tanks for 16 weeks and the non-immersed blank (B0). The mean number of sequences per sample obtained in biofilm samples was 140,286 and 94,271 in water samples. The number of sequences ranged from 29,725 for the hot-water inlet sample at week 4 (4HW), to 315,298 for the biofilm sample from the second tank at week 24 (24B2). More than 17 million high quality sequences were thus processed with a mean of 108,453 sequences per sample. Each sequence was labeled, generating 147,640 ASVs. After a rarefaction process, taxonomy assignment was applied using the SILVA 132 database and the bacterial compositions were studied at different taxonomic levels.
## 3.2. Bacterial composition

### 3.2.1. Taxonomic identification

At the phylum level (Fig. 1), results attributed 94.38% of microbial diversity, all sampling dates and compartments (biofilm or water samples) combined, to six major phyla: Proteobacteria, Bacteroidetes, Verrucomicrobia, Planctomycetes, Chloroflexi and Thaumarchaeota. Proteobacteria and Bacteroidetes represented more than 80% of the diversity with 54.52% and 26.85%, respectively. Moreover, Gamma-, Alpha- and Deltaproteobacteria were the three main classes composing the Proteobacteria phylum with 55.88%, 37.20% and 6.82%, respectively. Secondly, because the phylum taxonomic level did not distinguish among samples, we preferred the family taxonomic level. Thus, the taxonomy of water samples was assigned based on 1,291 ASVs at the family level, whereas biofilm samples were taxonomically identified from 1,009 ASVs (Table S4, available online).

<figure><img src="image_72.png" /><figcaption>Figure 1: The six most abundant phyla (from all samples, over all sampling times), representing 94.38% of all sequences. The number above each bar indicates the percentage of each phylum. The percentage of each class of Proteobacteria is shown in the blue donut chart.</figcaption></figure>
### 3.2.2. Composition across both datasets across time

#### 3.2.2.1. Water dataset

The samples from the water dataset were separated into three compartments: the cold-water inlet (Fig. 2A), the hot-water inlet (Fig. 2B) and tank water samples (Fig. 3). The samples of the three tanks at the same sampling date for tank water were combined. No significant differences were observed between tanks at each week according to the pairwise comparisons of the diversity indices with the Kruskal-Wallis test. From April to October, bacterial compositions were obtained from 22 sampling weeks. The major phyla represented in water dataset were Proteobacteria, Bacteroidetes and Verrucomicrobia. Among them, Vibrionaceae, Rhodobacteraceae, Flavobacteriaceae and Rubritaleaceae were the most frequently observed families. The relative abundance of the bacterial families seemed more similar between cold- and hot-water inlets samples than with the tank water samples, particularly for Vibrionaceae. In fact, the relative abundance of Vibrionaceae barely reached 5% in either water inlet sample (Fig. 2A, 2B), whereas it reached 50% in tank water samples, with the highest abundances for 18W, 24W and 28W (Fig. 3). Inversely, the relative abundance of Rhodobacteraceae and Flavobacteriaceae accounted for around 20% of the bacterial communities for each water inlet across time, and their abundance fluctuated more drastically in tank water samples from around 20% at week 0 to barely 10% at week 30. To a lesser extent, the SAR, Halieaceae, Desulfobulbaceae, Pirellulaceae and Cyanobiaceae families were specifically detected in samples from hot- and cold-water inlets and Colwelliaceae were mostly observed in tank water samples.
<figure><img src="image_73.png" /><figcaption>Figure 3: Bar plots of relative abundance (%) of bacterial families composing the tank water samples throughout the 30 sampling weeks. Bacterial families are grouped by phylum with the second color scale on the top. Families unassigned or that did not reach a minimum of 3% in at least one sample were grouped in the “Others” category.</figcaption></figure>

<caption>Figure 2: Bar plots of relative abundance (%) of bacterial families throughout the 30 sampling weeks composing A) the cold-water inlet samples, B) the hot-water inlet samples. Bacterial families are grouped by phylum with the second color scale on the top. Families unassigned or that did not reach a minimum of 3% in at least one sample were grouped in the “Others” category.</caption>
### 3.2.2.2. Biofilm dataset

As in the tank water samples, the biofilm samples from the three tanks at the same sampling date were combined. No significant differences were observed between tanks at each week according to the pairwise comparisons of the diversity indices using the Kruskal-Wallis test. The biofilm dataset was composed of the blank samples (three non-immersed pieces of concrete, named B0) and the samples from the tanks during 16 sampling weeks (Fig. 4). The blank B0 was mostly represented by specific phyla and families that were not identified in the other samples. The Actinobacteria, Firmicutes and Cyanobacteria phyla and the Burkholderiaceae family were only detected in the B0 samples, and no Planctomycetes were identified. At week 0, after 2 h immersion, the 0B biofilm sample already harbored more Flavobacteriaceae (20.38%), Vibrionaceae (14.84%), Rhodobacteraceae (7.02%), Colwelliaceae (2.74%) and Saprospiraceae (4.30%) than the B0 sample. These relative abundances observed were quite similar to the proportions observed in 0W, i.e. in the tank water samples from the same sampling week. Proteobacteria, Bacteroidetes and Verrucomicrobia were represented in the biofilm dataset, as in the water dataset. Results also brought to light the presence of Planctomycetes, Chloroflexi and Thaumarchaeota. Among these phyla, Flavobacteriaceae, Rhodobacteraceae, Saprospiraceae, Rubritelaceae, Pirellulaceae, Vibrionaceae, Halieaceae and Rubinisphaeraceae were the most common families observed across time in biofilm samples. Three other families, Nitrosomonadaceae (from less than 0.01% in 2B to 1.71% in 30B), Nitrosopumilaceae (from less than 0.01% in 2B to 8.14% in 30B) and Nitrospiraceae (from less than 0.01% in 2B to 1.20% in 30B) were increasingly present over time at the expense of the Flavobacteriaceae (from 37.89% in 2B to 23.23% in 30B). All together, the phyla, Planctomycetes, Chloroflexi and Thaumarchaeota appeared to be specific to biofilm samples and their relative abundance increased over time.
<figure><img src="image_74.png" /><figcaption>Figure 4: Bar plots of relative abundance (%) of bacterial families across biofilm samples across sampling weeks. Bacterial families are grouped by phylum and by family (color scale on the right). Unassigned families or those that did not reach a minimum of 1% in at least one sample were grouped in the “Others” category.</figcaption></figure>

## 3.3. Analyses of alpha-and beta-diversity

### 3.3.1. Comparison of alpha-diversity indices

Significant differences were always observed when comparing mean values of alpha-diversity indices between datasets. The biofilm dataset had significantly the greatest diversity than the water dataset both with the richness indicator Chao1 (H=13.87, p-value < 0.001) and the richness/evenness indicator Shannon (H=70.32, p-value < 0.001).

More specifically, there were no differences in richness between the bacterial communities of tank water and biofilm samples, whereas the latter were significantly richer than samples from either water inlet (p-value < 0.001, Fig. 5A). The Chao1 values revealed that tank water samples harbored as many bacterial communities as biofilm samples, but both compartments had more bacterial communities than either water inlet. Shannon's index values were significantly different between biofilm, water inlet and tank water samples (p-value < 0.001, Fig. 5B), this latter compartment being also significantly different from cold-water inlet samples (p-value < 0.05,
Fig. 5B). Hot-water inlet samples did not show differences with tank water samples or cold-water inlet samples. Shannon's index, based on richness and evenness, estimated that the microbiome was richer in biofilm samples than in other compartments, meaning that there were more bacterial communities and their presence was more evenly distributed. The bacterial communities were also richer and more evenly distributed in the tank water samples than in the cold-water inlet samples.

<figure><img src="image_75.png" /></figure>

Figure 5: Mean values and standard deviations of Chao1 (A) and Shannon (B, C, D) alpha-diversity indices plotted by compartment (A, B) or by sampling week for tank water samples (C) and for biofilm samples (D). All purple and gray box plots respectively represent tank water samples and biofilm samples from the three breeding tanks. Blue and red box plots correspond to cold- and hot-water inlet samples, respectively. Kruskal-Wallis tests for pairwise comparisons were carried out and different letters indicate significant differences.

Some significant differences were observed when comparing the mean values of Shannon's index between sampling weeks within tank water samples or biofilm samples. Concerning tank water samples (Fig. 5C, p-value < 0.05), at weeks 16, 19 and 26 (16W, 19W, 26W Fig. 3), Shannon's index values showed that the bacterial communities in tank water samples were more diverse and evenly distributed than at weeks 2 and 28 (2W, 28W, Fig. 3). Regarding the biofilm samples
(Fig. 5D, p-value < 0.05), except for week 2 (2B, Fig. 4), none of the bacterial communities were significantly different regarding their richness and evenness.

Alpha-diversity results indicated that the bacterial communities were more numerous and evenly distributed within the biofilm samples than in the tank water samples. Both communities from biofilm and tank water samples were more numerous and evenly distributed than communities from either water inlet.

### 3.3.2. Comparison of beta-diversity

We used a principal coordinate analysis (PCoA) to compare each microbiome sample with every other sample in each dataset (water and biofilm) and each compartment (hot- and cold-water inlets, tank water and biofilm). The qualitative comparison of microbiomes (Fig. 6A) based on presence or absence of considered taxa showed that two major axes explained almost 15% of the beta-diversity of all samples. Along these axes, samples from both water inlets could be grouped together, as well as biofilm samples on one end and tank water samples on the other end. The weighted UniFrac distance greatly increased the ability to discriminate samples by compartment (Fig. 6B). Moreover, the two main axes accounted for almost 50% of the variance. PERMANOVA analyses for both beta-diversity UniFrac distances tested if the hypothesis that one compartment was different from another was correct. Thus, compartments were statistically different between another by both the unweighted (R² = 0.14, p < 0.001) and the weighted UniFrac (R² = 0.46, p < 0.001) distances.

The comparison between both UniFrac distances on the same data showed that the relative abundance of taxa in our samples was more compartment-discriminating than the presence/absence of different considered taxa, whereas presence/absence better dissociated the samples from each other.
<figure><img src="image_76.png" /></figure>

Figure 6: Beta-diversity of the 159 samples. (A) Unweighted UniFrac and (B) weighted UniFrac distances were analyzed and plotted using principal coordinates analysis (PCoA). Each point represents a microbiome sample (composed of 29,725 sequences corresponding to the rarefaction depth). Rings and stars distinguish the water dataset from the biofilm dataset, respectively. Blue and red colors correspond to cold- and hot-water inlet samples, respectively, whereas the three shades of purple indicate the water or biofilm samples from the three breeding tanks. The brown star indicates the B0 sample.

## 3.4. Detection and identification of the Vibrionaceae family

The study of the distribution of the Vibrionaceae family was performed on sequences identified by genus within this taxonomic level. Almost 15% of all sequences were identified as Vibrionaceae, namely 2,575,816 sequences (Table S4, available online), 90% of which (2,343,502 sequences) came from the water dataset. Six different already identified genera and two other groups defined as uncultured and unassigned were determined within the Vibrionaceae family (Table 1). *Vibrio* spp. (1,796,843 sequences), *Photobacterium* spp. (404,774 sequences), *Aliivibrio* spp. (328,413 sequences) and *Enterovibrio* spp. (29,895 sequences) were the most identified ASVs.
<table><caption>Table 1: Number of sequences identified, using the SILVA 132 database, in the Vibrionaceae family and number of samples</caption><thead><tr><th colspan="7">Taxonomy of Vibrionaceae detected</th></tr><tr><th rowspan="2">Phylum</th><th rowspan="2">Class</th><th rowspan="2">Order</th><th rowspan="2">Family</th><th rowspan="2">Genus</th><th rowspan="2">Species</th><th colspan="3">All samples</th></tr><tr><th>Number of sequences</th><th>Number of samples observed</th><th>Number of total samples</th></tr></thead><tbody><tr><td rowspan="15">Proteobacteria</td><td rowspan="15">Gammaproteobacteria</td><td rowspan="15">Vibrionales</td><td rowspan="15">Vibrionaceae</td><td>Vibrio</td><td>-</td><td>1,796,843</td><td>159</td><td>159</td></tr><tr><td>-</td><td>1,750,394</td><td>159</td><td>-</td></tr><tr><td>Vibrio ponticus</td><td>24,369</td><td>68</td><td>-</td></tr><tr><td>Vibrio aestivus</td><td>16,467</td><td>20</td><td>-</td></tr><tr><td>uncultured bacterium</td><td>2,803</td><td>67</td><td>-</td></tr><tr><td>Vibrio sinaloensis</td><td>2,436</td><td>6</td><td>-</td></tr><tr><td>Vibrio anguillarum</td><td>336</td><td>28</td><td>-</td></tr><tr><td>uncultured Shewanella sp.</td><td>29</td><td>10</td><td>-</td></tr><tr><td>uncultured Thalassotalea sp.</td><td>9</td><td>1</td><td>-</td></tr><tr><td>Photobacterium</td><td>-</td><td>404,774</td><td>150</td><td>159</td></tr><tr><td>-</td><td>198,025</td><td>136</td><td>-</td></tr><tr><td>Photobacterium damselae subsp. damselae</td><td>203,839</td><td>98</td><td>-</td></tr><tr><td>Photobacterium swingsii</td><td>2,878</td><td>71</td><td>-</td></tr><tr><td>Photobacterium gaetbulicola Gung47</td><td>14</td><td>4</td><td>-</td></tr><tr><td>uncultured gamma proteobacterium</td><td>11</td><td>1</td><td>-</td></tr><tr><td>Photobacterium rosenbergii</td><td>5</td><td>1</td><td>-</td></tr><tr><td>Photobacterium damselae subsp. piscicida</td><td>2</td><td>1</td><td>-</td></tr><tr><td>Aliivibrio</td><td>-</td><td>328,413</td><td>130</td><td>159</td></tr><tr><td>-</td><td>273,651</td><td>123</td><td>-</td></tr><tr><td>Aliivibrio fischeri</td><td>54,762</td><td>108</td><td>-</td></tr><tr><td>Enterovibrio</td><td>-</td><td>29,895</td><td>77</td><td>159</td></tr><tr><td>-</td><td>29,812</td><td>74</td><td>-</td></tr><tr><td>Enterovibrio nigricans</td><td>42</td><td>5</td><td>-</td></tr><tr><td>uncultured bacterium</td><td>41</td><td>5</td><td>-</td></tr><tr><td>Salinivibrio</td><td>-</td><td>55</td><td>9</td><td>159</td></tr><tr><td>Grimontia</td><td>-</td><td>29</td><td>7</td><td>159</td></tr><tr><td>uncultured</td><td>-</td><td>57</td><td>6</td><td>159</td></tr><tr><td>-</td><td>-</td><td>15750</td><td>140</td><td>159</td></tr></tbody></table>
Moreover, Shannon's index was applied to Vibrionaceae within the tank water samples (Fig. 7). Significant differences were observed for 19W and 20W samples (both in August) compared with samples from 0W to 10W (except 6W) and from 26W to 30W. During these two weeks in August, the mean temperature was 20.5 °C (±0.19 °C) and pH ranged from 7.24 to 7.40 (Table S1, available online).

<figure><img src="image_77.png" /><figcaption>Figure 7: Mean values and standard deviations of Shannon alpha-diversity index of the Vibrionaceae family, plotted by sampling week for tank water samples. Kruskal-Wallis tests for pairwise comparisons were carried out and different letters indicate significant differences.</figcaption></figure>

**4. Discussion**

Here, we conducted a seasonal survey in the North of France, from April to October 2018 in a seabass farm that undergoes repeated summer outbreaks of vibriosis. Although no acute vibriosis outbreak occurred during this survey, the characterization of the microbiomes from the marine aquaculture environment was of paramount importance to evaluate and monitor the bacterial communities and their potential interactions over time. Specifically, the characterization of the Vibrionaceae population dynamics in the various aquaculture compartments shed light on which compartments (water inlets, water tank and biofilm) may be a source or a reservoir of
Vibrionaceae. Using Illumina MiSeq technology targeting the V4-V5 variable region of the 16S rRNA gene, we detected and identified the bacterial communities through a weekly sampling campaign. The presence of Vibrionaceae was ubiquitous and detected in all samples (Table 1). Despite a common microbiome shared between all samples, alpha-diversity indices were significantly different between sample compartments (Fig. 5A, 5B).

**4.1. Common microbiome composition**

A common microbiome was detected in samples from all compartments. This commonality is likely due to the source of the water used to supply the tanks in water and in which the pieces of concrete were immersed. Taking all the results together, the Proteobacteria (54.52%) and Bacteroidetes (26.85%) phyla formed the main components of the microbiomes from the water and biofilm compartments (Fig. 1). These phyla have already been detected in a seabass farm (Duarte et al., 2019) and were also predominant in the seabass skin microbiome (Chiarello et al., 2015, Pimentel et al., 2017, Rosado et al., 2019). Moreover, Gamma-, Alphaproteobacteria and Bacteroides appear to be the first classes to form a biofilm in a RAS (Wietz et al., 2009).

A core microbiome analysis (data not shown) revealed that Vibrionaceae (*Vibrio* genus, Table 1), Rhodobacteraceae (with the *Loklanella*, *Sulfitobacter*, *Planktotalea* genera), Rhizobiaceae, Flavobacteriaceae and Saprospiraceae were the families (belonging to the Proteobacteria and Bacteroidetes phyla) shared among all samples. These results were also consistent with those of Wang *et al.* (2018), who found that genera from the Saprospiraceae, Flavobacteriaceae and Rhodobacteraceae families were shared between RAS tank water and the RAS biofilter. Moreover, Flavobacteriaceae and Rhodobacteraceae have also been reported in association with zooplankton, notably because of their respective capacities to participate in the carbon cycle by degrading high-molecular-weight organic matter, and to contribute to biofilm formation (De Corte *et al.*, 2018).
## 4.2. Overall microbiota differences between sampling compartments

The alpha-and beta-diversity indices used in this study (Chao1, Shannon, unweighted and weighted UniFrac distances) highlighted differences in the bacterial composition between the compartments and between samples within each compartment (Fig. 5, Fig. 6). The biofilm samples and tank water samples harbored the most diverse bacterial populations compared with the water inlet samples (Fig. 5A, 5B). These observations could be partly explained by the presence of seabass in the tanks. The Colwelliaceae family was more abundant in the tank water samples than in the water inlet samples (Fig. 2, Fig. 3). Pimentel et al. (2017) highlighted that the accumulation of uneaten feed in the tank generate anoxic areas, where the genus *Colwellia* could be found and contribute to anaerobic processes. Otherwise, previous studies have shown that biofilm communities are distinct from other microbial communities, for example in raw water (Luo et al., 2013). The increasing presence of the Nitrosomonadaceae, Nitrospiraceae and Nitrosopumilaceae families appeared to be specific to the biofilm samples (Fig. 4). In fact, these three families are involved in nitrification processes and are known as ammonium-oxidizing bacteria and ammonium-oxidizing archaea (Rurangwa and Verdegem, 2015). Luo et al. (2013) revealed that the presence of Firmicutes and *Nitrospiraes* spp. in microbial biofilms improves the quality of the raw water by dissolving oxygen and modifying nitrogen levels. Planctomycetes, such as Pirellulaceae (Fig. 4), also participate in ammonium oxidation in anaerobic conditions (Schreier et al., 2010). The presence of these bacterial populations in a biofilm is crucial because they oxidize ammonia and nitrite into less toxic compounds, such as nitrates (Rurangwa and Verdegem, 2015).

Moreover, our results showed that bacterial populations composing biofilm established between the 2 hours immersion and the second sampling week. Bacterial communities on the non-immersed blank disappeared after just 2 h of immersion, as manifested by the Actinobacteria, Cyanobacteria and Firmicutes families. Firmicutes have been found in bacterial communities from RAS biofilters or in biofilms in raw water distribution systems (Luo et al., 2013, Schreier et al., 2010). These families have often been observed in various inhabited areas, such as utensils or on stones (Ng et al., 2016). The populations unique to biofilms changed rapidly, despite the
presence and constancy of families common to all compartments. Our results showed that Planctomycetes, Chloroflexi and Thaumarchaeota phyla as being specific to biofilm samples.

During this seasonal campaign, the cold-water inlet underwent the most temperature changes (from 9.08 °C to 21.1 °C), due to warmer waters in the summer, but no major differences in bacterial communities were observed in this compartment. The water temperature in the tanks was maintained approximately constant by mixing in variable proportions the hot- and cold-water inlets (from 17.97 °C to 21.17 °C). Nevertheless, the bacterial communities did not change with the slightly higher temperatures during summer (Fig. 5C, 5D). Moreover, a Spearman correlation test was performed between all physical parameters (temperature, pH, oxygen, salinity and conductivity) measured in the tanks, the diversity indicators (Chao1 and Shannon from tank water and biofilm samples) and the relative abundance of Vibrionaceae (from the tank water and biofilm samples). No significant association was revealed between evenness and richness variations from the tank water and biofilm samples and temperature, oxygen, salinity or conductivity considered independently. A negative correlation was revealed only between pH and both diversity indicators in biofilm samples. Bacterial communities of biofilm samples appeared more diverse when the pH was lower. The pH variation ranged from 6.35 to 7.97 during the sampling campaign (Table S1, available online). The pH decrease may be linked to the nitrification process occurring in biofilm samples. Finally, the relative abundance of Vibrionaceae from water tank samples correlated negatively with pH and oxygen levels in the tanks, whereas the relative abundance of Vibrionaceae from biofilm samples correlated positively with the temperature in the tanks. Then the temperature rising seemed promote Vibrionaceae proliferation in biofilm samples and their role as reservoir in case of dysbiosis.

### 4.3. Potential pathogens detected

We then focused on the Vibrionaceae family in tank water samples, because this compartment contained more Vibrionaceae than the other compartments. Our results suggested that biofilms may be a reservoir of Vibrionaceae, and tank water seemed amenable to its proliferation. A lower Shannon index was indeed obtained (18W, 24W and 28W, Fig. 5C) when the Vibrionaceae were
most abundant (18W, 24W and 28W, Fig. 3). The balance between bacterial communities was thus disturbed, because one bacterial population had proliferated in particular. Among the Vibrionaceae family, four genera were frequent, in order of importance: *Vibrio*, *Photobacterium*, *Aliivibrio* and *Enterovibrio*. More precisely, the species level determination identified *Vibrio ponticus*, *Vibrio aestivus*, *Vibrio sinaloensis* and *Vibrio anguillarum*, as well as *Photobacterium damselae* subsp. *damselae*, *Photobacterium swingsii*, *Photobacterium gaetbulicola* Gung47, *Photobacterium rosenbergii*, and *Photobacterium damselae* subsp. *piscicida*, and *Aliivibrio fischeri* and *Enterovibrio nigricans* (Table 1). Most pathogenic *Vibrio* species, such as *Vibrio harveyi* or *Vibrio vulnificus*, were not identified, although we expected to find *V. harveyi* since it has already been identified as major pathogens in this aquaculture infrastructure (Mougin *et al.*, 2020). The Vibrionaceae population more than doubled during the sampling campaign (20% at 0W and 50% at 28W, Fig. 3). The peaks of diversity were reached at weeks 19 and 20 (during August), suggesting that all genera were present and in stable proportions (Fig. 7). Despite this variation, the equilibrium between genera remained relatively stable throughout the sampling weeks.

Our results revealed that the water from the tanks contained Vibrionaceae and may be sufficiently rich in bacteria to harbor pathogens and their antagonistic members. Some studies have indeed reported that *Roseobacter* spp. (family Rhodobacteraceae) may establish antagonistic activity against Vibrionaceae and particularly against *V. anguillarum* (Balcázar *et al.*, 2010, Hjelm *et al.*, 2004). Among the Flavobacteriaceae, Polaribacter is an opportunist common fish farm bacteria colonizing an already weakened fish and has already been detected in the core microbiome of seabass and seabream (Rosado *et al.*, 2019). Our results showed that increases in Rhodobacteraceae and Flavobacteriaceae were associated with decreases in Vibrionaceae. The absence of acute vibriosis outbreaks during the campaign suggests that there is an equilibrium between pathogenic Vibrionaceae and the other antagonistic bacterial communities. It would be relevant to study the putative antagonistic activity between sampled Vibrionaceae, Rhodobacteraceae and Flavobacteriaceae. Simultaneously, identification and detection of biofilm microbiomes now makes it possible to investigate the capacity of horizontal
gene transfer of the different species and to investigate antibiotic resistance. Despite the presence
of potential pathogens, there were no disease outbreaks; therefore, the vaccination campaign
appears to have worked. In the present study, no sampling was carried out directly on fish firstly
for ethical reasons and secondly because the main objective was to consider the aquaculture
compartments. Nevertheless, future studies should compare our results with fish microbiomes to
confirm the efficiency of vaccination. Finally, our results demonstrated the importance of
simultaneously studying all bacterial communities.

**5. Conclusion**

In conclusion, this is the first study to compare bacterial communities from several water
compartments and from biofilms in an aquaculture farm over time with a seven-month weekly
sampling campaign. Proteobacteria and Bacteroidetes, which are common phyla in water
ecosystems, were abundant. The diversity of bacterial communities differed within
compartments, although there were no significant correlations with water quality parameters.
Biofilm samples contained higher bacterial diversity than water samples and tank water samples
were richer than samples from water inlets. The proportion of Vibrionaceae was high in tank
water samples. All six genera composing the Vibrionaceae family were identified. The natural
environment seemed to be a source of Vibrionaceae, which found a favorable environment to
develop and then persist in biofilms in the aquaculture system. In the future, we plan to
investigate the antagonistic effects between Vibrionaceae and other already present bacteria to
design solutions such as probiotics use to monitor bacterial populations and promote sustainable
aquaculture. Moreover, the aquaculture environment can be monitored using a
metatranscriptomic analysis. The bacterial populations detected can be linked to the primary
and secondary metabolic functions observed, such as virulence.

Supplementary data to this article can be found online at

https://doi.org/10.1016/j.aquaculture.2020.735987.
## Funding

This work was supported by the French government, IFREMER and the Hauts-de-France Regional Council as part of the CPER 2018-2019 MARCO project. This work was also supported by the Hauts-de-France Regional Council and ULCO, which provided funding for Julia Mougin's PhD studies.

## Declaration of competing interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

## Acknowledgements

Authors wish to thank Aquanord-Ichtus as a contributor to this project, especially Guillaume Tielie, Anabelle Duhamel and Céline Doyen. Authors want to thank Dominique Hervio-Heath for her advice on sequencing targets. We would also like to thank the Hauts-de-France Regional Council and ULCO (PhD funding for Julia Mougin); and the CPER 2014-2020 MARCO project (post-doctoral funding for Roxane Roquigny).

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Résumé

Les bactéries du genre Vibrio et plus particulièrement celles du clade Harveyi, sont responsables de la vibriose, entraînant des mortalités de masse associées à des pertes économiques considérables pour le secteur aquacole. Vibrio harveyi est un agent pathogène émergent pour le bar commun d'élevage (Dicentrarchus labrax), capable de former des biofilms. Etant donné qu'aucune méthode prophylactique n'est aujourd'hui commercialisée, la lutte contre la vibriose réside dans la prévention et le développement de méthodes de surveillance et de diagnostic. L'objectif de cette thèse consiste à apporter des éléments de compréhension, via la combinaison d'approches microbiologiques et moléculaires, visant à optimiser la gestion du risque Vibrio pour le bar commun d'élevage. Dans un premier temps, des méthodes de quantification et d'identification ont été mises au point. Une méthode de PCR en temps réel, ciblant le gène mreB, a été développée et optimisée pour quantifier V. harveyi à la fois dans l'eau et les biofilms. La « Luvibase », une base de données composée de 23 profils de spectres de masse de souches de Vibrio de collection a ensuite été élaborée, permettant d'identifier les espèces du clade Harveyi par MALDI-TOF MS. Dans un second temps, ces méthodes ont été appliquées lors d'une campagne hebdomadaire d'échantillonnage d'avril à octobre 2018 dans une ferme d'élevage de bars communs. Des échantillons d'eau et de biofilms ont été collectés à partir des bassins d'élevage et des arrivées d'eau chaude et froide, alimentant la ferme. L'abondance de V. harveyi et des Vibrionaceae cultivables à 37 °C, mais aussi leur diversité relative ont ensuite été déterminées. Plusieurs espèces potentiellement pathogènes ont régulièrement été identifiées, dont V. harveyi, tout au long de la campagne. L'étude de l'influence des paramètres environnementaux a mis en évidence une corrélation positive entre la température de l'eau et l'abondance de ces bactéries à la fois dans l'eau et les biofilms. Sachant que ces biofilms peuvent induire une persistance de V. harveyi au sein de la ferme, et que leur abondance pourrait prochainement augmenter du fait du réchauffement climatique, il est urgent de développer des solutions anti-biofilms, afin de limiter le risque Vibrio.

Mots clés : Aquaculture, Biofilm, Dicentrarchus labrax, Luvibase, MALDI-TOF MS, PCR en temps réel, Vibrio harveyi

Abstract

Vibrio bacteria, and particularly members of the Harveyi clade, are the causative agents of vibriosis, responsible for mass mortality events and important economic losses on aquaculture farms. Vibrio harveyi, which can form biofilms on aquaculture facility surfaces, has recently been identified as an emerging threat for seabass (Dicentrarchus labrax) aquaculture. As no prophylactic method is available on the market, it is urgent to improve surveillance and diagnosis tools to successfully manage vibriosis outbreaks. This thesis focuses on the optimisation of vibriosis risk reduction strategies in seabass farms, using a combination of microbiological and molecular approaches. First, we developed quantification and identification methods. A mreB-targeted SYBR Green I real-time PCR method was developed to quantify V. harveyi directly from environmental samples, including seawater and biofilms. A new in-house database named Luvibase, composed of 23 reference MALDI-TOF mass spectra profiles from Vibrio collection strains, was elaborated in order to identify Vibrio species belonging to the Harveyi clade, by MALDI-TOF MS. Second, those methods were applied during a sampling campaign from April to October 2018 in a seabass aquaculture facility. Seawater and biofilm samples were collected from rearing tanks, and from the cold- and hot-water inlets, supplying the farm. The abundance of V. harveyi and Vibrionaceae cultivable at 37 °C and their relative diversity were then determined. Several pathogens for seabass were constantly isolated during the sampling campaign. A positive correlation was highlighted between seawater temperature and their abundances in both types of samples. As these biofilms may act as a reservoir of V. harveyi in the farm, and as its abundance is likely to increase due to global warming, anti-biofilm solutions are urgently needed, in order to manage the risk of Vibrio outbreak.

Keywords : Aquaculture, Biofilm, Dicentrarchus labrax, Luvibase, MALDI-TOF MS, Real-time PCR, Vibrio harveyi