(https://www.mrdnalab.com/mrdnafreesoftware/fastq-splitter.html). À la suite de cette étape, chaque échantillon est associé à 2 fichiers FASTQ, l'un contenant les séquences « forward » et l'autre les séquences « reverse » (aussi nommées R1 et R2). Le package DADA2 propose une succession de lignes de commandes qui ont été réalisées au cours de cette thèse avec le logiciel RStudio (RStudio Team, 2020) selon les étapes successives suivantes: identification, filtres, estimation du taux d'erreur, dé-réplication, construction des ASVs, assemblage, suppression des chimères et assignation taxonomique (Fig. 49) (https://benjjneb.github.io/dada2/tutorial.html).

### III.3.2.a- Identification des séquences

La première étape de ce pipeline consiste à identifier les fichiers FASTQ, mentionnés précédemment, à l'aide des fonctions « list.files » et « basename ». Pour cela, les fichiers contenant les séquences démultiplexées doivent être regroupées dans un dossier. Le chemin d'accès à ce dossier est précisé dans R afin de trier les fichiers « forward » et « reverse » et d'extraire les noms de chaque échantillon.

### III.3.2.b- Filtres

Après une étape de contrôle de la qualité des séquences (fonction « PlotQuality »), la fonction « filterAndTrim » permet de tronquer les séquences à une longueur définie dépendante du couple d'amorces choisi, de retirer les séquences plus courtes que cette longueur et de retirer les séquences selon un score qualité minimum et un nombre d'erreurs attendues. Seules les séquences « forward » et « reverse » qui remplissent les conditions des filtres sont conservées.

### III.3.2.c- Taux d'erreur

Grâce à la fonction « learnErrors », l'algorithme entraîne un modèle d'erreur paramétrique à partir de l'ensemble des données filtrées dans le but d'identifier les erreurs liées aux étapes de PCR et de séquençage. Ce modèle quantifie un taux $\lambda_{i,j}$ pour lequel une séquence i est considérée comme une erreur d'une séquence j, en calculant la probabilité de substitutions des nucléotides au sein des séquences en fonction de leur composition et de leur score qualité. L'hypothèse nulle selon laquelle l'abondance de la séquence i est trop importante pour être expliquée par des erreurs expérimentales est ensuite testée. Des p-values basées sur les abondances sont ainsi calculées puis corrigées selon la méthode de Bonferroni (Bland & Altman 1995).
<figure><img src="image_59.png" /><figcaption>FIGURE 49 – Schéma du pipeline DADA2 implanté dans le package DADA2 sous R (Callahan et al. 2016).</figcaption></figure>
### III.3.2.d- Dé-réplication

La fonction « derepFastq » proposée dans le package DADA2 permet de dé-répliquer les séquences. Le résultat de cette étape est une liste de séquences uniques associées à leurs abondances respectives.

### III.3.2.e- Construction des ASVs

La fonction « dada » implémente un algorithme de partitionnement des séquences qui commence par placer toutes les séquences dé-répliquées dans une partition aussi appelée cluster. La séquence la plus abondante est considérée comme la séquence centrale de cette partition. Toutes les autres séquences sont ensuite comparées à cette séquence centrale selon le modèle d'erreur présenté précédemment. En utilisant un processus itératif, l'algorithme vise à créer des clusters (aussi appelés groupes ou partitions) de séquences de telle sorte que toutes les séquences d'une même partition soient considérées comme des erreurs de la séquence centrale. Lorsque les clusters sont construits, toutes les séquences d'un cluster sont remplacées par la séquence centrale de ce dernier. Les partitions ainsi formées sont nommées ASVs pour Amplicon Sequence Variants (variants de séquence d'amplicon). Cette notion sera abordée dans la suite de ce chapitre.

### III.3.2.f- Assemblage

Une fois les erreurs de séquençage éliminées, les séquences pairées présentant un alignement strict sont assemblées selon la méthode globale de Needleman-Wunsch (Needleman & Wunsch 1970). L'alignement est testé pour toutes les séquences « forward » et « reverse ». Si l'alignement n'est pas strict il est rejeté. Le chevauchement minimal est déterminé par la longueur de l'amorce « reverse » moins un nucléotide. Par exemple, pour une amorce de 20 nucléotides le chevauchement minimal sera de 19 paires de bases. La fonction « makeSequenceTable » permet d'établir une table d'ASVs contenant l'ensemble des séquences identifiées et leurs abondances par échantillon. Cette table est ensuite utilisée pour l'identification de chimères.

### III.3.2.g- Suppression des chimères

Les chimères sont retirées avec la fonction « RemoveBimeraDenovo ». Plusieurs méthodes sont proposées dans ce pipeline : « consensus », « per-sample » et « pooled ». La méthode « consensus » a déjà été présentée dans l'étape de suppression des chimères du workflow FROGS. L'approche « per-sample » identifie les chimères de chaque échantillon et les supprime obligatoirement sans comparaison avec le reste du jeu de données. Inversement, la méthode « pooled » regroupe l'ensemble des séquences de tous les échantillons et supprime les chimères trouvées au sein de ce pool de séquences. Dans tous les cas, les chimères sont identifiées selon une approche *de novo*. Les séquences sont classées selon leur abondance par ordre décroissant. Elles sont ensuite alignées selon un alignement global aux séquences les plus abondantes pour chercher de potentiels parents. Seules les séquences présentant un alignement strict sont supprimées.

### III.3.2.h- Assignation taxonomique

Grâce à l'implémentation de la méthode de classification naïve bayésienne (Wang et al. 2007), la fonction « assignTaxonomy » considère l'ensemble des séquences à identifier ainsi qu'un ensemble de séquences de référence avec une taxonomie connue (ici une base de données SILVA).
## III.3.3- Unité Taxonomique Opérationnelle (OTU) et Variants de Séquence d'Amplicon (ASV)

### III.3.3.a- Différences entre un OTU et un ASV

Une Unité Taxonomique Opérationnelle, ou OTU, est un regroupement de séquences identiques entre elles jusqu'à un certain seuil. Deux méthodes de construction sont possibles : à partir d'une banque de données déjà connues (« closed-reference ») ou à partir du jeu de données étudié (de novo). Dans le premier cas, une base de données de séquences connues est nécessaire. Les séquences du jeu de données d'intérêt sont alignées à cette base de référence en respectant un seuil d'identité préalablement fixé. Dans le cas d'une méthode de novo, seules les séquences des données d'intérêt sont utilisées et regroupées entre elles selon un pourcentage d'identité défini. Classiquement, en taxonomie des procaryotes, le seuil admis est de 97%. Dans cette situation, si nous considérons des séquences de 100 nucléotides regroupées au sein d'un même OTU alors les séquences les plus éloignées au sein du cluster peuvent différer de 3 nucléotides. Pour des séquences plus longues, comme celles considérées au cours de cette thèse (300pb), le nombre de nucléotides différents augmente. Dans le cas du pipeline FROGS, les seuils d'identité sont plus stricts que ceux généralement admis. En effet, des sous-clusters sont d'abord formés en prenant un seuil de différence égal à 1 puis des clusters de ces sous-groupes sont construits avec un seuil de 3. Malgré ces contraintes, les OTUs formés par FROGS ne sont pas composés de séquences identiques à 100%. Il serait d'ailleurs aberrant de construire de tels OTUs puisque regrouper des séquences identiques à 100% implique l'absence d'erreurs de séquençage (Callahan et al. 2017). Ce problème d'erreur de séquençage est corrigé par le pipeline DADA2 qui permet ainsi la construction d'ASVs ; regroupements de séquences identiques à 100% après modélisation des erreurs de séquençage. Plus précisément, un ASV consiste en un regroupement de variants d'amplicon considérés comme des erreurs d'une séquence centrale.

### III.3.3.b- Comment choisir ?

Plusieurs études sont disponibles pour comparer les méthodes de construction des ASVs et des OTUs et apportent des conclusions similaires. En 2017, Allali et al. (2017) ont analysé les jeux de séquences obtenus via 3 méthodes différentes : DADA2 (qui forme des ASVs), QIIME (qui forme des OTUs de novo) et UPARSE (qui forme des OTUs « closed-reference »). Leurs résultats d'analyses par les 3 méthodes conduisent à des conclusions biologiques similaires. En effet, ils concluent qu'aucune méthode universelle unique ne peut être utilisée et conseillent plutôt de choisir une méthode adaptée aux données d'intérêt (Allali et al. 2017). L'année suivante, une nouvelle étude comparative apporte des conclusions similaires (Glassman & Martiny 2018). Les méthodes ASV et OTU testées mènent à des compositions microbiennes similaires au niveau du genre et les conclusions biologiques ne diffèrent pas. À nouveau le choix d'une méthode d'analyse adaptée aux données d'intérêt s'impose comme étant plus judicieux. De manière générale, d'un point de vue biologique, aucune méthode n'apparaît comme obsolète mais l'approche OTU est généralement employée pour évaluer la diversité globale d'une communauté alors que l'approche ASV est conseillée dans le cas d'une compréhension plus précise d'un écotype. Plus récemment, Porter et Hajibabeai (2020) apportent la même conclusion mais conseillent tout de même l'utilisation des ASVs pour des raisons de reproductibilité, de comparaisons inter-études et de résolution au niveau nucléotidique. Des arguments similaires ont été avancés par d'autres auteurs pour promouvoir l'utilisation d'une méthode ASV, comme leur spécificité et leur
sensibilité (Callahan et al. 2017; Prodan et al. 2020). En effet, les modèles d'erreur implantés dans les méthodes ASVs permettent de différencier des séquences différentes d'un nucléotide et augmentent donc la sensibilité et la spécificité de ces méthodes. Ces modèles limitent également la formation de « fausses » séquences, contrairement aux méthodes OTU (Prodan et al. 2020), et permettent ainsi de faciliter l'assignation taxonomique. Les OTUs formés de novo par la méthode FROGS, dépendent du jeu de données dans lequel ils sont identifiés et de leur abondance relative à l'intérieur de ce dernier. En conséquence, ces OTUs sont considérés comme étant non reproductibles et ne peuvent pas être utilisés pour des comparaisons inter-études. En revanche, les ASVs sont construits sur la base d'un modèle d'erreur et corrigés, ils reflètent donc une réalité biologique et sont reproductibles. Les ASVs issus d'analyses différentes peuvent donc être regroupés dans une même table afin d'effectuer des méta-analyses (Callahan et al. 2017). Les OTUs formés à partir d'une base de référence peuvent également être utilisés dans des méta-analyses car ils existent en dehors du jeu de données d'intérêt, mais l'utilisation de telles bases de données limite la diversité d'une communauté d'intérêt. En effet, cette méthode est dépendante de l'information disponible à un instant t dans une base de données et est donc limitée pour des environnements moins étudiés. Pour résumer, malgré des conclusions biologiques proches entre ASV et OTU, le choix d'utiliser des ASVs permet d'obtenir des jeux de données plus précis, reproductibles, exhaustifs et utilisables en méta-analyses. Dans le cadre de ce projet de thèse, ce dernier critère a été le plus important pour motiver le choix d'une méthode ASV. En effet, l'un des axes d'étude de cette thèse était de regrouper l'ensemble des données issues d'expérimentations de différentes écloseries et réalisées entre 2018 et 2020 pour pouvoir effectuer une méta-analyse. Or la méthode FROGS et l'utilisation d'OTUs formés de novo ne permettait pas de telles analyses. La méthode DADA2 a donc été choisie pour analyser l'ensemble des données de séquençage considérées au cours de cette thèse.

## III.4- Normalisation

Le traitement des données de séquençage permet l'obtention d'une table de comptage, quelle que soit la méthode d'analyse choisie. Cette table comprend les ASVs (ou les OTUs) en lignes et les échantillons en colonnes. Chaque ASV ou OTU représente une séquence nucléotidique assignée à une taxonomie allant du règne à l'espèce (Tab. 10). Avant de pouvoir analyser cette table, une dernière étape de normalisation est nécessaire. Dans cette partie, nous aborderons les explications relatives à l'importance de la normalisation, aux différentes méthodes disponibles ainsi que les méthodes choisies dans le cadre de cette thèse.

<table><caption>TABLEAU 10 – Exemple d'une table d'ASVs.</caption><thead><tr><th></th><th>Séquence</th><th>Règne</th><th>Phylum</th><th>Classe</th><th>Ordre</th><th>Famille</th><th>Genre</th><th>Espèce</th><th>Éch_1</th><th>Éch_2</th><th>Éch_3</th></tr></thead><tbody><tr><td>ASV1</td><td>CGGAATTA...</td><td>Bacteria</td><td>Proteobacteria</td><td>Gammaproteobacteria</td><td>Enterobacterales</td><td>Pseudoalteromonadaceae</td><td>Pseudoalteromonas</td><td>NA</td><td>1151</td><td>917</td><td>541</td></tr><tr><td>ASV2</td><td>CGGAATC...</td><td>Bacteria</td><td>Bacteroidota</td><td>Bacteroidia</td><td>Chitinophagales</td><td>Saprospiraceae</td><td>Phaeodactylibacter</td><td>NA</td><td>50</td><td>162</td><td>271</td></tr><tr><td>ASV4</td><td>CGCATTCT...</td><td>Bacteria</td><td>Bdellovibrionata</td><td>Bdellovibriona</td><td>Bdellovibrionales</td><td>Bdellovibrionaceae</td><td>OM27</td><td>NA</td><td>11</td><td>18f</td><td>0</td></tr></tbody></table>
## III.4.1- Pourquoi normaliser ?

En observant une table d'ASVs non normalisée, deux problèmes majeurs se posent. Dans un premier temps, le nombre total de séquences d'un échantillon à l'autre varie grandement. Ce nombre total de séquences au sein d'un échantillon est aussi appelé une librairie (Tab. 11). Au sein d'une table d'ASVs, il existe autant de librairies qu'il existe d'échantillons. Des librairies de tailles différentes peuvent être causées par des biais expérimentaux tels que des variations induites par l'échantillonnage ou des profondeurs de séquençage différentes (Weiss et al. 2017). La profondeur de séquençage reflète le nombre total de séquences détectées, plus la profondeur est grande plus le nombre de séquences mises en évidence augmente. La taille des librairies ne reflète donc pas forcément une réalité biologique et peut fausser les analyses et les conclusions tirées à partir d'une table d'ASVs. On parle alors d'hétéroscédasticité (variances différentes) qui signifie que l'incertitude associée à chaque valeur dans la table est liée au nombre total d'observations et non à la réalité biologique des échantillons. Dans un second temps, outre des librairies de tailles différentes, des profondeurs de séquençage différentes peuvent entraîner un grand nombre de zéro au sein des tables d'ASVs. En effet, une table regroupe l'ensemble des ASVs mis en évidence dans l'ensemble des échantillons. Dans cette situation, un ASV très abondant au sein d'un échantillon peut être absent ou très faiblement présent dans un autre. Les ASVs très rares ou absents ne seront ainsi détectés qu'en augmentant le nombre de séquences mises en évidence. On parle alors de surdispersion. Cela signifie que la variabilité des données est plus importante que la variabilité attendue selon un modèle statistique donné. Le rôle de la normalisation est de minimiser les biais expérimentaux pour mettre en évidence la réalité biologique d'un jeu de données. En d'autres termes, la normalisation est le processus de transformation des données permettant de comparer statistiquement différents échantillons (Weiss et al. 2017). Dans le cadre de cette thèse, les données étudiées ont été obtenues au cours de différents séquençages, effectués entre 2018 et 2021, réalisés par le même prestataire (MrDNA, USA) via la même machine mais avec des profondeurs de séquençage différentes. Il était donc indispensable de trouver une méthode de normalisation adaptée pour pouvoir comparer l'ensemble des échantillons récoltés. Malheureusement, il n'existe pas de méthode consensus établie et le choix doit être fait selon les besoins de l'expérimentateur et les données mises à disposition (Evans et al. 2018). Les méthodes existantes les plus utilisées ainsi que celles testées et choisies dans le cadre de cette thèse sont présentées ci-dessous.

<table><caption>TABLEAU 11 – Illustration de trois librairies au sein d'une table d'ASVs.</caption><thead><tr><th></th><th>Éch_1</th><th>Éch_2</th><th>Éch_3</th></tr></thead><tbody><tr><th>ASV1</th><td>a</td><td>d</td><td>g</td></tr><tr><th>ASV2</th><td>b</td><td>e</td><td>h</td></tr><tr><th>ASV3</th><td>c</td><td>f</td><td>i</td></tr><tr><th>Librairies</th><td>a + b + c</td><td>d + e + f</td><td>g + h + i</td></tr></tbody></table>
## III.4.2- Principales méthodes de normalisation

### III.4.2.a- La raréfaction

La méthode de raréfaction, implémentée dans le package phyloseq disponible dans RStudio (McMurdie & Holmes 2013), est une normalisation couramment utilisée actuellement. Cette méthode se découpe en 2 étapes. La première consiste à sélectionner une taille de librairies minimale. Afin de limiter les biais liés à la subjectivité de l'expérimentateur, elle correspond souvent à la plus petite librairie du jeu de données. Ensuite, la deuxième étape est un sous-échantillonnage aléatoire de la table permettant une suppression aléatoire de certaines séquences au sein des échantillons pour arriver à des tailles de librairies égales. Ce tirage aléatoire entraîne la perte de certains ASVs rares au sein du jeu de données (Fig. 50).

<figure><img src="image_60.png" /><figcaption>FIGURE 50 – Normalisation par la méthode de raréfaction.</figcaption></figure>
## III.4.2.b- Méthodes basées sur les proportions

Il existe une variété de méthodes de normalisation dites « simples » telles que le comptage total (« Total Sum Scaling » ou TSS), le quartile supérieur (« Upper Quartile » ou UQ), la médiane (« Median » ou Med) ou le « Count Per Million » (CPM). Ces méthodes sont basées sur les proportions c'est-à-dire que chaque comptage pour chaque échantillon est divisé par la taille de la librairie correspondante. Les ratios obtenus sont ensuite multipliés par une valeur définie (100, 1 000 000, etc.) dans le cas du TSS et du CPM, ou par la médiane ou le quartile supérieur de l'ensemble des séquences dans le cas des méthodes UQ et Med (Dillies et al. 2013) (Fig. 51).

<figure><img src="image_61.png" /><figcaption>**FIGURE 51** – Exemples de méthodes de normalisation basées sur les proportions.</figcaption></figure>

Les méthodes TSS (Total Sum Scaling), UQ (Upper Quartile), Med (Médiane) et CPM (Count Per Million) sont montrées.
### III.4.2.c- Méthodes plus complexes

Il existe des méthodes de normalisation plus complexes : DESeq2 (Differential Expression Analysis for Sequence count data), edgeR et CSS (Cumulative Sum Scaling). Chacune de ces méthodes existe sous forme de packages disponibles dans le logiciel Rstudio.

La méthode DESeq2 (Love et al. 2014) commence par créer un pseudo-échantillon de référence regroupant la moyenne géométrique de chaque ASV. Chaque comptage est ensuite divisé par cette moyenne géométrique. La médiane des ratios ainsi calculés est estimée pour chaque échantillon. Chacune des médianes obtenues correspond à un facteur échantillon-spécifique qui est utilisé pour normaliser les comptages initiaux (Fig. 52).

<figure><img src="image_62.png" /><figcaption>FIGURE 52 – Normalisation par la méthode DESeq2 (Differential Expression Analysis for Sequence count data).</figcaption></figure>
La méthode edgeR (Robinson et al. 2009) nécessite également un échantillon de référence mais, contrairement à la méthode DESeq2, ce dernier est choisi au sein du jeu de données. Pour cela, l'algorithme calcule le ratio de chaque comptage par rapport à la taille des librairies. Ensuite, pour chaque échantillon, le quartile supérieur est calculé et la moyenne des quartiles est estimée. L'échantillon présentant le quartile supérieur le plus proche de la moyenne calculée est considéré comme étant l'échantillon de référence. Une fois cet échantillon déterminé, edgeR créé 2 tables distinctes et calcule le logarithme des ratios ainsi que la moyenne des logarithmes de l'échantillon par rapport à la référence. Les ASVs présents dans les 2 tables (> 0) sont conservés et le facteur de normalisation de l'échantillon considéré est alors calculé. Ce facteur correspond à la moyenne pondérée des logarithmes des ratios entre la référence et l'échantillon d'intérêt. Une fois le facteur obtenu, les comptages initiaux sont normalisés (Fig. 53).

<figure><img src="image_63.png" /><figcaption>FIGURE 53 – Normalisation par la méthode edgeR.</figcaption></figure>
La méthode CSS (Cumulative Sum Scaling) implémentée dans le package R metagenomeSeq (Paulson et al. 2013) vise à diviser les comptages par la somme des comptages contenus dans un quantile spécifiquement défini pour chaque échantillon. Ce quantile est déterminé en se basant sur une loi binomiale donnée-dépendante qui identifie les ASVs dominants dans chaque échantillon. Plus spécifiquement le quantile est choisi de telle sorte que la distribution des comptages au sein de l'échantillon soit significativement différente d'une distribution de référence préalablement définie par un modèle mathématique. Une fois le quantile déterminé, les comptages normalisés sont obtenus selon la relation suivante :

$$c_{i,j} \text{ normalisé} = \frac{c_{i,j}}{s_j} \times N$$

avec : $c_{i,j}$ la somme des comptages de l'ASV i dans l'échantillon j, $s_j^1$ la somme des comptages de l'échantillon j jusqu'au 1ère quantile ($q^1$) déterminé et $N$ la constante de normalisation déterminée par l'algorithme et commune à l'ensemble des comptages de la table.
Une transformation logarithmique est finalement appliquée aux comptages normalisés obtenus (Fig. 54).

<figure><img src="image_64.png" /><figcaption>FIGURE 54 – Normalisation par la méthode CSS (Cumulative Sum Scaling).</figcaption></figure>
## III.4.3- Quelle normalisation choisir ?

Comme pour le choix entre OTU et ASV, il n'existe pas de méthode de normalisation qui fasse consensus, le choix doit être fait selon le type de données puisque chaque méthode possède des avantages, des inconvénients et des limites qu'il est important de connaître. La raréfaction est devenue relativement courante pour normaliser les données de métabarcoding. Pourtant, en raison de la perte aléatoire d'ASVs rares, cette méthode est déconseillée par certains auteurs et notamment par le créateur du package phyloseq qui propose cette fonction (McMurdie & Holmes 2013; 2014). Même si le sous-échantillonnage aléatoire peut être rendu reproductible par l'utilisation d'une « graine » (ou seed en anglais, nombre utilisé pour l'initialisation d'un générateur de nombres pseudo-aléatoires), McMurdie & Holmes (2014) ont conclu que la perte d'information liée à la perte d'ASVs pouvait influencer la qualité des analyses de clustering (ordonnancement des échantillons) et de différentiel d'abondances réalisées en aval. Cette méthode ne peut être utilisée que dans le cas de tailles de librairies homogènes, ce qui n'était pas le cas des jeux de données considérés au cours de ce projet. C'est également pour cette raison que la méthode CSS ne pouvait pas être utilisée car cette dernière est déconseillée lorsqu'il existe une trop grande différence entre les tailles de librairies. La méthode edgeR a également été écartée à cause de la sélection d'un échantillon de référence au sein du jeu de données. En effet, cette étape n'aurait pas eu de sens biologique au regard des jeux de données considérés et des questions scientifiques posées au début de ce manuscrit. De plus, en raison d'un nombre important de 0 au sein des tables de comptage considérées, la méthode edgeR aurait supprimé de nombreux ASVs lors des calculs logarithmiques effectués, pouvant ainsi affecter le calcul des facteurs de normalisation et la normalisation en aval (Weiss et al. 2017). En conséquence, les méthodes basées sur les proportions ainsi que la méthode DESeq2 ont été considérées au cours de cette thèse. Cependant l'inconvénient principal de la méthode DESeq2 est qu'elle génère des valeurs négatives. Bien que ces valeurs puissent être artificiellement remontées (ajout de la valeur négative la plus basse à toutes les valeurs de la table), ce procédé peut impacter les analyses de distances réalisées en aval, et notamment les distances non euclidiennes utilisées au cours des analyses d'écologie microbienne (comme Bray-Curtis par exemple) (Weiss et al. 2017). Les méthodes basées sur les proportions possèdent également leurs limites puisque, contrairement à DESeq2, elles ne parviennent pas à corriger la surdispersion des données et peuvent ainsi entraîner la mise en évidence de faux positifs au moment des analyses de différentiels d'abondance. En considérant l'ensemble des critères énoncés ci-dessus, la méthode CPM, une méthode de normalisation basée sur les proportions et très similaire à la méthode TSS, a été choisie pour analyser les données obtenues au cours du projet de thèse. Pour cela, la fonction « normalize » du package phyloseq a été utilisée en sélectionnant la méthode « CPM ». Cependant, pour la méta-analyse, les différences de tailles de librairies trop importantes nous ont menés à utiliser la méthode DESeq2. Les résultats sont présentés dans le chapitre VII (partie VII.1).
# CHAPITRE IV

Des transmissions horizontales et verticales modulent le microbiote actif des œufs et des nauplii de *Penaeus stylirostris*
# IV- Chapitre IV – Des transmissions horizontales et verticales modulent le microbiote actif des œufs et des nauplii de Penaeus stylirostris

## IV.1- Étude A – Premières preuves de transmission horizontale

### IV.1.1- Introduction

Tous les organismes vivants, dont les animaux d'intérêt aquacole, établissent des relations symbiotiques avec diverses communautés microbiennes impliquées dans de nombreux processus physiologiques essentiels (chap. I, partie I.3). Plusieurs études suggèrent d'ailleurs qu'un déséquilibre de ce microbiote pourrait être corrélaté à une dégradation de l'état de santé de l'hôte (chap. I, partie I.3). Dans ce contexte et dans le cadre de mortalités larvaires massives constatées dans les écloseries de Nouvelle-Calédonie, des hypothèses quant à une variation des microorganismes procaryotiques associés aux larves et / ou à leur environnement ont été émises pour expliquer les problèmes rencontrés (chap. I, partie I.1.2.c). Afin de mieux comprendre cet écosystème microbien au sein des élevages, le premier objectif de cette thèse a été de caractériser les procaryotes présents au moment de la mise en place du microbiote larvaire et de déterminer les facteurs susceptibles de les influencer. Pour cela, des échantillons d'œufs et de larves au stade nauplius (premier stade larvaire) ainsi que des échantillons d'eau de réserves primaire (eau provenant du lagon) et secondaire (Fig. 32, chap. II, partie II.1.1.a) ont été prélevés à la Station Aquacole de Saint Vincent (SASV) au cours de 3 campagnes d'échantillonnage distinctes : M1 (septembre 2018, saison fraîche), M3 (novembre 2018, saison intermédiaire) et M4 (février 2019, saison chaude) (Fig. 31, chap. II, partie II.1). Dans un premier temps, la qualité des échantillons d'eau récoltés a été évaluée par analyse de la matière organique dissoute colorée (CDOM). Deux paramètres ont été considérés : le coefficient d'absorbance à 325nm (aλ325), qui est un indicateur de la concentration en CDOM, et le ratio des pentes spectrales (SR) qui est un indicateur du poids moléculaire de la matière organique dissoute (chap. II, partie II.4.2). Dans un second temps, les ADNc obtenus par rétrotranscription des ARNs extraits à partir de l'ensemble des échantillons ont été séquencés au cours de 2 séquençages Illumina HiSeq (2 x 150pb) distincts (chap. II, partie II.5.1). L'ensemble des échantillons de la M1 et de la M3 ont été séquencés en février 2019 avec une profondeur de séquençage de 20K alors que les échantillons de la M4 ont été séquencés en novembre 2019 avec une profondeur de séquençage de 50K (chap. III, partie III.4.1). Les échantillons d'œufs et de nauplii de la M3 ont également été reséquencés en novembre 2019. Ces différences de séquençage ont mené aux questionnements exposés dans le chapitre III (parties III.3.3.b, III.4.1 et III.4.3). Les données issues de l'ensemble des séquençages ont été analysées avec le package DADA2 (Callahan et al. 2016) (chap. III, partie III.3.2), afin d'obtenir une table d'ASVs (chap. II, partie II.5.2). Après normalisation par méthode CPM (chap. III, partie III.4.2.b), une classification ascendante hiérarchique basée sur une matrice de distance de Bray-Curtis a été construite en utilisant la méthode de Ward. Des comparaisons par diagrammes de Venn ont également été effectuées afin de mettre en évidence les microorganismes communs ou spécifiques à chaque type d'échantillon analysé (détaillé dans le chap. II, partie II.5.3).
IV.1.2- Article 1 – Potential lineage transmission within the active microbiota of the eggs and the nauplii of the shrimp *Litopenaeus stylirostris*: possible influence of the rearing water and more

<figure><img src="image_65.png" /></figure>

Potential lineage transmission within
the active microbiota of the eggs and
the nauplii of the shrimp *Litopenaeus
stylirostris*: possible influence of the
rearing water and more

Carolane Giraud<fnref n="1" />,<fnref n="2" />, Nolwenn Callac<fnref n="1" />, Maxime Beauvais<fnref n="1" />,<fnref n="3" />, Jean-René Mailliez<fnref n="1" />,
Dominique Ansquer<fnref n="1" />, Nazha Selmaoui-Folcher<fnref n="2" />, Dominique Pham<fnref n="1" />, Nelly
Wabete<fnref n="1" /> and Viviane Boulo<fnref n="1" />,<fnref n="4" />

Submitted 23 July 2021
Accepted 12 September 2021
Published 15 November 2021

Corresponding authors
Carolane Giraud, cgiraud@ifremer.fr
Nolwenn Callac, ncallac@ifremer.fr

Academic editor
Konstantinos Kormas

Additional Information and
Declarations can be found on
page 17

DOI 10.7717/peerj.12241

© Copyright
2021 Giraud et al.

Distributed under
Creative Commons CC-BY 4.0

OPEN ACCESS

ABSTRACT

**Background.** Microbial communities associated with animals are known to be key elements in the development of their hosts. In marine environments, these communities are largely under the influence of the surrounding water. In aquaculture, understanding the interactions existing between the microbiotas of farmed species and their rearing environment could help establish precise bacterial management.

**Method.** In light of these facts, we studied the active microbial communities associated with the eggs and the nauplii of the Pacific blue shrimp (*Litopenaeus stylirostris*) and their rearing water. All samples were collected in September 2018, November 2018 and February 2019. After RNA extractions, two distinct Illumina HiSeq sequencings were performed. Due to different sequencing depths and in order to compare samples, data were normalized using the Count Per Million method.

**Results.** We found a core microbiota made of taxa related to *Aestuariibacter*, *Al-teromonas*, *Vibrio*, *SAR11*, *HIMB11*, *AEGEAN* 169 marine group and *Candidatus En-dobugula* associated with all the samples indicating that these bacterial communities could be transferred from the water to the animals. We also highlighted specific bacterial taxa in the eggs and the nauplii affiliated to *Pseudomonas*, *Corynebacterium*, *Acinetobacter*, *Labrenzia*, *Rothia*, *Thalassolituus*, *Marinobacter*, *Aureispira*, *Oleiphilus*, *Profundimonas* and *Marinobacterium* genera suggesting a possible prokaryotic vertical transmission from the breeders to their offspring. This study is the first to focus on the active microbiota associated with early developmental stages of a farmed shrimp species and could serve as a basis to comprehend the microbial interactions involved throughout the whole rearing process.

**Subjects** Aquaculture, Fisheries and Fish Science, Marine Biology, Microbiology, Molecular Biology, Zoology

**Keywords** Active microbiota, Lineage transmission, Eggs, Nauplii, Shrimps, Rearing water

**How to cite this article** Giraud C., Callac N., Beauvais M., Mailliez J-R., Ansquer D., Selmaoui-Folcher N., Pham D., Wabete N., Boulo V. 2021. Potential lineage transmission within the active microbiota of the eggs and the nauplii of the shrimp *Litopenaeus stylirostris*: possible influence of the rearing water and more. *PeerJ* 9:e12241 http://doi.org/10.7717/peerj.12241

<footnote n="1">¹ Ifremer, IRD, Université de la Nouvelle-Calédonie, Université de La Réunion, CNRS, UMR 9220 ENTROPIE, Noumea, New Caledonia</footnote>

<footnote n="2">² University of New Caledonia, Institut des Sciences Exactes et Appliquées (ISEA), Noumea, New Caledonia</footnote>

<footnote n="3">³ Sorbonne Université, UMR 7261, Laboratoire d'Océanographie Microbienne, Observatoire Océanologique de Banyuls-sur-Mer, CNRS, Banyuls-sur-Mer, France</footnote>

<footnote n="4">⁴ IHPE, Université de Montpellier, CNRS, Ifremer, Université de Perpignan via Domitia, Montpellier, France</footnote>
INTRODUCTION

It is widely recognized that microorganisms, and more particularly prokaryotes, colonize all types of habitats (Whitman, Coleman & Wiebe, 1998; Mora et al., 2011) and are involved in crucial reactions in various biogeochemical cycles (Falkowski, Fenchel & Delong, 2008). Microorganisms have also been extensively studied for their implication in the human microbiota (Huttenhower et al., 2012) and thus for their involvement in health, development and behavior (Cho & Blaser, 2012; Moloney et al., 2014). Marine animals also host essential microbial communities associated with their skin (ex. mammals, fishes) or their exoskeleton (crustaceans) as well as their digestive and respiratory systems (Apprill, 2017). These communities are largely influenced by the marine environment which is colonized by all types of microorganisms (Sehnal et al., 2021). More and more studies suggest that comprehending the interactions existing among marine animals, their microbiota and their aquatic environment could help understand the response of these animals to climate change and pollution and could also help improve management of farmed species (Egan & Gardiner, 2016; Sehnal et al., 2021).

In New Caledonia, the Pacific blue shrimp (*Litopenaeus stylirostris*) is a valuable farmed species and represents an economical challenge for the territory (Beliaeff et al., 2009).
Indeed, since 1970, the sector has become the island's leading food-processing exporter (Rural Agency of New Caledonia) with a semi-intensive annual production of 1,500 tons.
Unfortunately, the production has been declining due to seasonal vibriosis touching adult individuals (Goarant et al., 1999; Goarant et al., 2006) and to larval mortalities that have not yet been explained. Knowing that natural seawater is used during the rearing process and that the New Caledonian lagoon has been more and more exposed to anthropic pressures over the years, hypothesis have been made regarding the water quality in order to explain these early mortalities. Thus, understanding the interactions that exist between the microbial communities associated with the rearing water and the different larval stages of *L. stylirostris* could help lead to precise bacterial management during shrimp farming.

As a first step in understanding the microbiota of *L. stylirostris*, we studied the active microbial communities associated with the eggs, the first larval stages (nauplii) and the rearing water. Samples were collected at three different times in September 2018 (cool season), November 2018 (transitional season) and February 2019 (warm season). Bacterial lineages affiliated to *Aestuariibacter*, *Alteromonas*, *Vibrio*, *SAR11*, *HIMB11*, *AEGEAN 169 marine group* and *Candidatus Endobugula* were found in all the samples but in different proportions suggesting that some bacterial communities in the rearing environment could potentially colonize the eggs and the nauplii. We also found specific taxa related to *Pseudomonas*, *Corynebacterium*, *Acinetobacter*, *Labrenzia*, *Rothia*, *Thalassolituus*, *Marinobacter*, *Aureispira*, *Oleiphilus*, *Profundimonas* and *Marinobacterium* genera in the eggs and the nauplii that were not found in the water samples and could have been vertically transmitted from the breeders.
# MATERIALS & METHODS

## Study design and sample collection

Eggs and nauplii of *L. stylirostris* were supplied by the experimental shrimp hatchery located at the Saint Vincent Bay (Ifremer, Boulouparis, New Caledonia) in September 2018 (M1), November 2018 (M3) and February 2019 (M4). For all experiments, breeders were reared in maturation tanks according to the method described in Pham *et al.* (2012). Tanks in the maturation and in the hatchery were filled with natural seawater from the Saint Vincent Bay. Natural seawater was pumped through a 1 cm pore size strainer in a primary reservoir (ResI), was then filtered on a 10 µm pore size filter and stored in a secondary reservoir (ResII). In the secondary reservoir, seawater was circulated for 3 days, through a loop composed of 1 and 5 µm pore size filters and a skimmer. Seawater was briefly passed through a UV chamber before filling the hatchery tanks. In each tank, 5 g m⁻³ of ethylenediaminetetraacetic acid (EDTA) were finally added (Fig. S1). Seawater from the primary reservoir was sampled 5 days before artificial inseminations. Seawater from the secondary reservoir was sampled after filtration and skimmer treatments, on the day the eggs were collected. For each experiment, 1L of seawater from the primary and the secondary reservoirs were collected and were filtered on a 0.2 µm pore size filter (S-PAK membrane filter, Millipore). Filters were kept for RNA extractions and were stored at -80 °C; filtrates were used, within the 2 h of sampling, to analyse the Colored Dissolved Organic Matter (CDOM) as described in Helms *et al.* (2009) and Sadeghi-Nassaj *et al.* (2018).

After artificial insemination and spawning, around a hundred eggs were collected in a 2ml sterile microtube using sterilized pliers. The following day, before transfer into rearing tanks for larval rearing, around a hundred nauplii were sampled in the same way as the eggs using sterilized pliers. All egg and nauplius samples were stored at -80 °C until further RNA extractions. For the M4 experiment, eggs and nauplii were sampled in replicates (M4_Egg1, M4_Egg2, M4_Nii1, M4_Nii2) whereas unique egg and nauplius samples were collected during the M1 and the M3 experiments.

## RNA extractions and sequencing

Due to different sample types, RNA extractions were performed using the RNeasy PowerWater kit (Qiagen) for the filters, and using the RNeasy minikit (Qiagen) for the eggs and the nauplii. Total RNAs were reverse-transcripted into complementary DNA (cDNA) by adding 200 ng of RNAs to a reaction mix (buffer 5X, dNTP 10 mM, random hexamers 50 µm, reverse transcriptase M-MLV (PROMEGA) 200 µl⁻¹, RNAse/DNAse free water). Reverse-transcription was performed in a thermocycler (VerityTM, Applied Biosystems) during 10 min at 25 °C, 2 h at 42 °C and 5 min at 85 °C.

All cDNAs were sent to MrDNA (Shallowater, TX, United States) in order to amplify and sequence the V4 region of the 16S rRNA gene using the 515F/806R primers (*Caporaso et al., 2011*). A HiSeq Illumina sequencing was conducted using a 2 × 300 pb paired-end run.

Due to different sampling times, two distinct sequencings were performed. All samples from the M1 and the M3 (M3a, first sequencing) experiments were sequenced in February
2019 with an average sequencing depth of 20k raw reads per samples. All samples from the M4 experiment as well as the egg and the nauplius samples from the M3 experiment (M3b, second sequencing) were sequenced in November 2019 with an average sequencing depth of 50k raw reads per sample.

Microbiota analysis

Raw sequences were demultiplexed using the MrDNA tool fastqSplitter (https://www.mrdnalab.com/mrdnafreesoftware/fastq-splitter.html). Demultiplexed sequences were treated using the DADA2 (Callahan et al., 2016) package available in the RStudio software (RStudio Team, 2020). Briefly, sequences were filtered using a maximum expected error of 2. Filtered sequences were used to estimate error rates and dereplication. Dereplicated samples and error rates were implemented in the DADA2 error model in order to correct sequencing errors and construct Amplicon Sequence Variants (ASVs) as described in Callahan et al. (2016) and Callahan et al. (2016). Paired-end reads were merged and chimeras were removed using the consensus method. Taxonomy was finally assigned using the Silva 138 database (Quast et al., 2013).

Data were normalized with the Counts Per Million (CPM) method using the cpm function available in the edgeR package under RStudio (Robinson, McCarthy & Smyth, 2009). All libraries were normalized to 1,000,000 reads prior to microbiota analysis. A dendrogram based on a Bray-Curtis dissimilarity matrix and Ward method was obtained using the vegan, ggplot2, dplyr and dendextend packages in RStudio (Galili, 2015; Oksanen et al., 2020; Wickham, 2016; Wickham et al., 2021). Histogram tables were obtained using the dplyr package in RStudio (Wickham et al., 2021). Venn diagrams were made using the open-source component for web environment jvenn (http://venn.toulouse.inra.fr/app/example.html) (Bardou et al., 2014).

All the 16S rRNA data are available in the NCBI SRA repository (Submission ID SUB9828953, BioProject ID PRJNA736535).

RESULTS

CDOM (Colored Dissolved Organic Matter) measurements

Two parameters were analyzed during CDOM measurements of the water samples:
the absorption coefficient at 325nm (aλ₃₂₅) and the ratio of the spectral slopes (SR)
((aλ₂₇₅-₂₉₅)/(aλ₃₅₀-₄₀₀)) which are respectively used as a proxy for CDOM concentration
and as an indicator of the molecular weight of Dissolved Organic Matter (DOM) (Sadeghi-
Nassaj et al., 2018). The a λ₃₂₅ values of all the reservoir samples ranged from 0.8 to 1.1
m⁻¹. For all experiments, the SR values varied from 5.98 to 7.67 in the primary reservoirs
(ResI) and from 4.79 to 8.12 in the secondary reservoirs (ResII). For the M1 experiment, the
SR value was higher in the ResI sample (7.06) than in the ResII sample (4.79). For the M3
and the M4 experiments, trends were inverted (Table 1) as SR values respectively equaled
7.67 and 5.98 in the primary reservoirs; and 8.12 and 6.27 in the secondary reservoirs.

Microbial diversity

A total of 2,987,759 sequences were obtained from the Illumina sequencings of all samples (M1, M3a, M3b and M4). Sequences were clustered into 4,950 distinct ASVs.
<table><caption>**Table 1** CDOM concentrations and slope ratios in the reservoirs. Values of CDOM concentration (aλ325) and slope ratios (SR) from 275 to 295 nm and from 350 to 400 nm of the primary and the secondary reservoir (ResI and ResII) samples of the M1, M3 and M4 experiments.</caption><thead><tr><th></th><th></th><th>ResI</th><th>ResII</th></tr></thead><tbody><tr><td rowspan="2">M1</td><td>aλ325 (m<sup>-1</sup>)</td><td>0.99</td><td>1.01</td></tr><tr><td>SR</td><td>7.06</td><td>4.79</td></tr><tr><td rowspan="2">M3</td><td>aλ325 (m<sup>-1</sup>)</td><td>0.83</td><td>0.82</td></tr><tr><td>SR</td><td>7.67</td><td>8.12</td></tr><tr><td rowspan="2">M4</td><td>aλ325 (m<sup>-1</sup>)</td><td>1.06</td><td>0.98</td></tr><tr><td>SR</td><td>5.98</td><td>6.27</td></tr></tbody></table>

The smallest and largest libraries were respectively composed of 15,399 and 554,056 reads and corresponded to the M3a_Egg and M3b_Nii samples. All libraries were normalized to 1,000,000 reads using the CPM method.

Hierarchical clustering based on Bray-Curtis similarity separated the samples into 2 distinct groups (Fig. 1A). The cluster 1 gathered all the water samples while the cluster 2 contained all the egg and the nauplius samples. All the water samples from the primary reservoirs clustered together with the ResII sample from the M1 experiment. The water samples from the other secondary reservoirs (M3a and M4) clustered separately. Eggs and nauplii sampled during the M3 experiment and sequenced twice formed a unique subgroup while the egg and the nauplius samples from the M1 and M4 experiments clustered together.

For all samples, 11 dominant classes were highlighted (Fig. 1B). Gammaproteobacteria (45% of the total ASV relative abundance) and Alphaproteobacteria (19%) were the most abundant classes followed by Bacteroidia (10%) and Verrucomicrobiae (5%). Acidimicrobiia, Planctomycetes and Cyanobacteriia each represented 3% of the ASVs table. Water samples from all the primary reservoirs showed consistent total bacterial compositions with five predominant classes: Alphaproteobacteria, Gammaproteobacteria, Acidimicrobiia, Cyanobacteriia and Bacteroidia. The bacterial diversity of the M1_ResII sample was very similar to the one of the primary reservoirs. The other water samples from the secondary reservoirs also contained Alphaproteobacteria and Gammaproteobacteria but Planctomycetes dominated the M3_ResII sample while the M4_ResII sample was mainly composed of Verrucomicrobiae. Global bacterial communities associated with the egg and the nauplius samples majorly displayed Gammaproteobacteria, Alphaproteobacteria and Bacteroidia (over 70% of the total ASV relative abundance). Eggs were also composed of Fusobacteriia, especially for the M1 experiment, while nauplii were partly composed of Desulfuromonada.

## Specific and shared ASVs among samples

In order to highlight specific and shared ASVs among all samples, we constructed several Venn diagrams. First, four distinct Venn diagrams were built to find common ASVs among all experiments for each sample type (Fig. S2). A total of 330 ASVs were shared among all the primary reservoir samples (Fig. S2A). All the secondary reservoir samples owned 215 ASVs (Fig. S2B) whereas the egg (Fig. S2C) and the nauplius (Fig. S2D) samples respectively
<figure><img src="image_66.png" /><figcaption>**Figure 1** Bacterial compositions and clustering of all samples. (A) Hierarchical clustering based on Bray-Curtis dissimilarity. In order to define clusters, a 1.5 threshold was set (represented by the red dotted line). All the water samples are clustered in Cluster 1, in light grey. All the egg and the nauplius (nii) samples are clustered in Cluster 2, in dark grey. (B) Bacterial compositions of the primary reservoir (ResI), the secondary reservoir (ResII), the egg and the nauplius (nii) samples. The highlighted bacterial classes have a total relative abundance higher than 1%.</figcaption></figure>

Full-size 🐶 DOI: 10.7717/peerj.12241/fig-1

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

shared 270 and 259 ASVs. We used these four ASV lists to build a final Venn diagram allowing us to highlight 623 distinct ASVs; specific and common among the compartments (Fig. 2). A total of 257 ASVs were uniquely found in the water samples (Fig. 2A). The primary and the secondary reservoirs respectively showed 145 and 33 specific ASVs while they shared 79 ASVs. All the considered samples shared 62 ASVs (Fig. 2B). In the same way as the water samples, 233 ASVs were only found in the egg and the nauplius samples (Fig. 2C); 88 ASVs were specific to the egg samples while 80 ASVs were uniquely highlighted in the nauplius samples. The eggs and the nauplii shared 65 ASVs that were not identified in the water samples.

## Specific and shared bacterial communities among samples

Based on the Venn diagram (Fig. 2), we analyzed the relative abundance of each subset of ASVs selected above by constructing 100% stacked histograms. The water samples showed specific ASVs (Fig. 3A). The microbiota specifically associated with the primary reservoirs showed 4 dominant groups which accounted for 30% of the total relative abundance
<figure><img src="image_68.png" /></figure>

**Figure 2** Venn diagram of shared ASVs among experiments and sample types. The green ellipse represents the ASVs common to all the egg samples. The blue ellipse represents the ASVs common to all the nauplius (nii) samples. The red ellipse represents the ASVs common to all the primary reservoir (ResI) samples. The yellow ellipse represents the ASVs common to all the secondary reservoir (ResII) samples. Numbers noted in the overlapping areas correspond to shared ASVs among compartments. Numbers noted outside of the overlapping areas correspond to specific ASVs. (A) Specific and shared ASVs among the water samples. (B) Shared core microbiota among all sample types. (C) Specific and shared ASVs among the egg and the nauplius samples.

Full-size

<figure><img src="image_69.png" /></figure>

DOI: 10.7717/peerj.12241/fig-2

(Fig. 3B): OM60 *Clade*, Sva0996 *marine group*, MB11c04 *marine group* and NS5 *marine group*. One ASV was not affiliated to the genus level (*Nitrincolaceae*) and represented 20% of the total relative abundance. *Candidatus Actinomarina*, *Synechococcus* and *SAR116 Clade* were shared between the primary and secondary reservoirs and accounted for 49% of the total relative abundance (Fig. 3C). The secondary reservoir samples highlighted a very specific and highly represented ASV affiliated to *Pedosphaeraceae* family which accounted for 80% of the total abundance (Fig. 3D).

A total of 62 ASVs were shared among all the samples (Fig. 4A). In this microbiota, 15 genera and 4 ASVs which were not affiliated to the genus level were highlighted. Even though all these bacterial lineages were found in all samples, their total relative abundance varied. Indeed, the ResI samples were dominated (60% of the total relative abundance) by *SAR11 Clade Ia*, *HIMB11* and by an ASV affiliated to the *AEGEAN 169 marine group* (Fig. 4B). The secondary reservoirs showed higher abundances for 3 ASVs which were affiliated to *Candidatus Endobugula*, *Gimesiaceae* and *NRL2* (Fig. 4C). In both egg (Fig. 4D) and nauplius (Fig. 4E) samples, *Aestauriibacter*, *Alteromonas* and *Vibrio* genera represented over 50% of the total relative abundance.

<figure><img src="image_70.png" /></figure>
<figure><img src="image_71.png" /></figure>

**Figure 3** Bacterial communities associated with the primary and the secondary reservoirs. Top bacterial lineages (up to genus when possible) with a relative abundance higher than 1% and specifically found in (A) the primary and the secondary reservoir water samples (ResI and ResII). Histograms were built using the abundance of the selected subset of ASVs. Top bacterial lineages (B) uniquely found in the primary reservoir (ResI) samples (148 ASVs, in red in the Venn diagram), (C) shared between all the water samples (79 ASVs, in the intersection of the red and yellow ellipses) and (D) uniquely found in the secondary reservoir (ResII) samples (33 ASVs, in yellow in the Venn diagram). The arrows represent the potential ASV transmission among compartments.

Full-size 🅒 DOI: 10.7717/peerj.12241/fig-3

<figure><img src="image_72.png" /></figure>
<figure><img src="image_73.png" /></figure>

**Figure 4** Bacterial communities associated with all samples. Top bacterial lineages (up to genus when possible) composing the (A) shared core microbiota among all samples (62 ASVs). Histograms were built using the abundance of the selected subset of ASVs. The highlighted bacterial lineages have a total relative abundance higher than 1% but are distributed unevenly among (B) the primary reservoir (ResI) samples, (C) the secondary reservoir (ResII) samples, (D) the egg samples and (E) the nauplius (nii) samples. The arrows represent the potential ASV transmission among compartments.

Full-size

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DOI: 10.7717/peerj.12241/fig-4
Bacterial compositions of communities specifically associated with the egg and the nauplius samples were evaluated alongside with transmitted ASVs between the two sample types (Fig. 5A). In the egg samples, 21 bacterial lineages accounted for more than 80% of the total relative abundance (Fig. 5B). *Pseudomonas* and *Corynebacterium* each had a relative abundance of 18%. *Acinetobacter* represented 8% of the total relative abundance while *Labrenzia* and *Rothia* each accounted for 6%. These five genera alone represented more than 55% of the total relative abundance among the bacterial communities specifically associated with the egg samples. Some ASVs seemed transmitted from the eggs to the nauplii (Fig. 5C). Among those ASVs, 21 bacterial lineages were highlighted and accounted for 90% of the total relative abundance. The most abundant genera were *Thalassolituus* (20%), *Vibrio* (18%), *Marinobacter* (5%), *Aureispira* (5%) and *Oleiphilus* (4%). One ASV accounted for 5% of the total relative abundance but could not be affiliated to the genus level (MBAE14 Order). Specific ASVs were also found in all the nauplius samples; 17 bacterial lineages were highlighted and represented 85% of the total relative abundance (Fig. 5D). *Profundimonas* and *Marinobacterium* respectively represented 15% and 8% of the total relative abundance. Two ASVs accounted for 28% of the abundance and were affiliated to SGC AAA286 E23 and Bacteroidia (unknown order).

DISCUSSION

Dealing with sequencing depths

Due to different sampling times and sequencing runs, the choice of bioinformatic
techniques for data analysis was very important. Thus, we considered and compared
several methods to cluster sequences and to normalize the considered dataset. During
the last decade, two ways have been used to cluster sequencing reads: the Operational
Taxonomic Units (OTUs) or the ASVs. OTUs cluster sequences which are identical up to
a fixed similarity threshold (usually 97%) while ASVs regroup identical sequences after
a sequencing error correction step (Callahan et al., 2016). Comparing the 2 methods,
it appears that biological conclusions are very similar (Allali et al., 2017; Glassman &
Martiny, 2018). However, ASVs are advised for improved reproducibility, comparison
across studies, meta-analysis and nucleotide-level resolution (Callahan, McMurdie &
Holmes, 2017; Porter & Hajibabaei, 2020). As the samples considered in our study had
been collected during 3 different experiments and were sequenced separately, we chose
to analyze sequencing data using the ASV method implemented in the DADA2 package
under RStudio (Callahan et al., 2016) in order to enable comparison among samples. After
sequencing data analysis, library sizes among samples varied from 15399 to 554056 reads
because of different sequencing depths. As a consequence, hierarchical clustering based
on Bray-Curtis dissimilarity of non-normalized data showed a clear sequencing depth
effect as samples sequenced together gathered together (Fig. S3). In order to compare
samples, data needed to be normalized. The rarefaction method has been extensively used
to normalize metabarcoding data but it leads to non-reproducible tables and randomly
suppresses OTUs or ASVs (McMurdie & Holmes, 2014). The edgeR and DESeq methods
have been advised but involve the use of logarithms and do not handle well null values
<figure><img src="image_75.png" /></figure>

**Figure 5** Bacterial communities associated with the eggs and the nauplii. Top bacterial lineages (up to genus when possible) with a realtive abundance higher than 1% and specifically found in (A) the egg and the nauplius (nii). Histograms were built using the abundance of the selected subset of ASVs. Top bacterial lineages (B) uniquely found in the egg samples (88 ASVs, in green in the Venn diagram), (C) shared between all the egg and the nauplius (nii) samples (65 ASVs, in the intersection of the green and blue elipses) and (D) uniquely found in the nauplius (nii) samples (88 ASVs, in blue in the Venn diagram). The arrows represent the potential ASV transmission among compartments.

<figure><img src="image_76.png" /></figure>
(Weiss et al., 2017) which were quite abundant in our dataset. A recent study showed that simpler methods lead to satisfying normalizations (Bushel et al., 2020). Thus, we chose the Count Per Million (CPM) method which enabled hierarchical clustering of the samples according to their bacterial compositions rather than their sequencing runs.

Chemicophysical and microbial quality of the water samples

CDOM concentrations were constant throughout all the water samples. However, SR values decreased from the primary to the secondary reservoirs during the M1 experiment and opposite tendencies were observed during the M3 and M4 experiments (Table 1). Shifts in SR can be correlated to molecular weight and composition modifications of the dissolved organic matter (Helms et al., 2009) suggesting that such changes occurred in the reservoirs and were of different natures during the M1 experiment compared to the M3 and M4 experiments. Overall, all the water samples showed a large abundance of Gammaproteobacteria and Alphaproteobacteria (Fig. 1B) which are respectively dominant classes in pelagic and benthic marine environments (Zinger et al., 2011). The primary reservoirs and the M1_ResII sample were also majorly composed of Acidimicrobia, Cyanobacteriia and Bacteroidia, 3 classes previously identified in pelagic environments and marine ecosystems (Giovannoni & Stingl, 2005; Zinger et al., 2011). The M3 and M4 secondary reservoirs respectively displayed important abundances of Planctomycetes and Verrucomicrobiae which have been found in soil, freshwater (Australian dam, Hungarian pond) and seawater (the Californian Current) (Fuerst, 1995; Freitas et al., 2012). CDOM measurements and bacterial compositions in water are known to be correlated factors (Judd, Crump & Kling, 2006). A change in the CDOM measurements can result in a change of the microbiota and vice versa. Thus, it is quite difficult to distinguish the cause and the consequence. However, both these factors can also be influenced by seasonal variations (Osterholz et al., 2018). As our samplings occurred between September 2018 and February 2019, seasonal variations could explain the differences observed in our samples in terms of SR tendencies and differential bacterial communities. However, as the primary reservoirs were quite similar and were a closer reflection of the natural conditions, the differences observed in the secondary reservoirs may also be explained by other factors. As we performed RNA extractions, we studied active prokaryotic communities (Blazewicz et al., 2013). We can thus hypothesize that differences in bacterial compositions, and therefore in CDOM profiles, may be due to different active microorganisms throughout the water treatments.

Specific bacterial communities associated with the primary reservoir samples were dominated by 4 marine groups (OM60 Clade, Sva0996, MB11c04 and NS5) (Fig. 3B). They have been identified as crucial communities in the marine environment and have been found in various ecosystems ranging from marine sediments and surface seawaters to polar and coastal marine ecosystems (Suzuki et al., 2001; Cho et al., 2007; Gómez-Pereira et al., 2010; Orsi et al., 2016). Specific ASVs were common to the primary and the secondary reservoirs (Fig. 3C) and were dominated by members of Candidatus Actinomarina, Synechococcus and SAR116. These bacterial groups are ubiquitous in marine ecosystems and are known as or linked to primary producers (Britschgi & Giovannoni, 1991;
Partensky & Vaulot, 1999; Ghai et al., 2013). This could suggest that these communities are present in the Saint Vincent Bay and persist throughout the reservoirs. However, specific ASVs associated with the secondary reservoirs (Fig. 3D) were largely dominated by an ASV affiliated to the *Pedosphaeraceae* family (Verrucomicrobiae class) suggesting that the water treatments between the primary and secondary reservoirs could select some bacterial communities. This has already been noticed by Vadstein et al. who showed that UV treatment of rearing water induced selection of fast-growing and opportunistic bacteria (Vadstein et al., 2018). The water samples considered in this study were collected before addition of ethylenediaminetetraacetic acid (EDTA) in the hatching tanks. To our knowledge, there is very few information about the effect of this metal chelator on active microbial communities in water. Thus, further investigations will be necessary to evaluate the role of EDTA on the microbiotas associated with the rearing water and the animals in shrimp hatcheries.

## Putative factors influencing the microbial communities associated with the eggs and the nauplii of *L. stylirsotris*

In the egg and the nauplius samples, Gammaproteobacteria, Alphaproteobacteria and Bacteroidia dominated (Fig. 1B). These classes have been identified in larvae of the Pacific white shrimp (*Litopenaeus vannamei*) at early developmental stage (Pangastuti et al., 2009; Zheng et al., 2017) but also in the intestines of adult black tiger shrimps (*Penaeus monodon*) (Rungrassamee et al., 2013). Eggs showed members of the Fusobacteriia class which have also been identified in adult shrimps (Rungrassamee et al., 2016; Zeng et al., 2017) indicating a possible preservation of bacterial communities throughout the whole lifecycle of shrimps. Overall, all the considered samples displayed similar bacterial compositions suggesting that the microbiotas of the eggs and the nauplii could also be influenced by their rearing environments (Fig. 1B). A total of 62 ASVs were shared among all compartments (Fig. 2B) and were considered to be the core microbiota of all our samples (Shade & Handelsman, 2012). Aesuariibacter, *Alteromonas* and *Vibrio*, which are ubiquitous genera in marine (Chan et al., 1978; Yi, Bae & Chun, 2004; Farmer et al., 2015), were present in all the samples but dominated the eggs and the nauplii (Figs. 4D and 4E). Bacteria affiliated to *Alteromonas* and *Vibrio* produce extracellular hydrolases involved in the biodegradation of various organic carbon sources (Chan et al., 1978; Farmer et al., 2015). *Vibrios* are also known to express genes involved in the metabolic degradation of chitin (Hunt et al., 2008), an important component of the exoskeleton of the nauplii. As mouth opening occurs after the nauplius stage in *Litopenaeus* shrimps (Wang et al., 2020a; Wang et al., 2020b), the vitellus is the only nutrient and energy resource until the zoae stage (Harrison, 1997). Thus, these bacterial communities may be less represented in water and could find favorable organic carbon sources and growth conditions on the external layer of the eggs and the exoskeleton of the nauplii (Hansen & Olafsen, 1999). Indeed, the microorganisms that were more represented in the water samples were completely different and belonged to very common marine groups (HIMB11, SAR11, AEGEAN) (Morris et al., 2002; Durham et al., 2014; Cram et al., 2015). As it has been shown for other animal species (brown trout, coho salmon), the total
microbiota of the eggs and the nauplii may embrace the epibiota at the surface but also the endobiota (Nyholm, 2020).

Bacterial communities specifically associated with the egg samples (Fig. 5B) were dominated by *Pseudomonas*, *Acinetobacter* and *Labrenzia*, 3 genera found in marine environments (Fournier & Richet, 2006; Palleroni, 2015; Raj Sharma et al., 2019) and which had already been studied during the development of *L. vannamei* and *P. monodon* (*Rungrassamee et al.*, 2013; *Wang et al.*, 2020a; *Wang et al.*, 2020b). The egg samples were also composed of *Corynebacterium*, a genera isolated from coral mucus (Ben-Dov et al., 2009). The eggs finally displayed large abundances of *Rothia*, usually found in human mouth cavities (*Tsuzukibashi et al.*, 2017) and considered as sample contaminants in vent shrimps (*Methou et al.*, 2019). Like *Alteromonas* and *Vibrios*, *Pseudomonas* and *Corynebacterium* are able to degrade different organic carbon sources using extracellular enzymes and could thus find optimal growth conditions with shrimp eggs and the nauplii explaining why they were so abundant among these samples (Palleroni, 2015; Tsuzukibashi et al., 2017). Concerning *Labrenzia*, they are known to produce labrenzbactin, a catecholate-containing siderophore which demonstrates an antimicrobial activity against *Micrococcus luteus* (Raj Sharma et al., 2019). Interestingly, *M. luteus* and *Rothia* are both affiliated to the *Micrococcaceae* family and have been respectively identified in the hepatopancreas of healthy adult shrimps (*L. vannamei*) and in the eggs of vent shrimps (Durán-Avelar et al., 2018; *Methou et al.*, 2019). This suggests that *Rothia*, may not be a sample contaminant as previously stated by *Methou et al.* (2019). Further investigations will be necessary in order to determine if this genus is part of the core microbiota of the eggs of *L. stylirostris* or not. Either way, all the ASVs that were not identified in the water environment could have been potentially vertically transmitted from the breeders. Vertical transmission has also been suggested in hydrothermal shrimps where the eggs were associated with specific bacterial communities that were not found in the environment (Methou et al., 2019). Intraovum vertical transmission has already been highlighted in farmed fish eggs, accounting for 20% of the total bacterial relative abundance in eggs (Hansen & Olafsen, 1999). Furthermore, a vertical transmission of 2 virus types has been proved using molecular techniques in the redclaw crayfish cultured in Australia (Jaroenram et al., 2021). Thus, supporting our hypothesis of a potential vertical transmission in the Pacific blue shrimp.

Just as the eggs, the nauplii also displayed specific bacterial communities that were not found in the other samples (Fig. 5D). The most abundant genera were *Profundimonas* and *Marinobacterium* which are both aerobic Gammaproteobacteria isolated from seawater samples (González et al., 1997; Cao et al., 2014). *Marinobacterium* have also been highlighted in pipefish as a potential beneficial bacterium to larvae (*Beemelmanns et al.*, 2019) and some strains are also known to hydrolyse chitine suggesting that they could play an important role in growth at nauplius stage. These ASVs were specifically identified in the nauplius samples and could have been vertically transmitted as well as for the eggs. Once again, as we studied active microbial communities, we can hypothesize that some bacterial communities are acquired from breeders and are activated at different developmental stages.
The nauplius samples could also be under the influence of bacterial communities associated with the eggs as specific ASVs seemed to be transmitted between the 2 sample types (Fig. 5C). These communities were mainly composed of *Vibrio* and *Oleiphilus* which have been identified in several marine environments and animals (Golyshin et al., 2002; Farmer et al., 2015). Aureispira bacteria were also abundant. They have been isolated from marine sponges and algae and are thus frequently found in marine organisms (Hosoya et al., 2006). Marinobacter and Thalassolituus have been isolated in farmed shrimp experiments (Dineshkumar et al., 2014). Thalassolituus has also been identified at the nauplius stage of *L. vannamei* and has been suggested as a beneficial bacterium for larval development (Wang et al., 2020a; Wang et al., 2020b). Vibrio and Thalassolituus have also been identified in healthy adult shrimps (Rungrassamee et al., 2016; Wang et al., 2020a; Wang et al., 2020b) supporting our previous theory that some bacterial communities may be acquired from the breeders and kept throughout the whole lifecycle. Even though Vibrio sp. can be associated with diseases, they have been identified in adults and nauplii of *L. vannamei* shrimps regardless of their health status (Vandenberghe et al., 1999; Cornejo-Granados et al., 2017). Some species are even known to be selected in the environment by their host as symbionts (Nyholm & McFall-Ngai, 2004).

## Possible dynamic interactions among compartments

Interestingly, five genera (*Vibrio*, *Marinobacter*, *Thalassotalea*, *Alteromonas* and *Aestuaribacter*) were found in the ASVs shared between the eggs and the nauplii (Fig. 5C) but also in the ASVs shared between the water reservoirs and the animals (Fig. 4). *Marinobacter* and *Aestuariibacter* have been identified in the rearing environment of adult *L. vannamei* and brackishwater shrimps (Chen et al., 2019; Dineshkumar et al., 2014). *Vibrio*, *Alteromonas* and *Thalassotalea* have all been spotted in *L. vannamei* at different developmental stages as well as in the surrounding water (Wang et al., 2020a; Wang et al., 2020b; Zhang et al., 2014; Zheng et al., 2017). In our study, these genera were highlighted from different ASVs in different compartments, suggesting that some taxa may be acquired from the aquatic environment by the eggs and the nauplii but also by the breeders which then potentially transmit them to their offspring, supporting the hypothesis of a possible vertical transmission of bacterial communities in *L. stylirostris* shrimp larvae (Fig. 6). This also shows that the microbiota associated to aquatic organisms and to their eggs are shaped by complex interactions between environmental and transmitted microorganisms (Sylvain & Derome, 2017).

## CONCLUSIONS

To our knowledge, this study is the first to focus on the active microbiota of the eggs and the nauplii of a farmed shrimp species while considering the impact of the rearing water. Taken together, our results provide evidence of a core microbiota (*Aestuariibacter*, *Alteromonas*, *Vibrio*, *SAR11*, *HIMB11*, *AEGEAN 169 marine group*, *Candidatus Endobugula*) among all samples suggesting a microbial transmission from the surrounding environment to the animals. As several ASVs were co-owned between the eggs and the nauplli, we also highlight a possible vertical transmission between the two compartments and potentially from the
<figure><img src="image_77.png" /><figcaption>**Figure 6** Schematic representation of potential microbial transmission and expression throughout the lifecycle of Litopenaeus stylirostris. The arrows represent potential lineage transmissions.</figcaption></figure>

Full-size Ⓟ DOI: 10.7717/peerj.12241/fig-6

<figure><img src="image_78.png" /></figure>

breeders as specific ASVs were not found in the rearing water (affiliated to *Pseudomonas*, *Corynebacterium*, *Acinetobacter*, *Labrenzia*, *Rothia*, *Thalassolituus*, *Marinobacter*, *Aureispira*, *Oleiphilus*, *Profundimonas*, *Marinobacterium*) (Fig. 6). This last point has not yet been confirmed as several male breeders are used to inseminate several females which produce eggs that are pooled before larval rearing, thus making it quite difficult to determine the genealogy of the shrimps and their offspring.

**ACKNOWLEDGEMENTS**

We are grateful to the SASV teams for their help throughout all the experiments: zootechny and laboratory support. We especially thank Jean-Sébastien Lam and Julien Le Rohellec for their precious help in the hatchery; Florence Antypas and Etienne Lopez for their contribution; as well as Valentine Ballan and Gwenola Plougoulen for their valuable biomolecular work.
**ADDITIONAL INFORMATION AND DECLARATIONS**

**Funding**

This work was supported by the RESSAC project (LEAD-NC, Ifremer New-Caledonia) within the framework agreement with the New Caledonian Provinces and Government and by the Pacific Doctoral School. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

**Grant Disclosures**

The following grant information was disclosed by the authors:
The RESSAC project (LEAD-NC, Ifremer New-Caledonia) within the framework agreement with the New Caledonian Provinces and Government and by the Pacific Doctoral School.

**Competing Interests**

The authors declare there are no competing interests.

**Author Contributions**

* Carolane Giraud analyzed the data, prepared figures and/or tables, authored or reviewed drafts of the paper, and approved the final draft.

* Nolwenn Callac conceived and designed the experiments, performed the experiments, analyzed the data, prepared figures and/or tables, authored or reviewed drafts of the paper, and approved the final draft.

* Maxime Beauvais performed the experiments, analyzed the data, authored or reviewed drafts of the paper, and approved the final draft.

* Jean-René Mailliez, Dominique Ansquer and Dominique Pham performed the experiments, authored or reviewed drafts of the paper, and approved the final draft.

* Nazha Selmaoui-Folcher conceived and designed the experiments, authored or reviewed drafts of the paper, and approved the final draft.

* Nelly Wabete and Viviane Boulo conceived and designed the experiments, performed the experiments, authored or reviewed drafts of the paper, and approved the final draft.

**Data Availability**

The following information was supplied regarding data availability:
Data is available at NCBI SRA repository under the submission ID SUB9823633 and BioProject ID PRJNA736535.

**Supplemental Information**

Supplemental information for this article can be found online at http://dx.doi.org/10.7717/peerj.12241#supplemental-information.

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## IV.1.3- Synthèse des résultats et perspectives

Les premiers résultats de cette étude concernent la qualité des eaux de réserve primaire (eau de remplissage provenant du lagon) et secondaire (eau traitée stockée) récoltées au cours des 3 campagnes d'échantillonnage considérées (M1, M3, M4). Alors qu'une concentration constante en matière organique dissoute (CDOM, aλ325) a été observée dans l'ensemble des échantillons analysés, les SR ont montré des profils de variations différents d'une réserve à l'autre et d'une campagne à l'autre. En effet, au cours des campagnes M3 et M4, les valeurs de SR ont diminué entre les réserves primaire et secondaire alors qu'une tendance inverse a été constatée pour la campagne M1. Le SR étant un reflet de l'évolution du poids moléculaire de la matière organique dissoute, les variations observées témoignent de compositions physico-chimiques potentiellement différentes entre les réserves (Helms et al. 2009). En parallèle, des profils de diversité bactérienne distincts ont également été mis en évidence. Malgré des proportions relatives d'Alphaproteobacteria et de Gammaproteobacteria similaires dans l'ensemble des échantillons, des abondances relatives plus importantes de Cyanobacteria, d'Acidimicrobia et de Bacteroidia ont été observées dans toutes les réserves primaires ainsi que la réserve secondaire de la M1, alors que les réserves secondaires de la M3 et de la M4 étaient respectivement plus riches en Planctomycetes et Verrucomicrobiaea. Dans l'eau de mer, la matière organique dissoute et la diversité microbienne sont des paramètres corrélés et sont susceptibles d'être influencés par des facteurs naturels comme les saisons (Osterholz et al. 2018). Les échantillonnages de notre étude ayant été réalisés à des périodes différentes entre 2018 et 2019, les fluctuations observées en termes de SR et de compositions procaryotiques pourraient ainsi être expliquées par les variations saisonnières. Cependant, malgré des environnements d'élevage larvaire changeants, un core microbien de 62 ASVs a été mis en évidence. Bien que ces ASVs aient été présents dans l'ensemble des échantillons considérés, nous avons constaté des variations de leurs abondances relatives. En effet, des proportions plus importantes des genres Aestuariibacter, Alteromonas et Vibrio ont été observées dans les œufs et les larves au stade nauplius alors que les réserves montraient des abondances relatives plus importantes de bactéries affiliées aux groupes marins HIMB11, AEGEAN 169 et SAR11. Au cours de cette étude, les prélèvements d'eau ont été effectués en amont des prélèvements d'œufs et de larves nauplii. La persistance de certaines bactéries, provenant de l'environnement d'élevage, au moment de la mise en place du microbiote larvaire suggère ainsi un potentiel impact du microbiote de l'eau d'élevage. Certains taxa bactériens pourraient être horizontalement transmis de l'environnement d'élevage vers la surface des œufs et des larves au stade nauplius et ainsi former l'épibiote de ces derniers. Cependant, une internalisation de certains microorganismes pourrait également être possible. Au-delà du core microbien mis en évidence, plusieurs ASVs spécifiquement associés aux œufs et aux nauplii ont également été observés. Les œufs présentaient des ASVs spécifiques, affiliés aux genres Pseudomonas, Acinetobacter, Labrenzia, Corynebacterium et Rothia alors que les larves au stade nauplius étaient distinctement associées à des Profundimonas et des Marinobacterium. Les ASVs partagés par les œufs et les larves au stade nauplius étaient affiliés aux genres Vibrio, Oleiphilus, Aureispira, Marinobacter et Thalassolituus. Certains de ces taxa bactériens (Pseudomonas, Acinetobacter, Labrenzia, Corynebacterium, Marinobacter et Thalassolituus) ont été mis en évidence, dans la littérature, chez d'autres espèces de crevettes à différents stades de développement suggérant une conservation de ces communautés microbiennes tout au long du cycle de vie des animaux. Ces microorganismes
n'ayant pas été observés dans les eaux de réserve au cours de l'analyse, nous avons émis l'hypothèse de l'existence d'une potentielle transmission microbienne verticale des géniteurs vers les larves. Cependant, l'absence de données concernant les microorganismes associés aux géniteurs limitaient nos interprétations. De plus, les échantillonnages d'eau de réserve ayant été réalisés en amont du remplissage des bacs d'élevage et donc en amont du traitement aux ultra-violets et de l'ajout d'EDTA (chap. II, partie II.1.1.a), il est possible que les échantillons considérés ne soient pas un reflet exact de l'environnement d'élevage des œufs et des larves au stade nauplius. Une étude complémentaire a donc été conduite afin d'évaluer plus précisément les facteurs influençant la mise en place du microbiote des œufs et des larves nauplii de la crevette bleue du Pacifique.

## IV.2- Étude B – Contributions des géniteurs et de l'eau d'élevage au cours de la mise en place du microbiote larvaire

### IV.2.1- Introduction

La découverte de communautés procaryotiques spécifiquement associées aux œufs et aux larves nauplii de *P. stylirostris* au cours de l'étude A (article 1, partie IV.1.2) suggérait l'influence d'une potentielle transmission verticale de certains procaryotes des géniteurs vers les larves. Ce processus a déjà été décrit chez l'humain où l'acquisition du microbiote intestinal, initié dès la naissance des nouveaux nés, est dépendante de certaines communautés microbiennes provenant de la mère. En effet, il a été montré que des nourrissons nés par voie basse présentaient des bactéries intestinales proches de celles du vagin alors que ceux nés par césarienne possédaient des communautés similaires à celles de la peau. Il a également été prouvé que les bactéries productrices d'acide lactique (*Lactobacilli*) provenant du lait maternel prédominaient les premiers microorganismes du tube digestif (Cho & Blaser 2012). Chez les organismes marins, et plus particulièrement chez la truite arc-en-ciel *Oncorhynchus mykiss*, une transmission verticale de la bactérie pathogène *Piscirickettsia salmonis* a été mise en évidence. Des fécondations *in vitro* réalisées à partir de gamètes provenant de géniteurs infectés ont montré que la présence d'un seul gamète infecté suffisait à assurer une transmission bactérienne aux larves (même si les larves obtenues ne présentaient aucun signe de maladie). Par une approche de microscopie électronique à balayage, les auteurs ont également montré que les bactéries étaient capables d'adhérer à la surface de l'ovule puis d'être complètement internalisées 5 minutes après la première exposition bactérienne (Larenas et al. 2003). De manière générale, plusieurs mécanismes de transmission verticale de symbiontes ont été mis en évidence chez les poissons. Les microorganismes peuvent ainsi être directement transmis à l'intérieur ou à la surface de l'œuf mais peuvent aussi être présents dans l'environnement des œufs ou des larves (Nyholm, 2020). Par exemple, chez le *Discus Symphysodon aequifasciatus*, les larves se nourrissent du mucus de leurs parents au cours des 3 premières semaines post-éclosion et acquièrent ainsi certaines des communautés microbiennes épibiotiques de leurs géniteurs (Sylvain & Derome 2017). En 2019, une étude comparative des pléopodes et des œufs de crevettes hydrothermales *Rimicaris exoculata* a mis en évidence un ordre bactérien (*Entomoplasmatales*) exclusivement présent à la surface des œufs. Les auteurs ont suggéré qu'il pourrait provenir du microbiote intestinal maternel et être émis au moment de la ponte (Methou et al. 2019). Ces différentes études laissent à penser que les géniteurs pourraient jouer un rôle important dans la mise en place des communautés microbiennes de leurs
Chapitre IV – Des transmissions horizontales et verticales modulent le microbiote actif des œufs et des nauplii de Penaeus stylirostris

progénitures. Afin d’évaluer la possibilité d’un tel processus de transmission verticale bactérienne chez *P. stylirostris*, des inséminations artificielles ont été réalisées pour produire 3 familles biparentales. Pour chaque famille, des échantillons d’organes reproducteurs femelles et mâles ainsi que d’œufs et de larves au stade nauplius ont été prélevés (chap. II, partie II.2.2). L’eau contenue dans chaque pondoir a également été échantillonnée après traitement et ajout d’EDTA (chap. II, partie II.1.1.a). Après des extractions d’ARNs et des rétrotranscriptions en ADNc réalisées à partir de l’ensemble des échantillons considérés, un séquençage Illumina HiSeq (2 x 150pb) a été effectué. Les données de séquençage ont été traitées avec le package DADA2 (Callahan et al. 2016) (chap. III, partie III.3.2) sous RStudio (chap. II, partie II.5.2). Une classification ascendante hiérarchique basée sur une matrice de distance de Bray-Curtis et sur la méthode de Ward, ainsi que des histogrammes et des diagrammes de Venn ont été construits (détailleé dans le chap. II, partie II.5.3).
## IV.2.2- Article 2 – The active microbiota of the eggs and the nauplii of the Pacific blue shrimp *Litopenaeus stylirostris* partially shaped by a potential vertical transmission

# The Active Microbiota of the Eggs and the Nauplii of the Pacific Blue Shrimp *Litopenaeus stylirostris* Partially Shaped by a Potential Vertical Transmission

### OPEN ACCESS

**Edited by:**
Seyed Hossein Hoseinifar,
Gorgan University of Agricultural
Sciences and Natural Resources, Iran

**Reviewed by:**
Zhijian Huang,
Sun Yat-sen University, China
Tomoo Sawabe,
Hokkaido University, Japan

*Correspondence:
Carolane Giraud
cgiraud@ifremer.fr
Nolwenn Callac
ncallac@ifremer.fr

¹Present address:
Viviane Boulo,
IHPE, Université de Montpellier,
CNRS, Ifremer, Université de
Perpignan Via Domitia, Montpellier,
France

**Specialty section:**
This article was submitted to
Microbial Symbioses,
a section of the journal
Frontiers in Microbiology

Received: 28 February 2022

Accepted: 22 April 2022

Published: 12 May 2022

**Citation:**
Giraud C, Callac N, Boulo V, Lam J-S, Pham D, Selmaoui-Folcher N and Wabete N (2022) The Active Microbiota of the Eggs and the Nauplii of the Pacific Blue Shrimp *Litopenaeus stylirostris* Partially Shaped by a Potential Vertical Transmission. *Front. Microbiol.* 13:886752. doi: 10.3389/fmicb.2022.886752

Carolane Giraud¹,²*, Nolwenn Callac¹*, Viviane Boulo¹†, Jean-Sébastien Lam¹,
Dominique Pham¹, Nazha Selmaoui-Folcher² and Nelly Wabete¹

¹UMR 9220 ENTROPIE, Ifremer (LEAD-NC), Noumea, New Caledonia, ²Institut des Sciences Exactes et Appliquées (ISEA), University of New Caledonia, Noumea, New Caledonia

The many ecological niches present in an organism harbor distinct microorganisms called microbiota. Different factors can influence the establishment of these commensal microbial communities. In a previous article, we have concluded that some bacterial lineages associated with the early larval stages of the Pacific blue shrimp *Litopenaeus stylirostris* could be acquired from the breeders *via* a potential vertical transmission. The present study was conducted in order to investigate this hypothesis. Using HiSeq sequencing of the V4 region of 16S rRNA gene, we analyzed the active microbiota associated with the eggs and the nauplii of *L. stylirostris* as well as with the reproductive organs of their breeders. Microbial communities associated with the rearing water were also considered to discriminate environmental microbial lineages. Using these analyses, we highlight a set of core bacterial families present in all samples and composed of members of *Colwelliaceae*, *Alteromonadaceae*, *Pseudoalteromonadaceae*, *Saccharospirillaceae*, *Oceanospirillaceae*, *Vibrionaceae*, *Burkholderiaceae*, *Rhodobacteraceae*, *Flavobacteraceae*, and *Corynebacteriaceae*; showing the importance of the environment in the establishment of the larval microbiota. We also present specific bacteria affiliated to the *Arcobacteraceae*, *Rhodobacteraceae*, *Comamonadaceae*, and *Colwelliaceae* families, which were only found in the breeders and their offspring strengthening the hypothesis of a potential vertical transmission shaping the active microbiota of the eggs and the nauplii of *L. stylirostris*.

**Keywords:** active microbiota, shrimps, eggs, nauplii, vertical transmission, microbial colonization, core microbiota

### INTRODUCTION

In animals and plants, temperature, pH, and oxygen levels as well as nutrient accessibility vary throughout the organism resulting in many different available ecological niches (Ikeda-Ohtsubo et al., 2018). These contrasting environments can be colonized by various and distinct microorganisms. Microorganisms associated with organs covered by epithelial cells and in
contact with the external environment of the host (gut, skin, oral cavity, urogenital, and respiratory tracts) are referred to as commensal microorganisms (Tlaskalová-Hogenová et al., 2004; Aas et al., 2005; Whiteside et al., 2015; Ikeda-Ohtsubo et al., 2018). All the microbial taxa inhabiting a given environment in a host form a microbiota (Ursell et al., 2012). In all organisms, the microbiota is known to have great importance in the health status of the host as it plays a key role in digestion, immunity, nutrient intake, development, and behavior (Cho and Blaser, 2012). In addition, studies have reported that microbiota dysbiosis is often linked to disease outbreak (Egan and Gardiner, 2016). Even though evidence is mounting on the essential role of the microbiota, very little is known about the establishment of these commensal microbial communities in a given tissue. Some studies suggest that environmental factors shape the microbiota. For example, studies have shown that diet influences the microorganisms associated with the gut of humans, fish, shrimps, and crabs (Nayak, 2010; Ursell et al., 2012; Sun and Xu, 2021). However, other factors can also be involved in the modulation of the microbiota such as host selection pressure and genetic background (Li et al., 2012; Sun and Xu, 2021). Interestingly, commensal microorganisms can also be transmitted from one generation to another *via* a vertical transmission. In humans, studies have shown that the microbiota of infants was similar to the vaginal microbiota of their mother after natural birth but mimicked the skin microbiota when caesarean section was performed (Cho and Blaser, 2012; Ursell et al., 2012). Vertical transmission of bacteria has also been suggested in marine organisms such as fish and sponges and has been experimentally proved in salmons (Larenas et al., 2003; Lee et al., 2009; Ikeda-Ohtsubo et al., 2018; Nyholm, 2020). Furthermore, mother to offspring bacterial transmission has been strongly hypothesized to modulate the microbiota of the eggs of the vent shrimp *Rimicaris exoculata* (Guri et al., 2012; Methou et al., 2019).

In a previous study that we conducted, we have also suggested that some bacterial strains could be vertically transmitted in the Pacific blue shrimp *Litopenaeus stylirostris* (Giraud et al., 2021). Indeed, studying the active microbiota associated with the early larval stages (eggs and nauplii) of *L. stylirostris* as well as with their rearing environment, several bacterial families were specifically associated with the eggs and the nauplii and were not found in the environment leading us to hypothesize that they had been acquired from the breeders. In order to determine if microbial vertical transmission is indeed involved in the establishment of commensal microbial communities in *L. stylirostris*, we present a new study focusing on the active microbiota of the eggs and the nauplii of *L. stylirostris*. We also investigate the microbial communities associated with their rearing environment as well as with the female and male reproductive organs of their breeders. We highlight a core microbiota associated with all the samples showing the importance of environmental factors in the establishment of commensal microorganisms in the animals. We also identify several bacterial families specific to the breeders and their offspring strengthening our hypothesis of a vertical transmission in *L. stylirostris*. Some specific bacterial lineages associated

with the eggs and the nauplii also suggest the influence of other underestimated factors. Nevertheless, this study brings insight on the establishment of the active microbiota associated with the first larval stages of a farmed shrimp species.

## MATERIALS AND METHODS

### Study Design and Sample Collection

Eggs and nauplii of *L. stylirostris* were supplied by the experimental shrimp hatchery located at the Saint Vincent Bay (Ifremer, Boulouparis, New Caledonia) in May 2021. Breeders were reared in earthen ponds before transfer to maturation tanks according to the method described in Pham et al. (2012). Tanks in the maturation and in the hatchery were filled with treated natural seawater from the Saint Vincent Bay as described in Giraud et al. (2021). Water from three different hatchery tanks was sampled after addition of ethylenediaminetetraacetic acid (EDTA; **Figure 1**). For each tank, 1L of seawater was filtered on a 0.2µm pore size filter (S-PAK membrane filter, Millipore) and filters were stored at -80°C until further RNA extractions.

In order to study two-parent families, artificial inseminations were conducted using one male to inseminate one female on the day the hatchery tanks were filled and sampled. After artificial inseminations, females were transferred into the hatchery tanks at a rate of 1 individual per tank and left to spawn (Figure 1). Males were dissected using sterilized tweezers and scissors to sample their reproductive organs (testicular diverticula). After spawning, females were retrieved and dissected in the same way as the males in order to collect their ovarian tissues. All the collected reproductive organs were rinsed with sterilized seawater and stored in 2ml sterile microtubes. After spawning, around a hundred eggs from each tank were collected in a 2ml sterile microtube using sterilized pliers. The following day, before transfer into rearing tanks for larval rearing, nauplii were separated from non-hatched eggs and abundantly rinsed. Around a hundred nauplii were sampled from each tank in the same way as the eggs. All the collected samples were stored at -80°C until further RNA extractions.

A total of three two-parent families were considered for this study. For each family unique samples of male and female reproductive organs, eggs, and nauplii were collected (Figure 1). All water samples collected from the hatchery tanks as well as male and female reproductive organs are respectively called “Tank,” “Male,” and “Female” in the rest of the article. Egg and nauplius (nii) samples are referred to as such in the text. All sample names are followed by a number ranging from 1 to 3. Samples with the same number belong to the same two-parent family.

### RNA Extractions and Sequencing

In order to study the active microbial communities (Yates et al., 2021) associated with all the considered samples, RNA extractions were performed using the RNeasy PowerWater kit (Qiagen) for the filters, the eggs and the nauplii; and using the RNeasy minikit (Qiagen) for the male and the female reproductive organs. All the extracted RNAs were reverse-transcribed into complementary
<figure><img src="image_79.png" /></figure>

**FIGURE 1** | Sample collection. Sample collection was performed using an eight-steps technique. First, water was sampled from the hatchery tanks after ethylenediaminetetraacetic acid (EDTA) addition. Then, on the day the tanks were filled and sampled, artificial inseminations were performed. In order to consider two-parent families only, one male was used to inseminate one female for all the artificial inseminations. After artificial inseminations, males were dissected in order to sample their reproductive organ and females were transferred into the hatchery tanks at a rate of 1 individual per tank and were left to spawn. After spawning, females were retrieved to dissect their ovarian tissue, and eggs were sampled from all the hatchery tanks. To allow larval hatching, eggs were left until the next day in the hatchery tanks. Before transfer into rearing tanks, the nauplii were sampled from each tank. A total of three two-parent families were considered for this study.

DNAs (cDNAs) using a two-step technique. The first step consisted in adding 200 ng of RNAs to a reaction mix [buffer 5X, dNTP 10 mM, random hexamers 50 µM, reverse transcriptase M-MLV (PROMEGA) 200 u.µl⁻<fnref n="1" />, RNAse/DNAse free water] and performing reverse transcription in a thermocycler during 10 min at 25°C, 2h at 42°C, and 5 min at 85°C. The second step was performed using the Second Strand cDNA Synthesis kit (ThermoFisher).

All the double-stranded cDNAs obtained were sent to MrDNA (Shallowater, TX, United States) in order to amplify and sequence the V4 region of the 16S rRNA gene using the 515F/806R primers (Caporaso et al., 2011). An HiSeq Illumina sequencing was conducted using a 2 x 300 pb paired end run and an average sequencing depth of 50 K raw reads per sample.

## Microbiota Analysis

Raw sequences were treated using the DADA2 package under RStudio (Callahan et al., 2016; RStudio Team, 2020) as described in Giraud et al. (2021). After the obtention of the ASV table, sequences affiliated to eukaryotas, chloroplasts, and mitochondrias were removed before further analysis.

A dendrogram based on a Bray-Curtis dissimilarity matrix and Ward method was constructed using the vegan package in RStudio (Oksanen et al., 2020). Histogram tables and diversity

indexes were obtained using the phyloseq package in Rstudio (McMurdie and Holmes, 2013). Significative differences among diversity indexes were evaluated performing a Kruskal-Wallis test and a Dunn post-test. Venn diagrams were made using the open-source component for web environment jvenn (Bardou et al., 2014).<fnref n="1" /> Core microbiotas were determined using the constructed Venn diagrams based on shared membership of ASVs (Shade and Handelsman, 2012).

All the 16S rRNA data are available in the NCBI SRA repository under the BioProject PRJNA736535 (SRA accession numbers from SRR17301487 to SRR17301501).

## RESULTS

### Total Microbial Diversity of all the Samples

A total of 5,409,237 reads were obtained from the HiSeq Illumina sequencing of all samples. Sequences were clustered into 2,344 ASVs. The smallest and largest libraries were respectively composed of 109,259 and 955,820 reads and respectively corresponded to the Egg_3 and Nii_1 (nauplii) samples.

<footnote n="1">¹http://jvenn.toulouse.inra.fr/app/example.html</footnote>
Hierarchical clustering based on Bray-Curtis dissimilarity separated the samples into two distinct groups (Figure 2A). The first cluster (Cluster 1) gathered all the tank water and the nauplius samples, while the second cluster (Cluster 2) grouped all the breeder and the egg samples. For all samples, six dominant bacterial classes with a total relative abundance higher than 1% were highlighted (Figure 2B). *Gammaproteobacteria* and *Alphaproteobacteria*, respectively, accounted for 72 and 8% of the total relative abundance. *Bacteroidia* and *Actinobacteria* each represented 6% of the ASV table. Finally, *Clostridia* and *Bacilli*, respectively had a total relative abundance of 3 and 1%. The tank waters and the nauplii showed consistent total microbial compositions with high proportions of *Gammaproteobacteria* as well as *Alphaproteobacteria* and *Bacteroidia*. All the male samples also had homogeneous microbial communities but with larger proportions of *Actinobacteria* and *Clostridia* compared to the water and nauplii. Unlike the other samples (water, nauplii, and males), the females and the eggs displayed important inter-individual variabilities. Indeed, the Egg_2 and the Female_3 samples had similar compositions to the male samples with a microbial diversity mainly composed by *Clostridia*,

*Actinobacteria*, and *Proteobacteria*; whereas the other female and egg samples showed higher abundances of *Proteobacteria*.

## Shared ASVs Among Sample Types

Several Venn diagrams were constructed in order to highlight shared ASVs among the five considered sample types: eggs, nauplii, tank waters, female ovarian tissue, and male reproductive organs (Supplementary Figure S1). All the tanks shared 546 ASVs which represented 96% of the total relative abundance of all the water samples (Supplementary Figure S1A). The female reproductive organs displayed a core microbiota composed of 269 ASVs (86%; Supplementary Figure S1B). A total of 271 ASVs (83%) were shared among the male reproductive organs (Supplementary Figure S1C). Finally, the egg (Supplementary Figure S1D) and the nauplius (Supplementary Figure S1E) samples respectively shared 338 (85%) and 1,025 ASVs (99.5%). These five ASV lists were used to build two global Venn diagrams (Figure 3). The first global Venn diagram showed the specific and shared ASVs among the tank water, the egg and the nauplius samples (Figure 3A), whereas the second Venn diagram focused on the breeders and their offspring (Figure 3B).

<figure><img src="image_80.png" /><figcaption>**FIGURE 2** | Clustering and total microbial composition of all the samples. (A) Hierarchical clustering based on Bray-Curtis dissimilarity and Ward method. Clusters were defined using a 1.5 threshold depicted by the red dotted line. All the tank water and the nauplius (nii) samples are gathered in Cluster 1, in light gray. All the female, the male and the egg samples are clustered in Cluster 2, in dark gray. (B) Bacterial compositions of the tank water, the nauplius (nii), the egg, the female and the male samples. Major bacterial classes were defined as the bacterial classes with a total relative abundance higher than 1%.</figcaption></figure>
<figure><img src="image_81.png" /></figure>

**FIGURE 3** | Global Venn diagrams of shared and specific ASVs among sample types. (A) Venn diagram of specific and shared ASVs among the tank water, the egg and the nauplius (nii) samples. The blue ellipse represents the ASVs shared by all the hatchery tank water samples. The red ellipse represents the ASVs shared by all the egg samples. The yellow ellipse represents the ASVs shared by all the nauplius (nii) samples. (B) Venn diagram of specific and shared ASVs among the breeders (female and male), the egg and the nauplius (nii) samples. The green ellipse represents the ASVs shared by all the female reproductive organ samples. The purple ellipse represents the ASVs shared by all the male reproductive organ samples. The red ellipse represents the ASVs shared by all the egg samples. The yellow ellipse represents the ASVs shared by all the nauplius (nii) samples. In both Venn diagrams, numbers noted in the overlapping areas correspond to the number of shared ASVs among sample types, while numbers noted outside of the overlapping areas correspond to the numbers of specific ASVs associated with each sample type.

## Shared Bacterial Lineages Among all the Samples

The tank water, the egg and the nauplius samples shared 268 ASVs among which 11 dominant bacterial families with a total relative abundance higher than 1% were highlighted (Figure 4A). Comparing the microbial compositions of these samples, *Burkholderiaceae* seemed to be more abundant as the larvae grew, while the proportions of *Colwelliaceae* and *Alteromonadaceae* decreased. Furthermore, the abundance of *Vibrionaceae* was constant in the tank waters, the eggs, and the nauplii (Figure 4A). The eggs and the nauplii also shared 172 ASVs with the male and the female reproductive organs.

When considering this subset of ASVs, 15 dominant bacterial families were highlighted (Figure 4B). The distribution profiles of these families varied between sample types. Indeed, the female and the male reproductive organs as well as the eggs showed higher abundances of *Burkholderiaceae*, *Corynebacteriaceae*, and *Micrococaceae* compared to the nauplii. On the contrary, abundances of *Colwelliaceae*, *Alteromonadaceae*, and *Pseudoalteromonadaceae* increased in the nauplius samples (Figure 4B). Comparing the two histograms, 10 families were shared between the two sets of core microorganisms: *Colwelliaceae*, *Alteromonadaceae*, *Vibrionaceae*, *Rhodobacteraceae*, *Saccharospirillaceae*, *Pseudoalteromonadaceae*, *Burkholderiaceae*, *Oceanospirillaceae*, *Flavobacteriaceae*, and *Corynebacteriaceae*. These bacterial lineages respectively represented 97 and 86% of the total relative abundance of the ASVs shared among the tank waters, the eggs and the nauplii (Figure 4A) and among the breeders and their offspring (Figure 4B).

## Shared Bacterial Lineages Among the Eggs, the Nauplii, and the Breeders

The eggs and the nauplii also shared specific ASVs with the female and the male samples taken separately. Indeed, the male, the egg, and the nauplius samples shared 22 ASVs, whereas 37 ASVs were uniquely found in the female reproductive organs, the eggs, and the nauplii. When considering the ASVs common to the males and their offspring, 14 dominant families were highlighted (Figure 5A). A total of 11 dominant families were observed when analyzing the ASVs shared by the females, the eggs, and the nauplii (Figure 5B). Even though different ASVs were involved in each group, four common families were found in the two constructed histograms: *Arcobacteraceae*, *Rhodobacteraceae*, *Comamonadaceae*, and *Colwelliaceae*. In both cases, the abundance profiles of the breeders, the eggs and the nauplii were very different and sometimes even opposite. Indeed, the breeders showed higher abundances of *Arcobacteraceae* than their offspring. On the contrary, the eggs and the nauplii respectively displayed larger proportions of *Comamonadaceae* and *Spongiibacteraceae* compared to the male samples. Furthermore, *Cryomorphaceae* were less abundant in the male reproductive organs than in the egg and the larval stages. Finally, *Saccharospirillaceae* were less highlighted in the ovarian tissues than in the nauplii.

## Shared Bacterial Lineages Among the Eggs and the Nauplii

The egg and the nauplius samples showed specific and shared ASVs in both global Venn diagrams. Indeed, the egg samples displayed a total of 27 specific ASVs in Figure 6A, whereas 22 specific ASVs were highlighted in Figure 6B. Analyzing the dominant bacterial families from these ASVs, *Longimicrobiaceae* and *Burkholderiaceae* appeared to be highly abundant in both groups. The eggs also shared ASVs with the nauplii in the two Venn diagrams. In the first Venn diagram, 35 ASVs were common to the both sample types (Figure 6C), while 72 ASVs were highlighted in the second Venn diagram (Figure 6D). The dominant bacterial families highlighted from
<figure><img src="image_82.png" /><figcaption>**FIGURE 4** | Shared bacterial lineages among the eggs, the nauplii, the tank waters, and the reproductive organs of the breeders. Top bacterial families with a total relative abundance higher than 1% shared among (A) the tank water, the egg, and the nauplius (nii) samples (268 ASVs) and (B) the female and the male reproductive organs, the egg and the nauplius (nii) samples (172 ASVs). The bacterial families in blue are those shared between the two histograms which were constructed by averaging the triplicates for each sample type.</figcaption></figure>

these ASV groups showed five consistent families: *Rhodobacteraceae*, *Nitrincolaceae*, *Vibrionaceae*, *Neisseraceae*, and *Burkholderiaceae*. In both cases, abundance profiles differed between the two sample types as bacterial lineages abundant in the eggs decreased in the nauplii and vice versa. Finally,

the nauplius samples showed a large amount of specific ASVs in both Venn diagrams (512 and 639 ASVs; **Figures 6E,F**). These groups of ASVs each showed various dominant bacterial families with homogeneous abundances. However, 11 families were common to both histograms: *Rhodobacteraceae*,
<figure><img src="image_83.png" /><figcaption>**FIGURE 5** | Shared bacterial lineages among the eggs, the nauplii, and the breeders. Top bacterial families with a total relative abundance higher than 1% shared among (A) the male, the egg and the nauplius (nii) samples (22 ASVs) and (B) the female, the egg and the nauplius (nii) samples (37 ASVs). The bacterial families in blue are the bacterial families shared between the two histograms which were constructed by averaging the triplicates for each sample type.</figcaption></figure>

Saprospiraceae, Saccharospirillaceae, Nitrincolaceae, Colwelliaceae, Thiotrichaceae, Vibrionaceae, Cryomorphaceae, Arcobacteraceae, Hyphomonadaceae, and Schleiferiaceae.

## DISCUSSION

### Total Microbial Compositions Throughout all the Samples

The tank water samples were majorly composed of *Gammaproteobacteria* as well as *Alphaproteobacteria* and *Bacteroidia* (**Figure 2B**). These three major bacterial classes had been previously identified in water samples collected from the

water reservoirs of the Saint Vincent experimental shrimp hatchery in October and November 2018 and February 2019. However, these reservoir samples also showed high abundances of *Acidimicrobiia*, *Cyanobacteriia*, and *Planctomycetes* (Giraud et al., 2021). Thus, differences in total microbial compositions were observed between the water in the tanks and the water used to fill the tanks. As samples were collected at different periods and seasons, these differences could be explained by both seasonal and annual variations impacting the bacterial composition of the natural seawater in the Saint Vincent Bay. Such variations have been shown in the coastal seawaters surrounding the subtropical Xiamen Island in China, where *Proteobacteria*, *Bacteroidetes*, *Cyanobacteria*, and *Firmicutes* dominated all samples even though more than 400 bacterial groups were identified as unique to at least one season (Wang et al., 2019). Similar conclusions were drawn from a 5-year study focusing on the bacterial genera associated with seawater from the Ofunato Bay in Japan (Kobiyama et al., 2021). Beyond seasonal variations, another hypothesis can be made in order to explain the differences observed in total microbial compositions between the tanks and the reservoirs. Indeed, unlike our previous study, water from the hatchery tanks was sampled after EDTA addition. A recent research has shown that microbial communities can be influenced by different EDTA concentrations in different soils (Wei et al., 2020) making it possible for EDTA to also influence microorganisms associated with seawater. In any case, tank water samples were dominated by *Gammaproteobacteria*, *Alphaproteobacteria*, and *Bacteroidia* which are common and important bacterial classes in various seawaters ranging from pelagic to benthic environments (Giovannoni and Stingl, 2005; Zinger et al., 2011). These bacterial classes were also very abundant in the nauplius samples which showed similar total microbial compositions than the water samples (**Figure 2B**). This was expected as these microbial communities have been shown in larvae and adults of different shrimp species under farmed conditions (Rungrassame et al., 2013; Zheng et al., 2017). Furthermore, the rearing environment is known to be a key factor in the structure of the microbiota associated with fish larvae (Giatsis et al., 2015; Vadstein et al., 2018). Contrary to what we had shown previously, in this study, the nauplius samples grouped with the tank water samples and not with the eggs (Giraud et al., 2021). Indeed, when considering the microbial communities associated with the reproductive organs of the breeders, egg samples clustered with the male and the female samples (**Figure 2A**). In these samples, except for the *Egg_3* and *Female_2* samples which were clustered in a separate subgroup (**Figure 2A**), inter-individual variability was greater as abundances of *Clostridia*, *Bacilli*, and *Actinobacteria* varied (**Figure 2B**). High variability among the microbiotas of reproductive organs have already been shown in insects. Indeed, a study focusing on the microbiotas associated to different organs of nine mosquito species has shown that even though organs share core microbiotas, a tissue-specific tropism exists (Mancini et al., 2018). These results showed that the nauplii and the water from the hatchery tanks had similar microbial compositions, while the eggs were closer to the breeders. This suggested that the microbiota of the eggs could be under the influence of
<figure><img src="image_84.png" /><figcaption>**FIGURE 6** | Specific and shared bacterial lineages among the eggs and the nauplii. Top bacterial families with a total relative abundance higher than 1% (A) specifically found in the eggs in Venn1 (27 ASVs) and (B) in Venn2 (22 ASVs), (C) shared between the egg and the nauplius (nii) samples in Venn1 (35 ASVs) and (D) in Venn 2 (72 ASVs), (E) specifically associated with the nauplii (nii) in Venn1 (512 ASVs), and (F) in Venn2 (639 ASVs). The bacterial families in blue are the bacterial families shared between histograms which were constructed by averaging the triplicates for each sample type.</figcaption></figure>

vertically transmitted microorganisms from the breeders and that after hatching, the microbiota of the nauplii could then be shaped by the rearing environment. In order to confirm our hypotheses, Venn diagrams were constructed to investigate the specific and shared bacterial communities among all the considered sample types (Supplementary Figure S1; Figure 3).

## Influence of the Rearing Environment on the Microbiota Associated With the Animals

Analyzing the constructed Venn diagrams, we were able to identify a core microbiota shared by the tank waters, the eggs and the nauplii and composed of 268 ASVs from which 11

dominant bacterial families were highlighted (Figure 4A). Out of these 11 families, 10 were also found in the core microbiota of the males, the females, the eggs, and the nauplii (Figure 4B): *Colwelliaceae*, *Alteromonadaceae*, *Vibrioaceae*, *Rhodobacteraceae*, *Saccharospirillaceae*, *Pseudoalteromonadaceae*, *Burkholderiaceae*, *Oceanospirillaceae*, *Flavobacteriaceae*, and *Corynebacteriaceae*. *Colwelliaceae*, *Alteromonadaceae*, and *Pseudoalteromonadaceae* are all affiliated to the *Alteromonadales* order and the *Gammaproteobacteria* class. Members of these bacterial families are found in various environments ranging from soil and sediments to coastal and open seawaters. They can also be associated with organisms such as algae, fish, and marine invertebrates. This ability to colonize very diverse habitats can
be explained by the production of various primary and secondary
metabolites (hydrolytic enzymes, cyclic peptides, proteins,
pigments, exopolymers, alkaloids...) which enable these
microorganisms to decompose different organic compounds
like hydrocarbons, lipids, proteins, and polysaccharides
(Ivanova and Mikhailov, 2001; Bowman, 2014; Ivanova et al.,
2014). *Vibrionaceae*, *Burkholderiaceae*, *Flavobacteriaceae*,
*Corynebacteriaceae*, and *Rhodobacteraceae* are also known for
their metabolic versatility and ecological diversity as they can
be found in fresh and seawaters, soil, mud, plants, animals,
and even humans (Riegel, 2006; Bernardet, 2015; Farmer and
Janda, 2015; Garrity et al., 2015a,c). *Saccharospirillaceae* and
*Oceanospirillaceae* are ubiquitous as well but are restricted to
aquatic environments (Garrity et al., 2015b; Kevbrin et al.,
2020). In 2011, research on the intestinal microbiota of the
Chinese shrimp *Fenneropoeaneus chinensis* highlighted four out
of eight bacterial genera (*Vibrio*, *Loktanella*, *Roseobacter*, and
*Flavobacterium*) affiliated to the *Vibrionaceae*, *Rhodobacteraceae*,
and *Flavobacteriaceae* families (Liu et al., 2011). The three
same bacterial families were also highlighted in a study focusing
on the intestinal microbial communities of post-larvae and
juveniles of the Pacific white shrimp *Litopenaeus vannamei*.
The authors even suggested that *Flavobacteriaceae* and
*Rhodobacteraceae* formed the core microbiota of all the samples
they considered (Huang et al., 2016). More recently, in 2020,
studies focusing on the microbiota associated with the
larval stages of *L. vannamei* highlighted *Vibrionaceae*,
*Pseudoalteromonadaceae*, *Oceanospirillaceae*, *Flavobacteriaceae*,
*Colwelliaceae*, *Rhodobacteraceae*, and *Alteromonadaceae* in their
samples (Wang et al., 2020a,b). The same bacterial families
were highlighted in the eggs and the nauplii of *L. stylirotris*
(Giraud et al., 2021). Thus, *Vibrionaceae*, *Rhodobacteraceae*,
*Flavobacteriaceae*, *Pseudoalteromonadaceae*, *Oceanospirillaceae*,
*Colwelliaceae*, and *Alteromonadaceae* were found in larvae and
adults of different shrimp species. As these families were also
associated with the tank water samples and as water was
collected before transfer of the females into the hatchery tanks,
we can hypothesize that some or all bacterial communities
may be acquired from the rearing environment by the animals
at larval or adult stages. As gut opening occurs after the
nauplius stage in penaeid shrimps, water may modulate the
epibota of the larvae, while breeders could acquire
microorganisms from the water and transmit them on to their
offspring *via* a potential vertical transmission shaping the
endobiota. In order to investigate this possible vertical
transmission, we analyzed the ASVs shared among the larvae
and the breeders taken separately.

## Arguments in Favor of a Potential Vertical Transmission

Even though the males were never in direct contact with
their offspring, they shared 22 ASVs and 14 bacterial families
(Figure 5A). The females, the eggs and the nauplii had 37
ASVs and 11 bacterial families in common (Figure 5B).
Among these two sets of core bacterial families,
*Arcobacteraceae*, *Rhodobacteraceae*, *Comamonadaceae*, and

*Colwelliaceae* were found. *Arcobacteraceae* are members of the *Campilobacteria* class and have been found in the intestinal microbiota associated with juveniles of *L. vannamei* (Zeng et al., 2017). This bacterial clade has also been identified in the eggs of the vent shrimp *Rimicaris exoculata* (Guri et al., 2012; Methou et al., 2019). Vertical transmission of *Arcobacteraceae* has been proved in poultry and in pigs under farmed conditions (Collado and Figueras, 2011). *Comamonadaceae* belong to the *Burkholderiales* order and are thus metabolically diverse explaining why they are found in various environments such as soil, mud, and water in natural and anthropic environments (Willems and Gillis, 2015). Members of this bacterial family have been found in the discus fish *Symphysodon aequifasciata* in adults and their fry. The authors suggested that these microorganisms were vertically transferred from the parents to their offspring (Sylvain and Derome, 2017). *Comamonadaceae* have also been identified in the gut microbiota of *L. vannamei* at post-larval stage (Huang et al., 2016; Li et al., 2018). In order to explain the presence of these bacterial communities in the intestines of the post-larvae, Huang et al. (2016) hypothesized that these microorganisms were acquired from the *Artemia* larvae used for feeding. In our study, focus was made on the eggs and the first larval stages; thus, feeding could not explain the presence of *Comamonadaceae* in the eggs and the nauplii. However, they were found in the reproductive organs of the breeders. In the other mentioned studies, all bacterial strains were found in the intestinal microbiota as well. Relationships between the microbial communities of the gut and the reproductive organs are not well-documented but studies have established a link between the gut microbiota and the reproductive endocrine system in women. Indeed, disruption in the bacterial communities associated with intestines in women has been proved to cause reproductive diseases and syndromes. Overall, the gut microbiota is known to have various important effects on the intestines and to influence more distinct organs and molecular pathways in the human body (Qi et al., 2019, 2021). Taken together, these results suggest a potential vertical transmission of bacteria affiliated to the *Arcobacteraceae*, *Rhodobacteraceae*, *Comamonadaceae*, and *Colwelliaceae* families from the breeders to their offspring. However, further studies will be necessary in order to prove this possible vertical transmission. For example, in salmonids, vertical transmission of the pathogenic bacteria *Piscirickettsia salmonis* was proved using indirect immunofluorescence technique and scanning electron microscopy (Larenas et al., 2003). However, this was possible for only one bacterial strain while we have highlighted several possible microbial families. Applying such technique in shrimps would require more investigations and important technical developments. Indeed, validating vertical transmission involves understanding the underlying cellular mechanisms. The development of specific probes could be worth considering for qualitative and quantitative tracking of specific bacterial strains (Lee et al., 2009). However, it is important to note that targeting certain microorganisms of interest is necessary.
Undetermined Factors May Shape the
Bacterial Communities Associated With
the Eggs and the Nauplii

Our first results suggested that the environment and the breeders
could shape the microbiota associated with the eggs and the
early larval stages of *L. stylirostris*. However, as the eggs and
the nauplii showed specific ASVs that were not highlighted in
the tanks nor in the male and female reproductive organs, it
seemed possible that other factors were involved. In both
constructed Venn diagrams, ASVs which were only found in
the egg samples were highlighted (**Figures 6A,B**). Only two
dominant active bacterial families were highlighted from these
ASVs: *Longimicrobiaceae* and *Burkholderiaceae*. Surprisingly,
*Longimicrobiaceae* members (*Gemmatimonadota* phylum) are
very common in soil and represent up to 2% of the soil microbial
community (Bertrand et al., 2018). To our knowledge, these
microorganisms have never been identified in marine seawaters
or aquatic organisms. However, they have been found in the
sediments of urbanized rivers (Ge et al., 2021). Adult shrimps
are cultured in earthen ponds and their intestinal microbiota
can share up to 80% ASVs with the sediments in their rearing
environment (Hou et al., 2018). The potential influence of the
gut microbiota has previously been discussed. However,
*Longimicrobiaceae* were not highlighted in the reproductive organs
of the breeders. As we studied active microbial communities,
it is possible that this bacterial family was present and inactive
in the breeders and became active when transmitted on to the
ggs. Further investigations will be necessary to understand the
role of *Longimicrobiaceae* in early larval stages of *L. stylirostris*.
On the other hand, the presence of *Burkholderiaceae* was not
surprising as they have been found in the intestines of post-
lvae and juveniles of *L. vannamei* (Huang et al., 2016). As
previously suggested for *Commamonadaceae*, *Burkholderiaceae*
may be acquired at very early larval stages and may persist
throughout the whole lifecycle of the shrimp. The egg samples
also shared specific bacterial lineages with the nauplii in the
Venn diagrams in **Figures 6C,D**. A total of five bacterial families
were consistently found: *Rhodobacteraceae*, *Nitrincolaceae*,
*Vibrionaceae*, *Neisseriaceae*, and *Burkholderiaceae*.
*Rhodobacteraceae*, *Vibrionaceae*, and *Burkholderiaceae* were
identified in all the samples but from different ASVs. Members
of these families are able to produce extracellular proteins enabling
them to colonize various habitats and to adapt, explaining why
they were found in every compartment of our experiment.
*Nitrincolaceae* has been identified in the gut microbiota of reared
and wild salmons (Lavoie et al., 2021) as well as in the rearing
water of sablefish larvae (Lee et al., 2021) and in *L. stylirostris*
larvae (Giraud et al., 2021). The ability of the *Nitrincolaceae*
members to colonize the intestines of marine species as well
as their rearing environments may support our idea that some
bacterial lineages could also be acquired from the environment.
As previously suggested before for the breeders, environmental
communities may activate when in contact with the larvae.
Finally, nauplii displayed many specific ASVs that were not
identified in the other sample types (**Figures 6E,F**). Considering
the major active bacterial families identified from these ASVs,
no family had a clear higher relative abundance. This was also

illustrated with the analysis of the diversity index
(Supplementary Figure S2). Indeed, the nauplius samples showed
a significantly higher Chao1 index reflecting a higher richness
of their microbial communities. However, no differences were
observed for the Simpson index reflecting no change in evenness
(Kim et al., 2017). This has already been reported in *L. vannamei*
as the nauplii presented higher richness index than the later
larval stages (zoea and mysis; Wang et al., 2020a). The increase
of microbial richness associated with the nauplii was explained
by yolk nutrient release and surface expansion at this stage.
The authors suggested that as larvae grew and as their intestinal
tract formed, food and environment tended to structure the
gut microbiota leading to less richness. The authors also suggested
that the microbial communities associated with the nauplii
originated from the eggs. However, our results suggest otherwise.
As previously mentioned, these communities may have been
acquired from the water and/or the breeders and activated as
the environmental conditions were more favorable. Other factors
could also influence the structure of the microbial communities
associated with the larvae such as competition between
microorganisms, genetic background, larval ontogenesis, and host
associated selection pressure (Vadstein et al., 2018; Sun and Xu,
2021). This is supported by the abundance difference of the
microbial communities that were shared by the samples. Indeed,
even when samples shared bacterial families, their relative
abundances varied greatly supporting our theory that microbial
communities may find more or less favorable conditions in each
compartment. This has been previously shown during larval
stages of *L. vannamei* where bacterial family abundances varied
from one stage to another and where several stage-specific groups
were highlighted (Zheng et al., 2017; Wang et al., 2020b). These
results suggest that even though environment and breeders could
influence the microbiota of the early larval stages, some
unconsidered factors could also structure the microbial
communities associated with the eggs and the nauplii.

CONCLUSION

In conclusion, we highlighted an active core microbiota associated
with all the considered samples. Indeed, the water filling the
hatchery tanks, the eggs and the nauplii as well as the female
and the male reproductive organs shared 10 bacterial families:
*Colwelliaceae*, *Alteromonadaceae*, *Pseudoalteromonadaceae*,
*Saccharospirillaceae*, *Oceanospirillaceae*, *Vibrionaceae*,
*Burkholderiaceae*, *Rhodobacteraceae*, *Flavobacteraceae*, and
*Corynebacteriaceae*. Several bacterial families were also shared
only by the breeders and their offspring: *Arcobacteraceae*,
*Rhodobacteraceae*, *Comamonadaceae*, and *Colwelliaceae*. Finally,
the egg and the nauplius samples showed unique bacterial
families that were not identified in the other sample types.
Taken together, our results suggest that the early larval stages
of *L. stylirostris* are shaped by their environment and by their
breeders. Some bacterial lineages may be horizontally acquired
from the rearing water through adsorption and adhesion process
at the surface of the larvae (Hansen and Olafsen, 1999). As
some microorganisms can enter the larvae before mouth opening,
<figure><img src="image_85.png" /><figcaption>**FIGURE 7** | Schematic representation of the factors influencing the microbiota of the Pacific blue shrimp at all developmental stages. The egg, nauplii, zoea, and mysis stages belong to the larval stages.</figcaption></figure>

these early acquired communities could shape the epibiota as well as the endobiota of the eggs and the nauplii. However, we also showed that some bacterial strains were shared with the breeders suggesting a potential vertical transmission affecting the structure of the larval microbiota. Furthermore, we highlighted bacterial families that were uniquely associated with the eggs and the nauplii suggesting that some unconsidered factors were involved (genetic background, host selection pressure, and so on). Thus, microbial communities associated with the eggs and the nauplii result in dynamic interactions that may persist through the whole larval cycle and even through the whole life cycle of the shrimp. Further investigations will be necessary in order to understand the evolution of the active microbial communities associated with later life stages as our results suggest that some bacterial lineages in shrimp larvae cultured in hatcheries may be determined way in advance by the breeders and their rearing environment. In addition, during further larval development, other factors like food could come into play to shape the microbiota (Figure 7). In aquaculture, microbial management of the rearing water is an important concern (Rurangwa and Verdegem, 2015). However, our study suggests that the microbiota associated with the animals can

also be modulated by other factors leading to rethink microbial management of the whole rearing process.

## DATA AVAILABILITY STATEMENT

The datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found in the article/ **Supplementary Material**.

## AUTHOR CONTRIBUTIONS

CG, NC, and NW conceived and designed the experiment. CG, NC, J-SL, DP, and NW performed the experiment and acquired the data. CG and NC analyzed the data, prepared the figures, and drafted the manuscript. VB, DP, NS-F, and NW reviewed the draft. All authors contributed to the article and approved the submitted version.

## FUNDING

This work was supported by the RESSAC project (LEAD-NC, Ifremer New-Caledonia) within the framework agreement with the New Caledonian Provinces and Government and by the Pacific Doctoral School.

## ACKNOWLEDGMENTS

The authors would like to thank Julien De Lorgeril as well as Vincent Douchet for their valuable work in the hatchery and for their help during the sample collection.

## SUPPLEMENTARY MATERIAL

The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fmicb.2022.886752/full#supplementary-material

**Supplementary Figure S1** | Venn diagrams of each sample type. Specific and shared ASVs among all (A) the tank water samples, (B) the female reproductive organ samples, (C) the male reproductive organ samples, (D) the egg samples, and (E) the nauplius (nii) samples. For all the Venn diagrams, each ellipse color corresponds to a two-parent family. All samples from the first family are represented in blue. All samples from the second family are represented in yellow. All samples from the third family are represented in red. In both Venn diagrams, numbers noted in the overlapping areas correspond to the number of shared ASVs among samples while numbers noted outside of the overlapping areas correspond to the numbers of specific ASVs associated with each sample.

**Supplementary Figure S2** | Alpha diversity index of all samples. (A) Chao1 and (B) Simpson index for all the tank waters, the egg, the nauplius (nii), the female and the male samples. Significant differences (value of *p* < 0.05) highlighted by a Kruskal-Wallis and Dunn test are represented by *.
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**Conflict of Interest:** The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

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## IV.2.3- Synthèse des résultats et perspectives

Cette étude a d'abord mis en évidence des communautés microbiennes partagées par les œufs et les larves nauplii avec leur eau d'élevage ainsi qu'avec les organes reproducteurs de leurs parents (géniteurs mâles et femelles). Au total, 10 familles bactériennes communes ont été identifiées : *Colwelliaceae*, *Alteromonadaceae*, *Vibrionaceae*, *Rhodobacteraceae*, *Saccharospirillaceae*, *Pseudoalteromonadaceae*, *Burkholderiaceae*, *Oceanospirillaceae*, *Flavobacteriaceae*, *Corynebacteriaceae*. Parmi ces familles, 7 (*Vibrionaceae*, *Rhodobacteraceae*, *Flavobacteriaceae*, *Pseudoalteromonadaceae*, *Oceanospirillaceae*, *Colwelliaceae* et *Alteromonadaceae*) ont déjà été mises en évidence au cours d'élevages d'autres espèces de crevettes pénéides, aussi bien dans l'eau qu'associées aux animaux et ce à différents stades de développement (Liu et al. 2011; Huang et al. 2016; Wang et al. 2020a,b; Heyse et al. 2021). Ces résultats suggèrent une influence de l'environnement sur les communautés microbiennes associées aux crevettes tout au long de leur développement et renforcent ainsi les conclusions apportées par l'étude A concernant une potentielle transmission horizontale. Afin d'évaluer la contribution des géniteurs dans la mise en place du microbiote larvaire, des comparaisons microbiennes ont ensuite été réalisées entre les œufs, les larves au stade nauplius et les testicules des mâles ainsi qu'entre les œufs, les larves au stade nauplius et les ovaires des femelles. Par cette démarche, 4 familles bactériennes communes ont été mises en évidence et 2 n'avaient pas été identifiées dans les eaux d'élevage, *Arcobacteraceae* et *Comamonadaceae*. La transmission verticale de bactéries affiliées à ces familles a été prouvée chez le porc et suggéré chez les volailles ainsi que chez le Discus (Collado & Figueras 2011; Sylvain & Derome 2017). Des taxa appartenant aux *Comamonadaceae* ont également été mis en évidence dans le microbiote intestinal de *Penaeus vannamei*. Les auteurs ont émis l'hypothèse que ces microorganismes pourraient provenir de la nourriture (Huang et al. 2016). L'ouverture de la bouche ayant lieu après le stade nauplius, les larves analysées au cours de cette étude n'ont été exposées à aucune source de nourriture. La présence de *Comamonadaceae* pourrait ainsi être expliquée par une potentielle transmission verticale des géniteurs vers les larves. En conséquence, les premières études menées dans le cadre de cette thèse suggèrent que la mise en place du microbiote larvaire est sous l'influence de communautés microbiennes présentes dans l'environnement d'élevage et associées aux géniteurs. Cependant, la persistance d'ASVs spécifiques aux œufs et aux nauplii laisse à penser que d'autres facteurs pourraient également être impliqués (sélection de l'hôte, facteurs génétiques, développement, etc.). Certaines bactéries maternelles associées, par exemple, à la cuticule ou aux fèces et non considérées lors de cette étude pourraient aussi être verticalement transmises aux œufs. Il est également important de noter que certains microorganismes pourraient trouver des conditions favorables au sein des œufs et des larves au stade nauplius et ainsi devenir actifs, expliquant qu'ils ne soient pas détectés dans l'eau ou les géniteurs. Néanmoins, les 2 premières études présentées ici offrent de nouvelles perspectives de travail. En effet, il pourrait être intéressant de comparer des œufs et des larves nauplii obtenus à partir de géniteurs élevés dans des conditions différentes (températures différentes, séquences alimentaires différentes, exposition à des stress ou non, etc.). Il est également envisageable que les communautés établies en amont des élevages larvaires persistent tout au long du développement et influencent ainsi la survie des animaux. Cette dernière hypothèse sera abordée lors de la méta-analyse présentée dans le chapitre VII (partie VII.2).
# CHAPITRE V

## Évolution des communautés procaryotiques associées à un élevage larvaire en bonne santé
# V- Chapitre V – Évolution des communautés procaryotiques associées à un élevage larvaire en bonne santé

## V.1- Introduction

Après la mise en évidence de potentielles transmissions verticales et horizontales au cours de la mise en place du microbiote larvaire de *Penaeus stylirostris*, la thèse a ensuite eu pour objectif de caractériser les microorganismes procaryotiques associés à des élevages larvaires en bonne santé, ne présentant pas d'épisode de fortes mortalités. En effet, afin de pouvoir évaluer si une dysbiose du microbiote larvaire et / ou du microbiote environnemental pouvait expliquer les mortalités massives observées dans les éclosures calédoniennes (chap. I, partie I.1.2.c), une étude focalisée sur l'évolution des communautés procaryotiques au sein d'élevages en bonne santé était nécessaire. Au début de ce projet, dans la littérature, les études relatives à l'écosystème microbien d'élevages larvaires de crevettes concernaient uniquement l'espèce *P. vannamei* (chap. I, partie I.3.4.a). Elles suggéraient que le microbiote des larves était sous l'influence du stade de développement des animaux et de la nourriture mais aussi des communautés microbiennes présentes dans l'eau d'élevage (Zheng et al. 2017; Xue et al. 2018; Wang et al. 2020a; b). Afin de déterminer si ces facteurs pouvaient également moduler les microorganismes procaryotiques associés aux larves de *P. stylirostris*, des échantillonnages quotidiens d'eau et de larves ont été réalisés au cours d'élevages larvaires conduits dans une écloserie privée du territoire (campagne d'échantillonnage E1 conduite dans écloserie privée 1, chap. II, partie II.1.2). Les intrants (eau de remplissage provenant du lagon, eau de réserve, nourriture) ont également été prélevés. Deux élevages larvaires, désignés par A et B, ont été considérés et réalisés chacun en triplicats à partir d'une même eau de réserve et ensemencés avec des larves issues de 2 lots de géniteurs différents. La qualité des eaux prélevées a été évaluée par des mesures de pH et de température ainsi que par des dosages d'ammonium (TAN), de phosphate (SRP) et de matière organique dissoute colorée (CDOM) (chap. II, partie II.4.2). Ces paramètres physico-chimiques ont été visualisés par une heatmap et par une analyse en composantes principales (ACP, Tab. 2, chap. I, partie I.4.1.a). La diversité bactérienne de l'ensemble des échantillons (eaux, aliments, larves) a été évaluée par séquençage Illumina HiSeq (2 x 150pb) des ADNc obtenus après rétrotranscription des ARNs extraits (chap. II, partie II.5.1). Après analyse des données de séquençage avec le package DADA2 (Callahan et al. 2016) (chap. III, partie III.3.2) sous RStudio et normalisation de la table d'ASVs avec la méthode CPM (chap. III, partie III.4.2.b et chap. II, partie II.5.2), une classification ascendante hiérarchique basée sur une matrice de distance de Bray-Curtis et sur la méthode de Ward a été construite. Les diversités alpha (indices de diversité) et bêta (ordonnancements par dendrogramme et PCoA) ont été analysées. Les taxa enrichis en fonction du jour d'élevage ou du stade développement des animaux ont été mis en évidence par des analyses discriminantes linéaires (LDA) avec effet de taille (LEfSe). Des corrélogrammes ont également été construits pour évaluer les corrélations existantes entre les paramètres physico-chimiques considérés et les taxa bactériens enrichis dans l'eau ou dans les larves (chap. II, partie II.5.3).
## V.2- [Article 3 – Environmental factors and potential probiotic lineages shape the active microbiota associated with healthy Penaeus stylirostris larvae and their rearing water](https://www.sciencedirect.com/science/article/pii/S0005-7497(20)30042-9)

L'article présenté dans cette partie est en cours de rédaction. Dans un souci de protection des données et dans l'attente de la signature d'une convention entre l'Ifremer et l'écloserie privée 1, cette dernière a été anonymisée. En conséquence, la liste des auteurs est susceptible d'être modifiée avant soumission.

### Environmental factors and potential probiotic lineages shape the active microbiota associated with healthy Penaeus stylirostris larvae and their rearing water

Carolane Giraud<fnref n="1" />,<fnref n="2" />, Nelly Wabete<fnref n="1" />, Célia Lemeu<fnref n="1" />, Nazha Selmaoui-Folcher<fnref n="2" />, Dominique Pham<fnref n="1" />, Viviane Boulo<fnref n="3" />, Nolwenn Callac<fnref n="1" />

**Keywords:** Active microbial communities, shrimp larval rearing, physico-chemical factors, commensal microorganisms, probiotics

### Abstract

**Background:** As most organisms, marine animals host microorganisms which are essential to their physiology and health. These commensal microbial communities maintain close interactions with the microbiota of their host's rearing water. In aquaculture systems, a disruption of these essential commensal and environmental microbial communities has been suggested to cause disease outbreaks. More specifically, such a microbial dysbiosis is hypothesized to cause larval mass mortalities in New-Caledonian shrimp hatcheries. In order to confirm this hypothesis and allow further microbial comparisons, this study focuses on the active microbiota of healthy *Penaeus stylirostris* larvae and their surrounding environment during the first 10 days of larval rearing.

**Results:** Using daily nutrient concentration quantitative analyses and spectrophotometric organic matter analyses, we highlight a global eutrophication of the rearing environment. We also evidence drastic microbial community modifications in the rearing water and the larvae samples using Illumina HiSeq sequencing of the V4 region of the 16S rRNA gene. We confirm that *Alteromonadales*, *Rhodobacterales*, *Flavobacteriales*, *Oceanospirillales* and *Vibrionales* members form the core microbiota of shrimp larvae. We also identify, in the water and the larvae samples, several potential probiotic microbial strains affiliated to the **AEGEAN 169 marine group** and the **OM27 clade** as well as to the *Ruegeria*, *Leisingera*, *Pseudoalteromonas* and *Roseobacter* genera. Finally, investigating the existing correlations between the considered environmental factors and the major bacterial taxa of the rearing water and the

<footnote n="1">¹Ifremer, IRD, Université de la Nouvelle-Calédonie, Université de la Réunion, CNRS, UMR 9220 ENTROPIE, Noumea, New Caledonia</footnote>

<footnote n="2">²University of New Caledonia, Institut des Sciences Exactes et Appliquées (ISEA), Noumea, New Caledonia</footnote>

<footnote n="3">³IHPE, Université de Montpellier, CNRS, Ifremer, Université de Perpignan via Domitia, Montpellier, France</footnote>
larvae samples, we suggest that deterministic and stochastic processes are involved in the assembly of microbial communities during the larval rearing of *P. stylirostris*.

**Conclusion:** During the first 10 days of larval rearing, we highlight important modifications of the microbiota associated with the rearing water and the larvae samples as well as physicochemical variations in the rearing environment. Overall, our results show that all these drastic changes mostly occurred during the zoea stages suggesting that this larval phase is crucial during shrimp larval development. Furthermore, the identification of different potential probiotics throughout the rearing could lead to adapt probiotic use in aquaculture facilities in New-Caledonia.

## Background

There is a significant contrast between the very small size of microorganisms and their essential role in all environments. Through their metabolic and genetic diversity, microorganisms are able to colonize all sorts of habitats therefore forming a microbiota [1,2]. In water and soil ecological niches, microbial communities are involved in nutrient recycling and biogeochemical cycles which are necessary processes in the sustainability of all ecosystems [3]. Beyond their ecological interest, microorganisms are also able to establish symbiotic relationships with multiple organisms ranging from plants to mammals. In animals, these microbial symbionts colonize various types of tissues and produce many metabolites influencing the digestion, the growth and the immune system of the host as well as other key physiological processes [2,4,5,6,7]. The establishment and the modulation of the microbiota of a given host often results from complex interactions between environmental factors and vertically transmitted microbial communities as well as host selection pressure and genetic background [2,8]. For example, such processes have been suggested to modulate the commensal microorganisms associated with the early life stages of Pacific blue shrimp, *Penaeus stylirostris*, larvae [9,10]. As a consequence of these very specific interactions, hosts harbor a very well adapted microbiota [11]. Under biotic or abiotic stress, this microbiota can be disrupted by an expansion of r-strategist microorganisms and opportunistic pathogens or by a loss of beneficial microorganisms and microbial diversity leading to a microbial dysbiosis [12,13,14]. In marine animals, such a dysbiosis has been suggested to lead to opportunistic or polymicrobial infections causing disease outbreaks in marine ecosystems [13] and more particularly in aquaculture systems [15]. For example, in the Pacific white shrimp *Penaeus vannamei*, modifications of the gut microbiota were correlated with disease severity and beneficial bacteria were associated with healthy individuals while potential pathogenic strains were highlighted in diseased shrimps [16,17]. Furthermore, microbial dysbiosis in the rearing water and the larvae of the Pacific blue shrimp, *P. stylirostris*, have been hypothesized to explain massive larval mortalities occurring in New-Caledonian hatcheries [18].

In New-Caledonia, the semi-intensive farming of *P. stylirostris* leads to an annual estimated production of 1500 tons and allows the employment of over 500 people. Shrimp farming is currently the island's first food-processing exporter and is therefore a very important socio-economic activity for the territory [19]. Unfortunately, due to the previously mentioned massive larval mortalities occurring in the hatcheries, the post-larvae production decreased by 25% between 2005 and 2019 and it is currently threatening the sustainability of shrimp farming in New-Caledonia [20]. In order to confirm the potential role of dysbiosis in the observed larval mortalities, a characterization of the microbiota associated with healthy *P. stylirostris* larvae and their rearing water seemed necessary to allow microbial comparisons
between healthy and moribund rearings. Thus, we present a new study focusing on the temporal evolution of the microbiota associated with healthy *P. stylirostris* larvae as well as with their surrounding environment during the first ten days of larval rearing. Using high-throughput Illumina sequencing (2x150 pb) of the active microbiota, we were able to identify the major bacterial orders daily associated with the water and the larvae samples and further investigated these microbial communities using Linear Discriminant Analyses (LDA) effect size (LEfSe) according to the rearing day and the larval developmental stage. We highlighted several potential probiotic strains with varying abundances throughout the experiment which could lead to adapt probiotic use during larval rearing in New-Caledonian hatcheries. Finally, we investigated the correlations existing between the highlighted microbial biomarkers and the physicochemical parameters of the rearing water. These results suggest that deterministic and stochastic processes may be involved in the assembly of the microbial communities associated with *P. stylirostris* larvae and their rearing water.

## Materials and Methods

### Study design

All samples were collected during an experiment conducted in a New-Caledonian private shrimp hatchery. Prior to larval rearing, mature male and female breeders were used to perform artificial inseminations, as described by Pham et al. (2012) [21]; and 250L hatching tanks were filled with treated natural seawater from the lagoon. Briefly, lagoon seawater was pumped through a sand filter and a 1µm pore size filter and stocked in a hatching reservoir equipped with heating and UV (ultraviolet rays) cycling systems. Before filling the hatching tanks, ethylenediaminetetraacetic acid (EDTA) was added to the hatching water reservoir (15ppm) (Fig. 1A). After artificial inseminations, inseminated females were placed in the hatching tanks at a rate of 1 individual per tank and were left to spawn in the dark. After spawning, females were retrieved while eggs were left in the hatching tanks. The next day, after hatching, the released nauplii (first larval stage) were pooled, abundantly rinsed and disinfected. On the same day, 10m³ rearing tanks were filled with treated natural seawater from the lagoon. Briefly, lagoon seawater was pumped the day before larval rearing through a sand filter and sent to a decantation reservoir equipped with a skimmer and a degassing system. Water from the decantation reservoir was then filtered through a series of 25, 10, 5 and 1µm pore size filters and stocked in a rearing reservoir equipped with heating and UV cycling systems (Fig. 1B). Before nauplii were added at a rate of 180 individuals per liter to the rearing tanks (Day 0 of the experiment), EDTA was added (15 ppm). Progressive bubbling was implemented during the whole rearing process, salinity was kept at 30ppt (± 1ppt) and no water renewal was performed. Water temperature was controlled by heating resistances placed in the tanks. Temperature was increased from 27°C to 30°C between D0 and D2 in order to help larval transitioning from nauplius to zoea stage and then remained stable until the end of the experiment (29.9°C ± 0.4°C). From Day 4 (D4) to the end of the experiment (D9), oxytetracycline (OTC) was daily added to the rearing tanks. The first antibiotic dose was added at 70ppm and was reduced to 17ppm the following days. From D2 to D9, larvae were fed with different sources of microparticles according to their larval stage (feeding protocol developed by the hatchery). Diet was supplemented with frozen *Artemia* sp. larvae from D4 to the end of the experiment (Fig. 1C). Larvae were fed every 2 hours from 4am to 10pm.

During this experiment, 2 larval rearing cycles were considered and were named A and B. These rearing cycles were conducted using the same reservoir water but larvae were
obtained from artificial inseminations performed using different breeders on two successive days. For each rearing cycle, triplicates were performed (A1, A2, A3, B1, B2, B3). A 250L tank filled with the same water as the rearing tanks and with no larvae or food addition was considered to be the control of our experiment (Ctrl).

## Sample collection

As larval rearing cycles A and B were conducted using the same reservoir water, unique samples of lagoon seawater (LS), decantation reservoir (DR) and rearing reservoir (RR) were collected prior to the beginning of the experiment (Fig. 1B). For each sample, 1l of seawater was collected using a 2l plastic vial. For each larval rearing cycle, on the day the hatching tanks were filled, 1l of water was collected from the hatching reservoir (named HR_A and HR_B). On the same day, after spawning, around a hundred eggs were sampled from the hatching tanks in 2ml sterile microtubes using a 100µm pore size net and sterilized tweezers. The next day, 1l of water was collected from all the rearing tanks after EDTA addition and before nauplii addition (W_D0). On the same day, around a hundred nauplii were sampled after the disinfection process and before addition to the rearing tanks (L_D0). From D1 to D9, prior to the first feeding of the day, larvae (L) and rearing water (W) were daily sampled from all the considered rearing tanks. Around 100 larvae were collected in sterile 2ml microtubes using a 120µm pore size net and sterilized tweezers. In order to sample 1l of rearing water with no larvae, a 120µm filtering device and a 2l plastic vial were used. Water samples were also daily collected from the control tank at the same sampling time as the rearing tanks. Every food type was sampled once during the experiment in 2ml sterile microtubes using sterilized tweezers (F1, F2, F3, F4, *Artemia* sp. larvae).

All the water samples were filtered using 0.2µm pore size filters (S-PAK membrane filter, Millipore). Filters as well as egg, larvae and food samples were stored at -80°C until further RNA extractions. Filtered water was used to fill 40ml sterile polyethylene vials which were stored at -20°C for further Total Ammonium Nitrogen (TAN) and Soluble Reactive Phosphorus (SRP) quantitative analyses. Filtered water was also used to fill 50ml pre-combusted (8h, 450°C) glass bottles in order to perform Colored Dissolved Organic Matter (CDOM) spectrophotometric analyses within hours after the sample collection.
<figure><img src="image_86.png" /><figcaption>**Figure 1 – Water treatments and larval rearing process.**</figcaption></figure>

Natural lagoon seawater was pumped, treated and used to fill (A) the hatching tanks and (B) the rearing tanks. (C) Larval rearing cycles started when nauplii (Nii) were added to the rearing tanks after ethylenediaminetetraacetic acid (EDTA) addition on Day 0 (D0). On Day 4 (D4), 70ppm of oxytetracycline (OTC) were added to the rearing tanks. Daily OTC doses were reduced to 17 ppm from Day 5 (D5) to Day 9 (D9). On Day 1 (D1), larvae were at nauplius stage (Nii) and were fed with a first type of microparticles (F1). From Day 2 (D2) to Day 4 (D4), larvae were at zoea 1 (Z1) and zoea 2 (Z2) stages and were fed with a second type of microparticles (F2). On Days 5 and 6 (D5 and D6), larvae were at stages zoea 3 (Z3) and mysis 1 (M1) and were fed with a third type of microparticles (F3). From Day 7 (D7) to Day 9 (D9), larvae were at mysis 1, 2, 3 (M1, M2, M3) and post-larvae (PL) stages and were fed with a fourth type of microparticles (F4). Microparticles were supplemented with frozen Artemia sp. larvae from Day 4 (D4) to Day 9 (D9).
## Zootechnical parameters

For each considered rearing tank, larvae contained in 1l of rearing water were daily observed under a binocular magnifying glass in order to determine larval stages and survival rates.

The larval survival rate (LSR), expressed in percent, was daily determined using the following equation: (n / T) x 100 where n is the number of counted living larvae in the considered rearing tank and T the number of larvae added to the rearing tank on D0.

The larval stage index (LSI) was daily determined using a modified equation of Maddox & Manzi (1976) [22]: (0*Nii + 1*Z1 + 2*Z2 + 3*Z3 + 4*M1 + 5*M2 + 6*M3 + 7*PL) / N where Nii corresponds to the number of larvae observed at nauplius stage and Z1, Z2 and Z3 indicate the number of larvae respectively observed at stages zoea 1, 2 and 3. In the same way, M1, M2 and M3 correspond to larval stages mysis 1, 2 and 3. PL stands for the number of observed post-larvae. Finally, N indicates the total number of observed larvae on the considered rearing day.

## Water quality analysis

For all water samples, temperature and pH were daily measured using the multiparameter probe pH/Cond 3320 (Mettler Toledo). Concentrations of Total Ammonia Nitrogen (TAN) and Soluble Reactive Phosphorus (SRP) were respectively measured using the methods described by Holmes et al. (1999) [23] and Murphy & Riley (1962) [24]. Colored dissolved organic matter (CDOM) analyses were performed by determination of the absorption spectra of the water samples from 200 to 700nm using a 10cm quartz tank and a SHIMADZU UV-1800 spectrophotometer [25]. MiliQ water was considered to be the reference of our analysis. The absorption coefficient at 325nm (aλ<sub>325nm</sub>) was used as a proxy of the CDOM concentration [26] and was determined using the following equation: (2.303 * a) / l where 2.303 is the logarithm transformation factor, a is the measured absorption at 325nm and l is the length of the quartz tank (10cm). The slope ratios of the absorption spectra from 275 to 295nm (S<sub>275-295</sub>) and from 350 to 400nm (S<sub>350-400</sub>) were determined in order to calculate the total slope ratio (SR) using the following equation: S<sub>275-295</sub> / S<sub>350-400</sub>.

## RNA extractions and sequencing

RNA extractions were performed using the RNeasy PowerWater kit (Qiagen) for the water samples and the RNeasy minikit (Qiagen) for the egg, the larvae and the food samples. All RNAs were «reverse»-transcribed into complementary DNAs (cDNAs) according to the protocol described in Giraud et al. (2022) [10].

All the obtained cDNAs were sent to MrDNA (Shallowwater, TX, United States) to amplify and sequence the V4 region of 16S rRNA gene using the 515F/806R primers [27]. Following this step, an HiSeq Illumina sequencing was performed using a 2x150pb paired-end run and an average sequencing depth of 50k raw reads per sample. Raw sequences obtained from the HiSeq Illumina sequencing were treated using the DADA2 package [28] implemented in RStudio [29] as described in Giraud et al. (2021) [9]. Prior to microbiota analysis, sequences affiliated to Eukaryotas, Chloroplasts and Mitochondrias were removed from the ASV table. A total of 68,490,986 reads were found and 13,291 ASVs were evidenced. Minimum library size was 117,730 reads and maximum library size was 1,474,219 reads. As a consequence, the final ASV table was normalized using the CPM method implemented in the normalize function of the phyloseq package in RStudio [30].
The datasets generated and analyzed during the current study are available in the NCBI SRA repository (BioProject PRJNA736535, SPR21099695 to SPR21099837).

## Downstream analysis

Using the ggplot2 package implemented in RStudio [31], a heatmap was built to visualize zootechnical and physicochemical parameters. In order to reduce dimensions and to simplify visual representations of these parameters, a standardized Principal Component Analysis (PCA) was performed using the dudi.pca function of the ade4 package in RStudio [32].

Microbial analyses were conducted using the phyloseq package in RStudio [30]. Alpha diversity indexes were determined using the plot_richness function and Principal Coordinates Analysis (PCoA) ordinations were built using the ordinate and plot_ordination function. A dendrogram based on a Bray-Curtis dissimilarity matrix and the Ward method was also constructed using the vegan package in RStudio [33]. Linear discriminant analyses (LDA) effect size (LEfSe) [34] were performed using the microbiomeMarker package in RStudio [35]. Relative abundances of the ASVs highlighted by the LEfSe with a threshold set at 4, were visualized by constructing scatterplots using the ggplot2 package in RStudio [31]. The microeco package in RStudio [36] was used to build correlograms between the physicochemical parameters and the ASVs highlighted by the LEfSe.

All the statistical differences highlighted for the environmental factors were evaluated using Kruskal-Wallis tests followed by Wilcoxon post-tests using the rstatix package in RStudio [37]. Differences were considered significant when p-values were inferior to 0.05.

## Results

### Zootechnical and physicochemical parameters

Larval survival rates (LSR) decreased in all the rearing tanks and the lowest value observed on the final day of the experiment was 69% (A3 and B3 tanks) (Fig. 2A). On the contrary, as the larvae grew, larval stage indexes (LSI) increased in all the rearing tanks (Fig. 2B) and all observed larvae were transitioning to post-larvae stage (LSI = 7) on Day 9 (D9). All zootechnical parameters were homogeneous between the 2 considered larval rearing cycles (A and B).

As expected, temperature increased in all the rearing tanks from D0 to D2 (27 to 30°C) and remained stable (29.9 ± 0.4°C) from D3 to D9 (Fig. 2C). Ammonium (TAN) and phosphorus (SRP) concentrations increased progressively during both larval rearing cycles compared to the control tank (Fig. 2D and 2E). Indeed, TAN and SRP concentrations were null or near 0µM in the rearing tanks at the beginning of the experiment and respectively reached 322 and 9.9µM on D9 while values remained low in the control tank. CDOM concentration (aλ<sub>325nm</sub>) values were also significantly different as they increased in all the rearing tanks compared to the control tank (Fig. 2F). More precisely, aλ<sub>325nm</sub> values (CDOM concentration proxy) shifted between D4 and D5 as they varied from 2 to 5 m<sup>-1</sup> from D0 to D4 and from 19 to 35 m<sup>-1</sup> from D5 to D9. On the contrary, pH (Fig. 2G) and slope ratio (SR) (Fig. 2H) values decreased from D5 to the end of the experiment. From D0 to D4 in the rearing tanks, pH values increased up to 8.2 and then decreased to 8 by the end of the experiment while pH was never inferior to 8.1 in the control tank. In the same way, SR values remained stable in the control tank while they equaled 5 from D0 to D4 and decreased to 3.8 from D5 to D9 in the rearing tanks.
<figure><img src="image_87.png" /><figcaption>Figure 2 – Heatmap of all zootechnical and physicochemical parameters in the rearing and the control tanks.</figcaption></figure>

(A) Larval Survival Rates (LSR) and (B) Larval Stage Indexes (LSI) in all the rearing tanks (larval rearing cycle A: A1, A2, A3, larval rearing cycle B: B1, B2, B3) from Day 0 (D0) to Day 9 (D9). As no larvae were added to the control (Ctrl) tank, no LSR or LSI were calculated. (C) Temperature, (D) Total Ammonium Nitrogen (TAN) concentration, (E) Soluble Reactive Phosphorus (SRP) concentration, (F) Colored Dissolved Organic Matter concentration (aλ<sub>325nm</sub>), (G) pH and (H) slope ratio (SR) values in the rearing and the control tanks from D0 to D9. In all the heatmaps, minimum values are represented in dark blue and maximum values are represented in yellow.
In order to allow better visual representation of all the physicochemical parameters in the rearing tanks, a standardized Principal Component Analysis (PCA) was performed and explained more than 87% of the total inertia for the first two principal axes (Fig. 3A). The correlation circle obtained from this analysis showed that the vectors associated with the $a\lambda_{325nm}$, TAN and SRP variables were oriented in the same way and were close to each other suggesting that these parameters were positively correlated. In the same way, pH and SR values also seemed to be positively correlated. As $a\lambda_{325nm}$, TAN, SRP, pH and SR were close to the horizontal axis, the PCA1 seemed to be driven by a pH and organic matter gradient. The vertical axis (PCA2) was driven by a temperature gradient which was not related to the other considered parameters. We observed how all the rearing samples clustered according to their physicochemical parameters, their sampling day and their larval stage (Fig. 3B and 3C).

<figure><img src="image_88.png" /><figcaption>**Figure 3 – Standardized Principal Component Analysis (PCA) of the physicochemical parameters in the rearing water of all the samples from Day 0 (D0) to Day 9 (D9).**</figcaption></figure>

(A) Correlation circle of the physicochemical parameters measured in the rearing tanks throughout the whole rearing cycle. Soluble Reactive Phosphorus (SRP) concentration, Total Ammonium Nitrogen (TAN) concentration, CDOM concentration ($a\lambda_{325nm}$), slope ratio (SR), pH and temperature, values were considered. (B) Visual representation of the samples according to their physicochemical parameters. Samples are clustered by rearing day, from Day 0 (D0) to Day 9 (D9). (C) Visual representation of the samples according to their physicochemical parameters. Samples are clustered by larval stage, from nauplii (Nii) to post-larvae stage. Larval transitioning phases from zoea to mysis and from mysis to post-larvae are respectively represented in orange and blue.