Ontogenèse Du Microbiote Chez Le Poisson Vivipare Brachyistius Frenatus

Ontogenèse du microbiote chez le poisson vivipare Brachyistius frenatus. Transmission verticale de symbiotes microbiens pionniers?

Mémoire

Aurélie Boilard

Maîtrise en biologie - avec mémoire Maître ès sciences (M. Sc.)

Québec, Canada

Ontogenèse du microbiote chez le poisson vivipare Brachyistius frenatus. Transmission verticale de symbiotes microbiens pionniers?

Mémoire

Aurélie Boilard

Sous la direction de :

Nicolas Derome, directeur de recherche

Résumé

Chez les Mammifères, le recrutement du microbiote débute in utero, ce qui restait à démontrer chez d'autres classes de Vertébrés. L’objectif général du projet était de tester si un tel recrutement se produit chez un Vertébré non Mammifère. Nous avons testé, chez le Poisson vivipare Brachyistius frenatus, l'hypothèse selon laquelle la poche utérine est colonisée par un microbiote transmissible aux alevins, conférant à leur propre microbiote une ontogenèse semblable à celle des Mammifères. Le projet visait l’atteinte des objectifs suivant : i) caractériser le mode de transmission du microbiote, ii) établir la composition, la diversité et les relations des communautés bactériennes du microbiote des femelles, des juvéniles et de leur environnement et iii) déterminer l’ontogenèse du microbiote chez B. frenatus. Ce projet a permis de caractériser le mode de transmission du microbiote, sa séquence de recrutement, ainsi que la contribution respective de différentes communautés sources en caractérisant la diversité bactérienne du microbiote des femelles, des juvéniles et de leur environnement avec une approche métagénomique de type code barre. La région V4 du gène de l'ARNr 16S a été ciblée comme marqueur taxonomique bactérien pour identifier les taxons des différents échantillons. Cette étude nous a permis d’identifier le premier cas d’une transmission verticale du microbiote in utero chez un vivipare non Mammifère et les résultats sous- entendent que B. frenatus est peut-être un tout nouveau modèle d’ontogenèse du microbiote. Cette étude a permis l’acquisition des connaissances sur la transmission du microbiote et, dans le contexte de convergence évolutive de la viviparité, elle ouvre à de nouvelles perspectives quant aux avantages évolutifs d'une telle transmission de symbiotes microbiens.

Abstract

In Mammals microbial recruitment starts in utero, something that had not been shown in any other Vertebrate class. The main goal of this project was to test whether this type of recruitment happens in a non-mammalian Vertebrate. We tested in the viviparous fish Brachyistius frenatus the hypothesis under which the uterine pouch is colonized by a microbiome transmissible to the juveniles, conferring them an ontogeny similar to Mammals. This project also aimed to i) characterize the mode of transmission of the microbiota, ii) establish the composition, diversity and relationships between the microbial communities of pregnant females, juveniles and their environment and iii) determine the ontogeny of the microbiota in B. frenatus. We characterized the mode of transmission of the microbiome, explored its recruitment and the contribution of different source communities with a metagenomic approach (bar coding). We targeted the hyper variable region V4 of the small subunit (16S) rRNA gene to determine the presence of a vertical transmission of the microbiome In this study, we confirmed the presence of a vertically transmissible microbiome in the viviparous fish B. frenatus. We documented for the first time an in utero transmission of the microbiota in a non-mammalian viviparous species. Our results also hint that B. frenatus might be a new model of microbiota ontogeny. This study contributes to the acquisition of knowledge on microbiome transmission and, in the context of evolutionary convergence of viviparity, allows the formulation of hypotheses concerning the evolutionary advantages of in utero microbiome transmission.

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Table des matières

Résumé ...... ii Abstract ...... iii Table des matières ...... iv Glossaire ...... v Remerciements ...... vii Avant-propos ...... viii Introduction ...... 1 1.1 Stratégies de reproductions chez les Vertébrés ...... 1 1.2 L’holobionte ...... 2 1.2.1 L’importance du microbiote pour la santé de l’hôte ...... 2 1.2.2 L’acquisition du microbiote chez les Mammifères ...... 5 1.2.3 L’ontogenèse et stratégies d’acquisition initiale du microbiote ...... 6 1.3 Approche expérimentale ...... 8 1.4 Brachyistius frenatus, espèce modèle pour notre étude ...... 9 1. 5 Objectifs et hypothèses ...... 11 Chapitre 1 Viviparity : cutting edge strategy for microbiota ontogeny ...... 12 1.1 Résumé ...... 12 1.2 Abstract ...... 12 Introduction ...... 13 Methodology ...... 16 Sampling and dissections ...... 16 Bacterial DNA extraction and amplification ...... 16 Bioinformatic analyses ...... 17 Statistical analyses ...... 17 Results ...... 19 Discussion ...... 21 Conclusion ...... 27 Acknowledgements ...... 28 Conflicts of Interests ...... 28 References ...... 29 List of tables and figures ...... 33 Conclusion ...... 45 Bibliographie ...... 48 Annexe A : Supporting informations ...... 58

Glossaire

ARNr 16S Sous unité 16S du gène de l’ARN ribosomique mitochondrial (ARNr 16S). Fréquemment utilisé comme marqueur taxonomique bactérien puisque ces régions hyperconservées sont entourées de régions hypervariables, qui permettent l’identification des taxons bactériens jusqu’au genre ou à l’espèce.

Acquisition horizontale du microbiote Acquisition de symbiotes microbiens par contact direct avec l’environnement.

Acquisition verticale du microbiote Transmission de symbiotes microbiens d’un parent à sa progéniture.

Dysbiose État de déséquilibre entre le microbiote et l’hôte, peut être causé par différents facteurs intrinsèques et extrinsèques, mène potentiellement à des maladies voir à la mort.

Eubiose État d’équilibre au sein de l’holobionte.

Gonopodium Modification de la portion antérieure de la nageoire anale chez les mâles de B. frenatus qui forme une structure complexe tubulaire pour le transfert du sperme lors de la reproduction.

Holobionte La combinaison de l’organisme hôte et de son microbiote en tant qu’unité interdépendante.

Lécithotrophie Stratégie de nutrition du juvénile chez les vivipares au cours du développement in utero où les nutriments proviennent d’un vitellus.

Longueur standard Méthode de mesure des poissons allant de l’extrémité de la bouche à celle de la queue (i.e. base de la nageoire caudale).

Longueur totale Méthode de mesure des poissons allant de l’extrémité de la bouche jusqu’à celle de la queue.

Matrotrophie Stratégie de nutrition du juvénile chez les vivipares au cours du développement in utero où les nutriments sont exclusivement de provenance maternelle.

Microbiote Ensemble des microorganismes (Archées, Bactéries, Virus, et Eucaryotes unicellulaires) colonisant les surfaces corporelles d’un organisme hôte.

Ontogenèse Processus du développement et maturation d’un juvénile, de la fécondation à l’âge adulte.

Ontogenèse du microbiote Processus de la formation du microbiote par le recrutement et la sélection (active et passive) de souches microbiennes tout au long du cycle de vie de l’organisme hôte.

PCR Technique moléculaire pour amplifier de l’ADN : Réaction de polymérase en chaîne, « Polymerase Chain Reaction ».

Poche utérine Ovaire modifié en une structure formant un réseau de poches interconnecté pour le développement des juvéniles chez les poissons vivipares de la famille des Embiotocidae.

Remerciements

Je tiens à remercier mon Directeur, Nicolas Derome, et Giacomo Bernardi, collabotateur du projet, pour m’avoir donné l’occasion de travailler sur un projet aussi ambitieux et passionnant, et pour leur soutien tout au long du projet. Je tiens aussi à remercier May Roberts, Daniel Wright et Alessio Bernardi pour leur participation aux efforts d’échantillonnage, sans lesquels le projet n’aurait pu avoir lieu. Un grand merci à la chercheure Rachel Meyers pour m’avoir permis de travailler dans son laboratoire et bénéficier des équipements nécessaires pour la réalisation des travaux laboratoires (dissections et extractions d’ADN) en conditions stériles. Un merci tout particulier à Jim et Midjann Velzy de m’avoir accueilli chez eux durant mon séjour à Santa Cruz et durant la pandémie. Merci à Sidki Bouslama et François-Étienne Sylvain pour leurs contributions aux analyses bioinformatiques et aussi à tous les membres des équipes des laboratoires Derome et Bernardi.

Le soutien financier du Conseil de Recherche en Sciences Naturelles et en Génie (CRSNG), du Fond de Recherche Québec – Nature et Technologies (FRQNT) et de Ressources Aquatiques Québec (RAQ) a permis la réalisation de ce projet.

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Avant-propos

L’article joint à ce mémoire de maîtrise ne fait office d’aucun état de publication au moment de la soumission du mémoire. Toutes modifications proposées suite à la soumission dans le journal choisi seront dans la version finale de l’article, lors de la publication. L’auteure principale du mémoire, qui est aussi l’auteure principale de l’article joint à ce mémoire, a participé à toutes les étapes de la réalisation du projet (échantillonnage, préparation des échantillons, analyses des données) et a procédé à la rédaction de l’article. Les coauteurs ont participé à la préparation des échantillons (Pierre-Luc Mercier) et aux analyses bioinformatiques (Sidki Bouslama). Giacomo Bernardi, collaborateur au projet, a effectué les échantillonnages en mer, coordonné les opérations terrain, participé à la conception du projet et révisé le manuscrit. Le directeur, Nicolas Derome, a conçu et coordonné le projet, et révisé le manuscrit.

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Introduction

1.1 Stratégies de reproductions chez les Vertébrés Lorsqu’il est question de mode de reproduction chez les Vertébrés, il existe deux grandes stratégies évolutives : l’oviparité et la viviparité. L’oviparité est caractérisée par la production et la ponte d’œufs par la femelle, dans lesquels les embryons se nourrissent d’une substance riche en réserves énergétiques, le vitellus (Wourms and Lombardi 1992, Blackburn 1999). Pour sa part, la viviparité est caractérisée par le développement des jeunes à l’intérieur du tractus reproducteur de la femelle, avec la mise-bas de jeunes complètement développés (Blackburn 1999, 2015). La viviparité présente un gradient en ce qui concerne la nutrition fœtale, allant d’un extrême, où les nutriments sont exclusivement de provenance maternelle (matrotrophie), à l’autre, où les jeunes sont nourris par un vitellus tout au long de leur développement (lécithotrophie) (Wourms 1981, Blackburn 1992, 2015). Ce qui est appelé ovoviviparité correspond à une série peu définie de stratégies qui se situent entre l’oviparité et la viviparité, incluant une oviparité avec rétention d’œufs et une viviparité complètement lécithotrophe (Blackburn 1999). De manière générale, les Mammifères sont l’exemple par excellence de la viviparité, étant tous vivipares à l’exception des Monotrèmes (un rare cas d’oviparité matrotrophe) (Blackburn 1999). La viviparité est cependant un trait beaucoup plus répandu, qui a fait l’objet de convergences évolutives chez les Vertébrés : au cours de l’évolution, elle est apparue de façon répétée et indépendante au moins 150 fois dans l’ensemble des classes de Vertébrés, à l’exception des Oiseaux (Wourms 1981, Blackburn 2015). On note par exemple sa présence chez des espèces telles que le Requin tigre (Galeocerdo cuvier) chez les Poissons (Whitney and Crow 2007), la Salamandre tachetée fastueuse (Salamandra salamandra fastuosa) chez les Amphibiens (Buckley et al. 2007) et le Boa constricteur (Boa constrictor) chez les Reptiles (Bauer 1998). Les diverses conséquences liées à la viviparité ont été largement documentées en termes de physiologie (un espace limité menant à une réduction du nombre de jeunes produits et qui peut affecter négativement la locomotion de la mère et sa capacité à se nourrir (Blackburn 1999)) et d’écologie (la protection contre les variations des conditions environnementales (Lambert and Wiens 2013), la prédation et les infections microbiennes (Blackburn 1999)). À ce jour, bien que cet aspect soit capital (Huurre et al. 2008, Sevelsted et al. 2015, Tamburini et al. 2016), ces avantages n’ont pas été abordés en ce qui a trait à la transmission verticale de symbiotes microbiens (i.e. de la mère à sa progéniture).

1.2 L’holobionte Le microbiote désigne l’intégralité de la flore microbienne symbiotique qui colonise nos surfaces corporelles et forme des écosystèmes microbiens, appelés microbiomes (O'Hara and Shanahan 2006, Consortium 2010, Clemente et al. 2012, Cui et al. 2016). Des organismes des trois règnes y sont présents, soit des Bactéries, des Eucaryotes, au moins une espèce d’Archée (Methanobrevibacter smithii) (Chow et al. 2010, Clemente et al. 2012, Lozupone et al. 2012) et une grande diversité de virus (Minot et al. 2011, Columpsi et al. 2016, De La Cruz Peña et al. 2018). On estime que le corps humain (Homo sapiens) serait l’hôte d’environ 3.8*1013 cellules microbiennes (Sender et al. 2016b, a), ainsi que de 1015 virus (Haynes and Rohwer 2011). La surface cutanée et les muqueuses (buccales et digestives) sont les principaux habitats colonisés par ces organismes (Shanahan 2002, Chow et al. 2010, Doré and Corthier 2010). Plusieurs facteurs sont susceptibles d’influencer la composition du microbiote humain, comme des facteurs environnementaux (Thompson et al. 2008, Benson et al. 2010, Spor et al. 2011), génétiques (Benson et al. 2010, Huttenhower et al. 2012, Parks et al. 2013, Tamburini et al. 2016), alimentaires (Turnbaugh et al. 2009, Maslowski and Mackay 2010, Walker et al. 2011) ou encore l’âge (Tiihonen et al. 2010, Yatsunenko et al. 2012).

Ensemble, le microbiote et l’hôte forment une entité appelée l’holobionte (Margulis 1991), un métaorganisme dont les parties sont interdépendantes (Simon et al. 2019). L’équilibre au sein de l’holobionte est fragile, des perturbations peuvent potentiellement amener l’holobionte d’un état d’eubiose (symbiotique) à un état de dysbiose (état malsain) (Iebba et al. 2016). C’est pourquoi le microbiote a un impact direct sur la prédisposition de nombreuses maladies à caractère inflammatoire ou infectieux (Khosravi and Mazmanian 2013, Kho and Lal 2018). Il est donc critique pour un organisme de recruter un microbiote avec toutes les fonctions essentielles à sa santé et son développement.

1.2.1 L’importance du microbiote pour la santé de l’hôte Chez les Mammifères, cette symbiose est très bien documentée, et des recherches à ce sujet ont permis de mettre en évidence la coévolution hôte-symbiote (bactéries à génome réduit auxquelles des gènes essentiels sont manquants (Andersson and Kurland 1998, Moran and Wernegreen 2000) et/ou bactéries possédant des gènes qui bénéficient principalement à l’hôte (Ochman and Moran 2001, Xu and Gordon 2003, Heinken et al. 2013)). Jusqu’à présent, l’essentiel des études sur les interactions hôte-microbiote se sont concentrées sur le microbiote intestinal. Ces études ont permis d’établir que l’intégrité du microbiote intestinal est indissociable de l’état de santé de son hôte

autant chez les Mammifères (Sekirov et al. 2010, Holmes et al. 2012, Marchesi et al. 2015), que chez les Poissons (Legrand et al. 2020).

Chez les Mammifères, comme chez les Poissons, les interactions symbiotiques entre les communautés bactériennes et l’hôte ont des impacts à plusieurs niveaux essentiels : le métabolisme (Gill et al. 2006, Hooper et al. 2012, Tremaroli and Bäckhed 2012, Butt and Volkoff 2019, Legrand et al. 2020), le comportement (Logan and Katzman 2005, Desbonnet et al. 2010, Cryan and Dinan 2012, Butt and Volkoff 2019), le développement (Hooper et al. 2012, Sommer and Bäckhed 2013, Dinan and Cryan 2017) et les prédispositions à certaines maladies (Pérez et al. 2010, Yang et al. 2013, Keku et al. 2014, Louis et al. 2014).

Au niveau du métabolisme, le microbiote intestinal influence la performance énergétique (Gill et al. 2006, Hooper et al. 2012, Tremaroli and Bäckhed 2012) en permettant par exemple l’absorption de nutriments autrement indigestes pour l’hôte, tel que certains polysaccharides (Smith et al. 2007, Sommer and Bäckhed 2013), ou par la sécrétion de certaines vitamines essentielles à la santé de son hôte (par exemple, B et K chez les Mammifères) (Smith et al. 2007, LeBlanc et al. 2013) ou d’enzymes digestives chez les Poissons (Legrand et al. 2020).

Les implications comportementales sont tout aussi importantes, chez l’Humain des recherches récentes ont commencé à associer la santé du microbiote intestinal et la santé mentale. Par exemple, l’anxiété (Logan and Katzman 2005, Desbonnet et al. 2010, Cryan and Dinan 2012), la dépression (Stevens et al. 2020), l’humeur (Steenbergen et al. 2015), la cognition (Heijtz et al. 2011, Cryan and Dinan 2012) et la douleur (Niedzielin et al. 2001, Cryan and Dinan 2012) sont tous des éléments pouvant être influencés par le microbiote intestinal grâce à la communication entre le système nerveux entérique et le système nerveux central via le nerf vague (i.e. «gut-brain axis »). Cette voie de communication bidirectionnelle entre le microbiote et le système nerveux central s’effectue par le biais de signaux immunitaires, neuronaux et endocriniens (Romijn et al. 2008, Grenham et al. 2011, Capuco et al. 2020). Des bactéries des genres de Lactobacilli et Bifidobacteria sont bénéfiques à la santé de l’hôte en diminuant sa perception de la douleur, voire en la soulageant, dans les cas de douleurs viscérales causées par le stress ou des maladies inflammatoires (Niedzielin et al. 2001, Cryan and Dinan 2012). De plus, ces taxons ont un effet bénéfique sur la santé mentale en réduisant les symptômes de détresse psychologique (Logan and Katzman 2005, Desbonnet et al. 2010, Cryan and Dinan 2012, Pirbaglou et al. 2016). Chez les poissons, des évidences pointent dans la même direction, où le microbiote aurait des impacts sur l’anxiété (Davis et al. 2016).

En ce qui concerne les prédispositions à certaines maladies, les recherches chez l’Humain démontrent que des perturbations au niveau de l’intestin favorisent le développement de maladies inflammatoires chroniques (syndrome du côlon irritable, maladie de Crohn ou des colites ulcératives) (Yang et al. 2013, Keku et al. 2014) et du cancer du côlon. De plus, l’inflammation chronique mène à des risques plus élevés de cancer du côlon (Yang et al. 2013, Keku et al. 2014). Le régime alimentaire serait un élément clé dans le maintien de cet équilibre et l’induction de déséquilibre (Keku et al. 2014). Par exemple, un régime alimentaire riche en fibre réduit les risques de cancer du côlon (Louis et al. 2014) en diminuant les risques d’inflammations chroniques, en induisant l’apoptose et en inhibant la prolifération des cellules tumorales (Keku et al. 2014). D’un autre côté, un régime alimentaire riche en protéines augmente les risques d’inflammation et de cancer du côlon, puisque plusieurs produits du métabolisme des protéines sont mutagènes et génotoxiques (Keku et al. 2014).

Enfin, le microbiote intestinal des Mammifères et des Poissons joue un rôle crucial au niveau du développement du système immunitaire (Chow et al. 2010, Hooper et al. 2012, Kelly and Salinas 2017), de la maturation du cerveau (Heijtz et al. 2011, Sampson and Mazmanian 2015, Phelps et al. 2017), en contribuant notamment à l’initiation de mécanismes de signalisation, qui ont un impact sur les circuits neuronaux et qui affectent les capacités motrices et l’anxiété (Heijtz et al. 2011). D’ailleurs, en ce qui a trait au développement du cerveau, le microbiote aurait un effet sur le développement social (Archie and Tung 2015, Dinan et al. 2015) et sur le développement de l’autisme (Li and Zhou 2016, Vuong and Hsiao 2017) chez l’Humain, et sur l’hyperactivité chez les Poissons (Phelps et al. 2017).

Les souches bactériennes composant le microbiote intestinal possèdent différentes fonctions pour contribuer à la santé de l’hôte. C’est pourquoi, tout au long du développement de l’hôte, différentes souches avec des fonctions bien spécifiques devront être recrutées. Pour assurer le bon développement de l’hôte, il est donc essentiel de recruter toutes les souches nécessaires aux étapes clés du développement, car l’équilibre écosystémique du microbiome est particulièrement instable aux premiers stades de développement (Schluter and Foster 2012) et des perturbations peuvent avoir des impacts considérables sur la santé présente et future de l’organisme (Guaraldi and Salvatori 2012, Yang et al. 2013, Keku et al. 2014).

1.2.2 L’acquisition du microbiote chez les Mammifères Pendant longtemps, il était établi que le milieu utérin était stérile et que l’inoculation initiale se faisait durant l’accouchement par le contact du jeune avec la muqueuse vaginale (Tissier 1900). En outre, la présence de bactéries dans le milieu utérin était fortement associée à des infections menant à des naissances prématurées (Gonçalves et al. 2002). Par contre, au cours des dernières années, la recherche a établi que les techniques employées auparavant n’étaient pas assez puissantes pour détecter la présence des bactéries pouvant être transmise dans le milieu utérin (DiGiulio 2012). Des preuves que le transfert initial du microbiote de la mère vers son jeune se ferait in utero ont été récemment mises au jour (Jiménez et al. 2005, Jiménez et al. 2008, Collado et al. 2016, Neu 2016, Younge et al. 2019). En effet, des bactéries de plusieurs genres (e.g. Firmicutes, Tenericutes, Proteobacteria Bacteroidetes, Fusobacterium(Aagaard et al. 2014)) ont finalement été détectées au niveau du placenta (Younge et al. 2019) et des différents fluides irriguant les structures placentaires (Collado et al. 2016, Younge et al. 2019), comme le sang du cordon ombilical (Jiménez et al. 2005) et le fluide amniotique (Jiménez et al. 2008). L’analyse du méconium des nouveau-nés appuie aussi cela, puisqu’il n’est pas stérile et contient des communautés bactériennes (entre 1 et 5 espèces ont été identifiées, dont les principales sont Enterococcus fecalis, Staphilococcus epidermidis et Escherichia coli), quoi que beaucoup moins diversifiées que ce qui est retrouvé dans le microbiote intestinal des adultes chez l’Humain (Jiménez et al. 2008).

Chez l’Humain, le recrutement du microbiote intestinal commence donc in utero (Jiménez et al. 2005, Jiménez et al. 2008, Neu 2016), et se poursuit très tôt après la naissance sous l’influence de quatre facteurs principaux : i) le mode d’accouchement (naturel ou par césarienne) (Tormo‐Badia et al. 2014, Mueller et al. 2015) ; ii) la nourriture donnée au nouveau-né (lait maternel ou préparation pour nourrissons) (Guaraldi and Salvatori 2012) ; iii) l’environnement (contact avec la peau maternelle, avec le personnel de l’hôpital ou les appareils) (Dominguez-Bello et al. 2010, Fallani et al. 2010), et iv) l’exposition à des antibiotiques (Tormo‐Badia et al. 2014, Mueller et al. 2015). D’autres facteurs peuvent influencer le recrutement et la composition du microbiote, mais puisque ceux-ci ne peuvent être directement modifiés ou ne s’appliquent pas à ce stade (génétique (Benson et al. 2010, Parks et al. 2013, Tamburini et al. 2016), âge (Tiihonen et al. 2010, Yatsunenko et al. 2012), régime alimentaire (De Filippo et al. 2010, Graf et al. 2015)), ils ne seront que mentionnés.

Le mode d’accouchement exerce une grande influence sur le recrutement subséquent des symbiotes. La voie naturelle met le nouveau-né en contact avec la flore vaginale, voire même intestinale de la

mère (Di Mauro et al. 2013, Tamburini et al. 2016) permettant une transmission verticale de divers taxons bactériens (e.g Lactobacillus spp. (Tamburini et al. 2016)) entre la mère et son enfant (Dominguez-Bello et al. 2010, Fallani et al. 2010). Chez les enfants nés par césarienne, les communautés bactériennes transmises sont essentiellement celles de la peau de leur mère (Dominguez-Bello et al. 2010) et de l’environnement (Fallani et al. 2010) (Staphyloccucus, Streptocuccus, Propionibacteria (Tamburini et al. 2016)). Cette différence dans la transmission du microbiote affecte le développement du système immunitaire (Huurre et al. 2008, Sevelsted et al. 2015) et favorise la prédisposition à certaines pathologies à base inflammatoire telles que l’asthme (Renz‐Polster et al. 2005, Tollånes et al. 2008, Sevelsted et al. 2015), les allergies (Grönlund et al. 2000, Renz‐Polster et al. 2005) et d’autres maladies inflammatoires (Sevelsted et al. 2015, Kristensen and Henriksen 2016).

En ce qui concerne le type de nourriture donné au nourrisson, l’allaitement est à prioriser pour plusieurs raisons. D’une part, le colostrum permet la transmission de l’immunité de la mère à son enfant (Ogra et al. 1977). D’autre part, toujours chez l’Humain, le lait maternel contient une grande quantité de symbiotes (Staphylococcus, Streptococcus, Lactobacillus, Bifidobacterium (Tamburini et al. 2016)). Des évidences de transmission de symbiotes intestinaux maternels via le lait maternel ont récemment été découvertes (Perez et al. 2007, Jost et al. 2014) et les symbiotes recrutés dans la petite enfance sont déterminants pour la tolérance et la réponse immunitaire au niveau des muqueuses (De Palma et al. 2012). À court terme, la transmission de symbiotes microbiens clés via l’allaitement pourrait contribuer à prévenir les entérocolites nécrosantes (Barlow et al. 1974), les diarrhées chez les nouveau-nés (Lopez-Alarcon et al. 1997, Morrow et al. 2005), les allergies (Kull et al. 2002), l’obésité (Von Kries et al. 1999, Arenz et al. 2004, Owen et al. 2005) et les maladies auto-immunes dans la petite enfance (maladie coeliaque (Ivarsson et al. 2002, Fasano and Catassi 2005, De Palma et al. 2012). À plus long terme, l’allaitement est associé à une réduction des risques de maladies inflammatoires, cardiovasculaires (Rich-Edwards et al. 2004), d’obésité (Horta et al. 2015, Tamburini et al. 2016) et de diabète de type 2 (Stuebe et al. 2005, Owen et al. 2006, Horta et al. 2015).

1.2.3 L’ontogenèse et stratégies d’acquisition initiale du microbiote Ainsi, le microbiote joue des rôles essentiels à la biologie de son organisme hôte (Hooper et al. 2012, Sommer and Bäckhed 2013, Dinan and Cryan 2017). Par conséquent, le microbiote a un effet direct sur la prédisposition à certaines maladies (Yang et al. 2013, Keku et al. 2014, Louis et al. 2014). Il est donc essentiel pour un organisme de recruter un microbiote doté de toutes les fonctions

indispensables à son développement. L’étude de l’ontogenèse du microbiote intestinal, soit le processus de recrutement des espèces de microorganismes tout au long du développement d’un organisme, devient donc incontournable pour mieux comprendre les rôles clés de ces microorganismes. L’acquisition initiale du microbiote s’opère selon deux grandes stratégies : l’acquisition horizontale, par le contact direct avec les communautés microbiennes de l’environnement, et la transmission verticale du microbiote d’un parent à sa progéniture. Chez les Ovipares, il est généralement admis que l’acquisition du microbiote se fait principalement par acquisition horizontale via l’environnement (Romero et al. 2014, Egerton et al. 2018), même si la présence de symbiotes a récemment été détectée au niveau du sac vitellin avant la formation de la coquille de l’œuf (Trevelline et al. 2018).

Chez les Vivipares, la transmission initiale verticale du microbiote de la mère à sa progéniture est suivie par une acquisition horizontale de symbiotes de l’environnement. Chez les Mammifères, la transmission verticale de symbiotes commence in utero, pour se poursuivre lors de l’accouchement (Collado et al. 2016, Perez-Muñoz et al. 2017), puis post-partum via le lait maternel (Jiménez et al. 2005, Jiménez et al. 2008, Neu 2016). Parallèlement, l’acquisition horizontale se fait par contact avec l’environnement. Chez plusieurs ovipares, notamment chez les Insectes, les Oiseaux et les Poissons, il existe une gamme de stratégies alternatives pour l’intégration d’une transmission verticale de symbiotes microbiens à un mode de transmission favorisant une acquisition horizontale. Chez l’Abeille (Kwong and Moran 2016), le Pigeon (Gillespie et al. 2012), le Discus et potentiellement plus de 28 autres espèces de poissons (Sylvain and Derome 2017), une transmission verticale du microbiote par le nourrissage parental a effectivement été observée. En ce sens, la caractérisation d’un mode de transmission vertical des symbiotes microbiens pionniers chez des organismes non-mammifères ouvrira des pistes de réflexion quant au rôle potentiellement critique des microorganismes symbiotes dès les premiers stades de développement de l’organisme hôte.

Considérant d’une part l’importance du microbiote pour la santé de l’organisme hôte, et d’autre part, la présence de stratégie alternative pour l’intégration d’une transmission verticale à un mode de reproduction favorisant une acquisition horizontale, une question s’impose. La viviparité serait- elle, entre autres choses, une stratégie d’optimisation de la transmission du microbiote? Cette question demeure non résolue puisqu’une telle transmission verticale in utero du microbiote n’a pas encore été mise en évidence chez un Vertébré non-mammifère. C’est que nous avons proposé de

faire avec cette étude en étudiant le mode de transmission du microbiote chez un poisson vivipare.

1.3 Approche expérimentale Considérant l’importance du microbiote, beaucoup d’efforts ont été mis en œuvre pour développer des techniques pour son analyse. L’approche métagénomique, soit une analyse génomique des communautés de microorganismes à l’aide de techniques de biologie moléculaire (amplification par PCR, séquençage parallèle à haut débit, approche code barre, etc.) est très prisée actuellement, car elle permet une caractérisation exhaustive de la composition taxonomique et fonctionnelle des communautés de microorganismes à analyser (Frank and Pace 2008), ce que ne permet pas la méthode de culture avec laquelle seuls 5% des souches en présence sont détectées. Deux approches métagénomiques sont possibles: la caractérisation des communautés à l’aide d’un seul gène ou la caractérisation du profil de tous les gènes de la communauté avec une approche « shotgun » (fragmentation génomique aléatoire) (Gilbert and Dupont 2010). L’analyse génétique par code- barre (DNA barcoding) se base sur la première voie et rend possible l’identification d’organismes à l’aide d’un marqueur génétique spécifique, soit une séquence conservée entre organismes d’une même espèce, mais qui diffère entre organismes d’espèces différentes (Hebert et al. 2003, Hajibabaei et al. 2007). Parmi ces marqueurs fréquemment utilisés se trouve la sous-unité 16S du gène de l’ARN ribosomique mitochondrial (ARNr 16S) (Hebert et al. 2003, Hajibabaei et al. 2007, Janda and Abbott 2007). D’ailleurs, c’est la technique la plus utilisée de nos jours pour l’étude de la phylogénie et de la taxonomie bactérienne. La présence de ce gène est presque universelle chez les Bactéries et son évolution est relativement lente. Sa séquence d’environ 1500 pb est suffisante pour identifier les espèces, voire les souches bactériennes, grâce à l’analyse bioinformatique et les banques de données pour l’identification des bactéries à partir de ce gène contiennent un nombre grandissant de taxons (Janda and Abbott 2007). Parmi les banques de données utilisées pour l’assignation taxonomique, on retrouve SILVA, RDP-II (« Ribosomal database project »), Greengenes, NCBI (« National Center for Biotechnology Information ») ou OTT (« Open tree of life taxonomy ») (Balvočiūtė and Huson 2017). Le choix d’utilisation d’une banque de données varie en fonction des besoins, comme le niveau d’assignation taxonomique recherché (genre ou espèce) et le type d’organisme à identifier (Eucaryote, Archées, Bactéries) (Balvočiūtė and Huson 2017). De plus, le mode d’assignation taxonomique peut varier entre les différentes banques de données, en fonction des différentes sources d’informations considérées (Balvočiūtė and Huson 2017). Par contre, la plateforme la plus utilisée pour les études basées sur le gène de l’ARNr 16S reste SILVA, mais la plateforme NCBI est aussi utilisée pour les études avec le gène de l’ARNr 16S et lors de

séquençage à l’aide d’une approche « shutgun » (Balvočiūtė and Huson 2017). Le choix des amorces pour l’amplification du gène de l’ARNr 16S est aussi un enjeu. D’une part, elles doivent correspondre à des séquences très conservées afin de permettre l’amplification d’ADN chez un maximum de taxons. D’autre part, elles doivent entourer une séquence dite hyper variable, soit une séquence propre à chaque taxon, pour permettre une assignation taxonomique fiable (Hartmann et al. 2010). Par exemple, la région hypervariable V4 est une bonne séquence à amplifier puisqu’elle est entourée de régions très conservées. Cependant, les techniques actuelles de séquençage à haut débit ne permettent que d’amplifier des fragments de 300 à 400 nucléotides, ce qui rend l’assignation taxonomique possible jusqu’au rang du genre, voire de l’espèce (Vincent et al. 2017).

1.4 Brachyistius frenatus, espèce modèle pour notre étude Le modèle que nous avons utilisé pour cette étude est la Perche côtière des forêts de varech (Brachyistius frenatus). Il s’agit d’une espèce relativement peu étudiée, n’étant pas une espèce d’intérêt commerciale vu sa petite taille (Feder et al. 1974). Cette espèce est un Poisson vivipare de la famille des Embiotocidae (Reisser et al. 2009, Longo and Bernardi 2015). Les Embiotocidae, famille de poissons appartenant à l’infraclasse des Téléostéens et qui comprend 24 espèces dont 18 vivants le long de la côte californienne (Reisser et al. 2009, Longo and Bernardi 2015), possède la particularité d’avoir un mode de reproduction sans stade larvaire pélagique : les mères portent les jeunes dans une poche utérine (Behrens 1977, Wourms 1981, Reisser et al. 2009, Longo and Bernardi 2015). Chez les Vertébrés non-mammifères, la tendance est à la viviparité lécitotrophe, mais l’une des grandes particularités des Embiotocidae dans ce contexte d’étude est sa viviparité matrotrophe, soit le même trait qui caractérise les Mammifères euthériens (Blackburn 2015). Les jeunes se développent dans la poche utérine jusqu’à un stade juvénile avancé, en étant nourris par une sécrétion de la muqueuse utérine au début du développement, puis par diffusion du sang maternel via leurs nageoires (Behrens 1977, Wourms 1981, Reisser et al. 2009, Longo and Bernardi 2015).

Chez les Embiotocidae, les cycles de vies sont variables, avec une espérance de vie variant entre 2 et 10 ans et un âge à la première reproduction allant de 1 à 3 ans (Baltz 1984). Malgré la présence de ces variations, le cycle de vie semble être très consistant selon la taille des poissons. Baltz (1984) propose un tel classement des espèces et a établi trois classes selon la longueur totale: i) petites (jusqu’à 215mm) , ii) moyennes (215 à 335mm) et iii) grandes (335mm et plus). B. frenatus est une petite espèce, avec une longueur standard entre 84 et 114mm. Cette espèce vit dans la zone intertidale (Baltz 1984) en très intime association avec les forêts

d’algues (varech) du genre Macrocystis, où tous les stades de développement de l’espèce vivent (Hubbs and Hubbs 1954). B. frenatus est une espèce carnivore, qui se nourrit entre autres d’ectoparasites présents sur les algues de M. pyrifera et sur d’autres poissons (Feder et al. 1974). Comme la plupart des autres petites espèces, B. frenatus a une espérance de vie relativement courte, soit de 2 ans (Hubbs and Hubbs 1954, Baltz 1984). Les mâles et les femelles sont de même taille et ont un taux de croissance similaire et l’âge à la première reproduction est d’un an (Hubbs and Hubbs 1954, Baltz 1984). Contrairement à ce qui est observé chez d’autres espèces d’Embiotocidae, les juvéniles ne sont pas sexuellement matures à la naissance (Hubbs and Hubbs 1954). Le nombre de juvéniles par portée est très variable (entre 2 à 50) et dépendrait de la qualité de l’habitat (i.e la canopée formée par les algues), qui est lui-même variable d’une année à l’autre (Baltz 1984). Les juvéniles naissent entre 32-33 mm, entre avril et juillet, et se reproduisent l’année suivante (Hubbs and Hubbs 1954, Feder et al. 1974). Chez B. frenatus, les femelles sont polyandres (elles se reproduisent avec plusieurs mâles) (DeMartini 1988, Tootell and Steele 2012) et la reproduction a lieu en automne (débutant entre septembre et octobre et se poursuivant jusqu’en décembre) (Feder et al. 1974). Les mâles déposent le sperme dans la région urogénitale des femelles (Wourms 1981) à l’aide du gonopodium (DeMartini 1988, Tootell and Steele 2012), une modification de la portion antérieure de la nageoire anale pour former une structure complexe tubulaire pour le transfert du sperme (Wiebe 1968).

1. 5 Objectifs et hypothèses L’objectif général de ce projet était de caractériser le mode de transmission du microbiote chez la Perche côtière des forêts de varech et l’hypothèse générale testée était la suivante : il y a transmission verticale du microbiote maternel in utero lors de la gestation chez B. frenatus, comme observé chez les Mammifères. En effet, puisque la poche utérine est reliée au milieu externe par l’orifice génital, ses parois devraient être colonisées par un microbiote. De plus, au début de la gestation, une sécrétion utérine est produite et les alevins s’en nourrissent durant les premières étapes du développement (Wourms 1981) de telle sorte que : (1) le microbiote de la muqueuse utérine pourrait changer de composition selon le mode de nutrition principal (sécrétion/diffusion) des alevins in utero et (2) les alevins, au contact de la muqueuse utérine et en ingérant sa sécrétion, seraient colonisés par des souches microbiennes de la muqueuse utérine. Ainsi, la transmission initiale du microbiote se ferait, in utero, dès l’éclosion des œufs, pour s’intensifier dès la première alimentation. L’approche pour tester les hypothèses vise les objectifs spécifiques suivants : i) déterminer la composition, la diversité et les relations des communautés microbiennes des femelles gestantes (mucus cutané, intestinal et utérin), de juvéniles et du milieu naturel de B. frenatus (algues et eau de mer), ii) déterminer l’ontogenèse (la séquence de recrutement) du microbiote des juvéniles, ainsi que l’origine des symbiotes recrutés. L’approche métataxonomique a été employée afin de caractériser la diversité et la structure taxonomique des organismes microbiens retrouvés chez la mère, les juvéniles et l’eau du milieu afin d’identifier l’origine des différents symbiotes bactériens des juvéniles (parentale vs environnementale). La région hypervariable V4 de la sous-unité 16S du gène d’ARNr est ciblée comme marqueur taxonomique bactérien universel. L’analyse de la composition taxonomique des librairies d’amplicons d’ARNr 16S a été effectuée avec dada2 et phyloseq. Des analyses statistiques telles que PERMANOVA, effectuées avec le logiciel R/vegan, ont permis notamment de déterminer dans quelle mesure il y a transmission in utero de symbiotes microbiens pionniers.

Chapitre 1 Viviparity : cutting edge strategy for microbiota ontogeny

Boilard, Aurélie1, S. Bouslama1,2, P-L. Mercier1,2, G. Bernardi3 et N. Derome1,2 1 Département de biologie, Université Laval 2 Institut de biologie intégrative et des systèmes 3 Department of Ecology and Evolutionary Biology,University of California Santa Cruz.

1.1 Résumé Chez les Mammifères, le recrutement du microbiote débute in utero. L’objectif du projet était de tester si un tel recrutement se produit chez le Poisson vivipare Brachyistius frenatus. Nous avons testé l'hypothèse selon laquelle la poche utérine est colonisée par un microbiote transmissible aux alevins. Nous avons caractérisé le mode de transmission du microbiote, sa séquence de recrutement, et la contribution des communautés sources en caractérisant la diversité bactérienne des femelles, des juvéniles et leur environnement avec une approche métagénomique de type code barre. La région V4 du gène de l'ARNr 16S a été ciblée comme marqueur taxonomique bactérien pour identifier une transmission verticale du microbiote. Cette étude nous a permis d’identifier la première instance d’une transmission verticale du microbiote in utero, chez un vivipare non mammifère et que B. frenatus pourrait être un tout nouveau modèle d’ontogenèse du microbiote.

1.2 Abstract In Mammals microbial recruitment starts in utero, something that had not been proven in any other Vertebrate class. The main goal of this project was to test whether this type of recruitment happens in a non-mammalian Vertebrate. We tested in the viviparous fish Brachyistius frenatus the hypothesis under which the uterine pouch is colonized by a microbiome transmissible to the juveniles, conferring them an ontogeny similar to Mammals. We characterized the mode of transmission of the microbiome, explored its ontogeny and established the bacterial diversity of the microbiome of the females, juveniles and their environment with a metagenomic approach (bar coding). We targeted the hyper variable region V4 of the small subunit (16 S) rRNA gene to determine the presence of a vertical transmission of the microbiome. Our results confirmed the presence of a vertically transmissible microbiome in B. frenatus. This study contributes to the acquisition of knowledge on microbiome transmission and, in the context of evolutionary convergence of viviparity, allows the formulation of hypotheses concerning the evolutionary advantages of in utero microbiome transmission.

Introduction There are two main reproductive strategies in Vertebrates: Oviparity and Viviparity. Oviparity is characterized by the females laying eggs and juveniles sustained by a vitellus during development (Wourms and Lombardi 1992, Blackburn 1999), while Viviparity is characterized by juveniles developing in their mother’s reproductive tract (Blackburn 1999, 2015). When it comes to fetal nutrition, viviparity offers a gradient ranging from nutrients exclusively provided maternally (matrotrophy) to nutrients provided to juveniles by a vitellus (lecitotrophy) (Wourms 1981, Blackburn 1992, 2015). Matrotrophy is relatively rare, but characteristic of eutherian Mammals (Blackburn 1999). Viviparity in itself is far more common, as the trait appeared independently over 150 times in all Vertebrate classes, except Birds (Wourms 1981, Blackburn 2015). In terms of physiology, viviparity implies a limited space for juvenile development (Blackburn 1999) which translates into a limited number of juveniles. In terms of ecology, viviparity provides protection against predators and varying environmental conditions (Lambert and Wiens 2013). Advantages and disadvantages of viviparity have been highly documented, but the advantages of viviparity regarding microbiota acquisition have yet to be explored. The microbiota regroups all microorganisms (Archeae, Bacteria, Viruses, etc.) colonizing a host and form ecosystems called microbiomes (O'Hara and Shanahan 2006, Consortium 2010, Clemente et al. 2012, Cui et al. 2016). In the past decades, increasing evidence has supported the essential roles of the microbiota in its host health and development has become unavoidable. Together microbiome and host form the holobiont, a tightly interconnected and interdependent symbiotic whole (Simon et al. 2019). For example, on the metabolic level, the microbiota plays a critical role in the absorption of otherwise indigestible nutrients (i.e. polysaccharides (Smith et al. 2007, Sommer and Bäckhed 2013)) and vitamin secretion (such as vitamin B and K (LeBlanc et al. 2013)). The microbiota will also play a critical role in immune development (Chow et al. 2010, Hooper et al. 2012) and brain maturation (Heijtz et al. 2011, Sampson and Mazmanian 2015).

Many factors can affect the microbiota (diet, environment, genotype), potentially leading the holobiont from eubiosis (healthy state) to dysbiosis (unhealthy, diseased state) (Iebba et al. 2016). Thus, the microbiota can have direct impacts on the predisposition to some diseases, such as chronic inflammatory diseases (irritable bowel syndrome, Crohn's disease and ulcerative colitis (Yang et al. 2013, Keku et al. 2014)) and some cancers (e.g. colorectal, pancreatic, oral, etc. (Karpiński 2019)). Holobiont equilibrium is especially fragile in early development (Schluter and Foster 2012), where disruptions (dysbiosis) can lead to considerable long-term impact on the hosts’

health (Guaraldi and Salvatori 2012, Yang et al. 2013, Keku et al. 2014).Therefore, it becomes critical for organisms to recruit a microbiota with all the essential functions to its healthy development. There are two main acquisition strategies when it comes to the recruitment of the microbiota: horizontal and vertical. The horizontal acquisition is the recruitment of microbial symbionts through direct contact with the environment (Romero et al. 2014, Egerton et al. 2018), while vertical acquisition is the transmission of microbial symbionts from parent to young. In oviparous species the acquisition is mainly horizontal, with the possible vertical transmission of a few symbionts through the vitellus before the formation of the eggshell (Trevelline et al. 2018) whereas the opposite pattern is typical of viviparous Mammals. The initial acquisition of microbial symbionts begins in utero and intensifies during delivery (Collado et al. 2016, Perez-Muñoz et al. 2017), and continues post-partum through maternal milk (Jiménez et al. 2005, Jiménez et al. 2008, Neu 2016) and contact with the environment (Moeller et al. 2018). Alternative strategies have been observed in oviparous species to integrate a significant vertical transmission of a microbiome to a reproduction strategy favoring a horizontal acquisition (Gillespie et al. 2012, Sylvain and Derome 2017). In the oviparous fish Discus (Symphysodon aequifasciata), parents will feed their young a mucosal substance from their skin that will transmit key bacterial symbionts (Sylvain and Derome 2017). In fact, more than 28 more species of fish (Sylvain and Derome 2017), including many species of the Cichlid family such as the Midas Cichlid (Amphilophus citrinellus), the Green Guapote (Cichlasoma beani) and the Orange chromide (Etroplus maculatus) have been found to have similar feeding strategies (Noakes 1979).

The ontogeny of the microbiome (sequence of recruitment of microbial symbionts) is highly affected by the main acquisition strategy. Oviparous species, such as the zebrafish, have an ontogeny characterized by a massive recruitment of microbial symbionts upon the larvae’s contact with the environment. The recruited diversity then decreases as the juvenile grows, before settling at maturation (Stephens et al. 2016). In viviparous mammals, the ontogeny starts with the transmission of a few key symbionts in utero, which is followed by a progressive acquisition of the rest of the microbiota by the young post-partum for stabilization at around 3 years of age (Koenig et al. 2011, Nieuwdorp et al. 2014).

Considering the existence of alternative strategies to integrate a vertical transmission of microbial symbionts to an otherwise predisposed reproduction tactic, could viviparity be an optimization strategy for the transmission of the microbiota? To date, few studies focused on the initial vertical

transmission of the microbiota in non-mammalian viviparous species. In this context, our study aimed to contribute to resolving this issue by studying the transmission of the microbiome in the kelp surfperch Brachyistius frenatus, a matrotrophic viviparous fish species from the Embiotocideae family (Reisser et al. 2009, Longo and Bernardi 2015).

The Embiotocidaea is a fish family from the Teleostean infraclass comprising 24 species of fish native of the North Pacific, all of which have matrotrophic viviparity. The young develop in a modified ovary, forming a uterine pouch (Behrens 1977, Wourms 1981, Reisser et al. 2009, Longo and Bernardi 2015). During early development nutrition is provided by ovarian secretion (histidine) and in later stages provided by nutrient diffusion (Behrens 1977, Wourms 1981, Reisser et al. 2009, Longo and Bernardi 2015). We selected the kelp surfperch, as our model considering it is a relatively abundant species, is easy to sample, and because this species lives in close association with kelp forests of the Macrocystis genus (Hubbs and Hubbs 1954). Therefore, all life stages can be found living in one place. B. frenatus is a carnivorous species (Feder et al. 1974), and can be found living on the coasts of Baja California in Mexico, in the south, to the coasts of Alaska, in the north (Feder et al. 1974, Csepp and Wing 1999). Reproduction happens throughout fall and up until December (Feder et al. 1974). Females are polyandrous (DeMartini 1988, Tootell and Steele 2012), and gestation lasts about 5 months. Juveniles are born between April and July (Hubbs and Hubbs 1954), with an average in May (Tocts 2018). Because the uterine pouch is linked to the environment by a cloaca, it was reasonable to assume that the uterine pouch is colonized by a microbiota. In early pregnancy, juveniles are fed by ovarian secretions, but in later stages they are fed through nutrient diffusion (Behrens 1977, Wourms 1981, Reisser et al. 2009, Longo and Bernardi 2015). More specifically, we hypothesized that the microbiota of the uterine pouch changes depending on the main source of nutrition and that the juveniles, through ingestion of secretion and by contact with the uterine pouch, would be colonized by maternal microbial symbionts. Our main goal was to characterize the mode of transmission of the microbiota in the kelp surfperch and we tested the hypothesis that there is a vertical transmission of the maternal microbiota in utero during pregnancy in, like observed in Mammals. We also aimed to i) determine the composition, diversity and relations of the bacterial communities of pregnant females and juveniles of B. frenatus and their environments, and ii) to characterize the ontogeny of the microbiome in B. frenatus. In this study, we successfully identified the first in utero vertical transmission of the microbiota in a non-mammalian matrotrophic viviparous species.

Methodology

Sampling and dissections In order to characterize the ontogeny of the microbiota of B. frenatus, we collected samples throughout the gestational period (February to May 2020) in Monterey Bay, California, at about 300 meters at sea from Perkins Park in Monterey (36’’37.724N, 121’’55.119W). Fish were captured by divers through spear fishing and then individually placed in a Ziploc bag. Two liters of seawater and a sample of kelp (M. pyrifera) were also taken in the field to document the diversity and composition of the microbiota present in the environment of B. frenatus. Sterilized propylene bottles were rinsed two to three times with seawater before use. Samples were brought back to the laboratory at the UCSC Coastal campus, on ice. Fishes and kelp samples were then transferred to - 80oC, while seawater was filtered with an electric pump system through 3µm and 0.22µm sterile nitrocellulose membranes. Skin mucus, gut and uterine pouch samples were taken on pregnant females in order to characterize their microbiota. Skin mucus samples were taken directly in the field, before transport, by gently running a sterile swab on the surface for about 30 seconds. The same procedure applied to kelp samples surfaces. Dissections were conducted under a laminar flow hood, with sterile instruments (autoclaved) and equipment. Surfaces of the laminar flow hood were disinfected with Ethanol 70%, Bleach 10%, DNA Away and 20 minutes of UV light, before and after use. The digestive tract of B. frenatus being relatively small, the gut was only separated in three (proximal, mid and distant gut) before sampling. The uterine pouch was sampled whole considering the small size of the structure, especially in early pregnancy. Juvenile larvae were counted before being individually sampled, they were also sampled whole, as it was impossible for us to dissect structures so small. Notes on the presence of parasites (mainly isopods in the gills) were taken as well as the number of juveniles per females. All samples taken on fish specimens were transferred in sterile 2mL Eppendorfs microtubes and stored at -80oC until the extraction step. In order to sample only adherent bacterial communities, samples were rinsed with 25 mL of sterile PBS 1X solution.

Bacterial DNA extraction and amplification Bacterial DNA was extracted with the salt extraction protocol of Aljanabi and Martinez (1997), a reliable method for the extraction of bacterial DNA. Sterile distilled water samples were used as negative extraction controls. The dosage of bacterial DNA was done by spectrophotometry, with UVstar plates and Tecan, for quality control on three levels: i) making sure the extractions worked, ii) verifying that samples were amplified enough by PCR and iii) that enough DNA remains (5-

15ng) for sequencing. Afterwards, two steps of amplification were done (PCR1 and PCR2) on the samples.

We used the QIAGENÒ Multiplex PCR Kit and the Quick-Start Protocol (40 amplification cycles) for the PCR1. Bacterial DNA was amplified with the 519-F and 745-R primers (Sigma-Aldrich – Missouri USA) targeting the hypervariable region V4 of the subunit 16S of the ribosomal RNA gene. Amplification of bacterial DNA was subsequently visualized with a gel electrophoresis (2% agarose m/v, buffer SB, 100V for an hour). Samples with successful amplification were then purified with magnetic beads (Axygen) in order to get rid of unwanted DNA (genomic DNA, leftover primers, DNTPs). The PCR2 step allowed us to add a unique combination of two molecular markers to identify each sample, for sequencing.

Bioinformatic analyses Libraries were sequenced on Illumina NovaSeq6000 (250pb) at the next generation sequencing platform of the Centre de recherche CHU de Québec de l’Université Laval for a paired-end sequencing, and depth of 400 000 to 500 000 reads per sample. Amplicon Sequence Variants (ASV) inference was done with dada2 and taxonomical identification was done with the NCBI databases with 99% matching sequence identity. We proceeded to an in silico decontamination of our dataset using our extraction and PCR negative controls. Subsequent statistical analyses were done with the software R and mainly using the packages Phyloseq (package used to manage microbiota datasets), Vegan (package for statistical analyses adapted for communities of organisms), ggplot2 (package for improved data visualization) and GUniFrac (package to calculate Generalized UniFrac distances).

A measure of relative abundance was calculated in order to determine the main taxa in the various types of samples (juveniles, mother’s skin mucus and gut, sea water and kelp). Samples were grouped according to their type, absolute abundance was then transformed into relative abundance. Uterine pouch and juvenile samples were grouped according to developmental stages. ASVs with an abundance inferior to 0.001% were removed.

Statistical analyses In order to determine if there was a vertical transmission of the microbiota and its source, we used the method proposed by Sylvain and Derome (2017) to calculate recruitment indexes. The recruitment indexes compare the abundance of shared OTUs between juveniles and potential microbial reservoirs. The average relative abundance of shared OTUs is calculated for both

juveniles and reservoirs. This average relative abundance of shared OTUs of juveniles is then divided by the average relative abundance in the reservoirs. The resulting index is an indicator of the strength of recruitment by juveniles depending on each reservoir. The higher the index, the more juveniles recruit from this reservoir (Sylvain and Derome 2017). The Shannon index was calculated to establish the diversity (heterogeneity) of the bacterial communities colonizing the various tested surfaces (skin mucus, gut, uterine pouch, juveniles, sea water and kelp). This measure takes into account the relative abundance of each taxon and species richness (number of taxa). We also bonified our alpha diversity measures with an equitability index (Piélou) and Faith’s phylogenetic diversity. Piélou’s equitability index measures the difference between the observed diversity and a uniform distribution and informs us of the presence of dominant taxa in our communities (Pielou 1966, Pielou 1977). Faith’s phylogenetic diversity index is a diversity measure based on phylogenetic distances (Faith 1992). On our various indexes we followed with a Bartlett test to determine variance homogeneity (which was not satisfied in any of our groups), followed by the non-parametric Kruskal-Wallis test of variance analysis. Posthoc tests (multiple t tests) were then realized to distinguish which groups were significantly different from one another.

Beta diversity measures were calculated in order to test if the taxonomical composition of the microbiome of our groups were significantly different from one another. Beta diversity measures are based on the calculation of phylogenetic distances (UniFrac) measured between taxa in their respective communities. There are three types of UniFrac distances; weighted, which takes relative abundance of each taxon into account; unweighted, which does not ; and generalized, a measure taking both weighted and unweighted distances into account. Here we used Generalized UniFrac (GUniFrac). We calculated the distance matrices using the GUniFrac package (v.1.1) with an alpha parameter of 0.5 (recommended by the authors). PERMANOVAs were performed with 999 permutations on the results in order to determine the presence of significantly different groups. The results were visualized with both 2D and 3D Principal Coordinate Analysis (PCoA).

We also performed a comparison of GUniFrac distances. We used this analysis to corroborate the results of our recruitment indexes during juvenile development. Developing juveniles’ communities of bacterial symbionts (stages 1 to 5) were compared to their putative origin sites on pregnant females (uterine pouch and midgut) and in the environment (seawater and M. pyrifera). GUniFrac distances were calculated with the same method described above. Afterwards, distance matrices were split according to each site of putative origin of juveniles’ microbiota, to plot and compare

them throughout development. We used locally weighted scatterplot smoothing (LOWESS), a non- parametric flexible regression method, to model this relationship. Additionally, we used a confidence interval of 99%, calculated using a t-based approximation method, as well as Bonferroni corrected pairwise MW tests to determine significant variations in GUniFrac distances between groups during juvenile development.

Covariance networks of bacterial species were built for a better visualization of similarities/differences between the bacterial communities of each group. Covariance networks should also allow confirming the results of the PERMANOVA. This was done with a Spearman correlation and the p correction method Benjamini & Hochberg (BH) (Benjamini and Hochberg 1995, Weiss et al. 2016) In order to only retain significant interactions of our networks, we selected interactions with Spearman correlation and [0.5 ; 1] and with p values < 0.05 after BH correction. The networks themselves were made with the software Cytoscape (v. 3.8.2.).

Results A total of 63 fish were collected throughout the sampling period (Table 1), of which 31 were pregnant females. A total of 332 juveniles were sampled from 29 of the gestating females, with an average of 18 juveniles per female. We also captured 21 fish of which we could not identify the sex, and captured 9 confirmed males. For the following analyses, juveniles were sorted into 6 developmental stages determined by visual criteria (Fig S1).

Following in silico decontamination, our dataset comprised 56 852 ASVs, distributed among 25 phylum, 261families and 1086 bacterial species. We first identified the presence of bacterial DNA in uterine pouches and in juveniles during PCRs. Sequencing confirmed the presence of 2015 ASVs distributed among 16 phylum, 82 families and 235 species in the uterine pouch of pregnant females and 4237 ASVs distributed in 16 Phylum, 115 Families and 417 bacterial species in juveniles.

The recruitment index of the microbial symbionts of kelp surfperch juveniles was the highest for the uterine pouch (19.40). Recruitment of the microbial symbionts was affected by juvenile developmental stages (Fig.1 and 2). Recruitment index from the uterine pouch decreased as development progressed. At stage 5, bacterial symbionts originated mainly from pregnant females’ mid gut. Dissimilarity index also followed this trend (Fig. S2), dissimilarity between bacterial communities of uterine pouch and juveniles increased from stage 3 through 5, but decreases with

pregnant females’ mid gut. The kelp had the second highest index on average, a trend consistently followed during development through stages 1 to 5.

Relative abundance of the top 20 ASVs revealed that Aquabacterium parvum as the most present species in all tested fish tissues except in the mid gut, where both A. parvum and Thermus thermophilus were similarly abundant (Fig. 3-4). Juveniles and uterine pouches shared most of their top 20 most abundant ASVs (Fig 3), but four species (Bradyrhizobium lupini, Bradyrhizobium vignae, Geobacillus stearothermophilus and Streptococcus pseudoporcinus) were unique to the juveniles and represented a relatively constant background signal throughout development. Skin mucus had the highest number of species in the top 20 most abundant ASV (Fig. 4) and hosted 4 species (Psychrobacter muriicola, Psychrobacter namhaensis, Rubripirellula obstinate and Thermus scotoductus) that were not shared in the most abundant ASVs of other tested groups. Most of the top 20 abundant ASVs in the seawater were uniquely observed there (Fig. 4) (Marinomonas posidonica, Nitrosopumilus cobalaminigenes, Pseudoalteromonas agarivorans, Pseudoaltermonas haloplanktis and Sulfitobacter faviae), except for 2 species shared with the uterine pouch (Amylibacter ulvae and Pseudoalteromonas neustonica) (Fig.4). Aeromonas lacus was, by far, the most abundant taxa in M. pyrifera (Fig. 4). Relative abundance calculated by taxa showed 5 bacterial species (Aeromonas lacus, Aquabacterium parvum, Brachybacterium faecium, Pseudomonas knackmusii and Thermus thermophilus) in the top 15 most abundant species of all the tissues of pregnant females, their juveniles and the kelp (Supporting Information, Table S5-S16). While not in the top 15, those species were also found in seawater.

From the alpha diversity measures, three bacterial community structures emerged: i) juveniles, uterine pouches and the gut of pregnant females, ii) pregnant females’ skin mucus and sea water and iii) kelp (Fig. 5). Shannon indexes of pregnant females’ skin mucus and seawater were significantly higher (p < 0.001) than other tested groups and Evenness was significantly lower (p < 0.05) for kelp. Phylogenetic diversity of juveniles, uterine pouch and pregnant females’ mid gut all had significantly lower indexes (p < 0.001) than the environment and groups in direct contact with the environment. Alpha diversity measures were also influenced by juvenile developmental stages (Fig 6). Shannon indexes significantly increased between stages 1 and 2 (p<0.05), but decreased between stages 2 and 4 (p<0.001). Stage 5 Evenness was significantly higher (p<0.1) than the indexes from stages 1 through 3, but was not different from stages 4 and 6. Phylogenetic diversity

increased between stages 1 and 3 of development (p<0.05) and decreased between stages 3 and 4 (p < 0.1).

The PERMANOVA analysis performed on the GUniFrac distances showed that the taxonomical composition of the bacterial communities was influenced by both sample type (skin, mucus, juvenile, sea water, etc.) and developmental stage in juveniles (Tables 2-3). Each sample type (p <0.005), and developmental stages (p <0.05) tested were significantly different from one another. The clustering of samples on the PCoAs were concordant with these results (Fig. 7-8). There was no significant changes in the composition of the bacterial communities in the uterine pouch throughout the development of juveniles (Table 4).

55% of all taxa are shared between all tested sample type and 84% of all taxa found in B. frenatus microbiome were shared across the uterine pouch, juveniles, mid gut and skin mucus (Fig. S3). Unique taxa were either 1% or less for each sample type tested. During development, the number of shared taxa between juveniles and the uterine pouch varied little (76 to 85%), but declined in the last two stages of development (Fig. S4).

The co-occurrence analysis network of the intestinal and skin mucus microbial communities had the highest number of interactions (35) and the kelp communities the lowest (3) (Fig. 9). The majority of significant interactions were positive and the skin mucus communities presented the highest number of negative interactions (8). The average number of interactions per taxa was low (between 0.5 and 1.25), the kelp having the lowest. During development, the number of significant interactions and number of interactions per taxon were highest at stage 3 and lowest at stage 4.

Discussion Recently, we witnessed a paradigm shift in the scientific community: the sterility of the uterine environment has been challenged. An increasing number of studies have been supporting the presence and transmission of a microbiome in utero (Collado et al. 2016, Perez-Muñoz et al. 2017, Walker et al. 2017, Younge et al. 2019). Focusing on the mice and the human models, these studies brought a new vision of the Mammalian uterine environment and raised interesting questions regarding the possible advantages of an in utero transmission of the microbiome. Our study is the first investigating such transmission in a non-mammalian viviparous species. Because the uterine pouch of female kelp surfperch is linked to the environment by a cloaca, it was reasonable to assume that the uterine pouch was colonized by a microbiota. The detection of 2015 ASVs

distributed in 235 know bacterial species, in the uterine pouch undoubtedly indicated it is a non- sterile environment. While we did not use any culture methods or transcriptomics in order to confirm the presence of living bacteria, it is highly unlikely that all of the identified species of bacteria would come from dead, non-active microorganisms. Recently, Roth et al. (2020) identified and characterized the microbiome of parental gonads of the pipefish (Sygnatus typhle), a species with male pregnancy, reinforcing the idea of the presence of an in utero microbiota in B. frenatus.

In mammals, the main sources of recruitment of microbial symbionts are structures linked to the uterine environment (placenta, uterus and amniotic membranes) and from other sources, notably the mother’s feces, in smaller proportions (Younge et al. 2019). A total of 4237ASVs belonging in 417 bacterial species were found in juveniles, a result hinting to an in utero vertical transmission of bacterial symbionts. Indeed, juveniles recruited the majority (19.40) of their bacterial from the uterine pouch. Therefore, most of the pioneering microbiota in B. frenatus appear to result from a vertical transmission in utero. Interestingly, all other putative origin group tested (pregnant females’ gut, seawater and kelp), contributed to the juvenile microbial recruitment to a certain degree, with indexes ranging from 3.48 to 7.40.

Microbial composition of the vertically transmitted microbiota of B. frenatus is relatively different than what has been found in mammals, but considering that fishes and mammals live in highly different environments this is somewhat expected (Collado et al. 2016, Perez-Muñoz et al. 2017, Younge et al. 2019). It is generally accepted that microbiota is species specific and that it is highly affected by the surrounding environment (Merrifield and Rodiles 2015, Lavoie et al. 2018). Despite interspecific variations, Proteobacteria is the most represented phylum in fish microbiome (Legrand et al. 2020). B. frenatus is no different in this regard, every surface tested (skin, gut and uterine pouch) had Proteobacteria dominated communities. Most abundant taxa identified at the Order level in B. frenatus are commonly found in fish microbiomes: Aeromonadales, Burkholderiales, Pseudomonadales, Alteromonadales, Actinomycetales (Legrand et al. 2020) and Rhizobiales (Bradyrhizobium) (Merrifield and Rodiles 2015). When it comes to diversity, B. frenatus gut microbiome is within expected diversity for marine fish with the presence of 12 phyla (Egerton et al. 2018), but over 14 known phyla were part of the overall microbiota. In comparison, 22 known phyla have been identified as a part of the human microbiome (Pasolli et al. 2019). Fishes harbour unique microbial communities depending on body surfaces (i.e. skin, gills, gut), each tissue providing with a specific niche (Legrand et al. 2020). We also confirmed that although most taxa might be shared between communities, taxa interact differently from one niche to

another. Taxa differed and showed no consistent pattern of interaction (interacted with different taxa and held varying amounts of interactions) from one niche to another. Similar patterns have been found in the human microbiome, where evidence points to strong niche specialization (Faust et al. 2012). In this study, we were able to successfully identify a great proportion of bacterial symbionts to the species level, including the most abundant bacteria, something that has yet to become widespread in microbiota studies in fishes. This can be explained by the use of the NovaSeq600 sequencing technology of Illumina that allowed for a deeper sequencing depth than other widespread sequencing technology like MiSeq of Illumina (Singer et al. 2019). As sequencing technologies continue to evolve and become more accessible, higher resolution for metagenomic analysis of microbiome will most probably become the norm. Although we could not compare our results to other studies at the species level, they proved to be relatively normal when compared to other studies on a less stringent taxonomic level (Phylum, Class, Order, Family and Genus). Considering that very few studies have reported results at the species level, it is reasonable that most species identified in this study had not been documented as possibly being a part of a marine fish microbiome. It still remains difficult to conclude on the level of accuracy we achieved for species’ level identification considering the small size of the amplified V4 hypervariable subunit (250pb).

Despite salient differences, we did notice interesting similarities with human and mice in utero vertical transmission of microbiota: the same three bacterial families (Pseudomonaceae, Streptoccocaceae, and Bacillaceae) were vertically transmitted in utero. In addition, the presence of the genus Brachybacterium was detected in the uterine environment of the mouse (Mus musculus) (Younge et al. (2019). According to Collado et al. (2016), Streptococcaceace and Bacillaceae bacteria families were also detected in mothers, infants and placenta. It is unclear as to why these specific families are transmitted across our models and what roles they could have. As far as we know, these results could be a functional convergence that has yet to be accurately characterized.

It has long since been accepted that viviparity, in contrast to oviparity, implies a great investment from the mother for a limited number of juveniles (Blackburn 1999), heavily relying on juvenile quality instead of quantity. In oviparous species, the acquisition of the microbiome is highly variable and depends on environmental conditions (Romero et al. 2014, Egerton et al. 2018), whereas, in viviparous species, a selection of symbionts is transmitted to the young (Collado et al. 2016, Younge et al. 2019). Our results confirm the presence of a controlled, non-random, acquisition of the microbiome in the kelp surfperch and show the establishment of highly structured

microbial communities before birth. In the context of our results, and considering the importance of the microbiome for its host health (Sekirov et al. 2010, Holmes et al. 2012, Marchesi et al. 2015), it is possible to hypothesize that the microbiome is indistinguishable from juvenile quality, making the in utero transmission of microbial symbionts an integrant part of this reproductive strategy. Not only can the transmission of key symbionts optimize health (Legrand et al. 2020), but also constitute niche occupation and colonization resistance (Lawley and Walker 2013, Kim et al. 2017) . This may reduce intrauterine infections as well as optimizing a selective strategy during postpartum recruitment. Matrotrophic viviparity could even take this optimization of microbiome acquisition further of the microbiome through microbial translocation from various body sites, such as the mother’s intestine (Younge et al. 2019), because of how juveniles and mother are tightly linked. This might be limited in lecitotrophic viviparity where there are none, or less, maternally provided nutrients (Blackburn 1999).

Mammals have evolved an even more advanced matrotrophic viviparity through several structures (placenta, umbilical cord, etc.). Interestingly we observed the transmission of a higher diversity of symbionts in B. frenatus compared to that of matrotrophic mammalian models, the human and the mice, and oviparous model, the zebrafish (Danio rerio). We believe these results can be explained by slight differences in the overall transmission strategy. For example, some of these differences are tightly linked to the ecology of B. frenatus. B. frenatus is a short-lived species (2 years), and it has been estimated it is highly unlikely that many individuals reach this age (Hubbs and Hubbs 1954). This means that most B. frenatus females will only reproduce once in their lifetime. Coupled to the fact that B. frenatus’ average brood (2 to 50 young) is highly dependent on habitat quality (i.e. kelp canopy quality), which is highly unpredictable from one year to another (Baltz 1984), optimal health of juveniles at birth becomes crucial. Juveniles are also born at a much more developed stage than it is the case in humans (Hubbs and Hubbs 1954, Feder et al. 1974). Without being sexually mature, they reach in utero the age at which oviparous fish have been observed to have a stable gut microbiota (between 50 and 75 dph (McIntosh et al. 2008, Stephens et al. 2016)) in utero, therefore reaching « microbiota » maturity before birth. In Mammals, in utero transmission is the first element in a wide spectrum of vertical transmission of the microbiota. Delivery mode (Tormo‐Badia et al. 2014, Mueller et al. 2015), exposure to mother’s stool during delivery (Ferretti et al. 2018), breast-feeding (Tamburini et al. 2016) and skin-young contact (Luna 2020) are all an integral part of the vertical microbiota acquisition. Whereas in B. frenatus, in utero transmission involves both contact with the uterine pouch and nutrition via the excreted uterine substance. When taking into account various microbiota sources,

and an extraordinarily long time of ontogeny in utero among other life history traits, it is unsurprising that a higher diversity of bacterial symbionts is transmitted in B. frenatus. Early on, we assumed that B. frenatus ontogeny would be similar to the mammalian model: with an initial transmission of a few taxa in utero, followed by a progressive recruitment of other bacterial symbionts post-partum. Even without post-partum data in B. frenatus, there are still key differences to observe compared with our initial expectations. In this case, there is a vertical transmission of a high phylogenetic diversity in B. frenatus, something which was neither recorded in the oviparous zebrafish (Stephens et al. 2016), or human as the viviparous mammalian model (Koenig et al. 2011). While we cannot rule out that once delivery, B. frenatus juveniles could follow either oviparous or viviparous trend, it is unlikely that it will do either. In the zebrafish oviparous model, juveniles are born with significantly higher microbial diversity and evenness relatively to later developmental stages and their parents. In the human matrotrophic viviparous model, juveniles are born with significantly lower microbial diversity relative to later developmental stages and their parents. However, in both cases, juveniles have to recruit selectively microbial species from their environment in order to build a well-structured and specialized adult microbiota (Koenig et al. 2011, Nieuwdorp et al. 2014, Stephens et al. 2016). In contrast, in B. frenatus, except for the communities of the skin mucus of pregnant females, juveniles and pregnant females’ bacterial communities share a highly structured microbiota with identical phylogenetic diversity. Both female and juvenile microbiota have low phylogenetic diversity and high structuration clearly contrasted with all other groups in direct contact with the environment (female skin mucus and bacterioplankton). In addition, the great majority of taxa found in pregnant adult females internal tissues (gut, uterine pouch) are present in the environment. Finally, highly structured microbial communities displaying highly connected interacting networks with elevated ratio of positive/negative interactions (Fig. 13) are deemed hallmarks of resilience capacity (Vázquez-Baeza et al. 2016, Cheaib et al. 2020, Cheaib et al. 2021), conferring an efficient colonization resistance (Cheaib et al. 2021). Therefore, the high resilience capacity of juvenile microbiota we observed (Fig. 13) suggests that an important recruitment phase of microbial symbionts postpartum in B. frenatus is unlikely. Instead, a slight postpartum restructuration of juveniles’ bacterial communities, with moderate shifts in taxa distribution is much more likely to occur. In this respect, gut microbiota characterization of oldest juveniles obtained from a single very late pregnancy female hint at such restructuration. Indeed, major structural changes occurred in this most advanced developmental stage, leading to an extensive convergence in terms of richness and evenness towards the adult female gut microbiota.

Therefore, B. frenatus might represent a new model for microbiota ontogeny, with the initial transmission of a high phylogenetic diversity in utero followed by a restructuration of the communities postpartum. That is, B. frenatus would be born will all the pieces to its adult microbiota, ready to be assembled correctly. These results are highly coherent with our vision of the microbiota being intrinsically linked to juvenile viability and the matrotrophic viviparous strategy. If this is the case, this strategy would be optimal for the transmission of the microbiota, guaranteeing the transmission of all critical taxa needed for optimal general and immune development of juveniles. Admittedly, microbiota data on juveniles born in nature are necessary to fully confirm this hypothesis.

Regarding the recruitment of microbial symbionts, while juveniles mainly recruit from the uterine pouch, this tendency is not maintained throughout development. Thus, the role of the uterine pouch as main source of microbial symbionts for recruitment decreased during gestation. Coherently, at the beginning of development microbial communities of juveniles were found to be the most similar to the one of the uterine pouch, but as development progressed they diverged from one another. In later stages, the mother’s gut plays a bigger role in recruitment. While selection could explain these changes, they could also be related to B. frenatus’ biology on two levels: juvenile nutrition in utero and juvenile mobility in utero. In early gestation, the uterine pouch produces a secretion to feed juveniles, but in late gestation juveniles are sustained through diffusion via hypertrophied caudal, dorsal and pectoral fins (Behrens 1977, Wourms 1981, Reisser et al. 2009, Longo and Bernardi 2015). As development progresses, less and less secretions are ingested, which could explain the decrease in the role of the uterine pouch in bacterial colonization of the juveniles. Juveniles are also free to move around between the interconnected network of pouches in utero in early stages of development (Behrens 1977). As gestation progresses, less and less space is available leading to the immobilization of juveniles in later stages (observations by Dr. G. Bernardi).

The major part of microbial diversity recruitment in juveniles seems to happen within the first 3 stages of development, as phylogenetic diversity increases. This trend was broken between stage 3 and 4, where phylogenetic diversity decreased. Between these two stages, major changes to the level of interaction between microbial symbionts were also observed. The early developing juvenile’s bacterial community network showed the highest number of interactions, but shifted to a much less stable state between stage 3 and 4, translating into fewer interactions. The communities recovered in later stages, matching the structure observed in adult tissues. It is unlikely that these

changes are due to environmental disturbances, considering that juveniles from stages 3 and 4 were collected from March to May and many fish from both stages were often sampled together. This means that intrinsic factors probably come into play in the differences observed between stage 3 and 4.

Compositional changes of the microbiota during development has been shown to happen in some host species, notably in mice (Younge et al. 2019), and a fish species with male viviparity S. typhle (Roth et al. 2020). In B. frenatus, each developmental stage also has a unique microbial composition and a distinct microbial interaction network. Not only do taxa interact differently depending on their niche, but also according to the developmental stages of their host. Contrastingly, uterine pouch microbiota did not change depending on juvenile mode of nutrition (secretion/diffusion). The amount of shared taxa between juveniles and uterine pouch was relatively constant from stage 1 to 5. Therefore, differences in the individual abundance of juveniles associated taxa seem responsible for the microbiota compositional uniqueness of each developmental stage. The microbiota of the uterine pouch was also constant throughout gestation, hinting that intrinsic factors to juvenile development are responsible for these changes. In humans a range of bacterial functions are promoted at different maturation stages (Tanaka and Nakayama 2017). Therefore, the observed succession of microbial symbionts in B. frenatus most probably affect the functional diversity of each stage, where different functions may be promoted throughout development to ensure optimal juvenile health at the time of birth. Considering the documented possible impact of perturbations on the recruitment of the microbiota in vertebrates (Guaraldi and Salvatori 2012, Yang et al. 2013, Keku et al. 2014), it is reasonable to assume that symbionts from the uterine pouch play an important role for the building of a highly resilient and efficient microbiota in juveniles.

Conclusion In this study we demonstrated the presence of a vertical transmission of the microbiota in utero in the viviparous fish B. frenatus, analogous to the vertical transmission documented in Mammals. Our results also hint that B. frenatus might be a new model for the ontogeny of the microbiome. The next step will be to obtain data from postpartum juveniles to confirm our hypothesis pertaining to the ontogeny of B. frenatus. Considering that it is possible to have juveniles developing in a culture media, outside of the uterine pouch (Triplett 1960), it raises possibilities for the realization of an axenic/gnotobiotic model. If so, this model may provide new knowledge about the importance of the microbiome acquired in utero on the general and immune development of juveniles and

therefore provide a better understanding of the advantages of such transmission. More generally, studies on the transmission and ontogeny of the microbiome contribute to gathering information on the potentially critical roles of microorganisms for the development and health of host organisms. In the context of evolutionary convergence of viviparity, results of such studies will allow us to investigate the possible evolutionary advantages of such initial transmission.

Acknowledgements We would like to thank the Natural Sciences and Engineering Research Council of Canada (NSERC), Fonds de Recherche du Québec – Nature et Technologie (FRQNT) and Ressources Aquatiques Québec (RAQ) and the University of California in Santa Cruz (UCSC) for their financial support of the project.

Conflicts of Interests The authors declare no conflict of interest.

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List of tables and figures Table 1 B. frenatus captured in Monterey Bay, California. Fish were captured by harpoon fishing between January 2020 and May 2020...... 35 Table 2 P-value of PERMANOVA performed on GUniFrac distances of microbial communities of B. frenatus pregnant females’(uterine pouch, midgut, skin mucus), juveniles and environment (seawater and M. pyrifera). Bacterial communities are significantly different between tested sample types. “***”< 0.001 “**” < 0.05 ; “*”< 0.01 ; “ ” n.s...... 35 Table 3 P-value of PERMANOVA performed on GUniFrac distances of microbial communities of B. frenatus juveniles during development. Bacterial communities are significantly different depending on developmental stages of juveniles. “***”< 0.001 “**” < 0.05 ; “*”< 0.01 ; “ ” n.s...... 36 Table 4 P-value of PERMANOVA performed on GUniFrac distances of microbial communities of B. frenatus pregnant females’ uterine pouch during juvenile development. Bacterial communities of the uterine pouch did not change throughout development. “***”< 0.001 “**” < 0.05 ; “*”< 0.01 ; “ ” n.s...... 36

Fig. 1 Recruitment indexes of B. frenatus juveniles (n=228) according to the putative origin of the bacterial symbionts. Juveniles recruit the majority of their microbiota directly from their mother’s uterine pouch and, to a lesser extent, from their mother’s gut and environment...... 37 Fig. 2 Recruitment indexes of B. frenatus’ juveniles throughout development according to the putative origin of the bacterial symbionts. In the first stages of development (1-3), juvenile recruit the majority of their microbial symbionts from their mother’s uterine pouch, but in later stages (4-6) this trend shifts. In the latest stages (5-6) female’s midgut and M. pyrifera play a greater role in the recruitment of juveniles’ microbiota...... 38 Fig. 3 Normalized (%) relative abundance of the top 20 ASV identified at species’ level in a. B. frenatus pregnant females’ uterine pouch and b. juveniles depending on juvenile developmental stages. A. parvum is highly present in all groups throughout development. Most species of the top 20 ASV are shared between pregnant uterine pouch and juveniles. Four species: B. lupini, B. vignea, G. stearthermophilus and S. pseudoporcinus, are unique to the top 20 ASV of juveniles and are a relatively constant background signal during development. .... 39 Fig. 4 Normalized (%) relative abundance of the top 20 ASV identified at species’ level in a. B. frenatus pregnant females’ skin mucus n=19, b. mid gut n=18 and in the environment (c. seawater n=12 and d. M. pyrifera n=7). A. parvum is also the most abundant taxa in B. frenatus except in the midgut, where both A. parvum and T. thermophilus are similarly abundant. Most of the top 20 abundant ASVs in the seawater are unique, except for 2 species shared with the uterine pouch (A. ulvae and P. neustonica). A. lacus is the most abundant taxa in M. pyrifera 40 Fig. 5 Alpha diversity measures of B. frenatus’ pregnant females (uterine pouch n=, midgut and skin mucus), juveniles and their environment (seawater and M. pyrifera) measured by a. Shannon indexes, b. Pielou’s evenness index and c. Faith’s phylogenetic diversity index. Shannon indexes of pregnant females’ skin mucus and seawater are significantly higher, and evenness was significantly lower for M. pyrifera. Phylogenetic diversity of juveniles, uterine pouch and pregnant females’ mid gut all had significantly lower indexes than the environment and groups in direct contact with the environment...... 41

Fig. 6 Alpha diversity measures for all six developmental stages of B. frenatus’ juveniles measured by a. Shannon indexes, b. Pielou’s evenness index and c. Faith’s phylogenetic diversity index. Shannon indexes increased between stages 1 and 2, but decreased between stages 2 and 4. Stage 5’s Enveness was significantly higher (p<0.1) than the indexes from stages 1 through 3, but was not different from stages 4 and 6. Phylogenetic diversity increased between stages 1 and 3 of development (p<0.05) and decreased between stages 3 and 4 (p < 0.1)...... 41 Fig. 7 Beta diversity calculated with GUniFrac distances and graphically represented by a. 2D PCoA and b. 3D PCoA for B. frenatus’ pregnant female tissues (uterine pouch, midgut and skin mucus), juveniles and their environment (seawater and M. pyrifera). Communities tend to cluster with their respective group...... 42 Fig. 8 Beta diversity calculated with GUniFrac distances and graphically represented by a. 2D PCoA and b. 3D PCoA throughout B. frenatus’ juvenile development. Communities tend to cluster with their respective developmental stage...... 43 Fig. 9 Co-occurrence network analysis of B. frenatus pregnant females (uterine pouch, midgut, skin mucus), juveniles and environment (seawater and M. pyrifera) calculated based on Spearman correlations ([-1 ; -0.5] and [0.5 ; 1], p<0.05 post Benjamini & Hochberg correction) at species’ level. Bacterial taxa interact differently depending on niche and developmental stages of juveniles...... 44

Table 1 B. frenatus captured in Monterey Bay, California. Fish were captured by harpoon fishing between January 2020 and May 2020.

January February March April May Total

Total 2 13 26 19 3 63 captures

Gestating - 2 12 15 2 31 females

Males - - 4 4 1 9

Unknown 2 11 10 - - 23

Table 2 P-value of PERMANOVA performed on GUniFrac distances of microbial communities of B. frenatus pregnant females’(uterine pouch, midgut, skin mucus), juveniles and environment (seawater and M. pyrifera). Bacterial communities are significantly different between tested sample types. “***”< 0.001 “**” < 0.05 ; “*”< 0.01 ; “ ” n.s.

Juvenile Uterine Skin mucus Midgut M. pyrifera Seawater pouch

Juvenile -

Uterine 0.001*** - pouch

Skin mucus 0.001*** 0.001*** -

Midgut 0.001*** 0.001*** 0.003** -

M. pyrifera 0.001*** 0.001*** 0.001*** 0.001*** -

Seawater 0.001*** 0.001*** 0.002** 0.003** 0.001*** -

Table 3 P-value of PERMANOVA performed on GUniFrac distances of microbial communities of B. frenatus juveniles during development. Bacterial communities are significantly different depending on developmental stages of juveniles. “***”< 0.001 “**” < 0.05 ; “*”< 0.01 ; “ ” n.s.

1 2 3 4 5 6

1 -

2 0.005** -

3 0.006** 0.005** -

4 0.001*** 0.001*** 0.001*** -

5 0.01* 0.002** 0.001*** 0.003** -

6 0.001*** 0.001*** 0.001*** 0.043** 0.001*** -

Table 4 P-value of PERMANOVA performed on GUniFrac distances of microbial communities of B. frenatus pregnant females’ uterine pouch during juvenile development. Bacterial communities of the uterine pouch did not change throughout development. “***”< 0.001 “**” < 0.05 ; “*”< 0.01 ; “ ” n.s.

1 2 3 4 5 6

1 -

2 0.425 -

3 0.608 0.573 -

4 0.745 0.741 0.556 -

5 0.525 0.488 0.157 0.897 -

6 0.4 0.4 0.6 1 0.25 -

Fig. 2 Recruitment indexes of B. frenatus’ juveniles throughout development according to the putative origin of the bacterial symbionts. In the first stages of development (1-3), juvenile recruit the majority of their microbial symbionts from their mother’s uterine pouch, but in later stages (4-6) this trend shifts. In the latest stages (5-6) female’s midgut and M. pyrifera play a greater role in the recruitment of juveniles’ microbiota.

Fig. 3 Normalized (%) relative abundance of the top 20 ASV identified at species’ level in a. B. frenatus pregnant females’ uterine pouch and b. juveniles depending on juvenile developmental stages. A. parvum is highly present in all groups throughout development. Most species of the top 20 ASV are shared between pregnant uterine pouch and juveniles. Four species: B. lupini, B. vignea, G. stearthermophilus and S. pseudoporcinus, are unique to the top 20 ASV of juveniles and are a relatively constant background signal during development.

Fig. 4 Normalized (%) relative abundance of the top 20 ASV identified at species’ level in a. B. frenatus pregnant females’ skin mucus n=19, b. mid gut n=18 and in the environment (c. seawater n=12 and d. M. pyrifera n=7). A. parvum is also the most abundant taxa in B. frenatus except in the midgut, where both A. parvum and T. thermophilus are similarly abundant. Most of the top 20 abundant ASVs in the seawater are unique, except for 2 species shared with the uterine pouch (A. ulvae and P. neustonica). A. lacus is the most abundant taxa in M. pyrifera

Fig. 5 Alpha diversity measures of B. frenatus’ pregnant females (uterine pouch n=, midgut and skin mucus), juveniles and their environment (seawater and M. pyrifera) measured by a. Shannon indexes, b. Pielou’s evenness index and c. Faith’s phylogenetic diversity index. Shannon indexes of pregnant females’ skin mucus and seawater are significantly higher, and evenness was significantly lower for M. pyrifera. Phylogenetic diversity of juveniles, uterine pouch and pregnant females’ mid gut all had significantly lower indexes than the environment and groups in direct contact with the environment.

Fig. 7 Beta diversity calculated with GUniFrac distances and graphically represented by a. 2D PCoA and b. 3D PCoA for B. frenatus’ pregnant female tissues (uterine pouch, midgut and skin mucus), juveniles and their environment (seawater and M. pyrifera). Communities tend to cluster with their respective group.

Fig. 8 Beta diversity calculated with GUniFrac distances and graphically represented by a. 2D PCoA and b. 3D PCoA throughout B. frenatus’ juvenile development. Communities tend to cluster with their respective developmental stage.

Fig. 9 Co-occurrence network analysis of B. frenatus pregnant females (uterine pouch, midgut, skin mucus), juveniles and environment (seawater and M. pyrifera) calculated based on Spearman correlations ([-1 ; -0.5] and [0.5 ; 1], p<0.05 post Benjamini & Hochberg correction) at species’ level. Bacterial taxa interact differently depending on niche and developmental stages of juveniles.

Conclusion

Il est généralement accepté que la viviparité, en contraste avec l’oviparité, implique un investissement supérieur de la mère pour un nombre limité de juvéniles par portée (Blackburn 1999), conférant une plus grande importance à la qualité des juvéniles. Considérant l’importance du microbiote à la santé de son hôte (Sekirov et al. 2010, Holmes et al. 2012, Marchesi et al. 2015), il est désormais bien établi que la viabilité des juvéniles est intimement liée au recrutement de symbiotes microbiens bénéfiques. Ainsi, la transmission verticale in utero de symbiotes microbiens pionniers, sélectionnés par les tissus maternels (e.g. poche utérine, intestin) est potentiellement un élément clé de cette stratégie de reproduction. La viviparité se présenterait donc comme une stratégie optimale de transmission de symbiotes clés au développement et à la santé des juvéniles, notamment en favorisant une occupation exhaustive des niches microbiennes, conférant une forte résistance à la colonisation par des souches exogènes opportunistes. Cette forte résistance à la colonisation préviendrait d’une part les infections in utero, et d’autre part permettrait un recrutement post-partum très sélectif de souches environnementales par les juvéniles. En ce sens, la viviparité matrotrophe amènerait un degré d’optimisation de plus, en incluant l’acquisition de symbiotes microbiens par translocation de souches d’autres niches chez la mère, tel que l’intestin (Younge et al. 2019), via une nutrition fœtale par diffusion sanguine.

Au cours de ce projet, nous avons identifié de nombreuses évidences de convergence au niveau de la transmission verticale in utero du microbiote chez deux classes d’Animaux ayant indépendamment développé une viviparité matrotrophe : non seulement avons-nous confirmé la présence d’une transmission verticale du microbiote chez le poisson vivipare Brachyistius frenatus, comme chez les Mammifères. Mais nous avons aussi mis en évidence un recrutement de symbiotes microbiens provenant de plusieurs sources différentes chez la mère (milieu utérin et intestin), tel qu’observé chez les Mammifères (Younge et al. 2019). Nous avons aussi identifié trois familles bactériennes, soit : Pseudomonaceae, Streptoccocaceae, et Bacillaceae, qui sont transmises à la fois chez notre modèle et chez l’Humain et ce, malgré de grandes différences au niveau des profils microbiens. La raison pour laquelle ces familles sont transmises et quelles fonctions elles pourraient avoir reste à être déterminé. Bien sûr, ce pourrait être une question d’occupation de niche, ou d’une convergence évolutive de fonctions qui n’ont pas encore été identifiées. Plus de recherche en ce sens sera nécessaire pour conclure.

Concernant la poche utérine, aucun changement n’a été détecté au niveau de la composition de son microbiote durant le développement des juvéniles. Quant à ces derniers, ils sont bels et bien colonisés par le microbiote de la poche utérine de leur mère, mais le rôle de la poche utérine dans le recrutement des symbiotes microbiens diminue tout au long de la gestation. Nous estimons que cette inversion est causée par les changements au niveau du mode de nutrition et de la possibilité de déplacements des juvéniles in utero. En ce qui a trait à nos objectifs, deux d’entre eux ont été atteints : i) nous avons bel et bien caractérisé le mode de transmission du microbiote chez B. frenatus et ii) établi la diversité et les relations des communautés microbiennes des mères, des juvéniles et de leur environnement. Il ne nous est pas encore possible de conclure quant aux résultats en lien avec notre dernier objectif qui était de comparer l’ontogenèse du microbiote de B. frenatus avec les modèles vivipares mammaliens et ovipares poissons disponibles dans la littérature. Par contre, nos résultats suggèrent que B. frenatus serait un nouveau modèle d’acquisition du microbiote avec une acquisition presque complète du microbiote in utero. Les avantages potentiels d’une telle transmission sont considérables, sachant qu’avec cette stratégie il y aurait une transmission assez complète des symbiotes microbiens essentiels au bon développement général et immunitaire des juvéniles, et ce, à l’abri des aléas du milieu environnant. Ce pourrait aussi être un compromis nécessaire pour empêcher les infections intra-utérines. Occupation de niche (Lawley and Walker 2013, Kim et al. 2017)

La capture de juvéniles post-partum, en nature, permettrait la complétion de la courbe d’ontogenèse du microbiote de B. frenatus permettrait de confirmer/infirmer si nous sommes vraiment en présence d’un nouveau modèle d’acquisition du microbiote. Considérant qu’il est possible d’élever des juvéniles en milieu artificiel, en dehors de la poche utérine (Triplett 1960), cela amène des possibilités pour la réalisation d’un modèle axénique. Ce type d’expérience a le potentiel d’explorer en profondeur l’effet des symbiotes microbiens transmis in utero aux juvéniles sur leur développement général et immunitaire.

Dans la littérature il existe une hypothèse selon laquelle au début du développement, les juvéniles se nourrissent des restes de spermes non utilisés pour la fécondation en plus des sécrétions (Dobbs 1975, Warner and Harlan 1982). Considérant qu’un microbiote a été identifié dans les gonades mâles et femelles chez l’anguille vésarde (Sygnatus typhle) (Roth et al. 2020), cela soulève un questionnement par rapport au rôle potentiel des mâles dans la transmission du microbiote.

Ce projet de maîtrise, de nature fondamentale, aura permis l’acquisition de nouvelles connaissances sur les différents modes de transmission du microbiote et, dans le contexte de convergence évolutive de la viviparité, de formuler des hypothèses quant aux avantages évolutifs d'une telle transmission de symbiotes microbiens. Les bénéfices de l’enrichissement général des connaissances scientifiques en ce sens trouveraient une application à plusieurs niveaux. En médecine, par exemple, l’étude de la transmission du microbiote chez les vivipares matrotrophes pourrait contribuer à la compréhension des mécanismes responsables d’infections intra-utérines et au développement de traitements et par une meilleure connaissance des taxons essentiels à la santé des juvéniles au développement de traitements probiotiques pour les nourrissons nés par césarienne.

L’identification des fonctions clés de chaque stade de développement permettrait d’enrichir notre compréhension du rôle du microbiote dans le développement in utero et les conséquences de dysbioses à ce niveau. Dans un contexte plus large, étudier l’ontogenèse et la composition du microbiote pourrait avoir des applications chez des espèces ovipares, particulièrement en écologie et en aquaculture. En écologie, l’application de ces connaissances pourrait éventuellement permettre d’améliorer la santé et la survie des populations de poissons en milieux perturbés, par exemple grâce à l’ensemencement d’individus incubés avec l’eau de la rivière (Lavoie et al. 2018) ou par le développement d’un indice de dysbiose pour le dépistage de maladies (Vázquez-Baeza et al. 2016, AlShawaqfeh et al. 2017, Xia et al. 2019).

En aquaculture, la production de juvéniles de qualité reste un élément problématique (Vadstein et al. 2013). Les taux de survie des juvéniles du stade œuf au stade larvaire restent excessivement bas, soit de 10-15%, au niveau industriel pour plusieurs espèces aquacoles (Vadstein et al. 2018). Or, l’industrie aquacole d’aujourd’hui représente une importante ressource alimentaire et économique (FAO 2020) qui risque de gagner en importance, considérant les estimations sur l’augmentation de la population humaine globale (Nations 2017). Dans cette optique, l’optimisation de la production aquacole est critique à une exploitation durable. En ce sens, étudier si et comment la viviparité permet une optimisation de la transmission des symbiotes clés à la santé des juvéniles présente une alternative potentiellement intéressante à ces problèmes chez des espèces ovipares, puisque celles-ci constituent l’essentiel de la production aquacole. L’enrichissement du milieu de culture des larves en symbiotes parentaux et un contrôle de l’ontogenèse pour l’intégration d’une stratégie d’acquisition semblable à ce qui est observé chez les vivipares présentent des avenues de recherche d’intérêt. Ce genre d’études favoriserait d’autant plus la compréhension des besoins des poissons au niveau microbien, pour garantir leur santé et une croissance maximale pour une production plus rapide et accrue, et ce, en évitant les pertes économiques occasionnées par les maladies.

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Annexe A : Supporting informations

Viviparity : cutting edge strategy for microbiota ontogeny

Supporting Informations

Modifications of Aljanabi and Martinez DNA extraction protocol:

Before the overnight incubation, we added an incubation step (one hour, at 37°C) with 20 μL lysozyme (25 mg/mL). After the overnight incubation, in order to minimize RNA contaminants, we included another incubation step (one hour, at 37°C) with 4 μL Rnase A (10 mg/mL). Afterwards, 300 μL volume of NaCl (6M) solution was added to each sample. Samples were then vortexed (5 sec) before centrifugation (4°C, 16 000g for 20 minutes). Supernatants were transferred in new tubes before repeating the centrifugation step (4°C, 16 000g for 20 minutes). One volume of cold isopropanol (stored at -20 °C) was added to the supernatant, and mixed by inversion. Tubes were incubated for at least 30 minutes at -20 °C before centrifugation (20 minutes at 4°C, 16 000g). Pellets were washed with 200 μL cold 70% ethanol (stored at -20 °C) and centrifuged one last time (10 minutes at 4°C, 16 000g). Pellets were dried prior to resuspension in 50 μL sterile milli-Q water. DNA quality as well as concentration was estimated by spectrophotometry.

Table S1 Reagents used for the PCR1 (QIAGENÒ Multiplex PCR Quick-Start Protocol)

Reagents Volume (µl) 2x QIAGEN Multiplex PCR Master Mix 12.5

10x primer mix (2 µM) 5

5X Q-Solution 10 RNase-free sterile water 20.5 DNA template (10 à 15 ng/µl) 2 Total 50

Table S2 Amplification conditions for the PCR1 (QIAGENÒ Multiplex PCR Quick-Start Protocol) 40 cycles of the following steps ; « Denaturation», «Annealing» and «Elongation » were performed.

Step Temperature (oC) Duration

o Initial heat activation 95 C 15 min Denaturation 94oC 30 sec Annealing 60oC 90 sec Elongation 72oC 90 sec

Final Elongation 72oC 10 min

Table S3 Reagents used for the PCR2 (New England BioLabs)

Reagents Volume (µl) Reaction Buffer (Q5) 5X 10 dNTPs (10 mM) 1 Forward Primer (10 µM) 1 Reverse Primer (10 µM) 1 High GC enhancer (Q5) 5X 10 Q5 High-Fidelity DNA Polymerase 0.5

H2O sterile 24.5 DNA template (10 à 15 ng/µl) 2 Total 50

Table S4 Amplification conditions for the PCR2. 12 cycles of the following steps; « Denaturation», «Annealing» and «Elongation » were performed

Step Temperature (oC) Duration

o Initial Denaturation 98 C 2 min Denaturation 98oC 10 sec Annealing 60oC 30 sec Elongation 72oC 30 sec

Final Elongation 72oC 10 min

Fig. S1 The six developmental stages of B. frenatus juveniles in utero in contrast to the adult, by A. Boilard.

Fig. S2 Comparison of GUniFrac distances of juvenile’s microbial communities from developmental stages 1 through 5 to microbial communities of pregnant females (uterine pouch and midgut) and the environment (seawater and M. pyrifiera). Microbial communities of juveniles are more similar to the uterine pouch of pregnant females in the beginning of development, but this trend shifted at the end of development, where microbial communities of juveniles are more similar to pregnant females’ midgut. This corroborates the recruitment indexes results. Juvenile microbial communities are more similar to the tissues they are recruiting from.

Fig. S3 Venn diagram analysis (%) comparing taxa of a. B. frenatus pregnant females tested tissues’ (uterine pouch, midgut and skin mucus) microbiota and b. pregnant female’s total microbiota to juveniles’, seawater and M. pyrifera’s. More than half of taxa are shared between all tested groups and the majority of taxa in B. frenatus’ microbiome are shared with juveniles.

Fig. S4 Venn diagram analysis (%) comparing the microbiota of B. frenatus pregnant females’ uterine pouch and juveniles’ throughout development. The majority of taxa are shared between juveniles and pregnant females’ uterine pouch from stages 1 through 5. The proportion of shared taxa declines in in the last two stages of juvenile development.

Table S5 Relative abundance (%) of the top 30 species in B. frenatus juveniles. S.e.m. Standard error of the mean.

Species mean (%) s,e,m Aquabacterium parvum 25.01 0.82 Pseudomonas knackmussii 9.69 0.85 Thermus thermophilus 4.55 0.43 Geobacillus stearothermophilus 2.74 0.33 Bradyrhizobium vignae 2.48 0.12 Bradyrhizobium lupini 1.99 0.09 Aeromonas lacus 1.57 0.32 Brachybacterium faecium 1.56 0.23 Thermus scotoductus 1.31 0.17 Pseudoalteromonas neustonica 1.23 0.14 Thermothrix azorensis 1.14 0.14 Aeribacillus composti 1.12 0.17 Streptococcus pseudoporcinus 1.12 0.04 Hyphomicrobium facile subsp. ureaphilum 1.06 0.05 Hydrotalea flava 1.03 0.07 Meiothermus ruber 0.85 0.11 Pelomonas puraquae 0.82 0.09 Polymorphum gilvum 0.79 0.08 Methylorubrum populi 0.53 0.06 [Flavobacterium] thermophilum 0.52 0.08 Vibrio splendidus 0.50 0.15 Alcanivorax gelatiniphagus 0.47 0.08 Citrobacter koseri 0.44 0.03 Simplicispira soli 0.38 0.03 Bradyrhizobium cytisi 0.38 0.03 no identification 0.37 0.02 Janthinobacterium lividum 0.37 0.03 Sphingomonas echinoides 0.34 0.07 Meiothermus silvanus 0.33 0.06 Psychrobacter muriicola 0.30 0.04

Table S6 Relative abundance (%) of the top 30 species in B. frenatus pregnant females’ uterine pouch. S.e.m. Standard error of the mean.

Species mean (%) s.e.m Aquabacterium parvum 14.36 2.69 Aeromonas lacus 6.21 2.18 Pseudomonas knackmussii 5.20 1.55 Brachybacterium faecium 4.37 1.65 Thermus thermophilus 3.55 0.93 Vibrio splendidus 2.96 1.51 Geobacillus stearothermophilus 1.58 0.60 Pseudoalteromonas neustonica 1.11 0.38 no identification 1.03 0.15 Rubripirellula obstinata 0.97 0.41 Corynebacterium tuberculostearicum 0.93 0.92 Thermus scotoductus 0.91 0.33 Bradyrhizobium lupini 0.88 0.24 Thermothrix azorensis 0.87 0.35 Meiothermus ruber 0.80 0.32 Bradyrhizobium vignae 0.60 0.22 Alcanivorax gelatiniphagus 0.46 0.25 Amylibacter ulvae 0.46 0.38 Streptococcus pseudoporcinus 0.42 0.08 Pelomonas puraquae 0.36 0.11 Hydrotalea flava 0.36 0.11 Acinetobacter johnsonii 0.32 0.29 Bordetella tumulicola 0.32 0.15 Janthinobacterium lividum 0.31 0.13 Brevundimonas intermedia 0.30 0.12 Effusibacillus lacus 0.30 0.30 Staphylococcus aureus 0.30 0.20 Psychrobacter muriicola 0.29 0.11 Aliivibrio sifiae 0.27 0.21 Polymorphum gilvum 0.24 0.14

Table S7 Relative abundance (%) of the top 30 species in B. frenatus pregnant females’ midgut. S.e.m. Standard error of the mean.

Species mean (%) s.e.m Brachybacterium faecium 8.73 3.24 Aquabacterium parvum 7.99 2.08 Thermus thermophilus 4.35 0.95 Aeromonas lacus 1.85 1.60 no identification 1.54 0.24 Thermus scotoductus 1.31 0.50 Geobacillus stearothermophilus 1.19 0.25 Rubripirellula obstinata 1.15 0.52 Aeribacillus composti 0.80 0.42 Thermothrix azorensis 0.74 0.36 Meiothermus ruber 0.68 0.23 Bradyrhizobium lupini 0.66 0.26 Vibrio splendidus 0.59 0.54 Bradyrhizobium vignae 0.53 0.13 Pseudoalteromonas neustonica 0.41 0.16 Sulfitobacter noctilucicola 0.41 0.27 Pseudomonas knackmussii 0.35 0.19 Staphylococcus aureus 0.35 0.27 Alcanivorax borkumensis 0.35 0.15 Flavobacterium cheonhonense 0.35 0.35 Alcanivorax gelatiniphagus 0.31 0.17 Pseudoruegeria haliotis 0.31 0.22 Pseudopuniceibacterium sediminis 0.29 0.25 Psychrobacter maritimus 0.29 0.25 Streptococcus pseudoporcinus 0.26 0.09 Hyphomicrobium facile subsp. ureaphilum 0.22 0.07 Meiothermus silvanus 0.21 0.07 Litoreibacter ponti 0.20 0.19 [Flavobacterium] thermophilum 0.20 0.08 Sphingomonas echinoides 0.20 0.11

Table S8 Relative abundance (%) of the top 30 species in B. frenatus pregnant females’ skin mucus. S.e.m. Standard error of the mean.

Species mean (%) s.e.m Aquabacterium parvum 4.98 1.25 Thermus thermophilus 3.31 0.90 Brachybacterium faecium 2.76 1.12 Pseudoalteromonas neustonica 2.66 0.71 Psychrobacter namhaensis 2.48 1.82 Psychrobacter maritimus 2.14 0.55 Thermus scotoductus 1.76 1.17 Pseudomonas knackmussii 1.50 0.52 Amylibacter ulvae 1.44 0.46 Psychrobacter celer 1.34 0.38 Geobacillus stearothermophilus 1.33 0.52 Alcanivorax gelatiniphagus 1.28 0.56 Psychrobacter muriicola 1.23 0.27 no identification 1.22 0.13 Mesonia algae 1.09 0.37 Thermothrix azorensis 1.04 0.43 Rubripirellula obstinata 0.92 0.22 Meiothermus ruber 0.84 0.40 Coraliomargarita akajimensis 0.81 0.45 Psychrobacter marincola 0.71 0.18 Formosa arctica 0.65 0.13 Rubritalea marina 0.63 0.24 Halomonas lutescens 0.58 0.21 Vibrio splendidus 0.55 0.21 Tenacibaculum sediminilitoris 0.48 0.12 Alcanivorax borkumensis 0.47 0.16 Bradyrhizobium lupini 0.46 0.11 Sulfitobacter faviae 0.43 0.10 Bradyrhizobium vignae 0.42 0.11 Idiomarina loihiensis 0.41 0.29

Table S9 Relative abundance (%) of the top 30 species in the seawater. S.e.m. Standard error of the mean.

Species mean (%) s.e.m Amylibacter ulvae 6.09 2.15 Pseudoalteromonas haloplanktis 2.18 0.58 no identification 1.80 0.26 Sulfitobacter faviae 1.39 0.28 Pseudoalteromonas neustonica 1.26 0.40 Marinomonas posidonica 1.10 0.40 Psychrobacter maritimus 1.05 0.52 Nitrosopumilus cobalaminigenes 1.02 0.51 Vibrio splendidus 0.85 0.26 Pseudoalteromonas agarivorans 0.82 0.19 Planktomarina temperata 0.82 0.11 Hellea balneolensis 0.78 0.15 Thermus thermophilus 0.76 0.68 Acidovorax defluvii 0.75 0.72 Formosa arctica 0.74 0.36 Rubripirellula obstinata 0.52 0.20 Psychrobacter marincola 0.50 0.26 Tenacibaculum sediminilitoris 0.50 0.13 Polaribacter insulae 0.45 0.15 Psychromonas marina 0.45 0.13 Mariniblastus fucicola 0.42 0.17 Sinobacterium norvegicum 0.39 0.15 Loktanella acticola 0.39 0.15 Geobacillus stearothermophilus 0.35 0.30 Halocynthiibacter arcticus 0.31 0.11 Vibrio superstes 0.29 0.06 Aquabacterium parvum 0.28 0.06 Lutimonas halocynthiae 0.28 0.07 Alteromonas naphthalenivorans 0.26 0.12 Thermus scotoductus 0.25 0.24

Table S10 Relative abundance (%) of the top 30 species in M. pyrifera. S.e.m. Standard error of the mean.

Species mean (%) s.e.m Aeromonas lacus 56.44 4.58 Brachybacterium faecium 8.74 1.01 Aquabacterium parvum 1.37 0.35 Psychrobacter maritimus 0.98 0.25 no identification 0.95 0.27 Psychrobacter celer 0.45 0.12 Aeribacillus composti 0.34 0.03 Psychrobacter muriicola 0.33 0.09 Pseudomonas knackmussii 0.32 0.05 Psychrobacter marincola 0.27 0.07 Pseudoalteromonas neustonica 0.22 0.08 Photobacterium angustum 0.19 0.15 Bradyrhizobium lupini 0.18 0.05 Brevundimonas intermedia 0.18 0.06 Lactobacillus apinorum 0.13 0.13 Hyphomicrobium facile subsp. ureaphilum 0.13 0.04 Paracoccus stylophorae 0.12 0.02 Streptococcus pseudoporcinus 0.11 0.03 Rubritalea marina 0.10 0.02 Streptococcus periodonticum 0.10 0.03 Geobacillus stearothermophilus 0.10 0.02 Bradyrhizobium vignae 0.10 0.02 Methylorubrum populi 0.09 0.02 Staphylococcus aureus 0.09 0.02 Rubripirellula obstinata 0.09 0.02 Exiguobacterium acetylicum 0.09 0.09 Dietzia maris 0.09 0.02 Ornithinimicrobium kibberense 0.09 0.04 Polymorphum gilvum 0.08 0.03 Bordetella tumulicola 0.07 0.02

Table S11 Relative abundance (%) of the top 30 species in B. frenatus juveniles at developmental stage 1. S.e.m. Standard error of the mean.

Species mean (%) s.e.m Aquabacterium parvum 23.17 1.54 Pseudomonas knackmussii B13 18.21 2.35 Thermus thermophilus 7.83 1.12 Geobacillus stearothermophilus 5.43 1.18 Bradyrhizobium vignae 2.46 0.26 Thermus scotoductus 2.30 0.37 Thermothrix azorensis 2.10 0.38 Bradyrhizobium lupini 1.87 0.16 Meiothermus ruber 1.70 0.34 Hyphomicrobium facile subsp. ureaphilum 1.18 0.11 Methylorubrum populi BJ001 0.94 0.16 Streptococcus pseudoporcinus LQ 940-04 0.92 0.07 Hydrotalea flava 0.84 0.11 Meiothermus silvanus 0.66 0.16 Polymorphum gilvum SL003B-26A1 0.62 0.10 Pelomonas puraquae 0.60 0.07 Carboxydocella manganica 0.53 0.14 Janthinobacterium lividum 0.51 0.07 [Flavobacterium] thermophilum 0.44 0.10 Citrobacter koseri 0.43 0.08 Pseudoalteromonas neustonica 0.39 0.10 no identification 0.38 0.03 Aeromonas lacus 0.35 0.07 Sphingomonas echinoides 0.33 0.13 Micrococcus luteus 0.31 0.08 Simplicispira soli 0.29 0.04 Klebsiella aerogenes KCTC 2190 0.29 0.09 Pseudomonas lactis 0.29 0.06 Staphylococcus aureus 0.28 0.10 Enterobacter cloacae 0.27 0.07

Table S12 Relative abundance (%) of the top 30 species in B. frenatus juveniles at developmental stage 2. S.e.m. Standard error of the mean.

Species mean (%) s.e.m Aquabacterium parvum 29.57 1.81 Pseudomonas knackmussii B13 7.09 1.77 Thermus thermophilus 6.62 1.29 Bradyrhizobium vignae 3.38 0.27 Geobacillus stearothermophilus 2.79 0.48 Bradyrhizobium lupini 2.30 0.18 Thermus scotoductus 1.61 0.36 Thermothrix azorensis 1.46 0.27 Hyphomicrobium facile subsp. ureaphilum 1.46 0.13 Streptococcus pseudoporcinus LQ 940-04 1.38 0.08 Meiothermus ruber 1.13 0.20 Hydrotalea flava 1.11 0.12 Pelomonas puraquae 1.11 0.16 Pseudoalteromonas neustonica 1.05 0.25 [Flavobacterium] thermophilum 0.86 0.29 Aeribacillus composti 0.81 0.43 Polymorphum gilvum SL003B-26A1 0.78 0.09 Citrobacter koseri 0.67 0.11 Janthinobacterium lividum 0.65 0.10 Methylorubrum populi BJ001 0.63 0.16 Simplicispira soli 0.55 0.09 no identification 0.55 0.04 Bifidobacterium asteroides 0.45 0.44 Staphylococcus aureus 0.42 0.10 Aeromonas lacus 0.41 0.05 Bradyrhizobium cytisi 0.35 0.05 Stenotrophomonas acidaminiphila 0.34 0.08 Acinetobacter vivianii 0.33 0.29 Sphingomonas echinoides 0.32 0.22 Meiothermus silvanus 0.32 0.09

Table S13 Relative abundance (%) of the top 30 species in B. frenatus juveniles at developmental stage 3. S.e.m. Standard error of the mean.

Species mean (%) s.e.m Aquabacterium parvum 28.49 2.54 Pseudomonas knackmussii B13 11.01 2.67 Thermus thermophilus 3.05 1.38 Bradyrhizobium vignae 2.59 0.23 Geobacillus stearothermophilus 2.35 0.98 Aeromonas lacus 2.12 0.81 Aeribacillus composti 1.89 0.64 Bradyrhizobium lupini 1.79 0.18 Pseudoalteromonas neustonica 1.67 0.46 Hydrotalea flava 1.35 0.17 Hyphomicrobium facile subsp. ureaphilum 1.33 0.13 Streptococcus pseudoporcinus LQ 940-04 1.15 0.10 Alcanivorax gelatiniphagus 1.14 0.48 [Flavobacterium] thermophilum 0.86 0.36 Pelomonas puraquae 0.79 0.08 Polymorphum gilvum SL003B-26A1 0.75 0.11 Sphingomonas echinoides 0.73 0.35 Brachybacterium faecium DSM 4810 0.70 0.45 Meiothermus ruber 0.62 0.36 no identification 0.60 0.06 Citrobacter koseri 0.57 0.08 Simplicispira soli 0.53 0.09 Janthinobacterium lividum 0.47 0.10 Thermothrix azorensis 0.47 0.23 Bradyrhizobium cytisi 0.46 0.06 Mesonia algae 0.43 0.11 Methylorubrum populi BJ001 0.43 0.12 Psychrobacter muriicola 0.39 0.16 Alcanivorax borkumensis SK2 0.38 0.16 Halomonas sediminicola 0.37 0.06

Table S14 Relative abundance (%) of the top 30 species in B. frenatus juveniles at developmental stage 4. S.e.m. Standard error of the mean.

Species mean (%) s.e.m Aquabacterium parvum 16.55 2.03 Brachybacterium faecium DSM 4810 5.82 1.07 Thermus thermophilus 4.89 1.03 Pseudomonas knackmussii B13 4.25 0.96 Aeromonas lacus 1.94 0.95 Thermus scotoductus 1.90 0.71 Geobacillus stearothermophilus 1.84 0.46 Bradyrhizobium lupini 1.83 0.39 Corynebacterium otitidis 1.65 1.64 Bradyrhizobium vignae 1.62 0.40 Thermothrix azorensis 1.35 0.52 Pelomonas puraquae 1.01 0.54 Vibrio splendidus 0.97 0.33 no identification 0.92 0.13 Hydrotalea flava 0.88 0.33 Streptococcus pseudoporcinus LQ 940-04 0.86 0.11 Thermoanaerobacter brockii subsp. finnii 0.77 0.61 Pyrinomonas methylaliphatogenes 0.63 0.63 Enterovibrio calviensis 0.59 0.32 Methylorubrum populi BJ001 0.55 0.21 Meiothermus ruber 0.52 0.16 Hyphomicrobium facile subsp. ureaphilum 0.51 0.12 Psychrobacter maritimus 0.43 0.17 Enterobacter cloacae 0.40 0.22 Polymorphum gilvum SL003B-26A1 0.38 0.08 Klebsiella aerogenes KCTC 2190 0.37 0.21 [Flavobacterium] thermophilum 0.37 0.16 Pseudoalteromonas neustonica 0.36 0.11 Aeribacillus composti 0.35 0.13 Corynebacterium tuberculostearicum 0.34 0.26

Table S15 Relative abundance (%) of the top 30 species in B. frenatus juveniles at developmental stage 5. S.e.m. Standard error of the mean.

Species mean (%) s.e.m Aquabacterium parvum 15.36 5.02 Vibrio splendidus 7.54 3.46 Thermus thermophilus 6.89 2.91 Photobacterium carnosum 4.58 2.76 Geobacillus stearothermophilus 4.02 2.46 Aliivibrio sifiae 3.56 1.42 Thermus scotoductus 3.06 1.66 Meiothermus ruber 2.02 1.40 Polymorphum gilvum SL003B-26A1 1.66 1.30 Bradyrhizobium vignae 1.54 0.89 Psychrobacter maritimus 1.35 0.63 Bradyrhizobium lupini 1.30 0.75 Aeromonas lacus 1.21 0.99 Acinetobacter vivianii 1.09 1.09 Streptococcus pseudoporcinus LQ 940-04 1.03 0.38 Meiothermus silvanus 0.94 0.79 Thermothrix azorensis 0.87 0.33 Pseudoalteromonas neustonica 0.87 0.39 no identification 0.84 0.18 Rubripirellula obstinata 0.82 0.40 Pseudoalteromonas haloplanktis ATCC 14393 0.64 0.38 Psychrobacter muriicola 0.57 0.34 Granulicatella balaenopterae 0.55 0.42 Hydrotalea flava 0.45 0.29 Pseudomonas knackmussii B13 0.36 0.13 Aquabacterium commune 0.36 0.36 Psychrobacter marincola 0.32 0.17 Pseudoalteromonas agarivorans DSM 14585 0.32 0.17 Sphingomonas echinoides 0.31 0.27 Sabulilitoribacter multivorans 0.29 0.26

Table S16 Relative abundance (%) of the top 30 species in B. frenatus juveniles at developmental stage 6. S.e.m. Standard error of the mean.

Species mean (%) s.e.m Aquabacterium parvum 30.46 1.82 Brachybacterium faecium DSM 4810 6.05 0.94 Thermus thermophilus 3.42 0.92 Bradyrhizobium vignae 1.98 0.20 Bradyrhizobium lupini 1.38 0.19 Pseudomonas knackmussii B13 1.26 0.73 Thermothrix azorensis 1.23 0.72 Geobacillus stearothermophilus 1.14 0.46 no identification 1.13 0.17 Skermanella aerolata 0.98 0.98 Thermus scotoductus 0.94 0.48 Polymorphum gilvum SL003B-26A1 0.88 0.49 Hydrotalea flava 0.84 0.15 Streptococcus pseudoporcinus LQ 940-04 0.82 0.09 Hyphomicrobium facile subsp. ureaphilum 0.72 0.12 [Flavobacterium] thermophilum 0.62 0.44 Aeromonas lacus 0.62 0.16 Meiothermus ruber 0.52 0.22 Sphingomonas echinoides 0.51 0.37 Pelomonas puraquae 0.46 0.07 Aliivibrio sifiae 0.42 0.38 Carboxydocella manganica 0.39 0.35 Pseudoalteromonas neustonica 0.36 0.09 Bradyrhizobium cytisi 0.29 0.04 Nitrospira moscoviensis 0.27 0.05 Vibrio splendidus 0.23 0.11 Aeribacillus composti 0.22 0.10 Citrobacter koseri 0.20 0.05 Janthinobacterium lividum 0.20 0.05 Simplicispira soli 0.18 0.03