LES DIFFERENTS MICROBIOTES DE L’HOLOBIONTE GRAND COREGONE (COREGONUS CLUPEAFORMIS) DANS UN CONTEXTE DE SPECIATION

Thèse

Maelle Sevellec

Doctorat en biologie

Philosophiae doctor (Ph. D.)

Québec, Canada

© Maelle Sevellec, 2018

LES DIFFERENTS MICROBIOTES DE L’HOLOBIONTE GRAND COREGONE (COREGONUS CLUPEAFORMIS) DANS UN CONTEXTE DE SPECIATION

Thèse

Maelle Sevellec

Sous la direction de :

Louis Bernatchez directeur de recherche

Nicolas Derome, codirecteur de recherche

Résumé

Les animaux ont toujours évolué avec leur microbiote. Toutefois, peu d’études ont analysé le rôle des bactéries sur la spéciation. Il a été démontré que le microbiote oriente la spéciation de son hôte. L’objectif principal de cette thèse est d’évaluer l’influence des bactéries sur la spéciation du grand corégone (Coregonus clupeaformis). Sous certaines conditions, il existe deux espèces de corégone qui ont évolué de façon parallèle ; l’espèce naine et l’espèce normale qui sont caractérisées par des niches trophique et écologique différentes. Plusieurs types d’interaction hôte-bactéries ont été analysés au niveau de populations de corégones sauvages et de corégones captifs, qui ont été élevés dans des conditions identiques. Chez les corégones sauvages, les résultats supportent un effet de parallélisme au niveau de la diversité bactérienne, mais cet effet n’a pas été observé au niveau de la structure des communautés bactériennes entre l’espèce naine et normale. La présence d’un noyau bactérien très conservé au niveau du microbiote intestinal démontre une influence marquée de l'hôte sur ses communautés bactériennes. L’ensemble de ces résultats soulignent la complexité de l'holobionte (hôte + bactéries) en démontrant que la direction de sélection peut différer entre l'hôte et son microbiote. Chez les corégones captifs, la différence de la structure bactérienne chez les nains, les normaux et les hybrides F1 réciproques met en évidence l’influence de l’hôte sur le microbiote. Finalement, cette étude pionnière des communautés bactériennes du grand corégone ouvre la voie à des nouvelles directions de recherche liées à l’évolution de l’holobionte.

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Abstract

Animals have evolved with their microbiota. However, little is known about the role of bacteria over the course of this evolution. These last few years, it has been shown that the microbiota influences speciation by controlling the pre-zygotic and post-zygotic reproductive isolation. The main goal of the thesis is to analysis for the first time the influence of the microbiota on the Lake Whitefish (Coregonus clupeaformis) which represents a continuum in the early stage of ecological speciation. Some species pairs of dwarf (limnetic niche specialist) and normal (benthic niche specialist) evolved in parallel inside several lakes in northeastern North America. Bacterial communities have been compared among five wild sympatric species pairs of whitefish as well as captive dwarf, normal and hybrids whitefish reared in identical controlled conditions. For the wild fish, difference of the bacterial community structure was highlighted in the three studied interactions between the bacterial community and the host: host-pathogen, host-symbiont and host-transient bacteria. Although no parallelism was detected for the bacterial community structure for these interactions, it was highlighted at the bacterial diversity level. Moreover, parallelism was not observed within the bacteria community structure, likely because the water bacterial communities, studied in this thesis, and the biotic and abiotic factors were characterized by important variation between the lake populations of whitefish. Furthermore, results revealed a strong influence of the host (dwarf or normal) on the bacterial communities with pronounced conservation of the core intestinal microbiota. Finally, our result highlighted the complexity of the holobiont (host + bacteria), suggesting that the direction of selection could differ between the host and its microbiota. In fact, three evolutionary directions have been highlighted between the fish and its bacterial communities. For the captive whitefish, an influence of host (normal, dwarf and hybrids) was also detected on bacterial taxonomic composition. Hybrid microbiota was not intermediate and its composition fell outside of that observed in the parental forms. This pioneer study on the bacterial communities of the Lake whitefish opens new research fields related to the evolution of the holobiont.

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Table des matières

Résumé ...... iii Abstract ...... iiv Table des matières ...... v Liste des tableaux ………………………………………………………………………………...vii Liste des figures ...... viii Liste des abréviations ...... iix Remerciements ...... xi Chapitre 1 : Introduction ...... 1 1.1 Le surprenant potentiel des microorganisms ...... 2 1.1.1 Chez les animaux et les plantes ...... 2 1.1.2 Dans les cycles biogéochimiques ...... 2 1.1.3 Découverte des bactéries par l’Homme ...... 3 1.2 Les différents types d’interaction hôte-bactéries ...... 3 1.3 Le concept de l’hologénome et l’évolution ...... 5 1.3.1 Le concept de l’hologénome ...... 5 1.3.2 Diversité et abondance du microbiote ...... 5 1.3.3 La transmission de génération en génération du microbiote ...... 7 1.3.4 Les propriétés du microbiote sur la valeur sélective ...... 7 1.4 Evolution de l’holobionte : cas de la spéciation...... 8 1.5 Le Grand corégone ...... 10 1.6 Objectifs de la thèse ...... 12 Chapitre 2 : Microbiome investigation in the ecological speciation context of Lake Whitefish (Coregonus clupeaformis) using next generation sequencing ...... 15 2.1 Résumé...... 16 2.2 Abstract ...... 17 2.3 Introduction ...... 18 2.4 Material and methods ...... 21 2.5 Results ...... 25 2.6 Discussion ...... 29 2.7 Conclusion ...... 38 2.8 Acknowledgments ...... 39 2.9 Data Accessibility ...... 40 2.10 Tables ...... 41 2.11 Figures ...... 46 Chapitre 3 : Holobionts and ecological speciation: the intestinal microbiota of Lake Whitefish species pairs...... 51 3.1 Résumé...... 52 3.2 Abstract ...... 53 3.3 Introduction ...... 54 3.4 Methods ...... 57 3.5 Results ...... 61 3.6 Discussion ...... 66 3.7 Conclusion ...... 73 3.8 Availability of data and material ...... 74 3.9 Acknowledgements ...... 75 3.10 Tables ...... 76 3.11 Figures ...... 79 3.12 Supplementary Tables ...... 83

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Chapitre 4 : Intestinal microbiota of whitefish (Coregonus sp.) species pairs and their hybrids in natural and controlled environment...... 88 4.1 Résumé...... 89 4.2 Abstract ...... 90 4.3 Introduction ...... 91 4.4 Material and methods ...... 94 4.5 Results ...... 99 4.6 Discussion ...... 103 4.7 Conclusion ...... 108 4.8 Acknowledgement ...... 109 4.9 Tables ...... 110 4.10 Figures ...... 113 4.11 Supplementary Data ...... 119 4.12 Supplementary Tables ...... 121 4.13 Supplementary Figures ...... 131 Chapitre 5 : Conclusion ...... 132 5.1 Principaux résultats ...... 133 5.2 Perspectives ...... 136 Chapitre 6 : Bibliographie ...... 138

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Liste des tableaux

Table 2. 1 :Number of infected and non-infected whitefish for each form in each lake according to the results of double nested PCR...... 41 Table 2. 2 : PCR primers used for the double nested PCR...... 42 Table 2. 3 : Statistic values on the number of infected and non-infected whitefish for lake according the results of double nested PCR...... 43 Table 2. 4 : eighteen pathogenic species of 10 identified putative pathogen genera which were identified using the BLAST algorithm...... 44 Table 2. 5 : Number of putative pathogens and opportunistic bacteria in whitefish according to 454 sequencing results...... 45

Table 3. 1 : Number and location of samples, sampling dates, FST and core microbiota for each species in each lake...... 76 Table 3. 2 : Summary of weighted Unifrac and the PERMANOVA test statistics...... 77 Table 3. 3 : Summary of GLM and ANOVA test statistics on the alpha diversity within- and between-lakes of whitefish species microbiota...... 78

Table S 3. 1 : Steps used to reduce sequencing and PCR errors...... 83 Table S 3. 2 : Bacterial taxa found in the PCR negative control...... 84 Table S 3. 3 :Bacterial species specific to a single whitefish species within lake obtained with Metastats and BLAST...... 86

Table 4. 1 : Number and locations of samples, sampling dates for each captive and wild whitefish populations or group...... 110 Table 4. 2 : Summary of PERMANOVA test statistics on microbiota taxonomic composition...... 111

Table S 4. 1 : Steps used to reduce sequencing and PCR errors...... 121 Table S 4. 2 : Matrix of bacterial abundance and Good’s coverage per captive whitefish sample...... 122 Table S 4. 3 : Summary of ANOVA test statistics on microbiota alpha diversity (inverse Simpson index)...... 124 Table S 4. 4 : Four Metastats tables with details of one-species-specific genera...... 125

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Liste des figures

Figure 2. 1 : Map of the study area...... 46 Figure 2. 2 : Plot of bacterial diversity estimated with the Simpson index for all ten populations...... 47 Figure 2. 3 : Relative abundance of phyla members found in kidney whitefish bacterial community for dwarf and normal whitefish in each lake...... 48 Figure 2. 4 : Principal components analysis per rank (PCA per rank) of Operational Taxonomic Units (OTU) present in whitefish kidney differentiated by lake...... 49 Figure 2. 5 : Differences between dwarf and normal whitefish kidney in selected pathogenic genera...... 50

Figure 3. 1 : Taxonomic composition at the phylum and genus levels...... 79 Figure 3. 2 : Principal coordinate analyses (PCoAs) of all the bacterial communities...... 80 Figure 3. 3 : Network analysis of intestinal microbiota for dwarf and normal whitefish within- and between-lakes...... 81 Figure 3. 4 : Heatmap of relative abundances of the most important metabolic pathways inferred by PICRUSt in the whitefish intestinal microbiota for each sample in all lakes...... 82

Figure 4. 1 : Network analysis of intestinal microbiota of dwarf and normal wild whitefish and intestinal microbiota of dwarf, normal and hybrids captive whitefish...... 113 Figure 4. 2 : Relative abundance of phyla representatives found in intestinal microbiota for dwarf and normal wild whitefish in each lake as well as in intestinal microbiota for dwarf, normal and hybrids captive whitefish...... 114 Figure 4. 3 : Discriminant analysis histogram off all wild whitefish species microbiota...... 115 Figure 4. 4 : Principal coordinate analyses (PCoAs) within- and between-lake of wild whitefish species microbiota...... 116 Figure 4. 5 : Principal coordinate analyses (PCoAs) between the microbiota of the four captive whitefish groups...... 117 Figure 4. 6 : Metastats results on dwarf, normal and hybrids captive whitefish...... 118

Figure S 4. 1 : Network analysis of intestinal microbiota of dwarf and normal wild whitefish and intestinal microbiota of dwarf, normal and hybrids captive whitefish...... 131

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Liste des abréviations ANOVA Analysis of variance PCoA Principal Coordinates Analysis RNA Ribonucleic acid (Acide ribonucléique) 16s rRNA 16S ribosomal RNA BDM Bateson-Dobzhansky-Muller BKD Bacterial kidney disease BLAST algorithm Basic local alignment search tool CO2 Carbon dioxide (dioxyde de carbone) DGGE Denaturing gradient gel electrophoresis DNA Deoxyribonucleic acid Fst Fixation index GLM Generalized linear model KO KEGG Orthology MCMC Markov Chain Monte Carlo MID-tags Multiplex identifiers MS-222 Tricaine Methanesulfonate mtDNA Mitochondrial DNA NLME Mixed effects linear random model NMS Nonmetric Multidimensional Scaling OTU Operational taxonomic unit PAUS Pea aphid U-type symbiont PCA Principal component analysis PCR Polymerase chain reaction (réaction en chaîne par polymérase) PERMANOVA Permutational analysis of variance PICRUSt Phylogenetic Investigation of Communities by Reconstruction of Unobserved States QTL Quantitative trait locus RDP Ribosomal Database Project RFLP Restriction fragment length polymorphism TGGE Temperature gradient gel electrophoresis YBP Years before present (années avant ce jour)

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À ma sœur, Auregan Sevellec pour m’avoir montré ce qu’était le courage À mon grand-père, Pierre Le Douaran pour m’avoir appris à saisir les opportunités de la vie

« The microbial world is a sleeping giant of biology »

Mark L. Wheelis 1998

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Remerciements

Il y a quelques années, je n’aurais jamais songé faire un doctorat. Je ne pensais pas avoir le caractère, ni les compétences intellectuelles et pourtant aujourd’hui, j’écris ces lignes … Et tout cela, c’est grâce à toi Louis. Merci Louis Bernatchez pour m’avoir donné cette liberté de tâtonner, de reculer, de réussir à faire des découvertes surprenantes et inattendues ! Merci de m’avoir soutenu durant tout le temps de ce long doctorat ! Merci de m’avoir fait confiance ! Merci pour la maturité apportée par ce doctorat et qui fait de moi une meilleure personne. Merci de m’avoir donné cette chance.

Je voudrais aussi remercier mon codirecteur, Nicolas Derome. Merci pour ton soutien, tes suggestions et ton enthousiasme pour ce projet malgré mon côté farouche !

J’aimerais aussi remercier les membres de mon comité d’encadrement : Steve Charette, Marie Filteau, Christian Landry et Julie Turgeon. Merci pour votre confiance aux différentes étapes de ce doctorat et pour vos suggestions.

Je souhaiterais remercier spécialement trois personnes, Anne Dalziel, Bérénice Bougas et Julie Grasset. Sans vous, je ne me serais pas lancée dans cette folle aventure et surtout je n’y serais pas restée.

Je voudrais également remercier mes plus proches collaborateurs, sans qui cette thèse n’existerait probablement pas. Scott Pavey, Sébastien Boutin, Éric Normandeau, Martin Laporte, Guillaume Côté et Cécilia Hernandez, merci à vous tous.

Merci aussi à tous les anciens et les nouveaux membres du laboratoire Bernatchez ! Les membres du laboratoire vont et viennent mais l’ambiance, la coopération et le soutien sont toujours les mêmes ! J’aimerais tout particulièrement remercier Anaïs Lacoursière- Roussel, Anne-Laure Ferchaud, Fabien Lamaze, Ben Sutherland, Anne-Marie Dion-Côté, Amanda Xuereb, Clément Rougeux, Alex Bernatchez et Jérémy Gaudin.

Il me reste à remercier également tous les organismes qui m’ont soutenu financièrement : le département de biologie de l’Université Laval, Québec-Océan et l’IBIS.

Je ne serais jamais arrivée à cette étape sans eux… Merci maman, depuis toute petite, tu me soutiens comme personne, tu me pousses toujours plus haut et ça fonctionne. J’ai la chance d’avoir une famille géniale, merci Boris Thomas, Patrick Sevellec, Auregan Sevellec, Elouenn Sevellec, Jacqueline Le Douaran, et Pierre Le Douaran d’avoir toujours été là pour moi. Aucun mot ne suffit à vous exprimer ma gratitude.

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Avant-propos

Cette thèse est organisée en 5 chapitres, incluant l’introduction générale (Chapitre 1) et la conclusion (Chapitre 5). Les chapitres 2, 3 et 4 sont publiés, soumis ou en voie d’être soumis dans des revues scientifiques.

Le chapitre 2 est publié sous la référence : Sevellec M, Pavey S A, Boutin S, Filteau M, Derome N, Bernatchez L. 2014. Microbiome investigation in the ecological speciation context of lake Whitefish (Coregonus clupeaformis) using next generation sequencing. Journal of Evolutionary Biology 27 : 1029–1046.

LB a conçu et a supervisé le projet. SAP a échantillonné. MS a produit les données. MS, SAP, SB et MF ont analysé les données. MS a écrit le manuscrit en collaboration avec SAP, ND et LB.

Le chapitre 3 a été accepté par la revue Microbiome : Sevellec M, Derome N, Bernatchez L. Holobionts and ecological speciation : the intestinal microbiota of Lake Whitefish species pairs.

MS et LB ont conçu le projet. LB a supervisé le projet. MS a échantillonné, a produit les données, a analysé les données. MS a écrit le manuscrit en collaboration avec ND et LB.

Le chapitre 4 n’a pas encore été soumis: Sevellec M, Laporte M, Bernatchez A, Derome N, Bernatchez L. Investigation of the intestinal microbiota within a host under speciation in natural and controlled environment: the case of the Lake Whitefish pairs and hybrids.

MS et LB ont conçu le projet. LB a supervisé le projet. MS et ML ont échantillonné. MS et AB ont produit les données, MS et ML ont analysé les données. MS a écrit le manuscrit en collaboration avec ML, ND et LB.

En plus de ces trois chapitres, je suis premier et second auteur de deux articles publiés au cours de mes travaux de doctorat :

Sevellec M, Boutin S, Pavey S A, Bernatchez L, Derome N. 2012. A fast, highly sensitive double-nested PCR-based method to screen fish immunobiomes. Molecular Ecology Resources 12: 1027-1039.

Pavey S A, Sevellec M, Adam W, Normandeau E, Lamaze F C, Gagnaire P-A., Filteau M, Herber F O, Maaroufi A, Bernatchez L. 2013. Nonparallelism in MHCIIβ diversity accompanies nonparallelism in pathogen infection of lake whitefish (Coregonus

xii clupeaformis) species pairs as revealed by next-generation sequencing. Molecular Ecology 22: 3833–3849

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Chapitre 1 : Introduction

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1.1 Le surprenant potentiel des bactéries

« La terre est une planète microbienne où les macroorganismes sont un ajout récent » (Mark L. Wheelis (Woese 1998)). En effet, les dernières estimations indiquent que l’apparition des Procaryotes a eu lieu il y a 3.9 milliards d’années et celles des Eucaryotes il y a 1.8 milliards d’années (Parfrey et al. 2011; Wacey et al. 2011).

1.1.1 Chez les animaux et les plantes

À ce jour, il n’existe pas dans la nature des animaux ou des plantes sans microbiote. Le microbiote, dans le cadre de cette thèse, est défini comme l’ensemble de la communauté bactérienne présente sur un organisme hôte. Les autres microorganismes, bien que jouant un rôle tout aussi important que les bactéries au sein d’un hôte ne seront pas abordés ici. Les animaux et les plantes ont donc évolué, évoluent et vont évoluer avec leur microbiote (Rawls et al. 2006). De plus, quelques indices laissent à penser que les Eucaryotes ont évolué à partir des Procaryotes. Les Eucaryotes utilisent les mêmes réactions biochimiques élémentaires que les Procaryotes (Rosenberg & Zilber 2016). Environ 60% des gènes humains sont homologues aux gènes procaryotes (Domazet 2008). En outre, la théorie de l’endosymbiose, bien que controversée, suggère que la cellule eucaryote provient de l’incorporation d’un procaryote dans un autre procaryote (Sagan 1967). L’avènement du séquençage de nouvelle génération a eu un énorme impact sur le monde microbien permettant de révéler le rôle considérable des bactéries. Chez l’Homme, le ratio entre le nombre de cellules humaines et le nombre de cellules bactériennes a récemment été estimé à 1:1. Il y a donc autant de cellules bactériennes que de cellules humaines (Sender et al. 2016). Ces bactéries interviennent au niveau de la régulation du système immunitaire, éliminent les agents pathogènes potentiels, extraient l’énergie de la nourriture, synthétisent des vitamines essentielles et des cofacteurs, améliorent les fonctions intestinales, inhibent certaines toxines et facteurs cancérigènes (Qin et al. 2010; Consortium 2012; Enders & Enders 2015).

1.1.2 Dans les cycles biogéochimiques

Les bactéries interviennent aussi dans l’environnement comme par exemple, dans les cycles biogéochimiques, dans la composition de l’atmosphère. Elles influencent le climat,

2 participent au recyclage de certains nutriments et de la décomposition de certains polluants (Huse et al. 2008). Sans cette régulation bactérienne, la vie multicellulaire telle qu’on la connait serait impossible. De plus, il a été récemment démontré que certaines bactéries étaient capables de métaboliser 99% du méthane en dioxyde de carbone (CO2).

Le méthane a un pouvoir de réchauffement de 25 fois plus puissant que le CO2 et des milliards de mètres cubes sont emprisonnés sous les glaces de l’Arctique et l’Antarctique. La libération de ce méthane suite au réchauffement climatique pourrait significativement amplifier ce phénomène (Michaud et al. 2017). Ainsi, les bactéries peuvent également intervenir dans l’atténuation du réchauffement climatique.

1.1.3 Découverte des bactéries par l’Homme

La première observation des bactéries par l’Homme eu lieu en 1683 par Antonie Van Leeuwenhoek qui observa, à l’aide d’un microscope, des bactéries buccales. Cependant, dès 1546 Girolamo Fracastoro suggère que des microorganismes peuvent être un facteur de maladie (Prescott et al. 2003). Au 19ème siècle deux écoles de microbiologie émergent (Rosenberg & Zilber 2013) :

• L’école de Robert Koch et de Louis Pasteur se focalisant sur la lutte contre les bactéries pathogènes entrainant des incroyables découvertes comme les vaccins et les antibiotiques. • L’école de Sergueï Vinogradski et de Martinus W. Beijerinck se focalisant sur les bénéfices que les bactéries peuvent apporter notamment en écologie.

De nos jours, même si beaucoup d’études médicales se concentrent sur les bactéries pathogènes, de plus en plus d’études analysent les interactions bénéfiques des bactéries sur leur hôte.

1.2 Les différents types d’interaction hôte-bactéries

Il existe différentes interactions hôte-bactéries qui dépendent du type de la souche bactérienne mais également du type d’hôte. Par exemple, une bactérie pathogène chez un hôte ne sera pas obligatoirement pathogène chez une autre espèce hôte. Seuls les trois types d’interactions hôte-bactéries principales seront développées ici.

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Il y a encore quelques années, les bactéries commensales étaient considérées comme faisant partie d’un processus unidirectionnel où elles tiraient avantage de l’hôte sans que celui-ci soit affecté (Prescott et al. 2003). Cependant, les bactéries commensales protègent l’hôte contre les bactéries pathogènes en échange d’un milieu protégé et de nutriments. En effet, les bactéries commensales occupent les sites de fixation ne permettant pas aux bactéries pathogènes de se fixer sur l’hôte (Rosenberg & Zilber 2013). Bien qu’elle ne soit pas neutre, cette interaction n’est pas coûteuse pour l’hôte ou les bactéries. Une souche bactérienne commensale peut survivre sans son hôte et en absence de cette souche, l’hôte favorisera la fixation d’une autre espèce bactérienne commensale (Alberdi et al. 2016; Macke et al. 2017). De plus, de nombreuses études montrent que les bactéries commensales coopèrent avec le système immunitaire de l’hôte (Arpaia et al. 2013).

La grande différence entre les bactéries commensales et les bactéries symbiotiques est la durée de l’interaction. Il existe une relation durable entre les bactéries symbiotiques et leur hôte. De plus, il arrive que l’hôte et les bactéries symbiotiques dépendent métaboliquement l’un de l’autre. Cette relation est autant bénéfique pour les deux protagonistes (Prescott et al. 2003). Un exemple bien connu est celui du ver tubicole géant (Riftia pachyptila) et de son symbionte composé de Gammaprotéobactéries. Ce ver vit dans les fonds marins, à proximité des cheminées hydrothermales. Ces dernières rejettent du liquide ayant une forte concentration en sulfure d’hydrogène. Le ver absorbe le sulfure d’hydrogène qui est métabolisé par les bactéries symbiotiques en matière organique (le malate et le succinate) qui sont les nutriments du ver (Klose et al. 2016).

Le dernier type d’interaction est la relation hôte pathogène conduisant aux maladies infectieuses. Il existe des agents pathogènes primaires qui causent la maladie chez un hôte sain par interaction directe. Il existe des agents pathogènes opportunistes qui font partie du microbiote normal de l’hôte. Ces derniers deviennent pathogènes dans certaines conditions, notamment si le système immunitaire de l’hôte s’affaiblit (Prescott et al. 2003). Lewis Thomas remarqua que « Le pouvoir pathogène n’est pas la règle. En effet, il est si peu fréquent et n’implique qu’un si petit nombre d’espèces dans l’immense population des bactéries, qu’il a un aspect insolite. La maladie résulte généralement de négociations symbiotiques peu concluantes, un dépassement de la ligne d’un côté ou de l’autre, une mauvaise interprétation biologique des frontières ». Il est généralement admis que la

4 relation hôte pathogène se termine soit par l’extermination de l’un des protagonistes soit par une évolution en une interaction commensaliste ou symbiotique (Rosenberg & Zilber 2013).

1.3 Le concept de l’hologénome et l’évolution

Les variations génétiques sont les principaux facteurs permettant l’évolution. Chez les animaux et les plantes, ces variations génétiques sont la conséquence de plusieurs processus comme la reproduction sexuée, les réarrangements chromosomiques, l’épigénétique et la mutation de l’hôte (Thomas et al. 2010). Cependant, les animaux et les plantes sont encore plus complexes et hébergent des milliards de microorganismes. Ces derniers ont aussi des mécanismes permettant des variations génétiques. Ainsi pour avoir une vue holistique de l’évolution des animaux et des plantes, il est nécessaire d’inclure leur microbiote. L’holobionte est le terme employé pour décrire l’hôte ainsi que son microbiote. Et l’hologénome est la somme de l’information génétique de l’holobionte comprenant le génome de l’hôte et le microbiome (somme de l’information génétique du microbiote).

1.3.1 Le concept de l’hologénome

Le postulat du concept de l’hologénome est que l’holobionte (hôte + microbiote) fonctionne comme une seule et même entité biologique face à la sélection naturelle. Le concept de l’hologénome est composé de trois principes (Rosenberg & Zilber 2016) :

• Tous les êtres multicellulaires comprennent un microbiote abondant et diversifié. Souvent, le nombre de microorganismes ou la somme de leur information génétique est supérieur aux nombres de cellules et de gènes de l’hôte. • L’hologénome est transmis de génération en génération avec une conservation partielle du microbiome permettant le maintien des propriétés de l’holobionte. • Le microbiote et son hôte interagissent de manière à influencer la valeur sélective de l’holobionte.

1.3.2 Diversité et abondance du microbiote

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La diversité et l’abondance microbienne au sein de l’holobionte dépendent de la variété des sites de fixation pour les bactéries des différents tissus, de l’activité du système immunitaire et des changements des conditions environnementales, incluant l’état physiologique de l’organisme hôte. Il existe un noyau stable au sein du microbiote qui est composé d’espèces bactériennes communes à tous les individus d’une espèce hôte. La plupart du temps, les bactéries du noyau stable sont présentes en grand nombre. À l’opposé, il existe un non-noyau du microbiote qui inclut les espèces bactériennes qui sont facilement échangeables et qui varient en fonction des conditions environnementales (Rosenberg & Zilber 2013; 2016). Ce sont cette diversité et cette abondance du microbiote qui permettent à l’holobionte d’acquérir trois autres mécanismes de variations génétiques. Premièrement, l’amplification microbienne est le mode de variation le plus rapide face à des changements tels que la disponibilité des nutriments ou de la température ou l’exposition aux antibiotiques ou un changement environnemental (Rosenberg & Zilber 2013; 2016). L’amplification de certaines bactéries et la diminution d’autres influencent le réservoir de gènes bactériens, qui permet une adaptation rapide aux nouvelles conditions. Par exemple, les enfants ayant une alimentation riche en fibre ont un microbiote intestinal dominé par les genres Prevotella et Xylanibacter permettant l’hydrolyse de la cellulose et du xylane. Ces genres bactériens sont absent dans le microbiote intestinal des enfants ayant une alimentation riche en glucide (De Filippo et al. 2010). Deuxièmement, la variation génétique provient également des bactéries acquises de l’environnement. Au vu des milliards de bactéries avec lesquelles les hôtes sont en contact, il est raisonnable de considérer qu’une bactérie provenant de l’environnement peut coloniser de manière définitive un hôte. De plus, sous certaines conditions favorables, cette bactérie nouvellement acquise, peut amplifier et affecter le phénotype de l’hôte. Il est cependant difficile de distinguer si une bactérie provient de l’environnement ou est déjà présente de manière mineure chez son hôte. Finalement, le dernier type de variation est le transfert horizontal de gènes, se définissant par un mouvement de matériel génétique d’un organisme donneur à un autre organisme receveur. L’organisme receveur n’est pas le descendant de l’organisme donneur. Ces transferts de matériels génétiques peuvent se faire de bactérie à bactérie mais aussi de bactérie à son hôte. Chez l’humain, 145 gènes sont attribués au transfert horizontal de gènes dont la plupart ont pour origine des bactéries ou des protistes (Crisp et al. 2015).

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Ce mécanisme est particulièrement puissant pour générer de la variation dans le réservoir de gènes. Ces trois mécanismes de variations génétiques dus aux bactéries sont rapides et sensibles au changement de l’environnement. Alors que l’évolution des gènes de l’hôte est lente et moins sensible à l’environnement (Alberdi et al. 2016; Macke et al. 2017).

1.3.3 La transmission de génération en génération du microbiote

Durant ces dernières années, il est devenu évident que le microbiome est transmis de façon verticale, notamment par la mise en évidence du noyau du microbiote au sein de nombreuses espèces hôtes. Il existe beaucoup de méthodes de transmission du microbiome de génération en génération. Chez les humains, le microbiome est majoritairement transmis par contact direct pendant et après la naissance entre l’enfant et ses parents (Blaser & Falkow 2009; Gilbert 2014). Chez certains Mammifères comme les éléphants, les koalas ou les hippopotames, la progéniture mange les fèces de leur mère pour l’acquisition de bactéries (Rosenberg & Zilber 2013). Beaucoup d’oiseaux nourrissent leur progéniture par régurgitation permettant la transmission de la nourriture mais aussi des bactéries (Rosenberg & Zilber 2013). Les poissons apposent des composés antimicrobiens sur leurs œufs qui permettent de sélectionner les bactéries. Elles seront les premières à coloniser les poissons après l’éclosion (Hanif et al. 2004; Wilkins et al. 2015). Les bactéries peuvent aussi être recrutées et transmises via l’environnement par l’eau, l’air et la nourriture partagés entre la progéniture et les parents (Rosenberg & Zilber 2016). Même si ce n’est pas le microbiome qui est transmis mais une espèce bactérienne, certaines bactéries sont transmises par les cellules reproductives de l’hôte comme le genre Wolbachia. Wolbachia est un endosymbionte présent dans une large proportion d’insectes et maternellement transmise par les œufs (Brucker & Bordenstein 2012b).

1.3.4 Les propriétés du microbiote sur la valeur sélective

« Ce n’est pas l’espèce la plus forte ou la plus intelligente qui va survivre mais celle qui est la plus réactive aux changements » (Darwin 1909). Cette citation de Charles Darwin correspond parfaitement au potentiel adaptatif des bactéries. L’adaptation rapide aux changements des bactéries peut augmenter la valeur sélective de l’holobionte.

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La contribution la plus générale et la plus importante du microbiote est la protection contre les pathogènes. Les animaux gnotobiotiques, qui ne possèdent pas de microbiote, qui sont nés et ont grandi dans un milieu stérile, sont beaucoup plus sensibles aux infections et meurent de façon systématique s’ils ingèrent des agents pathogènes (Formal et al. 1961; Shanmugam et al. 2005). De plus, certains processus métaboliques, qui ne peuvent pas être effectués par l’hôte, sont réalisés par son microbiote. Par exemple, certains animaux ne pourraient pas puiser l’énergie de leur alimentation sans leur microbiote. Le Termite se nourrit de bois qu’il peut digérer parce qu’il possède un microbiote permettant la fermentation de la lignine et de l’hémicellulose (Brune 2012). Chez l’Humain, le microbiote synthétise et excrète des vitamines en excès pour subvenir au besoin de son hôte. En outre, le microbiote protège aussi l’hôte contre les molécules toxiques provenant de l’environnement comme les métaux lourds, de certains champignons et de plantes toxiques (Monachese et al. 2012). Le microbiote influence aussi le développement des animaux et leur comportement. En effet, chez les Vertébrés, la comparaison entre des animaux conventionnels et gnotobiotiques a permis de montrer une divergence d’expression de centaines de gènes (Hooper et al. 2001; Rawls & Samuel 2004). Ces gènes induits par le microbiote permettent le développement complet du système immunitaire et du système digestif (Ley et al. 2006; Lee & Mazmanian 2010). Il a aussi été démontré que le microbiote intestinal peut influencer le cerveau par modulation des hormones influant le nerf vague (Heijtz et al. 2011). De plus, ces influences du microbiote sur le cerveau de l’hôte modifient son comportement. Les souris gnotobiotiques sont plus actives, moins anxieuses et prennent plus de risque que les souris conventionnelles. Si un microbiote est inoculé aux souriceaux gnotobiotiques, le comportement redevient celui d’une souris conventionnelle. Cependant ce changement de comportement ne fonctionne pas suite à l’inoculation d’un microbiote sur des souris gnotobiotiques adultes (Foster & Neufeld 2013). Le microbiote influencerait donc aussi le développement du cerveau de l’hôte. En outre, le microbiote est également moduler les molécules odorantes de leur hôte et ainsi jouer un rôle dans le choix du partenaire sexuel (Rosenberg & Zilber 2013).

1.4 Evolution de l’holobionte : cas de la spéciation

La spéciation est un processus évolutif permettant la formation de nouvelles espèces distinctes (Cook 1906). Ainsi, la biodiversité de cette planète est générée par la spéciation.

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Lors de ce processus, des barrières reproductives se mettent progressivement en place permettant l’apparition de lignées évolutives génétiquement divergentes. Il existe plusieurs mécanismes conduisant à l’élévation des barrières reproductives au sein d’une population comme la spéciation dite allopatrique où la séparation est due à un obstacle physique comme un cours d’eau ou un relief. La population d’origine est ainsi divisée en deux populations ne pouvant plus partager leur réservoir de gènes (Thomas et al. 2010). Un autre mécanisme de spéciation peut avoir lieu par la colonisation d’une nouvelle niche où des individus fondateurs se séparent de la population mère pour coloniser une nouvelle niche trophique ou écologique. Cette niche peut être isolée comme, par exemple, une île ou bien adjacente à la niche de la population mère mais soumise à d'autres pressions de sélection. C’est la spéciation péripatrique ou parapatrique (Thomas et al. 2010). Pour ce type de mécanisme, un cas de spéciation dû au microbiote a été rapporté. Une bactérie endosymbiotique facultative nommée ‘pea aphid U-type symbiont’ (PAUS) permet aux pucerons verts du pois (Acyrthosiphon pisum) l’acquisition d’un nouveau phénotype lui permettant de coloniser une nouvelle niche trophique. En effet, les bactéries PAUS confèrent à leur hôte la possibilité de digérer de la luzerne (Medicago sativum). Cette nouvelle colonisation a amené la population de pucerons possédant cette bactérie à diverger de la population d’origine (Tsuchida et al. 2004). Le dernier mécanisme de spéciation est la spéciation sympatrique où une divergence se crée au sein d’une population sans aucune séparation physique (Thomas et al. 2010). Dans ce cas, l’apparition d’un nouveau caractère chez certains individus les isole de la population d’origine alors que le contact physique est toujours possible. Cette divergence provient souvent de préférences dans l'accouplement. En 1989, il a été mis en évidence qu’au sein d’une même population de Drosophila melanogaster, les mouches élevées sur de la mélasse s’accouplaient avec des ‘mouches à mélasse’ et que les mouches élevées sur de l’amidon s’accouplaient avec des ‘mouches à amidon’. Le nombre de génération étant trop court pour venir d’une divergence de l’hôte mouche, les chercheurs ont donné des antibiotiques aux mouches, dans le but de tester l’implication du microbiote. En présence d’antibiotique, les mouches ont arrêté la préférence d’accouplement mélasse-mélasse et amidon-amidon. En effet, le choix du partenaire chez Drosophila melanogaster dépend de la présence ou de l’absence de Lactobacillus plantarum qui modifie les niveaux de phéromones sexuelles de la mouche (Sharon et al. 2011). Le microbiote peut donc influencer la préférence d'accouplement et ainsi peut aussi influencer la spéciation sympatrique.

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Il est maintenant bien établi que les bactéries contribuent à l'odeur des animaux, même celle des poissons. Or les odeurs ont un rôle déterminant dans le choix du partenaire sexuel. Il existe deux mécanismes permettant au microbiote de moduler la préférence d’accouplement. Le microbiote peut produire des molécules chemosensorielles ou, comme vu précédemment, en modulant les molécules odorantes de l’hôte (Brucker & Bordenstein 2012b; Damodaram et al. 2016; Shropshire & Bordenstein 2016). Le microbiote influence donc les barrières pré-zygotiques mais il peut aussi influencer les barrières post-zygotiques. Le modèle de spéciation Bateson-Dobzhansky-Muller (BDM) stipule que si deux populations d’une même espèce évoluent de façon isolée l’une de l’autre, des incompatibilités génétiques apparaissent induisant de la mortalité hybride ou de la stérilité hybride (Dobzhansky, 1937; Muller, 1942). L’introduction du microbiote au sein de ce modèle accélère l’incompatibilité génétique (Brucker & Bordenstein 2012a; b). Brucker et al. (2012) ont croisé deux espèces de guêpe Nasonia (Nasonia vitripennis et Nasonia giraulti) afin de créer des larves d’hybride F2 élevées en présence de leur microbiote (élevage conventionnel) et sans leur microbiote (élevage gnotobiotique). La mortalité des F2 est clairement plus importante avec le microbiote (élevage conventionnel) que sans le microbiote. Aucune mortalité n’a été recensée chez les larves ayant un microbiote et appartenant à Nasonia vitripennis ou Nasonia giraulti. Cette expérience prouve donc l’accélération du modèle BDM par le microbiote, entrainant la mortalité hybride. De plus, il est connu que les hybrides sont plus sensibles aux maladies infectieuses dues à la rupture d’association entre gènes du système immunitaire (Burke & Arnold 2001; Dhanasiri et al. 2011). Des bactéries pathogènes ou opportunistes provenant de l’environnement pourront alors provoquer plus facilement une infection. Cependant, la rupture de l’association des gènes de l’immunité peut aussi entrainer un déséquilibre microbiote-hôte pouvant mener le microbiote de l’hybride à infecter son hôte. Ce phénomène peut contribuer à la réduction de la valeur sélective de l’hybride. L’holobionte étant encore peu étudié dans un contexte évolutif, d’autres processus influençant l’hôte dans un contexte de spéciation restent à découvrir.

1.5 Le Grand corégone

Les Corégonidés appartiennent à la famille des Salmonidés et sont distribués de façon circumpolaire dans l’hémisphère nord. La plupart des Corégonidés vivent dans des

10 rivières où dans des habitats lacustres, comme le grand corégone (Coregonus clupeaformis).

Le grand corégone est un poisson distribué en Amérique du nord qui comprend deux espèces sympatriques référées comme l’espèce naine et l’espèce normale résultant d’une récente divergence adaptative. Le grand corégone est ainsi actuellement en voie de spéciation. En effet, les populations de grand corégone ont été isolées géographiquement lors d’une ère glaciaire au cours du Pléistocène qui a recouvert de glace le Canada et une partie des États-Unis. Cet isolement a mené à la divergence génétique de l’espèce en fonction des refuges glaciaires notamment le refuge glaciaire Acadien et Atlantique. Un contact secondaire entre les lignées acadiennes et atlantiques s’est produit, il y a 12 000 ans, dans au moins 6 lacs du bassin de la Rivière Saint-Jean au sud-est du Québec. Les divergences génétiques et l’opportunité de coloniser une niche trophique différente ont permis la divergence adaptative du corégone nain. Il a ainsi divergé en sympatrie de l’espèce ancestrale benthique, représentée par le corégone normal, pour devenir une espèce limnétique typique, appelée corégone nain (Lu & Bernatchez 1999; Bernatchez et al. 2010).

De plus, une évolution parallèle a eu lieu pour le grand corégone. Le phénomène d’évolution parallèle se produit quand deux espèces indépendantes l’une de l’autre développent des phénotypes similaires dans un écosystème similaire. Ainsi les corégones nains et normaux ont respectivement un phénotype similaire et diffère génétiquement, morphologiquement, physiologiquement, métaboliquement, écologiquement mais aussi au niveau du comportement (Bernatchez et al. 2010; Jeukens et al. 2010; Pavey et al. 2013; Dalziel et al. 2015; Laporte et al. 2016). En effet, le grand corégone normal est un poisson benthique se nourrissant de proies diverses comme des mollusques et du zooplancton, présentant une croissance rapide, une maturité tardive et une longue durée de vie (Bodaly 1979; Landry & Bernatchez 2010). À l’opposé, le corégone nain est un poisson limnétique, se nourrissant presque exclusivement de zooplancton, présentant une faible croissance, une maturité rapide et une durée de vie courte par rapport à l’espèce normale (Bodaly 1979; Bernatchez et al. 1999). L’évolution parallèle a aussi été mise en évidence par des études transcriptomiques qui ont également montré une différence d’expression génique importante entre les espèces naine et normale. Le corégone nain montre une surexpression des gènes impliqués dans la fuite face aux prédateurs alors que le

11 corégone normal montre une surexpression des gènes impliqués dans la croissance (StCyr et al. 2008; Bernatchez et al. 2010).

Les espèces de corégone nain et normal sont actuellement partiellement isolées au niveau reproductif (Gagnaire et al. 2013) et des croisements contrôlés entre les nains et les normaux sont possibles. Beaucoup d’hybrides F1 résultant de ce croisement connaissent une mortalité embryonnaire élevée (50-70%) ou un développement anormal (10-30%) suggérant l’élévation de barrières post-zygotiques (Renaut et al. 2009; Dion- Cote et al. 2014).

Le grand corégone est un modèle étudié depuis des années. Les différences, à tous les niveaux biologiques, entre les deux espèces de corégone en font un excellent système pour étudier le microbiote dans un contexte de spéciation.

1.6 Objectifs de la thèse

L’objectif général de cette thèse est de tester l’implication du microbiote dans le phénomène de spéciation via un hôte particulièrement étudié, le grand corégone. Pour cela, les bactéries potentiellement impliquées dans le processus de spéciation ont été étudiées en recherchant la présence de bactéries spécifiques à une forme de corégone dans différents microbiotes ou communautés bactériennes. En effet, nous avons étudié trois communautés bactériennes ayant trois niveaux d’interactions avec l’hôte. Dans un premier temps, nous avons analysés la communauté bactérienne du rein, nous permettant de tester la relation hôte-pathogène de l’holobionte grand corégone. Puis, dans un deuxième temps, nous avons étudié le microbiote intestinal adhérent à la paroi intestinale, nous permettant d’analyser une relation de type hôte-symbionte. En effet, ces bactéries qui aident le corégone à la digestion des aliments sur de longues périodes de temps peuvent être considérées comme des symbiontes (Rosenberg & Zilber 2013). Enfin, nous avons étudié le microbiote intestinal non-adhérent à la paroi, nous permettant d’analyser une relation hôte-microbiote plus neutre que la relation hôte-symbionte.

De plus, la description des différentes compositions taxonomiques bactériennes trouvées chez le grand corégone est une étape très importante pour mieux comprendre les relations hôte-microbiotes. Bien que les Poissons soit le groupe le plus diversifié des Vertébrés, il existe encore trop peu d’études sur le microbiote des poissons, en particulier chez les poissons sauvages (Pascoe et al. 2017). Par ailleurs, encore moins d’études se

12 sont focalisées sur le microbiote dans un contexte de spéciation chez le poisson alors que la spéciation est le processus évolutif par lequel est générée la biodiversité (Hata et al. 2014; Sevellec et al. 2014; Baldo et al. 2015; Smith et al. 2015; Sullam et al. 2015; Baldo et al. 2017).

Dans le deuxième chapitre, nous avons analysé les communautés bactériennes du rein des deux espèces du grand corégone sauvage par séquençage 454 du gène de la sous- unité 16S de l’ARN ribosomique. Le rein ayant une fonction immunitaire importante chez les Téléostéens, la présence de bactéries, dont les souches pathogènes, peut y être détectée chez les poissons valétudinaires. Notre premier objectif était de tester l’existence d’une différence d’infection entre les espèces au sein des lacs étudiés. Puis nous voulions tester l’existence de parallélisme au niveau du taux d’infection entre les deux espèces du grand corégone. Enfin, nous voulions également référencer les genres bactériens impliqués dans l’infection des espèces de corégone ou présent au sein des lacs. En effet, les espèces sympatriques du grand corégone ayant des niches écologique et trophique différentes, notre hypothèse était que les deux espèces de corégone ne sont pas en contact avec les mêmes pathogènes.

Dans le troisième chapitre, nous avons analysé le microbiote intestinal adhérent des deux espèces de grand corégone sauvage par séquençage Illumina du gène de la sous-unité 16S de l’ARN ribosomique. Ce microbiote stable peut interagir plus étroitement avec l’hôte que les autres bactéries intestinales. En outre, chez les Vertébrés, le microbiote intestinal est le microbiote qui possède la plus grande influence sur son hôte (Alberdi et al. 2016; Macke et al. 2017). Dans la majorité des cas, le changement de régime alimentaire, comme c’est le cas chez le grand corégone, semble être la principale force motrice de l’évolution. Aux vues de ces connaissances, nous voulions, dans un premier temps, savoir s’il existait des microbiotes intestinaux différents entre les espèces et/ou entre les populations de lacs. Puis nous avons testé l’hypothèse de parallélisme entre les microbiotes intestinaux des corégones nains et normaux. Finalement, nous avons référencé les taxons bactériens impliqués. De plus, dans ce chapitre, nous avons également étudié les communautés bactériennes de l’eau à différentes profondeurs pour chaque lacs afin d’analyser les relations entre les bactéries environnementales et celle de l’hôte.

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Dans le quatrième chapitre, nous avons analysé le microbiote intestinal transitoire des deux espèces de grand corégone sauvage ainsi que des espèces normales, naines et hybrides réciproques de grand corégone captif, élevés en conditions contrôlées. Ces microbiotes ont été analysés par séquençage Illumina du gène de la sous-unité 16S de l’ARN ribosomique. Comme pour les chapitres précédents, nous avons cherché pour le grand corégone sauvage (i) une variation entre le microbiote des espèces naines et normales au sein et entre chaque lacs, (ii) des preuves de parallélisme, (iii) l’identification des taxons bactériens. De plus, nous avons testé l’existence de microbiotes intestinaux différents entre les quatre groupes de corégone présents en conditions contrôlées. Nous avons aussi étudié l’effet parental sur le microbiote en identifiant notamment les pleins frères et pleines sœurs à l’aide d’une analyse mitochondriale et de marqueurs microsatellites.

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Chapitre 2 : Microbiome investigation in the ecological speciation context of Lake Whitefish (Coregonus clupeaformis) using next generation sequencing

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2.1 Résumé

Peu d’études ont utilisé le séquençage nouvelle génération pour analyser le microbiome des vertébrés dans leur environnement naturel, notamment celui des poissons d’eau douce. Dans cet article, nous séquençons le gène ribosomal 16s pour (i) tester s’il existe des différences de communautés bactériennes présentes dans les reins des espèces sympatriques naines et normales du grand corégone ; (ii) tester l’hypothèse d’une plus grande diversité bactérienne pour l’espèce normale et (iii) tester s’il existe une occurrence de parallélisme au niveau de la présence et de la composition des communautés bactériennes entre les différentes paires de corégone sympatriques présent dans différents lacs. La communauté bactérienne du rein de 253 corégones (nains et normaux) de cinq lacs a été analysée par combinaison d’une double PCR imbriquée et d’un séquençage 454. Des bactéries ont été détectées dans 52.6% des reins de corégone analysés. Il n’existe pas de différence globale significative entre les lacs et entre les espèces de corégone. Cependant, l’interaction entre les lacs et les espèces de corégone est significative. Nous avons identifié 579 genres bactériens différents et 18 espèces bactériennes connues pour être pathogènes. Ces résultats sont substantiellement plus importants que les descriptions précédentes utilisant des techniques moins sensibles. En outre, les différences dans les compositions bactériennes entre les espèces de corégone ne sont pas parallèles entre les lacs. Les reins des normaux ont une diversité bactérienne systématiquement plus importante, pouvant être le reflet de leur niche trophique plus diversifiée. Cette diversité bactérienne montre un patron parallèle entre les lacs. Ces résultats contribuent à mettre en évidence que la divergence adaptative des corégones nains et normaux a été conduite à la fois par des conditions écologiques parallèles et non parallèles entre les lacs.

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2.2 Abstract

Few studies have applied NGS methods to investigate the microbiome of vertebrates in their natural environment, and in freshwater fishes in particularly. Here, we used pyrosequencing of the 16S gene rRNA to; i) test for differences in kidney bacterial communities (i.e. microbiota) of dwarf and normal whitefish found as sympatric pairs; ii) test the hypothesis of higher bacterial diversity in normal compared with dwarf whitefish and iii) test for the occurrence of parallelism with the presence and composition of bacterial communities across species pairs inhabiting different lakes. The kidney microbiota of 253 dwarf and normal whitefish from five lakes was analysed combining a double-nested PCR approach with 454 pyrosequencing. Bacteria were detected in 52.6% of the analysed whitefish. There was no overall significant difference among lakes and forms, although the lake × form interaction was found significant. We identified 579 bacterial genera, which is substantially more than previous descriptions using less sensitive techniques of fish bacterial diversity in kidney, pathogenic or not. Ten of these genera contained eighteen pathogenic species. Differences in bacteria composition between whitefish forms were not parallel among lakes. In accordance with the higher diversity of prey types, normal whitefish kidney tissue consistently had a more diverse bacterial community and this pattern was parallel among lakes. These results add to building evidence from previous studies on this system that the adaptive divergence of dwarf and normal whitefish has been driven both by parallel and non-parallel ecological conditions across lakes.

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2.3 Introduction

Wild vertebrate species host a considerable bacterial diversity, which may influence their development, physiology, immune system and nutrition (Hooper et al., 2002; Bäckhed et al., 2005; Turnbaugh et al., 2007). Four main types of relationships between bacteria and their hosts have been documented. The first two types are commensal bacteria (Cahill, 1990), which may either have beneficial or neutral effects on the host (Prescott, 1995). The second type has a symbiotic obligatory relationship with the host, thus allowing a mutual benefit between symbiotic bacteria and host (Perru, 2006). The third type is opportunistic bacteria, which are facultative pathogenic bacteria that may become actively pathogenic when the host immune system is impaired and unable to fight off infection (Berg et al., 2005). The fourth type of relationship pertains to pathogenic bacteria which are responsible for infectious diseases (Falkow, 1997). Species and populations may differ in their susceptibility to infection due to differences in their immune systems (White et al., 2009), which may have evolved in response to selection from exposure to changing microbial communities (Nakajima et al., 2011).

Methods of measuring bacterial communities are rapidly improving. The earliest and most traditional technique is the culture-dependent method. Because of functional interdependency for most of the bacterial community members (Laplante et al., 2013), many bacterial species cannot be cultured, and others vary greatly in their culture requirements. As a result, culture-based approaches may suffer from inconsistencies, low sensitivity and a biased global overview of the bacterial diversity. In recent decades, microbiologists have developed new culture independent techniques to obtain a better representation of bacterial communities present in host organisms, for example denaturing gradient gel electrophoresis (DGGE) and temperature gradient gel electrophoresis (TGGE) (Muyzer & Smalla, 1998). Despite their usefulness, these methods are also limited by the resolution of band detection with complex bacterial communities and microbes of low abundance may easily be missed (Danilo, 2004).

Advanced culture-independent techniques, such as 16S rRNA massively parallel pyrosequencing, allow a more complete description of complex bacterial communities. The 16S rRNA gene is composed of conserved and “hypervariable” regions (Amann & Ludwig, 2000; Huse et al., 2008). Thus, it is possible to design nearly universal primers in the conserved regions that capture enough sequence diversity to delineate bacterial genera or

18 even species. This technique has demonstrated its effectiveness in studies of environmental bacterial communities (Huber et al., 2007; Roesch et al., 2007; Ghiglione & Murray, 2011; Kuffner et al., 2012; Roesch et al., 2012; Collin et al., 2013), and in investigating the microbiome of plant, human and mouse (White et al., 2009; Arumugam et al., 2011; Buffie et al., 2011; Lopez-Velasco et al., 2011; Siqueira Jr et al., 2011; Grice & Segre, 2012; Lebeis et al., 2012). In contrast, there have been very few studies using this approach documenting the microbiome of vertebrate species in their natural environment (Yildirim et al., 2010; Lavery et al., 2012; McKenzie et al., 2012; Larsen et al., 2013; Linnenbrink et al., 2013) and, to our knowledge, no study has yet documented the microbiome of any freshwater wild fish species using 16S rRNA pyrosequencing.

Lake Whitefish (Coregonus clupeaformis) comprises sympatric species pairs referred to as dwarf and normal whitefish that are found in several lakes of the St. John River drainage in the Province of Québec, Canada, and Maine, United States. A recent period of adaptive radiation (postglacial, 12,000 years before present (YBP)) has led to the parallel phenotypic and ecological evolution in different lakes of the dwarf whitefish derived from the ancestral normal whitefish (Bernatchez, 2004). Dwarf and normal whitefish are partially reproductively isolated in each lake (Gagnaire et al., 2013), differ in genetically-based morphological, physiological, behavioral, ecological and life history traits (Fenderson, 1964; Bernatchez et al., 2010), and occupy the limnetic and benthic habitat, respectively. Dwarf and normal whitefish also differ at the immune system level whereby evidence of parallelism of genes relative to immunity was highlighted among whitefish sympatric species forms (St-Cyr et al., 2008; Jeukens et al., 2010).

A recent study also revealed variable patterns of divergence at the MHCIIβ genes between different pairs of dwarf and normal whitefish, although there was no evidence for parallelism in patterns observed among lakes (Pavey et al., 2013). This study also found no parallel patterns in a small subset of genera where pathogenic bacteria have been identified in the literature. In order to further investigate the possible role of bacteria in the parallel ecological speciation of whitefish, this study considers the entire microbiota found in the kidney tissue of dwarf and normal whitefish from different lakes. The kidney of teleost fish, which include whitefish, is known to play several functions, including urinary and a major immune function (Danguy et al., 2011). Furthermore, the presence of bacteria in kidney has been considered evidence of a pathogen infection (Cahill, 1990). Two previous studies performed on the kidney microbial community in salmonids found 10

19 genera and 27 genera with DGGE technique and culture-dependent technique respectively (Dionne et al., 2009; Evans & Neff, 2009). Based on previous studies in other groups of organisms, it is expected to detect a greater diversity of genera in using the more sensitive technique of 16S rRNA pyrosequencing on the infected whitefish individuals. In fact, this technique may even be sensitive enough to detect bacterial DNA that is the result of successful immune responses (Pavey et al., 2013). However throughout the manuscript, we will refer to individuals with bacteria amplified from their kidney tissue as “infected”. In this context, our first objective is to test for differences in kidney bacterial communities between dwarf and normal whitefish found in sympatry in the same lake. The dwarf whitefish form feed almost exclusively on small zooplankton whereas normal whitefish feed on a wider diversity of prey types, including zooplankton, but predominantly zoobenthos, molluscs and small fish (Bernatchez et al., 1999; Bernatchez et al., 2010). Because the digestive tract is one of the major infection routes in fish (Ringø & Olsen, 1999), bacteria have the opportunity to colonize kidneys after passing through the intestinal epithelium (Hart et al., 1988; Jutfelt et al., 2006; Knudsen et al., 2008). Thus, these parallel ecological differences in habitat use and diet suggest that dwarf and normal whitefish from a given lake could be exposed to different bacterial communities, whereas whitefish from the same form but from different lakes could be exposed to similar ones. Some of these could be pathogenic and thus potentially imposing differential selection between dwarf and normal whitefish which could have contributed to the parallel divergence of these species pairs. Therefore, our next objectives were to, second, test the hypothesis of higher bacterial taxonomic diversity in normal vs. dwarf, given their broader range of prey types and, third, test for the occurrence of parallelism at the presence and composition of bacterial communities across species pairs inhabiting different lakes.

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2.4 Material and methods

Biological material

Sympatric dwarf and normal whitefish samples were collected in five different lakes (Cliff, East, Témiscouata, Webster and Indian) from the St John River drainage, Québec, Canada and Maine, United States (Figure 1). The lakes are geographically and hydrographically isolated from one another (Lu & Bernatchez, 1999). A total of 253 apparently healthy whitefish (from external appearance) were sampled with gill nets between June 14th and July 15th 2010 (Table 1). Fish were dissected on the field in sterile conditions; ventral belly surface of fish were rinsed with ethanol, nondisposable tools were rinsed with ethanol and heated over a blow torch between samples, and kidneys were individually stored in a sterile Eppendorf© tube and flash-frozen in liquid nitrogen. The samples were then transported to the laboratory and kept at -80°C until further processing.

Bacterial detection using double nested PCR

In order to diagnose the presence of bacteria in whitefish kidney, a double-nested PCR was performed. Due to the high concentration of host genomic DNA in kidney tissue relative to potential bacterial DNA, none of our extractions amplify bacterial DNA with repeatability using the standard techniques of DNA amplification (Sevellec et al., 2012). Detailed protocols of the DNA extraction, double nested PCR, library construction and 454 pyrosequencing are provided in Boutin et al. (2012). In brief, DNA from kidney tissue was extracted with a modified QIAamp DNA minikit protocol for tissues in sterile way, the double nested PCR was based on the same principle of the nested PCR described by Yourno (1992), except that three successive amplifications steps were done with three different primer pairs (Table 2). The full 16S rDNA (1380bp) was amplified using primers 1389R and 9F. For the second step, primers 907R and 23F allowed a specific reamplification of the hypervariable region V1-V2-V3-V4-V5 (884bp). Finally, primers 519R and 63F were used to specifically reamplify the rDNA hypervariable region V1-V2-V3 (456bp). For each step of amplification, two negative controls (extraction was replaced with 2 µL of sterile nuclease-free water (DEPC-treated Water Ambion®)) and one positive

21 control (extraction was replaced with 2 µL of bacterial cultures in liquid medium) were done. All positive controls were positive and out of 28 negative controls, 3 only showed fainted bands of contamination. A double nested PCR was performed on each sample twice independently. In the case of conflicting results (one positive DNA amplification result and one negative DNA amplification result), a final double nested PCR was done, serving as a “tie-breaker”. This double nested PCR enabled us to significantly increase a very low amount of bacterial DNA while avoiding eukaryotic DNA contamination (Sevellec et al., 2012). The library was done during third-step primers of the double nested PCR summing the A and B adapters required for 454 pyrosequencing and 45 different bar- coded MID-tags (Multiplex identifiers) to the primers 519R and 63F. All the PCR results were purified by AMPure bead calibration method and were sequenced using the GS-20 (Genome Sequencer 20) (Roche, Basel, Switzerland) at the Plateforme d’Analyses Génomiques (Université Laval, Québec, Canada).

Amplicon analysis

First, CLC Genomics Workbench 3.1 (CLC Bio, Aarhus, Denmark CLC work bench BIO®) was used to trim sequences for quality and remove primer sequences and tags (minimum average quality score: 35 for a window of 50, number of differences to the primer sequence = 0, maximum number of differences to the barcode sequence = 0, number of ambiguous base calls = 0, maximum homopolymer length = 8). Second, pre-processing and analysis was done with the microbial ecology community software MOTHUR (version 1.22.2) (Schloss et al., 2009) following the protocol of Costello stool analysis (http://www.mothur.org/wiki/Costello_stool_analysis). This allowed identifying and deleting chimeras, and removing smaller sequences that were either smaller than 300 base pairs or that contained pyrosequencing errors. Among the three analytical options available in MOTHUR, the OTU-based analysis protocol was used. We generated alpha diversity results, defined by the diversity in an individual fish kidney sample, and the richness estimators (Mc Cure et al., 2002). The richness or number of species in an individual sample was measured using two indexes: the Chao index was used as the richness estimator and the Simpson index was used as the diversity index (Magurran, 2003). The Chao index is the simplest richness index based on the number of rare species (Magurran, 2003). The Simpson index measures the probability that two randomly selected individuals

22 belong to the same taxa. Consequently, a higher Simpson index value is correlated with a lower diversity (Peet, 1974; Sepkoski, 1988). The OTU-based analysis using MOTHUR was also used for taxonomic identification (using 98% bootstrap score). Taxonomic identification was also performed by using the Ribosomal database Project (using the maximal criterion of 95% bootstrap score available in this method) (Maidak et al., 2001). Unweighted UniFrac tests were performed with the Phylotype-based analysis using MOTHUR. Finally, putative fish pathogen bacterial genera were identified according to Austin & Austin (2007). Furthermore, the species of selected putative pathogen genera were investigated using the BLAST algorithm (Altschul et al., 1997). For each putatively pathogenic genus that we described in the MS, we pooled sequencing from all populations and individuals in the study. We considered the top blast hit to be the bacterial species for that sequence. Then, for the globally most abundant species, we performed a literature search to determine if there are indications of pathogenicity in fishes. We restricted subsequent pathogen analyses to these genera.

Statistical analyses

We constructed a matrix containing the number of bacterial sequences for each bacterial genus in each fish sample from the MOTHUR taxonomy file (stool.final.an.0.02.cons.taxonomy). This matrix was used to perform a principal component analysis per rank (PCA per rank) (Baxter, 1995) using PC-ORD (Mc Cure et al., 2002). Samples were ranked as a function of the number of sequences found for each OTU. Nonmetric Multidimensional Scaling (NMS) analysis was not used in this case because the final stress was above recommended interpretable range (final stress = 25). Therefore, ranked-based PCA was preferred to NMS. Since absolute abundance may be influenced by sequence specific fidelity in the double nested PCR method, we also performed a non-parametric ranking method of abundance using the METASTATS software (White et al., 2009) to detect differentially abundant OTUs between dwarf and normal whitefish. The OTU by sample abundance matrix was also used for this analysis with standard parameters (p value ≤ 0.05 and number of permutations = 1000).

In order to determine if there were statistically significant differences in the proportion of infected fish among lakes and between forms, we used a generalized linear model (GLM) with a binomial family followed by an ANOVA with lakes and forms as factors and also

23 including their interaction. We then used the same GLM procedure in order to test for differences in the proportions of putative pathogenic bacteria between forms within and among lakes. Then, a GLM with Gaussian family was used to test for differences in both the Simpson and Chao indices and again between forms and among lakes. The Levene test was applied to test for differences in inter-individual variance of the Simpson and Chao indices between forms, among lakes, and their interaction (Snedecor, 1980).

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2.5 Results

The presence of bacteria was detected in kidneys of 52.6% (133 infected samples) of the analyzed whitefish (Table 1). The vast majority of samples produced consistent results between the two independent double nested PCR’s and did not require a tie breaker (83.6%). There were no overall significant differences in infection levels among lakes

(GLM, PLakes = 0.16) or between forms (GLM, PForms = 0.33), but there was a significant interaction of the lake and form terms (GLM, PLakes*Forms = 5.5e-5) (Table 3). The level of infection in the lakes were 46.6%, 54.0%, 66.1%, 46.2% and 48.0% for Cliff, East, Indian, Témiscouata and Webster lakes, respectively. In East and Témiscouata lakes, the dwarf whitefish infection rate was significantly higher than that of normal whitefish (GLM, PEast =

0.02 and PTémiscouata = 0.04), whereas, in Indian Pond, the normal whitefish infection rate was higher than that of dwarf whitefish (GLM, PIndian = 0.001). In Cliff and Webster, the infection rates were similar between forms (GLM, PCliff = 0.330 and PWebster = 0.571). Therefore, the infection rate in these samples was not globally influenced by a lake effect or form effect separately.

A total of 79,146 reads were obtained, after trimming, for the entire dataset of 133 infected samples. Four samples only containing chimeras or very few sequences were eliminated by CLC Genomics Workbench 3.1 and the MOTHUR preprocessing. Six other samples were not sequenced because their bar-coded MID-tags (Multiplex identifiers), MID 20 and MID 21, did not work, leaving 123 samples. Detailed results for the quality of this 454 pyrosequencing experiment are given in Sevellec et al. (2012). In brief, the Good's coverage estimator for the set of the lakes was 95.4%, a low occurrence (3%) of chimeric amplicons was observed, 77% of the data set was composed of sequences longer than 300 bases, Chao’s curves indicated a deeply and a representative sequencing, and a weighted UniFrac test done on a fragment of our data highlighting a large variation of bacterial communities diversity which indicating than bias caused by preferential amplification or primer selection during the double nested PCR, which could obscure initial abundance, does not affect our interpretation.

According to the Simpson index, the bacterial diversity in dwarf whitefish was significantly lower than that observed in normal whitefish diversity in all lakes (GLM, Pforms= 0.01) (Figure 2). In contrast, there was no significant difference in bacterial diversity among lakes (GLM, PLakes = 0.16). Similar results were found using the Chao index (GLM, Pforms =

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0.04 and Plakes = 0.11) (Supplementary Figure 1). Thus, the levels of bacterial diversity between dwarf and normal whitefish were parallel across all lakes. Also, the inter- individual variance in diversity measured by the Simpson index was significantly higher in all dwarf whitefish populations compared to normal whitefish samples (Levene Test, Pforms = 0.04). However, there was no difference in inter-individual variance in diversity between forms based on the Chao index (Levene Test, Pforms = 0.80).

Three phyla were predominantly represented across all samples: Proteobacteria, Actinobacteria and Firmicutes (Figure 3). Different bacterial communities at the genus level between dwarf and normal whitefish were observed within lakes (unweighted

UniFrac, Pforms Cliff<0.001, Pforms East<0.001, Pforms Indian<0.001, Pforms Témiscouata<0.001, Pforms

Webster<0.001) and among lakes (unweighted UniFrac, Pforms all the lakes<0.001). These differences were also highlighted for both forms among lakes (unweighted UniFrac, PLakes <0.001) and the interaction term between lakes and forms was also significant

(unweighted UniFrac, PLakes*forms<0.001). A total of 579 genera were detected (Supplementary Table 1), the most frequent ones being (by number of whitefish individuals infected by the same genus): Propionibacterium, Sphingomonas, Acinetobacter, Clostridium, Methylobacterium, Pseudomonas, Microcella, Kocuria, Staphylococcus and Polynucleobacter (Supplementary Figure 2 and Supplementary Table 2). The most abundant genera (quantity of reads of the genus present in all whitefish individuals) were Propionibacterium, Sphingomonas, Clostridium, Acinetobacter, Microcella, Altererythrobacter, Nitrosococcus, Croceicoccus, Sarcina and Polynucleobacter (Supplementary Figure 3 and Supplementary Table 3).

Two different PCA per rank were performed (Figure 4). The first two principal components explained 20.0% of the variance for the analysis comparing all five dwarf whitefish populations for all bacterial genera and 23.5% of the variance for all five normal whitefish populations for all bacterial genera. The PCA per rank on all the bacterial genera found in the normal individuals was also used in PC-ORD to generate four figures showing only the Altererythrobacter, Croceicoccus, Flavobacteria and Kocuria genera using the same axes calculated for all the genera. Comparing dwarf whitefish populations for these genera revealed no difference among lakes (results not shown), which corroborates the absence of differentiation when considering all bacterial genera. In contrast, results for normal whitefish revealed a more pronounced differentiation among lakes although with some overlap. In particular, Indian Pond, and East Lake bacterial communities were the most

26 distinct and clustered in the form of elliptical clouds whereby individual bacterial communities in Indian Pond were more spread along axis 2 whereas those from East Lake were more spread along axis 1. Cliff and Webster bacterial communities overlapped with those observed in Indian Pond and East Lakes whereas Témiscouata Lake displayed a pattern intermediate between Indian and East Lakes. These differences among the lakes were mainly explained by the genera Altererythrobacter and Croceicoccus that predominated in normal whitefish from East, Cliff and Webster Lakes. In contrast, Flavobacteria and Kocuria were the most abundant in some normal whitefish individuals from Indian pond but also of Cliff and Webster lakes.

Among the bacterial genera detected, 10 putative pathogenic bacterial genera were identified and further considered in our analysis because they were both identified as pathogenic genera in Austin & Austin (2007) and the most abundant species in our BLAST analysis were known fish pathogens: Acinetobacter, Aeromonas, Chryseobacterium, Corynebacterium, Flavobacterium, Janthinobacterium, Micrococcus, Pseudomonas, Shewanella, and Staphylococcus. Seven other putative pathogen genera (Citrobacter, Clostridium, Mycobacterium, Renibacterium, Enterococcus, Oxalobacter, and Streptococcus) were also detected but not considered as pathogen in our analysis because their frequency and their abundance were too low or fish pathogen species were not highlighted. Among these 10 putative pathogenic bacterial genera, 18 known fish pathogenic species were found (Table 4). Also in these 10 genera we found 14 other pathogenic bacteria which are known to infect other species (human, drosophila and plants) but not fish and finally 11 species which had no documented pathogenicity (Supplementary Table 4).

A total of 73% of the 133 whitefish samples for which bacteria were detected in kidney were infected by at least one putative pathogen (Table 5). There was no significant difference in the occurrence of the pathogenic bacteria between dwarf and normal whitefish within each lake (GLM, PCliff = 0.92, PEast = 0.29, GLM, PIndian = 0.15, PTémiscouata =

0.34, PWebster = 0.86), neither was there significant forms effect or lake effect (GLM, Pforms =

0.92, GLM, PLakes = 0.80).

Within the putative pathogen communities, some taxa were found only in one of the two forms in a given lake, but this varied among lakes (Figure 5). Pathogens of the genera Aeromonas, Flavobacterium, Micrococcus, Pseudomonas, Shewanella and

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Staphylococcus significantly infected normal whitefish. In particular, normal whitefish from Indian Pond were infected by pathogens belonging to five genera: Aeromonas, Flavobacterium, Pseudomonas, Shewanella and Staphylococcus. Bacterial communities of the normal whitefish of East and Webster were characterised by Micrococcus and Aeromonas respectively. For Cliff, dwarf whitefish were infected by Flavobacterium and normal whitefish were infected by Micrococcus. Finally, only Témiscouata dwarf whitefish were infected by Corynebacterium and Pseudomonas.

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2.6 Discussion

The general goal of this study was to investigate the kidney microbiome of sympatric dwarf and normal whitefish pairs in order to reach three objectives: i) to test for differences in kidney bacterial communities between dwarf and normal whitefish from the same lake; ii) to test the hypothesis of higher bacterial taxonomic diversity in normal than dwarf whitefish, and iii) test for the occurrence of parallelism in those patterns.

To this end, we analysed 253 whitefish samples from five lakes, and identified an unprecedented number of bacterial genera in kidneys, including 579 different genera among which 10 were pathogenic bacterial genera that comprised 18 known pathogenic species. This contrasts with previous studies that considered kidneys to be a sterile organ in healthy fish (Goldschmidt-Clermont et al., 2008; Dionne et al., 2009; Salgado-Miranda et al., 2010). Of course, one must consider that contamination could partly explain the level of bacterial diversity observed in whitefish kidneys. However, as mentioned in Materials & Methods, meticulous care was taken to avoid contamination by working in sterile conditions and avoiding cross-contamination between individuals. Also, the composition of kidney bacterial communities varied importantly between whitefish individuals, which is not compatible with contamination from the same manipulations in the same working environment. In addition, no PCR product was detected for 47% of the analysed individuals and on every plate there were both positive and negative reactions. The total absence of amplification for 47% of all individual analysed thus serve as additional negative controls. Thus, although it cannot be entirely ruled out, contamination during the manipulations is very unlikely to explain the main patterns we documented. This rather suggests that bacteria may pass to kidneys through by abrasions, injuries (Austin, 2006; Larsen et al., 2013), or by the digestive tract which is one of the major infection routes in fish (Ringø & Olsen, 1999). From there, bacterial translocation across the epithelia of the intestine could open a possible route for kidney infection (Hart et al., 1988; Jutfelt et al., 2006; Knudsen et al., 2008), thus offering a possible explanation for the much higher occurrence of bacteria in kidney than previously assumed.

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Variation of bacterial communities in whitefish kidney within lake

Regarding our first objective, we observed an overall significant lake effect on the composition of the different taxa. This general pattern can hypothetically be explained by the geographical distance and limited hydrological connectivity among the lakes, which could create different environmental conditions surrounding the lakes and lead to the presence of different bacteria (Lindström et al., 2005; Reche et al., 2005; Laplante et al., 2013). There was also a significant difference for form effect meaning that bacterial community compositions between dwarf and normal whitefish is distinct within and among the lakes. In fact, distinct bacterial communities may exist within a lake because the bacterial diversity and bacterial concentration decrease as a function of depth and due to differences in temperature, oxygen concentration and luminance (Ovreås et al., 1997; Bosshard et al., 2000; Koizumi et al., 2004; Landry et al., 2007). However, while whitefish forms occupy different depths in the water column, there is no barrier preventing contact between dwarf and normal whitefish (Bernatchez et al., 1999). Even if both dwarf and normal whitefish could come in contact with all of these bacterial communities, the probabilities of infection by the different taxa are probably different for the forms. In the absence of other similar studies on bacterial communities in natural populations of freshwater fishes, we cannot compare our observation of an overall relatively modest difference in bacterial communities between sympatric whitefish species pairs to other fish species. However, previous studies on eukaryote parasite communities revealed somewhat similar patterns with respect to benthic diet types having more diverse parasites, for instance between closely related sympatric cichlid species (Tropheus sp. Blais et al., 2007), sympatric incipient species of European whitefish (C. lavaretus; Knudsen et al., 2003) and threespine sticklebacks (Gasterosteus aculeatus; (MacColl, 2009; Natsopoulou et al., 2012)).

Bacterial diversity and whitefish diet

We observed that normal whitefish were characterized in all lakes by a greater average taxonomic diversity compared to dwarf whitefish, thus translating into parallelism in difference of taxonomic diversity. This apparently runs counter to the expectation of abundance of bacteria in the water column, which is expected to decrease with depth

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(Ovreås et al., 1997; Bosshard et al., 2000; Koizumi et al., 2004; Landry et al., 2007). However, assuming that bacterial transmission through diet may influence the bacterial community observed in kidney, this could perhaps be explained by difference in prey availability among lakes. Indeed, dwarf whitefish (and limnetic whitefish in general) feed almost exclusively on zooplankton, most often on the same taxa in different lakes (Bodaly, 1979.; Bernatchez et al., 1999). In contrast, normal whitefish are more generalists and feed on more diverse prey items including zoobenthos, molluscs, and fish prey, of which the composition varies among lakes and throughout the year (Bernatchez et al., 1999; Bernatchez, 2004). Another important observation is that we found no differences in kidney bacterial communities among dwarf whitefish from different lakes in our PCA (all dwarf lakes in the same cloud; Figure 4), which can be interpreted as mirroring evidence for parallelism in the composition of dwarf whitefish microbiota. In sharp contrast, we observed pronounced non-parallel differences in the kidney microbiota of normal whitefish from different lakes. Generally speaking, the zooplankton communities are more similar compared to the benthic and fish prey communities in the studied lakes (Landry et al., 2007; Landry & Bernatchez, 2010). Given that different diets may allow contact with different microbial communities (Gatesoupe & Lésel, 1998), it is thus plausible that parallelism in zooplankton community translates into parallelism in bacterial community of dwarf whitefish, whereas non-parallelism in benthic and fish prey community translates into non-parallelism in bacterial community of normal whitefish. This again suggests that the diet composition may impact the diversity of the bacterial community found in a given host (Ringø et al., 2006; Zhou et al., 2012). It thus appears that variation in patterns of zooplankton and benthic prey communities across lakes at least partly explain the differences in patterns of bacterial diversity and community composition observed between dwarf and normal whitefish.

Implication of patterns of parallelism and non-parallelism in whitefish diversification

In regard to our third objective, the above results show no strong parallelism in dwarf- normal bacterial community differences or infection rates. Yet, parallelism was observed among dwarf whitefish from different lakes, which had very similar microbiotas among lakes compared with normal whitefish which had wildly different microbiotas among lakes.

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Indeed, we systematically observed lower bacterial community diversity found in dwarf whitefish across all five lakes. This difference in bacterial diversity could potentially be explained by differences in prey diversity available to both forms. A nonexclusive hypothetical explanation for the observed parallel differences in bacterial diversity between dwarf and normal whitefish pertains to the trade-off made by these forms between resources devoted to growth and those devoted to the immune response. Thus, previous studies in the wild and common rearing conditions showed that normal whitefish have a genetically-based faster growth rate than dwarf whitefish (Bernatchez, 2004; Rogers & Bernatchez, 2007). Also, this parallel growth differential is accompanied by parallelism in patterns of gene expression whereby normal whitefish showed significant overexpression of genes involved with growth (protein synthesis, cell growth) (St-Cyr et al., 2008). In contrast, the limnetic life history of the dwarf whitefish requires a lot of energy be expended for constant swimming, for feeding on zooplankton and avoiding predators. Thereby, genes associated with metabolism, muscle contraction and detoxification were overexpressed in dwarf whitefish. According to Matarese & La Cava (2004), many genes have dual functions in both immunity and metabolism. Thus, dwarf whitefish could have a more efficient immune system than the normal whitefish. Besides, genes specific to the immune system are overexpressed in dwarf whitefish (St-Cyr et al., 2008; Jeukens et al., 2010). On the other hand, a recent study revealed no evidence for parallelism at the adaptive immune system in MHCIIβ gene diversity among whitefish sympatric species pairs, suggesting a minor role of pathogenic bacteria in the parallel evolution of whitefish species pairs (Pavey et al., 2013). In that study, specific lake effects associated with different environments appeared more important in explaining MHC variation in this system. However, a parallel evolution of innate immune system which eliminates bacteria in a non-specific manner is possible (Janeway, 2001). Indeed, some of the overexpressed genes documented by St-Cyr et al. (2008) and Jeukens et al. (2010) were complement C4, complement factor H1 protein, C1q-like adipose specific protein, implicated in complement system belonging the innate immune system and MHC class I antigen belonging the innate and the specific immune system This increased expression of genes of the innate immune system could be an adaptation of the dwarf whitefish exposure to pathogen (Goetz et al., 2010; Jeukens et al., 2010), although this remains to be investigated further. Finally, there is another non-exclusive interpretation regarding to the observed dwarf whitefish microbiota parallelism and the trade-off made by these forms between resources devoted to growth and those devoted to the immune response. Certain host genotypes

32 may have the capacity to recruit specific bacterial strains (McKnite et al., 2012). This capacity was recently demonstrated in another Salmonidae, the Brook Trout (Boutin et al., 2014), in which three QTLs were related to the relative abundance of three bacterial strains associated with skin and intestine tissue, all of them being documented to synthesize antimicrobial compounds. Therefore, it remains possible that dwarf form may preferentially recruit bacterial strains exerting strong antimicrobial properties in order to enhance its overall immune capacity against opportunistic pathogens. Though this would likely actively occur in the intestine, that may result in less bacteria infecting the kidney. As a trade-off, the bacterial strains that enhance energetic conversion efficiency may be disfavoured.

Nevertheless, testing for patterns of parallelism at many different levels may help identifying the main factors that are at play in driving the process of parallel adaptive divergence and ultimately reproductive isolation. In the case of whitefish, this strategy has clearly been efficient in achieving this goal. For instance, comparing the limnological settings of each lake allowed identifying the main biotic and abiotic factors (namely the level of oxygen depletion, lake depth, prey size distribution and biomass) that have most likely played a role in the level of adaptive divergence observed between dwarf and normal whitefish from different lakes (Landry et al., 2007; Landry & Bernatchez, 2010). Parallelism in gene expression patterns has also led to identifying the main physiological functions involved in the life history trade off between growth and survival observed in normal and dwarf whitefish, respectively (St-Cyr et al., 2008). In a recent investigation of respiratory, circulatory, and neurological traits across Lake Whitefish species pairs, Evans et al. (2013) found that in each of the species pairs, normal whitefish exhibited larger body size standardized gills compared to dwarf whitefish – a pattern that is suggestive of a common ecological driver of gill size divergence. Evans et al. (2013) also observed a trend toward larger hearts in dwarfs, the more active species of the two species, whereas brain size varied exclusively among the lakes but independent of form. In another recent study, Evans et al. (2012) tested for the parallel divergence of traits involved in oxygen transport in dwarf and normal Lake Whitefish. They found parallel differences in red blood cell morphology between the forms. Taken together, the results of these studies along with the present study imply that the diversification of whitefish has been driven both by parallel and non-parallel ecological conditions across lakes

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Whitefish microbiota

As mentioned above, it has traditionally been assumed that kidney tissue is free of bacteria in healthy fish (Nieto et al., 1984; Cahill, 1990; Goldschmidt-Clermont et al., 2008). However, an increasing number of studies have reported bacteria in healthy fish kidney. According to Dionne et al. (2009) and Evans & Neff (2009), 12.1% of juvenile salmon and between 9 and 29% of Chinook Salmon (Oncorhynchus tshawytscha) were infected by 10 and 27 genera respectively. Three other studies on liver and kidney of wild freshwater fish, turbot and five different salmonids respectively identified 8, 6 and 19 bacteria genera (Trust & Sparrow, 1974; Toranzo et al., 1993; Sousa & Silva-Souza, 2001). Furthermore, Flavobacterium psychrophilum infected kidney of 10.4% of Atlantic Salmon from the Baltic Sea (Ekman et al., 1999). In this study, 579 different genera representing nine phyla were found in kidney from 133 apparently healthy fish. This is a substantial diversity for an internal organ, particularly if one compares with the 10 genera and 27 genera respectively reported in two previous studies performed on the kidney microbial community in salmonids (Dionne et al., 2009; Evans & Neff, 2009). However, data from these studies was obtained by culture and DGGE, while a double nested PCR is much more sensitive to detect and specifically amplify DNA than PCR, nested PCR and culture methods (Sevellec et al., 2012). Thus the traditional assumption of kidney sterility may have simply been a factor of the limitations of the techniques available, as differing sensitivity even among modern techniques could largely explain the difference between the results of the present and the previous studies on the kidney bacterial diversity. Moreover, to our knowledge, a single study was done on wild freshwater fish with a Sanger DNA sequencing approach where they found 525 OTUs classified in 8 phyla on three zebrafish guts (Roeselers et al., 2011). Other than these studies, some microbiome studies in fish have analysed the digestive system and the skin with pyrosequencing of bar-coded 16S rRNA gene amplicons. Twelve and nine phyla were present in faeces of eight catfish individuals (Panaque sp.) and six marine fish, respectively (Di Maiuta et al., 2013; Larsen et al., 2013) and 6,058 OTUs were obtained from the seven grass carp (Ctenopharyngodon idellus) (Wu et al., 2012). In contrast, the Brook Trout (Salvelinus fontinalis) skin of 121 individuals had 16,904 genera distributed among 21 phyla (Boutin et al., 2013). Thus, while high relative to the a priori expectations the kidney diversity appears low compared to those recorded in both gut and skin microbiota. As mentioned in the introduction, kidney of teleost fish, which include whitefish, is known to play several

34 functions, including immune function, (Danguy et al., 2011). The anterior kidneys are composed almost exclusively by hematopoietic, lymphoid tissues and melanomacrophage centers where antibody-covered particles arrived through blood, including bacteria, are eliminated by phagocytosis (Press & Evensen, 1999; Agius & Roberts, 2003). It is thus possible that the double nested PCR is sensitive enough to detect and amplify the bacterial DNA from cells ongoing the process of elimination phagocytosis (Frank, 2002; Pavey et al., 2013).

Another factor that may explain the high level of kidney bacterial diversity in this study is that our sampling was done at the end of June and the beginning of July, when water temperatures are relatively high (Mackay & Kalff, 1969; Brunskill & Schindler, 1971). Generally speaking, bacteria abundances increase when water temperatures reach a certain threshold (Larsen et al., 2004; Dionne et al., 2009). For example, outbreaks of Vibriosis, a bacteria of the aquatic environment, happen when water temperatures exceed 15 °C (Larsen, 1981). Studies showed than the immune system efficacy could decline in presence of high bacteria concentrations in water (Buras et al., 1985; Cahill, 1990). In addition, there may be a reduction in the efficiency of fish immune system as a result of stress factors such as nutritional deficiencies, poorer water quality, overcrowding, parasitism, and temperature changes (Cahill, 1990). As such, whitefish may have been more susceptible to the colonization of internal organs from the external environment during the period at which they were sampled. Accordingly, the majority of genera identified in this study were associated with environmental water bacteria predominantly represented by Proteobacteria, Actinobacteria and Firmicutes. Proteobacteria and Actinobacteria were also predominantly present in all freshwater sites on a study analysing the typical freshwater bacteria (Zwart et al., 2002). Bacteria associated with the aqueous environment such as Sphingomonas, Methylobacterium, Lactobacillus, Roseomonas, Arthrobacter or Burkholderia were also found in Dionne et al. (2009) and Evans & Neff (2009).

Among 579 different genera found in our whitefish kidney, 14 genera are also described in the study of Evans & Neff (2009) on Chinook Salmon, and 6 genera in that of Dionne et al. (2009) on Atlantic Salmon. Both of these studies were conducted on young anadromous fish during their freshwater phase. Moreover, the genera Acinetobacter, Aeromonas, Citrobacter, Pseudomonas, and Staphylococcus that were observed in this study constitute the dominant microbiote of the adult freshwater fish digestive tract (Austin,

35

2006). Also, Enterobacter, Escherichia, Klebsiella, Serratia, Bacteroides, Bacillus and Propionibacterium, which are not considered as pathogens, were also found in whitefish kidney and previously reported in freshwater fish digestive tracts in general (Austin, 2006). Most of the other bacteria that we found in our study are ubiquitous and can be present in soil, water or plants, for instance Methylobacterium, Paracoccus, Stenotrophomonas, Bradyrhizobium, Altererythrobacter, Croceicoccus, Kocuria or Burkholderia (Kaneko et al., 2000; Lidstrom & Chistoserdova, 2002; Kelly et al., 2006; Kwon et al., 2007; Ryan et al., 2009; Xu et al., 2009; Bontemps et al., 2010; Savini et al., 2010).

Among all the 579 genera identified, there were 10 putative pathogen genera known to contain pathogenic species according to the literature (Austin & Austin, 2007). Indeed, the most abundant species representing these genera are known fish pathogens as revealed by our literature search. These genera are Acinetobacter, Aeromonas, Chryseobacterium, Corynebacterium, Flavobacterium, Janthinobacterium, Micrococcus, Pseudomonas, Shewanella, and Staphylococcus. Bacteria of the genus Acinetobacter sp., as Acinetobacter junii, and Acinetobacter lwoffii, can cause the acinetobacter disease which killed 92% of wild Atlantic Salmon in Norway (Roald & Hastein, 1980). Aeromonas hydrophila, Aeromonas salmonicida, Aeromonas sobria are known to cause septicaemia or ulcer disease in many freshwater fishes (Austin & Austin, 2007). Some species of Chryseobacterium are considered as potentially emerging pathogens and C. shigense was isolated from kidney of diseased Rainbow Trout (Zamora et al., 2012). Corynebacterium xerosis is implicated in bacterial kidney disease (BKD) in salmonid fish (Austin et al., 1983). Flavobacterium succinicans and Flavobacterium hydatis have been isolated from diseased fresh-water fish (Bernadet et al., 1996). Also Flavobacterium psychrophilum is an agent of bacterial cold-water disease and caused septicaemic disease (Nematollahi et al., 2003). Janthinobacterium lividum, Micrococcus luteus, Pseudomonas putida and Pseudomonas fluorescens caused serious diseases in Rainbow Trout (Sakai et al., 1989; Austin et al., 1992b; Austin & Stobie, 1992; Altinok et al., 2006). Shewanella algae were responsible of mass mortality of shellfish and fish (Hau & Gralnick, 2007). Also, many studies have reported infection of salmonids and other freshwater fishes by the genus Staphylococcus including S. warneri and S. Epidermidis (Nieto et al., 1984; Austin et al., 1992a; Gil et al., 2000). The majority of the above studies dealt with infection outbreaks in salmonid aquaculture conditions. Yet, this information provides a basis to identify potential pathogenic bacteria that may potentially affect whitefish in natural conditions (Pavey et al., 2013).

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The vast majority of the genera that we identified have not previously been described as comprising pathogenic bacteria (567/579). However, relatively little is known about pathogens in natural aquatic systems (Austin & Austin, 1999). It is also possible that some pathogens were not identified as such because it can be difficult to define the limit between pathogen and opportunistic bacteria and that bacteria have a continuous spectrum of pathogenicity (Casadevall & Pirofski, 1999). A box plot of the Simpson index of only the designated pathogenic bacteria did not reveal the same parallel pattern of diversity between dwarf and normal that we found for all bacteria (Supplementary Figure 4; Figure 2). Therefore, the main difference in infection between forms does not directly imply putative pathogenic bacteria. Thus, it is possible that the infection difference may be due to unknown pathogens or more likely opportunistic bacteria. In fact, the majority of genera could be opportunistic bacteria. This kind of bacteria colonizes tissues after a primary infection when the immune system of the host is impaired (Falkow, 1997; Casadevall & Pirofski, 1999). Some members of the most abundant and the most frequent genera, Propionibacterium, Sphingomonas, Polynucleobacter and Ralstonia are known to be opportunistic (Laskin & White, 1999; McDowell et al., 2011; Youngblut et al., 2013). This hypothesis of primary infection by at least one putative pathogen and second infection by opportunistic could explain the pattern of bacterial diversity we observed (Table 5). In fact, of the 133 fish that were found to be infected, 73% were infected by at least one putative pathogenic genus whereas, always under the hypothesis of primary infection, 23% of whitefish would have been infected by an unknown pathogen.

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2.7 Conclusion

In summary, this study represents the very first attempt to document fish microbiome in natural conditions by means of NGS-based approach. Here, our primary goal was to compare the bacterial communities present in kidney of sympatric species pairs of dwarf and normal whitefish in order to test whether parallel phenotypic and ecological evolution was accompanied by parallelism in their kidney microbiota, and in particular in the putative bacterial pathogenic community. We found bacteria in the kidney of more than half of the fish tested, and a diversity of bacterial genera well beyond previous descriptions of fish bacterial diversity in kidney, both pathogenic and not. Although there were differences in proportion of infected fish between dwarf and normal whitefish but these were not parallel among lakes. Also, there was similar bacterial diversity between dwarf whitefish from different lakes, providing evidence for parallelism in the bacterial diversity for this form, whereas pronounced differences in the bacterial diversity of normal whitefish from different lakes were found. However, in accordance with the higher diversity of prey types, normal whitefish kidney tissue consistently had more diverse microbiota, and this pattern was parallel among lakes. This again corroborates the expectations based on lake to lake differences and similarities in the benthic and limnetic prey communities, respectively. It is also possible that the trade-off between the immune system and growth functions implies globally weaker immune defences for faster growing normal whitefish relative to dwarf whitefish. While MHCIIβ patterns are not parallel among lakes (Pavey et al., 2013), this does not exclude parallel evolution in the innate immune system and other components, which will need to be investigated further in future studies.

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2.8 Acknowledgments

We thank Brian Boyle for his help with the 454 sequencing and Eric Normandeau for his bioinformatics skills and support. SP was supported through a fellowship from Fonds de la recherche en santé du Québec. We are also grateful for the two anonymous referees for their insightful comments and suggestions. This work was supported by a National Sciences and Engineering Council of Canada (NSERC) Discovery grant and a Canadian Research Chair to LB. It is also a contribution to the research programs of Québec-Océan and RAQ.

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2.9 Data Accessibility

Data have been deposited in Dryad data base: doi:10.5061/dryad.hv87d

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2.10 Tables

Table 2. 1 :Number of infected and non-infected whitefish for each form in each lake according to the results of double nested PCR. D: dwarf whitefish, N: normal whitefish and T: total of whitefish per lake.

Number of % non- Number of % Infected Sampling Lakes Species infected Total infected healthy fish fish date fish fish D 14 20 34 41 59 June 14-16 Cliff N 13 11 24 54 46 2010 T 27 31 58 47 53

D 17 7 24 71 29 July 13-14- East N 10 16 26 38 62 15 2010 T 27 23 50 54 46

D 14 17 31 45 55 June 15-16 Indian N 23 2 25 92 8 2010 T 37 19 56 66 34

D 15 11 26 58 42 July 6-7-8 Témiscouata N 3 10 13 23 77 2010 T 18 21 39 46 54

D 11 14 25 44 56 June 17 Webster N 13 12 25 52 48 2010 T 24 26 50 48 52

D 71 69 140 51 49 Total N 62 51 113 55 45 T 133 120 253 53 47

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Table 2. 2 : PCR primers used for the double nested PCR. The double nested PCR consists in three successive amplification steps conducted with three different primer pairs: 1389R-9F, 907R-23F and 519R-63F respectively.

Primer Sequence Reference

1389R 5′- ACGGGCGGTGTGTACAAG-3′ Marchesi et al. (1998) 907R 5'-CCGTCAATTCCTTTRAGTTT-3' Lane et al. (1985) 519R 5'-GWATTACCGCGGCKGCTG-3' Turner et al. (1999) 9F 5’-GAGTTTGATCCTGGCTCAG-3' Yoon et al. (1998) 5′- 23F TGCAGAYCTGGTYGATYCTGCC- Burggraf et al. (1991) 3′ 5′- 63F Marchesi et al. (1998) CAGGCCTAACACATGCAAGTC-3′

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Table 2. 3 : Statistic values on the number of infected and non-infected whitefish for lake according the results of double nested PCR. Three effects were tested thanks to a GLM followed by an ANOVA to determine if there were statistically significant differences in fish infection rate among lakes, between forms, and their interaction term. A statistically significant difference between forms within lake was also tested with a GLM.

Effect Statistic Tests Chisq Z value P value Lakes 6.6018 - 0.1585 GLM + Forms 0.9552 - 0.3284 ANOVA Lakes*forms 25.7886 - 5.49E-05

Cliff - 0.974 0.3303 Est - -2.249 0.0245 GLM Indian - 3.212 0.0013 Témiscouata - -1.97 0.0489 Webster - 0.566 0.5717

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Table 2. 4 : eighteen pathogenic species of 10 identified putative pathogen genera which were identified using the BLAST algorithm.

Species Reference(s) Evidence

Acinetobacter junii Navarrete et al. (2009) Aeromonas hydrophila Dopazo et al. (1988) Aeromonas salmonicida Langefors et al. (2001) Corynebacterium xerosis Austin et al. (1983) caused outbreak in salmonid Flavobacterium Nematollahi et al. (2003) fish aquaculture psychrophilum Micrococcus luteus Austin & Stobie (1992) Pseudomonas fluorescens Bruno et al. (2013) Pseudomonas putida Altinok et al. (2006)

caused outbreak in Pseudomonas chlororaphis Hatai et al. (1975) salmonids

Staphylococcus epidermidis Gil et al. (2000) caused outbreak in Shewanella algae Hau & Gralnick (2007) aquaculture

Acinetobacter lwoffii Li et al. (2006) Experimental fish infections Aeromonas salmonicida Langefors et al. (2001)

Chryseobacterium shigense Zamora et al. (2012) Flavobacterium hydatis Bernardet et al. (1996); Ekman (2003) isolated from diseased Flavobacterium succinicans Bernardet et al. (1996); Ekman (2003) salmonid Janthinobacterium lividum Austin et al. (1992) Staphylococcus warneri Gil et al. (2000)

Aeromonas sobria Olivier et al. (1981) Classified as enterotoxigenic

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Table 2. 5 : Number of putative pathogens and opportunistic bacteria in whitefish according to 454 sequencing results. D: dwarf whitefish, N: normal whitefish and T: total number of whitefish. The putative pathogen bacteria were determined according to Austin & Austin (2007).

Number of Number of fish fish infected % % Lakes Species infected by by only Total Pathogen Opportunistic pathogen opportunistic infected infected bacteria bacteria D 11 3 14 79 21 Cliff N 10 3 13 77 23 T 21 6 27 78 22

D 10 4 14 71 29 East N 9 1 10 90 10 T 19 5 24 79 21

D 7 5 12 58 42 Indian N 18 4 22 82 18 T 25 9 34 74 26

D 9 5 14 64 36 Témiscouata N 1 2 3 33 67 T 10 7 17 59 41

D 6 4 10 60 40 Webster N 7 4 11 64 36 T 13 8 21 62 38

D 43 21 64 67 33 Total N 45 14 59 76 24 T 95 35 130 73 27

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2.11 Figures

Figure 2. 1 : Map of the study area. The samples come from lakes Témiscouata, East, Cliff, Indian, Webster

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Figure 2. 2 : Plot of bacterial diversity estimated with the Simpson index for all ten populations. D: dwarf whitefish, and N: normal whitefish. The Simpson index is inversely correlated with bacterial diversity. Lower Simpson indices thus mean higher diversity.

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Figure 2. 3 : Relative abundance of phyla members found in kidney whitefish bacterial community for dwarf and normal whitefish in each lake. This taxonomy was constructed with the Ribosomal Database Project (RDP) with a confidence threshold at 95%. D: dwarf whitefish, N: normal whitefish.

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Figure 2. 4 : Principal components analysis per rank (PCA per rank) of Operational Taxonomic Units (OTU) present in whitefish kidney differentiated by lake. Each lake analyzed is represented by symbols: Cliff Lake (red triangle) East Lake (green dot) Indian Lake (blue square) Témiscouata Lake (pink inverted triangle) Webster Lake (blue diamond). The size of the symbol is proportional to OTU abundance. Two different PCA per rank analyses were performed. The first PCA per rank was performed only with genera present in the dwarf form, named dwarf total genera. The second PCA per rank was performed only with genera present in the normal form, named normal total genera. This last PCA per rank was used to generate the four supplementary PCA per rank displaying only the Altererythrobacter, Croceicoccus, Flavobacteria and Kocuria genera. The PCA per rank were produced by PC-ORD. Each PCA per rank had ten axes that were interpretable, but only axes one and two were used and explained a combined variance of 20% for dwarf total genera PCA per rank and 23.47% for normal total genera PCA per rank.

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Figure 2. 5 : Differences between dwarf and normal whitefish kidney in selected pathogenic genera. White squares represent putative pathogenic genera only found in normal whitefish and black those only found in dwarf whitefish in each lake. Grey refers to pathogenic genera found in both forms, and hashing refers the genera not found in either form in that lake.

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Chapitre 3 : Holobionts and ecological speciation: the intestinal microbiota of Lake Whitefish species pairs.

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3.1 Résumé

Il est maintenant bien établi que le symbionte a un impact considérable sur son hôte. Cependant l'étude du rôle possible de l'holobiont dans le processus de spéciation de l'hôte en est encore à ses balbutiements. Dans cette étude, nous avons comparé le microbiote intestinal de cinq paires sympatriques de grand corégone (Coregonus clupeaformis) qui sont caractérisés par un gradient de divergence génétique à un stade précoce de la spéciation écologique. Nous avons séquencé le gène ribosomal 16s au niveau des régions V3-V4 du microbiote intestinal présent dans un total de 108 corégones sauvages respectivement nommés nains et normaux, ainsi que la communauté bactérienne de l’eau de cinq lacs pour (i) tester s’il existe des différences entre le microbiote intestinal du corégone et la communauté bactérienne de l’eau et (ii) tester l’hypothèse de parallélisme au niveau du microbiote intestinal entre les espèces naine et normale. La communauté bactérienne de l’eau est distincte du microbiote intestinal du grand corégone suggérant que le microbiote intestinal n’est pas le reflet de l’environnement mais le reflet des propriétés intrinsèques de l’interaction hôte-bactéries. Nos résultats révèlent une forte influence de l’hôte (nain ou normal) sur le microbiote intestinal avec une conservation prononcée du noyau bactérien intestinal (moyenne de 44% de genres partagés). Même si aucune preuve du parallélisme n'a été observée, des différences non parallèles ont été observées entre les nains et les normaux dans trois des lacs. En conséquence, une composition taxonomique similaire a été observée pour deux autres paires de corégone. Cette absence de parallélisme entre le microbiote intestinal des nains et des normaux met en évidence la complexité de l’holobionte et suggère que la direction de sélection peut être différente entre l’hôte et son microbiote.

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3.2 Abstract

It is well established that symbionts have considerable impact on their host, yet the investigation of the possible role of the holobiont in the host’s speciation process is still in its infancy. In this study, we compared the intestinal microbiota among five sympatric pairs of dwarf (limnetic) and normal (benthic) Lake Whitefish Coregonus clupeaformis representing a continuum in the early stage of ecological speciation. We sequenced the 16s rRNA gene V3-V4 regions of the intestinal microbiota present in a total of 108 wild sympatric dwarf and normal whitefish as well as the water bacterial community from five lakes to (i) test for differences between the whitefish intestinal microbiota and the water bacterial community and (ii) test for parallelism in the intestinal microbiota of dwarf and normal whitefish. The water bacterial community was distinct from the intestinal microbiota, indicating that intestinal microbiota did not reflect the environment, but rather the intrinsic properties of the host microbiota. Our results revealed a strong influence of the host (dwarf or normal) on the intestinal microbiota with pronounced conservation of the core intestinal microbiota (mean ~ 44% of shared genera). However, no clear evidence for parallelism was observed, whereby non-parallel differences between dwarf and normal whitefish were observed in three of the lakes while similar taxonomic composition was observed for the two other species pairs. This absence of parallelism across dwarf vs. normal whitefish microbiota highlighted the complexity of the holobiont and suggests that the direction of selection could be different between the host and its microbiota.

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3.3 Introduction

Earth is dominated by approximately 1030 microbial cells (Whitman & Coleman 1998), which is two or three-fold more than the number of plant and animal cells combined (Huse et al. 2008). Therefore, it is important to consider that animal and plant evolution has and continues to occur in the presence of microbiota, which have either parasitic, mutualistic or commensal interaction with a host (Rawls et al. 2006). The ubiquity and importance of the microbiota is supported by its influence on host development, immunity, metabolism, behavior, and numerous other processes including speciation (Lee & Mazmanian 2010; Archie & Theis 2011; McCutcheon & Dohlen 2011; Ezenwa et al. 2012; Gilbert et al. 2012; McFall et al. 2012; Nicholson et al. 2012; Brucker & Bordenstein 2013; Wang et al. 2015). The host (animal or plant) and their microbiota are referred to as a “holobiont” (Rosenberg & ZilberRosenberg 2011; Gilbert et al. 2012; Brucker & Bordenstein 2012b; Bordenstein & Theis 2015), which represents a unique biological entity evolving through selection, drift, mutation and migration (Rosenberg & Zilber 2013). The concept of holobiont offers a new angle for the study of adaptive divergence ultimately leading to speciation. For instance, the role of microbiota on pre-zygotic isolation has recently been documented (Shropshire & Bordenstein 2016). Moreover, the host’s visual, auditory, and chemosensory signals implicated in mate choice could be influenced by its microbiota (Carlson et al. 1976; Matsuura 2001; De Cock & Matthysen 2005; Cator et al. 2009; Damodaram et al. 2016). Also, host populations sharing similar environment or diet have been shown to share similar microbiomes, known as a “socially shared microbiome” (Shropshire & Bordenstein 2016). The socially shared microbiome could recognize specific signals of the host population and thus influence its evolution in ways that are microbe- specific and microbe-assisted, which may lead to postzygotic isolation (Shropshire & Bordenstein 2016). The intestinal microbiota could be particularly prone to playing a key role in the process of population divergence and speciation given its broad array of functional impacts on its host (Pfennig et al. 2010) . The involvement of the intestinal microbiota in organismal functions comprises nutrition (David et al. 2014; Kohl et al. 2015), toxicity resistance (Kohl et al. 2014), energy metabolism (Kamra 2005; Taylor et al. 2007; McCutcheon & Dohlen 2011), morphology (Broderick et al. 2014) and behavior (Archie & Theis 2011; Cryan & Dinan 2012; Ezenwa et al. 2012; Lewis & Lizé 2015). On the other hand, the intestinal microbiota

54 can also promote host phenotypic plasticity, which may contribute to adaptation. For example, new intestinal microbiota genes can be acquired from the environment through acquisition of new bacteria (Hehemann et al. 2010; Dantas et al. 2013). The intestinal microbiota can also adapt in response to variation in the host’s physiological and environmental conditions (Alberdi et al. 2016b). Moreover, the short generation time of the intestinal microbiota and the horizontal transfer of genes can favor rapid microbiota evolution (Gogarten & Townsend 2005; Bromham 2009). While there are now a plethora of studies that have documented the positive influence of holobionts on hosts, including humans, relatively few studies have focused on fish microbiota in the wild even though they represent around 50% of the total vertebrate diversity (Nelson 2006; Sullam et al. 2012a). To date, about 20 studies have investigated fish intestinal microbiota in the wild (e.g. (Shiina et al. 2006; Roeselers et al. 2011; Ye et al. 2014; Llewellyn et al. 2015; Miyake et al. 2015)). Of these, very few concerned speciation and to our knowledge, none analyzed specifically the adherent bacteria present in the fish epithelial mucosa (Hata et al. 2014; Sevellec et al. 2014; Baldo et al. 2015; Smith et al. 2015; Sullam et al. 2015; Baldo et al. 2017). Adherent bacteria are of particular interest because they may interact more closely with their host than bacteria present in the alimentary bolus (Baldo et al. 2015). Lake Whitefish (Coregonus clupeaformis) comprises sympatric species pairs referred to as dwarf and normal whitefish that are found in five lakes of the St. John River drainage in the province of Québec, Canada and in Maine, United States. A relatively recent period of post-glacial adaptive radiation occurred approximately 12,000 years before present (YBP), leading to parallel phenotypic and ecological divergence in different lakes of the dwarf whitefish derived from the ancestral normal whitefish (Bernatchez 2004). Dwarf and normal whitefish are partially reproductively isolated in each lake (Gagnaire et al. 2013b), differ in genetically based morphological, physiological, behavioral, ecological and life history traits (Bernatchez et al. 2010; Jeukens et al. 2010; Pavey et al. 2013; Dalziel et al. 2015; Laporte et al. 2016) and occupy the limnetic and benthic habitat, respectively. Dwarf and normal whitefish also differ in trophic niche, where dwarf whitefish (and limnetic whitefish in general) feed almost exclusively on zooplankton (Bodaly 1979; Bernatchez et al. 1999) and normal whitefish are more generalist and feed on more diverse prey items including zoobenthos, molluscs, and fish prey (Bernatchez et al. 1999; Bernatchez 2004).

In this study, we investigate the within- and between-lake variation in the intestinal microbiota among these five sympatric pairs of dwarf and normal whitefish, representing a

55 continuum in the early stage of ecological speciation. We sequenced the 16S rRNA gene of adherent bacteria present in the intestinal tissue and in order to test for differences between intestinal microbiota of dwarf and normal whitefish pairs. We chose adherent microbiota present on intestinal tissues because this microbiota may be more involved in host-microbiota interactions. In parallel, we also sequenced the 16S rRNA gene of water bacterial communities from the five lakes in order to test the association between the water bacterial community and the whitefish intestinal microbiota. Ultimately, our main goal was to test for the occurrence of parallelism in the microbiota of sympatric dwarf and normal whitefish across different environments, where evidence for parallelism would provide strong indirect evidence for the role of natural selection in shaping host microbiota.

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3.4 Methods

Sample collection

Lake Whitefish were sampled with gill nets from Cliff Lake, Indian Pond and Webster Lake in Maine, USA, in June 2013, and from East and Témiscouata lakes in Québec, Canada during summer 2013, from May to July (Table 1). Fish were dissected in the field in sterile conditions. The ventral belly surface was rinsed with 70% ethanol and non-disposable tools were rinsed with ethanol and flamed over a blowtorch between samples. The intestine was cut at the hindgut level (posterior part of the intestine) and the digesta were aseptically removed. Then, the intestine was cut at the foregut level (anterior part of the intestine), removed from the peritoneal cavity and clamped on both extremities in order to isolate the adherent bacteria in the laboratory. The clamped intestines were individually stored in sterile cryotubes and flash-frozen in liquid nitrogen. Water samples (2 L) were collected in each lake at four depths (at the top of the water column, at 5 m, 10 m, and 15 m corresponding to 1 m above the lake bottom) with a Niskin© (General Oceanics). Water samples were filtered first with a 3.0 μm mesh, followed by a 0.22 μm nitrocellulose membrane using a peristaltic pump (Cole-Parmer: Masterflex L/S Modular Drive). The 0.22 μm membranes were placed into cryotubes and flash frozen with liquid nitrogen. All samples were transported to the laboratory and kept at -80°C until further processing.

DNA extraction, amplification and sequencing of intestinal bacteria

Adherent bacterial DNA from the intestinal segment was isolated by rinsing the interior of the intestines three times with 3 ml of sterile 0.9% saline (Ringø et al. 2006) and extracted using a modification of the QIAmp© Fast DNA stool mini kit (QIAGEN). To maximize DNA extraction of gram-positive bacteria, temperature and time were increased during the incubation steps, and the volume of supernatant and all of the products used with the supernatant (Proteinase K, Buffer AL and ethanol 100%) were doubled. Thus, 1200 µl were transferred into the column (in two subsequent steps) and bacterial DNA was eluted from the column with 100 μl of ultrapure water (DEPC-treated Water Ambion®). Bacterial DNA from the water samples was also extracted using a modified QIAmp© Fast DNA stool mini kit (QIAGEN) protocol. The 0.22 μm membranes were transferred with 1 ml InhibitEX

57 buffer to bead beating tubes (Mobio), incubated overnight at 50°C, and then vortexed for 1h. The same modified protocol used for the adherent bacterial DNA was used. In order to test the sterility during the extraction manipulation, seven blank extractions were done with buffer only. Moreover, the same extraction kit was used between fish microbiota and water bacterial community in order to avoid bias during extraction. Extracted DNA was quantified with a Nanodrop (Thermo Scientific) and stored at −20°C until use.

The partial DNA fragments of bacterial 16S rRNA genes were amplified by touchdown PCR for adherent bacterial DNA. Touchdown PCR is the optimal method to avoid eukaryotic contamination, potentially due to cross amplification with host DNA (Don et al. 1991; Korbie & Mattick 2008). A region ~250 bp in the 16S rRNA gene, covering the V3– V4 region, was selected to construct the community library using specific primers with Illumina barcoded adapters Bakt_341F-long and Bakt_805R-long (Klindworth et al. 2012) in a dual indexed PCR approach. The touchdown PCR of adherent bacterial DNA used 25 µl of NEBNext Q5 Hot Start Hifi PCR Master Mix, 1 µl (0.2 µM) of each specific primer, 15 µl of sterile nuclease-free water, and 8 µl of DNA. The PCR program consisted of an initial denaturation step at 98°C for 30s, followed by 20 cycles at 98°C for 10 s, 67–62 °C (touchdown PCR annealing step) for 30 s, and 72°C for 45 s. After the initial touchdown PCR cycles, an additional 15 cycles were run at 98°C for 10 s (denaturation), 62°C for 30 s (annealing) and 72°C for 45 s (extension), and a final extension of 72°C for 5 min.

The PCR amplification for water bacterial DNA comprised a 50 µl PCR amplification mix containing 25 µl of NEBNext Q5 Hot Start Hifi PCR Master Mix, 1 µl (0.2 µM) of each specific primer, 21 µl of sterile nuclease-free water, and 2 µl of water bacterial DNA. The PCR program consisted of an initial denaturation step at 98°C for 30s, followed by 30 cycles, with 1 cycle at 98°C for 10 s (denaturation), 56°C for 30 s (annealing) and 72°C for 45s (extension), and a final extension of 72°C for 5 min. Negative and positive controls were included for all PCRs. All the PCR results, including the negative controls, were purified using the AMPure bead calibration method. The purified samples were quantified using a fluorometric kit (QuantIT PicoGreen; Invitrogen), pooled in equimolar amounts, and sequenced paired-end using Illumina MiSeq Bakt_341F-long and Bakt_805R-long at the Plateforme d’Analyses Génomiques (IBIS, Université Laval, Québec, Canada). To prevent focusing, template building, and phasing problems due to the sequencing of low diversity libraries such as 16S rRNA amplicons, 50% PhiX genome was spiked in the pooled library.

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Amplicon analysis

Raw forward and reverse reads were quality trimmed, assembled into contigs for each sample, and classified using Mothur v.1.36.0 (Schloss et al. 2009a; Kozich et al. 2013). Contigs were quality trimmed with the following criteria: (i) when aligning paired ends, a maximum of two mismatches were allowed; (ii) ambiguous bases were excluded; (iii) homopolymers of more than eight bp were removed; (iv) sequences with lengths less than 400 bp and greater than 450 bp were removed; (v) sequences from chloroplasts, mitochondria and nonbacterial were removed; and (vi) chimeric sequences were removed using the UCHIME algorithm (Edgar et al. 2011). Moreover, the database SILVA was used for the alignment and the database RDP (v9) was used to classify the sequences with a 0.03 cut-off level. The Good's coverage index, Shannon index, inverse Simpson diversity and Unifrac weighted tests were estimated with Mothur. The Good's coverage index estimates the quality of the sequencing depth whereas alpha diversity (diversity within the samples) was estimated with the inverse Simpson index and the Shannon index. Beta diversity (diversity between samples) was calculated using a weighted Unifrac test (Lozupone & Knight 2005), which was performed using thetayc distance.

Statistical analyses

A matrix containing the number of bacterial sequences was constructed for each genus in each fish sample from the two Mothur taxonomy files (stability.an.shared and stability.an.cons.taxonomy). Therefore, OTUs (Operational Taxonomic Unit) with the same taxonomy were merged. This genus-merged matrix was used to perform the taxonomic composition analysis at the phylum and genus level, the principal coordinates analyses (PCoA), the permutational analysis of variance (PERMANOVA), the Metastats analysis, and the network analysis. Moreover, to determine if there was a significant difference at the alpha diversity level between species within and among lakes, we used a generalized linear model (GLM) with a Gaussian family followed by an ANOVA. In order to build the PCoAs, a Jaccard distance matrix was made from the genus-merged matrix after Hellinger transformation using the vegan package (Oksanen et al. 2006) in R (R Core Team 2016). The PERMANOVA analysis (number of permutations = 10,000) was also performed with the vegan package in R to test the species effects, the lake effects, and their interaction.

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The METASTATS software with standard parameters was also used (p ≤ 0.05 and number of permutations = 1000) to detect differential abundance of bacteria at the genus level between dwarf and normal whitefish (White et al. 2009). Network analyses, based on a Spearman’s correlation matrix, were performed to document the interaction between dwarf and normal whitefish microbiota. The Spearman’s correlation matrix was calculated using R on the Hellinger transformed matrix. Moreover, P-values and Bonferroni corrections were calculated for Spearman’s correlations for each sample. Then, the different networks were visualized using Cytoscape version 3.2.1, a software for visualizing networks (Shannon et al. 2003). Finally, PICRUSt (Phylogenetic Investigation of Communities by Reconstruction of Unobserved States, version 1.0.0) was used to predict putative functions for the whitefish microbiota based on the 16S rRNA sequence data set (Langille et al. 2013). To this end, our OTU data was assigned against the Greengenes database (released August 2013) and we used the Mothur command “make.biom” to obtain a data file compatible with PICRUSt.

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3.5 Results

Sequencing quality

A total of 1,603,342 sequences were obtained after trimming for the entire data set composed of 108 whitefish intestinal microbiota (44 dwarf and 64 normal whitefish) and 36 bacterial water samples (Table S1). Among these sequences, 24,308 different operational taxonomic units (OTUs) were identified with a 97% identity threshold, representing 544 genera. The average Good’s coverage estimation, used to estimate the quality of the sequencing depth, was 99% ± 2% of coverage index.

Very few sequences were obtained from the five PCR negative controls (Table S2). Although there were no bands after PCR amplification, 95 sequences in total were obtained from the five PCR negative controls, representing 0.006% of the total dataset. Sixty-one different species were identified with a range of 1-11 reads per bacterial species. Some of these sequences represented bacteria that are typically associated with fish, seawater, or freshwater environments, but also with fish pathogens (Table S2). None were associated to humans or to the laboratory environment. This suggests that contamination was very low, but not completely absent, as typically observed in similar studies (Baldwin et al. 2015; Gu et al. 2015; Wilkins et al. 2015).

Whitefish intestinal microbiota vs. water bacterial communities

Highly different communities at the genus level were observed with weighted UniFrac and PERMANOVA tests between the water bacterial community and whitefish microbiota within each lake and among the lakes (Table 2). Moreover, water bacterial communities as well as dwarf and normal whitefish microbiota had distinct dominant phyla composition (Fig.1-A). The water bacterial community was composed of Proteobacteria (38.7%), Actinobacteria (33.5%), Bacteroidetes (10.6%), Verrucomicrobia (4.4%), OD1 (2.0) and Firmicutes (1.9%). The five most abundant phyla of dwarf intestinal microbiota were Proteobacteria (40.6%), Firmicutes (17.8%), Actinobacteria (6.1%), OD1 (5.5%) and Bacteroidetes (3.4%), whereas the five most abundant phyla of normal microbiota were Proteobacteria (39.0%), Firmicutes (20.1%), Fusobacteria (4.1%), Actinobacteria (4.1%)

61 and Tenericutes (2.5%). Thus, the phylum Proteobacteria dominated all sample types, but other phyla differed between the fish microbiota and water bacterial communities. Moreover, even if Proteobacteria, Firmicutes and Actinobacteria were present in similar abundances between dwarf and normal microbiota, the phyla OD1 and Bacteroidetes were more present in dwarf whitefish and the phyla Fusobacteria and Firmicutes were more present in the normal whitefish.

Dwarf vs. normal whitefish microbiota: parallelism or not parallelism?

There was a significant difference between the dwarf and the normal whitefish microbiota at the genus level across all lake populations combined (Table 2). When treating each lake separately, the PERMANOVA tests revealed significant differences between dwarf and normal whitefish in Cliff, East and Témiscouata lakes whereas no significant differences were found in Indian and Webster lakes (Table 2). Moreover, there is a gradient of genetic population distance between dwarf and normal whitefish from different lakes (Table 1) (Bernatchez et al. 2010; Renaut et al. 2011). Namely, sympatric whitefish from Cliff Lake are the most genetically differentiated (FST=0.28) whereas those from Témiscouata Lake are the least differentiated (FST=0.01). Thus, if there was some association between the extent of genetic divergence and the difference in microbiota, dwarf and normal whitefish from Cliff should have the most differentiated intestinal microbiota and Témiscouata should have the least differentiated ones. This was not the case as species specific microbiota was observed in the latter lake, whereas no significant difference was found in both Indian and Webster lakes where genetic differentiation between dwarf and normal whitefish is more pronounced (FST Indian=0.06 and FST Webster=0.11)

The weighted UniFrac, which took into account the bacterial abundance rather than simply the presence or absence of taxa in the samples, were significant in all lake populations (Table 2). Therefore, the taxonomic composition of the microbiota was not always different between whitefish species depending on the lake but the abundance of microbiota always differed between whitefish species within each lake. No global differentiation was observed between whitefish species or lakes when all samples were included in the PCoA (Fig 2-A). However, the analysis revealed partially overlapping clusters corresponding to dwarf and normal whitefish in Cliff, East, Témiscouata, and Webster lakes (Fig 2-B:F). Dwarf and normal whitefish clusters were close to each other but nevertheless distinct. For example,

62 in Cliff Lake, the dwarf cluster was more separated by axis one, whereas the normal cluster was more differentiated by axis two. In East, Témiscouata and Webster Lakes, the opposite pattern was observed: dwarf and normal clusters were better separated by axis two and axis one, respectively. However, only three dwarf whitefish from Webster Lake could be collected resulting in low power of discrimination in that lake. Finally, dwarf and normal whitefish clusters almost completely overlapped in Indian pond.

Based on the network analysis, the five networks corresponding to each lake gave results that were similar to those obtained with the PCoA analysis, further supporting the observation that the dwarf-normal difference in microbiota varies according to the lake (Fig. 3). Although the network analysis containing all the fish samples revealed no clear pattern, lake-specific networks tended to cluster dwarf and normal samples separately in Cliff and Témiscouata Lakes. Even if the pattern is less clear for East Lake, the dwarf whitefish microbiota from this lake tended to cluster together (but not the normal whitefish microbiota). Also, no clear difference was observed in Indian Pond and as in previous analyses, interpreting patterns observed in Webster Lake was hampered by the small sample size of dwarfs, although microbiota of normal whitefish clustered together.

Functional annotation of whitefish microbiota

Putative microbiota functions were predicted using PICRUSt by assignment of the predicted metagenome. The gene category, which represented a set of genes influencing the same functional profile, varied widely according to the whitefish species or lake. Only one gene category, cell communication, was stable and had very low gene abundance. Some gene categories, including membrane transport, transcription, or energy metabolism, had high gene abundance in all dwarf and normal whitefish. However, the predicted microbiota functions revealed no significant functional differences between dwarf and normal whitefish microbiota within a given lake except for Témiscouata Lake (Table 2). There, dwarf whitefish had higher gene abundance than normal whitefish in all gene categories. In Cliff and Indian lakes, the genes representing the predicted microbiota functions were high for the majority of gene categories. On the contrary, in East Lake we observed low gene abundance in all the gene categories. In Webster Lake, six normal whitefish had high gene abundance, whereas the other six individuals had low gene abundance. Also, in Webster Lake, the three dwarf whitefish had low gene abundance.

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Globally, there was no significant functional difference between dwarf and normal whitefish microbiota across all lakes combined. Instead, gene abundance differed among lakes and the interaction term between lake populations and species was significant, indicating a strong lake population effect but no significant functional differences between species (Table 2).

Complementary analysis on whitefish microbiota: diversity, core intestinal microbiota and Metastats

There was no difference between the dwarf and the normal whitefish in terms of bacterial diversity. Thus the inverse Simpson index was not significant either between species within lakes or between lakes (Table 3). Similar results were also obtained using the Shannon index.

The core intestinal microbiota was defined as the microbial component shared by 80% of the samples. Three genera were shared among all the lake whitefish populations: OD1, Methylobacterium and Clostridium. Additionally, all dwarf whitefish populations shared Flavobacterium, TM7 and Pseudomonas, whereas all normal whitefish populations shared Aeromonas. Within a given lake, more genera were shared between dwarf and normal whitefish, their number varying between four and 11 depending on the lake (Fig. 1-B). Moreover, dwarf whitefish individuals shared more genera than normal whitefish did in Cliff, Indian, Témiscouata and Webster Lakes. In East Lake, the same number of shared genera was observed between both species. Although the number of shared genera among populations of each species or among lakes was modest, they represented on average 49.5% of all dwarf whitefish shared sequences and 39% of all normal whitefish shared sequences (Table 1).

The Metastats analysis did not allow identifying any genera that were only present in one species. However, several genera were found in only one species within a given lake. These genera were blasted to identify the bacterial taxa being represented (Table S3). Most of them were bacteria from the environment found in soil, plant, or freshwater. Interestingly, several bacteria previously found in seawater and human clinical specimens (but not found here in the negative control) were also found in intestinal whitefish microbiota, such as Arsenicicoccus piscis, Lactococcus lactis or Plesiomonas shigelloides (Aldova et al. 1999; Itoi et al. 2008; Hamada et al. 2009). We also found bacteria known to

64 be pathogenic in fish and humans, such as Flavobacterium spartansii and Clostridium baratii as well as Bifidobacterium thermophilum, which is a probiotic bacterium (McCroskey et al. 1991; Domig et al. 2007; Loch & Faisal 2014).

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3.6 Discussion

We investigated the intestinal microbiota of sympatric dwarf and normal whitefish pairs in order to: (i) test for differences in whitefish intestinal microbiota and water bacterial community from the same lake; (ii) test for differences in intestinal microbiota between dwarf and normal whitefish from the same lake; and (iii) test for the occurrence of parallelism in those patterns. Below we discuss the main results obtained for each of these objectives, as well as their relevance in the context of ecological speciation.

Quality control

In order to improve the laboratory protocol and avoid bacterial contamination, meticulous care was taken by working in sterile conditions, performing blank extractions, using positive and negative PCR controls, and sequencing negative PCR controls. These controls revealed very few sequences in negative PCR controls (representing 0.006% of our dataset; Table S2). These low contamination sequences were typically associated with fish or fish environments and were represented, in a large majority, by one unique sequence. This contamination is therefore too low to influence the fish mucosa dataset and as such is unlikely to explain the lack of consistent parallelism observed in our data set. Of the few previous studies that sequenced PCR negative controls, many found contamination without bands following PCR amplification (Baldo et al. 2015). Therefore, the PCR negative controls seemed not to be an adequate quality step and in order to know and reduce the risk of contamination, sequencing of PCR negative controls in the case of 16s rRNA gene pyrosequencing should be applied systematically, as we have done here.

Whitefish microbiota vs. water bacterial community

The whitefish intestinal microbiota was not reflective of the whitefish environment. Therefore, host physiology, immunity and genetic background may play a role in determining the internal intestinal microbiota (Baldo et al. 2015; Sullam et al. 2015; Alberdi et al. 2016b; Macke et al. 2017). The taxonomy between the fish intestinal microbiota and

66 the bacterial water community was highly distinct among lakes. The water and the fish bacterial community shared 23%, 21%, 29%, 27% and 23% of genera for Cliff, East, Indian, Témiscouata and Webster lake populations, respectively. These values are substantially greater than the 5% shared OTUs reported recently between Trinidadian guppies (Poecilia reticulata) and their environment (Sullam et al. 2015). However, this could be due to the fact that these authors compared the fish microbiota with the bacterial community from both water and sediments. There are two major ways to colonize the fish intestine: via maternal microbial transmission (Llewellyn et al. 2014; Wilkins et al. 2015) or via the environment, which is the primary mechanism of microbiota acquisition for fish (Nayak 2010). However, Smith et al. (2015) showed that the intestinal microbiota of threespine stickleback (Gasterosteus aculeatus) tends to be more similar to food- associated bacteria rather than water-associated bacteria. Although we did not sample the whitefish prey, our data demonstrate that around 25% of bacterial genera were shared between water and whitefish microbiota. Moreover, some of the main genera from whitefish microbiota were found at very low frequency in the environment. Therefore, even if the shared bacteria could come from the whitefish diet, it is quite likely that an important proportion of the intestinal microbiota could be attributed to the colonization of bacteria from the water.

Whitefish intestinal vs. kidney microbiota and host effect

In this study only the bacteria that formed a stable and specific association with the whitefish were analyzed. In fact, only the intestinal adherent microbiota of whitefish was selected, allowing for an indirect investigation of the host effect. In freshwater fishes, the dominant Proteobacteria is reported to be the most abundant phylum (Sullam et al. 2012a). Also, the occurrence Firmicutes, Bacteroidetes, Actinobacteria, Acidobacteria, Chlamydiae, Fusobacteria, Planctomycetes, Spirochaetes, TM7, Verrucomicrobia and Tenericutes has been reported in many freshwater fishes (Roeselers et al. 2011; Sullam et al. 2012a; Li et al. 2013; Ye et al. 2014; Eichmiller et al. 2016). However, the phyla OD1, which was present at a relatively low frequency in both dwarf and normal whitefish, has usually been reported in freshwater samples but not freshwater fish, further supporting the acquisition of part of whitefish microbiota from the environment (Smith et al. 2012; Qiu et al. 2016).

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Globally, we observed a total of 421 different genera in the intestinal mucosa from 108 fish. This is comparable to the level of diversity reported in other recent studies that analyzed 30 intestinal contents of five wild African cichlid fish species (tribe Perissodini) and 72 feces of the wild Amazonian fish tambaqui (Colossoma macropomum) that reported 121 and 525 genera, respectively (Baldo et al. 2015; Sylvain et al. 2016). Therefore, the number of genera adherent to whitefish intestinal mucosa was similar to the number of genera found in feces or intestinal content in other wild freshwater fish. In a previous study of the kidney bacterial community in Lake Whitefish (Sevellec et al. 2014), the observed genera diversity (579 genera from 133 apparently healthy fish) was higher than that observed here for the intestinal mucosa. However, many more OTUs (24,308 OTUs) were found in the intestinal mucosa than in the kidneys (2,168 OTUs). In both studies, mature fish were sampled in the same environment and they were sampled at the same period of time but in different years. The difference in genera diversity may result from both host genetic and immunity effects. Although the intestinal tract of animals contains the largest number of bacteria, which explains the difference between the intestinal mucosa and the kidney microbiomes at the OTU level, bacterial selection by the host may stabilize the number of intestinal genera (Brucker & Bordenstein 2012b; Rosenberg & Zilber 2013; Shropshire & Bordenstein 2016; Macke et al. 2017). Such host- driven selection was highlighted in a zebrafish (Danio rerio) intestinal microbiota study, where the number of OTUs decreased during zebrafish development until reaching an equilibrium at fish maturity (Stephens et al. 2016a).

Interestingly, our data revealed no difference in diversity between intestinal microbiota of dwarf and normal whitefish found in sympatry within a given lake. This is in contrast with our previous study on kidney tissues where normal whitefish harbored a higher diversity than dwarf whitefish in all five lakes studied (Sevellec et al. 2014). We had proposed that this difference may come from the distinct trophic niche of the two whitefish species. Dwarf whitefish feed almost exclusively on zooplankton (Bodaly 1979; Bernatchez et al. 1999), whereas normal whitefish are generalists and feed on zoobenthos, molluscs and fish prey (Bernatchez et al. 1999; Bernatchez 2004). Moreover, Bolnick et al. (2014) observed a less diverse intestinal microbiota when the food was more diversified in both threespine stickleback and Eurasian perch (Gasterosteus aculeatus and Perca fluviatilis), suggesting that the host had an effect on bacterial diversity. Thus, the strikingly different diets between dwarf and normal whitefish had no apparent effect on the diversity of the adherent intestinal microbiota. As mentioned above, host genetic effects could select

68 commensal bacteria in its intestine, which could perhaps explain the similar diversity level observed between dwarf and normal whitefish. Indeed, while the intestinal microbiota lives in a tight symbiotic relationship with the host, this is less so the case for kidney where the kidney microbiota has more of a pathogenic relationship with the host (Rosenberg & Zilber 2013; Sevellec et al. 2014). Therefore, the comparison between symbiotic and pathogenic relationship could highlight the important host effect on the stabilization of the intestinal microbiota but not in the kidney.

Sequencing the microbial world has revealed an overwhelming intestinal microbiota impact on the host and has allowed documenting the core intestinal microbial communities in mammalian and teleost fish (Rawls et al. 2006; Tap et al. 2009; Turnbaugh et al. 2009; Roeselers et al. 2011; Wong et al. 2013; Llewellyn et al. 2015; Miyake et al. 2015; Sullam et al. 2015). The core intestinal microbiota corresponds to the OTUs or the genera shared among close host relatives, and could be horizontally transmitted and/or selected as a common set of bacteria (Rawls et al. 2006; Baldo et al. 2015). For example, Roeselers et al. (2011) documented the occurrence of core intestinal microbiota between the domesticated and wild Zebrafish (Danio rerio). Here, our core microbiota data represented between 22% and 65% (mean ~ 44%) of genera shared between both species in each lake (Table 1). This percent of shared sequences is higher than that reported by Baldo et al. (2015), which found that the intestinal microbiota of cichlid species shared between 13 and 15% of sequences, but was equivalent to Sullam et al. (2015), which reported around 50% of shared sequences in the intestinal microbiota of Trinidadian guppy ecotypes. Therefore, the conservation of the core microbiota was strong within each whitefish species for each lake, further supporting the hypothesis of a strong host selective effect on its microbiota.

No clear evidence for parallelism in intestinal microbiota between dwarf and normal whitefish

Parallelism is the evolution of similar traits in independent populations (Schluter & Nagel 1995). In the case of Lake Whitefish, the test for patterns of parallelism at many different levels may help identify the main factors that are at play in driving the process of ecological speciation in this system of repeated sympatric pairs. Here, given the many differences in their ecology and life history traits, we expected to observe some parallelism

69 in differential intestinal microbiota between dwarf and normal whitefish species pairs. Indeed, parallelism between dwarf and normal whitefish has previously been documented for morphological, physiological, behavioral, and ecological traits (Lu & Bernatchez 1998; StCyr et al. 2008; Jeukens et al. 2009; Landry & Bernatchez 2010; Dion-Cote et al. 2014; Dalziel et al. 2015; Laporte et al. 2015; Dalziel et al. 2016; Laporte et al. 2016). Parallelism was also documented at the gene expression level, whereby dwarf whitefish consistently show significant over-expression of genes implicated with survival functions whereas normal whitefish show over-expression of genes associated with growth functions (StCyr et al. 2008; Bernatchez et al. 2010). Therefore, the apparent lack of parallelism in intestinal microbiota is somewhat surprising, especially given the known difference in trophic niches occupied by dwarf and normal whitefish. Indeed, fish diet is known to alter microbiota composition (Nayak 2010; David et al. 2016; Haygood & Jha 2016; Zarkasi et al. 2016; Koo et al. 2017). Moreover, microbiotas have been reported to change in parallel with their host phylogeny (Bordenstein & Theis 2015; Shropshire & Bordenstein 2016). This phenomena coined “phylosymbiosis” has been reported in organisms as phylogenetically diverse as hydra, fish and primates (Ochman et al. 2010; Franzenburg et al. 2013; Miyake et al. 2015). Here, we performed seven different types of analyses to test whether there were differences in the intestinal microbiota of the five whitefish species pairs that could have highlighted the occurrence of parallelism. However, while a clear difference between dwarf and normal whitefish microbiota composition was observed in three lakes, these differences were not parallel among lakes. Moreover, there was no difference between dwarf and normal whitefish from the other two lakes. Although the bacterial abundance (weighted UniFrac) differed between species in all five lakes, again those differences were not parallel across lakes.

All in all, we found no clear evidence of parallelism in the intestinal microbiota across the five dwarf and normal whitefish species pairs. Instead, our results suggested than the main source of variation in whitefish microbiota was the lake of origin. As mentioned above, an important proportion of the intestinal microbiota could be attributed to the colonization by bacteria from the water. However, each lake studied had a distinct water bacterial community (PERMANOVA, water bacterial community all the lakes = 0.0025). Although the whitefish host could modulate the intestinal microbiota, the lake bacterial variation could positively or negatively influence the intestinal microbiota of whitefish species. Cliff, Webster and Indian lakes harbor the most genetically divergent species pairs, whereas East and Témiscouata species pairs are the least differentiated (Renaut & Bernatchez

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2011; Gagnaire et al. 2013a). These two groups of lakes are characterized by important environmental differences (Landry et al. 2007). More specifically, lakes with the most divergent populations are characterized by the greatest oxygen depletion and lower zooplankton densities, suggesting harsher environmental conditions favoring more pronounced competition for resources between the two species (Landry et al. 2007). On the contrary, lakes with the less divergent populations were characterized by more favorable environmental conditions (Landry et al. 2007). Among the three lakes with the most genetically divergent species pairs, dwarf and normal whitefish differed in their intestinal microbiota only in Cliff Lake. East and Témiscouata species pairs (the two least differentiated populations) were also characterized by distinct intestinal microbiota. These observations suggest that while the lake of origin explains the composition of whitefish intestinal microbiota better than the species, there is no clear association between lake abiotic and biotic characteristics and the fish microbiota, suggesting that other factors that still need to be elucidated are at play.

Whitefish microbiotas and their possible role in ecological speciation

Most of adherent bacteria living on the intestinal mucosa are not randomly acquired from the environment (Bolnick et al. 2014), but are rather retained by different factors in the host (Brucker & Bordenstein 2012b). These symbiotic bacteria may play an essential role in the ecology and evolution of their hosts. Indeed, certain symbionts may affect evolutionary trajectories by conferring fitness advantages (Tsuchida et al. 2004). For example, the microbiota of the desert woodrats (Neotoma lepida) enables its host to feed on creosote toxic compounds, suggesting a fitness advantage by limiting resource competition (Kohl et al. 2014). Symbionts can also influence speciation in several ways. First, there are two main processes that could influence pre-zygotic isolation: (i) microbe-specific, which involves bacterium-derived products such as metabolites and (ii) microbe-assisted, which involves bacterial modulation of the host-derived odorous products (Brucker & Bordenstein 2012b; Shropshire & Bordenstein 2016). In a recent study, Damodaram et al. (2016) showed that the attraction of male to female fruit flies is abolished when female flies are fed with antibiotics, implying a role of the fly’s microbiota in mate choice. Second, symbionts can influence post-zygotic reproductive isolation with, for example, cytoplasmic incompatibilities leading to hybrid inviability (Brucker & Bordenstein 2012b). These authors

71 made crosses between two species of Nasonia wasp (Nasonia vitripennis and Nasonia giraulti) to create F2 hybrid larvae raised with their symbionts (conventional rearing) and without the symbionts (germ free). The F2 lethality was clearly more important with symbionts (conventional rearing) than without symbionts (germ free). Moreover, this lethality was not seen in pure larvae of both species reared with symbionts. Symbionts can also increase the host phenotype plasticity (Bolnick et al. 2014). For example, a facultative endo-symbiotic bacterium called pea aphid U-type symbiont (PAUS) allowed the pea aphid (Acyrthosiphon pisum) to acquire a new phenotype: the digestive capability of alfalfa (Medicago sativum) (Bolnick et al. 2014). This new phenotype supports a niche expansion that leads to geographic isolation between aphid populations and therefore indirectly confers a mechanism for pre-zygotic isolation. Given the absence of clear association between whitefish intestinal microbiota and whitefish species, it thus seems unlikely that any of these processes are at play in the speciation of the whitefish species pairs. This absence of parallelism across dwarf vs. normal whitefish microbiota highlights the complexity of the holobiont and suggests that the direction of selection could be different between the host and its microbiota.

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3.7 Conclusion

In summary, we analyzed the intestinal microbiota in the context of population divergence and speciation in the natural environments. We selected the whitefish mucosa; only the bacteria which formed a stable and specific association with the whitefish were analyzed. To our knowledge, this is the very first study which sequenced the intestinal adherent microbiota in fish host. Our main goal was to test for the occurrence of parallelism in the microbiota of dwarf and normal whitefish that evolved in parallel across different environments. However, no clear evidence for parallelism was observed at the bacterial level. We found distinct microbiota between the dwarf and the normal species in three of the five lake populations suggesting more selective pressure from the environment. This absence of parallelism across dwarf vs. normal whitefish microbiota highlighted the complexity of the holobiont and suggests that the direction of selection could be different between the host and its microbiota. Furthermore, the comparison of the adherent microbiota with the water bacterial environment and whitefish kidney bacterial community (Sevellec et al. 2014) provided evidence for selection of the adherent bacteria composition made by the host as well as bacterial diversity stabilization. Finally, an experiment without environmental variation would be useful to limit the effect of this in order to determine whether differences between whitefish species remain as large as observed here.

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3.8 Availability of data and material

Sequencing results are available in the Sequence Read Archive (SRA) database at NCBI under BioProject ID PRJNA394764.

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3.9 Acknowledgements

We thank G. Côté and the members of the Bernatchez laboratory for fieldwork, C. Hernandez-Chàvez and Alex Bernatchez for their laboratory advice and support, B. Boyle for his help with the Illumina MiSeq sequencing, B.J.G. Sutherland and A. Xuereb for comments on an earlier version of this manuscript.

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3.10 Tables

Table 3. 1 : Number and location of samples, sampling dates, FST and core microbiota for each species in each lake. The FST estimates are based on SNP (single-nucleotide polymorphism) results published previously (Renaut et al. 2011). The core microbiota is represented by percent of shared sequences for each form in each lake. D: dwarf whitefish, N: normal whitefish, T: total number of whitefish per lake, W: Number of water samples.

Normal Water Number -dwarf Percent of sample of fish Species/Wat pairwis shared s at Samplin Localizatio Lakes intestin er e sequence differen g date n al s t F mucosa ST depths D 12 35.7 - 46°23'59'' N 12 51.6 - June 13- N, Cliff 0.28 T 24 - - 14 2013 69°15'11'' W - - 6 W

D 8 60.4 - 47°11'15'' N 12 39.2 - July 2-4 N, East 0.02 T 20 - - 2013 69°33'41'' W - - 8 W

D 11 64.9 - 46°15'32'' N 15 36.1 - June 10- N, Indian 0.06 T 26 - - 11 2013 69°17'29'' W - - 8 W

D 10 44.5 - 47°40'04'' Témiscouat N 14 46.6 - May 28- N, 0.01 a T 24 - - 30 2013 68°49'03'' W - - 6 W

D 3 41.9 - 46°09'23'' N 11 22.2 - June 12- N, Webster 0.11 T 14 - - 13 2013 69°04'52'' W W - - 8

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Table 3. 2 : Summary of weighted Unifrac and the PERMANOVA test statistics. Three comparisons are shown: (i) comparison within and between lakes for the whitefish microbiota (dwarf and normal) and the water bacterial communities, (ii) comparison within and between lakes for the dwarf whitefish microbiota and the normal whitefish microbiota, (iii) comparison of the PICRUSt results between dwarf and normal microbiota for all lakes combined and for each lake. For the three comparisons, we tested the lake effect and the interaction between the bacterial communities (water, dwarf and normal whitefish) using PERMANOVA. Unifrac test is based on beta diversity and cannot be done with PICRUSt results. Unifrac PERMANOVA Comparison Lakes/Effects WSig F-Value R2 Pr(>F) water- whitefish <0.0010 33.834 0.185 <0.0010 Both

whitefish lakes - 2.774 0.061 <0.0010 species microbiota- water- water whitefish*lake - 1.278 0.028 0.074 bacterial Cliff <0.0010 3.818 0.124 <0.0010 communities East <0.0010 6.910 0.210 <0.0010 (sequence Indian <0.0010 6.653 0.172 <0.0010 data set) Témiscouata <0.0010 6.218 0.182 <0.0010 Webster <0.0010 7.341 0.269 <0.0010

species <0.0010 2.273 0.019 0.002

dwarf- lakes - 2.812 0.096 <0.0010 normal species*lake - 1.493 0.051 0.0021 whitefish Cliff <0.0010 1.931 0.081 0.006 microbiota East <0.0010 1.821 0.092 0.019 (sequence Indian <0.0010 0.913 0.037 0.530 data set) Témiscouata <0.0010 1.848 0.077 0.025 Webster <0.0010 1.396 0.104 0.145

species - 0.448 0.003 0.697 lake - 6.761 0.200 <0.0010 dwarf- normal species*lake - 2. 273 0.067 0.016 whitefish Cliff - 0.152 0.007 0.958 microbiota East - 1.642 0.083 0.114 (PICRUSt Indian - 0.413 0.017 0.793 results) Témiscouata - 5.052 0.186 0.019 Webster - 2.562 0.176 0.108

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Table 3. 3 : Summary of GLM and ANOVA test statistics on the alpha diversity within- and between-lakes of whitefish species microbiota. These tests were performed with the inverse Simpson index and similar results were observed with the Shannon index. Three effects are tested using a GLM followed by an ANOVA: the lake effect, the species effect and their interaction.

Effect F value t value P value GLM + ANOVA lake 0.833 - 0.507 species 0.035 - 0.852 lake*species 0.537 - 0.708

GLM Cliff - 0.186 0.853 East - -0.508 0.612 Indian - -0.697 0.487 Témiscouata - 0.478 0.633 Webster - -1.240 0.218

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3.11 Figures

Figure 3. 1 : Taxonomic composition at the phylum and genus levels. A: Relative abundance of representative phyla found in water bacterial communities and intestinal microbiota for dwarf and normal whitefish in each lake. This taxonomy is constructed with the database Silva and MOTHUR with a confidence threshold of 97%. B: Relative abundance of genera observed in the core intestinal microbiota of dwarf and normal whitefish for each lake. In this study, the genera selected to constitute the bacterial core is present in 80% of the samples. D: dwarf whitefish, N: normal whitefish.

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Figure 3. 2 : Principal coordinate analyses (PCoAs) of all the bacterial communities. These PCoAs are based on Jaccard index after a Hellinger transformation. A: comparison between water bacterial community and whitefish intestinal microbiota. Although the water bacterial communities come from five different lakes at different depths, all water samples are represented by a blue point. Each lake analyzed is represented by a different color: Cliff Lake (red), East Lake (blue), Indian Lake (orange), Témiscouata Lake (green) and Webster Lake (purple), and each whitefish species is represented by symbols: dwarf (circle) and normal (cross). B-F: comparison between dwarf and normal microbiota for each lake. Cliff Lake, East Lake, Indian Pond, Témiscouata Lake and Webster Lakes are represented by B, C, D, E and F respectively. Each whitefish species is represented by different symbols: dwarf (circle) and normal (cross); ellipses of 95% confidence are illustrated and were done with dataEllips using R car package. The red and green ellipses represent the dwarf and normal species, respectively.

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Figure 3. 3 : Network analysis of intestinal microbiota for dwarf and normal whitefish within- and between-lakes. The nodes represent a dwarf or a normal whitefish microbiota. The link (edge) between two samples highlights a Spearman correlation index and a significant p-value corrected with Bonferroni correction. A: Network analysis of whitefish microbiota among lakes. B-F: Network analysis of dwarf and normal microbiota for each lake. Cliff Lake, East Lake, Indian Pond, Témiscouata Lake and Webster Lakes are represented by the letter B, C, D, E and F respectively.

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Figure 3. 4 : Heatmap of relative abundances of the most important metabolic pathways inferred by PICRUSt in the whitefish intestinal microbiota for each sample in all lakes.Gene category represented a set of genes with the same functional profile. Warm colors represent high abundances and clear colors represent low abundances C: Cliff, E: East, I: Indian, T: Témiscouata, W: Webster, N: normal whitefish and D: dwarf whitefish.

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3.12 Supplementary Tables

Table S 3. 1 : Steps used to reduce sequencing and PCR errors. We followed the step recommended by MOTHUR in the MiSeq SOP protocol.

Number of Number of Number of all the microbiota water Main Step of filtration Sequences whitefish bacterial (Whitefish sequences sequences + Water) After make contigs 3681300 1801488 5482788 Remove sequences with ambiguous 1470160 400368 1870528 bases and lengths more than 450 bp Aligning paired ends (maximum two mismatches) and remove sequences with 1255617 393786 1649403 homopolymers of more than eight bp and with lengths less than 400 bp Remove chimeric sequences 1251463 388501 1639964 Remove sequences from chloroplasts, 1229767 373569 1603342 mitochondria and nonbacterial Number of final sequences 1229767 373569 1603342 Number of OTUs 10324 14717 24308 Number of genera 421 359 544 Coverage 99.20% 98.22% 98.98%

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Table S 3. 2 : Bacterial taxa found in the PCR negative control. The bacterial taxa were identified using BLAST and sources of isolation are provided. None of those species were associated with human or laboratory environment.

BLAST Number of BLAST E- Species Percent Isolated Sequences value identity Acholeplasma multilocale 1 88,63 5,00E-160 Mammal Aciditerrimonas ferrireducens 1 91,42 3,00E-172 Soil Human clinical Aerococcus urinae 1 83,72 4,00E-121 specimen Aeromonas sobria 3 98,92 0 Fish Anaerovorax odorimutans 1 93,24 0 Water sediments Human clinical Atopobium parvulum 1 88,22 9,00E-148 specimen Bacillus aquimaris 2 97,64 0 Seawater Bacillus toyonensis 1 96,15 0 Probiotic Bradyrhizobium lupini 1 99,54 0 Soil Brevinema andersonii 1 90,6 1,00E-175 Mammal Bythopirellula goksoyri 1 88,6 9,00E-158 Seawater Candidatus Planktoluna 1 99,55 0 Freshwater Cellulosilyticum lentocellum 1 92,38 5,00E-180 Mammal Chthoniobacter flavus 2 89,21 3,00E-163 Soil Clostridium algifaecis 1 78,24 6,00E-70 Marine plant Clostridium baratii 4 98,01 0 Pathogen Clostridium gasigenes 11 98,59 0 Food Clostridium huakuii 1 94,34 0 Soil Coxiella cheraxi 1 90,99 7,00E-179 Fish Delftia acidovorans 1 98,28 0 Environment Desulfitispora alkaliphila 2 87,99 7,00E-154 Water sediments Desulfobacca acetoxidans 1 91,22 0 Soil Desulfomonile limimaris 1 84,57 3,00E-127 Marine sediments Desulfomonile tiedjei 1 93,79 0 Soil Erysipelothrix rhusiopathiae 1 88,2 1,00E-156 Fish pathogen Eubacterium tarantellae 2 95,44 0 Fish Eubacterium tortuosum 2 85,06 9,00E-128 Chicken Gaiella occulta 1 86,26 6,00E-140 Freshwater Gloeobacter kilaueensis 1 90,54 1,00E-165 Lava Cave Haloactinobacterium album 7 94,97 0 Salt lake

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Hespellia porcina 1 96,83 0 Mammal Intestinimonas butyriciproducens 1 92 1,00E-176 Mammal Iodobacter fluviatilis 1 98,28 0 Water sediments Lactobacillus amylovorus 1 98,92 0 Probiotic Lactobacillus crispatus 1 99,14 0 Chicken Lactobacillus johnsonii 1 99,35 0 Probiotic Legionella worsleiensis 2 95,29 0 Freshwater Lonsdalea quercina 1 93,36 0 Plant Luteolibacter luojiensis 1 97,84 0 Soil Marinobacter litoralis 1 90,36 7,00E-174 Seawater Human clinical Massilia consociata 1 98,28 0 specimen Methylopila capsulata 1 96,81 0 Soil Natranaerovirga hydrolytica 1 89,31 1,00E-156 Freshwater Natranaerovirga pectinivora 1 82,96 3,00E-108 Freshwater Neorickettsia sennetsu 1 77,09 1,00E-62 Fish Paludibacter propionicigenes 1 93,28 0 Plant Pelomonas aquatica 1 98,49 0 Freshwater Pirellula staleyi 1 88,25 2,00E-155 Freshwater Planctomyces maris 1 87,82 3,00E-152 Seawater Psychrosinus fermentans 1 97,63 0 Freshwater Rhodoplanes piscinae 1 95,92 0 Fish Roseimicrobium gellanilyticum 1 89,81 9,00E-168 Soil Rothia sp. 1 99,33 0 Environment Serratia liquefaciens 3 96,36 0 Fish pathogen Simkania negevensis 1 90,19 3,00E-172 Fish pathogen Stenotrophomonas chelatiphaga 1 83,92 4,00E-121 Soil Stenotrophomonas sp. 6 97,56 0 Soil Thermogutta hypogea 1 86,75 9,00E-143 Soil Verrucomicrobium spinosum 1 88,25 7,00E-154 Freshwater Zavarzinella formosa 2 91,56 0 Soil

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Table S 3. 3 :Bacterial species specific to a single whitefish species within lake obtained with Metastats and BLAST. No specific bacterial species were found only on dwarf whitefish or only on normal whitefish. However, all these bacterial species could be implicated of the whitefish speciation process. C: Cliff, E: East, I: Indian, T: Témiscouata, W: Webster, N: normal whitefish and D: dwarf whitefish.

Lakes and Bacterial Species Whitefish Isolated Species Aerococcus viridans EN Mammals Human clinical Anaerococcus prevotii CD specimens Aquabacterium parvum CD Water Arsenicicoccus bolidensis EN Soil Arsenicicoccus piscis EN Fish Bacillus aquimaris CN Seawater Bacteriovorax stolpii EN Feces Animals feces Bifidobacterium thermophilum TD probiotics Brachymonas denitrificans CD Soil Cetobacterium somerae CN-WN Seawater and feces Cloacibacterium normanense ED Wastewater Clostridium baratii CN Agent of botulism Clostridium disporicum CN Feces Clostridium gasigenes CN Mammals Clostridium sardiniense CN Soil and feces Clostridium subterminale CN Soil and feces Deefgea rivuli CN -EN Freshwater Delftia acidovorans CD Environment Dermacoccus profundi CD Seawater Flavobacterium spartansii CN Fish pathogen Flavobacterium succinicans WD Freshwater and soil Flavobacterium terrigena WD Soil Giesbergeria anulus CD Wastewater Hydrogenophilus thermoluteolus CN Freshwater Lactobacillus graminis ED Plant Lactococcus lactis CD Fish Human clinical Leptotrichia wadei ED specimens Microbacterium luteolum CD-EN Soil Micrococcus aloeverae CD Plant

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Micrococcus cohnii CD Clinical environment Micrococcus luteus CD Environment Human clinical Mycoplasma faucium ED specimens Myxococcus fulvus EN Soil Nakamurella flavida CD Plant Neisseria mucosa CD Plant Neorickettsia risticii EN Water animals Nocardioides ginsengagri CD Plant Novosphingobium kunmingense CN Soil Paracoccus marinus CD Seawater Human clinical Peptoniphilus gorbachii CD specimens Freshwater, freshwater Plesiomonas shigelloides CD fish Rickettsia prowazekii EN Feces Rickettsia typhi EN Insect Serratia ficaria EN Plant Serratia fonticola EN Freshwater Serratia liquefaciens EN Freshwater and feces Serratia plymuthica EN Soil and plant Serratia proteamaculans EN Plant Streptomyces manipurensis EN Soil Human clinical Turicibacter sanguinis CD specimens Human clinical Veillonella tobetsuensis CN specimens

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Chapitre 4 : Intestinal microbiota of whitefish (Coregonus sp.) species pairs and their hybrids in natural and controlled environment.

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4.1 Résumé

Il est devenu évident que les animaux sauvages n’ont jamais existé sans leur microbiote ni en tant qu’entité biologique autonome. En conséquence, l’analyse de la relation entre le microbiote et son hôte est essentielle pour comprendre l’évolution des animaux et leur adaptation à l’environnement. Le grand corégone (Coregonus clupeaformis) est un modèle très documenté pour étudier la spéciation écologique puisque l’espèce naine (spécialiste de la niche limnétique) a évolué de façon indépendante et répétée de la forme ancestrale normale (spécialiste de la niche benthique). Dans cette étude, nous avons comparé le microbiote intestinal transitoire de cinq paires sympatriques de corégones sauvages ainsi que de corégones captifs (nain, normal et hybride F1 réciproque) qui ont été élevés dans des conditions contrôlées identiques. Nous avons séquencé le gène ribosomal 16s des régions V3-V4 du microbiote intestinal présent de 185 corégones pour (i) tester s’il existe du parallélisme au niveau du microbiote intestinal transitoire des cinq paires sympatriques de corégone sauvage, (ii) tester s’il existe un microbiote intestinal différent entre les nains, normaux et hybrides réciproques élevés dans des conditions contrôlées et identiques, puis (iii) comparer le microbiote intestinal entre les corégones sauvages et captifs. Pour les corégones sauvages, un effet significatif de l’hôte sur la composition taxonomique a été observé lorsque tous les lacs sont analysés ensemble. Cependant, une analyse lac par lac montre un effet hôte présent uniquement dans trois paires sympatriques sur cinq. Pour les corégones captifs, une influence de l’hôte (nain, normal et hybride) a également été détectée sur la composition taxonomique du microbiote ainsi que des dizaines de genres bactériens, spécifiques aux nains, normaux ou hybrides, ont été mis en évidence. Nous avons également observé que les hybrides ne sont pas des intermédiaires des espèces naines et normales mais montrent un patron en dehors de celui des pures. Etonnamment, un noyau bactérien, composé de six genres bactériens, est présent au sein des corégones sauvages et captifs. Cela suggère une transmission horizontale du microbiote. Même si le régime alimentaire apparait comme un facteur majoritaire de l’évolution, nos résultats suggèrent une interaction plus complexe entre l’hôte, le microbiote et l’environnement menant à trois voies évolutives différentes entre le corégone et son microbiote intestinal.

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4.2 Abstract

It is becoming increasingly clear that wild animals have never existed as autonomous entities and without their microbiota. As a consequence, investigating relationships between microbiota and their host is essential towards a full understanding of how animal evolve and adapt to their environment. The Lake Whitefish Coregonus clupeaformis is a well-documented model for the study of ecological speciation, where the dwarf species (limnetic niche specialist) evolved independently and repeatedly from the normal species (benthic niche specialist). In this study, we compared the transient intestinal microbiota among five wild sympatric species pairs of whitefish as well as captive dwarf, normal and hybrids whitefish reared in identical controlled conditions. We sequenced the 16s rRNA gene V3-V4 regions of the intestinal microbiota present in a total of 185 whitefish to (i) test for parallelism in the transient intestinal microbiota among sympatric pairs of whitefish, (ii) test for transient intestinal microbiota differences among dwarf, normal and both hybrids reared under identical conditions and (iii) compare intestinal microbiota between wild and captive whitefish. A significant effect of host species on microbiota taxonomic composition was observed in the wild when all lakes where analyzed together, with species effect observed in three of the five species pairs (Cliff, East and Indian lakes). In captive whitefish, an influence of host (normal, dwarf and hybrids) was also detected on microbiota taxonomic composition and tens of genera specific to dwarf, normal or hybrids were highlighted. Hybrid microbiota was not intermediate; instead its composition fell outside of that observed in the parental forms and this was observed in both reciprocal hybrid crosses. Interestingly, six bacterial genera formed a bacterial core which was present in all whitefish, suggesting a horizontal microbiota transmission. Although diet appeared to be a major driving force for microbiota evolution, our results suggested a more complex interaction among the host, the microbiota and the environment leading to three distinct evolutionary paths of the intestinal microbiota.

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4.3 Introduction

Woese and Wheelis (1998) referred the Earth as a microbial planet, where macro- organisms are recent additions. Similar biochemical reactions between eukaryotes and prokaryotes in addition to the endosymbiotic theory suggest that eukaryotes evolved from prokaryotes (Sagan 1967; Stein 2004). In addition, eukaryote-prokaryote cooperation is a necessity to assemble more complex structure as multicellular organisms (Nowak 2006). Hence, animals and plants have never been autonomous entities as they have always co- evolved in closed association with microbes (Gilbert et al. 2012). Recent studies highlighted the huge impact of microbiota on their host such as the expression of hundreds of genes by comparing of germ-free and conventional organisms in several species (Hooper et al. 2001; Rawls et al. 2004). Furthermore, it is now clear that part of the microbiota is transmitted across generations in many animals and plants through a variety of methods (Rosenberg & Zilber 2016). In fishes in particular, the mother allocates antimicrobial compounds to the eggs before spawning (Hanif et al. 2004; Wilkins et al. 2015). This maternal selection of bacteria influence the first bacteria to be in contact with the sterile larvae during the hatching (Llewellyn et al. 2014). Clearly then, a holistic understanding of macroorganisms biodiversity requires the investigation of their association with microbiota and their co-evolution.

The hologenome concept stipulates that the genome of the host and the microbiome (i.e. sum of the genetic information of the microbiota) act in consortium as a unique biological entity, namely the holobiont (Rosenberg & Zilber 2013). Besides playing a role in their host adaptation to their environments (Alberdi et al. 2016), the microbiota may also been involved in reproductive isolation, either in the form of a pre-zygotic barrier by influencing the host’s mate choice by chemosensory signals (Brucker & Bordenstein 2012; Damodaram et al. 2016; Shropshire & Bordenstein 2016) or post-zygotic barrier by producing genome and microbiome incompatibilities in hybrids, in accordance to the Bateson, Dobzhansky and Muller model of genetic incompatibilities (BDM; (Dobzhansky 1937; Muller 1942; Brucker & Bordenstein 2012). Because bacterial community of the gastrointestinal tract is implicated in many critical functions essential for development and immune responses, such as fermentation, synthesis and degradation functions, the adaptive potential of the host could be substantially influenced by the intestinal microbiota (Rosenberg & Zilber 2013; Chevalier et al. 2015; Alberdi et al. 2016; Macke et al. 2017).

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Fishes as a group comprises the greatest taxonomic diversity of vertebrates and a major food resource for human populations (Nelson 2006; Béné et al. 2015; Bernatchez et al. 2017), yet little is known about the relationship with their microbiota and it evolution. A better understanding of fish intestinal microbiota is thus necessary towards filling the knowledge gap relative to the already well characterized mammals and insect microbiota (Clements et al. 2014). This could potentially lead to a better management of fisheries and aquaculture, given that fish microbiota could play a significant role in host health and growth performance (Ringø et al. 2010).

The Lake Whitefish (Coregonus clupeaformis) represents a continuum in the early stage of speciation where species pairs of dwarf (Acadian lineage) and normal (Atlantic lineage) species evolved independently in several lakes in northeastern North America (Lu & Bernatchez 1998; Bernatchez et al. 2010). The normal species is specialized to a benthic niche, feeds on diverse prey as zoobenthos and molluscs and is characterized by rapid growth, late sexual maturity and a long lifespan (Bodaly 1979; Landry & Bernatchez 2010). On the contrary, the dwarf whitefish is a limnetic fish, which feed almost exclusively on zooplankton and is characterized by slower growth, early sexual maturation and a shorter lifespan compared to the normal species (Bodaly 1979). Gene expression show significant over-expression of genes implicated with survival functions in dwarf whitefish, whereas normal whitefish show over-expression of genes associated with growth functions (St-Cyr et al. 2008; Bernatchez et al. 2010). Moreover, many other physiological, morphological and behavioral traits display parallel differences among these two whitefish species that correspond to their respective trophic specialization (Jeukens et al. 2009; Bernatchez et al. 2010; Pavey et al. 2013; Dalziel et al. 2015; Laporte et al. 2015; 2016). Because of this recent speciation process and this clear trophic segregation, the Lake Whitefish is an excellent model to study the possible role of the microbiota in the process of trophic niche adaptation. Moreover, the fact that dwarf and normal whitefish are only partially reproductively isolated offers the unique possibility of investigating for the faith of microbiota in hybrids relative to parental forms.

The main goal of this study is to document the intestinal microbiota of Lake Whitefish species pairs and their hybrids in natural and controlled environment. First, we sequenced the 16S rRNA gene of transient intestinal microbiota in five wild species pairs of whitefish to estimate the within- and between-lake variation and tested for parallelism among intestinal microbiota. Secondly, we sequenced the 16S rRNA gene of transient intestinal

92 microbiota on dwarf, normal and first generation hybrids reared in common garden in order to test the influence of the whitefish host on the microbiota in the same controlled conditions and under two different diets. Moreover, we tested the maternal effects (i.e. effect of a mother’s phenotype on her offspring’s phenotype) on the offspring intestinal microbiota.

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4.4 Material and methods

Sample collection for wild whitefish

Lake Whitefish were sampled with gill nets from Cliff, Indian and Webster lakes in Maine, USA, in June 2013, and from East and Témiscouata lakes in Québec, Canada from May to July (Table 1). Fish were dissected in the field in sterile conditions; ventral belly surface of fish was rinsed with ethanol, non-disposable tools were rinsed with ethanol and heated over a blow torch between samples. The intestine was cut at the hindgut end level (posterior part of the intestine) and the digesta were aseptically squeezed and harvested out to isolate the alimentary bolus. All samples of alimentary bolus were transported to the laboratory and kept at -80°C until further processing.

Experimental crosses, rearing conditions and sample collection for captive whitefish

In November 2013, 32 fish representing four cross types; dwarf (D♀×D♂), Normal (N♀×N♂), and their reciprocal hybrids (F1 D♀×N♂ and F1 N♀×D♂) were pooled together in three tanks (eight whitefish per forms per tank). Protocols for crosses were described previously (Dalziel et al. 2015; Laporte et al. 2016) and information regarding whitefish eggs fertilization and the parental generation are available in the section of the Supplementary Data. These fish were separated in three tanks sharing the same experimental conditions (water, food, pH and temperature) for 7 months. Juvenile whitefish were fed on two types of food; Artemia and dry food pellet (Flüchter 1982; Zitzow & Millard 1988). Although the fish were pooled without being marked, the four groups were easily genetically differentiated following sampling (see below). In June 2014, fish were euthanatized with MS-222. Sampled fish were dissected immediately in sterile conditions, with the same protocol and sterile tools as for wild whitefish. Samples were kept at 80°C until further processing. This study was approved under Institutional Animal Care and Use Committee protocol 2008–0106 at Laval University.

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Whitefish host: DNA extraction, amplification and genetic identification of captive whitefish lineages

A fin clip was collected from all fish used to study the intestinal microbiota and were preserved in 95% ethanol. DNA was extracted using a salt extraction method (Aljanabi & Martinez 1997) with slight modifications (Valiquette et al. 2014). Mitochondrial (mtDNA) and nuclear DNA were used to identify the whitefish dwarf (DD) and normal (NN), and their hybrids DH and NH (hybrid D♀N♂, and hybrid N♀D♂). First, an analysis of mtDNA restriction fragment length polymorphism (RFLP) was done. Dwarf and normal species have different mitochondrial DNA haplotypes (Jacobsen et al. 2012). The protocol used for the mitochondrial DNA analysis is provided in Dalziel et al. (2015). In brief, after the amplification of the cytochrome b by PCR, the amplified products were digested by the restriction enzyme SnaBI which cuts the amplified cytochrome b of the normal whitefish haplotype but not of the dwarf. Second, 12 microsatellite loci were genotyped on all juvenile whitefish and their known parents to differentiate them at nuclear DNA level (Rico et al. 2013) (details in Supplementary Data). Amplified loci were migrated via electrophoresis using an ABI 3130xl capillary DNA sequencer (Applied Biosystems Inc.) with a molecular size standard (GeneScan-500 LIZ, Applied Biosystems). Genotypes were scored using genemapper 4.0 (Applied Biosystems Inc). A combination of three software, STRUCTURE v2.3.4, GENECLASS2 v2.0 and PAPA v2.0 was used to reassign each studied fish to its group of origin (Pritchard et al. 2000; Duchesne et al. 2002; Piry et al. 2004). STRUCTURE was performed assuming an admixture model without priors with a burn-in period of 50 000 followed and 100,000 Markov Chain Monte Carlo (MCMC) steps. GENECLASS2 was conducted using the simulation test of (Rannala & Mountain 1997) based on 100,000 simulated individuals. Finally, PAPA was performed for the parental allocation procedure with a uniform error model (error sum = 0.02).

Whitefish microbiota: DNA extraction, amplification and sequencing

The alimentary boluses from captive and wild fish were extracted using a modification of the QIAmp© Fast DNA stool mini kit (QIAGEN). To maximize DNA extraction of gram- positive bacteria, temperature and time were increased during the incubation steps and all products used were doubled (Proteinase K, Buffer AL and ethanol 100%). Thus, 1200 µl

95 were transferred into the column (in two subsequent steps) and bacterial DNA was eluted from the column with 100 μl of ultrapure water (DEPC-treated Water Ambion®). DNA extractions were quantified with a Nanodrop (Thermo Scientific) and stored at −20°C until use. Five blank extractions were done.

In order to construct the community library, a region ~250 bp in the 16S rRNA gene, covering the V3–V4 region, was amplified using specific primers with Illumina barcoded adapters Bakt_341F-long and Bakt_805R-long in a dual indexed PCR approach (Klindworth et al. 2012). The PCR amplification comprised 50 µl PCR amplification mix containing 25 µl of NEBNext Q5 Hot Start Hifi PCR Master Mix, 1 µl (0.2 µm) of each specific primers (Bakt_341F-long and Bakt_805R-long), 15 µl of sterile nuclease-free water and 8µl of specify amount of DNA. PCR program consisted of an initial denaturation step at 98°C for 30s, followed by 30 cycles, where 1 cycle consisted of 98°C for 10 s (denaturation), 56°C for 30 s (annealing) and 72°C for 45s (extension), and a final extension of 72°C for 5 min. Negative controls and positives controls were also performed for all the PCR.

All the PCR results, including the negative controls, were purified using the AMPure bead calibration method. The purified samples were quantified using a fluorometric kit (QuantIT PicoGreen; Invitrogen), pooled in equimolar amounts, and sequenced paired-end using Illumina MiSeq at the Plateforme d’Analyses Génomiques (IBIS, Université Laval, Québec, Canada). To prevent focusing, template building, and phasing problems due to the sequencing of low diversity libraries such as 16S amplicons, 50% PhiX genome was spiked in the pooled library.

Whitefish microbiota: Amplicon analysis

Raw forward and reverse reads were quality trimmed, assembled into contigs for each sample, and classified using Mothur v.1.36.0 following the protocol of MiSeq SOP (https://www.mothur.org/wiki/MiSeq_SOP) (Schloss et al. 2009; Kozich et al. 2013). In brief, contigs were quality trimmed with several criteria. First, a maximum of two mismatches were allowed when aligning paired ends and ambiguous bases were excluded. Second, homopolymers of more than eight, sequences with lengths less than 400 bp and greater than 450 bp, sequences from chloroplasts, mitochondria and nonbacterial were removed. Thirdly, chimeric sequences were found and removed using

96 the UCHIME algorithm (Edgar et al. 2011). Moreover, the database SILVA was used for the alignment and the database RDP (v9) was used to classify the sequences with a 0.03 cut-off level. The Good's coverage index which was used to evaluate the quality of the sequencing depth, was estimated in Mothur (Hurlbert 1971).

Whitefish microbiota: Statistical analyses

The analyses for the captive and wild whitefish microbiota were performed with Mothur and Rstudio v3.3.1 (Team 2015). We first constructed a matrix of taxonomic composition (wild and captive included) with the number of Operational taxonomic units (OTUs) after merging them by genus. The bacterial genera were considered as variables and fish as objects according to Mothur taxonomy files (stability.an.shared and stability.an.cons.taxonomy).

We then investigated the microbiota difference between the captive and wild whitefish using a network analysis. A Spearman’s correlation matrix following a Hellinger transformation on the matrix of taxonomic composition was performed to document interactions between all the captive and wild whitefish microbiota. The network was visualized using Cytoscape v3.2.1 where node were illustrated in two different versions: (i) according to their sampling sites (eight groups: five lakes and three tanks) and (ii) according to their genetic group (the two wild species pairs and the four groups: dwarf, normal, hybrid D♀N♂, and hybrid N♀D♂) (Shannon et al. 2003). We also tested for the effect of captivity (wild and captive conditions) on whitefish microbiota taxonomic composition (PERMANOVAs; 10,000 permutations) and alpha diversity (inverse Simpson diversity) with an ANOVA following a fitted gaussian family generalized model (GLM) {Magurran:2004di}. This was performed on: i) all fish, ii) on dwarf whitefish only and, iii) on normal whitefish only. Finally, we identified the bacterial genera present in 80% of all fish which constituted the bacterial core of the whitefish.

The variation within and among wild whitefish populations was then investigated. We tested for an effect of ‘host species’, ‘lake’ and their interaction, with ‘mass’ as a covariate on the taxonomic composition, using a permutational analysis of variance (PERMANOVA; 10,000 permutations). This procedure was run for each of the five lakes independently after removing the explanatory variable ‘lake’ of the analysis. The ‘host species’, ‘lake’ effects and their interaction were also tested for an effect on the inverse Simpson diversity

97 with an analysis of variance (ANOVA) following a fitted gaussian family generalized model (GLM). Allometric effect on inverse Simpson diversity was first tested with a linear regression on mass. As for the taxonomic composition, we ran this procedure for each lake independently. We also tested for differences in the microbiota taxonomic composition between dwarf and normal wild whitefish by means of a discriminant analysis. A principal component analysis (PCA) on the transformed Hellinger matrix was conducted to avoid collinearity and over fitting problem. Only the axes explaining at least 1% of the variation were kept for the discriminant analysis. We validated the discriminant analyses according to the method described in Evin et al. (2013). Finally, principal coordinates analyses (PCoAs) was built on a Bray-Curtis distance matrix after a Hellinger transformation to visualize variation at the genus level between dwarf and normal wild whitefish among and within the lakes (Legendre & Legendre 1998; Oksanen et al. 2006).

We then tested for differences between the four groups of the captive whitefish by investigating the effect of ‘host group’ (Dwarf, Normal, hybrid D♀N♂, and hybrid N♀D♂), ‘diet’ (A: feeding on a mix of dry food and Artemia, B: feeding on Artemia only) and their interaction with ‘mass’ and ‘tank’ as covariates on taxonomic composition (PERMANOVA; 10,000 permutations). The effect of diet was added in the analysis because fish bolus exhibited a clear distinction between two observed feeding habits during the controlled condition experiment. For the alpha diversity, the effect of ‘host group’, ‘diet’ and their interaction on the inverse Simpson diversity were tested with a mixed effects linear random model using the ‘nlme’ package in R, with tank as a random effect and individual fish nested within tank (Pinheiro et al. 2009). As for the analyses on wild whitefish, we first tested for an allometric relationship with mass using a linear regression and used the residuals in all cases showing a significant relationship. Principal coordinates analyses (PCoAs) built on a Bray-Curtis distance matrix after a Hellinger transformation were also used to visualize variation at the genus level as described above. Discriminant analyses were also performed on captive whitefish but results were not considered any further because we observed a negative cross-validation according to the method of Evin et al. (2013). Moreover, in order to test for the presence of bacterial genera that were private to any of the captive whitefish group, we used the Metastats software with standard parameters (p ≤ 0.05 and number of permutations = 1000) to detect differential abundance of bacteria at the genus level between two host populations (White et al. 2009). We performed four Metastats analyses on the captive whitefish between: Dwarf vs. Normal, Dwarf vs. hybrid D♀N♂, Normal vs. hybrid N♀D♂, and hybrid D♀N♂ vs. hybrid N♀D♂.

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4.5 Results

Sequencing quality

A total of 2,498,271 sequences were obtained after trimming for the entire data set composed of 185 whitefish intestinal microbiota (67 dwarf whitefish, 79 normal whitefish and 39 hybrids whitefish) from natural and controlled condition (Table S1). Among these sequences, 189,683 different operational taxonomic units (OTUs) were identified with a 97% identity threshold, representing 710 bacterial genera. Two wild whitefish samples were removed because of low coverage and low number of sequences and 22 captive whitefish samples were not included from various reasons (empty intestine, no PCR amplification or ambiguous allocation to a host group).

The average Good’s coverage estimation for all intestinal microbiota (wild and captive whitefish) was 92.3±7.6% of coverage index. While this global Good’s coverage may seem relatively low, the Good’s coverage from wild whitefish microbiota (n=111) and captive whitefish microbiota with a diet of Artemia only (n=27) were respectively 95.4±2.8% and 98.2±1.4% suggesting the good sequencing quality of our data. Thus, the low Good’s coverage came from captive whitefish microbiota with a diet of mixing Artemia and dry food (n=47), with a 82.8±3.4% coverage index. We consider these data reliable for further analysis for three reasons. First, this second diet group was composed of 341 bacterial genera where the distribution showed an unusual high abundance (i.e. number of read) found in few bacterial genera (Table S2). This unusual abundance distribution is known to decrease the Good’s coverage which is defined as 1-(Number of OTUs that have been sampled once / total number of sequences) (Hurlbert 1971). Second, the Illumina MiSeq sequencing occurred in the same chip, suggested no sequencing problem in the light of the excellent coverages for the other groups. Third, a low Good’s coverage is supposed to reflect a low number of sequences per sample because of the different filtration steps which eliminated reads generated by poor quality sequencing. In our case, the low Good’s coverage observed in the captive group that fed on both food type shows a total number of sequences per sample similar to the other captive group (Table S2).

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Wild whitefish vs. captive whitefish intestinal microbiota

We observed a pronounced intestinal microbiota differentiation between the wild and captive whitefish based on the network analysis among all our samples (Fig. 1). This differentiation between wild and captive whitefish microbiotas was also supported by a significant effect of captivity on taxonomic composition (PERMANOVA, P < 0.001; Table 2) for all fish, dwarf only and normal only and on alpha diversity (ANOVA, P < 0.001; Table S3) for all fish. Furthermore, although the major of phyla (Firmicutes, Proteobacteria, Actinobacteria and Planctomycetes) were similar between wild and captive whitefish, the bacterial abundance was clearly different (Fig. 2). A second level of differentiation based on diet was observed when considering the captive samples only (Fig. 1). More specifically, the first and most abundant group was composed by most of the wild whitefish (only one dwarf and two normal all from East Lake were excluded from this group). There was no clear pattern of differentiation between dwarf and normal whitefish microbiota (Fig. S1). However, the five lake populations tended to cluster together. The second and the third groups were composed by all captive whitefish. Few interactions were observed between these two groups despite the fact that they were composed by all four groups (dwarf, normal and hybrids). Furthermore, no pattern of interaction was observed within the second or third group (e.g. between fish belonging to a specific group) (Fig. S1). Finally, among the 710 bacterial genera found among all captive and wild whitefish, six bacterial genera were shared by all fish: Acinetobacter, Aeromonas, Clostridium, Legionella, Methylobacterium and Propionibacterium. Thus, these genera constitute the core intestinal microbiota which was defined as the microbial component shared by 80% of the samples.

Wild dwarf and normal whitefish microbiota

At the phylum level, dwarf and normal wild whitefish intestinal microbiota was characterized by identical dominant phyla with a similar bacterial abundance (Fig. 2). The five first main phyla for intestinal whitefish species microbiota were Firmicutes, Proteobacteria, Planctomycetes, Verrucomicrobia and Actinobacteria. However, taxonomic variation between dwarf and normal species were observed by minority phylum. For example, Tenericutes and Fusobacteria were more represented in normal whereas

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Bacteroidetes was more represented in dwarf whitefish. We observed a more pronounced influence of the lake whereby dwarf or normal microbiota within a given lake shared more similarities than microbiota from different lake populations within a same species (Fig. 2).

Although no effect of lake or species on alpha diversity was observed (Table S3), a lake effect was highlighted on taxonomic composition (Table 2), as well as a significant effect of host species (Table 2). The discriminant analysis performed on all wild whitefish confirmed this overall difference between dwarf and normal intestinal microbiota albeit reflecting a substantial overlap (Fig. 3). Within each lake, the PERMANOVA tests revealed significant differences between dwarf and normal whitefish in Cliff, East and Indian lakes only, whereas no significant difference was observed in Témiscouata and Webster lakes (Table 2). Together, these observations suggested that lake effect was stronger than the host species effect. This is also supported by the PCoA analyses that revealed no global differentiation between all dwarf and normal whitefish intestinal microbiota. However, a clusterization by lake population was observed as for the network analysis (Fig. 4-A). Lake by lake, PCoAs also revealed partially overlapping clusters corresponding to dwarf and normal whitefish in Cliff, East Indian and Webster lakes (Fig. 4-B:F). In Cliff Lake, the dwarf cluster was mostly aligned on the second axis, whereas the normal cluster was more aligned on the first one. In East Lake, the opposite pattern was observed, as well for Indian Lake, despite that the two clusters mostly overlap. It should also be note that the results of Webster Lake should be take cautiously considering the small sample size of the dwarf whitefish (n=3). Finally, dwarf and normal whitefish clusters completely overlapped in Témiscouata Lake.

Pure and hybrid whitefish microbiota in controlled environment

Although all fish were all within the same environment and the same food (both Artemia and dry fish food), we observed that some whitefish refused to eat the dry fish food. As for the network analysis, the two distinct diet groups were highlighted by a significant effect of diet on both taxonomic composition microbiotas (PERMANOVA, P < 0.001; Table 2) and alpha diversity (ANOVA, P = 0.001; Table S3). The PCoA analysis clearly separated two distinct clusters on axis one corresponding to the two diet groups and independent of the genetic background (either pure forms or hybrids) (Fig. 5). Furthermore, the mixt diet group, composed of mixt of Artemia and dry food, was dominated by Firmicutes and the

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Artemia diet group was dominated by Proteobacteria (Fig. 2). The five first phyla composing the microbiota of the mixt diet group were Firmicutes, Proteobacteria, Actinobacteria, Bacteroidetes and TM7 whereas the five first phyla of the Artemia diet were Proteobacteria, Actinobacteria, Firmicutes, Planctomycetes and TM7. Within the diet mixt group, hybrids bacterial abundance was smaller than dwarf and normal abundance for Firmicutes and larger for Proteobacteria. The opposite phenomena was observed for the Artemia diet group: hybrids bacterial abundance was larger and smaller for Firmicutes and Proteobacteria respectively relative to the bacterial abundance observed for pure normal and dwarf whitefish. The PERMANOVA tests also revealed a significant effect of host group on captive whitefish (Table 2). The PCoA analysis within each of the two diet groups highlighted slight differentiation between hybrids and pure whitefish (Fig. 5). In the mixt diet group, dwarf and normal ellipses were mostly aligned on the second axis whereas the two hybrids ellipses were mostly aligned first axis. The inverse pattern was present in the Artemia diet group with the pure whitefish ellipses and the hybrid whitefish ellipses aligned on the first and second axis respectively.

We observed between eight and 42 bacterial genera specific to a whitefish group in both diet groups in controlled conditions (Fig. 6). The number of specific bacterial genera varied among the four groups. For example, the comparison DD vs. NN revealed 21 dwarf specific bacterial genera and 27 normal specific bacterial genera respectively. The comparison between hybrids D♀N♂ and N♀D♂ revealed 41 and 16 bacterial genera respectively. Moreover, 135 and 62 specific bacterial genera specific were detected respectively in the mixt diet group and the Artemia diet group (see Table S4 for details).

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4.6 Discussion

In this study, we investigated the relative role of symbionts on two whitefish species host and their hybrids. We sequenced the transient intestinal microbiota of whitefish in natural and controlled environment in order to (i) compare intestinal microbiota between wild and captive whitefish, (ii) test for parallelism in the transient intestinal microbiota among sympatric pairs of whitefish, and (iii) test for transient intestinal microbiota differences among dwarf, normal and both hybrids reared under identical conditions.

The intestinal microbiota in captive versus wild population of whitefish

Although an important part of bacteria which colonized the intestinal fish are random bacteria from water and food, the presence of an intestinal microbiota core is more and more observed among several species (Astudillo-García et al. 2017). The combination of all wild whitefish (dwarf and normal) and captive whitefish (dwarf, normal and hybrids) highlighted six genera shared by at least 80% of our samples. Interestingly, our intestinal core microbiota data represented 20% of shared sequences which is higher than the intestinal microbiota core of cichlid species (i.e. between 13 and 15% of shared sequences; Baldo et al. 2015). These shared genera could be horizontally transmitted and/or selected as a common set of bacteria (Rawls et al. 2006; Baldo et al. 2015). Therefore, the conservation of certain genera by many captive whitefish support the microbiota transmission in fish. Finally, we observed much more bacteria with an unknown taxonomy in wild whitefish in comparison to captive whitefish (Figure 1). This difference between controlled and uncontrolled environments remind that a considerable number of bacteria is waiting to be discovered in wild.

No clear pattern of parallel evolution in transient intestinal microbiota between dwarf and normal wild whitefish

Parallelism is the evolution of similar traits in independent populations (Schluter & Nagel 1995) and was observed in several fishes in north temperate regions (Schluter 2000; Østbye et al. 2006; Bernatchez et al. 2010). The parallelism between dwarf and normal

103 whitefish has previously been documented for morphological, physiological, behavioral, and ecological traits (Lu & Bernatchez 1998; StCyr et al. 2008; Jeukens et al. 2009; Landry & Bernatchez 2010; Dion-Cote et al. 2014; Dalziel et al. 2015; Laporte et al. 2015; Dalziel et al. 2016; Laporte et al. 2016). Given the difference of trophic niche and ecologic niche, we expected to observe parallelism in differential transient intestinal microbiota between dwarf and normal whitefish species pairs. Indeed the dwarf whitefish is a limnetic fish feeding on zooplankton whereas the normal whitefish is a benthic fish feeding on zoobenthos and molluscs (Bodaly 1979; Bernatchez et al. 1999), and such different diet should put the whitefish pairs in contact with different bacterial communities leading to a distinct transient intestinal microbiota. Moreover, differentiation of microbiota compositions correlated with diet was previously observed (Nayak 2010; David et al. 2016; Haygood & Jha 2016; Zarkasi et al. 2016; Koo et al. 2017). In fact, the utilization of novel nutrients frequently produced a change in the microbiota composition by increasing or decreasing different bacterial strain according to their metabolic potential (Rosenberg & Zilber 2013). This is also supported by the microbiota composition differentiation of the two diet groups in captivity. However, despite a global effect of species host on microbiotas, our results suggested no clear pattern of parallelism between dwarf and normal whitefish among all lakes. Indeed, a difference between dwarf and normal whitefish microbiota composition was observed in three of the five lakes only (Cliff, East and Indian). Together, this suggested a larger environmental effect than species host.

The water bacterial community of the same studied lakes was investigated previously, and all lakes was characterized by a specific water bacterial community (Sevellec submitted). In addition, distinct biotic and abiotic factors have been recorded among the lakes (Landry et al. 2007; Landry & Bernatchez 2010). More specifically, Cliff, Webster and Indian lakes are characterized by the greatest oxygen depletion and lower zooplankton densities whereas the lakes East and Témiscouata were characterized by more favorable environmental conditions with a more important density of zooplankton and well- oxygenated water (Landry et al. 2007). Therefore, the variation of water bacterial community and the biotic and abiotic factors could underlie the more important lake effect than species host effect observed in the transient intestinal microbiota. Nevertheless, highly distinct bacterial composition between the water bacterial community and the whitefish transient intestinal microbiota was observed among lakes. The water bacterial community was dominated by Proteobacteria (38.7%), Actinobacteria (33.5%) and Bacteroidetes (10.6%) whereas the whitefish transient intestinal microbiota was dominated

104 by Firmicutes (38.2%) and Proteobacteria (29.5%). Therefore, whitefish transient intestinal microbiota was not reflective of the whitefish environment and could highlight a microbiotas selection induced by host physiology, immunity and genetic background (Alberdi et al. 2016; Macke et al. 2017). In fact, a part of stable microbiota could be present in the transient intestinal microbiota. Furthermore, some transient bacteria strain could be selected because they contribute to the digestion (Smith et al. 2015; Rosenberg & Zilber 2013). Overall, these observations suggested that the major factor of the composition of whitefish transient intestinal microbiota was the lake of origin, but that a selection among water bacterial community is produced to favorize host digestion that could be, in some case, related to host species.

Comparison of transient and stable intestinal microbiota in wild whitefish and the host effect

The phylum present in the wild whitefish transient microbiota are Acidobacteria, Actinobacteria, Bacteroidetes, Chlamydiae, Chloroflexi, Firmicutes, Fusobacteria, Planctomycetes, Proteobacteria, Terenicutes, TM7 and Verrucomicrobia, which are common according to previous freshwater fishes study (Roeselers et al. 2011; Sullam et al. 2012; Larsen & Mohammed 2014; Li et al. 2014; Ye et al. 2014; Eichmiller et al. 2016). In comparison, we sequenced in a previous study the adherent intestinal mucosa microbiota on the same whitefish (Sevellec submitted). Interestingly, although the major phylum was similar, the abundance of these different phyla was clearly different. For example, the five first phylum of stable microbiota were Proteobacteria (39.8%), Firmicutes (19%), Actinobacteria (5.1%), OD1 (3.8%) and Bacteroidetes (2.8%) whereas the five first phylum of transient microbiota were Firmicutes (38.2%), Proteobacteria (29.5%), Verrucomicrobia (4.4%), Planctomycetes (4.1%) and Actinobacteria (3.7%). Moreover, the number of genera and the number of OTUs were more important in the transient microbiota (611 genera and 94,883 OTUs) than in the stable microbiota (421 genera and 10,324 OTUs). Most of the stable bacterial strain living on the intestinal mucosa are not randomly acquired from the environment (Bolnick et al. 2014) and are differently retained by the host (Brucker & Bordenstein 2012). As highlighted in Sevellec et al. (submitted), an important host effect is present for both whitefish species, which stabilize the number of genera in the stable intestinal microbiota. Thus, the comparison between whitefish

105 transient and stable microbiota support that the whitefish host have a substantial impact on the selection of its intestinal microbiota. More precisely, Cliff and East lakes present a distinct intestinal microbiota between whitefish pairs with both, the stable and the transient bacteria, whereas Webster lake species pairs shown a similar intestinal microbiota for both stable and the transient bacteria. However, the absence of differentiation in the Webster lake could be caused by the small sample size of the dwarf species (n=3). Interestingly, the stable intestinal microbiota was different between the Témiscouata whitefish pairs but the transient microbiota from the same whitefish was similar, suggesting an important host-species effect leading to two separated stable intestinal microbiotas of the whitefish species even if the bacterial strain present in the intestinal bolus were similar. In contrast, the opposite pattern was observed in Indian lake, suggesting that host species have likely no effect on microbiotas divergence and that the difference in transient microbiotas is likely caused by the respective species pairs trophic niches. Interestingly, it was previously suggested that the direction of selection could be different between the host and the microbiota of a holobiont system (Rosenberg & Zilber 2016). The whitefish species pairs of Indian and Témiscouata appears to be a good example to support such difference of evolution between the host and the microbiota. Together, this illustrates three distinct evolutionary paths among the holobiont’s whitefish species pairs, which evolved independently in the 18,000 years. Finally, our results combine with the previous study of Sevellec et al. (submitted) suggest that stable microbiotas should be considered a more reliable choice to study the effect of host species on microbiotas.

The modest whitefish host lineages effect on the transient intestinal microbiota in controlled condition

An unexpected variation occurred during the rearing experimentation of the whitefish pair species and the hybrids. Although all the captive whitefish were rearing in the same controlled condition for seven months, diet comportment split the whitefish into two groups independent to host group and basin in common garden. We used these two type of food (i.e. Artemia and dry food), because they was necessary for growth and survival of the juvenile whitefish (Flüchter 1982; Zitzow & Millard 1988). However, a total of 47 whitefish chose to feed on the two types of food, whereas 27 whitefish chose to feed on Artemia

106 only. Therefore, we study the effect of host group on all individuals, and individuals of different diet groups separately.

In a controlled environment, the intestinal bacterial variation should only depend on the host effect which integrated the influence of the host physiology, immunity, genetic background and behavior. In this study, we investigate the host lineage effect (i.e. genetic background) specific to two whitefish species and their hybrid on the variation of transient intestinal microbiota. In addition to a strong pattern caused by the host diet behavior, which was not related to host lineage, slight patterns of differentiation were observed between the whitefish species and hybrids with PCoA analysis. Furthermore, the PERMANOVA test highlighted significant difference on the transient microbiota among the whitefish host lineage groups. Moreover, a variation of the bacterial abundance at the phylum level was observable among host lineage groups. Finally, tens of genera specific to one whitefish species or hybrids were highlighted in both diet groups. These results suggested a modest but present host lineage effect on the transient intestinal microbiota. To our knowledge, only one other fish microbiota study produced in a controlled environment suggests a host lineage effect on the intestinal microbiota (Trinidadian guppies ecotypes, Poecilia reticulate; Sullam et al. 2015). However, the fish used growth to maturity in wild, and were keep in tank for 10 weeks only. Finally, it was important to notice that the phylum composition distinct hybrids from dwarf and/or normal whitefish. A study on two house mouse species (M. m. domesticus and M. m. musculus) and their F2 hybrids highlighted a significant differentiation of intestinal microbiota between the two mice species and their F2 hybrid in wild and laboratory conditions (Wang et al. 2015). These results suggest that hybrids are characterized by a specific microbiota and support the hybrid incompatibilities model in the hologenome concept.

Admittedly, the use of transient microbiota and juvenile fish in our study could also explained modest host lineage effect. Indeed, the transient microbiota is an indirect view of the stable intestinal microbiota, which is considered more accurate. Furthermore, the microbiota is changing during the development and is not stabilized until adult stage (Stephens et al. 2016). It thus possible that a stronger host group effect could be observed in other more favorable experimental conditions.

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4.7 Conclusion

Despite no clear evidence for parallelism was observed in the transient intestinal microbiotas of wild whitefish, a distinct transient intestinal microbiota between the dwarf and the normal species was observed among three of the five species pairs. However, the lake of origin shows a more stronger impact on transient intestinal microbiotas, suggesting that environmental variables specific to lake produced a more important selective pressure on bacterial composition than host species. In common garden, a modest but present host group effect on the transient intestinal microbiota was observed, but appear to better distinct hybrids from species instead of the two species. This support the view that hybrid incompatibilities could occurs inside the hologenome concept. Finally, our results on Indian and Témiscouata Lakes, combine to a previous study on the stable intestinal microbiotas support the idea that host genome and microbiome could evolve in different direction inside a same population.

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4.8 Acknowledgement

We thank G. Côté, A. Dalziel, A-M. Dion-Côté, S. Higgins and J-C Therrien for fieldwork and the crossing and rearing of captive whitefish at the LARSA, C. Hernandez-Chàvez for her laboratory advice and support, B. Boyle for his help with the Illumina MiSeq sequencing, A. Kusler Laporte for comments on an earlier version of this manuscript.

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4.9 Tables

Table 4. 1 : Number and locations of samples, sampling dates for each captive and wild whitefish populations or group. DD: dwarf whitefish, NN: normal whitefish, DH: Backcross hybrids with pure dwarf female and F1 hybrid male, NH: Backcross hybrids with normal pure female and F1 hybrid male. In localization, LARSA (Laboratoire de recherche en sciences aquatiques, Université Laval) is a research laboratory of aquatic science.

Sample Sampling Origin Form Localization size date DD 12 June 13- 46°23'59''N, Cliff NN 12 14 2013 69°15'11''W

DD 10 July 2-4 47°11'15''N, East NN 13 2013 69°33'41''W

DD 12 June 10- 46°15'32''N, Indian NN 13 11 2013 69°17'29''W

DD 10 May 28-30 47°40'04''N, Témiscouata NN 14 2013 68°49'03''W

DD 3 June 12- 46°09'23''N, Webster NN 12 13 2013 69°04'52''W

DD 7 November Common NN 5 12th 2013 LARSA Garden 1 DH 7 to June th NH 6 09 2014

DD 5 November Common NN 4 12th 2013 LARSA Garden 2 DH 6 to June th NH 6 10 2014

DD 8 November Common NN 6 12th 2013 LARSA Garden 3 DH 6 to June th NH 8 11 2014

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Table 4. 2 : Summary of PERMANOVA test statistics on microbiota taxonomic composition. All lakes refer to the analysis of effect of host species (dwarf and normal), lake (Cliff, East, Indian, Témiscouata and Webster) and its interaction with mass as a covariate on all wild fish. Second, the effect of host species and mass as a covariate is treated for each lake separately. Third, CAPTIVE refers to the analysis of effect of host group (dwarf, normal, hybrids D♀N♂ and N♀D♂), diet (Artemia only and Artemia with dry food) and it interaction with mass and tanks as covariates on all aptive fish. Fourth, effect of captivity (wild and captive) and mass as covariate on all fish, dwarf only and normal only. F-Value: value of the F statistic, R2: R-Squared Statistic, Pr(>F): p-value.

Source of PERMANOVA Group of fish variation F-Value R2 Pr(>F) WILD species 2.350 0.017 0.006** lakes 6.744 0.197 <0.001*** All lakes species:lakes 1.927 0.056 <0.001*** mass 1.628 0.012 0.067

Cliff species 5.253 0.180 <0.001*** mass 2.914 0.100 <0.001*** East species 1.889 0.085 0.047* mass 1.165 0.053 0.291 Indian species 2.032 0.083 0.041* mass 1.582 0.064 0.105 Témiscouata species 0.741 0.033 0.732 mass 0.920 0.041 0.447 Webster species 0.858 0.057 0.562 mass 2.142 0.143 0.015 CAPTIVE group 1.985 0.043 0.033*

diet 58.955 0.427 <0.001***

species:diet 1.557 0.034 0.108

mass 1.990 0.014 0.084*

tank 1.649 0.024 0.102

BOTH all fish captivity 64.457 0.260 <0.001*** mass 3.481 0.014 0.001** dwarf captivity 28.245 0.289 <0.001*** mass 4.517 0.046 <0.001***

111 normal captivity 16.371 0.180 <0.001*** mass 1.917 0.021 0.035*

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4.10 Figures

Figure 4. 1 : Network analysis of intestinal microbiota of dwarf and normal wild whitefish and intestinal microbiota of dwarf, normal and hybrids captive whitefish. Each node represents either a dwarf, a normal or a hybrid whitefish microbiota. The connecting lines between two samples represent their correlation and is highlighting by a Spearman index.

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Figure 4. 2 : Relative abundance of phyla representatives found in intestinal microbiota for dwarf and normal wild whitefish in each lake as well as in intestinal microbiota for dwarf, normal and hybrids captive whitefish. This taxonomy is constructed with the database Silva and MOTHUR with confidence threshold at 97%. C: Cliff, E: East, I: Indian, T: Témiscouata, W: Webster, N: normal whitefish, D: dwarf whitefish, DH: Backcross hybrids with pure dwarf female and F1 hybrid male; NH: Backcross hybrids with normal pure female and F1 hybrid male. Although all the whitefish was in contact with identical environment and the same food (Artemia and dry fish food), some of them refused to eat the dry fish food. This comportment leads to two groups: the diet group A (Artemia + dry food) and B (Artemia).

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Figure 4. 3 : Discriminant analysis histogram off all wild whitefish species microbiota. This discriminant analysis was performed on the axes of principal component analysis (PCA). T tests were done on the discriminant analysis results. The dwarf and the normal are represented by the black and white, respectively. Dwarf and normal whitefish with the overlapping discriminant scores are shown in grey.

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Figure 4. 4 : Principal coordinate analyses (PCoAs) within- and between-lake of wild whitefish species microbiota. These PCoAs are based on Jaccard index after a Hellinger transformation. Ellipses of 95% confidence are illustrated and were done with dataEllips using R car package. A: comparison among all the lake wild whitefish populations. Each lake analyzed is represented by a different symbol and ellipse color: Cliff Lake (red), East Lake (blue), Indian Lake (orange), Témiscouata Lake (green) and Webster Lake (purple) and each whitefish species is represented by symbols: dwarf (circle) and normal (cross). B-F: comparison between dwarf and normal microbiota within each lake. Cliff Lake, East Lake, Indian Pond, Témiscouata Lake and Webster Lakes are represented by B, C, D, E and F respectively. Each whitefish species is represented by symbols: dwarf (circle) and normal (cross); the red and green ellipse represent the dwarf and normal species, respectively.

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Figure 4. 5 : Principal coordinate analyses (PCoAs) between the microbiota of the four captive whitefish groups. These PCoAs are based on Bray-Curtis index after a Hellinger transformation. Ellipses of 95% confidence are illustrated and were done with dataEllips using R car package. A: comparison between the four whitefish groups intestinal microbiota for all the captive whitefish. B: comparison between the four whitefish groups intestinal microbiota in the mixt diet group. C: comparison between the four whitefish groups intestinal microbiota in the Artemia diet group. Dwarf and normal whitefish are represented by the symbol ○ and + respectively and their ellips are represented by continuous line. The hybrids F1 NH (Backcross hybrids with normal pure female and F1 hybrid male) and DH (Backcross hybrids with pure dwarf female and F1 hybrid male) are represented by the symbol × and □ respectively and their ellipses are represented by dotted line. Dwarf and hybrids DH are represented by the color red whereas normal and hybrids NH are represented by the color green.

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Figure 4. 6 : Metastats results on dwarf, normal and hybrids captive whitefish. Four side- by-side comparisons were performed: dwarf-normal, DH: Backcross hybrids with pure dwarf female and F1 hybrid male, NH: Backcross hybrids with normal pure female and F1 hybrid male and DH-NH. The comparisons are log transformed and each one-whitefish- specific genera are represented by a barplot. Mixt diet group and Artemia diet group are represented by yellow and grey bars, respectively.

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4.11 Supplementary Data

Experimental crosses of captive whitefish

Whitefish eggs used for this study were incubated at the Laboratoire de Recherche en Sciences Aquatiques (LARSA, Université Laval, Québec, Canada). The dwarf species came from Témiscouata Lake (47°40’04”N, 68°49’03”W) which is from the Acadian glacial lineage origin whereas the normal species came from Aylmer Lake (45°54”N, 71°20”W) corresponding to the Atlantic glacial lineage (Lu & Bernatchez 1998). Backcross F1- Hybrids were obtained by crossing a F1 hybrid laboratory strain and wild whitefish parents. More precisely, F1 hybrid (F1 D♀*N♂) were produced in crossing three wild dwarf females and two laboratory strain normal males by artificial fertilization. Same processes was used to produced F1 hybrid (F1 N♀*D♂) with crossing five laboratory strain normal females and twelve wild dwarf males (see figure 1 (Rogers et al. 2007)). The dwarf and normal whitefish crosses were also created by artificial fertilization with sperm and eggs were collected in the field and transported to the LARSA. No treatments, such as antibiotics or malachite green were delivered to the eggs.

Whitefish microsatellite markers PCR

Three different PCRs were performed for the whitefish microsatellite markers analysis. Details about primer sequences and PCR protocols are presented in Ciro et al. (2013). Briefly, the multiplex PCR A was performed with 2 µl (≈20 ng) of whitefish DNA, 5 μL Qiagen® multiplex reaction buffer, forward and reverse primers at different concentrations: 0.3 µm of Cocl32, Cocl lav41, Cocl Lav8 and 0.35 µm of Cocl Lav224; purified water adjusted the final volume at 10 µl. Multiplex PCR program was: 15 min at 94°C, and then 35 cycles of 30 sec at 94°C, 3 min at 58°C, 1 min at 72°C and 30 min at 60°C.

The multiplex PCR B were performed with 2 µl (≈20 ng) of whitefish DNA, 5 μL Qiagen® multiplex reaction buffer and forward and reverse primers at different concentration: 6 µm of Cocl15 et Cisco200 and 0.25 µm of Cocl 33; purified water adjusted the final volume at 10 µl. Multiplex PCR program was: 15 min at 94°C, and then 35 cycles of 30 sec at 94°C, 3 min at 60°C , 1 min at 72°C and 30 min at 60°C. The Simplex PCRs were performed with

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2 µl (≈20 ng) whitefish DNA, 0.2 µl GoTaq® DNA polymerase (PROMEGA), 0.5 µl of each forward and reverse markers (0.5 µm) (Osmo5, Cocl34, Cocl36, Bwf F-1 and Cocl Lav22) 2 µl of 5X Colorless GoTaq®, 0.6 µl of MgCl2 (0.5 mM), 0.8 µl dNTPs (200 µm) and purified water adjusted the final volume at 10 µl. Simplex PCR program was: 2 min at 94°C, and then 35 cycles of 30 sec at 94°C, 3 min at 58°C (Osmo5, Cocl36, Bwf F-1, Cocl Lav22) or 64°C (Cocl34), 1 min at 72°C and 30 min at 60°C

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4.12 Supplementary Tables

Table S 4. 1 : Steps used to reduce sequencing and PCR errors. We followed the step recommended by MOTHUR in the MiSeq SOP protocol.

Number of Number of Number of microbiota microbiota microbiota captive Main Step of filtration wild captive and wild whitefish whitefish whitefish sequences sequences sequences After make contigs 4765128 2498271 8299965 Remove sequences with ambiguous 1774729 885737 2660466 bases and lengths more than 450 bp Aligning paired ends (maximum two mismatches) and remove sequences with 1737037 872921 2609958 homopolymers of more than eight bp and with lengths less than 400 bp Remove chimeric sequences 1729598 868868 2598519 Remove sequences from chloroplasts, 1855778 845370 2498271 mitochondria and nonbacterial Nb of final sequences 1855778 845370 2498271 Nb of Otus 94883 85363 189683 Nb of genus 611 433 710 Good’s Coverage 95,06% 88,22% 91,86%

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Table S 4. 2 : Matrix of bacterial abundance and Good’s coverage per captive whitefish sample. DD: dwarf whitefish, NN: normal whitefish, DH: Backcross hybrids with pure dwarf female and F1 hybrid male, NH: Backcross hybrids with normal pure female and F1 hybrid male. The diet group A is composed of Artemia and dry food and B is composed of Artemia. Note: this table is a part the table S2.

Whitefish Diet Number of Good's Group species Group sequences coverage 04-B2F NH A 9554 83.63 04-B3F NH A 4047 78.95 05-B1F NN A 7950 87.38 05-B2F DD A 4272 77.76 05-B3F NH A 10535 84.72 06-B1F DH A 10626 86.82 06-B2F NH A 9598 85.02 07-B1F DD A 10220 83.05 07-B3F NN A 12571 88.09 08-B3F NH A 4981 84.08 09-B1F DD A 8830 86.87 09-B3F NN A 9552 78.43 10-B1F DD A 8965 82.96 10-B3F DD A 5368 80.61 11-B1F DD A 9494 83.98 11-B3F DD A 4764 76.20 12-B1F NH A 2616 81.04 12-B2F DH A 11443 81.56 12-B3F NH A 8438 84.55 13-B2F DH A 8778 84.43 14-B1F NH A 7496 80.31 14-B3F DD A 8842 80.32 15-B1F DH A 9462 86.81 15-B2F NN A 9109 82.89 15-B3F DH A 7660 79.13 16-B3F NH A 7866 78.85 17-B1F NN A 4621 78.84 17-B3F DD A 7478 80.82 18-B2F DD A 7387 81.18 19-B3F NN A 14935 82.68 20-B1F DD A 9012 80.42

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21-B2F NN A 9285 82.16 21-B3F NN A 10663 83.77 22-B1F NH A 10264 85.83 22-B2F DH A 14523 82.39 23-B3F DH A 5548 81.65 24-B1F DH A 12012 83.70 24-B2F DD A 3249 75.62 24-B3F DD A 7171 80.83 25-B1F NH A 10637 81.66 26-B2F NN A 7388 83.05 27-B2F NH A 11992 91.12 27-B3F DD A 12970 88.66 29-B1F NH A 16608 84.97 30-B2F DD A 8130 83.89 33-B2F DH A 7908 90.59 33-B3F DH A 7948 81.35 02-B1F NH B 7773 96.57 03-B3F NH B 14847 99.51 06-B3F DH B 9042 98.83 07-B2F DD B 3941 97.64 08-B1F DD B 16447 98.61 08-B2F NH B 12825 99.27 09-B2F NH B 24288 96.83 10-B2F DH B 20371 99.36 13-B1F DH B 7346 94.80 16-B1F DH B 13921 98.99 18-B1F DH B 10261 94.29 19-B2F NN B 16329 98.51 20-B2F NH B 13028 98.53 20-B3F NH B 14615 98.47 21-B1F NN B 6503 97.91 22-B3F DD B 9433 99.14 23-B1F DH B 11224 97.46 25-B2F DH B 19484 99.12 25-B3F DD B 15942 99.43 26-B1F NN B 11586 99.07 26-B3F NN B 14829 98.92 28-B3F NN B 17491 99.40 29-B3F DH B 7009 97.73 30-B3F DH B 28573 99.30 31-B1F DD B 17016 96.21 31-B3F NH B 12031 99.13 32-B1F NN B 13956 98.64

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Table S 4. 3 : Summary of ANOVA test statistics on microbiota alpha diversity (inverse Simpson index). All lakes refer to the analysis of effect of host species (dwarf and normal), lake (Cliff, East, Indian, Témiscouata and Webster). Second, the effect of host species is treated for each lake separately. Third, CAPTIVE refers to the analysis of effect of host group (dwarf, normal, hybrids D♀N♂ and N♀D♂), diet (Artemia only and Artemia with dry food) on all captive fish. Fourth, effect of captivity (wild and captive), dwarf only and normal only. F-Value is the value of the F statistic.

Degrees of Group of fish Source of variation F value P value freedom WILD species 1 0.439 0.510 All lakes lakes 4 2.304 0.064 lakes:species 4 1.152 0.337 Cliff species 1 0.109 0.744 Est species 1 0.025 0.876 Indian species 1 2.026 0.169 Témiscouata species 1 1.557 0.225 Webster species 1 0.824 0.380

CAPTIVE group 3 0.599 0.620

diet 1 40.471 0.001***

species:diet 3 1.930 0.134

BOTH all fish captivity 1 8.915 0.003** dwarf captivity 1 2.044 0.157 normal captivity 1 2.040 0.157

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Table S 4. 4 : Four Metastats tables with details of one-species-specific genera. The whitefish species are represented by DD: dwarf whitefish, NN: normal whitefish, DH: Backcross hybrids with pure dwarf female and F1 hybrid male, NH: Backcross hybrids with normal pure female and F1 hybrid male. The diet group A is composed of Artemia and dry food and B is composed of Artemia.

Metastats comparison DD and DH captive whitefish Genera Diet group Groups Aquicella B DD Flavobacterium B DD Fusobacterium B DD Lactobacillus B DD Leuconostoc B DD Sphingobium B DD Streptococcus B DD Weissella B DD Aeromonas A DD Kocuria A DD Nocardioides A DD Rhodobacter A DD Corynebacterium A DH Delftia A DH Labrenzia A DH Legionella A DH Paracoccus A DH Planctomyces A DH Pseudomonas A DH Stenotrophomonas A DH

Metastats comparison NN and NH captive whitefish Genera Diet group Groups Acinetobacter B NH Aerococcus B NH Brachymonas B NH Brevibacterium B NH Catonella B NH Cupriavidus B NH Dermacoccus B NH

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Escherichia_Shigella B NH Kocuria B NH Leptotrichia B NH Mycobacterium B NH Neisseria B NH Oceanobacillus B NH Paenibacillus B NH Pediococcus B NH Peptococcus B NH Rhizobium B NH Roseomonas B NH Sphingobacterium B NH Turneriella B NH Vibrio B NH Acinetobacter A NH Bifidobacterium A NH Brevundimonas A NH Chryseobacterium A NH Collimonas A NH Lactobacillus A NH Ohtaekwangia A NH Olsenella A NH Streptococcus A NH Weissella A NH Acetobacterium B NN Acidovorax B NN Aeriscardovia B NN Aneurinibacillus B NN Aquabacterium B NN Arcobacter B NN Arthrobacter B NN Bacillus B NN Bifidobacterium B NN Brevibacillus B NN Cellvibrio B NN Devosia B NN Duganella B NN Enhydrobacter B NN Ethanoligenens B NN Gemella B NN Haemophilus B NN Hyphomicrobium B NN Legionella B NN

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Massilia B NN Megasphaera B NN Methyloversatilis B NN Novosphingobium B NN Prauserella B NN Pseudomonas B NN Psychrilyobacter B NN Rothia B NN Sphaerobacter B NN Sphingobium B NN Sphingomonas B NN Undibacterium B NN Comamonas A NN Elizabethkingia A NN Flavobacterium A NN Ilumatobacter A NN Labrenzia A NN Leuconostoc A NN Listonella A NN Psychrobacter A NN Rhizobium A NN Rhodopirellula A NN Shewanella A NN

Metastats comparison NH and DH captive whitefish Genera Diet group Groups Aliivibrio B DH Aneurinibacillus B DH Arcobacter B DH Clostridium_sensu_stricto B DH Clostridium_XI B DH Comamonas B DH Deinococcus B DH Dickeya B DH Dokdonella B DH Elizabethkingia B DH Enhydrobacter B DH Exiguobacterium B DH Gemella B DH Legionella B DH Limnohabitans B DH

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Megasphaera B DH Novosphingobium B DH Phenylobacterium B DH Proteiniclasticum B DH Pseudomonas B DH Rhodobacter B DH Salinimicrobium B DH Shewanella B DH Sphingobacterium B DH Sphingobium B DH Thermomonas B DH Wautersiella B DH Aerococcus A DH Alkanindiges A DH Aquabacterium A DH Delftia A DH Desulfovibrio A DH Flavobacterium A DH Labrenzia A DH Legionella A DH Psychrobacter A DH Stenotrophomonas A DH Listonella A DH Trichococcus A DH Methylobacterium A DH Paracoccus A DH Aerococcus B NH Leptotrichia B NH Pediococcus B NH Photobacterium B NH Turneriella B NH Aquitalea A NH Bifidobacterium A NH Brevundimonas A NH Devosia A NH Gemmata A NH Hyphomicrobium A NH Lactobacillus A NH Oerskovia A NH Ohtaekwangia A NH Olsenella A NH Pasteuria A NH

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Metastats comparison NN and DD captive whitefish Genera Diet group Groups Acinetobacter B DD Actinomyces B DD Anoxybacillus B DD Aquicella B DD Barnesiella B DD Brevundimonas B DD Campylobacter B DD Cerasibacillus B DD Faecalibacterium B DD Oceanobacillus B DD Ohtaekwangia B DD Prevotella B DD Pseudolabrys B DD Rhodococcus B DD Sphingobium B DD Sphingomonas B DD Thermoflavimicrobium B DD Vogesella B DD Gp6 A DD Lactobacillus A DD Streptococcus A DD Alishewanella B NN Aneurinibacillus B NN Aquabacterium B NN Arcobacter B NN Arthrobacter B NN Bifidobacterium B NN Cellvibrio B NN Corynebacterium B NN Devosia B NN Duganella B NN Ethanoligenens B NN Gemella B NN Haemophilus B NN Hyphomicrobium B NN Megasphaera B NN Methyloversatilis B NN Novosphingobium B NN Photobacterium B NN Prauserella B NN

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Pseudomonas B NN Rothia B NN Shewanella B NN Sphaerobacter B NN Tissierella B NN Undibacterium B NN Labrenzia A NN Planctomyces A NN

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4.13 Supplementary Figures

Figure S 4. 1 : Network analysis of intestinal microbiota of dwarf and normal wild whitefish and intestinal microbiota of dwarf, normal and hybrids captive whitefish. The nodes represent a dwarf or a normal or a hybrid whitefish microbiota. More precisely, the whitefish species are represented by DD: dwarf whitefish, NN: normal whitefish, DH: Backcross hybrids with pure dwarf female and F1 hybrid male, NH: Backcross hybrids with normal pure female and F1 hybrid male. The connecting lines between two samples represent their correlation and is highlighting by a Spearman index.

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Chapitre 5 : Conclusion

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L’objectif général de cette thèse était d’étudier la potentielle implication du microbiote dans la spéciation du grand corégone. Ultimement, nous cherchions à savoir si les communautés bactériennes du microbiote étaient, soit impliquées dans l’évolution parallèle du grand corégone, soit influencées par celle-ci. Cet objectif général a été effectué en caractérisant le microbiote à différents niveaux d’interactions avec l’hôte, soit avec les bactéries pathogènes, avec les bactéries symbiotiques et avec les bactéries transitoires, en étudiant respectivement les reins (chapitre 2), la paroi intestinale (chapitre 3), et le bolus (chapitre 4) du grand corégone. Plus précisément, les lignes directrices de cette thèse étaient i) de répertorier et de comparer les communautés bactériennes chez deux espèces de corégone et leur hybrides, ii) de tester la présence de parallélisme au niveau du microbiote sur 5 paires d’espèces, et iii) d’identifier les taxons bactériens présents dans les espèces naines-normales ainsi que les espèces pures-hybrides. Le grand corégone a été choisi comme modèle d’étude puisqu’il s’agit d’un système très bien étudié et les connaissances accumulées sur cet hôte permettent de mieux appréhender les questions relatives à l’évolution de l’holobionte. Ainsi, les résultats de cette étude ont permis d’approfondir notre compréhension de l’évolution du microbiote et de son interaction avec l’hôte dans un contexte de divergence adaptative et de spéciation.

5.1 Principaux resultats

Des différences entre les deux espèces du grand corégone ont été mise en évidence à tous les niveaux d’interaction hôte-bactéries. Lors du second chapitre, il a été montré que les infections sont différentes entre les espèces de corégone, et varient en fonction du lac d’origine. De plus, les taxons bactériens impliqués dans ces infections varient également entre les espèces et en fonction du lac d’origine, suggérant une grande influence de l’environnement. L’étude du microbiote des bactéries adhérentes (i.e. symbiotiques ; chapitre 3) et non adhérentes (i.e. transitoires ; chapitre 4) à la paroi intestinale a aussi montré une différenciation des microbiotes en fonction des espèces. Il est intéressant ici de préciser que le microbiote intestinal symbiotique est différent pour les populations de Cliff, Est et Témiscouata mais similaire pour les populations d’Indian et Webster, tandis que le microbiote intestinal transitoire est différent pour Cliff, Est et Indian mais similaire pour Témiscouata et Webster. Au vu des régimes alimentaires différents entre les deux espèces, l’obtention d’un parallélisme au niveau du microbiote intestinal était attendue. En effet, l’ingestion et la digestion d’un nouveau type de nourriture pouvant favoriser des

133 communautés bactériennes distinctes (Rosenberg & Zilber 2013). Le fait de ne pas observer cet important effet environnemental sur le microbiote intestinal met en évidence un autre facteur : l’hôte. Il a été démontré que la physiologie de l’hôte, son système immunitaire ainsi que son bagage génétique pouvaient influencer le microbiote intestinal (Baldo et al. 2015; Sullam et al. 2015; Alberdi et al. 2016; Macke et al. 2017). Il est ainsi possible que l’effet de l’hôte a atténué le parallélisme attendu par la divergence de régime alimentaire entre les deux espèces. La comparaison des deux types de microbiote intestinal est très intéressante pour construire des hypothèses sur l’évolution de l’holobionte. En effet, le microbiote intestinal symbiotique est caractérisé par une interaction particulièrement stable et étroite avec l’hôte, alors que le microbiote intestinal transitoire est plus influencé par l’environnement dont fait partie le régime alimentaire. Les paires d’espèces des lacs Indian et Témiscouata sont particulièrement intéressantes puisque leurs microbiotes intestinaux symbiotique et transitoire diffèrent. La présence de différents microbiotes intestinaux transitoires et l’absence de différence pour le microbiote intestinal symbiotique au sein du lac Indian suggèrent qu’il existe un effet de l’hôte qui maintient un microbiote similaire entre les deux espèces malgré une alimentation différente. L’effet opposé se produit au sein du lac Témiscouata où l’absence de différence pour le microbiote intestinal transitoire et la présence de différence pour le microbiote intestinal symbiotique suggèrent un effet hôte, favorisant la divergence du microbiote malgré un régime alimentaire plus similaire entre les deux espèces. Ainsi, même si nous n’avons pas trouvé un fort parallélisme au niveau des microbiotes intestinaux, le grand corégone semble être un bon exemple pour souligner la complexité de l’holobionte, où la sélection peut affecter différemment le microbiote et son hôte (Rosenberg & Zilber 2016). En excluant le lac Webster à cause d’une puissance statistique trop faible (n=3 chez l’espèce naine), les résultats suggèrent trois chemins évolutifs distincts entre les corégones nains et normaux (1 : Cliff et Est ; 2 : Indian et 3 : Témiscouata).

De plus, il existe de grandes fluctuations biotiques et abiotiques entre les cinq lacs étudiées (Landry et al. 2007; Landry & Bernatchez 2010). De plus, toutes les communautés bactériennes de l’eau des lacs sont caractérisées par une structure taxonomique distincte. Ainsi, même si la sélection des bactéries peut être différente entre les nains et les normaux, le contexte environnemental peut atténuer le parallélisme de la structure taxonomique bactérienne du grand corégone. Il a cependant été possible d’obtenir du parallélisme au niveau de la diversité bactérienne du microbiote des reins (i.e. pathogènes de l’hôte). En effet, la diversité alpha de la communauté bactérienne des reins

134 est systématiquement plus élevée pour l’espèce normale. Deux hypothèses mutuellement non exclusives peuvent expliquer ce résultat. Premièrement, la plus grande diversité alpha observée chez le corégone normal peut être le reflet de sa nourriture plus variée par rapport à l’espèce naine (Bodaly 1979; Landry & Bernatchez 2010). Pour autant il existe un mécanisme de stabilisation de la diversité bactérienne intestinale, mis en évidence chez le grand corégone ainsi que pour le microbiote intestinal du poisson zèbre (Danio rerio) (Stephens et al. 2016), nous amenant ainsi à la seconde hypothèse liée à un mécanisme défensif du système immunitaire. En effet, une étude a montré une surexpression de gènes impliqués dans le système immunitaire inné chez l’espèce naine (St-Cyr et al. ; 2008; Jeukens et al.; 2010). Le système immunitaire inné est un mécanisme de défense intervenant rapidement et détruisant les corps étrangers de manière non-spécifique. Ainsi le système immunitaire inné des corégones nains serait plus performant et stabiliserait mieux la diversité alpha dans le microbiote des reins. L’espèce naine subissant une plus forte pression pathogénique dû à sa niche écologique (i.e. la colonne d’eau est caractérisée par une abondance et une diversité bactérienne plus importante que les sédiments), a développé des mécanismes immunitaires plus efficaces afin de mieux contrôler les infections. De plus, une étude sur le complexe majeur d’histocompatibilité IIβ (Pavey et al. 2013) n’a pas mis en évidence de parallélisme au niveau du système immunitaire spécifique qui reconnait et intervient de façon distinctive contre les pathogènes. L’absence de parallélisme au niveau du système immunitaire spécifique souligne que celui-ci a évolué dans un environnement où les pathogènes étaient différents. Cela confirme que la variation environnementale est trop fluctuante pour favoriser des bactéries spécifiques à une espèce de corégone.

Il est intéressant de noter que la communauté bactérienne des reins est composée de 579 genres bactériens, que celle du microbiote intestinal symbiotique de 421 et celle du microbiote intestinal transitoire de 611. Ce nombre de genres bactériens confirme une stabilisation plus importante produite par l’hôte sur le microbiote intestinal symbiotique. Ces résultats suggèrent aussi que l’hôte a moins d’effet sur la stabilisation des microbiotes transitoires et infectieux. Le nombre de genres bactériens dénombrée dans les reins de corégone est plus important en comparaison à d’autres études précédentes. Cette différence peut être justifiée par une nouvelle méthodologie développée au cours de ma thèse. En effet, un nouveau type de PCR spécialement conçu pour cette analyse, la double PCR imbriqué (Sevellec et al. 2012) a permis de mieux amplifier les bactéries présentes dans les reins. Finalement, cette grande diversité de genres bactériens

135 identifiés donne une image plus complète de la structure des communautés bactériennes présentes chez le poisson sauvage d’eau douce, permettant ainsi une meilleure compréhension des interactions entre l’hôte et ses microbiotes stables, transitoires et pathogènes.

Afin de confirmer les différents résultats effectués sur le corégone sauvage mettant en évidence un effet de l’hôte, quatre groupes de corégone captifs ont été élevés dans des conditions contrôlés (nain, normal et hybrides F1 réciproques). La présence de microbiotes intestinaux transitoires différents ainsi que des genres bactériens spécifiques à un groupe de corégone mettent en évidence un effet divergent dû à l’hôte. De plus, le microbiote intestinal transitoire des hybrides se distingue des patrons observés chez les espèces pures (nain et normal). Ainsi les microbiotes des hybrides ne sont pas les intermédiaires aux deux espèces de corégone. Des patrons similaires ont été retrouvés chez la souris grise (M. m. domesticus et M. m. musculus) et ses hybrides F2 (Wang et al. 2015). Cette différence du microbiote intestinal transitoire entre les hybrides et les pures peut être dû à une architecture génétique aberrante des hybrides, mise en évidence chez le grand corégone. Cette différence de microbiote peut également provenir de rupture de gènes coadaptés du système immunitaire suggérant une possible incompatibilité des hybrides chez l’holobionte (Renaut et al. 2009; Dion-Cote et al. 2014; Brucker & Bordenstein 2012a)

5.2 Perspectives

Ces trois études pionnières sur le grand corégone ouvrent la voie à une grande variété d’analyses. En effet, cette thèse a été réalisée d’un point de vue génétique, et peu d’un point de vue fonctionnel. En effet, la métatranscriptomique ou la métabolomique permettraient de mieux comprendre les interactions hôte-microbiotes sur ce système et de répondre à la question suivante. Existe-il des patrons transcriptomiques ou des métabolites (primaires ou secondaires) différents entre les espèces de corégone ?

De plus, cette thèse a pour la première fois mis en évidence, chez les poissons, la présence de différents patrons évolutifs de l’holobionte. Une étude de plus grande envergure comparant l’évolution de l’holobionte du grand corégone et du corégone d’Europe (Coregonus lavaretus) qui a une histoire évolutive similaire au grand corégone, permettrait de confirmer ces résultats et d’approfondir nos connaissances sur les

136 interactions hôte-microbiotes. Il serait particulièrement intéressant de tester l’existence de patron de divergence au niveau du microbiote chez les six formes de corégone européen pouvant être présentes dans un même lac (Douglas et al. 1999) et de les associer à différentes variations environnementales.

Finalement, afin de savoir si le microbiote a un réel impact sur la spéciation, il serait préférable de comparer en laboratoire les populations conventionnelles et gnotobiotiques (Brucker & Bordenstein 2012). Il serait particulièrement intéressant de tester le taux de survie des embryons des corégones purs ainsi que des hybrides F1 et F2 avec et sans leur microbiote. Cette expérience pourrait contribuer à confirmer que le microbiote peut élever les barrières post-zygotiques en augmentant les incompatibilités génétiques du modèle Bateson-Dobzhansky-Muller comme l’a déjà démontré Brucker et al. (2012) avec le système Nasonia.

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