VWqPHV microbiens environnementaux et hôtes associés …

© Bachar Cheaib, 2020

Étude de l’évolution contemporaine de systèmes microbiens environnementaux et hôtes associés dans

un contexte d’écotoxicologie

Thèse

Bachar Cheaib

Doctorat en biologie

Philosophiæ doctor (Ph. D.)

Québec, Canada

Étude de l’évolution

contemporaine de systèmes

microbiens environnementaux et

hôtes associés dans un contexte

d’écotoxicologie

Thèse

Bachar Cheaib

Doctorat en biologie

Philosophiae doctor (Ph.D.)

Sous la direction de :

Nicolas Derome, directeur de recherche

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Résumé

Les microbes ou micro-organismes sont les producteurs primaires des

services écosystémiques pour les cycles biogéochimiques de la terre et les

systèmes biologiques. Les xénobiotiques marquent une nouvelle ère

anthropogénique « l’anthropocène », et ils représentent une source de

sélection artificielle de la structure et de la composition de la biodiversité

microbienne. Par conséquent, les perturbations anthropogéniques sont

néfastes pour les systèmes microbiens et induisent des changements

adaptatifs ou des dommages dans leurs répertoires génotypiques.

L’assemblage des communautés microbiennes durant la résistance et la

résilience est gouverné par des processus éco-évolutifs.

Ce travail découle de l’intersection transdisciplinaire de l’écotoxicologie,

l’écologie microbienne, la métagénomique et la bioinformatique. L’objectif de

ce travail consiste à étudier les signatures adaptatives de la résistance et de

la résilience microbienne selon deux modèles. Le premier est

environnemental (E) composé d’un bassin versant lacustre contaminé par des

métaux lourds. Le deuxième modèle est hôte-associé (HA), constitué d’un

système expérimental d’exposition de la Perchaude (Perca flavescens) au

chlorure de cadmium selon deux régimes constant et graduel.

Trois nouveautés résument les travaux de cette thèse de doctorat.

Premièrement, le phénomène de découplage taxon-fonction a été démontré

pour la première fois, dans le système E sous un gradient sélectif de pollution,

et au sein du microbiote cutané dans le système HA durant sa période de

résilience.

Deuxièmement, des altérations significatives de la diversité taxonomiques et

fonctionnelles mettent en évidence des signatures adaptatives du résistome

et de l’érosion des fonctions métaboliques dans le système E. Quant au

système HA, le stress métallique a augmenté la prévalence significative de

souches pathogènes et des opportunistes avec une dysbiose cutanée de la

perchaude accompagnée par une réduction de sa capacité de résistance à

une colonisation bactérienne massive.

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Troisièmement, la modélisation de l’assemblage bactérien de microbiote du

système HA montre des rôles confondus de l’ontogenèse et de la force de

sélection durant la période de résistance. La persistance des effets à long

terme de la sélection durant le stade de résilience a été expliquée par une

augmentation inattendue de la bioaccumulation du cadmium dans les tissus

hépatiques de l’hôte.

En conclusion, nos travaux montrent que l’adaptation des répertoires

métagénomiques peut être décelée par le phénomène de redondance

fonctionnelle observée à l’échelle de découplage taxon-fonction, ce qui reflète

potentiellement une stratégie adaptative par transfert horizontal de gènes

partagés entre les communautés microbiennes environnementales sous

perturbation graduelle.

Dans le système HA, l’assemblage de microbiote montre un gradient de

processus neutres et non neutres. Enfin, la dérive taxonomique serait une

force écologique non négligeable plus importante dans le système

environnemental que dans le système intestinal durant et après la

perturbation.

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Abstract

Microbes or microorganisms are the primary producers of ecosystem services

for biogeochemical cycles of the earth and biological systems. Xenobiotics

mark a new anthropogenic era, "the Anthropocene," and they represent a

source of artificial selection of the structure and composition of microbial

biodiversity. As a result, anthropogenic disturbances are detrimental to

microbial systems and induce adaptive changes or damage in their

metagenomic repertories. During resistance and recovery, the ecological

processes governing the assembly of microbial communities cannot be

dissociated from those of microbial evolution.

This work stems from the transdisciplinary intersection of ecotoxicology,

microbial ecology, metagenomics and bioinformatics. The main goal is to

understand the adaptive signatures of microbial resistance and resilience in

two models. The first is environmental (E) composed of a lake-bound

watershed contaminated by heavy metals. The second model is host-

associated (HA), consisting of an experimental system of perch (Perca

flavescens) intoxicated with cadmium using two steady and gradual regimes.

Three novelties summarize the work of this doctoral thesis. Firstly, the

phenomenon of taxon-function decoupling has been demonstrated for the

first time, in the E system under selective pollution gradient, and second,

within the cutaneous microbiota in the HA system during its recovery stage.

Third, the microbiota assembly modelling in the HA system suggested mixed

effects of ontogenesis, and selective pressure during the period of resistance

and recovery. The increase in cadmium bioaccumulation in liver tissues of

perch can argue the persistence of the long-term effects of selection during

the recovery stage.

In conclusion, our work showed that the adaptation of microbial metagenomic

repertories could be revealed through functional and taxonomic redundancy

patterns observed at the scale of taxon-function decoupling. The gap between

functional and taxonomic diversity reflects an adaptive strategy by horizontal

gene transfer among environmental communities microbial under gradual

disruption.

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In the HA system, the microbiota assembly shows a gradient of neutral and

non-neutral processes. Finally, the taxonomic drift is a significant ecological

force, more effective in the environmental system than in the intestinal

system during and after the disruption.

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Table des matières

Résumé ................................................................................................................ ii

Abstract .............................................................................................................. iv

Liste des tableaux .......................................................................................... x

Liste des Schémas ......................................................................................... xi

Liste des figures ............................................................................................ xii

Liste des abréviations ................................................................................ xiii

Remerciements .............................................................................................. xvi

Introduction Générale .................................................................................. 1

L’anthropocène: des effets anthropiques sur la biodiversité et

l’assemblage des communautés microbiennes ......................................... 2

Les questions et les chapitres de cette thèse ............................................ 4

Chapitre 1 – Méthodes et concepts de base en écologie

microbienne ...................................................................................................... 8

1.1 Les microbes sont ubiquitaires et l’environnement les filtre ... 9 1.2 Accès à la biodiversité microbienne, de la boite de Pétri au

métagénome ....................................................................................................................... 9 1.3 L’avènement du séquençage de nouvelle génération ................. 10 1.4 Les approches de la métagénomique .................................................... 12

1.5 L’approche d’amplicons basée sur un gène marqueur

universel .............................................................................................................................. 12 1.6 L’approche métagénomique globale basée sur le séquençage

de l’ADN total. .................................................................................................................. 14 1.7 Approche de l’opération taxonomique universelle (UTO) ........ 14 1.8 L’approche des variants des séquences d’amplicons (VSA).... 16 1.9 Les mesures écologiques et phylogénétiques de la diversité. 17 1.9.1 Les mesures de la diversité alpha ...................................................... 17 1.9.2 Les mesures de la diversité Beta ......................................................... 19 1.9.2.1 Les distances écologiques ..................................................................... 19 1.9.2.2 Les distances phylogénétiques .......................................................... 20 1.10 Réseaux d’interactions microbiennes ............................................... 20 1.11 Les quatre forces évolutives et processus écologiques ......... 22 1.11.1 La mutation ..................................................................................................... 22 1.11.2 La sélection ..................................................................................................... 24 1.11.3 La migration .................................................................................................... 26 1.11.4 La dérive génétique .................................................................................... 27 1.11.5 Interactions des forces évolutives .................................................... 28 1.11.6 De l’évolution à l’écologie ...................................................................... 30 1.11.6.1 L’adaptation Locale ................................................................................ 30 1.11.6.2 L’adaptation rapide ................................................................................ 31 1.12 Processus écologiques et modèles ..................................................... 32

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1.13 Introduction à la modélisation mathématique en écologie et

évolution microbienne................................................................................................. 33

1.13.1 Préambule historique de la modélisation ...................................... 34 1.13.2 Aperçu sur les modèles d’assemblage microbiens .................. 37 1.13.2.1 Les modèles métaboliques ................................................................. 37 1.13.3 Les modèles kinétiques ............................................................................ 38 1.13.4 Les modèles spatiaux ................................................................................ 38 1.13.4.1 Modèles basés à l’échelle individuelle ........................................ 39 1.13.4.2 Modèles basés à l’échelle populationnelle ............................... 39 1.13.4.3 Modèles basés à l’échelle de communauté .............................. 40 1.13.5 Les modèles neutres en écologie microbienne .......................... 40 1.13.6 Le modèle neutre de Sloan .................................................................... 42 1.13.7 Neutralisme et déterminisme dans l’assemblage du

microbiote de l’hôte. .................................................................................................... 45 1.13.8 Les modèles éco-évolutifs ...................................................................... 46 1.14 Récapitulatif ..................................................................................................... 48

Chapitre 2: Taxon-function decoupling as an adaptive

signature of lake microbial metacommunities under a chronic

polymetallic pollution gradient .............................................................. 50

2.1 Résumé ................................................................................................................... 51 2.2 Abstract .................................................................................................................. 52 2.3 Introduction ......................................................................................................... 53 2.4 MATERIALS AND METHODS ......................................................................... 57 2.4.1 Lake characteristics and locations ..................................................... 57 2.4.2 Metallic and chemical gradient surveys .......................................... 58 2.4.3 Water sampling .............................................................................................. 58

2.4.4 DNA extraction and metagenome sequencing ............................ 58 2.4.5 Bioinformatic and statistical analysis ............................................... 58 2.5 Results ..................................................................................................................... 61 2.5.1 Decoupling taxon-function ...................................................................... 61 2.5.2 Detangled taxonomic structure and function diversity ......... 62

2.5.3 Canonical correlations of taxon and function .............................. 62 2.5.4 Taxonomic variation signatures........................................................... 63 2.5.5 Role of trace metals in taxonomic variation signatures ........ 64 2.5.6 Function variation signatures ............................................................... 64 2.5.7 Role of trace metals in function variation signatures ............. 66 2.6 Discussion ............................................................................................................. 67 2.6.1 Decoupling taxon-function as a signature of adaptive

strategies ............................................................................................................................ 67 2.6.2 Taxonomic adaptive signatures ........................................................... 69 2.6.3 Functional adaptive signatures ............................................................ 71 2.7 Conclusions ........................................................................................................... 73 2.8 Figures ..................................................................................................................... 74 2.9 Supplementary figures .................................................................................. 82 2.10 Supplementary Material ........................................................................... 94

Chapitre 3: From networks to models: The Yellow Perch

(Perca flavescens) microbiome assembly under metal toxicity

.............................................................................................................................. 96

3.1 Resumé ................................................................................................................... 97

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3.2 Abstract .................................................................................................................. 98 3.3 Introduction ......................................................................................................... 99 3.4 Materials and methods ................................................................................ 102 3.4.1 Fish rearing. ................................................................................................... 102 3.4.2 Exposure regimes to cadmium. .......................................................... 102 3.4.3 Host-microbiota and water sampling. ............................................ 102 3.4.4 Metal concentration in water and fish liver. ............................... 103

3.4.5 DNA extraction to Illumina Miseq sequencing. ......................... 103 3.4.6 Analysis of 16S rDNA amplicons. ...................................................... 104 3.4.7 Correlational networks. .......................................................................... 104 3.4.8 Metacommunity assembly modelling. ............................................ 105 3.5 Results ................................................................................................................... 105 3.5.1 Metal concentrations in water and host livers .......................... 105 3.5.2 Mixed Effects of time and treatment on metacommunity

alpha diversity ............................................................................................................... 106 3.5.3 An important effect of time on the taxonomic composition

of metacommunities ................................................................................................... 106 3.5.4 Community-level phylogenetic divergence ................................. 107 3.5.5 Correlational metacommunity networks ...................................... 107 3.5.5.1 Substantial role of rare taxa in the metacommunity

network connectivity ................................................................................................. 107 3.5.5.2 Reduced network connectivity in gut communities under

cadmium stress ............................................................................................................. 107 3.5.5.3 Negative correlations in Skin Mucous Community

networks suggest dysbiosis ................................................................................... 108 3.5.5.4 Fragmentation of water microbial community networks . 108 3.5.6 Stochasticity in water community assembly and

determinism in that of host microbiota .......................................................... 109

3.6 Discussion ........................................................................................................... 109 3.6.1 Phylogenetic divergence at the community-level revealed

the impact of Cadmium exposure. ..................................................................... 110 3.6.2 Gradual disconnection of abundant taxa from the main gut

interacting network. ................................................................................................... 111 3.6.3 Rare OTUs play a pivotal role in community assembly. ....... 112 3.7 Conclusions ......................................................................................................... 112 3.8 Perspective ......................................................................................................... 113 3.9 Tables ..................................................................................................................... 114 3.10 Figures .............................................................................................................. 119 3.11 Supplementary Figures ........................................................................... 125 3.12 Supplementary Material ......................................................................... 131

Chapitre 4: Community recovery dynamics in yellow perch

microbiome after gradual and constant metallic perturbations

............................................................................................................................ 134

4.1 Resumé ................................................................................................................. 135 4.2 Abstract ................................................................................................................ 136 4.3 Introduction ....................................................................................................... 137 4.4 Methods ................................................................................................................ 139 4.4.1 Fish rearing .................................................................................................... 139 4.4.2 Exposure regimes to cadmium ........................................................... 140

4.4.3 Recovery after the exposure to Cadmium.................................... 140

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4.4.4 Host-microbiota and water sampling ............................................. 140 4.4.5 Metal concentration in water and fish liver ................................ 141 4.4.6 DNA extraction, libraries preparation and 16S amplicons

sequencing ....................................................................................................................... 141 4.4.7 Bioinformatics and biostatistics analyses .................................... 141 4.4.7.1 Reads preprocessing and OTUs clustering ................................ 141 4.4.7.2 Post-OTUs analysis, networks and function prediction. ... 143 4.5 Neutral and deterministic models to asses the recovery of

community assembly. ................................................................................................ 144 4.6 Results ................................................................................................................... 144 4.6.1 Cadmium concentration bioaccumulation in the fish liver

during recovery time .................................................................................................. 144 4.6.2 Genotypic signatures of community recovery ........................... 145 4.6.3 Microbial taxonomic composition change during recovery 146 4.6.4 Correlational networks of host and water microbiome ....... 148 4.6.5 Recovery of microbial functional diversity a time T5. .......... 149 4.6.6 The role of neutral and deterministic processes in the

recovery of host microbiota ................................................................................... 149 4.7 Discussion ........................................................................................................... 150 4.8 Conclusions ......................................................................................................... 154

4.9 Tables ..................................................................................................................... 155 4.10 Figures .............................................................................................................. 158 4.11 Supplementary figures ............................................................................ 166 4.12 Supplementary Material ......................................................................... 168

Discussion et conclusions générales.................................................. 170

Bibliographie ................................................................................................. 178

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Liste des tableaux

Table 3. 1 Statistics’ summary of Cadmium concentration variation over

time and treatments in water tanks and fish livers. ............................. 114

Table 3. 2 Statistical summary of alpha-diversity changes over time and

treatments. .................................................................................. 115

Table 3. 3 Phylogenetic divergence at the community level. ................. 117

Table 4. 1 Statistics of Cd concentrations in water and fish liver over time

and treatments. ............................................................................ 155

Table 4. 2 Phylogenetic divergence in host and water microbiomes. ...... 156

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Liste des Schémas

Schéma 1. 1 Approches métagenomiques de l’analyse de microbiote. ...... 15

Schéma 1. 2. Boite noire de l’écologie des communautés. ...................... 33

Schéma 1. 3. Espace des modèles éco-évolutifs. .................................. 48

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Liste des figures

Figure 2. 1 Bioinformatics analysis pipeline. ......................................... 74

Figure 2. 2 Composition of metacommunities based on the ORF approach. 75

Figure 2. 3 Function abundance classification based on ORF approach. ..... 76

Figure 2. 4 Polymetallic resistance genes (PMRG) abundance correlation

with trace metals. ........................................................................... 78

Figure 2. 5 Decoupling of taxon and function between metacommunities

based on the subsampled reads approach. .......................................... 80

Figure 2. 6 Coupling of taxon and function between metacommunities based

on the subsampled reads approach .................................................... 81

Figure 3. 1 Linear variations of alpha-diversity over time and between

treatments explained by the linear mixed model in water and host-microbial

communities ................................................................................ 119

Figure 3. 2 Phylogenetic divergence at the community level ................. 120

Figure 3. 3 Correlational co-abundance networks of gut microbial

community. .................................................................................. 121

Figure 3. 4 Correlational co-abundance networks of skin microbial

community. .................................................................................. 122

Figure 3. 5 Correlational co-abundance networks of water microbial

community. .................................................................................. 123

Figure 3. 6 Bar plots of neutral OTUs change at community and meta-

community levels. ......................................................................... 124

Figure 4. 1 Schematic illustration of the perch microbiome recovery

experiment. ................................................................................. 158

Figure 4. 2 Alpha-diversity dynamics in the water and perch microbiome.

.................................................................................................. 158

Figure 4. 3 Taxonomic composition dynamics of host communities ........ 159

Figure 4. 4 Heatmaps of differential abundance among host and water

communities ................................................................................ 159

Figure 4. 5 Function diversity dynamics in host and water microbiome. .. 160

Figure 4. 6 Recovery dynamics of the networks of host communities. .... 161

Figure 4. 7 Recovery dynamics of the network of water communities ..... 162

Figure 4. 8 Centrality plots of host microbiome networks ..................... 163

Figure 4. 9 Percentage of neutral OTUs over time and treatment. ......... 164

Figure 4. 10 Demographic variation of metacommunity neutrality across

water and host microbiome ............................................................. 165

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Liste des abréviations

16S rDNA: 16S Ribosomal DNA;

ACE: Abundance-based coverage estimator;

Al: Aluminium;

AMD: Acid Mine Drainage;

ANOVA: Analysis of variance;

BAR-mc: Arnoux Bay, medium contaminated;

BEB: back extraction buffer;

BH: Benjamini-Hochberg correction test;

CC : Cadmium Constant Concentration;

Cd : Cadmium;

CdCl2: Cadmium Salts;

CPAUL: Comités de protection des animaux de l’université Laval ;

Ctrl: regime of negative Control;

Cu: Cooper;

CV: Cadmium Variable Concentration;

DAS-lc: Dasserat Lake, low contaminated;

FDR: False Discovery Rate;

Fe: Iron;

HGT: Horizontal Gene Transfer;

LAR-hc: Arnoux Lake, high contaminated;

Lead: Pb;

Mn: Manganese;

MRPP: Multiple Response Permutation Procedure;

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NGS: Next generation sequencing;

NLS: non-linear least squares model;

NMDS: non-metric Multi-Dimensional Scaling;

OPA-nc: Opasatica Lake, not contaminated;

ORF: Open Reading Frame;

OTU: Operational taxonomic Unit;

PCA: Principal Component Analysis;

PCG: Polymetallic Contamination Gradient;

PCR: Polymerase Chain Reaction;

PERMANOVA: Permutational analysis of variance;

PMRG: Polymetallic resistance genes;

ppb: parts per billion;

rCCA: Regularized canonical correlation analysis;

RDP: Ribosomal Database Project;

TUR-hc: Turcotte Lake, high contaminated;

UTO : Unité Taxonomique Opérationnelle;

Zinc : Zn;

xv

À Hadi, Amar et Farah

À toute ma famille

À la révolution de 17 Octobre

xvi

Remerciements

En premier, je présente mes profonds et sincères remerciements à mon

directeur Pr. Nicolas Derome de m’avoir donné l’opportunité de continuer

dans la recherche scientifique, et la chance de saisir ma première opportunité

québécoise. Merci de m’avoir accueilli dans ton laboratoire comme un

assistant de recherche en premier temps (Décembre 2013-Mai 2014) et

comme un étudiant en doctorat en deuxième temps (Juin 2014-Octobre

2018) ! Merci de m’avoir accordé toute ta confiance pour mener mon projet

avec toute liberté ! Merci de m’avoir donné avec toute générosité tous les

matériels et les consommables nécessaires pour mener mes travaux dans ton

laboratoire ! Merci de m’avoir donné toute cette belle chance.

J’aimerais aussi remercier les membres de mon comité d’encadrement : Pr.

Connie Lovejoy, Pr. Louis Bernatchez et Pr. Jacques Corbeil. Merci pour votre

confiance aux différentes étapes de ce doctorat et pour vos suggestions.

Je voudrais remercier de tout mon cœur Dr. Martin LLewellyn de m’avoir

accordé également toute sa confiance pour réaliser des collaborations

scientifiques sur ses projets durant son post-doctorat dans notre laboratoire.

Merci Martin pour ton soutien tous les niveaux et merci de m’avoir accueilli

dans ton laboratoire comme un chercheur postdoctoral à l’université de

Glasgow, même avant de soutenir ma thèse.

Merci infiniment à Dr. Mohamed Alburaki de m’avoir soutenu durant cette

thèse. Merci Mohamed de m’avoir donné l’opportunité de collaborer sur ton

projet et de m’avoir transmis ta passion pour les abeilles, et de me faire

connaitre leur diversité génétique en Syrie et au Liban.

Grand merci à tous mes proches collaborateurs, Hamza Seghouani, Sarah El-

Khoury, François-Etienne Sylvain, Pierre-Luc Mercier et Dr. Umer Ijaz.

Je tiens à remercier particulièrement Jeff Gauthier d’avoir lu mon texte en

Français. Merci pour toutes les discussions infinies et intéressantes sur les

aspects différents de la recherche scientifique. Tu es un collègue agréable,

altruiste, et toujours prêt pour aider tous les collègues au laboratoire.

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Merci spécial à Amina Abed et Vani Mohit d’avoir corrigé minutieusement

plusieurs textes en Français à plusieurs occasions durant mon parcours. Merci

à toi Amina pour tes conseils concernant les protocoles de l’extraction d’ADN.

Merci à Dr. Anne Dalziel, Dr. Amanda Xuereb, Dr. Ciara Keating, Eleanor

Lindsay, et Matt Bywater d’avoir m’aidé dans les corrections de mes

manuscrits en anglais.

Merci à tous les anciens et les nouveaux membres du laboratoire Derome,

pour leur qualité humaine et leur aide durant des longues journées

d’échantillonnage au LARSA. Merci à Émie, Sidki, Laurence, Katherine,

Camille et Sara-Jane.

Merci aux professionnels de recherche du Laboratoire Bernatchez, Alysse et

Cecilia pour leur dépannage de matériels durant les longues semaines de

grève.

Je souhaiterais remercier spécialement Dr. Michel Lavoie pour ses conseils

d’or concernant la manipulation de Cadmium. Merci Michel, tu m’avais ouvert

les yeux sur des aspects que j’ignorais en Chimie environnementale.

Je tiens à remercier les personnels du LARSA particulièrement Jean-

Christophe Therrien, et je n’oublie pas la plateforme de séquençage de l’IBIS

particulièrement Dr. Brian Boyle.

À mes collègues co-fondateurs du Club Bioinformatique, Jeff Gauthier, Dr.

Anthony Vincent et Éric Normandeau, je vous exprime tous mes sincères

remerciements.

Je souhaiterais remercier Mario Boutin une personne qui a laissé une

empreinte humaine et un vide éternel à l’IBIS, et aux services de la Laverie.

Mario était toujours plein de joie et d’amour pour ses collègues, que ton âme

repose en paix.

À tous(tes) mes ami(e)s Nabil, Carlos, Aref, Yazan, Hayan, Mohamed, Fawzi,

Aoun, Rabih, Imad, Roba, Sarah, Émilie, Hector sans oublier personne, je

souhaiterais vous remercier pour votre encouragement et pour tous les bons

moments. Merci de m’avoir soutenu sur place et à distance durant toutes les

étapes de ma vie canadienne.

xviii

À ma mère, mon père, mes frères et sœurs, aucun mot ne suffit de vous

exprimer ma gratitude, sans vos encouragements, je ne serais jamais arrivé

à finir ma thèse.

xix

Avant-propos

Cette thèse de doctorat en biologie et bioinformatique comporte cinq

chapitres incluant une introduction et une conclusion générale. Mes travaux

sont constitués de trois articles de recherche, identifiés chapitres 2, 3 et 4

dans la table des matières. Les trois chapitres sont rédigés en anglais car le

premier fut publié et les deux derniers demeurent en révision dans des revues

scientifiques internationales utilisant la langue anglaise. Le thème global de

cette thèse s’articule sur l’axe de l’évolution contemporaine de deux modèles

systèmes microbiens : environnemental et hôte-associé dans un contexte

d’écotoxicologie.

Le 1er chapitre est une revue bibliographique des principes de bases en

écologie microbienne. Le deuxième chapitre fait l’objet d’une publication

scientifique dans le Journal « Frontiers in Microbiology » et présente les

signatures adaptatives détectées au sein d’un système microbien

environnemental sous un gradient de pression sélective, induite par une

exposition chronique aux métaux traces au cours de plus de 60 ans

d’exploitation minière.

Le 3ème et le 4ème chapitres présentent les empreintes de l’évolution

expérimentale d’un système microbien hôte-associé dans un contexte

d’intoxication métallique artificielle selon deux régimes de sélection, constant

et graduel. Le chapitre 3 se focalise sur les signatures de la résistance de

microbiote de l’hôte en fonction de l’intensité du polluant, tandis que chapitre

4 décrit la résilience de la structure de microbiote en fonction du gradient de

stress métallique. Les deux chapitres sont sous révisions depuis quelques

mois, dans les deux revues « Nature ISME » et « Microbiome »

respectivement.

Pour chacun des chapitres de ma thèse, j’ai formulé les hypothèses

scientifiques et la théorie des questions adressées avec l’appui de mon

directeur Nicolas Derome. Pour le deuxième chapitre la prise d’échantillons

sur le terrain a été assurée par Pierre-Luc Mercier le troisième co-auteur de

la publication. Le deuxième cosignataire, Malo Le Blouch a contribué

également dans l’analyse descriptive des données de séquençage. Pour les

chapitres 3 et 4, j’ai principalement contribué à la conception, la planification

xx

et à la conduite des expériences avec l’aide de mon directeur Nicolas Derome

et mon collaborateur Hamza Seghouani. La prise des échantillons et la

dissection des poissons ont été réalisée grâce à l’aide de tous les membres

du laboratoire. Après l’échantillonnage, j’ai effectué la filtration de l’eau,

l’extraction de l’ADN, les préparations des librairies, les analyses bio-

informatiques et biostatistiques des résultats et la rédaction des articles.

Dans le chapitre 3, les deux co-auteurs Katherine Vandal-Lenghan et Pierre-

Luc Mercier ont contribué à l’extraction de l’ADN de certains échantillons et

la quantification des librairies de séquençage.

Les coauteurs internationaux de l’Université de Glasgow, Martin LLewellyn et

Umer Ijaz ont contribué dans l’amélioration de l’analyse des résultats et dans

la qualité de la rédaction des manuscrits de chapitre 3 et 4.

Les détails des articles publiés ou soumis se trouvent ci-dessous :

Article I. Taxon-function decoupling as an adaptive signature of lake

microbial metacommunities under a chronic polymetallic pollution gradient

Auteurs : Bachar Cheaib, Malo Le Boulch, Pierre-Luc Mercier, et Nicolas

Derome

Publié le 3 Mai 2018 dans le journal Frontiers in Microbiology (Front Microbiol.

2018 ; 9 : 869.).

Article II. From networks to models: the Yellow Perch (Perca flavescens)

microbiome assembly under metal toxicity

Auteurs : Bachar Cheaib, Hamza Seghouani, Martin Llewellyn, Katherine

Vandal-Lenghan, Pierre-Luc Mercier, and Nicolas Derome.

Soumis le 05 Mars 2019 dans journal Nature ISME, rejeté uniquement par

l’éditeur (accepté par l’arbitre) le 30 septembre 2019 avec l’option de

resoumettre une version courte (Reference ISMEJ-19-00341A)

Article III. Community recovery dynamics in yellow perch microbiome after

gradual and constant metallic perturbations

Auteurs : Bachar Cheaib, Hamza Seghouani, Umer Zeeshan Ijaz, and Nicolas

Derome.

xxi

Cet article a été publié dans le journal Microbiome. Microbiome 8, 14 (2020)

https://doi.org/10.1186/s40168-020-0789-0

En parallèle, je me suis intéressé à l’évolution d’une métalloenzyme le

carbonique anhydrase. C’est une famille protéique ubiquitaire capable de

substituer les ions métalliques pour assurer la photosynthèse chez les

phytoplanctons. J’ai rédigé un manuscrit (Juin 2017) sur ce sujet sous forme

d’un chapitre supplémentaire sous la direction de Connie Lovejoy. Ce travail

serait bientôt envoyé à une revue scientifique afin de le publier.

D’un autre côté, les travaux de recherche de la thèse ont été communiqué

sous forme de présentations et affiches à des journées scientifiques

départementales et des organisations scientifiques québécoises et

canadiennes, et à des conférences internationales. J’ai personnellement

conçu et communiqué chacune de ces présentations et des affiches. Mes

participations en personne sont citées ci-dessous :

Conférences locales à l’Université Laval, Québec, Canada

Plusieurs éditions de la journée d’étudiante (2014, 2015, 2016 et 2018) de

l’Institut de Biologie Intégrative et des Systèmes (IBIS)

Deux éditions de colloque annuel de département de Biologie (2015,2018)

avec un prix de distinction (Edition 2015) de la fondation Richard Bernard.

Conférences et réunions annuelles canadiennes

Plusieurs éditions (2014, 2015, 2017) de la Réunion annuelle des Ressources

Aquatiques Québec (RAQ), Québec.

Colloque annuel conjoint RÉAQ-EcoBIM (ÉcoBIM 2015), INRS, Québec.

Conférence Genomes to / aux Biomes (2014), 1ère réunion conjointe de la

Société canadienne d'écologie et d'évolution (SCEE), de la Société canadienne

de zoologie (CSZ) et de la Société canadienne des limnologie (SCL), 25-29

mai, Montréal.

BISP 2016. Congrès BiSP (Bactériologie intégrative : Symbiose &

Pathogenèse), third edition, Université Laval Québec, Canada.

Conférences internationales

xxii

Le 17ème Symposium de la Société Internationale de l'Écologie Microbienne

(ISME17), du 12 - 17 Août 2018, Leipzig, Allemagne. Ma participation été

financé en partie par les Fonds général pour les études supérieures (FGES)

du Conseil de recherches en sciences naturelles et en génie (CRSNG).

Le 16ème Symposium de la Société Internationale de l'Écologie Microbienne

(ISME16), du 21 au 26 août 2016, Montréal, Canada.

GLBIO-CCBC 2016, Great Lakes Bioinformatics et Conférence Canadienne sur

la Biologie Computationnelle, du 16 au 19 mai 2016, Toronto, Canada.

Mes recherches dans le laboratoire de Nicolas Derome, ne se restreignent pas

aux chapitres présentés dans ce document. J’ai contribué en tant que co-

auteur aux analyses bioinformatiques et biostatistiques de quatre

publications sur des problématiques qui rentrent dans l’intérêt général de

cette thèse

La première porte sur l’impact de l’acidité de l’eau sur la résilience de

microbiote d’un poisson amazonien le Tambaqui publiée dans le journal

Scientifc Reports

Sylvain, F.-É., Cheaib, B., Llewellyn, M., Gabriel Correia, T., Barros

Fagundes, D., Luis Val, A., Derome, N., 2016. pH drop impacts differentially

skin and gut microbiota of the Amazonian fish tambaqui (Colossoma

macropomum). Sci. Rep. 6, 32032.

La deuxième porte sur la causalité entre pesticides, expression des gènes et

maladies infectieuses des abeilles (varroa) publié dans Journal of economic

entomology.

Alburaki, M. Cheaib B, et al. Agricultural Landscape and Pesticide Effects on

Honey Bees (Hymenoptera: Apidae) Biological Traits. Journal of economic

entomology 110, 835–847;2017.

La troisième propose un candidat probiotique pour réduire l’effet du

parasitisme sur la mortalité des abeilles, publié dans Frontiers in Ecology and

Evolution

El Khoury S, Rousseau A, Lecoeur A, Cheaib B, Bouslama S, Mercier PL,

Demey V, Castex M, Giovenazzo P, Derome N. Deleterious Interaction

xxiii

Between Honeybees (Apis mellifera) and its Microsporidian Intracellular

Parasite Nosema ceranae Was Mitigated by Administrating Either Endogenous

or Allochthonous Gut Microbiota Strains. Frontiers in Ecology and Evolution.

2018 May 23; 6:58. Doi: 10.3389/fevo.2018.00058.

La quatrième révèle une sélection diversifiante des protéases de surface de

Trypanosoma cruzi GP63 parmi les patients atteints de la maladie de Chagas

chronique et congénitale. Elle est publiée dans le journal PLOS Neglected

Tropical Diseases

Llewellyn MS, Messenger LA, Luquetti AO, Garcia AL, Torrico F, Tavares SBN,

Cheaib B, Derome N et al. 2015. Deep sequencing of the Trypanosoma cruzi

GP63 surface proteases reveals diversity and diversifying selection among

chronic and congenital Chagas disease patients. PLoS NTD.

Depuis le début de la dernière année de cette thèse, j’entame un stage post-

doctoral avec mes collaborateurs, William Sloan, Martin LLewellyn et Umer

Ijaz à l’école ingénieure de l’Université de Glasgow. Mes recherches portent

sur la modélisation écologique de la dynamique de microbiote des

salmonidés. Mes recherches se concentrent sur la quantification du rôle relatif

des processus neutres et non-neutres dans la colonisation du tube digestif

des juvéniles des salmonidés dans la nature et en aquaculture. Mes résultats

pour l’instant sont publiés dans « le journal of AEM Applied and Environmental

Microbiology »

Durant cette thèse, j’ai transformé mes difficultés financières en opportunités

pour développer un gout pour l’enseignement en assurant mes fonctions

d’auxiliaire d’enseignement de biostatistique sous forme des travaux dirigés

avec Frederic Maps. Durant mon post-doc, j’ai également saisi l’opportunité

d’assister des travaux dirigés sur les bases de programmation (Python) avec

mon collaborateur Umer Ijaz à l’université de Glasgow.

Sur le plan social et scientifique général, j’ai initié l’idée du Club

Bioinformatique de l’IBIS que j’ai co-fondé plus tard avec mes chers collègues

Jeff Gauthier, Antony Vincent, et Eric Normandeau de plusieurs laboratoires

de l’IBIS. Cette expérience m’a offert la chance de partager et d’améliorer

mes connaissances et d’acquérir des nouvelles problématiques récentes en

bioinformatique biostatistique et en biologie évolutive

1

Introduction Générale

L’anthropocène, soit l’époque géologique contemporaine, est caractérisée par

un impact colossal de l’activité humaine sur la biosphère (Balter 2013; Larson

et al. 2014; Waters et al. 2016; Tucker et al. 2018). De l’industrie chimique,

plastique et métallurgique, aux pesticides et insecticides, à la pollution des

eaux, et la perturbation des cycles biogéochimiques de la terre, la liste est

longue et ne cesse de s’allonger avec des centaines d’altérations irréversibles

des écosystèmes. Les effets anthropiques sur les écosystèmes terrestres,

fluviaux et marins et les systèmes biologiques sont devenus alors tout à la

fois néfastes et étendus à l’échelle mondiale. Ainsi, le progrès technologique

d’Homo sapiens (industriel, numérique, communication, énergétique,

transport, nucléaire, militaire, etc.), sa croissance démographique et la

prolongation de son espérance de vie ne pourraient plus continuer au

détriment des ressources de la biodiversité et de l’équilibre de nos

écosystèmes (Crutzen and Stoermer 2000; Pelletier and Coltman 2018). Au-

delà des enjeux économiques, politiques de la réalité et en dépit de l’absence

des règlementions éthiques efficaces par les nations unies, les chercheurs ne

cessent de saisir l’opportunité pour soulever des questions fondamentales

concernant l’accélération artificielle de l’évolution génotypique des systèmes

biologiques sous l’effet de la pression sélective induite par les effets

anthropiques. À court et long terme, les études de l’évaluation de l’impact

des xénobiotiques sur la biodiversité tout au long de la chaine trophique

démontrent des perturbations même à l’échelle la plus fine du vivant, les

micro-organismes ou les microbes. Les xénobiotiques sont des produits

chimiques trouvés mais non produits par les organismes ou l'environnement.

Certains produits chimiques naturels (endobiotiques) deviennent des

xénobiotiques lorsqu'ils sont présents dans l'environnement à des

concentrations excessives. Le terme « xeno » dans « xénobiotiques » vient

du mot grec « xenos » qui signifie un invité, ami ou étranger (Soucek 2011).

Sans métabolisme, de nombreux xénobiotiques atteindraient des

concentrations toxiques (Croom 2012).

2

L’anthropocène: des effets anthropiques sur la biodiversité et

l’assemblage des communautés microbiennes

Il est connu que les microbes (Bactéries, Archées, Eucaryotes unicellulaires,

Virus, etc.) sont ubiquitaires, vivant en communautés, en biofilms,

planctoniques ou associées avec d’autres forme de vie. Les communautés

microbiennes planctoniques se trouvent dans l’eau, le sol et l’air, et les

symbiotiques se sont associées aux Métazoaires et aux Plantes, et aux autres

microbes unicellulaires. Elles sont les productrices primaires des services

écosystémiques du sol, et de l’eau, ou de l’hôte et constituent la partie

majeure de la biodiversité sur terre.

Les communautés microbiennes impliquées dans ces services s’avèrent très

impactés dans leurs répertoires génotypiques et phénotypiques par les

xénobiotiques. Citons par exemple les antibiotiques, les métaux toxiques, les

plastifiants chimiques, les biocides comme les pesticides, insecticides, et

désinfectants. Ces polluants chimiques non dégradables sont souvent des

xéno-estrogènes synthétiques qui interfèrent avec les récepteurs des

systèmes biologiques comme des perturbateurs endocriniens. Dans ce

contexte de perturbation, des centaines d’exemples peuvent être cités, nous

citons ici quelques-uns.

L’exemple de traitement par des antibiotiques est un excellent argument qui

témoigne de l’adaptation rapide des microbes et l’accélération de la cadence

de l’évolution microbienne. Les études montrent que les souches bactériennes

traitées par des antibiotiques (Vincent et al. 2019) acquièrent de la résistance

par conjugaison, un mécanisme de transfert horizontal parmi d’autres

(transformation, transduction) bien connus chez les microbes. Cette

résistance prépondérante aux antibiotiques est acquise horizontalement et

avant de se transmettre verticalement d’une génération bactérienne à l’autre

par division clonale (Holmes et al. 2016; von Wintersdorff et al. 2016; Cesare,

Eckert, and Corno 2016). Les biocides (triclosan, toluène, méthyl-mercure,

proflavine, etc.) sont des polluants de classes chimiques différentes qui

conduisent aussi à l’évolution du résistome bactérien (l’ensemble de gènes

de résistance bactériens) par des mutations génomiques (substitutions,

délétions, insertions) ou par l’acquisition de nouveaux gènes par transfert

3

horizontal. Peu importe les mécanismes, si les mutations génétiques sont

fixées dans les populations à travers les générations, elles peuvent selon la

stabilité de l’environnement conférer des avantages phénotypiques qui

contribuent à des capacités adaptatives comme la dégradation des

xénobiotiques. D’autre part, les néonicotinoïdes (Clothianidine,

thiaméthoxame ou imidaclopride) affectent la physiologie, le comportement

des pollinisateurs, et par conséquent la santé de l’Homme et de notre

environnement (Raine 2018; Crall et al. 2018; Alburaki et al. 2016; Doublet

et al. 2015; van der Sluijs et al. 2013; Di Prisco et al. 2013). Par exemple,

les pesticides et les insecticides contiennent des molécules chimiques qui

perturbent non seulement les capacités cognitives (Zhang and Nieh 2015;

Siviter et al. 2018) et hygiéniques des abeilles (Boutin et al. 2015; E. Zhang

and Nieh 2015; Siviter et al. 2018), mais aussi leur microbiote (Raymann,

Shaffer, and Moran 2017; Motta, Raymann, and Moran 2018), ce qui

augmente leur susceptibilité aux pathogènes et de ce fait perturbe l’équilibre

immunité-symbionte/microbiote.

Le dernier exemple concerne les métaux lourds, ayant une masse atomique

élevée, certains laissent de traces toxiques (Cd, Ni, Cu, Co, Al etc.) qui

altèrent la biodiversité microbienne dans le sol, les milieux aquatiques et les

systèmes biologiques (Koschorreck 2008; Huang, Kuang, and Shu 2016;

Hudson-Edwards and Dold 2015). Les études publiées par notre laboratoire

ont montré une corrélation entre les concentrations des métaux avec la

variation la composition des communautés microbiennes (Laplante and

Derome 2011; Laplante, Boutin, and Derome 2013). Par exemple, les métaux

lourds déversés par le drainage minier acide, en particulier le cadmium,

augmente l’acidité de l’eau, et induit des changements dans la composition

taxonomique des communautés bactériennes favorisant les

Alphaprotéobactéries.

Les effets des métaux traces toxiques sur l’environnement (Nordstrom 2011;

Bejan and Bunce 2015; Lavoie, Fortin, and Campbell 2012; Wu et al. 2016;

X. Zeng, Chen, and Zhuang 2015) et les êtres vivants (Vymazal 1987;

Giguère et al. 2004; Lacroix and Hontela 2004) sont connus, mais très peu

étudiés dans le cas des symbiontes microbiens associées aux systèmes

biologiques aquatiques, par exemple les Poissons (S. Zhang et al. 2015;

4

Bridges et al. 2018)

En général, les xénobiotiques perturbent la diversité, la structure et les

fonctions des communautés microbiennes, leur assemblage ainsi que leurs

interactions, selon des processus moléculaires évolutifs et adaptatifs

méconnus.

Les études de l’assemblage du microbiote de l’Homme sous antibiothérapie

(Costello et al. 2012) ou du microbiote de poisson euryhalin lors de

l’acclimatation à la salinité (V. T. Schmidt et al. 2015), étaient principalement

centrées sur des processus déterministes, avec peu de preuves d’une

colonisation stochastique. De plus, le rôle des processus écologiques et

évolutifs dans la résilience de la structure des communautés microbiennes

après perturbation reste à déchiffrer. Théoriquement, la nature de ces

processus peut varier entre neutre (stochastique) (Sloan et al. 2006;

Jayathilake et al. 2017) et sélective (déterministe) (Stegen et al. 2012; Q.

Zeng et al. 2017). Dans le contexte de la résilience du microbiote de l’hôte

ou environnemental, peu d’études ont été dédiée à cette question. Ces

derniers ont révélé que ce sont les processus déterministes qui induisaient la

dynamique de la succession bactérienne étudiée dans un contexte du sol

perturbé soit par un gradient d’épuisement des éléments nutritifs (Song et

al. 2015), soit par un choc thermique (Jurburg et al. 2017), ou par une

réhydratation pluviale(Placella, Brodie, and Firestone 2012)

Néanmoins, il reste encore beaucoup à faire pour comprendre les mécanismes

de l’assemblage des communautés microbiennes résilientes chez diverses

espèces d’organismes hôtes sous un contexte de perturbation et de résilience.

Les questions et les chapitres de cette thèse

Dans cette thèse, les impacts des effets anthropiques sur la diversité, la

structure, la fonction et la composition des communautés microbiennes dans

deux écosystèmes ; environnemental, et en association avec un hôte seront

discutés. En admettant que les connaissances actuelles sur l’évolution

microbienne à partir des microbes cultivables ne soient pas représentatives

de celles de la majorité inconnue, les approches de séquençage de nouvelle

génération « Next generation sequencing » (NGS) et le progrès de la bio-

5

informatique (banques des séquences, réseaux, algorithmiques), ainsi les

modèles récents en écologie et évolution microbienne nous ouvrent alors des

voies interdisciplinaires et intégratives prometteuses pour trouver des

réponses à nos questions. Ainsi dans cette étude, nous nous sommes

intéressés à trois problématiques :

1) La dynamique de la structure et de la composition des

communautés microbiennes, et leurs fonctions écosystémiques

2) L’évolution de leurs interactions et leur assemblage dans un

contexte anthropogénique

3) La résilience de leur structure et assemblage et leurs interactions

avec leur écosystème après perturbation (environnement et hôte).

Le premier chapitre est une revue bibliographique qui résume les méthodes

et les concepts de base en écologie microbienne.

Le deuxième chapitre étaye l’hypothèse que les perturbations chroniques

des communautés microbiennes lacustres par des métaux traces

entraineraient un découplage entre la diversité taxonomique et fonctionnelle.

Pour vérifier cette hypothèse, des communautés microbiennes ont été

échantillonnées dans cinq lacs exposés à un gradient de contamination

polymétallique (PCG), appartenant à un même bassin versant, situé à

proximité d’une mine de cuivre historiquement active pendant plus de

soixante ans. Avec une approche métagénomique, bio-informatique, nous

avons alors caractérisé le niveau d’intégrité des fonctions représentant les

services écosystémiques.

Le troisième chapitre étaye l’hypothèse qu’un processus de gradient

sélectif gouvernerait l'assemblage du microbiote d’un organisme soumis à des

perturbations graduelles et constantes.

Pour évaluer cette hypothèse, une approche d’évolution expérimentale à

court terme a été utilisée (six mois), laquelle, compte tenu du temps de

génération des Bactéries (20 à 30 minutes chez E. coli) équivaut à plus de

8000 générations. Plus précisément, nous avons testé expérimentalement au

laboratoire comment l’exposition prolongée constante ou graduelle au

6

cadmium (Cd) de 1200 individus des perchaudes (Perca flavescens) avait

affecté l’assemblage de leurs microbiotes par séquençage d’amplicons du

gène de l’ARNr 16S. Nous avons constaté qu’un gradient de sélection induit

par un gradient de concentration d’un métal toxique a perturbé non

seulement la physiologie de l’hôte, mais également le recrutement et

l’assemblage de son microbiote.

Le quatrième chapitre traite de la dynamique de la résilience du microbiote

de la perchaude après l’arrêt de l’exposition à des quantités sous-létales de

cadmium. Le terme « résilience » est employé pour décrire le changement

qui se produit lorsqu'une communauté retourne à un autre état stable après

perturbation. Le rétablissement des communautés microbiennes dépend du

type, de la durée, de l’intensité et du gradient de perturbation.

Nous focalisons sur les modèles car les rôles des processus écologiques et

évolutifs dans la résistance et la résilience de la structure du microbiote des

hôtes restent à documenter. Théoriquement, et telle que mentionné ci-

dessus, la nature de ces processus varie entre neutralisme (stochastique)

(Stephen P. Hubbell 2006; Sloan et al. 2006) et sélection (déterministe)

(Webb et al. 2002; Chase 2003). Ces derniers opèrent soit par filtrage

environnemental et exclusion compétitive (Cadotte et al. 2010; Stegen et al.

2012). Le peu d’études disponibles a révélé que ce sont des processus

déterministes qui régissent la dynamique de la succession bactérienne durant

le rétablissement des communautés microbiennes des sols. Dans notre étude

expérimentale (chapitre 3 et 4), nous avons évalué la contribution relative

des processus neutres et non-neutres à la résistance et à la résilience de

l’assemblage du microbiote de la perchaude à la suite d’un gradient

expérimental d’exposition métallique. Étant donné que les juvéniles de

perchaude peuvent tolérer des doses non létales de cadmium sans subir de

dommages physiologiques importants (Giguère et al. 2006; Campbell et al.

2005a), notre modèle hôte-microbiote est optimal pour étudier la capacité de

résilience du microbiote après une perturbation liée à l'exposition au

cadmium. Premièrement, nous avons quantifié avec des méthodes

appropriées les niveaux de métaux traces dans des échantillons de foie et

d’eau. Ensuite, nous avons comparé l’état de résilience de la structure et de

la fonction de la communauté dans l’eau et du microbiote de l’hôte entre des

7

régimes constants et variables d’exposition au cadmium. Afin de démêler

l'effet du xénobiotique du développement de l'hôte (Sylvain and Derome

2017; Burns et al. 2016a) sur l'ontogenèse, l'assemblage du microbiote a

également été évalué dans des conditions stables en tant que régime témoin.

Deuxièmement, nous discutons des changements globaux de la diversité

taxonomique et fonctionnelle ainsi que la prévalence des agents pathogènes.

Nous évaluons la contribution relative des processus neutres et non-neutres

dans la résilience du microbiote de perchaude de deux régimes d’exposition

métallique constant et graduel.

8

Chapitre 1 – Méthodes et concepts de

base en écologie microbienne

Ce chapitre est une bibliographie des méthodes et concepts de base utiles

pour assurer la bonne compréhension des trois chapitres de recherche de

cette thèse.

9

1.1 Les microbes sont ubiquitaires et l’environnement les filtre

Que leur mode de vie soit libre ou associé à un hôte (microbiote) les micro-

organismes constituent le générateur fonctionnel des métabolites primaires

et secondaires de tout écosystème. Leur importance est donc centrale et

primordiale dans les cycles biogéochimiques de la terre, la qualité des eaux,

en agriculture (fixation de l’azote, biostimulation de la croissance par

solubilisation d’éléments minéraux, biocontrôle des phytopathogènes), en

médecine (la médecine personnalisée pour les diabétiques, la médecine

régénératrice après transplantation des cellules souches, le traitement des

maladies infectieuses par des probiotiques), en industrie agroalimentaire et

en sécurité alimentaire, entre autres. Les nouvelles découvertes en

microbiologie, en écologie et en évolution microbienne ont, clairement, mis

en évidence l’implication de l’environnement à sélectionner ses communautés

microbiennes. Un simple exemple très répandu, les nodules racinaires des

plantes recrutent les Bactéries bénéfiques fixatrices d’azote (Brill 1975; Dos

Santos et al. 2012; Yang et al. 2017). Le système immunitaire de l’Homme

joue le rôle du filtre qui retient les bactéries bénéfiques responsables de la

maintenance de son homéostasie (Belkaid and Hand 2014; Belkaid and

Harrison 2017; Belkaid and Segre 2014). Ainsi, le microbiote contrôle et

régule de nombreuses fonctions vitales de l’hôte telles que l’immunité et

l’assimilation des nutriments chez les modèles animaux en génétique et en

médecine (Rawls, Samuel, and Gordon 2004; Wong and Rawls 2012; Heys et

al. 2018; Gould et al. 2018; Wong et al. 2015).

1.2 Accès à la biodiversité microbienne, de la boite de Pétri au

métagénome

Les microbes cultivables au laboratoire ne représentent qu’une infime

minorité (< 5%) de la biodiversité microbienne (Donachie and Begg 1970;

Konopka 1984; Amann, Ludwig, and Schleifer 1995; Connon and Giovannoni

2002; Nichols et al. 2008; Staley and Konopka 1985). Jusqu’à récemment,

ceci représentait une contrainte majeure pour caractériser la composition

génétique de l’ensemble des communautés microbiennes dans un échantillon

d’un environnement donné. Après deux décennies du séquençage du premier

génome bactérien (Haemophilus influenzae par Fleischmann et al (1995)

10

(Fleischmann et al. 1995), l’avènement des technologies de séquençage de

nouvelle génération Next generation sequencing » (NGS) a favorisé l’accès

aux ressources génétiques microbiennes de différents écosystèmes (sol, eau,

hôte-symbionte) d’une manière exhaustive. L’ensemble du répertoire

génétique d’une communauté microbienne est appelé métagénome. Il faut

toutefois garder à l’esprit que les découvertes fascinantes basées sur la

minorité cultivable, sans le recours aux approches métagénomiques, ont

permis aux biologistes de construire des banques des données généralistes

(GenBank, KEGG. UNIPROT etc.) , spécialistes et expertes (SEED, IMG/M,

BRENDA, etc.) à l’échelle des fonctions, des domaines, des protéines, de

gènes, de génomes et même des populations (Benson et al. 2013; Kanehisa

et al. 2014; “UniProt: The Universal Protein Knowledgebase” 2017;

Schomburg et al. 2004; I.-M. A. Chen et al. 2019). Ces connaissances

acquises contribuent aujourd’hui à donner des réponses claires aux questions

centrales portant sur les bases génétiques de la diversité microbienne, de

leurs capacités adaptatives et de leurs potentiels évolutifs dans un contexte

anthropique, ou naturel.

1.3 L’avènement du séquençage de nouvelle génération

Le NGS (next-generation sequencing) désigne une nouvelle série de

technologies qui a révolutionné le séquençage des acides nucléiques et

aminés. Le rendement du séquençage a augmenté de manière exponentielle

alors que le prix ne cesse de diminuer rendant ces technologies très rentables.

Les technologies NGS ont prouvé leur efficacité, rapidité et surtout leur

exactitude (Goodwin, McPherson, and McCombie 2016). Plusieurs

méthodologies de NGS ont remarquablement influencé nos connaissances

dans presque tous les domaines de la biologie moderne, depuis l’évolution

moléculaire jusqu’au diagnostic des maladies contagieuses et infectieuses et

en passant par les thérapies des maladies génétiques, le champ d’application

est très large. Depuis la découverte de la structure de l’ADN en 1953, à

l’élucidation du code génétique, cela nous a pris plus d’un quart de siècle pour

développer la méthode Sanger, une première génération de séquençage de

l’ADN, basée sur une synthèse chimique connue sous le nom « séquençage

par synthèse » ; un seul acide nucléique est déterminé à la fois, en fonction

de la longueur des séquences d’ADN d’un gène ou d’un génome. Une décennie

11

plus tard, le séquençage Sanger a été automatisé par « Applied Biosystems »

en 1987, traçant depuis, la voie vers le séquençage du premier génome

bactérien (Haemophilus influenzae) en 1995 (Fleischmann et al. 1995), puis

la levure (premier génome eucaryote) en 1996 (Goffeau et al. 1996), et le

génome de l’Homme en 2001 (Lander et al. 2001). Ce dernier a coûté 150

millions de dollars (https://www.genome.gov/about-genomics/fact-

sheets/Sequencing-Human-Genome-cost) au consortium international du

projet « Human genome » et une longue décennie de cartographie physique

et génétique pour compléter le génome. À la fin du projet en 2001, le

séquençage de Sanger avait atteint ses limites théoriques et techniques. Par

la suite, afin d’accélérer le séquençage, augmenter le rendement et

notamment réduire le coût et le temps, une nouvelle ère des méthodes

s’ouvra en 2005 avec la technologie 454.

La démocratisation du NGS avait par conséquent réduit drastiquement le coût

du séquençage du génome humain de 2.7 milliards ou millions? aux environs

1 000 dollars.

Les méthodes NGS se divise en deux grandes catégories : séquençage par

ligation (« SOLiD », « Complete Genomics ») et séquençage par synthèse

(Illumina, 454, Ion Torrent). La plupart des technologies se base donc sur la

dernière, et se classifie en deux méthodes, le séquençage par clôture cyclique

(Illumina), et le séquençage par addition successive des nucléotides (454,

Ion Torrent). En seconde et en troisième générations le séquençage par

synthèse produit respectivement des courtes et des longues lectures

(Goodwin, McPherson, and McCombie 2016). Les technologies de courtes

séquences (Illumina, Qiagen, 454, Ion Torrent) se caractérisent par les coûts

les plus bas, et produisent des séquences de haute précision, utiles pour la

détection des variants génétiques à l’échelle populationnelle. Cependant, les

technologies de longues séquences (PacBio et ONT) assurent des longues

séquences afin d’assembler des génomes complets ou pour d’autres

applications comme le séquençage des isoformes. Ainsi, chaque technologie

présente ses avantages et ses limites en termes de précision, de fiabilité, de

longueur des séquences et d’erreurs de séquençage (Goodwin, McPherson,

and McCombie 2016; Kumar, Cowley, and Davis 2019; Vincent et al. 2017).

12

Par conséquent, les méthodes NGS procurent un accès aux ressources

génétiques de la biodiversité microbienne. Depuis plus qu’une décennie, elles

ont ouvert le chemin aux approches omiques « Omics », dont la

métagénomique, pour identifier les micro-organismes et caractériser leur

contenu génétique à l’échelle phénotypique, fonctionnelle et métabolique.

1.4 Les approches de la métagénomique

Tout d’abord un métagénome c’est l’ensemble des matériels génétiques des

microorganismes séquencés dans un échantillon d’un environnement donné

(sol, air, eau, animal hôte, plante hôte).

Les méthodes métagénomiques sont souvent subdivisées en deux approches.

La première consiste en l'amplification ou séquençage d’un seul gène

marqueur conservé universellement chez tous les micro-organismes, par

exemple le gène de la sous-unité 16S de l’ARN ribosomique chez les

Bactéries, de la sous-unité 18S chez les Eucaryotes microbiens (zooplanctons

et phytoplanctons). Alors que la deuxième approche consiste en un

séquençage de l'ensemble du contenu génétique d’une communauté

microbienne « Whole-Genome-Sequencing » (WGS), ce qui procure un

aperçu global du répertoire fonctionnel et du contenu génétique du

métagénome en question.

1.5 L’approche d’amplicons basée sur un gène marqueur universel

Cette approche consiste à amplifier un gène marqueur à partir d’un

échantillon d’ADN. Une amplification « PCR » est réalisée pour quantifier

l'abondance relative des variants du marqueur en question au sein de la

communauté microbienne présente dans un échantillon donné. Le gène

ribosomique 16S chez les Procaryotes et le gène 18S chez les Eucaryotes sont

donc des marqueurs universels couramment utilisés en phylogénie

moléculaire et plus généralement pour détecter les changements de

composition des communautés microbiennes.

Le gène de l’ARN ribosomique 16S a été proposé comme un marqueur de

phylogénie pour la première fois par Carl Woese (Woese 1987) . La taille de

ce gène fait environ 1500 paires de bases, il possède des propriétés qui

répondent aux critères d’un candidat d’un marqueur génétique universel pour

différencier les différentes lignées bactériennes. Le gène 16S est ubiquitaire,

13

très conservé entre les différentes lignées bactériennes et évolue lentement

(Gillespie et al. 2006; Pei et al. 2010). Il est composé de de neuf domaines

qui se subdivisent entre deux types de séquences conservées et variables.

Les domaines conservés évoluent très lentement avec peu de mutations

fixées. Les régions variables sont ciblées par des amorces universelles car

elles permettent généralement distinguer entre les différents genres

bactériens (Baker, Smith, and Cowan 2003; Guo et al. 2013; Kembel et al.

2012; Caporaso et al. 2011), même si certaines souches peuvent être autant

plus divergentes au sein d’un même genre que d’entre genres différents

(Johansen et al. 2017; Acinas et al. 2004; Janda and Abbott 2007).

L’amplification de la séquence entière du marqueur n’est pas obligatoirement

nécessaire, cependant une région hypervariable serait ciblée comme un code-

barre génétique. Après amplification de ce dernier, le produit de PCR est

purifié et enfin séquencé. Ensuite, les séquences lues des amplicons sont

analysées par des méthodes bio-informatiques, qui se résument par les

étapes suivantes :

Le contrôle de qualité consiste sur l'élimination de courtes séquences, la

filtration des homopolymères et des séquences ayant un score de qualité

moyen à très bas.

La correction du nombre de copies s’il s’agit du marqueur (16S/18S), et

filtration des chimères produites par le biais d'hybridation non spécifique des

amorces durant l'amplification PCR.

La classification des séquences filtrées en unités de base de diversité tout en

se basant sur un seuil de similarité nucléotidique (97% à 99%) arbitraire

communément utilisé en écologie microbienne, et à la fois contesté (Edgar

2018). Au sein d’une même unité de diversité obtenue avec une similarité de

97%, les études documentent une grande diversité des espèces, par exemple

au sein du genre Bacillus (Maughan and Van der Auwera 2011; Connor et al.

2010), d’où la contestation de l’approche de seuil de similarité. Les unités

taxonomiques sont alors annotées taxonomiquement et comparées

phylogénétiquement afin de comprendre la structure et la composition des

communautés microbiennes en question (Figure 1).

14

1.6 L’approche métagénomique globale basée sur le séquençage de

l’ADN total.

Pour cette approche, l’échantillon d’ADN est fragmenté aléatoirement par

fragmentation mécanique. Les fragments obtenus sont de différentes tailles,

ils sont clonés aléatoirement dans des vecteurs afin de construire une banque

de librairies à petits ou larges inserts. Tout dépend de l’intérêt de l’étude, les

librairies peuvent être criblées afin de cibler un gène d'intérêt biomarqueur

de l’écosystème en question, ou bien séquencées dans son intégralité afin de

permettre la caractérisation du contenu génétique et taxonomique du

métagénome (Schéma 1.1). Après le séquençage à haut débit, l’assemblage

des séquences en « contigs », suffisamment longs permet de prédire le

répertoire des gènes dans l’échantillon. Selon la complexité de la diversité de

l’environnement étudié, les assembleurs peuvent permettre la reconstitution

de génomes presque complets à partir d’un métagénome (Albertsen et al.

2013; Parks et al. 2015; Iverson et al. 2012; Albertsen et al. 2013; Mehrshad

et al. 2016). L’analyse bio-informatique des méta-communautés post-

assemblage se résume en trois étapes principales :

Prédire des cadres de lecture ouverts (« Open Reading Frame » ORF) des

gènes.

Assigner des affiliations taxonomiques de gènes prédits.

Annoter les fonctions de gènes en spécifiant les ontologies de leurs fonctions.

L’abondance de chaque gène est donc déterminée par la fréquence de son

occurrence et la profondeur des séquences lues qui ont servi à l’assemblage

de son contig prédit.

1.7 Approche de l’opération taxonomique universelle (UTO)

15

Schéma 1. 1 Approches métagenomiques de l’analyse de microbiote.

Édité de Morgan XC, Huttenhower C (2012) PLoS Comput Biol 8(12): e1002808.doi:10.1371/journal.pcbi.1002808

Une étape cruciale de toutes les études en écologie microbienne est le

regroupement des séquences des communautés de micro-organismes en

groupes (clusters en anglais) phylogénétiquement proches (97% de

similarité) appelés OTU (« operational taxonomic unit ») (Schéma 1.1).

Cependant, cette approche a été remise en question à plusieurs reprises, car

le concept OTU ne prend pas en compte la théorie de spéciation chez les

Bactéries (Preheim et al. 2013; T. S. B. Schmidt, Rodrigues, and Mering

2014). Des approches alternatives ont été proposé récemment pour

minimiser le biais introduit par le seuil d’identité.

16

La notion de l’OTU représente donc ici l’unité de base de diversité même si le

concept derrière la définition de l’espèce demeure problématique et un sujet

de débat actif (Doolittle and Zhaxybayeva 2009).

1.8 L’approche des variants des séquences d’amplicons (VSA)

Pour pallier le problème du seuil d’identité (97%) arbitraire de l’approche

UTO, il a été proposé récemment que les taxons devraient être définis avec

une approche d’identité exacte des séquences des gènes marqueurs

(Callahan et al. 2016). Cette approche alternative à l’UTO est basée sur la

détermination des taxons par des variants des séquences exacts (VSEs), et

connue sous le terme de variants des séquences d’amplicons (VSA)s « ASVs :

amplicons sequence variants » (Callahan, McMurdie, and Holmes 2017) ou

UTO-à-rayon zéro (zUTOs), tel que proposé par Edgar (2016).

Les VSAs de novo sont déduits d'un processus d’apprentissage automatique

dans lequel les séquences biologiques sont distinguées des erreurs sur la

base, d’un modèle d’erreur via un algorithme de débruitage « Denoising »

DADA2 (Callahan et al. 2016) et d’une fonction (f) de transition de probabilité

des erreurs. Le modèle d’erreur assume que les séquences répétées ont plus

de chances d'être observées que celles contenant des erreurs.

Par conséquent, l'inférence des VSAs de chaque échantillon ne peut pas être

effectuée indépendamment de chaque lecture considérée comme la plus

petite sous unité de données. Selon Callahan et al. (2017), contrairement aux

unités UTOs de novo, les VSAs sont des étiquettes cohérentes car ils

représentent une réalité biologique. Ces unités peuvent être déduites et

comparés avec différentes études ou différents échantillons. Cette approche

n’augmente pas seulement la résolution taxonomique, mais elle simplifie

également les comparaisons entre les études en éliminant la nécessité de

réannoter les taxons lorsque les ensembles de données sont fusionnés

(Glassman and Martiny 2018). Grâce à ces avantages, il y a eu une

augmentation du nombre de pipelines bioinformatiques cherchant à utiliser

les VSEs et à minimiser le biais d’inférence de la diversité (Callahan,

McMurdie, and Holmes 2017; Edgar 2016; Amir et al. 2017). En outre, les

auteurs de cette approche ont déclaré que les VSEs devraient remplacer les

17

OTUs (Callahan, McMurdie, and Holmes 2017). Cependant, avant l'adoption

de toute nouvelle approche, il est vraiment nécessaire de quantifier la

manière dont celle-ci se compare à un grand nombre de recherches

antérieures (Glassman and Martiny 2018). De plus, les classifications d’UTOs

restent biologiquement utiles pour comparer la diversité de grands ensembles

de données ou pour identifier des clades partageant des traits communs

(Delgado-Baquerizo et al. 2018). Parallèlement, une approche nommée Z-

UTOs a été proposée pour prendre en considération les variations intra-

taxons, elle consiste en la construction d’UTOs avec un seuil de 100% de

similarité de séquences (Edgar 2018).

Indépendamment de leur l’exactitude, les VSAs Z-OTUs et UTOs restent des

unités moléculaires de base approximatives de la diversité d’une

communauté. Comme les deux approches sont limitées quant au contexte

fonctionnel, nous aurons besoin de définir une l’unité moléculaire

fonctionnelle de la diversité.

1.9 Les mesures écologiques et phylogénétiques de la diversité

Les matrices d’abondance et d’occurrence des UTO permettent de calculer les

indices de la diversité intra-échantillons (Alpha) et inter-échantillons (Beta).

1.9.1 Les mesures de la diversité alpha

La diversité des UTO au sein d'un échantillon donné est connu sous le terme

diversité Alpha. On distingue deux types des mesures Alpha ; la richesse

spécifique (nombre des UTOs, « chao-1 index » et « ACE ») et l’équitabilité

de la distribution d’abondance des UTO (Shannon et Simpson). La

comparaison des distances écologiques et phylogénétiques inter-

communautés permet de mettre la loupe sur la divergence et la convergence

de leurs structure, fonction et diversité dans des contextes divers (pollution,

traitement, spatio-temporel).

L’indice de la richesse spécifique

Le moyen le plus simple de mesurer la diversité c'est d’énumérer les UTO

présents dans l'échantillon, appelé la richesse spécifique. La richesse ne

considère pas la structure de la communauté et elle ne détecte pas

l'abondance différentielle entre les UTO.

18

D’autres estimateurs de la diversité alpha « chao- index » (Chao 1984) et

« ACE» (Abundance-based coverage estimator of species richness) (Chao and

Lee 1992; Chao and Yang 1993) sont aussi utilisés.

Supposons que SObs soit le nombre des UTOs abondants plus que deux fois

dans un échantillon donné et a soit le nombre inconnu des taxons présents

mais qui ne sont pas observés dans la communauté, la richesse serait :

La richesse sans correction pour les taxons non observés serait

R=SObs

Dans le cas de la richesse avec correction pour les taxons non observés, on

utilise l’indice de choa1 ou l’indice :

L’indice de chao1 :

𝑆𝐶ℎ𝑎𝑜1 = 𝑠obs +𝐹1(𝐹1−1)

2(𝐹2+1)

F1 et F2 sont le nombre de singletons et de doublons, respectivement, et Sobs

est le nombre d'espèces observées

L’indice ACE :

𝑆𝐴𝐶𝐸 = 𝑠abund +𝑠rare

𝐶ACE+

𝐹1

𝐶ACE𝛾𝐴𝐶𝐸

2

Soit Sabund and Srare sont respectivement le nombre d’UTO abondantes et rares,

CACE est l’estimateur de la couverture d’abondance de l’échantillon, F1 est la

fréquence des singletons et 𝛾𝐴𝐶𝐸2 est le coefficient estimé de la variation pour

les OTUs rares.

Le coefficient de variation estimé serait défini par la formule suivante :

𝜸𝑨𝑪𝑬𝟐 = 𝒎𝒂𝒙[

𝑺𝐫𝐚𝐫𝐞

𝑪𝐀𝐂𝐄

∑ 𝒊(𝒊−𝟏)𝑭𝒊𝟏𝟎𝒊=𝟏

(𝑵𝐫𝐚𝐫𝐞)(𝑵𝐫𝐚𝐫𝐞−𝟏)− 𝟏, 𝟎]

Les indices basés sur l’abondance

Il y a plusieurs indices qui peuvent décrire la structure d’une communauté.

Les deux mesures les plus communément utilisées sont Shannon (Lemos et

19

al. 2011; Magurran and McGill 2011) et Simpson (Simpson 1949; Lemos et

al. 2011) . Le dernier ajoute plus de poids sur l'abondance des UTO.

L’indice de Shannon (H)

𝐻 = − ∑ (𝑝𝑖 ln 𝑝𝑖)𝑆𝑖=1

Où s est le nombre total d’UTO et pi est la proportion de la communauté

représentée par l’UTO i.

L’indice de Simpson (D)

𝐷 =1

∑ 𝑝𝑖2𝑠

𝑖=1

Où s est le nombre total des taxons et pi est la proportion de la communauté

représentée par l’UTO i.

Ces indices ne sont pas linéaires, ce qui signifie qu’un échantillon avec un

indice Simpson de 0.8 n'est pas deux fois plus diversifié qu’un échantillon

avec indice Simpson de 0,4. Un moyen plus représentatif de la diversité Alpha

contourne ce problème par le calcul de la diversité effective tel que proposé

par (Jost 2006; 2007).

1.9.2 Les mesures de la diversité Beta

Le changement de la diversité d’un d’échantillon à l’autre est connu sous le

nom de la diversité Beta. La comparaison de cette diversité implique des

calculs matriciels des distances entre les échantillons, basés sur leurs profils

taxonomique ou génétiques (UTO). Il existe plusieurs façons de calculer les

distances tout en se basant sur la similarité des profils d'abondance et la

proximité génétique des UTO. On distingue les distances écologiques des

distances phylogénétiques.

1.9.2.1 Les distances écologiques

Les distances de Jaccard (Jaccard 1912) et Bray-Curtis (Bray and Curtis

1957) sont les mesures les plus communément utilisées. L’indice de Jaccard

mesure les similitudes entre deux ou plusieurs groupes. L'indice de Bray-

Curtis (BC) est utilisé pour quantifier la dissimilarité compositionnelle entre

deux sites écologiques différents. Les abondances sur chaque site sont

exprimées en proportion. La dissimilitude de Bray-Curtis est comprise entre

20

0 et 1, 0 signifiant que les deux sites ont la même composition (c'est-à-dire

qu'ils partagent toutes les espèces) et 1, que les deux sites ne partagent

aucune espèce.

1.9.2.2 Les distances phylogénétiques

Les distances UniFrac (C. Lozupone and Knight 2005; C. A. Lozupone and

Knight 2015), prennent en compte la distance génétique des UTO dans

chaque échantillon. La distance Unifrac pondérée ajoute l'information de

l'abondance relative de chaque UTO à chaque distance génétique. La méthode

construit un arbre phylogénétique de tous les UTO, puis il calcule le ratio des

longueurs de branches d’UTO uniques versus celles d’UTO communs.

La version non pondérée de l’Unifrac transforme l’abondance en occurrence.

Les distances pondérées et non pondérées sont sensibles à la présence des

plus rares et des plus dominants. Une version plus équilibrée a été proposée,

dénommée distance Unifrac généralisée (J. Chen et al. 2012). La variante

Gunifrac prend en compte le paramètre d’équitabilité de l’abondance relative

des UTO dans le processus de pondération.

Les distances phylogénétiques ne peuvent pas décrire les corrélations ni les

co-occurrences des UTO. Pour ce faire les réseaux sont des outils importants

pour visualiser les interactions entre les communautés microbiennes.

1.10 Réseaux d’interactions microbiennes

Les réseaux, largement appliquées en génomique, sont des outils

extrêmement puissants afin d’explorer les relations entre les entités

biologiques, par exemple, la similarité entre deux gènes, les interactions

entre deux protéines, ou en écologie microbienne par la co-occurrence des

taxons (Weiss et al. 2016). Les corrélations de OTU, basées sur l’abondance

ou l’occurrence décrivent bien des liens cohésifs au sein d’une méta-

communauté, mais pas nécessairement leur causalité. En revanche, les

propriétés topologiques des réseaux peuvent prédire les processus

écologiques conducteurs de la dynamique spatiale et temporelle des

interactions microbiennes.

En considérant la simple formulation de Lotka-Volterra des interactions entre

deux espèces, les différents types d’association comme le mutualisme,

21

commensalisme ou le parasitisme ont été aussi élucidés (Lidicker 1979). Il

est important de citer qu’il existe au moins six types d'interactions

qualitativement distinctes.

Le mutualisme concerne les interactions qui sont bénéfiques pour les deux

partenaires d’interactions.

2) Le commensalisme désigne les interactions qui sont bénéfiques pour l'un

et sans effet pour l'autre.

3) Le parasitisme définit les interactions qui sont bénéfiques pour l'un et

néfastes pour l'autre.

4) Le neutralisme se produit quand les interactions n’ont pas d'effet sur les

deux partenaires.

5) L’amensalisme englobe les interactions qui ont des effets négatifs sur l’un

et pas d’effets sur l’autre.

6) La compétition concerne les interactions qui sont néfastes pour les deux

partenaires.

Du point de vue écologique, l’orientation de la direction des arrêtes dans les

réseaux d’interactions indique si une espèce affecte d’autres espèces. Par

exemple, cela voudrait dire, que les réseaux sans arrêtes orientés ne peuvent

pas décrire où distinguer qualitativement la nature des interactions entre OTU

à part les corrélations positives et négatives des abondances. À l'heure

actuelle, il est difficile d’inférer des réseaux dirigés sans l’acquisition des

séries des données temporelles car la modélisation mathématique de la

dynamique de communautés peut nous renseigner sur les types de ces

interactions.

Les fonctions métaboliques sont souvent plus informatives que les réseaux

de co-occurrence. Étant donné que les fonctions métaboliques des microbes

peuvent être différentes ou redondantes, elles peuvent indiquer une

complémentarité ou une redondance métabolique et ainsi déterminer si les

arêtes sont susceptibles de représenter un mutualisme ou une compétition.

Des outils basés sur des modèles métaboliques peuvent fournir de telles

prédictions. Par exemple, le calcul de la consommation et la sécrétion de

métabolites permettent de fournir des indices de complémentarité ou de

22

compétition (Cao et al. 2015). En revanche, d’autres méthodes se focalisent

sur l’analyse du bilan du flux métabolique pour calculer les taux de croissance

des espèces en monoculture et en co-culture. Ces outils permettent aux

chercheurs de prédire les interactions écologiques entre les espèces.

Cependant, les modèles métaboliques nécessitant la séquence complète du

génome, sont souvent limités par une annotation médiocre et peuvent être

inexacts sans vérification manuelle de l’annotation par des experts (Henry et

al. 2010). Par conséquent, leur utilisation est limitée à un petit nombre

d'espèces bien étudiées (Faust et al. 2018).

1.11 Les quatre forces évolutives et processus écologiques

1.11.1 La mutation

La variation génétique est une condition préalable aux changements évolutifs.

En l'absence d'une telle variation, aucun changement ne peut être réalisée.

La variation génétique est essentiellement générée par mutation. Il est donc

clair que la mutation est une force évolutive majeure et moteur de l'évolution

(Hershberg 2015; M. Lynch 2016). Lorsqu’une mutation se produit chez un

individu, sa fréquence augmentera au sein de la population ou disparaîtra de

la population. La fixation des mutations à travers les générations dépend

d’une combinaison avec la force de sélection naturelle, de la migration ainsi

que la dérive génétique.

Chez les bactéries, les gènes correspondent à 85–90% du génome et les

régions intergéniques sont petites et englobent de sites régulateurs (Mira,

Ochman, and Moran 2001). Les gènes ont peu ou pas d'introns et sont

généralement organisés en unités polycistroniques (opérons). Les mutations

ne se restreignent pas aux substitutions nucléotidiques synonymes et non-

synonymes. Rappelons qu’une substitution est un remplacement d’une seule

base nucléotide d’un codon au sein un gène. Si la protéine codée par ce gène

était modifiée, la substitution est synonyme (souvent troisième position du

codon), sinon la substitution est non-synonyme (deuxième position du

codon). Il existe plusieurs types de mutations telles que les insertions et de

délétions et les mécanismes de transferts horizontaux connus chez les

procaryotes ; la conjugaison la transduction et la transformation. Les

insertions et les délétions de matériel génétique ont généralement des effets

23

délétères sur l'expression des gènes (Price, Arkin, and Alm 2006). Les

mécanismes du transfert horizontal représentent une source d'hérédité

horizontale qui permet un flux des gènes transdomaines en éliminant les

barrières génétiques même entre des lignées évolutives distantes (Dagan,

Artzy-Randrup, and Martin 2008; Aminov 2011; Zhaxybayeva, Lapierre, and

Gogarten 2004). Le transfert horizontal est essentiel pour l’adaptation des

cellules aux nouvelles conditions écologiques, y compris les environnements

cliniques et ceux contenant des antibiotiques (Niehus et al. 2015). Les

mécanismes des transferts horizontaux chez les Bactéries sont fréquents et

se produisent naturellement à l’échelle des secondes (Kejnovsky and Trifonov

2016; Andam, Williams, and Gogarten 2010; Syvanen 2012). De l’autre coté,

les Bactéries n'échappent pas aux remaniements connus en génomique,

comme les phénomènes de duplication, de transposition et d'intégration qui

se produisent aussi spontanément et dépendamment du contexte évolutif.

Donc, la duplication des gènes ou des opérons peut engendrer une source de

variation importante dans les génomes bactériens. La transposition et les

transferts horizontaux de gènes notamment la conjugaison plasmidique sont

des mécanismes de transport génétique qui facilitent soit l’insertion des

éléments génétiques mobiles (mobilome) soit le flux de gènes accessoires

adaptatifs (Dabbs and Sole 1988; Pérez Carrascal et al. 2016; Ana Segura

2014; Gillings 2013; Dalia et al. 2015). L’intégration via des intégrons

(cassettes des gènes de résistance aux antimicrobiens ou de virulence) est

également connue chez les Bactéries (Frost et al. 2005; Starikova et al. 2012;

Oliveira-Pinto et al. 2016). De même, la recombinaison par site homologue

est largement connue et étudiée (Castillo-Ramírez et al. 2011). En somme,

les sources des mutations chez les Bactéries sont très variées, ce qui suggère

la mutation comme une force majeure génératrice permanente de la diversité

génétique moléculaire.

La mutation est souvent étudiée en assumant que certains types des

mutations d'ADN (par exemple les substitutions synonymes) évoluent

entièrement d'une manière neutre. Les premières études d’évolution

expérimentales des Bactéries (exposition des Bactéries aux virus) ont montré

pour la première fois que les mutations se sont produites avant la sélection

(Luria and Delbrück 1943).L’expérience de Salvador Luira et Max Delbrück

24

fut montré que les mutations surviennent de manière aléatoire spontanée et

ne sont pas induites par le force de sélection naturelle. Plusieurs méthodes

pour éliminer les effets de la sélection naturelle durant les études

expérimentales de mutation ont été discutées (Hershberg 2015), par exemple

les expériences d’accumulation des mutations (Elena and Lenski 2003; Lind

and Andersson 2008; Brockhurst, Colegrave, and Rozen 2011).

1.11.2 La sélection

La sélection naturelle est le concept central de la théorie de l'évolution de C.

Darwin. Cette force évolutive a révolutionnée nos idées sur la façon dont les

êtres vivants ont évolué depuis leur ancêtre commun. Elle affirme que les

variations spontanées étaient une caractéristique commune de toutes les

formes de vie(Moxon 2011). Elle explique comment les populations peuvent

évoluer de telle manière qu'elles deviennent mieux adaptées à leur

environnement au fil du temps et au cours des générations. Les variants

spontanés qui sont mieux adaptés augmentent en fréquence grâce à la

sélection naturelle parce que leur progéniture hérite de ces caractéristiques

avantageuses pour survivre.

Les effets de la sélection sur la fréquence des gènes au cours des générations,

ont été largement étudiés par les généticiens des populations (Dykhuizen

1990). Les causes et la façon dont la variation génétique dans un

environnement crée des différences sélectives (Gac et al. 2012; Wielgoss et

al. 2013; Good et al. 2017; Maddamsetti, Lenski, and Barrick 2015). La

compréhension du phénotype (par exemple les facteurs de la croissance

bactérienne, les voies métaboliques) et la définition des composants

importants de l'environnement comme les facteurs biotiques et abiotiques

deux défis majeurs dans les études expérimentales de la sélection.

La sélection naturelle peut fonctionner de différentes manières en distinguant

trois modes principaux: la sélection négative ou prufiante, la sélection

positive et la sélection balancée (Shapiro 2014). La sélection négative (parfois

appelée purifiante) est la tendance des individus inaptes à se reproduire

moins et donc à être éliminés de la population. La sélection positive favorise

25

la survie et la reproduction de variants avantageux et concurrentiels par

rapport au reste de la population. Lors d'un balayage sélectif, des variants

sélectionnés positivement remplacent des variants non sélectionnés. La

sélection diversifiante pourrait être considérée comme des balayages sélectifs

incomplets mais favorables dans une population (Shapiro 2014). Le balayage

sélectif est le processus par lequel un gène adaptatif ou un allèle

(éventuellement un variant spécifiant une niche) se répand dans une

population par recombinaison plus rapidement que par expansion clonale. Par

conséquent, la variante adaptative est présente dans plus d'un clone et la

diversité n'est pas purgée à l'échelle du génome (Shapiro 2014). La sélection

diversifiante est en cas particulier fréquence-dépendante négative, lorsqu’un

individu avec un phénotype considéré comme "rare" par rapport aux autres

individus pourra gagner en survie ou reproduction grâce à cette rareté. C'est-

à-dire qu'un individu sera favorisé et aura un gain plus important en fitness

si son phénotype est moins fréquent que les autres individus de la population.

C'est une des pressions de sélection les plus fortes, cependant les exemples

dans la nature sont assez rares, tout du moins difficilement observables

directement. La fréquence-dépendante négative a été observée chez les

gènes comportements rares des drosophiles (Fitzpatrick et al. 2007) et le

gènes de coloration des males chez les poissons guppies (Olendorf et al.

2006)

Dans un autre exemple, une mutation particulière est favorisée par la

sélection positive lorsqu'elle est à faible fréquence, mais elle devient délétère

à haute fréquence. La mutation ne balaye jamais toute la population, mais

fluctue autour d'une fréquence intermédiaire (Shapiro 2014). En fonction de

la délimitation des frontières entre les populations, il peut être difficile de

distinguer la sélection diversifiante et la sélection positive chez les micro-

organismes (Shapiro 2014).

Deux autres types de sélection sont aussi connues, la sélection par « auto-

stop » génétique et la sélection purifiante. Lorsqu'une mutation avantageuse

serait fixée dans une population par sélection positive, une autre mutation

neutre ou délétère physiquement liée, peut faire l'auto-stop avec la fixation

de la première. Cet effet est connu sous le terme de la sélection liée (linked

26

selection) par auto-stop génétique (Birky and Walsh 1988; Smith and Haigh

1974).

Les mutations non synonymes sont beaucoup plus nombreuses et ont plus

des effets plus importants sur le fitness que les mutations synonymes (Nei

and Gojobori 1986). Vu que ces effets sur le fitness sont généralement

négatifs, ces mutations sont progressivement éliminées des populations par

sélection purifiante conduisant à de faibles ratios de taux de substitution non

synonymes (dN) sur synonymes (dS) (dN/dS <1) (Kuo, Moran, and Ochman

2009). Toutefois, les substitutions non synonymes peuvent être également

affectées par la sélection naturelle, par exemple en raison du biais lié à

l'usage de codons (Gouy and Gautier 1982; Sharp et al. 2005), en particulier

chez les bactéries à croissance rapide (Vieira-Silva and Rocha 2010). Les

régions intergéniques correspondent rarement à plus de 20% du génome et

sont sujettes à une sélection purifiante à des niveaux intermédiaires entre

ceux des substitutions non synonymes et synonymes (Thorpe et al. 2017).

Globalement, cela signifie que la plupart des mutations du génome bactérien

sont délétères (Rocha 2018).

1.11.3 La migration

La migration n’est pas uniquement une force évolutive mais aussi une force

physique. La forme, la taille et la motilité des cellules microbiennes

apparaissent comme des facteurs cellulaires intrinsèques déterminants dans

le comportement rhéologique des cultures bactériennes vivantes au cours du

processus de croissance. Vu que le fonctionnement d’un écosystème dépend

des interactions sociales et de l'auto-organisation spatiale, ces derniers

dépendent de la motilité des cellules bactériennes (Ebrahimi, Schwartzman,

and Cordero 2019). Avec la migration, le taux et l'impact des transferts

horizontaux peuvent augmenter considérablement pour des locus sous

sélection positive. Sans migration le transfert horizontal devient rare (Niehus

et al. 2015).

La force de migration est très importante car elle nous permet de comprendre

la dynamique spatiotemporelle des communautés microbiennes symbiontes,

27

planctoniques ou biofilms. En écologie microbienne la migration est une force

majeure, mais nous comprenons très peu de choses sur la structure spatiale

et la dynamique temporelle de microbiote chez l'Homme ou n'importe quel

système biologique. Des travaux récents sur le microbiote du poisson zèbre

montrent que l'influence de la structure spatiale sur la dynamique des

populations bactériennes ne peut être révélée que par imagerie directe

(Jemielita et al. 2014; Hammers et al. 2015; Wiles et al. 2016). Comment les

populations microbiennes s'établissent, croissent, fluctuent et persistent ;

sont des questions d’actualités que nous élaborons dans la section des

modèles mathématiques.

1.11.4 La dérive génétique

Cette force est la base de la théorie neutre de l'évolution moléculaire et de la

génétique des populations. La théorie neutre affirme que la grande majorité

des changements évolutifs au niveau moléculaire sont causés par la fixation

aléatoire de mutations neutres, due à la perte aléatoire de certains génotypes

rares dans des populations finies (M. Kimura 1991). La théorie affirme

également que la majeure partie de la variabilité génétique au sein de

l'espèce (telle que le polymorphisme des protéines et de l'ADN) est

sélectivement neutre ou presque, et qu'elle est maintenue dans l'espèce par

l'équilibre entre mutation et extinction aléatoire. Cette théorie suppose que

seule une minorité des mutations de novo ont soit des effets suffisamment

délétères sur le fitness qu'elles ont peu de chances de se fixer dans la

population, soit sont soumises à une sélection positive suffisamment forte

pour contrer les effets de la dérive et se fixer. Il existe toutefois des mutations

soumises à une sélection faible, qui peuvent être fixées lorsque les allèles

alternatifs sont éliminés par dérive génétique (Jensen et al. 2019).

L’importance de la force de la dérive génétique pour dicter la trajectoire

évolutive microbienne à l’échelle de la complexité d’un génome entier a été

largement débattu (Michael Lynch 2006; Charlesworth and Barton 2004;

Daubin and Moran 2004; Michael Lynch and Conery 2003; Hershberg, Tang,

and Petrov 2007). La dérive génétique est considéré comme une force qui

contribue à la réduction de taille de génomes bactériens (Kuo, Moran, and

28

Ochman 2009), par exemple chez les endosymbiontes des insectes (Alleman,

Hertweck, and Kambhampati 2018) , mais pas chez les bactéries symbiontes

marines (Bobay and Ochman 2017).

Contrairement aux Eucaryotes, pour lesquels la densité génique est très

variable (Il n’y a pas de relation entre la taille du génome et le nombre de

gènes) (Ryan Gregory 2002; Michael Lynch and Conery 2003), la taille du

génome des bactéries est étroitement liée au nombre de gènes (Mira,

Ochman, and Moran 2001; Giovannoni et al. 2005). Par conséquent, les

forces évolutives et plus particulièrement les mutations agissent

principalement au niveau des gènes codants ou de leur séquence de

régulation et ont donc des effets importants sur l'architecture globale des

génomes bactériens (Kuo, Moran, and Ochman 2009). En raison des

mutations de novo (délétions, insertions) chez les génomes bactériens

(Andersson and Andersson 2001; Mira, Ochman, and Moran 2001; Nilsson et

al. 2005; Hershberg, Tang, and Petrov 2007), les gènes seront inactivés et

perdus s'ils ne sont pas conservés par sélection.

À l’extrême, les gènes essentiels doivent, par définition, être préservés,

tandis que ceux qui n'offrent aucun effet bénéfique perdront leur fonction

avec le temps. Cependant, la plupart des gènes se situent quelque part entre

ces extrêmes et l'étendue de la dérive génétique déterminera le nombre de

gènes conservés (Ochman and Davalos 2006; Bobay and Ochman 2017;

Khachane, Timmis, and Martins dos Santos 2007).

1.11.5 Interactions des forces évolutives

Il est généralement admis que la sélection naturelle favorise la diminution

des taux de mutation, vu que les mutations sont souvent délétères (Motoo

Kimura 1967; Drake 1991; Dawson 1998; Michael Lynch 2010). Drake (1991)

et collaborateurs postulaient que la réduction de taux de mutation

impliquerait certains coûts physiologiques et par conséquent que la sélection

naturelle entraînerait à la fois la réduction des taux de mutation et la

diminution progressive de cette réduction (Drake 1991). En revanche,

Michael Lynch a suggéré un modèle alternatif selon lequel le taux de mutation

29

n'est pas fixé par sélection naturelle sur le coût physiologique, mais plutôt

par la dérive génétique (Michael Lynch 2010).

Au fur et à mesure que les taux de mutations par site nucléotidique

diminuent, la sélection devient plus faible pour réduire encore les taux de

mutation, jusqu'à atteindre un point où la sélection n'est plus assez puissante

pour contrecarrer l'action de la dérive génétique (Hershberg 2015; Michael

Lynch 2010)

Conformément au modèle de Lynch, les taux de mutation par base étaient

inversement corrélés à la taille effective de la population (Ne) chez les

Procaryotes et les Eucaryotes (Michael Lynch 2010; Sung et al. 2012). Étant

donné que le « Ne » est inversement lié à la dérive, on peut donc affirmer

que les taux de mutation deviennent plus élevés à mesure que la force de la

dérive par rapport à la sélection devient plus forte.

Lynch a ensuite raffiné son modèle de « barrière de dérive » en montrant que

la régression des taux de mutation par rapport à Ne est élevée pour les

Procaryotes par rapport aux Eucaryotes (M. Lynch 2016). La capacité de la

sélection naturelle à raffiner un phénotype est finalement limitée par le bruit

créé par la dérive génétique qui est elle-même limitée par la taille finie de la

population et des effets stochastiques de mutations associées (M. Lynch

2016). Cette découverte suggère que, pour un Ne donné, la sélection peut

être moins efficace pour réduire les taux de mutation chez les Procaryotes.

Pour expliquer ce phénomène, Lynch a suggéré que la magnitude de la

sélection visant à réduire les taux de mutation ne soit pas simplement

fonction du taux de mutation par base, mais également du potentiel de

mutation délétère du génome.

Les communautés microbiennes possèdent des caractéristiques bien

différentes des Métazoaires, car elles évoluent principalement par mutation

(Hershberg 2015; M. Lynch 2016). Ainsi, les mécanismes des transfert

horizontaux chez les Bactéries sont fréquents et se produisent à l’échelle des

secondes(Kejnovsky and Trifonov 2016; Andam, Williams, and Gogarten

2010; Syvanen 2012). Ceci suggère un rôle dominant des forces

30

diversifiantes de l’évolution moléculaire, la mutation et la migration, cette

dernière facilite l’adaptation avec un flux de gènes par transfert horizontal.

1.11.6 De l’évolution à l’écologie

1.11.6.1 L’adaptation Locale

L'adaptation locale est le résultat d'une sélection diversifiante en fonction des

conditions locales, qui peut structurer des populations et des communautés

microbiennes. Formellement, la théorie de l’adaptation locale est souvent

appliquée à des métapopulations c.-à-d. groupes de populations qui

interagissent les unes avec les autres par la migration (S. A. Kraemer and

Boynton 2017). Une population dans une métapopulation est plus adaptée

localement lorsqu'elle est plus sélectionnée par son environnement d'origine,

avant que les populations d'autres habitats soient présents dans cet

environnement (S. A. Kraemer and Boynton 2017; Kawecki and Ebert 2004).

L'habitat ici fait référence aux conditions environnementales dans lesquelles

un génotype existe.

Les recherches expérimentales sur l'adaptation locale chez les microbes ont

porté sur des génotypes individuels en tant qu'unités d'adaptation, car la

caractérisation des populations microbiennes dans la nature reste difficile.

Chaque génotype est traité comme une population et un cadre d’adaptation

locale à la métapopulation est utilisé. À l'opposé, quelques études ont défini

des communautés entières comme unités de divergence (S. A. Kraemer and

Boynton 2017; Hoostal, Bidart-Bouzat, and Bouzat 2008; Z. I. Johnson et al.

2006). La sélection, le flux de gènes par migration, la recombinaison, et la

spéciation microbienne sont des processus principaux mis en œuvre durant

l’adaptation locale (S. A. Kraemer and Boynton 2017).

L'adaptation locale dans la nature a été largement étudiée dans les systèmes

macrobiens (Fraser et al. 2011; Anderson et al. 2010), mais des difficultés

techniques rendent difficiles les études de terrain sur l'adaptation

microbienne locale. Contrairement aux plantes et aux animaux, de nombreux

microbes sont faciles à manipuler en laboratoire, mais leur dynamique reste

difficile à étudier sur le terrain. Par conséquent, la plupart des informations

31

sur l'adaptation microbienne locale proviennent de l'évolution expérimentale

de microbes cultivables en laboratoire (S. A. Kraemer and Boynton 2017).

Deux principaux défis doivent être pris en compte lorsqu'on étudie

l'adaptation locale. Premièrement, des données historiques sont nécessaires

pour identifier les environnements, les échelles spatiales, les populations

microbiennes et les traits avantageux pour une sélection divergente.

Deuxièmement, les microbiologistes ont besoin d'outils pour mener des

expériences de transplantations réciproques rigoureuses. Seules les

expériences de transplantations réciproques fournissent les données de

fitness qui sont nécessaires pour confirmer l'adaptation locale. Ces deux défis

sont surmontables (S. A. Kraemer and Boynton 2017).

Les observations à l’échelle des séquences du génome entier sont

particulièrement utiles pour identifier les pressions sélectives potentielles

conduisant à une adaptation locale (Shapiro and Polz 2014). Ces études en

majorité analysent le génome d'individus appartenant à des populations

différentes afin de détecter la présence de régions génomiques divergentes.

Les statistiques de divergence utiles incluent les mesures FST et la divergence

nucléotidique (Ellison et al. 2011); Des études d'association à l'échelle du

génome peuvent également être utilisées pour détecter la base génétique des

phénotypes d'intérêt (Bush and Moore 2012; Shapiro et al. 2012; Dutilh et

al. 2014). Lorsque les données de séquence du génome entier sont

disponibles, la divergence peut être comparée à cette échelle alors que le

long du génome entier; lorsque seulement quelques gènes peuvent être

séquencés, les locus sélectionnés peuvent être comparés à des locus neutres

ou à des prédictions neutres basées sur un modèle (Sokurenko et al. 2004).

1.11.6.2 L’adaptation rapide

La dynamique active et cryptique, ainsi que l’évolution rapide des

communautés microbiennes peut certainement masquer les interactions

trophiques (Yoshida et al. 2007). Ce que nous appelons « dynamique

cryptique », est un phénomène dans lequel la force ou même l’existence d’un

lien trophique prédateur-proie est masquée par les cycles de dynamique

32

évolutive de la proie. Ce phénomène a été observé dans les chemostats des

rotifères-algues et bactériophages. Les observations expérimentales

confirment que l'évolution des proies peut altérer considérablement la

dynamique prédateur–proie et que, par conséquent, toute tentative de

comprendre les oscillations de la population dans la nature ne peut pas

négliger les effets potentiels de l'évolution rapide en cours (Yoshida et al.

2003).

Plusieurs études ont observé que l'évolution par sélection naturelle peut

fonctionner à la même échelle temporelle que la dynamique écologique, plus

précisément, les recherches éco-évolutives récentes sur la manière dont

l'adaptation rapide influence l'écologie et vice versa. L’évolution par des

forces non adaptatives peut se produire également rapidement, avec des

conséquences écologiques mais, pour tenir compte de l'ensemble des

interactions écologie-évolution (éco-évo), il faut aborder explicitement les

processus au niveau de la génétique et la démographie des populations

(Lowe, Kovach, and Allendorf 2017).

1.12 Processus écologiques et modèles

Par analogie aux quatre forces évolutives principales décrites en génétique

des populations : la mutation, la sélection, la dérive génétique, et le flux de

gènes, en écologie microbienne, quatre processus de base contribuant à

l'assemblage d'une communauté microbienne sont considérés (Vellend 2010;

Nemergut et al. 2013): la diversification, la dispersion, la sélection et la dérive

(Schéma 1.2).

33

Schéma 1. 2. Boite noire de l’écologie des communautés.

Édité de Mark Vellend 2010. Quarterly Review of Biology 85(2):183-206.

Les différentes combinaisons spatio-temporelles de ces processus, pourraient

être importantes dans la structuration des communautés microbiennes.

Les modèles en évolution moléculaire et génétique des populations consistent

donc à estimer les taux d'évolution, l'indice de différenciation du

polymorphisme génétique et la variation dans le temps des fréquences

alléliques alors que les modèles d'évolution en écologie microbienne se basent

principalement sur l’information de l'abondance, l’occurrence, et la distance

phylogénétique pour estimer la fréquence, la richesse des UTOs et leur taux

de migration (Vellend 2010).

1.13 Introduction à la modélisation mathématique en écologie et

évolution microbienne

Un modèle mathématique est défini comme une équation, ou un ensemble

d’équations, qui essayent d’expliquer des exemples de réalité d’une manière

simplifiée, en utilisant uniquement les propriétés les plus pertinentes d'un

système (Perez-Rodriguez and Valero 2013). Ensuite, une théorie scientifique

est fondée lorsqu'une explication mécanistique est donnée pour l’ensemble

de phénomènes observés.

34

L’occurrence et l’abondance des communautés microbiennes, ainsi que leurs

interactions sont de propriétés dynamiques qui changent continuellement

dans le temps (Zaccaria, Dedrick, and Momeni 2017). Il est donc nécessaire

de comprendre leur adaptation aux fluctuations des conditions

environnementales et leur évolution sous l’effet des perturbations

(changements de l'environnement, administration des antibiotiques, ou

l’invasion des pathogènes, xénobiotiques etc.) de ces dernières. La

modélisation mathématique peut alors prédire la dynamique de ces

communautés microbiennes perturbées, et la question majeure qui se pose

ici, est comment les modèles permettent-ils de prédire la dynamique

d'évolution des populations microbiennes ?

La modélisation d’une population consiste à prédire sa taille (N) au cours des

générations en utilisant des équations différentielles en fonction de temps, de

la disponibilité des ressources et des taux de croissance exponentiel (K).

1.13.1 Préambule historique de la modélisation

Initialement, l’idée de taux de croissance exponentielle a été exploré par

Thomas Robert Malthus (1766-1834) par dans un essai intitulé « Principe de

population » (Principle of Population, Six éditions, 1798 à 1826). Puis, le

mathématicien Benjamin Gompertz (1779-1865) avait suggéré les premières

équations différentielles en élucidant l’hypothèse : le taux de croissance (K)

décline exponentiellement avec le temps indépendamment la densité de

population.

En parallèle, le mathématicien Pierre-François Verhulst (1804-1849) fut

intéressé par la théorie des nombres pour décrire dynamique des populations.

Il fut publié dans correspondance mathématique et physique en 1838 :

"Notice sur la loi que la population poursuit dans son accroissement". En fait,

Il proposa l’équation logistique « Logistic equation » comme une alternative

plus réaliste à la loi de Malthus. Il fut assumé que le taux de croissance K est

une fonction de la disponibilité des nutriments. Dès lors, il publia en 1845 un

livre intitulé "Recherches mathématiques sur la loi d'accroissement de la

population" dans les Nouveaux Mémoires de l'Académie Royale des Sciences

et Belles-Lettres de Bruxelles.

35

Quelques décennies plus tard, Alfred James Lotka (1880-1949),

mathématicien, physicien chimiste et statisticien, célèbre pour ses travaux

sur la dynamique des populations et l’énergétique, publia en 1925 un livre

intitulé ‘Les éléments mathématiques pour la biologie’ "Elements of

Mathematical Biology". Lotka fut proposé que la sélection naturelle soit, à la

base, une lutte entre les organismes pour obtenir l’énergie disponible; les

organismes qui survivent et prospèrent sont ceux qui capturent et utilisent

l'énergie à une capacité plus efficace et élevée que ceux de leurs concurrents.

Alors, son contemporain, le mathématicien Vito Volterra (1860-1940), connu

pour ses contributions à la mathématique de biologie et aux équations

intégrales, publia "Leçons sur la théorie mathématique de la lutte pour la vie",

Paris, 1931. Lotka et Volterra ont tous les deux (indépendamment) proposé

des modèles d’écologie théorique décrivant les populations concurrentes et

les systèmes prédateurs-proies (Kingsland 2015; Guerraggio and Paoloni

2013) Ils ont suggéré des équations d'évolution des états d’équilibre stables

décrites avec des équations différentielles de la variation des proies et des

prédateurs dans le temps.

Quelques années plus tard, le biologiste russe, Georgyi Frantsevitch Gauze

(1910-1986) a travaillé sur la structure des populations de microorganismes

en culture, et en 1932 il publia « le principe de l’exclusion compétitive ». Son

travail expérimental était basé sur deux espèces des levures en culture,

formalisé théoriquement par le modèle Lotka & Volterra. Deux ans plus tard,

il a publié le livre "La lutte pour l'existence" (The struggle for existence,

1934). En parallèle, Kermack et McKendrick développèrent les premiers

modèles mathématiques épidémiologiques (Kermack, McKendrick, and

Walker 1927).

Comme la théorie de l’exclusion compétitive de Gauze était fondé uniquement

sur deux microorganismes, Robert McCredie May (1936 -) s’intéressait alors

aux interactions de N espèces et à la dynamique des populations animales et

aux relations entre complexité et stabilité dans les communautés naturelles.

Il a réalisé des avancées majeures en biologie des populations grâce à

l'application de méthodes mathématiques (Sugihara and May 1990). Il a

développé le cadre théorique de l'écologie des communautés (stabilité et

complexité dans des écosystèmes modèles). Il a également proposé des

36

outils de modélisation mathématiques à l’épidémiologie et à l'étude de la

biodiversité (May 1976).

Quant au développement des modèles mathématiques de l’évolution,

n’oublions pas la contribution cruciale des travaux de Karl Pearson et ses

collègues qui ont aussi mis au point un cadre mathématique pour la théorie

de l’évolution (Pearson and Henrici 1896; Gillham 2015) et les contributions

de généticien Sewall Wright et du mathématicien Ronald A. Fisher, au

développement théorique de la génétique des populations (Crow 2010).

Rappelons que l'un des principaux objectifs de la génétique des populations

est de comprendre l'effet de diverses forces évolutives, telles que la dérive,

la sélection, la mutation et la migration, sur la dynamique de fréquence des

allèles au sein d'une population. Le modèle nul de Wright-Fisher décrit

l'échantillonnage d'allèles dans une population sans sélection, sans mutation,

sans migration, sans chevauchement des temps de générations et sans

reproduction aléatoire. En réalité, les populations naturelles n'adhèrent pas à

ces prémisses, mais le modèle de dérive proposé par Wright-Fisher fournit un

outil pour étudier comment l'introduction de forces évolutives peut rendre

plus complexe un modèle relativement simple.

Ces efforts, ainsi que de nombreux autres travaux, ont contribué aux

développements théoriques de l’écologie et de l’évolution (Shou et al. 2015;

Haller 2014) qui sont récemment appliqués depuis peu aux systèmes

microbiens. Néanmoins, le développement de nouveaux modèles

mathématiques en biologie est souvent traité avec scepticisme (Zaccaria,

Dedrick, and Momeni 2017).

Ce scepticisme est en partie attribué à une incertitude sur l'utilité d'un

nouveau cadre de modélisation. Le cadre prend-il en compte les aspects

cruciaux du système biologique? Répond-il aux questions importantes

auxquelles sont confrontés les chercheurs sur le terrain ? Le modèle est-il

assez simple pour inspirer un aperçu des processus importants ? Le modèle

est-il général ou spécifique aux détails et nuances d'un phénomène biologique

particulier ? Ces questions se posent naturellement lors des études des

communautés microbiennes (Zaccaria, Dedrick, and Momeni 2017), et

reflètent les compromis intrinsèques de chaque cadre de modélisation ainsi

que discuté ci-dessous :

37

Les principes de base de la modélisation ont été présentés (Baldwin and

Donovan 1998; Curnow 1985) en classant les modèles comme suit: (1)

dynamique ou statique; (2) déterministe ou stochastique; et (3)

mécanistique ou empirique.

1.13.2 Aperçu sur les modèles d’assemblage microbiens

Cette section introduit une classification des modèles mathématiques

employés et expérimentalement testés en microbiologie et en écologie

microbienne. Il s’agit donc d’un aperçu de certains modèles qui ont bien

fonctionné expérimentalement.

1.13.2.1 Les modèles métaboliques

Le fondement d’un modèle métabolique se base une approche des réseaux

interconnectés des réactions métaboliques potentielles qui décrit les

interactions des produits finaux des gènes ou métabolites. Les données de

séquençage d’un génome complet permettent aux chercheurs de prédire le

potentiel métabolique de l'organisme (Raman and Chandra 2009). Chaque

fois que des données génomiques sont disponibles pour une nouvelle espèce

séquencée, l’annotation des voies métaboliques peut définir un réseau

métabolique complet, éventuellement affiné par l’addition de données

biochimiques de la littérature (Feist et al. 2009). La résolution du modèle peut

aller d’une voie métabolique unique (Fuhrer, Fischer, and Sauer 2005) au

métabolisme primaire entier (Almaas et al. 2004). Les données

stœchiométriques applicables aux réactions ou produits des gènes

précédemment établis sont ensuite assimilés au modèle. Les matrices des

coefficients stœchiométriques résultants rapportent les taux de flux des

réactions enzymatiques et les dérivés temporels des concentrations

métaboliques (Raman and Chandra 2009). Ce type de modèle peut ensuite

être utilisé pour l'analyse de la balance de flux (FBA) ce qui permet aux

chercheurs de corréler un génotype à son phénotype (dans une cellule

individuelle ou dans une communauté) par la dérivation des flux métaboliques

(Song et al. 2014; Hanemaaijer et al. 2015; T. Zhang 2017).

38

1.13.3 Les modèles kinétiques

Les descriptions initiales de communautés microbiennes complexes utilisaient

des approches « coarse-grained » (limitées aux intrants et aux rentrants,

sans mécanismes intermédiaires). La modélisation à gros-grain applique des

paramètres empiriques pour décrire la fonction cinétique de base de la

dynamique d’une communauté (Widder et al. 2016). En général, les modèles

cinétiques décrivent la croissance de cultures bactériennes à travers

l’utilisation des équations différentielles incorporant la concentration du

substrat limitant et les taux de croissance (ou d'absorption) correspondant à

cette concentration (Kessick 1974). Les équations de Monod et de Michaelis-

Menten sont deux équations cinétiques couramment utilisées, exprimant

respectivement la croissance cellulaire et l'absorption de substrat, basées sur

un seul substrat limitant la croissance et l'absorption catalysée par une

enzyme (Oh and Martin 2007; Hanemaaijer et al. 2015). Les équations

différentielles sont utiles pour prédire la vitesse d’une réaction enzymatique

lorsque les substrats sont abondants et que les concentrations du produit final

sont constantes. Afin de rectifier l’irréversibilité des équations de Michaelis-

Menten, Hoh et ses collaborateurs ont conçu un modèle cinétique qui prend

en compte les facteurs limitants, la croissance et la théorie thermodynamique

de l’équilibre d’une réaction (Hoh and Cord-Ruwisch 1996).

1.13.4 Les modèles spatiaux

Les modèles métaboliques et cinétiques décrivent les facteurs principaux qui

conduisent la dynamique d’une communauté microbienne (taux de

croissance, absorption du substrat et production de métabolites). En

comparaison, la dimension spatiale n’a pas été considérée attentivement, la

raison principale était de garder les modèles simples. Cependant, il existe de

nombreux systèmes microbiens pour lesquels la dispersion est essentielle

pour définir la dynamique au sein d'une communauté (Hellweger et al. 2016).

Les difficultés de modélisation de structures spatiales particulièrement les

biofilms, proviennent de la partition de la diversité microbienne en gradients

locaux, de la complexité métabolique de rétention des ressources. Elles

proviennent aussi de la synergie de coopération, ainsi que la compétition

continue pour la reproduction ou régénération dans le temps, et enfin les

39

biofilms comme des forteresses de résistance ayant des structures en

agrégats complexes (Flemming et al. 2016). Ajoutons en outre les écueils

méthodologiques liés à la paramétrisation et temps de calibrage et des calculs

des modèles

1.13.4.1 Modèles basés à l’échelle individuelle

Les premiers efforts pour développer un modèle de biofilm microbien ont été

axés sur l'équilibre de la croissance (Klapper and Dockery 2010). Ces types

de modèles étaient initialement unidimensionnels et incorporaient des

équations de réaction-diffusion pour des nutriments et d'autres composés

produits par les cellules (Klapper and Dockery 2010; Wanner and Gujer

1986). Au fil du temps, les modèles ont augmenté en dimension (2D et 3D)

et ont utilisé la modélisation basée à l’échelle individuelle (IBM) (Ferrer, Prats,

and López 2008; Volker Grimm and Railsback 2005) pour décrire de manière

plus précise le comportement hétérogène couramment observé dans un

biofilm (Kreft et al. 2001; Klapper and Dockery 2010). Bien que la croissance

reste l’objectif principal de nombreux modèles de biofilms, d'autres facteurs

tels que la détection du quorum (Klapper and Dockery 2010; Chopp et al.

2002) et la mécanique des biofilms (Körstgens et al. 2001; Klapper and

Dockery 2010) ont également été représentés.

1.13.4.2 Modèles basés à l’échelle populationnelle

Les efforts de modélisation au niveau de la population ont été résumés d’une

manière plus détaillée dans une revue récente (Hellweger et al. 2016). Ici,

nous résumons les traits saillants de modèles des populations. La

modélisation de population consiste en l’une des deux approches suivantes :

ascendante ou descendante. Dans les approches ascendantes, le niveau

inférieur est décrit afin de prédire le résultat au niveau supérieur.

Dans les approches ascendantes, le niveau inférieur est décrit afin de prédire

le résultat au niveau supérieur. Par exemple, un modèle à l’échelle

individuelle (IBM) peut caractériser un système microbien en utilisant des

interactions / caractérisations individuelles (Railsback and Grimm 2011;

DeAngelis and Mooij 2005); ces individus peuvent être des cellules uniques,

des espèces ou des groupes de microbes dans un contexte spatial et / ou

40

temporel particulier. L’information au niveau de la population apparaît comme

un sous-produit naturel de la description du model kinétique IBM (Hellweger

et al. 2016). Les IBM sont intrinsèquement plus complexes et spécifiques à

chaque cas, mais offrent des prédictions très descriptives et conviennent

mieux à la modélisation de l'hétérogénéité.

À l’inverse, les approches descendantes, telles que l’utilisation de modèles au

niveau de la population (PLM), décrivent les changements au niveau de la

population. Contrairement aux IBM, le temps et l’espace sont souvent

considérés comme continus. Les PLMs peuvent être basés sur des équations

différentielles ordinaires (ODE) ou des équations différentielles partielles

(PDE), en fonction des exigences de structure spatiale du modèle (Edelstein-

Keshet 2005; Gurney and Nisbet 1998).

1.13.4.3 Modèles basés à l’échelle de communauté

Ces modèles considèrent la communauté comme un super organisme, et ils

prédisent des réactions métaboliques en prenant en compte les interactions

entre les gènes (Khandelwal et al. 2013). Ils consistent en l’analyse spatiale

et temporelle de flux de balance métabolique, en utilisant la discrétisation

spatiale et la simulation dynamique et tout en adaptant une solution linéaire

basée sur des reconstructions métaboliques à l’échelle du génome (Henson

2015). Actuellement COMETS est le modèle le plus connu (Harcombe et al.

2014). Harcombe et ses co-auteurs en utilisant des cocultures de E. coli and

S. Enterica ont montré que le modèle dépend de la biomasse d’une souche

(S), de métabolite extracellulaire (M) et du taux d’absorption (T) de M par S.

1.13.5 Les modèles neutres en écologie microbienne

Les mécanismes de la coexistence des espèces et la maintenance de la

biodiversité dans les communautés écologiques ont longtemps été un thème

au cœur de la recherche fondamentale de l'écologie des communautés, dans

laquelle la théorie déterministe des niches et la théorie neutre stochastique

sont bien reconnues comme étant les deux plus influentes.

La théorie de niche stipule que les espèces coexistant dans une communauté

doivent avoir des niches différentes, et que les espèces avec les mêmes

41

exigences de niche ne pourraient pas coexister longtemps (Matthews and

Whittaker 2014)

Bien que la théorie de niche ait été soutenue par de nombreuses études de

terrain et de laboratoire, elle rencontre des difficultés pour expliquer les

mécanismes de coexistence d’espèces par exemple dans les forêts tropicales.

Hubbell (1997, 2001); Wills et al. (1997) ont introduit la théorie neutre de la

biodiversité qui fournissait des perspectives alternatives de la coexistence des

espèces (Stephen P. Hubbell 2001; S. P. Hubbell 1997; Wills et al. 1997). La

théorie neutre de la biodiversité de Hubbell est une théorie basée sur la

dynamique stochastique à l’échelle individuelle qui suppose des équivalences

entre les espèces en interaction, et formulée par une limite de dispersion et

par un modèle de distribution de l’échantillonnage (Etienne 2005; Alonso,

Etienne, and McKane 2006; Rosindell, Hubbell, and Etienne 2011; Rosindell

et al. 2012). Un modèle de distribution permet de tester statistiquement la

théorie neutre avec les données d’abondance des espèces obtenues en

macro-écologie (plantes et animaux) ou des données de séquençage

métagénomique (écologie microbienne).

Compte tenu des caractéristiques uniques de la distribution des données

métagénomiques de l'abondance des OTUs microbiennes, Sloan (Sloan et al.

2006; 2007) avait proposé un modèle neutre alternatif mettant l’accent sur

la neutralité au niveau de l’individu dans les communautés microbiennes.

Contrairement aux théories neutres traditionnelles qui ont été calibrées en

utilisant « une description presque complète de la distribution de l’abondance

des taxons pour la communauté », le modèle de Sloan peut se calibrer avec

des échantillons à petite taille recueillis par des approches moléculaires avec

peu d’espèces et il permet une différence de compétitivité entre les espèces

microbiennes en utilisant un modèle de distribution continue (Sloan et al.

2006; 2007) plutôt qu’un modèle discret utilisé par Hubbell.

La théorie neutre offre donc un outil quantitatif puissant pour identifier les

forces qui influencent l’assemblage des communautés microbiennes dans les

systèmes hôtes-associés. Les modèles neutres permettent de comprendre les

mécanismes qui maintiennent la diversité microbienne et l’impact des

perturbations sur l’assemblage des communautés.

Par exemple, le test de la théorie neutre peut aider à distinguer entre les

forces qui contrôlent la composition et la diversité du microbiote de l’hôte.

42

Est-ce que les facteurs déterministes de l'hôte tel que l'immunité ou plutôt

les forces stochastiques de la naissance, de la mort et de la migration seraient

à l’œuvre?

Si c’est le premier cas, cela suggère que la communauté est formée par la

partition de différentes niches, occupées par des OTUs ayant des fonctions

différentes et la diversité observée avec son hétérogénéité est déterminée

par les facteurs environnementaux déterministes qui délimitent différentes

niches.

Si c’est le deuxième cas, cela suggère que la communauté est essentiellement

composée en grande partie d’un mélange aléatoire d'espèces écologiquement

équivalentes, et que la diversité observée avec son hétérogénéité serait le

fruit des force stochastiques de naissance, de mort et de migration des

différentes espèces.

1.13.6 Le modèle neutre de Sloan

Le modèle de neutre de l’écologie microbienne (Sloan et al. 2006) dérive de

la théorie neutre de la diversité de Hubbell (Stephen P. Hubbell 2001). Le

modèle de Sloan visait à résoudre le problème d’inférer les distributions de

l’abondance des taxons des communautés microbiennes à partir des petits

échantillons métagénomiques. Il suppose qu’une communauté locale

(destination) est saturée avec un nombre total NT d’individus. Dans la

communauté locale, un individu, peut subir soit la mort, soit l’immigration

d’une communauté lointaine (source) qui se produit à un taux δ

indépendamment de l'espèce.

Un immigrant d'une communauté source, avec une probabilité m,

remplacerait immédiatement la personne décédée ou un membre localement

né avec une probabilité de remplacement 1-m.

Par conséquent, la communauté de destination est assemblée ou

réassemblée à travers un cycle continu d'immigration, de reproduction et de

mort. En outre, en supposant que les décès soient répartis uniformément

dans le temps, alors un seul décès est attendu pendant une période de temps

1/δ.

43

Pendant ce temps-là, la nième espèce, dont l’abondance absolue initiale était

de Ni, augmenterait d'un point, resterait le même ou diminuerait d'un point

avec la probabilité spécifiée par les trois expressions suivantes,

respectivement.

𝑃𝑟 (𝑁𝑖 + 1𝑁𝑖

⁄ ) = (𝑁𝑇 − 𝑁𝑖

𝑁𝑇) [ 𝑚𝑝𝑖 − (1 − 𝑚) (

𝑁𝑖

𝑁𝑇 − 1)] (1)

𝑃𝑟 (𝑁𝑖

𝑁𝑖⁄ ) = (

𝑁𝑖

𝑁𝑇) [ 𝑚𝑝𝑖 + (1 − 𝑚) (

𝑁𝑖 − 1

𝑁𝑇 − 1)]

+ (𝑁𝑇 − 𝑁𝑖

𝑁𝑇) [ 𝑚(1 − 𝑝𝑖) + (1 − 𝑚) (

𝑁𝑇 − 𝑁𝑖 − 1

𝑁𝑇)] (2)

𝑃𝑟 (𝑁𝑖 − 1𝑁𝑖

⁄ ) = (𝑁𝑖

𝑁𝑇) [ 𝑚(1 − 𝑝𝑖) + (1 − 𝑚) (

𝑁𝑇 − 𝑁𝑖

𝑁𝑇 − 1)] (3)

Soit xi la fréquence d’occurrence de la nième espèce (OTU) dans la

communauté de destination, c’est-à-dire xi =n/N, où n est le nombre

d'échantillons de la communauté locale où l’espèce i est présent et N est le

nombre total d'échantillons de la communauté locale (Burns et al. 2016a).

Soit pi est la fréquence d'occurrence de la nième espèce dans la communauté

source, c’est-à-dire la contrepartie de xi dans la communauté destination.

Sloan et al. (2006) a montré que xi devrait suivre la distribution bêta

suivante:

𝑥𝑖 ~ 𝐵𝑒𝑡𝑎[ 𝑁𝑇𝑚𝑝𝑖 , 𝑁𝑇𝑚(1 − 𝑝𝑖)] (4)

Plus spécifiquement,

φi(xi ; NT, m) = cxiNTmpi−1 (1 − xi)

NTm(1−pi)−1 , (5)

44

𝑐 =Γ(NTm)

Γ[𝑁𝑇𝑚(1 − 𝑝𝑖)]Γ(𝑁𝑇𝑚𝑝𝑖), (6)

où Ni et NT sont respectivement le nombre total d'individus d'espèce i et le

nombre total de d’individus (de toutes les espèces) dans les échantillons de

la communauté locale, m est la fréquence de migration, et φi représente la

fonction de densité de probabilité.

D'après Burns et al. (2016), le processus pour tester le modèle neutre de

Sloan et al. (2006) peut être résumé en trois étapes (W. Li et al. 2018) :

(i) Calculez pi et xi, avec pi et xi, on peut s’ajuster à la distribution

bêta (équations 2 et 3) et obtenir l’estimation de m.

(ii) Calculer la fonction φi, la fréquence d’occurrence théorique de

l’espèce i dans tous les échantillons de la communauté de

destination basés sur m et la distribution bêta.

(iii) Déterminez si le xi observé de l'espèce i se situe dans un intervalle

de confiance théorique prévu par le modèle neutre et obtenez une

liste des espèces neutres dont xi satisfait la prédiction du modèle

neutre.

Contrairement au modèle neutre de Hubbell, Il n’utilise pas un test statistique

p-valeur permettant de tester la neutralité à l’échelle de la communauté avec

le modèle de Sloan (Sloan et al. 2006; 2007), à part de discerner le

pourcentage d’espèces neutres ou non neutres (W. Li et al. 2018).

Évidemment, il n’est pas facile de définir à quel niveau arbitraire le

pourcentage d’espèces neutres peut être désigné comme majoritaire pour

que toute la communauté soit considérée neutre, comme c’est le cas pour le

modèle de Hubbell. Une autre mesure importante qui peut être utilisée pour

juger la pertinence de l’ajustement avec le modèle de Sloan est le coefficient

de détermination (R2). Nous utilisons un seuil subjectif de R2 = 0,5 pour

accepter le test du modèle Sloan.

Enfin, tester les modèles neutres est un défi en soit, avec un environnement

véritablement multisite ou compartimenté (permettant de calculer des taux

de migration variables entre différentes communautés locales) jusqu’à que

Harris et al. (2017) aient développé une méthode d’apprentissage

automatique basée sur la distribution de Dirichlet qui consiste à calculer une

45

fonction bêta multinomiale. Cependant, le temps de calcul de cette méthode

est très long, et proportionnel à la taille des données.

1.13.7 Neutralisme et déterminisme dans l’assemblage du

microbiote de l’hôte.

Les changements dans la composition et la diversité des espèces peuvent

avoir un prépondérant stochastique important (P. L. Chesson and Warner

1981). Telle que mentionnée, c’est seulement lorsque Hubbell (2001) a

importé la théorie neutre de la génétique des populations à l'écologie que la

dérive a été incorporée dans la théorie de neutralité comme un facteur étant

bien plus qu’un « bruit », et pouvant avoir un fort effet.

Les approches de modélisation mathématique évaluent la contribution

relative des processus stochastiques et déterministes dans de l’assemblage

des communautés microbiennes. L’une de ces approches consiste à appliquer

des modèles de communautés neutres (MCN) (Sloan et al. 2006). La théorie

neutre stipule que le dynamique des UTO varie au hasard, par accroissement

démographique, et des évènements de migration d’une communauté source

vers une communauté cible (Sloan et al. 2006; Burns et al. 2016a).

En revanche, la théorie des processus déterministes « non neutre » prédit

que les conditions environnementales (par exemple intra-hôte) et les

interactions interspécifiques, y compris l’exclusion compétitive, déterminent

l’abondance des espèces microbiennes (Chase and Leibold 2003). En raison

de leur grande pertinence, les MCN ont amélioré notre compréhension de

l’assemblage de communautés microbiennes et ont prédit avec succès la

dynamique de leurs structures (Woodcock et al. 2007; Jayathilake et al.

2017; Venkataraman et al. 2015; Ofiţeru et al. 2010). L’approche MCN la plus

robuste est celle de Sloan et al. (2006), utilisant la théorie de la neutralité de

Hubbell qui est calibrée et capable de reproduire des motifs sur des

échantillons de tailles multiples (Woodcock et al. 2007). Cependant, malgré

les avantages évidents de MCN, ils ne sont pas sans controverses, certains

auteurs affirmant qu'ils ne peuvent pas expliquer toute la variance réelle

entre les communautés microbiennes associées aux organismes hôtes

(McGill, Maurer, and Weiser 2006).

46

Des processus à la fois neutres et non neutres ont été mis en évidence comme

moteurs de l'assemblage de métacommunautés microbiennes dans de

nombreuses études de microbiotes de Vertébrés (Jeraldo et al. 2012). Par

exemple, des processus neutres jouent un rôle majeur dans l’ontogenèse des

communautés microbiennes lors du développement de l'hôte et au cours de

son cycle de vie dans différents modèles de Vertébrés et de Plantes (Jeraldo

et al. 2012; Maignien et al. 2014; McCafferty et al. 2013). À l'inverse, dans

le microbiote intestinal humain, le filtrage sélectif imposé par hôte, plutôt que

l'assortiment de souches, dominait les règles d'assemblage microbien,

suggérant ainsi que les processus non neutres en étaient les principaux

facteurs (Levy and Borenstein 2013; 2014).

D’autre part, des études sur l’assemblage de microbiote du poisson zèbre

(Danio rerio) avaient montré la contribution deux types de processus neutres

et non-neutres (Burns et al. 2016a). Chez trois autres espèces de poissons,

Les processus déterministes sont mis en œuvre durant l'assemblage du

microbiote intestinal au cours des premiers stades de développement et avant

de se réduire progressivement (Yan et al. 2016). Par conséquent, l'interaction

entre les processus évolutifs et leurs impacts sur l'ontogenèse du microbiote

associé à l’hôte (néanmoins chez les Vertébrés) reste ambiguë. Dans

l’ensemble, de nombreuses études sur les écosystèmes associés à l’hôte,

portant soit sur l’ontogenèse sous des conditions neutres, soit sur des

comparaisons cas-témoins, révèlent des ambiguïtés quant à l’influence

relative des processus neutres et non neutres sur l’ontogenèse du microbiote.

1.13.8 Les modèles éco-évolutifs

Les communautés microbiennes naturelles, et même celles sous conditions

expérimentales bien contrôlées sont capables de se diversifier et de s’adapter

via une dynamique combinant les processus d’écologie et d’évolution.

Les modèles théoriques de base (Volterra 1928; D. Tilman 1982) supposent

que les dynamiques écologiques et évolutives ont tendance à diminuer les

espèces co-existantes par exclusion compétitive ou par la sélection des plus

aptes. La contradiction apparente entre observations et théorie attribue un

paradoxe à la biodiversité époustouflante des communautés microbiennes

(Hutchinson 1961; U. Sommer and Worm 2002). Ceci a donc suscité un long

débat en cours sur le mécanisme sous-jacents à l'émergence et à la stabilité

47

de la diversité dans les communautés d'organismes compétitifs (Doebeli and

Ispolatov 2010; U. Sommer and Worm 2002; P. Chesson 2000)

Pour identifier les mécanismes candidats expliquant la différence entre

génération et maintien de la diversité, les modèles théoriques de base en

écologie et évolution ont été étendus par de nombreuses caractéristiques,

incluant la structure spatiale, l’hétérogénéité spatiale et temporelle, les

mesures topologiques des réseaux d’interactions, la prédéfinition des limites

spatiale des niches, les taux de mutation versus sélection, et le compromis

entre le taux de croissance et le rendement métabolique et énergétique.

Cependant, on ignore encore quels paramètres sont essentiels pour expliquer

la biodiversité microbienne, car cette diversité est observée également dans

des conditions stables et homogènes.

Jusqu'à présent, les modèles de dynamique éco-évolutive développés

rentrent dans trois grandes catégories (Farahpour et al. 2018):

1- Modèles dans l'espace génotypique, comme la génétique des

populations (Ewens 2004) et les modèles quasi-espèces (Nowak 2006).

2- Modèles dans l’espace phénotypique, comme modèles de la dynamique

adaptative (Doebeli 2011) et de web world (Drossel, Higgs, and

Mckane 2001).

3- Modèles dans l'espace d'interaction, comme les modèles Lotka-Volterra

(Coyte, Schluter, and Foster 2015; Ginzburg, Akçakaya, and Kim 1988)

et des réseaux évolutifs (Coyte, Schluter, and Foster 2015; Mathiesen

et al. 2011; Allesina and Levine 2011).

Chacune de ces catégories de modèles possède des avantages et des limites

et met l’accent sur un aspect particulier. Cependant, dans la nature, ces

aspects sont bruités par des réactions éco-évolutives mettant en lien le

génotype, le phénotype et le niveau d'interaction

Dans un système biologique en évolution, les mutations peuvent provoquer

des variations phénotypiques si elles sont associées à de nouveaux traits

phénotypiques dans l'espace phénotypique (Soyer 2012). Ces variations ont

un impact écologique uniquement si elles affectent les interactions biotiques

ou abiotiques des espèces; sinon, elles sont neutres sur le plan écologique.

Les variations adaptatives du réseau d'interaction qui en résultent modifient

la composition des espèces par le biais de la dynamique des populations.

48

Enfin, la sélection fréquence-dépendante parfois favorise les espèces ayant

des stratégies adaptatives à leur nouvel environnement.

Nous avons donc un lien entre les réseaux d’interactions et les dynamiques

éco-évolutives, et donc nous n’avons pas besoin de suivre tous les

changements évolutifs au niveau génétique ou phénotypique si nous nous

intéressons à la dynamique macro-éco-évolutive, et aux interactions

affectées par cette dernière (Schéma 1.3). L'évolution peut être considérée

comme une exploration temporelle de l'espace d'interaction, et la

modélisation à ce niveau peut nous aider à étudier comment les réseaux

d'interactions compétitives complexes évoluent et influencent la diversité

(Farahpour et al. 2018).

Schéma 1. 3. Espace des modèles éco-évolutifs.

1.14 Récapitulatif

Dans ce chapitre, nous avons discuté en premier les principes et les concepts

de base en écologie microbienne. Le progrès des nouvelles générations du

séquençage, qui en est à sa deuxième décennie, ne peut que promettre de

gagner encore plus de précision dans la caractérisation de la biodiversité

microbienne via les approches de métagénomique. L’approche des amplicons

basée sur un gène marqueur universel comme le 16S (chez les Bactéries et

Archées) ou le 18S (chez les microbes eucaryotes) permet quantitativement

et qualitativement de caractériser la diversité microbienne dans un

échantillon donné d’un environnement donné. L’approche métagénomique

globale basée sur le séquençage de l’ADN total permet de détecter le contenu

génétique et les fonctions moléculaires d’une communauté donnée. Les deux

approches permettent de détecter et d’énumérer les unités de base de la

49

biodiversité grâce au concept de l’unité taxonomique opérationnelle (UTO)

connu sous le terme anglophone OTU « Operational taxonomic Unit ».

Ensuite, les mesures de la diversité Alpha permettent de caractériser et

d’estimer la richesse dans un échantillon donné. Tandis que les distances

écologiques et phylogénétiques de la diversité Beta permettent de comparer

la divergence entre les communautés basées sur les différentielles de

l’abondance et les distances génétiques des UTO. En deuxième partie de ce

chapitre nous avons discuté les types des interactions hôte-symbionte et la

notion de l’hologénome inspirée du concept de l’unité

évolutive « l’holobionte ». En outre, nous avons discuté le concept des forces

évolutives commun en évolution moléculaire et génétique des populations

emprunté récemment à l’écologie microbienne. Il permet de distinguer entre

les forces diversifiantes (migration, mutation) de celles qui sont réductives

de la biodiversité génétique microbienne (sélection et dérive). D’ailleurs, le

rappel de notions de base en évolution microbienne permet d’assurer une

bonne compréhension de la nature déterministe versus neutre de

l’assemblage des communautés microbiennes planctoniques, en biofilms, ou

associées à l’hôte.

En résumé, toute étude en écologie microbienne hormis le contexte

écotoxicologique, requiert les nouvelles technologies de pointe en biologie

moderne afin d’accéder aux ressources génétiques de la biodiversité

microbienne. Les méthodes de bio-informatiques et biostatistiques comme

celles des réseaux et des modèles d’assemblage permettent efficacement de

caractériser et de quantifier la dynamique des répertoires génétiques au sein

des communautés microbiennes.

50

Chapitre 2: Taxon-function decoupling as an

adaptive signature of lake microbial

metacommunities under a chronic polymetallic

pollution gradient

Bachar Cheaib, Malo Le Boulch, Pierre-Luc Mercier, et Nicolas Derome

Front Microbiol. 2018; 9:869

Front. Microbiol., 03 May 2018 https://doi.org/10.3389/fmicb.2018.00869

51

Taxon-function decoupling as an adaptive signature of lake microbial

metacommunities under a chronic polymetallic pollution gradient

Bachar Cheaib, Malo Le Boulch, Pierre-Luc Mercier, et Nicolas Derome

2.1 Résumé

L'exposition des micro-organismes aux facteurs anthropogéniques de stress

peut entraîner une dégradation de la diversité microbienne. Une telle

adaptation peut laisser des empreintes moléculaires dans leurs répertoires

taxonomique et fonctionnel. Ici, nous avons évalué la diversité taxonomique

et fonctionnelle d'un système de méta-communautés microbiennes lacustres

tout au long d'un gradient de pollution polymétallique subi par un drainage

d’acide minier (DAM) durant plus que 60 ans d'exposition chronique. Par une

approche de séquençage métagénomique, nos résultats mettent en évidence

trois types de signatures adaptatives. Premièrement, une signature du

découplage taxon-fonction a été détectée dans les communautés

microbiennes de lacs à contamination forte et intermédiaire. Deuxièmement,

des changements parallèles dans la composition taxonomique ont été

détectés dans de lacs pollués versus non pollués. Troisièmement, les

variations dans l’abondance des modules fonctionnels furent suggérées pour

expliquer une détérioration progressive des services écosystémiques (par

exemple, la photosynthèse) et une érosion graduelle du métabolisme

secondaire dans les lacs pollués. Dans l'ensemble, les changements dans

l'abondance de taxons et fonctions et, ainsi que les gènes de résistance

polymétalliques tels que copA, copB, czcA, cadR, cusA, étaient corrélés avec

la teneur en métaux traces (principalement le cadmium) et l'acidité

52

2.2 Abstract

Adaptation of microbial communities to anthropogenic stressors can lead to

reductions in microbial diversity and disequilibrium of ecosystem services.

Such adaptation can change the molecular signatures of communities with

differences in taxonomic and functional composition. Understanding the

relationship between taxonomic and functional variation remains a critical

issue in microbial ecology. Here, we assessed the taxonomic and functional

diversity of a lake metacommunity system along a polymetallic pollution

gradient caused by 60 years of chronic exposure to acid mine drainage (AMD).

Our results highlight three adaptive signatures. First, a signature of taxon -

function decoupling was detected in the microbial communities of moderately

and highly polluted lakes. Second, parallel shifts in taxonomic composition

occurred between polluted and unpolluted lakes. Third, variation in the

abundance of functional modules suggested a gradual deterioration of

ecosystem services (i.e. photosynthesis) and secondary metabolism in highly

polluted lakes. Overall, changes in the abundance of taxa, function, and more

importantly the polymetallic resistance genes such as copA, copB, czcA,

cadR,cusA, were correlated with trace metal content (mainly Cadmium) and

acidity. Our findings highlight the impact of polymetallic pollution gradient at

the lowest trophic levels.

53

2.3 Introduction

Micro-organisms represent a significant portion of global biodiversity and are

the engine driving Earth’s biogeochemical cycles and primary production

(Falkowski, Fenchel, and Delong 2008; Green, Bohannan, and Whitaker

2008). Ecosystem services provided by microbes ensure optimal

environmental conditions for all multicellular life forms (Robinson, Bohannan,

and Young 2010). For decades, the implications of taxon-function

relationships in microbial communities have been debated by researchers

(Doolittle and Zhaxybayeva 2009; Bissett et al. 2013; A. C. Martiny,

Treseder, and Pusch 2013; Louca, Parfrey, and Doebeli 2016; Morrissey et

al. 2016). On one hand, researchers showed that even very closely related

taxa exhibited contrasting metabolic and ecological functions (e.g. distinct

growth rates and metabolic substrate utilization profiles), indicating a gap

between taxon phylogeny and the functional repertoires of some bacterial

genera (Jaspers and Overmann 2004; Maharjan et al. 2006; Doolittle and

Zhaxybayeva 2009). These studies employed molecular taxonomic profiling,

either by sequencing SSU (small subunit ribosomal ribonucleic acid) 16S rRNA

(Doolittle and Zhaxybayeva 2009; Jaspers and Overmann 2004) or specific

housekeeping genes (Maharjan et al. 2006). On the other hand, studies

focused on microbial molecular evolution and ecology reported a significant

relationship between phylogenetic groups or taxonomic composition at

different hierarchical levels (i.e. Phylum and Class) with ecological and

functional traits (Webb et al. 2002; Ward et al. 2006; J. B. H. Martiny et al.

2006; Gupta and Lorenzini 2007; Allison and Martiny 2008; Philippot et al.

2010; Gravel et al. 2011; A. C. Martiny, Treseder, and Pusch 2013). The

majority of these genomic studies have been limited to correlating traits with

taxa abundance variation. Additional evidence at the community level is

needed to predict the interplay of evolutionary processes (horizontal gene

transfer, gene loss, selective pressure) and ecological processes (spatial

dispersal limits, biotic interactions, neutral biogeography) drive

metacommunity composition and functional repertoires in complex ecological

contexts.

With advances in sequencing technologies, metagenomic approaches have

the potential to advance our understanding of both the taxonomic and

54

functional composition of complex microbial communities. In this respect,

metagenomic studies have revealed significant coupling between taxonomic

composition or phylogenetic lineages and ecological traits (Bouvier and del

Giorgio, 2002; Philippot et al., 2010) or functional gene repertoires (Debroas

et al. 2009; Goldfarb et al. 2011; Muegge et al. 2011; Fierer, Leff, et al.

2012; Bryant et al. 2012; Langille et al. 2013; A. C. Martiny, Treseder, and

Pusch 2013; Mayali et al. 2014; Forsberg et al. 2014; Vanwonterghem et al.

2014; Morrissey et al. 2016; Larkin and Martiny 2017). For example, in

natural lake communities, associations are reported between taxon

abundance and function (Debroas et al. 2009), and in soil communities from

multiple environments, with chemical substrate variation (Goldfarb et al.

2011) and functional attributes (Fierer, Leff, et al. 2012). Most of these

studies have been conducted in relatively unperturbed environments, and on

microbial communities facing moderate to low selective pressure.

Other microbial community studies, mostly based on 16S rRNA gene analysis,

and rarely complemented by whole metagenome shotgun sequencing,

revealed either partial or marked decoupling between taxonomic composition

and ecological traits (Lima-Mendez et al. 2015) or functional gene repertoires.

Patterns of complete to partial decoupling are often found in natural

environmental conditions (Hooper et al. 2008; Hooper, Mavromatis, and

Kyrpides 2009; Burke et al. 2011; Raes et al. 2011; Smillie et al. 2011;

Barberán et al. 2012; Louca, Parfrey, and Doebeli 2016). This taxon-function

decoupling has rarely been discussed in extreme environments such as acid

mine drainages (AMD) (J. Kuang et al. 2016). These findings highlight the

need to further investigate environments where initial conditions have been

perturbed by xenobiotic factors(Bowen et al. 2011).

The occurrence of taxon-function decoupling has been reported in other

metagenomic studies as functional redundancy between phylogenetically

distant taxa (Burke et al. 2011; Green, Bohannan, and Whitaker 2008; Stokes

and Gillings 2011) and divergent microenvironments (Hooper et al. 2008;

Hooper, Mavromatis, and Kyrpides 2009). To summarize, taxonomic and

functional features could be useful in assessing adaptive response of microbial

metacommunities in disturbed ecosystems. One study, to our knowledge, has

focused on the outcome of microbial taxon-function relationships under

55

selection gradients, indicating possible linkages between the structure and

functioning of soil microbial communities (Fierer, Lauber, et al. 2012). Thus,

it remains uncertain whether taxon-function decoupling is an adaptive

response to a gradual selective pressure. Xenobiotic stressors like antibiotics,

chemical and metallic pollutants erode microbial biodiversity (Parnell et al.

2009), which is predicted to impair or erode ecosystem services (Sandifer

and Sutton-Grier 2014). Therefore, the characterization of taxon-function

decoupling patterns will enhance our understanding of the robustness of

microbial functional networks that ensure key ecosystem services. Here, the

complex connections of microbial biodiversity and ecosystem services (Miki,

Yokokawa, and Matsui 2014) were addressed at the molecular level by

comparing variation in the taxonomic composition and molecular functions of

microbial communities.

We hypothesized that a stress gradient, specifically a polymetallic pollution

gradient over a relatively long evolutionary time scale in terms of bacterial

generation time, would result in adaptive signatures in taxonomic

composition and functional repertoires. Specifically, we predicted that stress

gradients would gradually induce selection for microbial metacommunities

with functional repertoires and a taxonomic composition capable of living in

this harsh environment. To test our hypothesis, we targeted lakes polluted

by a polymetallic gradient of acid mine waters. Heavy metals can originate

either from natural sources such as volcanic activity or anthropogenically by

mines tailings, an important source of AMD. Acidity gradients recorded in lake

waters surrounded by natural volcanic activity (e.g. Indonesian crater lake

Kawah Ijen, Argentinian volcanic lake in Patagonia), have significant effects

on the microbial community composition and biodiversity (Wendt-Potthoff

and Koschorreck 2002; Löhr et al. 2006). AMD is created by the exposure of

sulphidic minerals to air and water forming soluble sulphates (Almeida et al.

2008). Ferrous minerals become oxidized in contact with water producing

ferric ions and H2 (D. B. Johnson and Hallberg 2003; Edwards and Bazylinski

2008). Leached ions into streams generate acidic water by lowering the pH

(< 3). Consequently, other metal ions such as Zn, Hg, Ni, Cr, Cd, Cu, Mn, Al,

As and Pb appear in AMD waters at high concentrations. There are limited

descriptions of microbial diversity in AMD in the literature, especially in

56

impacted environments with high zinc and cadmium concentrations (Almeida

et al. 2008). In AMD polluted surface water, Almeida et al. (2008) showed

that bacterial diversity in Sepetiba Bay, Brazil, which is much higher than

archaeal diversity, was dominated by Proteobacteria, Actinobacteria,

Cyanobacteria and had a high abundance of unclassified bacteria (unknown

strains). Similar composition (dominance of Proteobacteria) was observed

over 59 microbial communities from physically and geochemically diverse

AMD sites across Southeast China (J.-L. Kuang et al. 2013). Kuang and al.

(2013) revealed that pH gradient is a major factor explaining community

differences between AMD communities regardless of the long-distance

isolation and the distinct substrate types. Likewise, the investigation of the

microbial diversity of an extremely acidic, metal-rich water lake (Lake Robule,

Bor, Serbia) revealed low diversity dominated by Proteobacteria strains

(Stankovic et al. 2014). Similar community composition was observed in

bacterioplankton communities exposed to cadmium in coastal water

microcosms (K. Wang et al. 2015). Similar to surface waters, Hemme et al., (2010,

2016) highlighted that chronic exposure to high concentrations of heavy

metals (~ 50 years) in groundwater caused a massive decrease in

biodiversity, characterized by a high abundance of Proteobacteria, as well as

a significant loss in allelic and metabolic diversity. More importantly, Hemme

et al., (2016) pointed to the importance of Horizontal Gene Transfer (HGT)

during the evolution of groundwater microbial communities in response to

heavy metal exposure. However, very few studies were carried out on water

polluted across a polymetallic gradient (J.-L. Kuang et al. 2013; Desoeuvre,

Casiot, and Héry 2016). One of those studies reported the impact of an

extreme poly-metallic gradient (including arsenic) on the diversity and

distribution of arsenic-related genes in river waters (Desoeuvre, Casiot, and

Héry 2016). Other studies on AMD polluted freshwater sediments (Sánchez-

Andrea et al. 2011; Jackson et al. 2015; Ni et al. 2016; Jie et al. 2016)

showed the dominance of Proteobacteria in microbial communities as well as

community specialization. In lake sediments exposed to AMD gradients, the

effects of different metals on specific microbes and microbial activities were

correlated with their respective chemical properties. All these studies used

16S rRNA gene analysis, except for one, which used deep coverage data from

shotgun metagenome sequencing (Hemme et al. 2016).

57

In our study, we used a shotgun metagenomic sequencing approach to

characterize the functional and taxonomic diversity of bacterioplankton from

five lakes within a catchment that was historically exposed to a polymetallic

contamination gradient (PCG) for over sixty years. As the PCG was previously

correlated with taxon abundance variation (Laplante, Boutin, and Derome

2013; Laplante and Derome 2011), taxon-function decoupling was expected

to occur in the most polluted lakes and be absent in less polluted or unpolluted

lakes. Our first objective was to assess the taxonomic and functional

signatures of bacterioplankton adaptation to PCG. Secondly, we aimed to

provide insight into the interplay of biodiversity and ecosystem services under

a stress gradient by analyzing taxon-function variation.

2.4 MATERIALS AND METHODS

2.4.1 Lake characteristics and locations

Over the last sixty years, the Rouyn-Noranda (Western Quebec, Canada)

mining sites have dumped acid mine drainage (AMD) with heavy polymetallic

traces (Laplante and Derome 2011) into surrounding lakes. We targeted five

lakes in this area (Supplementary Figure S2.1). Among them, three have

common surface water interconnected along the same hydrologic basin:

Arnoux Lake (LAR-hc; highly polluted), Arnoux Bay (BAR-mc; medium levels

of pollution), and Dasserat Lake (DAS-lc; the least polluted). The water

polluted by AMD spreads from Arnoux to Dasserat Lake generating a

polymetallic gradient over 20 km. Around 30 km to the south side of this

natural system of connected lakes, Opasatica Lake (OPA-nc), which is a

landlocked unpolluted site, was sampled and considered as an unpolluted

negative control, and ca. 40 km to the north side, Turcotte Lake (TUR-hc),

another landlocked site was selected as a highly polluted lake. Longitude and

latitude coordinates are given in Supplementary file 1. The abandoned mine

site is a source of tailings and eroded mine waste into the Arnoux River, which

drains west to Arnoux Lake, Arnoux Bay, and then Dasserat Lake. These lakes

are irregular in shape and the bathymetry reflects the relief of the underlying

bedrock. The immediate surrounding area consists of hilly terrain, volcanic

rocks, ultramafic rocks, mafic intrusions, granitic rocks, and early and middle

Precambrian sediments (Alpay 2016).

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2.4.2 Metallic and chemical gradient surveys

pH and Polymetallic concentration (Al, Cd, Cu, Fe, Mn, Pb, Zn) in the studied

lakes was measured in June 2010, a year prior to the present study, using

ICP VISTA Varian-axial mass spectrometer as described in Laplante and

Derome (2011). Trace metal profiles showed a polymetallic gradient in the

three interconnected lakes (Supplementary Figure S2.1). For each lake, we

measured temperature (OPA-nc: 12°C; DAS-lc: 10°C; BAR-mc: 9.9°C; LAR-

hc: 11.5°C; TUR-hc: 9.5°C). Dissolved organic carbon (DOC) were

determined in each sample using a total organic carbon (TOC) analyzer

(Shimadzu) following the non-purgeable organic carbon (NPOC) method

(Laplante and Derome 2011).

2.4.3 Water sampling

Sampling was carried out in September 2011 by collecting 6 L of water per

lake at a depth of 60 cm below the surface. Water samples were sequentially

filtered (3 filters per sample), first through a 47-mm polycarbonate filter with

3-micron pore size, followed by a 0.22 μm nitrocellulose membrane filter

(Advantec) using peristaltic filtration (Masterflex L/S Pump System with Easy-

Load II Pump Head; Cole-Parmer, Vernon Hills, IL, USA). Duplicates of the

0.22 μm filter were placed into cryotubes at -80 °C.

2.4.4 DNA extraction and metagenome sequencing

Filter duplicates were pooled, then genomic DNA was extracted as described

by (Laplante, Boutin, and Derome 2013). Library preparation (TruSeq DNA

Illumina) of paired-end reads (2 × 100 bp read length) was performed by the

McGill University/Genome Quebec Innovation Center for whole metagenomic

shotgun sequencing using a HiSeq™ 2000 Sequencing System. A total of 30

Gbps were obtained and the sequencing data summary is shown in

Supplementary file 2. The sequence files are available from the Sequence

Read Archive (http://www.ncbi.nlm.nih.gov/sra), BioProject ID:

PRJNA449990.

2.4.5 Bioinformatic and statistical analysis

Reads-based approach (Figure 2.1). To first discard methodological biases

including sequencing artifacts, we pre-processed data for quality filtering,

59

chimeric sequences, homopolymers and short reads (cutoff: 50 bp) using the

Nesoni Clip tool (https://github.com/Victorian-Bioinformatics-Consortium)

version 0.133. Overall, the quality of forward reads (R1) was better than

reverse reads (R2). This difference is related to sequencing quality decrease

over the length of reads, in addition to the loss of enzymatic specificity

overtime in the paired-end platform technology. Base calling quality was

selected at a Phred or Q score of 33 (Supplementary file 2). FLASH software

v1.2.11 (Magoč and Salzberg 2011) was used with default parameters (10

to 65 bp overlapping window) to merge paired-end reads.

As a second step, following the selection of good quality reads for all

five metagenomic samples, a sequence similarity search was performed

against the SEED database (Version: May 2015) (Overbeek et al. 2014) using

Diamond v0.7.9.58 (Buchfink, Xie, and Huson 2015). The taxonomic content

of each sample was assigned using the Lowest Common Ancestor (LCA)

method (Huson et al. 2007). Functional abundance was estimated using the

SUPERFOCUS software (Silva et al. 2015) with Diamond (1e-12 as p-value, 60

identity as threshold, 30 base pairs as minimum alignment length). To cope

with missing biological replicates and unequal read numbers across all five

lake samples (varying from 37 to 80 million paired-end reads before

filtration), a read subsampling approach without replacement was used

instead of rarefying or simulating reads from complete genomes. Accordingly,

each metagenomic sample was subsampled 12 times with an equal number

of reads (1 million reads). The uniformity in terms of number of subsampled

reads from all samples were largely respected as in previous studies using

simulated metagenomes (Garcia-Etxebarria, Garcia-Garcerà, and Calafell

2014; Mavromatis et al. 2007). The 60 generated metagenomic pseudo-

replicates of equal size were submitted to our custom pipeline of taxonomic

and functional abundance annotations.

Thirdly, to measure alpha and beta diversity based on feature

abundances, we employed the OTU concept of taxonomic units (Schloss et al.

2009). Considering each feature (genus, function) as an OTU, alpha and beta

diversity were computed using Mothur software (Schloss et al. 2009). UniFrac

distances based on shared and unshared features were computed for each

compared pair of samples. To inspect how environmental factors impact

60

metacommunity composition, subsampled sites were first plotted based on

their feature abundances with non-metric multidimensional scaling (NMDS)

using Bray-Curtis distance between samples. Hence, the OPA-nc sample was

used as control reference to compute the differential abundance of genera.

Then, the computed distance matrix was clustered with Ward’s method based

on minimum variance. Clusters of genus abundance were distinguished with

different colors on the NMDS plot. Next, mixed metals metadata were

projected on NMDS axes by fitting a regression model. The significance of the

“regression coefficient” of the model was computed using a random

permutation test (1000 iterations). Then the regression coefficient between

the randomized response and the fitted values from the model was computed.

The NMDS model was run using the VEGAN package (Oksanen et al. 2016) in

the R statistical environment (R Foundation for Statistical Computing 2008).

To test for a correlation between taxonomical and functional composition, a

Canonical Correlation Analysis using the CCA (González et al. 2008) and

mixOmics (Rohart et al. 2017) packages in R were applied. With CCA, the

function-taxon cross-correlation was computed by maximizing the linear

combinations between the two matrix vectors. Then a regularization function

of CCA from mixOmics was used to deal with the high number of features

(genus, function) compared to the low number of samples (60 subsamples).

Regularization parameters (lambda 1 and lambda 2) were determined

through a standard cross-validation (CV) procedure on a two-dimensional

surface. The optimal value for lambda was obtained by searching for the

largest CV-score on the 2D surface that requires intensive computing time to

converge for the optimal cross-validation value. Choice of canonical

dimensions and graphical representation of features and samples were

performed with mixOmics package.

ORFs-based approach (Figure1). To improve annotation accuracy in terms of

length and coverage, an Open Reading Frame (ORF) prediction approach was

used after de novo assembly. Collinear metagenomic reads belong to the

same genetic unit were merged into same contigs. Firstly, de novo assemblies

of raw reads were performed using the RAY Meta (Boisvert et al. 2012)

assembler. Secondly, to explore contig features and gene contents, contigs

were submitted to the MG-RAST webserver (Glass et al. 2010) and ORF

prediction was conducted using the FragGeneScan tool (Rho, Tang, and Ye

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2010). Afterwards, contigs were annotated with the BLAT tool implemented

in MG-RAST against the SEED database using stringent filtering parameters

(1e-12 as p-value, 85% identity as threshold, 50 base pairs as minimum

alignment length). Statistical summaries of annotated contigs are available

in Supplementary file 2. Customized microbial annotations from the MG-RAST

webserver were improved using the RESTful API tool (Wilke et al. 2015). An

additional similarity research step based on BLASTx (parameters; identity

threshold of 85%, e-value of 10-12 and minimum alignment length of 50 base

pairs) (Camacho et al. 2009) was performed on contigs against the BacMet

database (Pal et al. 2014) for annotating all polymetallic resistance genes

(hereafter termed PMRGs). After annotations, contig coverage information

determined by the Ray Meta assembler was added to normalize abundance

information. Then, both abundance matrices of taxon and function coverage

(both normalized and non-normalized) were analyzed with the STAMP

software using a differential proportion comparisons test (Parks and Beiko

2010). In a second additional workflow analysis, the ORFs were locally

annotated with Diamond as described above in the “Reads-based approach”

section. BLAT and Diamond provided similar annotation results. To measure

alpha and beta diversity within and between communities, abundance

matrices were adapted for the Mothur software. At the third step, metabolic

abundance was analyzed using MG-RAST metabolite annotations. The

metabolic differential abundance was surveyed using iPATH (Yamada et al.

2011); this tool offers the visualization of shared and specific pathways

between pairs of samples.

2.5 Results

2.5.1 Decoupling taxon-function

To investigate the impact of the polymetallic selection gradient on lake

metacommunity composition, we measured the pattern of decoupling

between taxon and function along the contamination gradient of the five

lakes. We hypothesized that taxon-function decoupling pattern is an adaptive

response of lake metacommunities. To detect this pattern, we performed two

independent analyses: i) taxonomic structure versus functional diversity and

ii) canonical correlation of taxon and function.

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2.5.2 Detangled taxonomic structure and function diversity

According to alpha-diversity analysis, the highest value of community

richness (chao index) at the genus level was recorded in BAR-mc (OPA-nc:

126.8, DAS-lc: 115.07, BAR-mc: 212.6, LAR-hc: 121.66, TUR-hc: 68). In

contrast to richness, community evenness (Shannon index) was lowest in

TUR-hc (0.116), intermediate in OPA-nc (2.24), gradually decreasing along

the metallic gradient from DAS-lc (2.85) < BAR-mc (2.58) < LAR-hc (2.47).

However, community evenness of functions (OPA-nc: 2.36, DAS-lc: 2.32,

BAR-mc: 2.92, LAR-hc: 2.62, TUR-hc: 2.87) was higher in BAR-mc, LAR-hc

and TUR-hc then OPA-nc and DAS-lc. Then, beta-diversity analysis at the

genus level (Figure 2.2f) revealed two patterns of structural convergence: (i)

between the two independent lake communities, namely the unpolluted

control (OPA-nc) and the low polluted lake (DAS-lc); (ii) between the

interconnected BAR-mc-LAR-hc and the polluted control TUR-hc

communities. Concerning functional diversity distribution, beta-diversity of all

subsystems (Figure 2.3b) revealed two convergent patterns: (i) between the

polluted control (TUR-hc), the highly-polluted gradient lake (LAR-hc), and the

medium-polluted lake (BAR-mc); (ii) between the independent lake

communities, namely the low-polluted DAS-lc and the negative control (OPA-

nc) communities.

2.5.3 Canonical correlations of taxon and function

Regularized canonical correlation analysis (rCCA) of function (subsystems

level 1, 2, 3) with taxon was assessed using a maximal cross-validation

criterion (see Materials and Methods). To detect linear combinations between

function and taxon we separately performed the same rCCA analysis for OPA-

nc / DAS-lc, and then for BAR-mc / LAR-hc / TUR-hc. For OPA-nc / DAS-lc

(Figure 2.6 a-d), we found a maximum variance of only 1% explained by the

first axis computed from the taxon covariance matrix, and 1% explained by

the first canonical correlation principal component computed from the

function subsystems level 1 (results not shown) covariance matrix, even

when we tested the canonical model at the most accurate hierarchical

functional resolution (subsystems level 3). Supported by a high cross-

validation score (0.975), this result suggested a strong coupling between

taxon and function. Conversely, in BAR-mc / LAR-hc / TUR-hc (Figure 2.5 a-

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d) we found the first axis explained 25% of the variance computed from the

taxon covariance matrix, and 3% (Subsystem level 1) to 6% (subsystems

level 3) explained by the first canonical axis computed from the functional

covariance matrix. This result, supported with a high cross-validation score

(0.99), revealed a weak correlation between taxon and function, thus

suggesting a strong taxon-function decoupling. Using the first two canonical

axes, in BAR-mc, LAR-hc and TUR-hc, a clear separation was observed

between taxon and function (Figure 2.5 c), while the axes are superimposed

in OPA-nc and DAS-lc (Figure 2.6 c).

2.5.4 Taxonomic variation signatures

The metacommunity composition analysis emphasized three major patterns

marked by abundance shifts within and between Proteobacteria,

Cyanobacteria and Actinobacteria phylum (Figure 2.2a). In the first pattern,

Proteobacteria mostly dominated by Betaproteobacteria (Supplementary file

3) reached a higher relative abundance in the highly-polluted (hc) lakes TUR-

hc (99%) and LAR-hc (35%) compared to the moderately-polluted (mc) lake

BAR-mc (20%), the least-polluted (lc) lake DAS-lc (19%), and the unpolluted

(nc) lake OPA-nc (27%). At the genus level, Polynucleobacter, unclassified

Burkholderia and Burkholderia were the most dominant within polluted lakes

TUR-hc, LAR-hc, and BAR-mc, respectively, while Polaromonas was the most

dominant in DAS-lc and OPA-nc (Supplementary file 3). In the second

pattern, Actinobacteria were the most dominant phylum (Supplementary file

3) in less polluted lakes [OPA-nc (53%) and DAS-lc (62%)] and their relative

abundance gradually decreased in more polluted lakes [BAR-mc (33%), LAR-

hc (10%) and completely disappeared in TUR-hc], mainly for the five most

abundant genera: Streptomyces, Frankia, Mycobacterium, Kribbella, and

Nocardioides (Supplementary file 3). In the third pattern, Cyanobacteria

(Supplementary file 3) were abundant in OPA-nc (15.4%) and BAR-mc

(42%), and much less frequent in LAR-hc (4.4%), DAS-lc (0.2 %) and TUR-

hc (< 0.01 %). At genus level, Synechococcus was most dominant,

accounting for 98% and 92% of Cyanobacteria genera in OPA-nc and DAS-lc,

respectively. In contrast, distinct Cyanobacteria genera were dominant in

polluted lakes: the filamentous Anabaena in BAR-mc, unclassified

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Cyanobacteria in LAR-hc, and both the diazotrophic Cyanothece, and the

filamentous Anabaena in TUR-hc.

Lake metacommunity abundance shifts were further documented using

bootstrapped hierarchical classification and PCA. At the genus level, both

methods showed similar pattern of clustering with high statistical support

(bootstrap values above 75%; more than 95% of explained variation by the

first two PCA components), with BAR-mc, LAR-hc, and TUR-hc grouped

separately from OPA-nc, DAS-lc (Figure 2.2b, c, d).

2.5.5 Role of trace metals in taxonomic variation signatures

NMDS analysis based on ORFs (Figure 2.2e) revealed interesting relationships

(significant R-squared indicating regression model’s goodness of fit) between

taxonomic abundance and different factors such as pH, DOC and trace metals

(mainly Cadmium). OPA-nc and DAS-lc were significantly correlated with DOC

and pH axes, while all other sites exposed to polymetallic gradient (BAR-mc,

LAR-hc, and TUR-hc) were significantly correlated with trace metals axes

(Figure 2.2e). To further analyze the link between abundance differences at

different taxonomic ranks and the trace metal gradient, the same NMDS

analysis was performed using the ORFs approach. The abundance of

Proteobacteria (Supplementary Figure S2.3a), Actinobacteria

(Supplementary Figure S2.3b) and Cyanobacteria (Supplementary Figure

S2.3c) were studied separately. NMDS analyses of abundance shifts at the

genus level revealed significant correlations with different metal axes, pH and

DOC. The shifts in composition within lake metacommunities were not

explained by the same factors. For example, variation in the abundance of

Proteobacteria among lakes was mainly explained by Cd, pH, Mn, Alu, while

Cd and Fe explained variation in the abundance of Actinobacteria, and Alu

and Mn were the main factors explaining variation in the abundance of

Cyanobacteria among lake metacommunities.

2.5.6 Function variation signatures

Our results showed 6801 annotated functions from all communities

distributed into 988 subsystems in level 3, 192 subsystems in level 2, and 28

subsystems in level 1 (see sheet 2 in Supplementary Figure S2.5). At the first

level (see Supplementary Figure S2.5 and file 5), our results of cross-

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metagenomes comparison suggested that the relative abundance of

“Photosynthesis”, “Cofactors, Vitamins, Prosthetic Groups, Pigments” and

“Respiration” subsystems was significantly highest in OPA-nc while the

“Stress response” was the lowest in this lake. However, Subsystems of “RNA

metabolism”, and mobile elements (Phages, prophages, plasmids and

transposable elements) showed the highest abundance in BAR-mc, followed

by LAR-hc and TUR-hc, and low abundance in OPA-nc and DAS-lc.

Furthermore, the relative abundance of the “carbohydrates” subsystem

decreased gradually in all lakes except from DAS-lc to OPA-nc

(Supplementary file 5). Interestingly, among the 28 subsystems (Level 1),

four subsystems “Nitrogen metabolism”, “Cell cycle and division”, “Sulfur

metabolism”, “and “Motility and Chemotaxis” decreased gradually along the

contamination gradient. In addition, three subsystems (Phosphorus and

Potassium metabolism, Membrane transport) were absent in LAR-hc and

showed specific profiles of low abundance (Supplementary file 5) varying

between 0.2% to 3.8% in BAR-mc and TUR-hc. For multiple subsystems in

Level 1 (n= 12), no gradual abundance variation was observed. However, at

a deeper resolution, many important functions related to metals transport

and resistance from the “Virulence defense and disease”, “Membrane

transport” and “Iron acquisition and metabolism” subsystems showed few

gradual (i.e. Cobalt-Zinc-Cadmium resistance) abundance profiles and high

specific abundance per lake (Supplementary Figure S2.7). At the functional

level, variation abundance was detectable within all subsystems where three

profiles of abundance variation were observed from OPA-nc to TUR-hc: i)

profile 1 (FP1) represents gradual function abundance decrease (106

functions) along the contamination gradient (Supplementary file 5 and Figure

S2.10), ii) profile 2 (FP2) represents gradual function abundance increase

(123 functions) along the contamination gradient Supplementary file 5 and

Figure S2.10, and iii) profile 3 (FP3) represents specific functional abundance

(Supplementary file 5 and Figure S2.11) in control negative OPA-lc (167

functions), or in polluted lakes (225 functions). These functional profiles were

not necessarily observed in one subsystem, but rather multiple profiles were

detectable within one subsystem (Supplementary file 5). For example, under

the “Virulence, Disease and Defense” subsystem, we observed all these

profiles with functions related to metal resistance FP2 (i.e Cobalt-zinc-

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cadmium CzcA protein, Cation efflux system protein CusA), and FP1 (i.e

Magnesium and cobalt efflux protein CorC), and FP3 (i.e Copper homeostasis)

OPA-nc (see Virulence subsystem in Supplementary file 5). However,

functions related to mobile genes and HGT agents (Supplementary Figure

S2.8) were significantly more abundant in polluted lakes (e.g. Gene transfer

agent proteins, conjugative transfer proteins, DNA repair, CRISPR associated

proteins, integrons). Classification of functional abundance (subsystem levels

1, 2, 3) identified two independent clusters. The first cluster grouped BAR-

mc, LAR-hc and TUR-hc, and the second grouped DAS-lc and OPA-nc

(Supplementary Figure S2.5 and S6). Similar topologies were obtained using

both approaches: ORF (Supplementary Figure S2.4b) and reads subsampling

(Supplementary Figure S2.4c, d). PCA analysis based on the ORF approach

produced the same results, where at least 71% of variance was explained on

the first PC for all subsystem function levels. We only presented a PCA plot

for subsystems abundance in level 1, where more than 82% of variation in

functional abundance was explained by the first component (Figure 2.3a). At

the metabolic level, analysis of enzymes abundance profiles cross-

metagenomes showed different topology which was a dichotomy between

OPA-nc and all others pollution gradient lakes (See Supplementary file 7 and

Supplementary Figure S2.9).

2.5.7 Role of trace metals in function variation signatures

NMDS analysis of functional abundance highlighted two main patterns of

correlation (significant R-squared indicating regression model’s goodness of

fit) with metadata (Figure 2.3c). First, BAR-mc, LAR-hc and TUR-hc were

correlated with Cadmium axis (p-value ≤ 0.05). Second, OPA-nc and DAS-lc

were correlated with pH axis (p-value ≤ 0.05). The same analysis performed

on the subsystems in level 2 (192 functional modules) suggested a significant

contribution of all studied factors (results not shown). At the finest functional

level, lakes ordination based on the NMDS of polymetallic resistance genes

(PMRG)s abundance showed a fit with the cadmium concentration gradient

(Figure 2.4), where DAS-lc was ordinated near BAR-mc and LAR-hc. In NMDS

analysis of PMRGs located on chromosomes (Figure 2.4a), only Cadmium

played a significant role in explaining abundance variation. Similarly, the

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NMDS analysis of PMRGs located on plasmids provided the same classification

profile even though they do not fit significantly with any metal traces (Figure

2.4b).

2.6 Discussion

2.6.1 Decoupling taxon-function as a signature of adaptive

strategies

Comparing the compositional signatures of taxon and function, we observed

that relative differences in taxon abundance could only partially predict the

impact of metallic toxicity on metacommunity structure (see section “Role of

trace metals in taxonomic variation signatures”). By considering the

signatures of functional abundance of the subsystems explained by pH and

Cadmium in polluted lakes, we could more accurately predict the impact of

metallic contaminants on ecosystem services of lake metacommunities. In

this respect, the contamination gradient explained much variation in

community function structure and provided a powerful way to further assess

the relationship between the distribution of functional abundance and

selective pressure, which may increase gradually with the expelled AMD flow

over time. The impact of the selection gradient on lake metacommunity

composition was tested through two independent analyses, first using

diversity measures, and second by detecting taxon-function decoupling

patterns. Alpha taxonomic diversity suggest a switch in BAR-mc, while the

gradual decrease in evenness based both taxon and function in OPA-nc:

(2.2t;2.3f) , DAS-lc (2.8t; 2.3 f), BAR-mc (2.5t;2.9f), LAR-hc (2.4t;2.6f), TUR-

hc (0.1t;2.8f) could be a potential consequence of composition homogeneity

in community type (e.g. Proteobacteria in TUR-hc). Indeed, this observation

may be related to the low complexity in AMD communities previously

documented for the same lake system (Laplante, Boutin, and Derome 2013;

Laplante and Derome 2011), and for other AMD metacommunities (Allen and

Banfield 2005; Huang, Kuang, and Shu 2016).

The rCCA analysis allowed for the detection of significant spatial correlation

between taxon and function in OPA-nc / DAS-lc, reflecting a coupling between

taxon and function. In these unpolluted lakes, as mentioned above, NMDS

analysis showed that environmental factors (Cadmium, pH and DOC)

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explained variation in the overall taxonomic and functional composition. At

high resolution (subsystems level 2, 3) NMDS showed a slight difference

between OPA-nc and DAS-lc, but we cannot unequivocally associate these

variations to trace metal ratios. We may have missed other explanatory

environmental and chemical variables (i.e. NO2, NO3, SO4, PO4), or the

potential variation resulting from neutral ecological process, drift or random

reproduction as observed in wastewater habitats (Ofiţeru et al. 2010). Such

coupling is not necessarily absolute but partial, owing to the presence of some

differentiated sub-communities performing the same ecosystem services. In

pristine natural conditions (without stressful anthropogenic inputs), coupling

between taxon and function was observed in freshwater lakes (Langenheder

et al., 2005; (Debroas et al. 2009), and decoupling was observed in oceanic

bacterial communities from contrasting environments (Louca, Parfrey, and

Doebeli 2016).

Overall, in the present study, we found that functional variation between

polluted and unpolluted lakes was better explained by environmental factors

than taxonomic variation between and within functional groups. Concerning

the three lake communities facing exposure to a polymetallic gradient (BAR-

mc / LAR-hc / TUR-hc), the explained variance between taxon (25%) and

function (6%) strongly suggests a decoupling between taxa and functions.

The shared functions in these three polluted lakes reflect a convergent

pattern, which in turn could be interpreted as a predictive signature of the

ecosystem service’s impairment associated with acid mine lake water. This

conclusion is further supported by the NMDS results, where the distribution

of polluted lakes fitted closely to Cadmium. In addition to rCCA, when

comparing tree topologies of structure and function (Figure 2.2f; 3c), we

detected additional patterns of taxon-function decoupling, like the

polymetallic contamination gradient. Such an approach offers interesting

insights into the adaptive strategies used by metacommunities facing long-

term exposure to polymetallic pollution. Often interpreted as an indicator of

HGT in natural communities (Burke et al. 2011; Green, Bohannan, and

Whitaker 2008; Louca et al. 2016; Louca, Parfrey, and Doebeli 2016; Ram et

al. 2005) and AMD communities (Navarro, von Bernath, and Jerez 2013;

Devarajan et al. 2015; L. Chen et al. 2016; Hemme et al. 2016), taxon-

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function decoupling may provide evidence for selective pressure on microbial

communities (e.g. exerted by metallic exposure). Indeed, as mentioned

above, multiple proteins playing a role in HGT, such as cassettes of integrons

and transposable elements, were present in polluted lakes, and absent in an

unpolluted lake (OPA-nc). We observed more than 14 mobile PMRGs located

on plasmids, and only two PMRGs on both plasmids and chromosomes. The

plasmid location of these PMRGs indicates that bacterial conjugation may be

a vector for HGT. Interestingly, a heatmap of abundance clustering from

chromosomal and plasmid PMRGs (Figure 2.not shown) produced a similar

topology of functional profiles (i.e. OPA-nc; DAS-lc-BAR-mc; LAR-hc-TUR-

hc).

Evolutionarily speaking, such taxon-function decoupling patterns are

expected to be signature of adaptation within communities between closely,

but also distantly, related bacterial strains. Consequently, community

composition in BAR-mc, LAR-hc or TUR-hc may have independently evolved

via HGT events of resistance and regulatory genes. According to functional

abundance results, the potential occurrence of HGT is higher in LAR-hc and

TUR-hc compared to BAR-mc, which is closer to DAS-lc and OPA-nc in terms

of functional distribution. A subset of adaptive beneficial transferred genes is

expected to reach fixation (Lind et al. 2010), but the long term metallic

contamination may have funneled the “metal resistance gene pool” into

different evolutionary trajectories due to the mounting selective pressure.

2.6.2 Taxonomic adaptive signatures

In this study, the overall taxonomic variation suggests three salient patterns

of abundance distribution. First, a “composition gradient” pattern constituted

three shifts in taxonomic structure: i) high abundance of Proteobacteria in

polluted sites (TUR-hc, LAR-hc; BAR-mc), ii) high abundance of

Actinobacteria in unpolluted sites (OPA-nc, DAS-lc), iii) intermediate levels of

Cyanobacteria in all sites, with Nostocales being abundant in polluted lakes

and Chroococcales abundant in unpolluted lakes (Supplementary file 3).

Second, a “community type” pattern suggests that the overall

metacommunity exhibited compositional shifts along the five lakes from wide

(phylum) to narrow (genus) taxonomic levels. Third, a “taxonomic

convergence" pattern highlights parallel changes of community taxonomic

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structure, thus confirming previous results based on semi-quantitative and

quantitative studies (Laplante and Derome 2011; Laplante, Boutin, and

Derome 2013).

To further reinforce the taxonomic composition analysis, we examined genera

abundance and ORF distributions. Similar ratios of ORFs/Taxa were observed

in the five studied metagenomes (Supplementary Figure S2.2). The number

of annotated ORFs in all metagenomes was comparable. Furthermore,

random subsampling analysis without replacement produced similar results

(slightly different in topology) compared to the ORFs approach, with

remarkable clustering fidelity of subsampled replicates from each

metagenome (supplementary Figure S2.4a). Here, the subsampling approach

revealed consistency in the molecular signal of each lake. We acknowledge

that the subsampling approach used in our analysis cannot replace real

biological replications, but it is rather an indicator of the metagenomic data

robustness to the metacommunity structure.

To understand the sources of variation in contributing to the three major

shifts of relative abundance in community type, combined NMDS and

correlational analyses were performed for each pattern of taxonomic

variation. First, the Proteobacteria genera distribution of eight predefined

clusters (Supplementary file 4) showed that abundance variation between

communities was mainly explained by synergistic interactions of Cd, pH, Mn,

and Alu (Supplementary Figure S2.3a). According to previous studies,

Proteobacteria were among the most abundant phyla in acid mine water

(Laplante, Boutin, and Derome 2013; Streten-Joyce et al. 2013) and in

freshwater lake sediments polluted by “heavy metals” (Ni et al. 2016).

Second, in contrast to Proteobacteria, our results divided Actinobacteria into

four genera abundance clusters (Supplementary file 4) constrained by two

main and opposite explanatory factors, Cd and Fe (Supplementary Figure

S2.3b). In fact, the most abundant Actinobacteria genera (Streptomyces,

Frankia, Mycobacterium), which varied between polluted and unpolluted

lakes, fall in the same abundance cluster (see Actinobacteria in

Supplementary file 4). Indeed, some Actinobacteria (e.g. Streptomyces)

strains are known to have different metal-resistance profiles (Álvarez,

Catalano, and Amoroso 2013). Interestingly, strains like Mycobacterium were

71

able to transport and uptake Cd (Dimkpa et al. 2009). On the other hand,

Cyanobacteria abundance showed different patterns of abundance in polluted

and unpolluted lakes (Supplementary file 4; Supplementary Figure S2.3c)

suggesting that Chroococcales (Cyanothece, Microcystis, Synechocystis,

Thermosynechococcus) and Synechococcales (Synechococcus,

Prochlorococcus) are much more affected by trace metals compared to the

Nostocales (Anabaena, Aphanizomenon, Cylindrospermopsis,

Dolichospermum, Nodularia, Nostoc, Raphidiopsis). Although Cd was not

identified here as a significant explanatory factor, diverse strains of

Nostocales were documented to have the capacity to adsorb Cadmium

(Pokrovsky et al. 2008) and trace metals (Mota et al. 2015). Interestingly,

the sudden break of Nostocales lineages (Supplementary file 3) between the

connected lakes DAS-lc, BAR-mc and LAR-hc is potentially related to

resistance thresholds to trace metals, as higher levels become toxic to

Synechococcus (Ludwig et al. 2015). Furthermore, the high relative

abundance of Chroococcales and Cyanobacteria in OPA-nc and DAS-lc is

potentially related to their role in photosynthesis and DOC mineralization

(Bittar et al. 2015). Overall, our results show that metallic toxicity impacts

metacommunity structure and provides a partial explanation for the relative

shifts in abundance found in the lakes we studied. The dominance of

Proteobacteria in over polluted communities confirms the result previously

observed in the same lake system (Laplante, Boutin, and Derome 2013), and

from various acid mine waters in the world (Almeida et al. 2008; Hemme et al. 2010;

J.-L. Kuang et al. 2013; Stankovic et al. 2014; K. Wang et al. 2015).

2.6.3 Functional adaptive signatures

At the general level (subsystems level 1), only four subsystems showed

gradual variation. At the function level, our results suggest deterioration in

ecosystem services along the contamination gradient, as relative abundance

of functional modules in 18 subsystems such as “Carbohydrates”,

“Photosynthesis”, “Cell division and cycle”, “DNA metabolism” and

“Respiration” decrease gradually. However, under “Virulence defense and

disease” and “Membrane transport” subsystems (level 1), many important

metals transport and resistance functions (i.e. Cobalt-Zinc-Cadmium

resistance) increased between OPA-nc and other lakes (Supplementary

72

Figure S2.7). These profiles of gradual changes were less observable at the

general level (subsystems level 1), and more detectable at the functional

level resolution of many subsystems. The gradual decrease and increase in

relative abundance proportions was clearly observed at le the lowest

molecular function (i.e. Photosynthesis functions) along the polymetallic

gradient. Overall, variation in the functional composition of metacommunities

suggests convergence between BAR-mc/LAR-hc and TUR-hc, two

geographically distant and independent lakes affected by independent AMD

sources.

In contrast to the community classification based on taxonomic composition,

BAR-mc is functionally closer to LAR-hc-TUR-hc than OPA-DAS. NMDS of

functional composition, community hierarchical clustering, and PCA analysis

all find the same classification results. Cadmium and pH were the main factors

explaining functional composition variability among lakes. However,

independent analysis performed on both PRMGs and enzymatic functions

abundance showed that DAS-lc fitted within the polluted lakes (BAR-mc-LAR-

hc-TUR-hc) instead of OPA-nc. PMRGs located on plasmids (Figure 2.4b) were

differentiated from those located on chromosomes (Figure 2.4a) since

plasmid genes are known to house more adaptive genes acquired via bacterial

conjugation (Li and Zhang 2015). Only two experimentally confirmed genes

(copA and actP) were found in both plasmids and chromosomes. CopA is

involved in silver/copper export and homeostasis (Banci et al. 2003; Behlau

et al. 2011; Cha and Cooksey 1991; Outten et al. 2001). Acetate Permease

(ActP) controls copper homeostasis in rhizobium preventing low pH-induced

copper toxicity (Reeve et al. 2002). NMDS analysis based on Chromosomal

PMRG abundance revealed that Cadmium plays a significant role (p-value ≤

0.05) in shaping the differential abundance of these genes. Alternatively,

analysis of plasmid PMRGs did not highlight any significant fit with metal axes

(Figure 2.4b), owing to the low number of annotated PMRGs on plasmids.

Using OPA-nc as an unpolluted reference in our comparative framework,

differential metabolic abundance variation revealed an erosion of biosynthesis

pathways along the contamination gradient (results of compared pathways

not shown). Eroded metabolic functions were associated to degradation of

aromatic compounds, amino acid biosynthesis, and carbohydrates, thus

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leading to the loss of major bacterial mediated ecosystem services. As

bacterial communities experienced a consistent metallic stress over sixty

years of mining activities, many functions associated with ecosystem services

likely became energetically too expensive to be maintained. Such a selective

environment may have led to community specialization. Community

specialization has recently been demonstrated in soil AMD communities

(Volant et al. 2014) and natural freshwater communities (Pérez, Rofner, and

Sommaruga 2015; Pernthaler 2013; Salcher 2013). In summary, the two

main elements (or factors) that explained the majority of the functional

variation between polluted vs unpolluted communities were pH and Cadmium

concentration. Nonetheless, other metal trace gradients offered partial

explanations for functional variation.

2.7 Conclusions

In this study, we examined adaptive signatures within natural lacustrine

microbial communities living under a gradient of selective pressure induced

by trace metal contamination from over 60 years of mining. Using a

metagenomic approach based on whole genome shotgun sequencing, we

identified a convergence in both taxonomic and function responses, thus

providing evidence for genotypic signatures of adaptive evolution. Strong

selective pressure may drive overall taxon-function decoupling, which may

reflect the occurrence of gene loss and Horizontal Gene Transfer (HGT)

induced by AMD gradient, or the result of strong selection exerted on existing

strains possessing the necessary resistance genetic background. This study

remains a preliminary assessment of decoupling phenomenon and further

studies are eventually needed to understand in a deeper manner the nature

of convergence between unpolluted environments versus polluted

environments in a context of stress gradient. At the taxonomic scale,

metacommunity composition showed marked relative abundance shifts of

major phyla, but was much more marked at the genus level, suggesting a

“community type” adaptation to the metallic gradient within each ecological

niche. At the function scale, we observed the erosion of metabolic pathways

along the metallic gradient despite the higher abundance of functional

categories like stress response, regulation, protein metabolism, and metallic

74

resistance in polluted lakes compared to unpolluted lakes. Investigating the

relationship of both taxonomic and functional signatures, we detected a

decoupling pattern between taxon and function in polluted lakes as an

indicator of adaptation potentially via horizontal gene transfer. These results

suggest, for the first time, a decoupling pattern of taxon-function within

natural communities adapted to a gradient of polymetallic contamination.

This decoupling pattern highlights the gap between microbial biodiversity and

ecosystem services in polluted environments.

2.8 Figures

Figure 2. 1 Bioinformatics analysis pipeline.

Two approaches were developed for this work. With the ORF-based approach,

de novo assembly was performed on raw reads data using Ray Meta software.

Then, the predicted ORFs (Open Reading Frames) were annotated using

Diamond similarity research tool against SEED, which is a curated database.

With the reads-based approach, merged (with FLASH algorithm) and filtered

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reads (with Nesoni) (length ~ 200 pb in average) were annotated using the

same tool as the ORF-based approach, Diamond algorithm and SEED

database. For both approaches, we used the lowest common ancestor (LCA)

algorithm in the taxonomic assignment and the subsystems hierarchy in

function classification. Diversity measures were computed using mothur

software.

Figure 2. 2 Composition of metacommunities based on the ORF approach.

(a) Metacommunity composition (y-axis) is shown in stacked bars for each

lake metagenome (x-axis). Only phyla with relative abundance (RA) greater

than 1% are shown. (b) Hierarchical clustering of samples based on genus

RA using Ward’s method and Bray-Curtis dissimilarity distance, bootstrap AU

(Approximately Unbiased) p-value and BP (Bootstrap Probability) values are

76

shown on the nodes. (c, d) Principal Component Analysis (PCA) of samples

based on genus RA with different annotation parameters of alignment length

cutoffs (50 pb in c and 30 bp in d) and identity threshold (85% in c and 60

% in d). (e) NMDS of genera abundance fitted to trace metals was performed

with Bray-Curtis distance, three dimensions were a priori defined for distance

rank ordination and stress value was below 0.05. Cadmium (Cd), Manganese

(Mn), and pH significantly fitted with NMDS axes are highlighted in red. NMDS

loadings (NMDS1, NMDS2), and P-value of correlation r2 of trace metals were

reported in Supplementary file 6. Each small dot represents the ordinated

genus, while each large point represents the lake communities’ samples using

a circle for OPA-nc in blue and the control TUR-hc in black, and the connected

lakes are illustrated with squares (LAR-hc in red, BAR-mc in orange and DAS-

lc in yellow). Genus plot coordinates, clusters and dot labels are shown in

Supplementary file 4. (f) Tree based Unifrac distance computed with mothur

is indicated by branch lengths. All these results were obtained using the ORF

based approach with 85% identity threshold, e-value of 10-12, minimum

alignment length of 50 base pairs, and the lowest common ancestor (LCA)

algorithm for taxonomic assignment. OPA-nc (Opasatica Lake) is the negative

control; DAS-lc (Dasserat Lake) is low polluted; BAR-mc (Arnoux Bay) is

medium polluted; LAR-hc (Arnoux Lake) is highly polluted, and TUR-hc

(Turcotte Lake) is the positive control of contamination.

Figure 2. 3 Function abundance classification based on ORF approach.

77

Composition analysis of

metacommunity functions

based on relative abundance

(RA) of subsystems using

principal component analysis

(PCA) (a), tree based Unifrac

distance (b) and NMDS (c)

identified the same pattern.

(a) PCA figure was obtained

from STAMP software (Parks

et al. 2014). (b) NMDS (three a priori predefined dimensions projected into

two dimensions, stress value <0.05, Bray-Curtis distance) axes of all

annotated subsystems level 1 fit significantly with Cadmium (Cd), and pH

using the ORF approach. In d, each small dot represents a subsystem, while

the large dot does represent the lake metagenome indicated with a circle for

the negative control lake (OPA-nc) in blue and the positive control lake (TUR-

hc) in black. The connected lakes are illustrated with squares (LAR-hc in red,

BAR-mc in orange and DAS-lc in yellow). NMDS loadings (NMDS1, NMDS2),

and P-value of correlation r2 of trace metals were reported in Supplementary

file 6. Subsystems plot coordinates, clusters and dot labels are resumed in

Supplementary file 4. (c) Tree based Unifrac distance computed with mothur

is indicated by branch lengths In the ORF based approach the following

parameters were strictly respected; 85% identity threshold, e-value of 10-12,

minimum alignment length of 50 base pairs, and the lowest common ancestor

(LCA) algorithm for taxonomic assignment. OPA-nc (Opasatica Lake) is the

negative control; DAS-lc (Dasserat Lake) is low polluted; BAR-mc (Arnoux

Bay) is medium polluted; LAR-hc (Arnoux Lake) is highly polluted, and TUR-

hc (Turcotte Lake) is the positive control of contamination.

78

Figure 2. 4 Polymetallic resistance genes (PMRG) abundance correlation with trace metals.

79

(a) For PMRG on chromosomes (72 genes), Cadmium (Cd) was significantly

correlated with NMDS axes and it was the main explanatory factor of

abundance variation of these genes between metacommunities. (b) NMDS

axes based on relative abundance of PMRG located on plasmids (27 genes)

do not significantly fit with any trace metal arrows. This NMDS analysis was

performed with Bray-Curtis distance, three dimensions were a priori defined

for distance rank ordination and stress value was below 0.05. NMDS loadings

(NMDS1, NMDS2), and P-value of correlated trace metals are reported in

Supplementary file 6. Each small dot represented an individual PMRG, while

each large point represents the lake communities’ samples using circles for

OPA-nc in blue and the control TUR-hc in black, and the connected lakes were

illustrated with squares, LAR-hc in red, BAR-mc in orange and DAS-lc in

yellow. Thresholds of 75% of identity, minimum alignment length of 50 base

pairs and e-value of 10-12 parameters were strictly respected. PMRG were

annotated by performing Blastn of ORFs against BacMet database using

Diamond software.

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Figure 2. 5 Decoupling of taxon and function between metacommunities based on the subsampled reads approach.

(a-d) Regularized canonical correlation analysis (rCCA) performed on BAR-

mc, LAR-mc and TUR-hc. (e-h) rCCA performed on OPAnc and DAShc. (a)

rCCA of taxon (genus relative abundance) showed 25% of explained variance

on the first canonical component. (b) rCCA of function (subsystems level3

relative abundance) showed only 6% of explained variance on the first

canonical component. (c) The canonical cross-correlation of taxon-function

identified a decoupling pattern. (d) Cross-validation score converged to a

maximum value of 0.99 when regularization parameters λ1 and λ2 were both

fixed at 0.375.

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Figure 2. 6 Coupling of taxon and function between metacommunities based on the subsampled reads approach

(a) rCCA of taxon (genus relative abundance) showed 1% of explained

variance on first canonical component. (b) rCCA of function (subsystems

level3 relative abundance) also showed 1% of explained variance on first

canonical component. (c) The canonical cross-correlation of taxon-function

identified a coupling pattern between taxon and function. (d) Cross-validation

scores converged to a maximum value of 0.975 when regularization

parameters λ1 and λ2 were both fixed at 0.0925. rCCA was applied using

mixOmics and CCA package in R.

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2.9 Supplementary figures

Supplementary figure S2.1. Geographical localisation of the

sampling sites.

This figure shows the geographical localisation (a) of the sampling sites

located in Ryoun-Noranda (West Quebec, Canada) visited in June 2011.

Latitute and longitude coordinates of sampling sites are 48.25005489 and -

79.40574646 in Opasatica lake (OPA-nc); 48.07601448 and -79.3082428 in

Dasserat lake (DAS-lc); 48.24090959 and -79.35012817 in Arnoux Bay

(BAR-mc); 48.25051211 and -79.333992 in Arnoux lake (LAR-hc);

48.30474963 and -79.07742262 in Turcotte lake (TUR-hc). This map was

produced using Arc GIS Esri ® Arc Map™ 10.1 under academic licence

certification. (b) Trace metals concentrations measured in the five sampled

lakes one year before this study (Laplante and Derome 2011). The x-axis

represents the log ratio of trace metal concentrations (mg/l) and the y-axis

represents detection limit in each lake. The metallic gradient showed that

83

Cadmium was under the detection limit in OPA-nc (negative control), at the

detection limit in DAS-lc (low contamination), three times more than the

detection limit in BAR-mc (medium contamination), LAR-hc (high

contamination), and TUR-hc (positive control). Contamination gradient

classification refers to the Cadmium log ratio across the five lakes.

Supplementary figure S2.2. Classification of lake metacommunities

based ORF approach at genus and phylum levels.

(a) Distribution of ORF and annotated genus in the five metagenomes. This

figure showed that not only the number of predicted ORFs (Supplementary

file 2), comparable between metagenomes but also the genus count

(Supplementary file 3). (b) Hierarchical clustering of samples using Ward’s

method and Bray-Curtis dissimilarity distance, bootstrap AU (Approximately

Unbiased) p-value and BP (Bootstrap Probability) value are shown on nodes.

(c, d) principal component analysis (PCA) of samples based on genus relative

abundance (RA) assigned with coverage (c) and without coverage (d)

normalization. Metacommunity clustering based on genus abundance is

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different at phylum level where BAR-mc was closer to OPA-nc and DAS-lc. (e)

PCA analysis of samples based on function RA with different annotation

parameters of alignment length cutoff (30 bp) and identity threshold (60 %).

(f) Distribution of filtered ORFs on different alignment length cutoffs. For the

ORF based approach (a,b,c,d), the 85% identity threshold, e-value of 10-12

and minimum alignment length of 50 base pairs parameters were selected in

filtering annotations , and the LCA (Lowest Common ancestor) algorithm was

used to assign taxonomy.

Supplementary figure S2.3. Composition of metacommunities based

on the ORFs approach.

NMDS (with Bray-Curtis distance) of genera abundance for major abundant

phyla fitted to trace metals for Proteobacteria (a), Actinobacteria (b),

Cyanobacteria (c). the water pH and trace metals which correlated

significantly with NMDS axes were highlighted in red. Each small point in

figures a, b, and c represented the genus abundance, while each big point

does represent the lake metacommunities samples using circle shape for

OPA-nc in blue and the control TUR-hc in black, and the connected lakes were

illustrated with square shape, LAR-hc in red, BAR-mc in orange and DAS-lc in

yellow. NMDS loadings (NMDS1, NMDS2), and P-value of correlation r2 of

85

trace metals were reported in Supplementary file 6. Genus plot coordinates,

clusters and dot labels are resumed in Supplementary file 4.

Supplementary figure S2.4. Hierarchical clustering of taxon and

function.

(a) Hierarchical clustering of artificial replicates based on genus abundance

using the subsampled reads approach. (b) Hierarchical clustering of samples

based on abundance of subsystem level 1 using the ORF approach. (c, d)

Hierarchical clustering of subsampled replicates based on subsystems level 1

and 3 using the reads approach. Hierarchical clustering was performed using

Ward’s method and Bray-Curtis dissimilarity distance; bootstrap AU

(Approximately Unbiased) p-value and BP (Bootstrap Probability) value are

shown on the nodes.

86

Supplementary figure S2.5. Heatmap of subsystems in level 1

This heatmap represents metagenomes classification based on subsystems in

level 1 (See supplementary file 5). Dendrogram’s topology identified two

clusters. The first cluster grouped BAR-mc, LAR-hc and TUR-hc, and the

second grouped DAS-lc and OPA-nc. The hierarchical clustering of relative

abundance proportions of subsystems, and of samples was performed using

Ward’s method and Bray-Curtis dissimilarity distance. The ORF approach was

used with identity threshold of 85%, e-value of 10-12 and minimum

alignment length of 50 base pairs parameters. Vegan package and heatmap

() function in R were used to produce this figure.

87

Supplementary figure S2.6. Heatmap of subsystems in all levels.

Subsystems relative abundance were clustered cross-metagenomes in

different levels, level2 (981 modules), level3 (192 modules) and function

level (6801 functions) (See supplementary file 5). The same topology was

observed in level 1(See supplementary figure S5) and in all levels. The

hierarchical clustering of relative abundance proportions of subsystems, and

of samples was performed using Ward’s method and Bray-Curtis dissimilarity

distance. The ORF approach was used with identity threshold of 60%, e-value

88

of 10-12 and minimum alignment length of 50 base pairs parameters. Vegan

package and heatmap () function in R were used to produce this figure.

Supplementary figure S2.7. Heatmap of multiple subsystems

abundant in function level.

This heatmap represents cross-metagenomes, the common and most

abundant functions (>2%) in 22 subsystems (See supplementary file 5).

Functions of polymetallic resistance (Cation efflux system protein CusA and

89

Cobalt−zinc−cadmium resistance protein CzcA) showed a profile of gradual

abundance increase along the pollution gradient. The hierarchical clustering

of relative abundance proportions of functions, and of samples was performed

using Ward’s method and Bray-Curtis dissimilarity distance. The ORF

approach was used with identity threshold of 60%, e-value of 10-12 and

minimum alignment length of 50 base pairs parameters. Vegan package and

heatmap () function in R were used to produce this figure.

90

Supplementary figure S2.8. Subsystem of “Phages, prophages,

plasmids, and transposable elements” cross-metagenomes.

91

Under this subsystem multiple relevant functions (level 3) related to mobile

elements and transfer vectors (Gene transfer agents, transposons,

prophages, conjugative plasmids, integrons) were shared between DAS-lc,

BAR-mc, LAR-hc, TUR-hc and depleted in OPA-nc. However, each

metagenome contains specific profile of mobile elements functions such like

agents of gene transfers and conjugative elements in TUR-hc. The

hierarchical clustering of relative abundance proportions of this subsystem

modules, and of samples was performed using Ward’s method and Bray-

Curtis dissimilarity distance. The ORF approach was used with identity

threshold of 60%, e-value of 10-12 and minimum alignment length of 50 base

pairs parameters. Vegan package and heatmap () function in R were used to

produce this figure.

Supplementary figure S2.9. Metabolic abundance cross-

metagenomes.

This heatmap represents 1842 annotated enzymes (See EC

number in supplementary file 7) in all samples. The

hierarchical clustering of relative abundance proportions of

enzymes, and of samples was performed using Ward’s

method and Bray-Curtis dissimilarity distance. The

dendrogram shows dichotomy between OPA-nc metagenome

and all others. The ORF approach was used with identity

threshold of 60%, e-value of 10-12 and minimum alignment

length of 50 base pairs parameters. Vegan package and


92

Supplementary figure S2.10. Gradual variation of functions cross-

metagenomes.

Two heatmaps represent gradual function abundance FP1 (106 functions) and

FP2 (123 functions) along the contamination gradient. The hierarchical

clustering of relative abundance proportions of functions was performed using

Ward’s method and Bray-Curtis dissimilarity distance. The ORF approach was

used with identity threshold of 60%, e-value of 10-12 and minimum

alignment length of 50 base pairs parameters. Vegan package and heatmap

() function in R were used to produce this figure.

93

Supplementary figure S2.11. Specific variation of functions cross-

metagenomes.

94

Two heatmaps represent specific function abundance FP3-OPA-nc (167

functions) and FP3 specific to pollution gradient (225 functions). The

hierarchical clustering of relative abundance proportions of functions was

performed using Ward’s method and Bray-Curtis dissimilarity distance. The

ORF approach was used with identity threshold of 60%, e-value of 10-12 and

minimum alignment length of 50 base pairs parameters. Vegan package and


2.10 Supplementary Material

The Supplementary Material for this article can be found online at this link:

https://www.frontiersin.org/articles/10.3389/fmicb.2018.00869/full#supple

mentary-material

Supplementary file 1. This file contains two tables. The first table

resumed the geographical coordinates of sampled sites. The second table

presented abiotic parameters measured for each sampled site one year

before this study (Laplante and Derome 2011).

Supplementary file 2. This file summarized statistics of reads, contigs, and

ORFs MG-RAST annotations per lake metagenome.

Supplementary file 3. This file summarized in one table the relative

abundance of major taxa at phylum, class, and genus levels.

Supplementary file 4. This file resumed details of NMDS plots. NMDS

loadings, abundance clusters and points labels of all genus (Table1),

Proteobacteria (Table 2), Actinobacteria (Table 3), Cyanobacteria (Table 4),

then of all subsystems (Table 5) were reported for assuming a better

understanding of NMDS figures and supplementary figures.

Supplementary file 5. This file reported subsystems annotations and data

analysis; All subsystems data (dataset output of STAMP software) in table 1,

subsystems level 1 relative abundance (RA) in table 2, list of all annotated

subsystems in table 3, function profiles classification of RA proportions in

table 4, different function profiles (FP) (table 5, 6, 7, 8), resume of FP

occurrence in subsystems level 1 (table 9), summary of most abundant

functions (table 10) and summary of the subsystem “virulence, disease and

defense” (table 11).

95

Supplementary file 6. This file summarized details of NMDS correlation

analysis with metadata. Each table resumed NMDS loadings (NMDS1,

NMDS2), P-value of correlated trace metals of taxa (Table1), subsystems

level 1 (Table 2) and PMRGs.

Supplementary file 7. This file resumed diversity measures based relative

abundance of taxa (genus) and function (subsystems) in table 1 and all

annotated enzymes by their EC-number in table 2.

96

Chapitre 3: From networks to models: The

Yellow Perch (Perca flavescens) microbiome

assembly under metal toxicity

Bachar Cheaib , Hamza Seghouani, Martin Llewellyn, Katherine Vandal-

Lenghan, Pierre-Luc Mercier , and Nicolas Derome

Submitted to the Animal microbiome journal

97

From networks to models: The Yellow Perch (Perca flavescens)

microbiome assembly under metal toxicity

3.1 Resumé

Des processus stochastiques et déterministes gouvernent la structure et

l'assemblage des symbiontes bactériennes. Ils sont très peu étudiés dans un

système hôte-microbiote, particulièrement dans un contexte de perturbation.

Pour évaluer le rôle de ces processus dans la structuration et la composition

du microbiote, deux types d’habitats microbiens, libres (eau) et symbiotiques

(cutané et intestinal) associés à la Perchaude (Perca flavescens), ont été

exposés à deux régimes de sélection, constant et graduel, induits par des

doses non létales de cadmium. Les deux régimes de sélection constant et

graduel montrent une augmentation significative de la diversité alpha et des

corrélations négatives dans les réseaux, ce qui reflète une dysbiose à la fois

dans le microbiote cutané et intestinal. Le modèle non linéaire de moindres

carrés (NLS) suggère une dérive taxonomique ce qui reflète une succession

stochastique durant l’assemblage des communautés d'eau sans cadmium,

alors que l'assemblage de microbiote de l’hôte exposé au cadmium évolue

sous pression de sélection.

98

3.2 Abstract

Stochastic and deterministic processes underpin host-associated microbiota

structure and assembly. To assess the impact of such processes on microbiota

composition, two types of microbial habitats, free-living (water), and host-

associated (skin and gut), were experimentally exposed to either a constant

or gradual selection regime exerted by two sublethal cadmium chloride

dosages. Yellow Perch (Perca flavescens) was used as a vertebrate

ecotoxicological model. Using 16S rRNA gene metabarcoding, quantitative

metrics of the three metacommunities were characterized along development

and across experimental conditions. Both constant and gradual selection

regimes drove a significant alpha diversity increase both in skin and gut

microbiota. Pervasive negative correlations between taxa in both selection

regimes in skin, in addition to the taxonomic convergence with the

environmental bacterial community, suggest a loss of colonization resistance

resulting in the dysbiosis of host-associated microbiota. Furthermore,

network connectivity under stress was exclusively maintained by rare OTUs,

while abundant OTUs were mainly composed of opportunistic invaders such

as Mycoplasma and other genera related to fish pathogens. Finally, non-linear

least squares models (NLS) suggested that stochasticity mainly drove

taxonomic drift in cadmium-free water communities, whereas host-

microbiota assembly evolved in a deterministic (non-neutral) manner. Our

findings enhance our understanding of microbiota community assembly under

anthropogenic selective pressure.

99

3.3 Introduction

Microorganisms drive the biogeochemical cycles of the earth and contribute

towards homeostasis, immunity, physiology, behaviour (K. V.-A. Johnson and

Foster 2018; Stephens et al. 2016) and development (Stephens et al. 2016;

Sylvain and Derome 2017) across a wide range of metazoan hosts (Sherrill-

Mix et al. 2018). Host-microbiota symbioses involve complex and dynamic

associations between obligate and facultative symbionts (Theis et al. 2016).

Disentangling the characteristics of microbial interactions within communities

improves our comprehension of metacommunity assembly (Faust and Raes

2012). Ecological processes (i.e., dispersal, selection and ecological drift)

shape these interactions and govern the assembly rules of the ecological

communities (Nemergut et al. 2013; Vellend 2010). The impact of ecological

processes on community assembly is a long-term debate in macroecology.

Stochastic neutral theory suggests ecological interactions have no impact on

species abundances (macroscopic organisms). In such cases, local

communities are randomly connected to a single metacommunity through

differing rates of migration, death and birth (Bell 2001; Hubbell Stephen P.

2005; Stephen P. Hubbell 2006). In contrast, niche-related deterministic

theory considers that environmental conditions and interspecific interactions,

including competitive exclusion, determine species’ abundance (Chase and

Leibold 2003). In microbial ecology, the advent of culture-independent

approaches such as high-throughput 16S rDNA metabarcoding paved the way

for the conceptual framework of the Operational Taxonomic Unit (OTU) as

unit of microbial diversity. Such advancements have opened new

mathematical (Harris et al. 2017; Jayathilake et al. 2017; Sloan et al. 2006;

Stegen et al. 2012; Q. Zeng et al. 2015; 2017) and network-based (Faust

and Raes 2012; Foster, Krone, and Forney 2008; Taxis et al. 2015) models

for predicting ecological interactions between microbial communities. These

models helped in constructing hypotheses on types of processes driving

microbiomes assemblies over evolutionary time.

Models for quantifying the neutral (Harris et al. 2017; Burns et al. 2016a;

Ofiţeru et al. 2010; Sloan et al. 2006) and deterministic (Morrison-Whittle

and Goddard 2015; O’Dwyer, Kembel, and Sharpton 2015; Stegen et al.

2012; Yeh et al. 2015) processes in different types of microbial ecosystems

100

continue to provide new comprehensive insights regarding the forces

governing microbiome assembly. Both neutral and non-neutral processes

have been evidenced as drivers of the microbial metacommunity assembly in

many vertebrate microbiomes (Jeraldo et al. 2012), as well as within

rainwater microcosms (Langenheder and Székely 2011). For instance, neutral

processes were identified to play a major role during development of host-

associated microbial communities in different domesticated vertebrate and

plant models (Jeraldo et al. 2012; Maignien et al. 2014; McCafferty et al.

2013). Conversely, in the human gut microbiome, network modelling has

revealed that host-filtering, rather than strains-assortment, dominates the

microbial assembly rules, thus suggesting that non-neutral processes are the

key drivers (Levy and Borenstein 2013; 2014).

Studies within the same host species have found contradictory results: in

zebrafish, neutral processes generated substantial variation in gut microbiota

composition across individual hosts, but non-neutral processes (i.e. microbe-

microbe interactions, dispersal, or host-filtering) increased along host

development (Burns et al. 2016a). In contrast, in three other fish species,

deterministic processes shaping gut microbiota assembly were mainly at play

during the first developmental stages before gradually reducing (Yan et al.

2016). Therefore, the interplay between the underlying evolutionary

processes governing microbiota ontogeny in vertebrates is still poorly

understood. Overall, many studies of host-associated ecosystems, focusing

either on ontogeny under neutral conditions or on case-control comparisons,

reveal ambiguities regarding the relative influence of neutral and non-neutral

processes on microbiota ontogeny.

When focusing on case-control surveys, the influence of a given selective

pressure on microbiota composition is more salient. For instance, from the

human gut and oral microbiome under antibiotic therapy (Costello et al.

2012) to the euryhaline fish microbiome during salinity acclimation (V. T.

Schmidt et al. 2015), metacommunity assembly was mostly driven by

deterministic processes, with little evidence for stochastic colonisation.

Nonetheless, much remain to be done for fully understanding the mechanisms

of microbial assembly across a diverse range of host species.

101

In the present study, we first predict that a selection gradient induced by a

concentration gradient of a toxic metal will not only disrupt the host

physiology (Pyle, Rajotte, and Couture 2005a), but will also overwhelm the

recruitment and the assembly of symbiont consortia. Second, we predict that

the host will lose its filtering capacity to recruit the appropriate symbionts,

which in turn, will translate into increased colonisation of opportunistic

strains, as predicted by the colonisation resistance model (Costello et al.

2012). Third, from the interaction networks of OTUs, we expect that the rare

taxa may play an essential role in the structure of metacommunity assembly

(Banerjee, Schlaeppi, and Heijden 2018; Fierer 2017; Jousset et al. 2017; M.

D. J. Lynch and Neufeld 2015; Pester et al. 2010). Finally, microbiota diversity

within late host development stages may decrease due to the increasing host-

filtering capacity that occurs along developmental and life stages (Llewellyn

et al. 2016; Stephens et al. 2016; Sylvain and Derome 2017).

To this end, we measured the effect of directional selection along the

developmental stages of the host organism -juvenile Yellow Perch(Perca

flavescens) were exposed to two selection regimes: a constant (9 ppb) and a

gradual (0.8 to 9 ppb) exposure to sublethal doses of cadmium chloride

(CdCl2), over 90 days. The taxonomic compositional dynamic of two types of

microbial habitats, free-living (water), and host-associated (skin and gut),

were characterised throughout the young developmental stages of the host

using a 16S SSU rRNA gene metabarcoding approach. Being able to cope with

polymetallic gradients generated by acid mine drainages (AMD), the Yellow

Perch is a well-established ecotoxicology vertebrate model: many studies

have measured the impact of heavy metals on their adaptive genetic diversity

(Bélanger-Deschênes et al. 2013), innate immune system (Dautremepuits et

al. 2009), metabolism (Couture and Rajender Kumar 2003), development

(Azizishirazi et al. 2014), parasitism (Marcogliese et al. 2005; 2010; Ryman,

Walleghem, and Blanchfield 2008) and transcriptional activity (Bougas et al.

2013b). As these host functions are closely related to gut microbiota

composition, the Yellow Perch is a convenient model to test the effects of

metal exposure on a vertebrate host-microbiota system. The impact of

cadmium on the genotype of microbial communities in AMD water have been

documented (Cheaib et al. 2018), but not within the host-microbiota system.

102

Using sublethal doses of cadmium as a selection pressure, we aimed at

disantangling the neutral and non-neutral processes shaping the microbiota

assembly, without triggering significant physiological damage and causing

host death.

3.4 Materials and methods

3.4.1 Fish rearing.

After an acclimation period of one month in a 1500 L container, 1200 juvenile

Yellow Perch were reared in 24 tanks (50 fish per tank) of 36 L, each of which

had an independent filtering system circuit. The fish juveniles were fed daily

with the same food from the beginning to the end of the experiment. A second

acclimation period of two weeks was carried out before the start of cadmium

exposure (Supplementary file 1).

3.4.2 Exposure regimes to cadmium.

Control (Control) and Cd treated tanks were randomly distributed in the

aquarium facility. The experiment was designed for two cadmium exposure

regimes (8 tanks per regime), and one negative control regime (8 tanks). In

treated tanks, fish were exposed to cadmium chloride (CdCl2) provided by

Sigma-Aldrich (> 99.9% purity). The cadmium was dissolved in water. For

the regime of cadmium constant concentration (CC), the cadmium chloride

was initially added at 0.8ppb (parts per billion), before gradually increasing

the concentration every five days to reach a maximal concentration at the

end of the first month (T1). This maximal concentration was maintained two

months until the end of treatment (third month, T3). For the regime of

cadmium variable concentration (CV), the compound was initially added at

0.6 ppb, before the concentration was increased every five days to reach the

maximal concentration at the end of treatment (third month, T3). The

maximal CdCl2 concentration was empirically set at 9 g/L as it is the highest

CdCl2 concentration tolerated by Yellow Perch in contaminated Canadian lakes

(Couture, Rajotte, and Pyle 2008).

3.4.3 Host-microbiota and water sampling.

A total of 432 mucosa host microbiota samples were collected for this study,

216 (3 times x 3 regimes x 8 tanks x 3 replicates) skin mucus swabs and 216

103

(3 times x 3 regimes x 8 tanks x 3 replicates) gut tract samples

(Supplementary file2). Water samples were stored in sterile bottles

(Nalgene), 2 liters per tank were filtered using a polycarbonate membrane of

0.22 μm. In total, 144 filters (3 times x 3 regimes x 8 tanks x 2 replicates)

were conserved in 2mL sterile micro-centrifuge tube and directly stored at -

80 C.

3.4.4 Metal concentration in water and fish liver.

Concentrations of metal traces (Cd, Cu and Zn) within the water and liver

were determined with the ICPMS (Ionization Coupled Mass spectrometry)

technology at the Department of Chemistry, Laval University for T0 and T1,

then at INRS (Institut National de la Recherche Scientifique), Quebec, for T1-

T3. Before ICPMS analysis of cadmium ions in water, the CdCl2 in water

samples was fixed by adding 4% of nitric acid. This analysis was performed

every week until the end of the CdCl2 exposure regimes. After lyophilisation,

liver samples were digested with purified nitric acid and kept at room

temperature for five days. The liver acid digestion protocol was adapted from

Pierron et al. (2009). For further details, see the Supplementary file 2. The

metal concentrations were analysed using two-way analysis of variance

(ANOVA) of two independent factors: time and treatment. The interactions

between time and treatment factors were analysed using Tukey’s test and

Wilcoxon rank test depending on the data (metal concentration), which was

assessed for normality with the Shapiro test.

3.4.5 DNA extraction to Illumina Miseq sequencing.

DNA was extracted from all skin mucus and water samples using the Qiagen

DNeasy blood and tissue kit (Supplementary file 2). For all intestine samples,

after an RNA extraction for a transcriptomic project, the DNA was extracted

from TRIzol organic phase using BEB (back extraction buffer) and PCI

(phenol/chloroform/isoamylalcohol 25:24:1) solution (Supp. File 3). The 16S

ribosomal DNA was amplified via PCR using universal primers specific to the

V3-V4 hyper-variable region of the rDNA 16S gene (Werner et al. 2012). The

purified product of first-round PCR was used as a template for the library

preparation by performing second-round PCR. Final amplified DNA was

verified by electrophoresis on 2% agarose gel, and finally, DNA concentration

104

of the product was quantified by florescence using Quant-iT™ PicoGreen™

dsDNA Assay Kit (Thermo Fischer Scientific) (Supplementary file 2).

3.4.6 Analysis of 16S rDNA amplicons.

Sequence analysis was performed with our bioinformatic pipeline as described

previously (Llewellyn et al. 2016; Sylvain et al. 2016). The computed

diversity variation distribution across time and per treatment was plotted and

analysed for significance with linear mixed models considering tank as

random effect and cadmium as fixed effect using the lme4 package in R

(model M1= lmer(diversity.index (Evenness or Richness) ~

Cd.Water*Cd.Liver+Time+(1|Tank)). The linear mixed model (M1) was

compared to a multiple linear regression model M2 [lm(diversity.index ~

Cd.Water*Cd.Liver+Time] without random effects using a simple ANOVA

[anova(M1, M2)]. The analysis of the diversity structure and composition of

metacommunities was performed using the Rhea package (Lagkouvardos et

al., 2017). Briefly, the significance of alpha-diversity indexes (richness and

evenness) and beta-diversity (phylogenetic distance) differences between

experimental groups was assessed using rank statistics tests (Kruskal-

Wallis/Wilcoxon). Beta-diversity was measured using generalised UniFrac (J.

Chen et al. 2012), which considers both dominant and rare OTUs. Calculated

p-values of pairwise comparisons in alpha and beta-diversity were validated

for significance using the B-H (Benjamini-Hochberg) multiple correction tests

(Benjamini and Hochberg 1995) for avoiding the Type I errors (false

positives). Additional details on this analysis were documented in

Supplementary file 2.

3.4.7 Correlational networks.

The Spearman correlation coefficient corrected with false discovery rate FDR

multiple correction test was used to compute the OTU co-abundance

correlation. This coefficient was recently demonstrated as a robust approach

in terms of sensitivity and precision of correlation detection (Weiss et al.

2016). Significant positive and negative correlations were selected with B-H

test. Strong positive (Corr > 0.6), negative (Corr < -0.6) and significant

correlations (p-value FDR < 0.05) were used to build OTU networks.

Cytoscape software (Shannon et al. 2003) was used to perform network

105

visualization and analysis. The number of components indicated the level of

fragmentation (number of subnetworks) in the community. Each node size in

the network was proportional to the average OTU' relative abundance in all

samples, and the edge length was inversely proportional to the significant

coefficient of correlation between two nodes.

3.4.8 Metacommunity assembly modelling.

To investigate the role of neutral processes in community assembly, we fit

the distribution of OTUs to a neutral model of microbial assembly (Sloan et

al. 2006) using a non-linear least squares approach and beta distributions,

which has recently been implemented by others (Burns et al. 2016a). The

neutral model compares the frequency of OTU occurrence to their abundance

in the metacommunity by estimating a parameter (m), which describes the

migration rate. The estimated migration rate (m) is the probability that a

random loss (death or emmigration) of an OTU in a local community is

replaced by dispersal from the metacommunity source (Burns et al. 2016a).

The temporal comparisons (T0-T1; T1-T3) of predicted versus observed OTU

frequencies from the neutral model were used to highlight the percentage of

OTUs fitting the model with a confidence interval of 95%. The goodness of fit

to the neutral model was assessed using R-square as the coefficient of

determination. R-squared = 0.5 was the goodness of fit threshold to Sloan’s

neutral model (Sloan et al. 2006).

3.5 Results

3.5.1 Metal concentrations in water and host livers

The ANOVA demonstrated significant changes in the concentration of metals

in both water and fish liver at a specific time point and over time (Supp. Table

3.1). The Cd concentration had significantly changed between experimental

groups in the fish liver at T3, and in water at T1 and T3. According to ANOVA,

the interaction of time and Cd treatment was significant in both water (p-

value <0.05) and in fish liver (p-value <0.05). At time T3, the total Cd

concentration in water was maximal in CC and CV and significantly different

between control and each Cd regime but was not significantly different

between CC and CV. In the fish liver, the difference in Cd concentration at T3

was significant between all treatments (CC, CV or Control) (Table 3.1).

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3.5.2 Mixed Effects of time and treatment on metacommunity

alpha diversity

Diversity indices (Table 3.2) in gut microbial community, skin microbial

community and water microbial community measured per treatment (CC, CV

& Control) and across time showed notable trends. Over time and treatment,

the alpha diversity (richness or evenness) varied significantly (Table 3.2-a,

Supp Figure 3.1, Supp. Table 3.2). In the gut microbial community, richness

significantly changed over time between all groups (Control, CC and CV)

(Table 3.2A). In the skin microbial community, significant changes between

groups were detected only at T1 (Table 3.2B, Supp Figure 3.1, Supp. Table

3.2). Statistical comparison using both diversity indices for evenness

(Shannon.effective) and richness (Chao1) demonstrated the importance of

time as a driver in microbial community alpha diversity, rather than treatment

(Figure 3.1).

3.5.3 An important effect of time on the taxonomic composition of

metacommunities

Hierarchical OTU classification at the genus and phyla levels revealed a

significant differential abundance of different taxonomic groups in both water

and host communities according to different conditions (treatment, time

point). These taxonomic differences were significant in Bray-Curtis-based

clustering (Supp. Figures 2.2 & 3.3). OTUs classified as Mycoplasma showed

increasing abundance across all treatments (CC, CV & control). Overall,

fluctuations of richness and evenness measured in water and host-associated

communities were explained, at least partly, by time and treatments.

Although time exerted an effect on richness and evenness in all communities,

the treatment showed a significant effect only on the evenness in skin

mucosal communities. Interaction between both factors (i.e. time and

treatment) revealed a significant effect on richness in all communities, and

on evenness in skin mucosal communities.

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3.5.4 Community-level phylogenetic divergence

Phylogenetic distances based Gunifrac between OTUs were compared using

PERMANOVA and a multivariate homogeneity test for group dispersions

(variances). Among all host and water communities, time had a significant

effect on community divergence (p<0.005). By T1, significant differences

(p<0.005) among treatments were only observed in microbial skin

communities (Table 3.3, Figure 3.2); however, by T3, both variable (CV) and

constant (CC) cadmium exposure had driven differences in both skin and gut

communities compared to the controls. Among water microbial communities,

phylogenetic distance between treatments was significantly different at all

time points (Table 3.3; Supp. Figure 3.4).

3.5.5 Correlational metacommunity networks

3.5.5.1 Substantial role of rare taxa in the metacommunity network

connectivity

The percentage of OTUs involved in significant correlations was higher in

water community networks compared to those of the skin and gut

communities (Supp. Table 3.3). Within gut communities, abundant OTUs

played peripheral roles in network structure, demonstrating low overall

connectivity (Figure 3.3). In contrast, most connections in each network

existed among rare OTUs – especially in skin communities early in both

cadmium exposure regimes (T1 of CC and CV, Figure 3.4).

3.5.5.2 Reduced network connectivity in gut communities under

cadmium stress

Exposure to cadmium had a significant impact on the network connectivity

and integrity in the gut microbial communities. By T3, significant correlations

involved 78%, 69% and 63% of OTUs from Control, CV, and CC treatments

respectively (Supp. Table 3.3). In the Control group, most abundant OTUs

were connected to a central hub (Figure 3.3). In contrast, in the Cd-treated

groups, abundant OTUs were gradually disconnected from the main network

in small independent hubs or sub-networks. These abundant OTUs were

mainly composed of Mycoplasmas and other genera among Firmicutes like

Bacillus (>6% in CC and CV).

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3.5.5.3 Negative correlations in Skin Mucous Community networks

suggest dysbiosis

In skin microbial community networks, at T1, network connectivity

represented by average neighbors (AN) was lowest in the Control network

(Control AN: 4) compared to cadmium treatment networks (CVAN: 10.7; CCAN:

24.6) (Supp. Table 3.4). Higher connectivity observed in CV and CC relative

to the Control treatment was manifest in the higher percentage of significant

negative correlations (red edges in networks of Figure 3.4) observed in those

groups (CV neg. corr.: 6.9%; CC neg. corr.: 6.3%) compared to control group

(Control neg. corr.: 2.2%). A significant increase in the abundance of Tenericutes

(Mycoplasma) and Proteobacteria nodes (OTUs) explained many of negative

interactions in CV (71 nodes) and CC (112 nodes) groups, when compared to

the Control group (50 nodes). Crucially, at T3 among both cadmium

treatments CV & CC, putative pathogenic genera were abundant at the

expense of other non-pathogens, as demonstrated by multiple negative

correlations between these genera (Figure 3.4). Bacteroidetes at T1 in CC

and CV treatments (Supp. Table 3.5-a) drove also many of these negative

correlations (11 nodes in CC, 8 nodes in CV, and 3 nodes in Control) (Figure

3.4). Interestingly, Bacteroidetes at T0 were significantly lower in skin

compared to gut and water for groups CC & CV & Control (Supp. Figure 3.5).

At T1, they significantly increased over time (Supp. Table 3.5-a) in skin for

CC& CV, not for control, and they show a significant higher abundance in skin

compared to water and gut communities for treatments groups (Supp. Figure

3.5).

3.5.5.4 Fragmentation of water microbial community networks

Water microbial community networks were fragmented, both over time and

treatment (Figure 3.5). There was a notable lack of Tenericutes (Mycoplasma)

in comparison to the skin and intestinal microbial communities’ networks. For

instance, only 2 to 3 nodes of Tenericutes (Mycoplasma) occurred in Control

and CC networks at T0 and T3, and with very low relative abundance. No

Mycoplasma nodes were observed in the CV network. Furthermore, the

number of network components was the highest in Control network at T0 and

T3, in CC at T1, and was always intermediate in the CV network (Supp. Table

109

3.4). Overall, topologies of the water microbial communities network

encompassed disconnected small independent hubs.

3.5.6 Stochasticity in water community assembly and

determinism in that of host microbiota

We applied Sloan’s model (Sloan et al. 2006) using a package developed by

Burns et al. (2016) to determine the relative importance of neutral and non-

neutral dispersal processes in community assembly. The percent of OTUs that

fit the neutrality model within a confidence interval of 95% tended to be

variable across time, community, and cadmium exposure regime (Figure

3.6). In gut and skin communities, less than 50% of the total number of OTUs

between time points (T0 – T1, or T1 - T3) were best explained by the neutral

model. This would suggest a deterministic role of the host in the recruitment

of the microbiota in every treatment. However, the percentage of neutral

OTUs was higher in the control treatment compared to the variable and

constant cadmium regimes. In water communities, the percentage of neutral

OTUs was more than 50% at each time point for control and at T0 and T1 for

both cadmium exposure regimes (CV and CC). At T3, the majority of OTUs

(>70%) were non-neutral for both cadmium exposure regimes (Figure 3.6).

Studying the relationship of OTU abundance with neutrality, our analysis

revealed that neutral OTU's percentage highly increased (25% to 80%) in

host and water communities when the rare OTUs were discarded from the

NLS models (Supp. Figure 3.6, Supp. Table 3.6).

3.6 Discussion

Our study evidenced salient differential shifts in community assembly across

three community types (environmental or host-associated), time and

xenobiotic exposure regimes. This highlights for the first time the relative

contribution of neutral and non-neutral factors in shaping the microbiota

during the early life-stages of an ecotoxicology vertebrate model. First,

significant increase of alpha diversity in skin and gut microbiota was detected

in both constant and gradual selection regimes. Then, at the community-

level, significant phylogenetic divergence was observed between the control

and treatment groups in the three community types at two specific time

points, T1 and T3. These two key time points were investigated further with

110

co-abundance network analysis. Frequent significant negative correlations

between taxa in both selection regimes in the skin and the increasing richness

of environmental bacterial strains, suggest a dysbiosis in the mucosal host-

associated microbiota. Furthermore, network connectivity under stress was

maintained by rare OTUs, while abundant OTUs were mainly composed of

opportunistic invaders such as Mycoplasma and other genera related to fish

pathogens like Aeromonas, Pseudomonas and Flavobacterium. Finally, to

predict the nature of evolutionary processes driving the metacommunity

changes, application of a non-linear least squares model (NLS) to the data

suggested that stochastic processes drove taxonomic drift in cadmium-free

water communities, whereas host-microbiota assembly evolved in a

deterministic (non-neutral) manner in treatments and the control.

3.6.1 Phylogenetic divergence at the community-level revealed

the impact of Cadmium exposure.

At T1, significant phylogenetic divergence occurred between treatments (CC

& CV) and control (Control). Most importantly, a taxonomic convergence

between treatments (CC & CV) not only in skin but also within the water

community occurred. This convergence mainly resulted from an invasion of

environmental bacterial strains in skin. The significant increase of

Bacteroidetes only in treatments groups (CC &CV) in skin communities

compared to water communities could strongly support this hypothesis

(Supplementary Figure 3.5). Such gain or loss of tissue-specific community

type suggests a disruption of the host’s ability to control the assembly of skin

microbiota, which is correlated with Cd exposure. This phenomenon is termed

“direct colonisation resistance” (Buffie and Pamer, 2013); however, we do

not exclude that this colonisation failure also resulted, at least partly, from a

host immune system failure (termed as “immune colonisation resistance”).

This compositional disruption translated into many negative correlations

between taxa in both selection regimes in the skin-associated microbiota at

T1. Furthermore, the impact of Cd exposure on skin community structure was

also observed at T3, where the phylogenetic distance became significantly

divergent, even between both selection regimes, where many negative

correlations were detected between taxa. In addition to the increasing

invasion of environmental bacterial strains in skin (i.e. failure of colonization

111

resistance, see (Buffie and Pamer 2013a), the rise of negative correlations

suggests a dysbiosis state of skin-associated microbiota (Vázquez-Baeza et

al. 2016). This dysbiosis might be associated not only with an increase in

evenness and phylogenetic convergence with the water bacterial community,

but also with the rise of antagonism among OTU co-abundance networks in

both selection regimes (CC & CV). Most of antagonism was mediated through

rare and abundant Tenericutes (Mycoplasma) and Proteobacteria. Depending

upon the strain, Mycoplasma are thought to be either fish pathogens,

opportunists, or innocuous commensals in fish (R. M. Brown, Wiens, and

Salinas 2018; Holben et al. 2002). In comparison to skin, the significant

divergence between control (Control) and treatments (CC, CV), and the rise

of negative correlations, appeared later in the gut community: at T3. This

delayed pattern of dysbiosis strongly suggests that the physiological impact

of cadmium exposure was mitigated more effectively within the gut. In fish

(and other vertebrates), the liver is the main organ to accumulate xenobiotics

including cadmium. Therefore, the late compositional shift in the gut

microbiota potentially occurred when bioaccumulation of cadmium within the

liver reached its maximum carrying capacity.

3.6.2 Gradual disconnection of abundant taxa from the main gut

interacting network.

Another noticeable compositional shift was the disconnection of abundant

taxa from the main gut interacting network, which was proportional to the

stress intensity (Fig. 3). By T3, the overall taxonomic network connectivity

was formed exclusively from rare OTUs. Contrastingly, abundant OTUs were

peripherals and disconnected from central hubs, mainly composed of putative

opportunistic invaders such as the Mycoplasmas and other genera

encompassing strains associated to fish pathogens, like Bacillus (>6% in CC

and CV). As observed in other fish species such as Atlantic salmon (Salmo

salar) (Llewellyn et al. 2016) and the longjaw mudsucker (Gillichythys

mirabilis) (Bano et al. 2007; Givens et al. 2015), Yellow Perch have intestinal

microflora dominated by Tenericutes (Mycoplasma sp.). It is therefore difficult

to conclude whether the increase of several Mycoplasma strains is beneficial

or not to the host. Concerning Bacillus, a similar increase was associated with

irritable bowel disease (IBD) in dogs and negatively correlated with bacterial

112

strains associated to healthy individuals (Vázquez-Baeza et al. 2016).

Interestingly, rare OTUs and negative correlations did not play an essential

role in water co-abundance networks, which were highly fragmented both in

the control and treatment regimes. The taxonomic composition of water

microbial communities was characterised by the low occurrence of

Tenericutes (Mycoplasma) compared to host communities.

3.6.3 Rare OTUs play a pivotal role in community assembly.

Rare OTUs have been demonstrated to play a pivotal role in community

assembly (Banerjee, Schlaeppi, and Heijden 2018; Fierer 2017; Jousset et al.

2017; M. D. J. Lynch and Neufeld 2015; Pester et al. 2010) either in

promoting homeostasis (Jousset et al., 2017) or dysbiosis (Hajishengallis et

al. 2012). Therefore, we have applied the non-linear least squares model

(NLS) to disentangle neutral and non-neutral evolutionary processes that

were at play in community assembly in both control and treatment groups.

The distribution of neutral OTUs across different relative abundance averages

(Supp. Figure 3.6) suggested a non-neutral role of rare OTUs in water and

host communities. Our data demonstrated that stochastic processes mainly

drove taxonomic drift in cadmium-free water communities (from 53% to 69%

of OTUs), in contrast to what was measured in cadmium-treated water at T3

(6% of OTUs for CV, 26% of OTUs for CC). Contrastingly, host-microbiota

assembly evolved under a deterministic (non-neutral) manner in both

experimental and control groups, although this trend was more salient in both

experimental groups at T3. The percentage of OTUs in the gut and skin that

fit the neutral model was significantly lower than that of non-neutral OTUs.

The assembly of gut microbial communities may have evolved under non-

neutral processes due not only to the cadmium as a disrupting factor in both

experimental groups, but also due to the selection imposed by the host

development, as documented in different fish species raised under normal

environmental conditions (Stephens et al. 2016; Sylvain and Derome 2017).

3.7 Conclusions

In this experimental evolution trial, the extensive involvement of rare taxa

throughout community assembly was highlighted by the alpha and beta

diversities, and more specifically by the pattern of correlational networks.

Furthermore, the rise of negative correlations between taxa was proportional

113

to the cadmium exposure, therefore providing a reliable indicator of

disturbance to the homeostasis of the host bacterial metacommunity. The

gradual taxonomic convergence between water- and skin-associated bacterial

communities across both cadmium exposure groups highlights the loss of the

colonization resistance capacity of the host. This was potentially due to

physiological stress experienced by the host: cadmium bioaccumulation in

Perch's liver has already been documented to disrupt host physiology (Pyle,

Rajotte, and Couture 2005a). Finally, mathematical modeling demonstrated

that stochastic processes such as drift drove taxonomic assembly of

cadmium-free water communities, whereas host-microbiota assembly mainly

evolved under deterministic (non-neutral) processes.

3.8 Perspective

The patterns of neutral and non-neutral assembly in contrasting types of

bacterial communities (i.e. one environmental and two host-associated)

described here provide novel key insights regarding our understanding of

evolutionary forces that are at play in shaping the host microbiota when

facing a sublethal environmental stress. Living organisms are currently facing

unprecedented levels of environmental stressors that impact their capacity to

cope with natural pathogens, essentially by altering their overall immune

defense. Therefore, there is an urgent need to accurately decipher the early

warning signals occurring at the first stages of xenobiotic exposure. By

highlighting the link between a loss of colonization resistance and dysbiosis

within the host (which in turn is known to induce an inflammatory response),

our results will be useful not only for the field of microbial ecology but also

for biomedical research, as dysbiosis of gut microbiome composition has been

shown to result in the onset of various inflammatory diseases such as

diabetes, IBD, Crohn disease, cancer, and obesity (Mathis and Benoist 2012;

Snedeker and Hay 2012; Tilg and Kaser 2011).

114

3.9 Tables

Table 3. 1 Statistics’ summary of Cadmium concentration variation over

time and treatments in water tanks and fish livers.

The average of Cadmium (Cd) concentrations in treatments is higher 3 to 4

folds than in the Control groups (Table 1.1A). The overtime variation of Cd

concentration treatments groups (summarized in Table 1.1B) demonstrated

significant accumulation of Cd in fish livers. In water, at T1 and T3, the Cd

concentration was significantly different between Control and treatments, but

it is not between treatments (CC, CV) at T3. Treatments comparisons against

Table 3.1-a Liver Water

Cadmium

average

concentration

(ng/ml)

T0 T1 T3 T0 T1 T3

Ctrl 0.086 0.099 0.15775 0.065 NA 0.075

CV 0.086 0.09 0.4 0.067 1.54 6.43

CC 0.086 0.107 0.5235 0.098 3.08 5.8

Table 3.1b.

Over time

comparisons

T0-T1 T0-T3 T1-T3 Comments

Tukey's

p.value

Tukey's

p.value

Tukey's

p.value

Ctrl.T0=CV.T0=CC.T0

(no Cadmium

Treatment) Ctrl-Ctrl 0.99899388 0.999999983 0.944720869

CV-CV 0.99808831 0.000348064 4.4923E-08

CC-CC 0.999838113 4.57148E-07 0

Table 3.1c.

Statistics tests

(Tukey, Wilcox)

T1 T3

Liver Water Liver Water

Groups

comparisons

Tukey's

p.value

Tukey's

p.value

Tukey's

p.value

Tukey's p.value

Ctrl-CV 1 0.0059 0.00002 0

Ctrl-CC 1 0.05 0.0009 0

CC-CV 1 0 0.0281 0.1304

Ctrl-CC-CV

(Kruskal−Wallis

Rank Sum

Test)

0.99 0 0.0002 0

115

the Control group showed significant changes in Cd concentration at T1 and

T3 in water tanks and T3 in fish livers and (Table 1.2C). Accumulated Cd

concentrations in water tanks are approximately the same in CC and CV at

T3.

Table 3. 2 Statistical summary of alpha-diversity changes over time

and treatments.

Table 3-2

Community Time T0-T1 T0-T3 T1-T3

Alpha-

diversity

per regime

Richness Evennes

s

Richness Evenness Richness Evenness

adj. p-value (BH) adj. p-value (BH) adj. p-value (BH)

Gut Ctrl 0 0 0 0 0.279 0.5707

CV 0 0 0 0 0.0054 0.5498

CC 0 0 0 0 0.0006 0.3929

Skin Ctrl 0 0.869 0 0.0001 0 0

CV 0 0.0002 0 0 0 0

CC 0 0.002 0 0.0025 0 0

Water Ctrl 0.9502 0.0245 0.4673 0.058 0.4948 0.818

CV 0.5479 1 0.0023 1 0.0012 1

CC 0.4313 0.4008 0.479 0.04 0.0547 0.172

Table 3-b

Community Regime Ctrl-CV Ctrl-CC CC-CV

Alpha-

Diversity/Tim

e

Richness Evennes

s

Richness Evenness Richness Evenness

Corrected p-value

(BH)

Corrected p-value

(BH)

Corrected p-value

(BH)

Gut T3 0.0304 > 0.05 0 > 0.05 0.0354 > 0.05

Skin T1 > 0.05 0.017 > 0.05 0.017 > 0.05 0.7415

Over time, the richness or evenness have significantly changed between

treatments and Control, in all type of communities. At each time point, the

richness in gut and skin microbial communities has changed substantially in

all groups (Control, CC and CV) except in the Control group of the gut at T3

(Table 1.2A). On the other hand, the evenness showed significant changes in

116

all host communities between T0 and T1 except in the Control group of skin,

then between T1 and T3 except all groups of Gut communities (Table 1.2A).

In the water microbial communities, evenness showed significant changes

over time only in the Control group between T0 and T1. The evenness has

significantly changed over time in the water, just in CC and CV between T1

and T3. The significant changes of alpha-diversity indexes difference between

treatments and Control were statistically compared (Table 1.2B) using rank

statistics tests (Kruskal-Wallis/Wilcoxon). The same statistics were used to

compare alpha-diversity overtime. Overall, the evenness in microbial skin

communities has significantly diverged between treatments and Control at

T1. The richness in Gut microbial communities was significantly different

between treatments and Control only at T3.

117

Table 3. 3 Phylogenetic divergence at the community level.

Time Groups

Permanova Multiple test correction Betadisper Mrpp

p value

ADONIS p values B-H

p values

dispersion

p value

MRPP

Ctrl = control regime; CC = concentration is constant; CV = concentration is variable

GUT T0 All groups 0.052 0.079

CC-Ctrl 0.042 0.0765 0.048 0.033

CC-CV 0.051 0.0765 0.141 0.049

Ctrl-CV 0.28 0.28 0.302 0.234

T1 All groups 0.151 0.954

CC-Ctrl 0.124 0.25 0.941 0.114

CC-CV 0.25 0.25 0.843 0.234

Ctrl-CV 0.247 0.25 0.783 0.224


CC-Ctrl 0.001 0.0015 0.916 0.001

CC-CV 0.006 0.006 0.145 0.008

Ctrl-CV 0.001 0.0015 0.295 0.001

SKIN T0 All groups 0.016 0.54

CC-Ctrl 0.166 0.166 0.649 0.154

CC-CV 0.081 0.1215 0.256 0.075

Ctrl-CV 0.026 0.078 0.599 0.02


CC-Ctrl 0 0.003 0.249 0.006

CC-CV 0.085 0.085 0.395 0.079

Ctrl-CV 0.002 0.003 0.07 0.006


CC-Ctrl 0.035 0.049 0.619 0.049

CC-CV 0.049 0.049 0.586 0.045

Ctrl-CV 0.021 0.049 0.872 0.009

Water


CC-Ctrl 0.018 0.027 1 0.009

CC-CV 0.002 0.006 0.291 0.002

Ctrl-CV 0.036 0.036 0.519 0.038


CC-Ctrl 0.009 0.0135 0.851 0.01

CC-CV 0.136 0.136 0.073 0.113

Ctrl-CV 0.002 0.006 0.238 0.001


CC-Ctrl 0.002 0.002 0.068 0.002

CC-CV 0.001 0.0015 0.02 0.001

Ctrl-CV 0.001 0.0015 0.546 0.001

The phylogenetic distances between OTUs were computed using Gunifrac

(distance Chen et al. 2012). The divergence between treatments and Control

was assessed using PERMANOVA and the homogeneity for group dispersions

118

(distance from centroid), was evaluated using two multivariate tests,

BETADISPER and Multi-Response Permutation Procedure (MRRP) of within

versus among group dissimilarities. The significance of divergence between

groups was measured using multiple correction tests with Benjamini-

Hochberg BH (p-value<0.05)). For Gut microbial community, the significant

phylogenetic divergence between all groups was observed at T3. For Skin

microbial community, the significance in phylogenetic divergence was

observed between Control-CV and Control-CC at T1, and between all groups

at T3. For water microbial community, the phylogenetic divergence between

groups was significantly different at each time point except between CC and

CV at T1.

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3.10 Figures

Figure 3. 1 Linear variations of alpha-diversity over time and

between treatments explained by the linear mixed model in water and host-microbial communities

The richness and evenness variations were considered as response variables

in the linear model with mixed effects. The fixed effects were defined by time

and cadmium concentration (in water and liver), and tanks have a random

120

effect. Using the lme4 package in R, the linear model (M1= lmer

(diversity.index (Evenness or Richness) ~ Cd.Water*Cd.Liver + Time +

(1|Tank) ) demonstrates sTable 1.profiles of alpha-diversity over time in the

Control group compared to treatments in both water and host (skin, gut)

microbial communities. Constant Cadmium regime (CC) is in orange, variable

Cadmium regime (CV) is in Yellow, and Control (Control) is in green.

Figure 3. 2 Phylogenetic divergence at the community level

NMDS (non-metric Multi-Dimensional Scaling) plot of generalized Unifrac

distances showed the distribution of host-microbial samples based on the

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phylogenetic content of their microbiota. The plot shows a significant

separation of sample groups according to treatment (see in Supp Table 1.5-

b, p-values of the PERMANOVA test indicating the significance of group

separations) at T1 in the skin, and at T3 in skin and gut.

Figure 3. 3 Correlational co-abundance networks of gut microbial

community.

The topology of the gut microbial community network at T3 revealed a

gradual loss of connectivity from between treatments and Control. These

networks are composed of 30, 46, 65 of CC (connected components or hubs),

respectively in Control, CV, and CC groups. Therefore, the nodes number

(NN) was lower in the Control group (ControlNN: 410) compared to Cd groups

networks (CVNN: 488; CCNN: 526). On the contrary, an average of neighbors

(AN) was higher in the Control network (Control AN: 4) than in Cd groups

networks (CVAN: 2.98; CCAN: 2.82). Each node size in the network is

proportional to the average of the OTUs relative abundance in all samples.

The dominant OTUs, Tenericutes (Mycoplasma), Proteobacteria

(Pseudomonas) and, Firmicutes (Bacillus) are peripherals in the network.

Only in the Control group, dominant OTUs were connected to the central hub.

In contrast, in the Cadmium groups, dominant OTUs were disconnected from

the central hub into small independent hubs. In all conditions, the rare taxa

or tiny nodes hold most networks connections. This network was built using

R and Cytoscape software, to overview the complete topological features of

networks see the supplementary Table 1.6.

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Figure 3. 4 Correlational co-abundance networks of skin microbial community.

In microbial skin networks, each node size in the network is proportional to

the average of the OTUs relative abundance in all samples. At time1, the

proportion of neighbors (AN) was lower in Control network (Control AN: 4)

compared to Cd groups networks (CVAN: 10.7; CCAN: 24.6). The main

difference in CV and CC relative to Control was manifest in the higher

percentage of significant negative correlations observed in those groups (CV

neg. corr.: 6.3%; CC neg. corr.: 6.9%) compared to Control group (Control

neg. corr.: 2.88%). The OTUs of Bacteroides significantly higher in CC and

CV groups were mostly involved in the negative correlations (red edges in

networks). Despite their high relative abundance in all groups, both of rare

and most abundant Tenericutes (Mycoplasma) nodes (OTUs) were involved

in negative interactions in CV and CC groups, but not in the Control group. At

T3, there was a loss of significant interactions compared to T1, which might

be partially related to the decrease of OTU numbers between T1 and T3 (-

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53.2 % in Control, - 57.7% in CV, and - 54.7 % in CC). In the Control group,

the invasion of Tenericutes (Mycoplasma) was not involved in negative

interactions. Rare OTUs from both phylum Firmicutes and Proteobacteria

represent most nodes in the central hub of the network. At T3, in the CV

group there were any negative correlations, and Tenericutes (Mycoplasma)

OTUs accounted among the rare taxa. In the CC group, negative interactions

represented over 6 % of the total edges of the network. This network was

built using R and Cytoscape software. To overview the complete topological

features of networks, see the supplementary Table 1.6.

Figure 3. 5 Correlational co-abundance networks of water microbial community.

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Water microbial community networks displayed fragmented interactions, over

time and between treatments — the topology of water microbial encompassed

sub-networks disconnected in small independent hubs. The number of

independent hubs was the highest in Control network at T0 and T3, at T3 in

CC, and it was always intermediate in CV network (see the supplementary

Table 1.3). This network was built using R and Cytoscape software.

Figure 3. 6 Bar plots of neutral OTUs change at community and

meta-community levels.

According to the non-linear least squares model (NLS), the proportion of OTUs

that fit neutrality model within a confidence interval of 95% tended to be

variable between communities across time, and cadmium treatments (Figure

2.6). In gut and skin communities, less than 50% of the total number of OTUs

over time was explained by the neutral model. However, the percentage of

neutral OTUs is higher in the Control treatment compared to the variable and

constant cadmium regimes. In the water communities, the percentage of

neutral OTUs is more than 50% at each time point. For cadmium CV and CC

at T3, the majority of OTUs (>70%) are non-neutral. At the meta-community

level including water and host-communities as a whole system, the

percentage of non-neutral OTUs was always higher than neutral OTUs in

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treatments and Control. The goodness of fit (R2) was lower than 0.5 in all

comparisons except in Control water communities.

3.11 Supplementary Figures

Supplementary Figure 3.1. Box plots of alpha-diversity variations

over time and between treatments in host and water microbial

communities.

The box plot of richness and evenness variations showed different trends

between treatments and Control. In the gut (left), the alpha-diversity showed

the same tendency in all groups, except at time3. In skin (middle), the

evenness at T1 was higher in Cadmium treatments compared to Control while

the opposite produced for richness at T3. In water (right), the evenness and

richness were intermediate in the Control group compared to variable and

constant Cadmium selection treatments, except the evenness which was the

highest in Control group at T1. Constant Cadmium regime (CC) is in orange,

variable Cadmium regime (CV) is in Yellow, and Control (Control) is in green.

126

Supplementary Figure 3.2. Heatmaps of significant taxonomic

variation at the phylum level.

127

This Figure represented with three heatmaps the significant changes over

time of taxonomic composition at the phylum Level in the gut (2a.), in the

skin (2b.) and water (2c.). The hierarchical clustering of the relative

abundance of phyla which significantly changed over time was performed

using Ward's method and Bray–Curtis dissimilarity distance. Vegan package

and pheatmap () function in R were used to produce these heatmaps.

Supplementary Figure 3.3. Heatmaps of significant taxonomic

variation at the genus level.

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This Figure represented with three heatmaps the significant overtime changes

of taxonomic composition at the genus Level in GMC (Gut Microbial

Community) (2a.), in SMC (Skin Microbial Community) (2b.) and WMC (Water

Microbial Community) (2c.). The hierarchical clustering of the relative

abundance of phyla which significantly changed over time was performed

using Ward's method and Bray–Curtis dissimilarity distance. Vegan package

and pheatmap () function in R were used to produce these heatmaps.

Supplementary Figure 3.4. Phylogenetic divergence at the

community-level in water microbiome.

NMDS (non-metric Multi-Dimensional

Scaling) plot of generalized Unifrac distances

showing the distribution of host-microbial

samples based on the phylogenetic content of

their microbiota. The plot shows a significant

separation of sample groups according to

treatment (see in Supp. Table 1.5-b, p-values

of the PERMANOVA test indicating the

significance of group separations) at each

time point, but treatments samples are closer

to each other than the Control group at T1

and T3.

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Supplementary Figure 3.5 Boxplots of Bacteroidetes variation

overtime and between treatments.

At T0, the relative abundance of Bacteroidetes was significantly lower in Skin

compared to water and gut microbial communities. However, at T1,

Bacteroidetes abundance was significantly higher (Wilcox ‘s P-value <0.05)

in Skin communities only in treatments groups (CC and CV), while in Ctrl

group they showed any significant variation.

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Supplementary Figure 3.6 Distribution of abundance versus

neutrality in the whole metacommunity , host and microbial communities

This Figure showed the distribution variation of neutral OTUs percentage (Y

axis) predicted by NLS models using 12 cutoffs of relative abundance

averages (X axis) in the entire metacommunity at T1 and T3.

131


Supplementary Table 1. ANOVA summary of metals concentrations in

water tanks and Perca flavescens fish livers. Tukey and Wilcoxon’s tests

showed that the Cadmium concentration in fish liver and has significantly

changed between treatments and Control; Constant Cadmium regime (CC),

Variable Cadmium regime (CV), and Control (Control) and over time. No

significant changes were observed between Zinc and Cooper between

treatments.

Supplementary Table 2. Summary of alpha-diversity over time and

treatments. This table summarises averages of richness and evenness in the

host (skin and gut) and water microbial communities at each time point

between treatments and Control groups.

Supplementary Table 3. Statistical summary of co-abundant OTUs in

correlational networks. The percentage of OTUs involved in significant

correlations was relatively higher in water compared to skin and gut microbial

communities’ networks. In gut microbial networks, this percentage decreased

between 78 % in the Control group to 69% in the CV group, and 63% in CC.

In microbial skin networks at T1, the significant part of OTUs varied between

89 %, 90 % and 97% in Control, CV and CC networks. At T3, the percentage

of OTUs in skin microbial communities varied between 56 %, 31 % and 54%

In water microbial communities, this percentage ranged between 46%, 9 %,

and 37% at T0; 31%, 33% and 34% at T1; then 51%, 7%, and 75% at T3,

respectively in Control, CV and CC networks. Per time and treatment, all the

networks nodes (OTUs) average relative abundance are reported in the

second sheet of this table.

Supplementary Table 4. Summary of correlation networks features in host

and water microbial communities. The networks of gut microbial communities

are composed of 30, 46, 65 of CC (connected components or hubs),

respectively in Control, CV, and CC groups (see sheet 1). Therefore, the

nodes number (NN) was lower in the Control group (ControlNN: 410)

compared to Cd regimes networks (CVNN:488; CCNN:526). On the contrary,

an average of neighbours (AN) was higher in the Control network (Control AN:

4) than in Cd groups networks (CVAN:2.98; CCAN:2.82). Skin microbial

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networks were composed of 47, 10, 3 of CC (connected components),

respectively in Control, CV, and CC groups (see sheet 1). The number of CC

was inversely proportional to nodes number, low in Control group network

(ControlNN: 651) in comparison with Cd groups networks (CV NN: 661; CC

NN:759). Similarly, the average of neighbours (AN) was lower in Control

network (Control AN: 4) compared to Cd groups networks (CVAN: 10.7; CCAN:

24.6). This connectivity higher in CV and CC at T1 was proportional to the

higher percentage of strong negative correlations (R neg. Corr. < -0.6; B-H

p-value < 0.05), observed in those groups (CV neg. corr.: 6.3%; CC neg.

corr.: 6.9%) compared to Control group (Control neg. corr.: 2.88%). Water

microbial networks displayed variables features over time and between

regimes (see sheet 2).

Supplementary Table 5-a. Significant taxonomic changes in water and

host-microbial communities over time and between treatments. In the gut,

the significant changes in the relative abundance between T0 and T1 were

detected for Synergistetes in CC and Tenericutes in CC and CV. Later,

between T1 and T3, the relative abundance of four phyla, Euryarchaeota,

Firmicutes, Proteobacteria, and Tenericutes has significantly changed in all

groups. In skin, between T0 and T1, the significant changes were observed

for Firmicutes and Fibrobacteres in the CV; Bacteroidetes and Fusobacteria in

CC and CV. However, between T1 and T3, the significant changes of relative

abundance concerned Actinobacteria in CC and CV; Proteobacteria,

Tenericutes, Bacteroidetes, and Firmicutes in all groups. In water, between

T0 and T1, the significant changes were observed for Bacteroidetes in

Control; Proteobacteria in the CV; Firmicutes and Tenericutes in all groups.

However, between T1 and T3, the significant changes concerned

Proteobacteria and Tenericutes in CV, and Firmicutes in all groups. Two phyla

showed significant changes at long-term between T0 and T3, Fibrobacteres

in Control, and Actinobacteria in Control and CV.

Supplementary Table 5-b. Temporal taxonomic changes in host and water

communities. The Significant temporal variation of taxonomic composition

involved 24 genera in Gut microbial community, 33 in skin microbial

community and 19 in water microbial community. The corrected p-values of

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paired Wilcoxon signed rank sum test are summarized in Supplementary

Table 5-a.

Supplementary Table 6. Features of water and host-microbial communities’

correlational networks per time and treatment. This table is composed of

multiple tables; each network features are represented in an independent

table. The most important features are the degree (connectivity) of nodes,

closeness centrality, betweenness centrality and their size proportional to

their average relative abundance in all samples.

Supplementary File 1. A brief description of the sample groups of host

microbiota and water.

Supplementary file 2. Materials and Methods (supplementary information)

Supplementary file 3. DNA extraction from TRIZOL from the organic phase

after RNA extraction

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Chapitre 4: Community recovery dynamics in

yellow perch microbiome after gradual and

constant metallic perturbations

Cheaib, B., Seghouani, H., Ijaz, U.Z. et al. Community recovery dynamics in

yellow perch microbiome after gradual and constant metallic perturbations.

Microbiome 8, 14 (2020). https://doi.org/10.1186/s40168-020-0789-0

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4.1 Resumé

Contexte : Les processus éco-évolutifs régissant l'assemblage microbien

post-perturbation restent mal étudiés, en particulier dans les systèmes hôte-

microbiome. La résilience de la structure d’une communauté microbienne

dépend non seulement du type, de la durée, de l'intensité et du gradient de

perturbation, mais également de la structure initiale de la communauté, et

du type de l'habitat. Dans cette étude, les microbiotes cutané et intestinal de

la Perchaude (Perca flavescens), ainsi que de l’eau ont été caractérisés avant,

pendant (90 jours) et après (60 jours) l’exposition au cadmium. Les

microbiotes de la peau, des intestins et des réservoirs d'eau dans les groupes

de contrôle et de traitement ont été caractérisés avant, pendant et après

l'exposition au cadmium en utilisant des librairies d'ADNr 16s et une

technologie de séquençage à haut débit (Illumina, Miseq).

Résultats : Les mesures spectrométriques ont révélé une augmentation de

bioaccumulation de cadmium dans le foie, même deux mois après l'arrêt de

l’exposition au cadmium. Le gradient de sélection montre des effets

différentiels sur la résilience, notamment par l’augmentation de diversité

alpha, et des transitions taxonomiques, ainsi qu'une divergence significative

de la diversité fonctionnelle et phylogénétique. La résilience des

communautés cutanées a montré une prolifération importante d'agents

pathogènes opportunistes du poisson (genre Flavobacterium). La neutralité

montre plus d’impact sur les communautés du groupe contrôle que ceux

exposés au cadmium. Le microbiome de la perche a atteint un état stable

alternatif dans la peau et une trajectoire de récupération presque complète

dans les communautés microbiennes intestinales.

Conclusions :

La perturbation métallique à court terme du développement des poissons

montre des répercussions à long terme pour la santé de l'hôte. La résilience

des communautés microbiennes après une exposition aux métaux dépend de

l'ampleur de l'exposition (constante, progressive) et de la nature de la niche

écologique (eau, peau et intestin). Dans l'ensemble, nos résultats montrent

que l'assemblage microbien pendant la résilience de du microbiote a été à la

fois orchestré par des processus neutres et déterministes.

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4.2 Abstract

Background: The eco-evolutionary processes ruling post-disturbance

microbial assembly remain poorly studied, particularly in host-microbiome

systems. The community recovery depends not only on the type, duration,

intensity and gradient of disturbance, but also on the initial community

structure, phylogenetic composition, legacy, and habitat (soil, water, host).

In this study, yellow perch (Perca flavescens) juveniles were exposed over 90

days to constant and gradual sublethal doses of cadmium chloride.

Afterwards, the exposure of aquaria tank system to cadmium was ceased for

60 days. The skin, gut and water tank microbiomes in control and treatment

groups, were characterized before, during and after the cadmium exposure

using 16s rDNA libraries and high throughput sequencing technology

(Illumina, Miseq).

Results: Our data exhibited a long-term bioaccumulation of cadmium salts

in liver even after two months since ceasing the exposure. The gradient of

cadmium disturbance had differential effects on the perch microbiota

recovery, including increases in evenness, taxonomic composition shifts, as

well as functional and phylogenetic divergence. The perch microbiome

reached an alternative stable state in the skin and a nearly complete recovery

trajectories in the gut communities. The recovery of skin communities showed

a significant proliferation of opportunistic fish pathogens (i.e.

Flavobacterium). Our findings provide evidence that neutral processes were

a much more significant contributor to microbial community turnover in

control treatments than in those treated with cadmium, suggesting the role

of selective processes in driving community recovery.

Conclusions: The short-term metallic disturbance of fish development has

important long-term implications for host health. The recovery of microbial

communities after metallic exposure depends on the magnitude of exposure

(constant, gradual), and the nature of the ecological niche (water, skin, and

gut). The skin and gut microbiota of fish exposed to constant concentrations

of cadmium (CC) were closer to the control negative than those exposed to

the gradual concentrations (CV). Overall, our results show that the microbial

assembly during the community recovery were both orchestrated by neutral

and deterministic processes.

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4.3 Introduction

Resilience refers to the capacity of a natural ecosystem to maintain a stable

state after facing different exogenous disturbances, both in terms of

amplitude and frequency (Holling 1973).Introduced first by Holling (1973),

the concept of resilience was redefined to incorporate the idea of recovery

following a temporary disruption (Pimm 1984; V. Grimm and Wissel 1997),

not simply the ability to resist this disturbance in the first place (Hodgson,

McDonald, and Hosken 2015). Both ecological concepts, ‘resistance’ and

‘recovery’, were simultaneously considered as measurable components that

together represent resilience (Hodgson, McDonald, and Hosken 2015). In

other microbial studies, the term ‘resistance’ is synonymous with

resilience(Ziegler et al. 2017) using Holling‘s definition. Notwithstanding,

‘sensitivity’ (inverse of resistance) is also sometimes used to represent the

degree to which a community changes in response to disturbance (Shade et

al. 2012). The recovery rate, time to reach an equilibrium state, and the

distance to an alternate stable state are quantitative measures that can be

used to compare the resilience (Cabrol et al. 2016; Hodgson, McDonald, and

Hosken 2015; Ingrisch and Bahn 2018; Shade et al. 2012) and improve our

understanding of ecosystem recovery (Scheffer et al. 2015; Ziegler et al.

2017). In this study we will employ the term ‘recovery’ to describe the pattern

of eco-evolutionary change that occurs when a community returns to an

alternative stable state.

The recovery of microbial communities depends on the type, duration,

intensity and variability of a disturbance. More importantly, microbial

recovery can be impacted by the initial community structure, phylogenetic

composition, legacy, and the type of habitat (soil, water, host). After

antibiotic treatment, the complete recovery of initial bacterial community

composition is rarely achieved, as reported in various host-microbiota

systems from honeybees (Raymann, Shaffer, and Moran 2017) to humans

(Dethlefsen and Relman 2011). The incomplete recovery of gut microbiota

ecosystems after antibiotic administration results in a shift of the microbial

composition to an alternative equilibrium called an “alternate stable state”

(Shade et al. 2012; C. A. Lozupone et al. 2012; F. Sommer et al. 2017). This

compositional shift occurs when resistance or recovery is weak, and/or when

138

the intensity of disturbance is high. Although the understanding of factors

that drive such regime shifts to an alternative equilibrium in microbial

ecosystems will have tremendous impacts in various fields of application (e.g.

personalized medicine, agriculture, bioremediation), this phenomenon is still

poorly studied.

The relative roles of ecological and evolutionary processes in the recovery of

the structure of microbial communities are still to decipher. Theoretically, the

nature of these processes can be neutral (stochastic) (Hubbell Stephen P.

2005; F. Sommer et al. 2017), or selective (deterministic) (Chase 2003;

Sloan et al. 2006), the latter being driven either by environmental filtering or

competitive exclusion (Cadotte et al. 2010; Webb et al. 2002), the former by

demographic sampling effects alone. In the context of community recovery,

a small number of studies revealed that deterministic processes drive

bacterial succession dynamics in a soil bacterial community disrupted either

by a depletion gradient of nutrients (Song et al. 2015), a thermal shock

(Jurburg et al. 2017),or a rainfall rehydration of dry soil (Placella, Brodie, and

Firestone 2012).

In the present study, we assessed the relative contribution of neutral and

deterministic processes in the recovery of the yellow perch (Perca flavescens)

microbiome assembly following an experimental metallic exposure gradient.

Polymetallic contamination in aquatic ecosystems mostly results from

exposure to acid mine drainages (AMD) occurring around the world (Wendt-

Potthoff and Koschorreck 2002; F. Wang et al. 2003; Gough et al. 2008;

Hudson-Edwards, Jamieson, and Lottermoser 2011; Moser and Weisse 2011;

Urbieta et al. 2012; Douglas et al. 2012; Masmoudi et al. 2013; Stankovic et

al. 2014; Valente et al. 2015; Cheaib et al. 2018). For instance, in the natural

Canadian lakes, the cadmium (Cd) concentration reaches 9 ppb (parts per

billion) in perch liver/water (Pyle, Rajotte, and Couture 2005b; Couture,

Rajotte, and Pyle 2008), and it has a clear quantitative impact on the perch

physiology, gene expression, and genotype diversity (Bougas et al. 2013b).

In the same polluted lake system studied by Couture et al. (2008), the

microbial communities’ assembly in water has evolved under chronic

exposure to a gradient of trace metals due to the AMD expelled in the

environment, leaving substantial genotypic signatures of adaption in the

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taxonomic and functional repertories of AMD communities (Cheaib et al.

2018). Given that yellow perch juveniles can tolerate sublethal doses of

cadmium without encountering significant physiological damage or death

(Giguère et al. 2004; Campbell et al. 2005b), this host-microbiota model

system is well suited to study microbiota recovery following metal exposure

stress. In the laboratory, yellow perch juveniles underwent exposure to

sublethal doses of cadmium chloride (CdCl2), the accumulation of which was

tested in the water and within perch liver. The recovery of community

structure and function in water and host microbiome were then studied and

compared between constant and variable regimes of metallic stress, which

was defined by the levels of Cd detected in liver and water samples. To

distangle the effect of the xenobiotic from host development (Sylvain and

Derome 2017; Burns et al. 2016b) on bacterial strain recruitment ontogeny,

microbiota assembly was also assessed in stable conditions as a control

regime. Our expectation was that constant exposure to cadmium chloride,

due to its severe implications for host and microbial community physiology,

would impede community recovery most severely than in the gradual

exposure experimental group.

4.4 Methods

4.4.1 Fish rearing

The experiment is described in Figure 4.1. Briefly, there were two acclimation

periods: one in a standard container (1500L) and the second in 24 tanks (36

L) with an independent filtering system circuit for each aquarium. The fish

juveniles were reared within the same physicochemical conditions

(photoperiod, pH, ammonia, nitrogen dioxide). Throughout the experimental

period, to maintain viable conditions for perch in each water tank, fecal and

uneaten food particles were removed daily using specific pressing tubes for

each set of experimental conditions. A volume of 15 L of water was renewed

two times a week for each tank (Figure 4.1).

140

4.4.2 Exposure regimes to cadmium

Cd-treated and Control (Ctrl) tanks were designed into two cadmium chloride

exposure regimes (8 tanks per regime), and one negative control regime (8

tanks) (Schema 1). The yellow perch in treated tanks were exposed to

cadmium chloride (CdCl2) dissolved in water. Under the regime of constant

CdCl2 concentration exposure (CC), the cadmium chloride was initially added

at 0.8 ppb, then increased to reach a target theoretical concentration of 9

ppb (parts per billion) by the end of the first month (T1). The CdCl2

concentration was adjusted to 9 ppb every five days during two additional

months until the end of treatment (third month, T3) where the measured

concentration reached an average of 5.8 ppb. Under the regime of variable

CdCl2 concentration (CV), the CdCl2 was initially added at 0.6 ppb, then the

concentration was gradually increased every five days to meet the target

theoretical concentration of 9 ppb by the end of the third month. The

measured concentration reached an average of 6.8 ppb at the end of

treatment (third month, T3). The maximal CdCl2 concentration was settled at

9 g/mL, which was within range of concentrations detected in yellow perch

liver in contaminated Canadian lakes (Pyle, Rajotte, and Couture 2005b;

Couture, Rajotte, and Pyle 2008).

4.4.3 Recovery after the exposure to Cadmium

The cadmium administration was stopped after the third month (T3). The

experiment was extended two months (T5) after T3 to test the recovery of

microbiome assembly in water and host.

4.4.4 Host-microbiota and water sampling

Briefly, we selected 144 mucosa samples of skin (2 times x 3 regimes x 8

tanks x 3 replicates) and 144 gut (2 times x 3 regimes x 8 tanks x 3

replicates) samples corresponding to T0 (no cadmium) and T3 (ultima

cadmium treatment). Also, 48 water samples (2 times x 3 regimes x 8 tanks

x 1 technical replicate) from T0 and T3 were included. At the end of recovery

time (T5), 72 skin mucosa samples (1-time x 3 regimes x 8 tanks x 3

replicates) and 72 gut samples (1-time x 3 regimes x 8 tanks x 3 replicates)

were collected from the host. In addition, 240 samples (5 times x 3 regimes

x 8 tanks x 2 technical replicates) of water (2 L) microbial filter (0.22 μm)

141

were sampled between T3 and T5 at an interval time of 15 days corresponding

to five recovery time points (TR1, TR2, TR3, TR4, and T5).

4.4.5 Metal concentration in water and fish liver

Every week until the end of the CdCl2 exposure regimes, we measured the

concentration of trace metals of cadmium (Cd), zinc (Zn) and copper (Cu) in

the yellow perch liver and water tanks using the ICPMS (Ionization Coupled

Mass spectrometry) technology available at INRS (Institut National de la

Recherche Scientifique). For further details of the measurement of Cd in liver

preceded by acid digestion and lyophilisation see our under review study

Cheaib et al. (2019). Two-way analysis of variance (ANOVA), Tukey’s test,

and Wilcoxon rank test were applied to test the significance of cadmium

accumulation in liver and water over time, and between treatment groups.

4.4.6 DNA extraction, libraries preparation and 16S amplicons

sequencing

DNA was extracted using the Qiagen DNeasy blood and tissue kit for skin

mucosa, and TRIzol organic phase followed by BEB (back extraction buffer)

and PCI (phenol/chloroform/isoamyl alcohol 25:24:1) solutions for all gut

samples. The V3-V4 hypervariable region of the universal rDNA 16S gene

(Werner et al. 2012)was amplified using universal specific primers. The

libraries of amplicons were prepared using a set of 384 combinations of

adaptors, processed in one sequencing run, on an Illumina Miseq sequencing

machine. Reactions of PCR were verified by electrophoresis on 2% agarose

gel, purified and quantified by fluorescence for the double strand DNA

concentration using Quant-iT™ PicoGreen™ dsDNA Assay Kit (Thermo Fischer

Scientific).

4.4.7 Bioinformatics and biostatistics analyses

4.4.7.1 Reads preprocessing and OTUs clustering

Sequence analysis was performed with our bioinformatic pipeline as described

previously (Sylvain et al. 2016; Llewellyn et al. 2016). In the first instance,

we used SICKLE Version 1.2 to trim the reads (> Q30 Phred quality score)

followed by utilizing PANDASEQ Version 2.11 (Masella et al. 2012) assembler

for merging paired-end read into a single merged reads ( ~ 350 bp)

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corresponding to the amplified 16S rRNA V3-V4 hypervariable region (347 F-

805 R). Based on an approach of De novo sequence clustering before the

taxonomic assignment, reads were clustered into OTUs at 97% identity with

USEARCH Version 9 (Edgar RC. 2010) and filtered out using UNOISE2

algorithm (Edgar 2016) to discard chimeric sequences, putatively produced

during PCR amplification cycles using OTUs were annotated using RDP

database as previously described in our pipeline (Sylvain et al. 2016;

Llewellyn et al. 2016). to. Community structure and composition of

metacommunities were analyzed across time and treatments by richness

(OTUs count), evenness (Shannon index) and the Gunifrac phylogenetic

distance (J. Chen et al. 2012) using the vegan (Oksanen et al. 2016) and

Rhea (Lagkouvardos et al. 2017) packages in R.

We then calculated the alpha-diversity indices (richness and evenness) and

beta-diversity (phylogenetic distance) differences between experimental

groups and used rank statistics tests (Kruskal-Wallis/Wilcoxon) to assess

their significance. The resulting p-values for pairwise comparisons in alpha

and beta-diversity were corrected for multiple testing using the Benjamini-

Hochberg method (Benjamini and Hoecheberg, 1995). Note that the Beta-

diversity was calculated using the generalized UniFrac metric (W. Chen et al.

2013), which considers both the dominant and the rare OTUs. The

permutational multivariate analysis of variance (henceforth referred to as

PERMANOVA) was applied to the Gunifrac distance matrices to explain the

sources of variations including the experimental groups. To test homogeneity

of variances, we performed the multivariate homogeneity test which a Multi-

Response Permutation Procedure (MRRP) of within versus among group

dissimilarities dispersions of Gunifrac distances. The Non-Metric Multi-

Dimensional Scaling (NMDS) was performed to visualize Gunifrac distances

in a reduced space with k=2 dimensions. For statistics comparison of one-

dimensional statistics of multiple groups, we used the non-parametric

Kruskal-Wallis Rank Sum test because of the strong assumption of the normal

distribution of OTUs abundance being rarely assumed.

The alpha-diversity variation across time and per treatment was predicted

and ploted with linear mixed effect models using the ratio of

richness/evenness as a response variable, time and cadmium concentrations

143

in water and liver as fixed effects, with the categorical variable tank as a

random effect.

Using the lmer R package, for water the model was used as following in R;

Model=lmer (Richness/Shannon.effective~Time+Cd.Water+(1|Tank),

data=mixdata, REML = TRUE), whereas for each host habitat (Skin, Gut),

we employed the following model;

Model_host=lmer(Richness/Shannon.effective~Time+Cd.Liver+Cd.Water+(

1|Tank), data=mixdata, REML = TRUE).

The confidence interval was then predicted using the predictInterval()

function in R.

4.4.7.2 Post-OTUs analysis, networks and function prediction.

Structure and diversity measures of groups (control & treatments) were

compared with rank statistics tests (Kruskal-Wallis/Wilcoxon) adjusted with

BH (Benjamin-Hochberg) test for multiple corrections, and the p-value <0.05

as a threshold of statistical significance. To understand the role of relative

abundance of OTUs on the similarities of community structure, correlation

networks of communities (samples) were constructed using the Spearman

coefficient as a robust approach of correlation detection (Weiss et al. 2016).

Significant positive and negative correlations were filtered and false discovery

rate (FDR) was assessed with B-H test for multiple corrections. Next, network

visualisation and analysis were performed with Cytoscape software (Shannon

et al. 2003).The network centrality was analysed using the “Network

Analyzer” plugin in Cytoscape. The betweenness centrality of a node was

calculated as the total number of the shortest paths from all nodes to all other

nodes that pass this node (Röttjers and Faust 2018). The centrality of the

nodes reflected their importance in transmitting information between hubs;

it does not depend on the feature of node degree, which describes the total

node connectivity. The size of nodes was proportional to the number of OTUs

in each sample, and the coefficient of the significant correlation between two

nodes was inversely proportional to the size of edge. Finally, functional

profiles of each community type at every time point were predicted using the

software TaxforFun (Aßhauer et al. 2015).

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4.5 Neutral and deterministic models to asses the recovery of

community assembly.

In the null hypothesis, the neutral model (Sloan et al. 2006) (Sloan et al.

2006) assumes a beta distribution of OTU abundance. Using the non-linear

partial least square method (Burns et al. 2016b), which estimates the

migration rate (m) of OTUs from their source to a destination community, the

model predicts the frequencies of OTUs. The estimated migration rate (m) is

the probability that a random loss (death or emigration) of an OTU in a

destination community is replaced by dispersal from the source community.

Comparing the predicted versus observed frequencies, we can determine

which OTU fits the model in each host and water community, at every time

point, across both control and treatments groups. The goodness of fit to the

model was measured using the coefficient of determination R-square (R2

>0.5) within a confidence interval of 95%, where increased strength of

goodness of fit to the model suggests an essential role of stochastic processes

in the microbiome assembly.

4.6 Results

4.6.1 Cadmium concentration bioaccumulation in the fish liver

during recovery time

Interestingly, the concentration of cadmium ions measured with ICPMS

increased significantly in the fish liver even after two months from stopping

exposure. The Cd concentration increased from 0.4 ppb to 1 ppb in the

variable CdCl2 regime (CV) and from 0.5 ppb to 1.17 ppb in the constant

CdCl2 regime (CC). However, in the water, as expected, the Cd concentration

significantly decreased from 6.4 ppb to 1.06 ppb in CV, and from 5.8 ppb to

1.34 ppb in CC (Table 4.1a-b). Consequently, the accumulation of Cd in liver

and water was always significantly higher in treatments CC and CV compared

to the control group (Table 4.1-c). Similar Cd concentrations observed among

treatment groups CC and CV in the water at times T3 and T5 (expected at

maximum Cd concentration added in tanks) was observed in fish liver only at

time T5 (Table 4.1-c).

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4.6.2 Genotypic signatures of community recovery

At the alpha-diversity level, to investigate how far diversity metrics could be

used as indicators for metacommunity structure recovery, both richness and

the evenness were calculated in water and host-microbial communities. In

the host microbiome, time had a significant effect on diversity measures

within all groups between times T3 and T5. The richness and evenness have

significantly increased overtime in skin microbiota, and significantly

decreased in the gut microbiota. Over the five recovery time points (TR1,

TR2, TR3, TR4, T5), temporal comparisons in the water microbial

communities associated with each experimental group did not show a

significant change of evenness for CC and CV, but did during TR2 – TR4 within

the control group (Ctrl). Within these communities, a significant change in

richness during the whole recovery period except TR2-TR3 was also found for

CC and CV (Additional Table 4.S1a-b).

In contrast, both richness and evenness in the control group of skin

microbiota significantly fluctuated over the recovery period (T3-T5, after

CdCl2 addition had stopped). At time T3, the pairwise comparison of CC and

CV against the control group (CC-Ctrl and CV-Ctrl) revealed significant

differences in microbial richness in the gut and evenness in the skin. At time

T5, statistical tests did not detect any significant change in diversity measures

between all groups for gut and skin microbiome (Additional Table 4.S1-c);

however, as at T3, the evenness of skin microbiome at T5 was significantly

divergent between cadmium treatments, (p-value = 0.0063) (Additional

Table 4.S1-c).

The comparative analysis of richness and evenness among water and host

microbiota showed convergent patterns of diversity between the water and

the skin communities before the disturbance and after the recovery

(Additional file 4.1: Figures 4.S1).

The predicted alpha-diversity values along with the fitted linear mixed effect

model for water communities showed a significant drop in treatment values

compared to the control group. On the other hand, for the host communities

(skin and gut), they increased under the selection regimes and decreased

during the recovery time span (Figure 4.2).

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In summary, except for the linear mixed effect model results, the observed

patterns of alpha diversity metrics changes across the experiment did not

show a clear trend over the course of the experiment. Nonetheless, increasing

evenness and richness was a general trend for the skin, while decreasing

evenness and increasing richness was representative of the gut microbiome

community recovery.

Beta diversity (Gunifrac) between samples was compared using a

PERMANOVA and a multivariate test for variance homoscedasticity. By T3 –

at peak cadmium exposure - significant differences (p < 0.05) among

treatments were observed in all microbial communities of water and host

(Table 4.2 ; Additional file 4.2 : Figures 4.S2); and by T5, both variable (CV)

and constant (CC) cadmium exposure treatments retained differences in skin

communities compared to the controls despite the recovery period.

Surprisingly, given our expectation that cadmium exposure would have a

major impact on community recovery, a high similarity in the community

phylogenetic structure between the control and CC groups was detected

among gut microbial communities at T5. Beta-diversity between treatments

(CC, CV, Ctrl) was always significantly divergent at each time point in water

samples except for the observed convergence between CC and CV at recovery

time TR2 (Table 4.2; Additional file 4.2: Figures 4.S2). The comparison of

beta-diversity showed a community structure divergence (p-value<0.001)

between water, skin and the gut microbiota before the disturbance and after

the recovery (Additional file 4.3: Figures 4.S3). The results show that water

microbiome at time T3 is not representative enough of fish microbiome (see

the blue cluster in the phylogram of CC, page 2 of figures 4.S3). However, at

the recovery time T5, the water was not representative of the fish microbiome

in gradual selection regime (see the blue Cluster in the phylogram of CV, page

3 of figures 4.S3).

4.6.3 Microbial taxonomic composition change during recovery

At T5, no significant changes were observed between groups at the phyla

level in the water, but Actinobacteria in the gut, and both Euryarchaeota and

Tenericutes in the skin, were significantly different between control and

treatments (CC & CV). Additional Table 4.S2 details several taxa that showed

significant differential abundance between treatments (Ctrl, CV, CC). Of

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particular importance putative pathogenic genus Flavobacterium was

significantly enriched in the skin for both groups of CdCl2 exposed fish at T5,

despite the recovery period. In the gut microbiome, Syntrophococcus was the

only genus to be significantly different between treatments (Figure 4.3;

Additional Table 4.S2). In the water, significant differences in taxonomic

abundance between CV and Ctrl were restricted to one genus (Kiloniella) at

recovery time TR1 and two genera (Marinobacter and Perlucidibaca) at T5.

No significant differences in taxonomic composition were detected between

CC and Ctrl in the water at T5. Overall, statistical analysis of taxonomic

composition dynamics over time within each treatment during the recovery

period revealed several minor differences (see Additional Tables 4.S3 for

more details).

On the other hand, the pairwise comparison of taxonomic composition

between different type of communities at each time point and for each

experimental group showed significant divergence between microbial

communities of gut, skin and water. At the recovery time (T5), Tenericutes,

Euryarchaeota, and Firmicutes were inherently associated (significantly up

regulated) with the gut microbiome; Actinobacteria and Bacteroidetes, on the

other hand were specific to skin microbiome; with Fibrobacteres and

Actinobacteria implicated with the water microbiome. Despite all this, the

Proteobacteria were found to be prevalent and common in water and skin

microbiome. At the selection time (T3), Fibrobacteria and Actinobacteria were

scarcely abundant and were picked up as differentially abundant (Additional

file 4.6: Figures 4.S6). To delineate the most relevant taxa (at the genus

level) significantly changing between the communities, we have performed a

pairwise test on an overall comparison of skin, gut and water at each time

point for different treatment groups. The results plotted in heatmaps in the

figure 4 clearly reveal that each community type has an inherent signature

and the corresponding proportions of genera differed between control and

treatments over time with high similarity between the CC and Ctrl at time T5

(Figure 4.4; Supplementary Tables 4.4).

Correlational analysis (Additional file 4.4: Figures 4.S4) revealed a positive

relationship between specific genera and concentrations of cadmium in perch

liver and water. In aquaria treated with CdCl2 (CV &CC), cadmium

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concentrations in water and liver showed strong significant positive

correlations with seven genera from gut microbiome, each of which

represented a different phylum and had a strong negative correlation with

relative abundance of Mycoplasma. In the skin microbiome of both CC and

CV, 15 genera (Sphingomonas; Haloarcula; Legionella; Flavobacterium;

Ameyamaea; Dokdonella; Shigella; Massilia; Mycoplasma; Polaromonas;

Pseudomonas; Rhodobacter; Rhodococcus ; Shewanella ; Syntrophococcus)

showed significant profiles of positive or negative correlations with the Cd

concentrations in liver (Additional file 4.4: Figures 4.S4). Divergent profiles

between CC and CV were only observed for the correlations of Shewanella

and Syntrophococcus with Cadmium concentrations. Similar correlation

profiles between these groups were observed in the water (Additional file 4.5:

Figures 4.S5).

4.6.4 Correlational networks of host and water microbiome

In the host and water communities, the network analysis of samples

correlations showed a partitioned community distribution between treatments

groups, at time T3, and overlapping patterns during recovery period (Figure

4.6; Figure 4.7). At time T0, the correlational networks of host microbiome

showed unstructured topology with on average a fewer number of edges. An

edge can represents significant low correlations (Spearman’s Rho correlation

> 0.5) between samples from different groups with the node size proportional

to the richness of each sample. The topological distribution of nodes in the

network were further analyzed by comparing the betweenness centrality to

the eigenvalue centrality (Figure 4.8). The results indicate a shift in mean of

the centrality metrics between the control (which is higher) and the cadmium

selection regimes. The plots of eigenvalue centrality versus betweenness

centrality clearly reveal that these communities shift at times T3 for skin

microbiome, as well as at time T3 for Gut microbiome. The high betweenness

centrality observed in control reflects the efficiency of network centrality

measure to predict the effect of perturbation on the community structure

during the selection phase, but not during the recovery time as centrality

median shifts at T5 was not observed (Figure 4.8). The same centrality

analysis was obtained for water microbiome networks resulting in similar

patterns at time T3 (results not shown).

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4.6.5 Recovery of microbial functional diversity a time T5.

At time T0, an ANOVA of functional richness within the metacommunity

showed a significantly higher average of functional diversity in gut and skin

microbiomes compared to water microbial communities. Surprisingly stable

in water communities, functional diversity did not show any significant

divergence among treatment and control group at T3 regardless of the

community type (skin, water, gut). The lack of treatment effect observed may

well have been masked by the strong influence of time over microbial

diversity (Cheaib et al. 2019 under review). However, at T5, the functional

diversity of skin microbiome was significantly higher in the control group than

in treatment groups according to the ANOVA (CC-CV(p-value) = 0.04; CV-

Ctrl(p-value) = 0.0055; CC-Ctrl(p-value) = 0.45) (Figure 4.5). In the gut

microbiota, no significant changes in functional diversity were detected

between treatments (CC-CV(p-value) = 0.3; CV-Ctrl (p-value) = 0.54; CC-

Ctrl (p-value) = 0.58).

4.6.6 The role of neutral and deterministic processes in the

recovery of host microbiota

The goodness of fit of host and water microbial communities to the non-linear

partial least square model (NLS) was high (R-square > 0.5), supporting the

theory of predominant neutrality (Additional Table 4.S5). To disentangle gut

and skin microbiota ontogeny from the cadmium effect, the NLS model was

deployed using the control as a reference. A comparison of observed versus

predicted OTU frequencies revealed that the percentage of neutral OTUs in

skin and gut microbiota (Figure 4.9) at the recovery time T5 is higher in

control group compared to those in treatments at T3 and T5. The same

analysis was undertaken in the water and the percentage of neutral versus

non-neutral OTUs showed the same trends across Ctrl, CC and CV at T5.

Overall, we noted a preponderance of OTUs that fitted the neutral model in

all comparisons. Majority of the OTUs that did not fit the neutral model were

assigned to Mycoplasma species (indeed no Mycoplasma sp. OTUs fitted the

neutral model), which can be seen in Figure 4.10 as well as Table 4.S6 and

Table 4.S7. The neutral process was much more prevalent in control group at

time T3 and T5 as compared to the treatment groups.

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4.7 Discussion

Our data clearly show that long term bioaccumulation of cadmium occurs in

the in the Perca flavescens liver on exposure to aqueous cadmium salts. Our

data also showed that the cadmium persists at high concentrations even once

the treatment has been stopped for two months. We have already shown that

cadmium treatment clearly impacts both the skin and gut microbial

communities, as compared to controls (Cheaib et al 2019, under review).

Recovery was the focus of the current study, and microbial communities post-

exposure showed different routes to (and extents of) recovery in those

associated with the skin and gut once cadmium treatment was ceased. In the

skin, evenness – the extent to which different microbes in a community share

similar abundances – and richness increased during the recovery phase in

cadmium-treated fish. Beta-diversity comparisons, meanwhile, revealed

significant differences between all experimental cohorts (Ctrl, CC, CV) in

water and skin niches. Among gut microbial communities, decreasing richness

and increasing evenness was observed over the recovery period. Beta-

diversity metrics indicated few significant differences between cadmium and

control treatments. Crucially, by the end of the recovery period in the gut,

functional richness was comparable between tests and control, a potential

signal of full community recovery. We used models to assess the relative roles

of microbial assembly in the different groups. We found evidence that neutral

processes were a more prevalent contributor to microbial community

turnover in control treatments than in those treated with CdCl2 – likely

indicating the role of selective processes in driving community recovery.

Overall our data do not strongly support our prediction that the most extreme

cadmium exposure (CC) would lead to the least successful recovery. Instead,

CC and CV treatments, especially in the gut, demonstrated a good degree of

recovery, both in terms of both alpha and functional diversity.

At the end of the third month of exposure (T3), the cadmium concentration

in the liver was significantly higher in CC and CV than in the control group.

These concentration differences were still observed two months (T5) after the

gradual clearing of Cd started. The liver plays a major role in the

accumulation, excretion and biotransformation of contaminants like

metalloids (Silver and Phung 1996; M. A. Defo, Spear, and Couture 2014),

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and bioaccumulated metals remain at high concentrations in the liver due to

its depuration function of other organs (such as gills and muscles) (Jezierska

and Witeska 2006). Long-term bioaccumulation of cadmium has been

documented in perch and other biological systems (Campbell et al. 2005b;

Giguère et al. 2006; Klinck et al. 2007; Xie et al. 2008; Nirola et al. 2016),

as has its effect on ecosystem services in soil and water (Cheaib et al. 2018;

Y. P. Chen et al. 2014) and metazoan gut ecosystems (Liu et al. 2014; S.

Zhang et al. 2015; Ba Qian et al. 2017; Šrut et al. 2019).This study not only

confirms the chronic bioaccumulative effect of Cd but also suggests that the

sequestered Cd in perch liver presumably cannot predict the regime of

exposure (CC, CV), as the concentration did not significantly vary in livers

between both regimes, CC and CV, at T5. Over the recovery period, the

concentration of Cd in water significantly decreased, but Cd was not

completely removed from the tank system since it has a strong affinity to the

tank silicon gaskets, and has a high competitiveness with Zn for the debris

of organic compounds always available in the water aquarium ecosystem

(Pinter and Stillman 2015).

The water microbial communities showed few differential abundances of taxa

differences during the recovery period (Figure 4.3). Furthermore, the

microbial functional diversity in water remained stable throughout the

experiment, and no significant differences between treatments were found

during the exposure or the recovery periods. However, the community beta-

diversity at the phylogenetical level between treatments (CC, CV, Ctrl)

showed significant difference at each time point, suggesting a pattern of

taxon-function decoupling as an adaptive strategy reported previously in

lacustrine water contaminated with cadmium (Cheaib et al. 2018).

To assess the yellow perch microbiome recovery, we examined alpha-

diversity (richness and evenness), beta-diversity (phylogenetic distance),

taxonomic composition and functional diversity (metabolic functions). Most of

these measurements are commonly used as community-wide metrics to

assess the recovery of microbial communities, for example in humans (Singh

et al. 2015; Dethlefsen and Relman 2011), soil(Jurburg et al. 2017; Griffiths

and Philippot 2013) and wastewater(De Vrieze et al. 2017) (Vrieze et al

2017).

152

In the skin microbiome, the disturbance intensity (cadmium gradient) had a

differential impact on the community recovery trajectories, resulting in a

significant difference of evenness (Additional Table S1c ) and functional

diversity (Figure 4.5) between CC & CV at time T5 (Table2-C). During the

gradual exposure regime (CV), the cadmium may provoke an endurance

effect on the skin microbiota which was progressively adapted to the cadmium

accumulating in the tank system, while within the constant exposure regime

(CC), abrupt diversity and taxonomic changes might have been triggered.

Gradual changes are evident under stress gradients, for example, within

bioreactors, the anaerobic microbiome has been shown to gradually adapt

following ammonium disturbance(Regueiro, Carballa, and Lema 2016).

Consequently, the significant divergence in the functional diversity between

CV-Ctrl and CV-CC, not between CC-Ctrl, perhaps indicate a unique adaptive

evolution signature of skin microbiome under CV regime. Therefore, the skin

communities from CV and CC may have followed a different recovery

trajectory after adaptation. Strikingly, the recovery of skin microbiota of the

most extreme exposure (CC) appeared to be the most successful, when

considering the convergence of richness, evenness and functional diversity

between CC and Ctrl. However, significant differences among CC, CV and Ctrl

in terms of phylogenetic divergence (Table 4.2) and taxonomic composition

shifts (Figure 4.3, Additional Table 4.S2; Additional Tables 4.S3; Additional

Tables 4.S4) suggest this recovery was incomplete. For instance, a significant

increase in fish pathogens like Flavobacterium, Legionella and opportunists

like Mycoplasma was detected in both cadmium groups (CC & CV) compared

to control. The relative abundance of Flavobacterium was significantly lower

in the control group with a low percentage (< 0.5 %). Perturbation with

cadmium can facilitate the proliferation of opportunistic pathogens, this

concern has been found in other studies of fish microbiota recovery after

exposure to antibiotic (Navarrete et al. 2008) and triclosan biocide (Narrowe

et al. 2015). Similar taxonomic changes in both exposure regimes (CC & CV)

were expected (Regueiro, Carballa, and Lema 2014). Overall, the cadmium

disturbance may cause a shift to an alternative stable state, demonstrating

differential and incomplete recovery of skin microbiota in CC and CV.

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In the gut microbiome, the recovery routes were different; at time T5, there

was only a significant evenness convergence between CC and CV. Overall,

the few significant differences in taxonomy, as well as the phylogenetic

divergence (Additional Table S2) between CC-CV and Ctrl-CV, but not

between CC-Ctrl, suggests a full recovery of the gut microbiota in CC and

gradual recovery in CV. At the level of taxonomic composition, overall the

dominance of opportunists Tenericutes was also a feature of farmed Eurasian

perch (Perca fluviatilis) gut microbiota studied in a context of stress predation

(Zha et al. 2018), although they were not found in the wild Eurasian perch

(Bolnick et al. 2014).

In the skin and gut microbiota, the significant increase in diversity (evenness

and richness) over the recovery period (T3-T5) was consistent with the

diversity increase in other host-associated studies such as the recovery of the

fathead minnow gut microbiome from a low-level triclosan exposure (Narrowe

et al. 2015), the human intestinal microbiota post-infection (Singh et al.

2015) , the murine gut microbiome exposed to antibiotics in early life (Cho

et al. 2012), and the molasses wastewater (De Vrieze et al. 2017).Further,

the functional redundancy observed in all water and gut microbial

communities is a major adaptive strategy behind resistance and recovery

(Cheaib et al. 2018; De Vrieze et al. 2017). Lastly, the significant divergence

of skin and gut microbiota diversity over time within the control group

suggests persistent divergence from the initial community structure due to

microbiota ontogeny through the developmental stage of fish juveniles (Burns

et al. 2016b).

Our findings demonstrate a relative role of neutral processes shaping the

bacterial communities recovery following exposition to metallic stressors.

According to the neutral model fit, the percentage of neutral OTUs in skin and

gut microbiota was significantly higher in the control group compared to CdCl2

treated groups, which provides evidence that neutral processes are the major

contributor in the microbiota assembly in non-stressed yellow perch,

therefore suggesting that selective processes are at play in driving the

community recovery in stress-exposed groups. Furthermore, Mycoplasma sp.

are a dominant species in perch microbiome, implicated in literature for other

fish species (Llewellyn et al. 2016; Holben et al. 2002). The inability of neutral

154

models to explain the abundance of any OTUs for Mycoplasma sp. in the

current study suggests that these bacterial strains can quickly adapt to the

host environment. Our study is the first to investigate the relative importance

of neutrality and determinism in driving post-disturbance assembly of the

host-associated microbiome.

4.8 Conclusions

This study not only elucidates the long-term bioaccumulation effect of toxic

metals on biological systems but also suggests that the sequestered cadmium

in fish liver will not likely predict the magnitude of exposure regime (constant

or variable). The effect of cadmium exposure on microbial communities is

also varying and dependent on the nature of the host it is originating from.

Surprisingly, after recovery, skin and gut microbiota of fish exposed to

constant concentrations of cadmium (CC) were closer to the control group

than those exposed to the gradual concentrations (CV). In the skin, the

metallic perturbation caused a shift to an alternative stable state, leading to

an incomplete recovery and therefore, facilitating the proliferation of

opportunistic pathogens (like Flavobacterium). In the gut, the functional and

phylogenetic diversity measurements suggest a complete community

recovery in the CC group and gradual recovery in the CV group. The selective

pressure exerted by cadmium on host and water microbiota may have left

adaptive evolution patterns conserving functional diversity at the expense of

taxonomic diversity. In both skin and gut microbiota, the recovery was

associated with a significant increase of evenness and richness in skin and

vice versa in the gut. In the control group, as expected, the significant

divergence from the initial community structure confirms the dynamic of

bacterial strains through the developmental stage of fish juveniles.

Consequently, the community recovery was affected by both cadmium

pressure and host development. In addition, our results have shown that the

microbial assembly rules during the community recovery were both

orchestrated by neutral and deterministic processes. In the water, community

recovery was driven by a substantial role of phylogenetic structuring resulting

from a combined pattern of stochasticity and cadmium-induced selective

pressure, in which the causality remains unknown. Further studies are needed

155

to quantify the interactions of neutrality and determinism in driving post-

disturbance assembly of host-associated microbiome during recovery.

4.9 Tables

Table 4. 1 Statistics of Cd concentrations in water and fish liver over time and treatments.

p value < = 0.05 : “*”

p value < = 0.001 : “***”

4.1-c T3 T5

Statistics tests (Tukey,

Wilcox) Liver Water Liver Water

Groups Tukey’s p

value

Wilcox’s p

value

Tukey’s p

value

Wilcox’s p

value

Ctrl-CV 0.00002 *** 0 *** 0.0000 *** 0.0002 ***

Ctrl-CC 0.0009 *** 0 *** 0.0000 *** 0.0002 ***

CC-CV 0.0281 * 0.1304 0.5362 0.1304

All (Kruskal−Wallis rank-

sum test) 0.0002 *** 0 *** 0.0000 *** 0.0003 ***

p value < = 0.05 : “*” ; p value < = 0.001 : “***”

These Tables summarise statistics of cadmium concentration variation over

time (Table 4.1.1B) and among treatments (Table 4.1.1C) in water tanks and

4.1-a Liver Water

Cadmium average concentration

(ng/ml) T0 T3 T5 T0 T3 T5

Ctrl 0.0860 0.1578 0.2330 0.0650 0.0750 0.0388 *

CV 0.0860 0.4000 1.0015 0.0670 6.4300 1.0600

CC 0.0860 0.5235 1.1700 0.0980 5.8000 1.3400

4.1-b Liver Water

Overtime

comparisons T0–T3 T0–t5 T3–t5 T0–t3 T0–t5 T3–t5

Groups Tukey’s p value Wilcox’s p value

Ctrl-Ctrl 1.0000 0.8253 0.8112 1.0000 1.0000 1.0000

Cv-CV 0.0003 *** 0.0000 *** 0.0000 *** 0.0000 *** 0.0000 *** 0.0000 ***

CC-CC 0.0000 *** 0.0000 *** 0.0000 *** 0.0000 *** 0.0000 *** 0.0000 ***

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fish livers. Table 4.1.1-a illustrated the average cadmium (Cd) concentrations

in each group.

Table 4. 2 Phylogenetic divergence in host and water microbiomes.

Time Groups


p value

ADONIS p values B-H

p values

dispersion

p value

MRPP

Ctrl = control regime; CC = concentration is constant; CV = concentration is variable

Gut

T0

All groups 0.052 0.079 0.279

CC-Ctrl 0.042 * 0.077 0.048* 0.033 *

CC-CV 0.051 0.077 0.141 0.049 *

Ctrl-CV 0.280* 0.280 0.302 0.234

T3

All groups 0.001** 0.367 0.647

CC-Ctrl 0.001** 0.002 ** 0.916 0.001***

CC-CV 0.006 ** 0.006 ** 0.145 0.008 *

Ctrl-CV 0.001*** 0.002 ** 0.295 0.001 ***

T5

All groups 0.006 * 0.217 0.199

CC-Ctrl 0.135 0.135 0.923 0.104

CC-CV 0.005 ** 0.012 * 0.084 0.009**

Ctrl-CV 0.008 ** 0.012 * 0.130 0.008 **

Skin

T0

All groups 0.016 * 0.540 0.500

CC-Ctrl 0.166 0.166 0.649 0.154

CC-CV 0.081 0.122 0.256 0.075

Ctrl-CV 0.026 * 0.078 0.599 0.020 *

T3

All groups 0.008 ** 0.820 0.580

CC-Ctrl 0.035* 0.049 * 0.619 0.049 *

CC-CV 0.049 * 0.049 * 0.586 0.045 *

Ctrl-CV 0.021* 0.049 * 0.872 0.009 **

T5

All groups 0.001*** 0.380 0.001 ***

CC-Ctrl 0.011 * 0.011 * 0.749 0.008 **

CC-CV 0.001*** 0.002 ** 0.307 0.002 **

Ctrl-CV 0.001 *** 0.002 ** 0.236 0.001 **

Water

T0

All groups 0.001 *** 0.656 0.886

CC-Ctrl 0.018 * 0.027 * 1.000 0.009 **

CC-CV 0.002 ** 0.006 ** 0.291 0.002 **

Ctrl-CV 0.036 * 0.036 * 0.519 0.038 *

T3

All groups 0.001 *** 0.036 * 0.596

CC-Ctrl 0.002 ** 0.002** 0.068 0.002 **

CC-CV 0.001 ** 0.002 ** 0.020 * 0.001 ***

Ctrl-CV 0.001 *** 0.002 ** 0.546 0.001 ***

157

Time Groups


p value

ADONIS p values B-H

p values

dispersion

p value

MRPP

TR1

All groups 0.001 *** 0.214 0.108

CC-Ctrl 0.001 *** 0.002 ** 0.305 0.002 **

CC-CV 0.001 *** 0.002 ** 0.064 0.001 ***

Ctrl-CV 0.007 ** 0.007 ** 0.653 0.007 **

TR2

All groups 0.001 *** 0.639 0.381

CC-Ctrl 0.003 ** 0.005 ** 0.395 0.003 **

CC-CV 0.096 0.096 0.892 0.077

Ctrl-CV 0.001 *** 0.003 ** 0.391 0.001 ***

TR3

All groups 0.001 *** 0.561 0.629

CC-Ctrl 0.001 *** 0.003 ** 0.343 0.001 ***

CC-CV 0.003 ** 0.005 ** 0.536 0.002 **

Ctrl-CV 0.007 ** 0.007 ** 0.620 0.006 **

TR4

All groups 0.001 *** 0.498 0.031 *

CC-Ctrl 0.009 ** 0.009 ** 0.536 0.007 **

CC-CV 0.006 ** 0.009 ** 0.179 0.009 **

Ctrl-CV 0.004 ** 0.009 ** 0.578 0.002 **

T5

All groups 0.001 *** 0.408 0.027 *

CC-Ctrl 0.002 ** 0.002 ** 0.331 0.001 **

CC-CV 0.001 *** 0.002 ** 0.578 0.003 **

Ctrl-CV 0.002 ** 0.002 ** 0.438 0.001 **

The phylogenetic distances between OTUs were computed using Gunifrac

distance (see “Methods” section). The divergence between treatments and

control was assessed using PERMANOVA and the homogeneity for group

dispersions (distance from centroid) was evaluated using two multivariate

tests, BETADISPER and multi-response permutation procedure (MRRP) of

within versus among group dissimilarities. The significance of divergence

between groups was measured by applying multiple correction tests with

Benjamini-Hochberg BH (p value < 0.05).

p value < = 0.001 : “***”, p value < = 0.01 : “**”, p value < = 0.05 : “*”.

158

4.10 Figures

Figure 4. 1 Schematic illustration of the perch microbiome recovery

experiment.

Figure 4. 2 Alpha-diversity dynamics in the water and perch microbiome.

Predicted alpha-diversity plots by linear mixed model. Alpha-diversity in

water and host-microbial communities over time and among treatments is

predicted using the linear mixed model. The richness/evenness ratio were

considered as response variables, the fixed effects were defined by time and

cadmium concentration (in water and liver), and tanks were taken as random

effects. Over time, the predicted alpha diversity in host microbial

communities (skin, gut) highlights stable trends of the Control group

compared to the treatments. However, all groups of the water microbial

communities decrease overtime. Constant cadmium regime (CC) is in orange,

variable cadmium regime (CV) is in yellow, and control (control) is in green.

159

Figure 4. 3 Taxonomic composition dynamics of host communities

Stacked Barplots show the most abundant taxa (>0.5%) overtime in the gut,

skin and water microbiomes. The genera that significantly changed among

treatments and control at T5 are summarised in Additional Table S2.

Figure 4. 4 Heatmaps of differential abundance among host and water communities

This figure from left to right includes 9 heatmaps of the significant taxonomic

fingerprints at the genus level between gut, the skin and the water at times

T0 (first column), T3 (second column) and T5 (third column) in the control

(first row), the CV (second row), and the CC (third row) groups. The

hierarchical clustering of the relative abundance of phyla which significantly

changed over time was performed using Ward's method and Bray–Curtis

160

dissimilarity distance. Vegan package and pheatmap () function in R were

used.

Figure 4. 5 Function diversity dynamics in host and water microbiome.

Boxplots of functions profiles were predicted from the matrices of taxa count

using the software Tax4Fun. The statistical significance (p-value< 0.05)

found using ANOVA followed by FDR (False Discovery Rate) test are

represented with asterisks points (0.001: "***", 0.01: "**", 0.05: "*").

161

Figure 4. 6 Recovery dynamics of the networks of host communities.

The networks organization is based on nodes betweenness centrality among

treatments and Control. Unstructured patterns in the networks were observed

at T0. Node size represents sample richness. The strength of correlation

(Spearman Correlation from 0.3 to 1) between two nodes is inversely

proportional to the size of the edge. This network was built using R and

Cytoscape software. Constant Cadmium samples (CC) are in orange, Variable

Cadmium samples (CV) are in Yellow, and Control (Control) samples are in

green.

162

Figure 4. 7 Recovery dynamics of the network of water communities

The networks organization at every resilience time TR1, TR2, TR3, TR4 and

WT5 is based on nodes betweenness centrality among treatments and

Control. The networks modules easliy distinguishable between groups since

T0. The size of nodes (sample richness) at the beginning of TR1 and at the

end of the time TR4 showed shifts in the community richness. The strength

of correlation (Corr. Spearman from 0.5 to 1) between two nodes is inversely

proportional to the size of the edge. This network was built using R and

Cytoscape software. Constant Cadmium samples (CC) are in orange, Variable

Cadmium samples (CV) are in Yellow, and Control (Control) samples are in

green.

163

Figure 4. 8 Centrality plots of host microbiome networks

This figure summarizes relationships of betweenness centrality versus

eigenvalue centrality of host microbiome networks among treatments and at

each time point. The results show evidence of shift in centrality medians

between the control regime (which is higher) and the cadmium selection

regimes. The plots of eigenvalue centrality versus betweenness centrality

clearly reveal that centrality shift at time T3 for skin and gut microbiome.

164

Figure 4. 9 Percentage of neutral OTUs over time and treatment.

165

Using the non-linear least squares model (NLS), the percentage of OTUs that

fit the neutral model within a confidence interval of 95% showed variable

trends between communities across time and treatments. A goodness of fit

R2> 0.5 was considered as the significant threshold of neutrality fit. The

cadmium treatment invoked stochasticity in the water communities, while in

gut and skin communities, the percentage of neutral OTUs remained higher

in the control compared to treatments.

Figure 4. 10 Demographic variation of metacommunity neutrality

across water and host microbiome

This figure summarizes the scatterplots of neutral model fitting the whole

metacommunity ( gut skin and water) at times T0 (first column), T3 (second

column) and T5 (third column) in the control (first row), the CV (second row),

and the CC (third row) groups. Neutral OTUs are shown in black, non-neutral

are depicted in grey, whilst the red is Mycoplasma sp. OTUs. We see no

Mycoplasma sp. OTUs that fit the neutral model in the whole metacommunity.

166

4.11 Supplementary figures

Additional file 4.1: Figures 4.S1. Dynamic of alpha-diversity divergence

between host and water communities. The significant ANOVA results of alpha

diversity between water (W), Skin(S) and Gut (Gut) communities in Control,

CV and CC groups before and during disturbance, and after recovery period

are represented with asterisks on the boxplots (0.001 : "***", 0.01 : "**",

0.05 : "*").

https://static-content.springer.com/esm/art%3A10.1186%2Fs40168-020-

0789-0/MediaObjects/40168_2020_789_MOESM1_ESM.pdf

Additional file 4.2: Figures 4.S2. Beta-diversity divergence at the

treatment level. This file combines all the NMDS (non-metric Multi-

Dimensional Scaling) plots showing first two dimensions in the ordination of

when using generalized Unifrac distance measure of water and host-microbial

communities. The NMDS plots and PERMANOVA revealed a significant

separation between different treatments and control (for the pairwise, see

Table 2 for adjusted p-values after Benjamini-Hochberg correction in

PERMANOVA and MRPP tests) at T0, T3, and T5 for skin and gut microbiota,

and at T0, T3, TR1-TR4, and T5 for water microbial communities.



Additional file 4.3: Figures 4.S3. Beta-diversity divergence at the

community level. This file combines all the NMDS (non-metric Multi-

Dimensional Scaling) plots and phylograms based on generalized Unifrac

distances between water and host-microbial communities. The NMDS plots

and PERMANOVA revealed a significant separation of among all type of

communities per time (T0, T3, and T5) and treatment (Control, CC, CV).



Additional file 4.4: Figures 4.S4. Heatmaps of cadmium with taxa

diversity and composition in host and water communities. The

correlations indicate a gradient from positive (blue) to negative (red) along a

colour gradient, with rows representing diversity measures (richness,

167

evenness) as well as cadmium concentrations, and columns indicating

taxonomic levels. The gut microbiome in the constant CdCl2 (CC) and variable

CdCl2 (CV) regimes showed a negative correlation between Mycoplasma and

diversity indices. A strong positive correlation between Actinomycetales is

noticeable in the CV. For the skin microbiome, not only Actinomycetales, but

also Burkholderiales, and Chromatiales showed strong positive correlations

with CV. This figure was produced using the Rhea package.



Additional file 4.5: Figures 4.S5. Heatmaps of cadmium with taxa

diversity and composition in water during the recovery period. The

correlations indicate a gradient from positive (blue) to negative (red) along a

colour gradient, with rows representing diversity measures (richness,

evenness) as well as cadmium concentrations, and columns indicating

taxonomic levels. In the constant CdCl2 (CC) and variable CdCl2 (CV) regimes,

correlations of cadmium with taxa abundance showed variable profiles over

time. This figure was produced using the Rhea package.



Additional file 4.6: Figures 4.S6. Heat trees and stacked bar plots of

water and host microbiome structure.

This figure summarizes pairwise comparison of the community composition

of water and each of the host communities for different treatments (Ctrl, CC

and CV). Additionally, stacked bar plots of relative abundance at phylum

level are provided for each community (water, skin, gut). The non-grey

coloring (which category the branches are upregulated in) indicates

significant differences in terms of log median ratios for samples from different

habitats (Gut, Skin and Water) as determined by a Wilcox rank-sum test

followed by a Benjamini-Hochberg (FDR) correction for multiple testing. The

heat trees were built using metacoder and stacked barplots were produced

using the Rhea package.

168




Additional Table 4.S1. Alpha-diversity dynamics over time and

treatments. This statistical summary reveals richness or evenness changes

over time (table 1 a, b) and between control and treatments (table 1c) in

water and host communities. The significant changes of alpha-diversity

indices between treatments and control were statistically tested using

Kruskal-Wallis and Wilcoxon tests by applying Benjamini-Hochberg

correction. The same statistics were used to compare alpha-diversity over

time.


0789-0/MediaObjects/40168_2020_789_MOESM7_ESM.xlsx

Additional Table 4.S2. Statistical summary of taxa divergence

between treatments and control after recovery. This table summarises

significant taxonomic changes in water and host-microbial communities

between control and treatments using the Fisher test, and by applying

Benjamini-Hochberg correction.



Additional Tables 4.S3. Statistical summary of taxa divergence over

time in host and water microbial communities after recovery. These

table summarise significant taxonomic changes over time in water and host-

microbial communities using Kruskal-Wallis and Wilcoxon tests, and by

applying Benjamini-Hochberg correction.



Additional Tables 4.S4. Statistical summary of differential abundance

between water and host microbial communities at all taxonomic

levels. These table summarise significant taxonomic changes between water

169

and each of the host communities (skin and gut) at times T0 (sheet1), T3

(sheet2), and T5 (Sheet3) using Kruskal-Wallis and Wilcoxon tests, and by

applying Benjamini-Hochberg correction.



Additional Table 5.S5. Statistics of the neutral model in host and

water microbiomes. This table summarises the neutral model fit based on

the following parameters; the migration rate (m.ci) within 95% of the

confidence interval, the goodness of fit(R2), number of samples, richness,

abundance cutoff, percentage of % neutral OTUs and non-neutral OTUs.



Additional Table 6.S6: List of OTUs that accounted for those that did not

fit the neutral model



Additional Table 7.S7: List of OTUs that accounted for those that did not

fit the neutral model and were assigned to Mycoplasma species.



170

Discussion et conclusions générales

Dans le cadre de ce projet de recherche, nous avons détecté des signatures

génotypiques de stratégies adaptatives de communautés microbiennes dans

un contexte de perturbations métalliques et ce en étudiant deux modèles

environnementaux et un système d’hôte-associé. Dans le deuxième chapitre,

un système microbien sous perturbation environnementale a été étudié. Les

communautés microbiennes lacustres ont été échantillonnées trois ans après

l’arrêt des exploitations minières. Ces mines se trouvaient à proximité du

bassin versant des lacs étudiés et leur exploitation a duré environ 60 ans. Ce

qui fait de ce système lacustre un excellent modèle naturel de pression

anthropique pour étudier la résilience des communautés microbiennes

exposées à un gradient de perturbations tels que les métaux traces. De plus,

un lac témoin non pollué a été utilisé comme contrôle négatif.

En utilisant une approche métagénomique de séquençage dans les deux types

de modèles microbiens (environnemental et hôte-associé), des empreintes

méta-génotypiques ont été identifiées sous forme de transitions

compositionnelles dans le répertoire taxonomique et fonctionnel des

communautés microbiennes. Les résultats du modèle environnemental

anthropique montrent une forte sélection exercée sur les gènes de résistance

aux métaux lourds. À l'échelle compositionnelle taxonomique des

métacommunautés, des changements évidents sont marqués par un

différentiel d’abondance relative des taxons. Ces changements du répertoire

taxonomique ont été beaucoup plus marqués au niveau du genre, suggérant

une adaptation « de type communauté » au gradient métallique dans chaque

niche écologique lacustre. À l'échelle compositionnelle des fonctions, nous

avons observé une faible adaptation révélée par l'érosion de voies

métaboliques (Carbohydrates et biosynthèse des protéines). À travers le

gradient métallique, l’abondance relative de certaines catégories

fonctionnelles, telles que la réponse au stress, la régulation, le métabolisme

des protéines et la résistance métallique, était beaucoup plus élevée dans les

lacs pollués que dans les lacs non pollués.

Les relations entre la diversité des taxons et des fonctions indiquent des

empreintes adaptatives persistantes lors de la phase de résilience du système

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microbien environnemental. En analysant la relation entre les signatures

taxonomiques et fonctionnelles des communautés lacustres, nous avons

détecté un découplage entre la diversité taxonomique et le répertoire

fonctionnel dans les lacs pollués, ce qui est un indice clair d’adaptation,

potentiellement via le transfert horizontal de gènes (Burke et al. 2011; Green,

Bohannan, and Whitaker 2008; Louca, Parfrey, and Doebeli 2016; Louca et

al. 2016; Ram et al. 2005; Hemme et al. 2016). Ce découplage révèle une

redondance fonctionnelle malgré une divergence taxonomique, ce qui met en

évidence que la diversité microbienne à l’échelle taxonomique ne reflète pas

nécessairement la diversité des services écosystémiques dans les

environnements fortement et graduellement perturbés. Ces résultats

suggèrent pour la première fois, à l’aide des méthodes basées sur des

corrélations canoniques et de régularisation (González and Déjean 2012), un

découplage taxon-fonction au sein de communautés microbiennes naturelles

adaptées à un gradient de contamination polymétallique.

Cette étude propose une vision préliminaire du phénomène de découplage.

Des études supplémentaires seront nécessaires pour comprendre de manière

plus approfondie la nature de la convergence des communautés microbiennes

dans le contexte d’un gradient sélectif. L’investissement de l’évolution

moléculaire des gènes de résistance aux métaux serait une bonne piste pour

tester l’hypothèse de HGT. Des données de séquençage avec une couverture

plus profonde seront nécessaires pour assembler des génomes complets à

partir de métagénomes. Les séquences des génomes complets permettent de

mesurer le taux d’évolution des gènes de résistance aux métaux par rapport

à d’autres gènes vitaux à l’échelle d’un génome entier. Finalement le balayage

sélectif de gènes de résistance mérite notre attention car un tel évènement

de purge de diversité n’a pas encore été caractérisé à l’heure actuelle chez

des communautés microbiennes dans le milieu naturel (Doolittle and

Zhaxybayeva 2009).

Le modèle de communautés hôte-associé sous perturbation a été caractérisé

expérimentalement. Nous avons modélisé l’environnement naturel au

laboratoire en conditions artificielles d’intoxication similaires à l’environnent

anthropique. Nous avons donc étudié l’impact de la toxicité du Cadmium (Cd)

sur les microbiotes intestinal et cutané de la Perchaude (Perca flavescens)

172

reconnu comme un poisson modèle en écotoxicologie. La Perchaude s’adapte

au faible gradient de traces polymétalliques (Bourret et al. 2008; Bélanger-

Deschênes et al. 2013). En dépit de sa tolérance, plusieurs études

d’écotoxicologie ont mis en évidence l’impact des traces du Cd sur cet

organisme, notamment au niveau de la diversité génétique de ses

populations, de l’expression de gènes du système immunitaire et des voies

de biosynthèse de l’acide rétinoïque, primordial pour sa croissance et son

développement (Bourret et al. 2008; Pierron et al. 2009; Bélanger-Deschênes

et al. 2013; Bougas et al. 2013a; Michel A. Defo et al. 2014).

Le stress métallique aurait potentiellement des répercussions sur les

fonctions cellulaires, physiologiques et hormonales qui sont sous contrôle du

microbiote. Dans notre expérience, nous avons utilisé des doses non létales

de cadmium comme agent de sélection sans provoquer de dommages

physiologiques importants ni entraîner la mortalité de l’hôte. Nous avons

étudié la composition, l’assemblage, la structure et les réseaux de corrélation

microbienne de trois niches écologiques : l’eau, le mucus cutané et le mucus

intestinal de la Perchaude et ce, en réponse à une exposition au Cd selon

deux régimes ; constant et graduel. Cette expérience d’évolution

expérimentale nous a permis d’étudier la résistance et la résilience des

communautés microbiennes, qui ont respectivement constitués, les troisième

et quatrième chapitres de la thèse.

Dans le troisième chapitre, l’étude de la résistance a été conduite avec une

approche métagénomique d’amplicons suivie par des mesures de la diversité

alpha et beta, puis avec des réseaux de co-abondance et des modèles

d’écologie microbienne. Les résultats montrent selon le régime de

perturbations, des signatures adaptatives différentes entre le mucus cutané

et le mucus intestinal à l’échelle de la composition, de la structure et de la

dynamique d’assemblage du microbiote de la Perchaude. Premièrement, les

propriétés topologiques des réseaux de co-abondance montrent non

seulement l'étendue de l’implication de taxons rares dans l'assemblage de

microbiotes, mais aussi des corrélations négatives entre les taxons qui

augmentent proportionnellement aux concentrations du Cd introduites dans

le système, c’est-à-dire plus élevées dans le régime constant que dans le

régime graduel. L’augmentation de la diversité alpha, la divergence

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phylogénétique révélée par l’analyse de la diversité bêta, et la convergence

taxonomique entre les communautés bactériennes de l'eau et la peau sous

les deux régimes d’exposition au Cd (constant et graduel) mettent en

évidence que l'hôte avait perdu la capacité de résister à la colonisation

bactérienne environnementale. Ce phénomène de «résistance à la

colonisation» (Buffie and Pamer 2013b) résultait théoriquement en partie

d'un dysfonctionnement du système immunitaire de l'hôte appelé «résistance

de la colonisation immunitaire».

Sous les deux régimes de sélection, la composition des communautés

microbiennes a été marquée par la prévalence des souches opportunistes,

telles que Mycoplasma, Pseudomonas, ainsi que d’autres souches connues en

aquaculture telle que le Falvobacterium, dont certaines sont pathogènes (R.

M. Brown, Wiens, and Salinas 2018; Holben et al. 2002).

Les perturbations induites par le Cd infligent à l’hôte des lésions hépatiques

identifiées par microscopie à haute résolution (résultats non présentés), avec

une bioaccumulation significative du cadmium dans le foie révélé par

spectrométrie de masse ionique à haute précision. Nos résultats confirment

bien les conclusions des études rapportées sur les perturbations induites par

le cadmium sur la physiologie non seulement de la Perchaude (Ponton et al.

2016; L. D. Kraemer, Campbell, and Hare 2006; Giguère et al. 2006; Klinck

et al. 2007), mais aussi chez d’autres espèces de poissons comme le Tilapia,

la Blennie paon et la Truite (Zhai et al. 2017; Naïja et al. 2017; Niyogi et al.

2004). Également, la dysbiose du microbiote de l’hôte causée par des métaux

ont été davantage reportées chez des modèles animaux comme la Souris (S.

Zhang et al. 2015; Ba Qian et al. 2017) que dans l’aquaculture. Pour tester

si la perte de résistance de l’hôte à la colonisation bactérienne s’est

accompagnée par des processus stochastiques d’assemblage microbien dans

l’eau et chez l’hôte, un modèle mathématique basé sur la régression des

moindres carrées non linéaires, a été appliqué pour estimer la contribution

relative de ces processus. Les résultats montrent qu’en absence de cadmium,

la dérive avait essentiellement conduit l'assemblage taxonomique des

communautés de l'eau, alors qu’en présence de cadmium, cet assemblage

était sous l’influence principale de processus déterministes (non neutres). En

revanche, peu importe le niveau d’exposition au cadmium, l'assemblage des

174

microbiotes se développait principalement selon des processus non-neutres.

L’assemblage du microbiote intestinal peut avoir évolué selon des processus

non neutres dus non seulement au cadmium en tant que facteur de

perturbation dans les deux groupes expérimentaux, mais également à la

sélection imposée par le développement de l’hôte, comme le démontrent les

résultats du groupe contrôle. Ce phénomène a été observé chez différentes

espèces de poissons élevées en conditions normales (Inoue and Ushida 2003;

Burns et al. 2016b; Sylvain and Derome 2017).

Le dernier chapitre de cette thèse s’est penché sur l’étude de la dynamique

de la résilience en étudiant les signatures adaptatives et les processus

écologiques mis en œuvre sous les régimes d’exposition métallique constant

et graduel et dans les différentes niches écologiques. Les résultats montrent

non seulement un effet de bioaccumulation à long terme des métaux traces

sur le système biologique étudié, mais suggèrent également que le cadmium

séquestré dans le foie du poisson ne permettrait probablement pas de prédire

l'intensité du régime d'exposition (constant ou variable).

L’effet du cadmium sur les communautés microbiennes varie en fonction de

la biologie et la physiologie de l’hôte. Étonnamment, après deux mois de

résilience, les microbiotes cutanés et intestinaux des poissons exposés au

régime constant se rapprochaient davantage du groupe témoin que ceux

exposés au régime graduel. Dans le mucus cutané, les résultats montrent un

processus de résilience incomplet ce qui conduit à un état intermédiaire stable

et facilite ainsi la prolifération de souches opportunistes (comme

Flavobacterium). Par conséquent, la prévalence de souches opportunistes

observées durant la phase de résistance caractérise également la phase de

résilience du microbiote cutané. En revanche, la phase de résilience témoigne

d’une augmentation significative de la richesse du microbiote dans la peau et

inversement dans les intestins.

Au sein du microbiote intestinal, les mesures de la diversité fonctionnelle et

phylogénétique suggèrent une résilience presque complète de la

communauté sous le régime constant et une résilience intermédiaire sous le

régime graduel. Comme attendu à la fin de l’expérience de résilience, dans le

groupe témoin, la divergence significative par rapport à la structure initiale

de la métacommunauté du départ confirme la théorie de l’ontogenèse (Inoue

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and Ushida 2003; Burns et al. 2016b; Sylvain and Derome 2017) du

microbiote et montre une dynamique de colonisation microbienne orchestrée

par l’hôte tout au long de son développement dès le stade juvénile. Cela

pourrait suggérer que la résilience du microbiote de la Perchaude dans les

deux régimes de sélection a été influencée à la fois par les forces sélectives

associées au cadmium et celles liées au développement de l'hôte. Nos

résultats ont montré que les règles de l'assemblage microbien lors de la phase

de résilience de la communauté semblent être influencées par un gradient de

processus neutres et déterministes. Des études de la colonisation

symbiotique seront nécessaires avec plusieurs séries temporelles pour

quantifier les contributions des processus neutres et déterministes dans la

structuration et de l'assemblage de microbiote durant la résilience.

Curieusement, la pression de sélection exercée par le cadmium sur les

communautés microbiennes de l'eau montre des empreintes adaptatives

témoignant une redondance dans la diversité fonctionnelle aux dépens de la

diversité taxonomique. Cette signature est semblable au découplage taxon-

fonction observé dans la première étude au sein des communautés

microbiennes lacustres sous haute pression de sélection polymétallique. Au

niveau du système hôte-microbiote, la dynamique semble être différente, la

résilience de la méta-communauté ayant été marquée par une structuration

phylogénétique implique une potentielle interaction des processus

stochastiques et déterministes.

En général, nos travaux offrent une perspective solide sur le phénomène de

découplage observé entre les répertoires taxonomique et fonctionnel au sein

des communautés microbiennes sous sélection graduelle. Des études

supplémentaires seraient nécessaires et complémentaires pour discerner

d’une manière plus approfondie les stratégies adaptatives des communautés

microbiennes dans l’environnement naturel et celles associées à l’hôte.

Le phénomène de découplage taxon-fonction observé durant la période de

résilience du microbiote cutané de Perchaude est issu d’une pression de

sélection graduelle. Notre nouvelle hypothèse serait que l’adaptation de

l’holobionte sous sélection graduelle impliquerait un rôle dominant des

transferts horizontaux (Panda et al. 2018; López-Madrigal and Gil 2017).

Plusieurs arguments de l’évolution microbienne peuvent appuyer cette

176

hypothèse. Par exemple, les symbiontes bactériens connus chez plusieurs

modèles de Métazoaires évoluent par réduction de taille de génome (Wolf and

Koonin 2013) et n’échappent pas aux transfert horizontaux de gènes

(Dufresne, Garczarek, and Partensky 2005; Mao, Yang, and Bennett 2018;

Tian et al. 2017; López-Madrigal and Gil 2017; Hendry et al. 2018 ; González-

Torres et al. 2019). Lorsque l’évolution implique une optimisation de taille,

les évènements de transfert horizontaux deviennent logiquement des sources

d’innovations adaptatives pour maintenir la coévolution avec leur hôte (B. P.

Brown and Wernegreen 2019; González-Torres et al. 2019). Nos résultats

montrent une forte dominance de souches opportunistes dans les microbiotes

tels que les Mycoplasmas. Ceux-ci se caractérisent par des génomes de très

petites tailles (Trussart et al. 2017).

De la résistance à la résilience, nos résultats montrent que le microbiote est

un outil de diagnostic environnemental puissant permettant d'évaluer la

capacité des écosystèmes à résister aux contaminants comme les métaux

traces. Les processus écologiques et évolutifs sous-jacents décrivent une

colonisation bactérienne stochastique dans le système Perchaude-microbiote

durant l’exposition initiale au Cadmium. L’identification de processus neutres

et non neutres régissant l'assemblage des différents types de communautés

bactériennes (environnementale et associées à l'hôte) offre de nouvelles

informations clés pour la compréhension des forces évolutives qui façonnent

la composition du microbiote de l'hôte face au stress environnemental (le

Cadmium). Les organismes vivants sont actuellement confrontés à des

niveaux sans précédent de facteurs de stress environnementaux qui ont une

incidence sur leur capacité à faire face aux agents pathogènes naturels,

essentiellement en modifiant leur défense immunitaire et physiologique. Par

conséquent, il est utile et nécessaire de déchiffrer avec précision les signaux

précoces d’alerte apparaissant aux premiers stades de l’exposition aux

xénobiotiques. En soulignant le lien entre perte de résistance à la colonisation

et dysbiose chez l'hôte (qui est connu pour induire une réponse

inflammatoire), nos résultats seront utiles non seulement pour le domaine de

l'écologie microbienne, mais également pour la recherche biomédicale,

notamment pour mieux comprendre le phénomène de la dysbiose. Il a été

démontré que l’altération de la composition du microbiote intestinal entrainait

177

l'apparition de diverses maladies inflammatoires telles que le diabète

(Upadhyaya and Banerjee 2015; Tilg and Moschen 2014; Tilg and Kaser

2011), la maladie de Crohn (Svolos et al. 2019) , le cancer (Helmink et al.

2019), l'obésité (Thaiss 2018), ainsi que plusieurs maladies mentales telle

que l’autisme.

D’une même importance, notre étude est l’une des rares à s’être focalisée sur

la phase de résilience du microbiote associé à l'hôte, analysée à l'aide de

modèles d’assemblage de communautés écologiques. Nous avons pu évaluer

la contribution de facteurs neutres et déterministes à la base de la résilience

de systèmes biologiques confrontés à un gradient de pressions de sélection

d’origine anthropique. De faibles concentrations de métaux traces peuvent

causer des dommages physiologiques et génétiques des systèmes

biologiques, et modifier considérablement leur microbiote, sans retour à l’état

initial ce qui rappelle les conséquences des traitements par des antibiotiques

chez l’Homme. En raison de leur complexité, de leur taille et de leur

composition, la modélisation des mutations dans le répertoire génétique

microbien reste une tâche ardue. Il sera intéressant de tester si l'état post-

perturbation est plus résilient face à une perturbation identique ou plus

sévère. Des nouvelles perturbations répétitives post-perturbation peuvent

aider à identifier les changements de la composition bactérienne à l’échelle

de ce qu’on appelle espèce. La caractérisation des biomarqueurs candidats

peut sans aucun doute contribuer à la biorestauration et la réhabilitation à

grande échelle (par exemple au Canada) des masses d’eau polluées et

contaminées par les rejets des industries minières. Au-delà de la limite des

outils d'écologie microbienne basés sur les informations d'abondance, des

études supplémentaires sont nécessaires pour produire des

données « Omics », à l’échelle fonctionnelle et métabolique afin de trouver

des cœurs de gènes interchangeables entre les communautés au cours de la

réponse post-stress. Les études de génomique et de génétique des

populations peuvent caractériser l’évolution des gènes de résistance aux

métaux traces et leur taux de mutation. De plus, pour améliorer la résilience

et la santé de l'aquaculture, des études supplémentaires seront nécessaires

pour comprendre l’évolution contemporaine autour de l’axe contaminant-

hôte-microbiote.

178

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