BOUETARD, A., 2013. Potentiel évolutif des réponses

Transcription

N° ordre : 2013-10
N° Série : D-70
THESE / AGROCAMPUS OUEST
Sous le label de l’Université Européenne de Bretagne
pour obtenir le diplôme de :
DOCTEUR DE L'INSTITUT SUPERIEUR DES SCIENCES AGRONOMIQUES,
AGRO-ALIMENTAIRES, HORTICOLES ET DU PAYSAGE
Spécialité : Ecologie
Ecole Doctorale : Vie Agro Santé
présentée par :
Anthony BOUETARD
POTENTIEL EVOLUTIF DES REPONSES TRANSCRIPTOMIQUES ET
PHENOTYPIQUES VIS-A-VIS DU STRESS D’ORIGINE ANTHROPIQUE : LE CAS
DE LYMNAEA STAGNALIS EXPOSEE AUX PESTICIDES
soutenue le 4 avril 2013 devant la commission d’Examen
Composition du jury :
Yannick OUTREMAN
Professeur, Agrocampus Ouest / président
Nico VAN STRAALEN
Professeur, Université libre d’Amsterdam / rapporteur
Patrice DAVID
Directeur de recherche, CNRS Montpellier / rapporteur
Laure GIAMBERINI
Professeur, Université de Lorraine / examinateur
Arnaud TANGUY
Maître de conférences, Station Biologique de Roscoff / examinateur
Mari-Agnès COUTELLEC
Chargée de recherche, INRA Rennes / responsable scientifique
Laurent LAGADIC
Directeur de recherche, INRA Rennes / directeur de thèse
Logo structure
d’accueil
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Résumé
Les contaminations répétées par les pesticides des milieux aquatiques lentiques situés dans les
zones agricoles, peuvent influencer l’évolution des populations d’organismes non-cibles qui y sont
inféodées, notamment pour les espèces ayant une faible capacité de dispersion. L’objectif de la thèse
fut d’évaluer le potentiel évolutif de réponses moléculaires au stress, chez un gastéropode aquatique,
Lymnaea stagnalis afin de tester l’influence de la sélection sur la divergence des populations.
L’hypothèse de divergence adaptative a été testée sur un ensemble de 14 populations naturelles
neutralement différenciées (FST = 0,282) et contrastées du point de vue de leur degré d’exposition aux
pesticides. Les traits d’histoire de vie étudiés montrent des patrons de divergence compatibles avec la
sélection diversifiante, homogénéisante, ou avec l’hypothèse neutre (QST-FST). Le type d’habitat
apparaît comme le principal facteur de divergence inter-population mais les résultats suggèrent une
action sélective de la pression pesticide vers une fécondité accrue, comme réponse possible à la
réduction de survie précoce.
Du point de vue moléculaire, la réponse transcriptionnelle de gènes candidats au diquat,
pesticide modèle pro-oxydant, s’est avérée plus marquée que les activités enzymatiques
correspondantes. A l’échelle du transcriptome (RNAseq), l’analyse préliminaire de la variation
génétique sur un sous-jeu de quatre populations, montre en premier lieu une concordance remarquable
avec la divergence neutre. L’effet du diquat semble s’exprimer essentiellement via son interaction
avec la population, suggérant des patrons de réponse très différents ainsi qu’une plus faible sensibilité
à l’herbicide dans les populations historiquement exposées. Globalement, cette étude révèle une
grande variabilité, indiquant un fort potentiel évolutif chez cette espèce, et fournit de nouveaux
arguments en faveur de la prise en compte de la variation génétique dans les procédures d’évaluation
du risque écologique.
Mots clés : Ecotoxicologie Aquatique, Génétique des populations, Génétique quantitative,
Transcriptomique, Pesticides, Evaluation du Risque Ecologique, Lymnaea stagnalis.
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Abstract
Repeated pesticides contaminations of lentic freshwater systems located within agricultural
landscapes may affect population evolution in non-target organisms, especially in species with a fully
aquatic life cycle. The main objective was to assess the evolutionary potential of molecular responses
to stress in a freshwater gastropod, Lymnaea stagnalis, through an approach designed to test for
population adaptive divergence due to pesticide pressure.
The hypothesis of adaptive divergence was tested using a QST-FST approach on a set of 14
natural populations neutrally differenciated (FST = 0,282), from contrasted environments with respect
to pesticide pressure. Different patterns were obtained according to the studied life history traits, from
neutrality, to divergent and homogenizing selection. Habitat type was shown to be the main factor
responsible for population genetic divergence. However, a possible selective effect of anthropogenic
stress on some traits was also suggested (enhanced reproduction ability as a response to decreased
survival at hatching).
Regarding molecular responses to diquat, a pro-oxidant herbicide, results based on candidate
genes revealed more pronounced or detectable effects at transcriptional than at more functional level
(enzyme activities). At the transcriptomic scale (RNAseq), preliminary investigations based on a
subset of four populations, revealed a pattern remarkably consistent with population neutral
divergence. Consistently, statistical analyses show that the effect of diquat expressed mostly through
its interaction with population, which suggests very different patterns of response according to
populations. Results also suggested a possibly reduced sensitivity to diquat in exposed populations, as
compared to reference ones. Overall, high genetic variability observed both within and between
populations, is indicative of high evolutionary potential, and provides additional arguments to take
genetic variation into account in procedures of ecological risk assessment.
Keywords: Aquatic Ecotoxicology, Population Genetics, Quantitative Genetics, Pesticides,
Transcriptomics, Ecological Risk Assessment, Lymnaea stagnalis.
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Remerciements
Quelle aventure ! Tout ce travail n’aurait jamais été possible sans l’aide de nombreuses
personnes.
Tout d’abord, je souhaite remercier Marie-Agnès Coutellec, pour avoir éloboré ce
projet original et ambitieux et pour m’avoir soutenu tout au long de ces trois années. Un grand
MERCI pour ton aide, ton implication dans ce travail, ta patience, ton professionnalisme et ta
sympathie. J’ai appris énormément à tes côtés, aussi bien scientifiquement qu’humainement
parlant, lors de ces nombreuses et riches discussions. Je t’adresse tout mon respect et mon
estime.
Merci aussi à Laurent Lagadic, directeur de thèse et reponsable de l’équipe
« Ecotoxicologie et Qualité des Milieux Aquatiques » (EQMA), pour nous avoir fait profiter
de son HDR, pour ses conseils avisés et pour l’entière confiance et l’autonomie qu’il m’a
accordées. Merci également pour m’avoir aidé quelques années plus tôt, à m’orienter sur la
voie de la biologie moléculaire et des sciences environnementales.
Merci à Jean-Luc Baglinière, Hervé Le Bris et Jean-Marc Roussel, directeurs de
l’UMR « Ecologie et Santé des Ecosystèmes » (ESE) pour leur accueil au sein du laboratoire,
leur écoute et leur disponibilité, ainsi qu’à Didier Azam, directeur de l’Unité expérimentale
d’Ecologie et d’Ecotoxicologie (U3E) pour m’avoir permis de réaliser les élevages et les
expérimentations dans les locaux de l’U3E.
Je tiens à remercier chaleureusement Nico van Straalen, Patrice David, Laure
Giamberini, Arnaud Tanguy et Yannick Outreman pour m’avoir fait l’honneur d’accepter de
faire partie de mon jury de thèse et pour avoir consacré du temps à l’évaluation de ce travail.
Merci également aux membres de mon comité de pilotage : Guillaume Evanno, Joëlle
Forget-Leray et Thierry Guillaudeux, mon tuteur de thèse, pour leurs conseils et remarques
pertinentes sur ce projet.
De nombreuses personnes ont contribué au bon déroulement de ce projet et m’ont aidé
à surmonter ces montagnes de limnées et de bases azotées. Merci à Marc Collinet pour son
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travail indispensable, sa minutie, sa bonne humeur et tous ces moments de franches rigolades
autour des innombrables rondelles de salade. Merci également à Thierry Caquet, qui malgré
ses responsabilités, a partager de son temps précieux pendant cette période d’élevage. Un
grand merci aussi à tous les membres de l’U3E, Antoine Gaillard, Alphonse Quemeneur,
Bernard Joseph, Cédric Lacoste et plus particulièrement Maïra Coke pour avoir participé à la
mise en place, l’entretien et avoir assuré toute la logistique indispensable à la bonne
réalisation de ce travail. Un grand merci Maïra pour la qualité de ton travail ainsi que pour
tous ces moments partagés, ton écoute et ton soutien.
Merci aussi à Anne-Laure Besnard pour sa sympathie, son dynamisme, sa rigueur et
son aide lors des tests d’écotoxicité, des manipulations de biologie moléculaire et son travail
réalisé aux plateformes génomiques du Rheu et de Toulouse. Merci également à Danièle
Vassaux pour son aide et l’énergie consacrée à la mise au point et à la réalisation des analyses
biochimiques. Merci aussi à Colette Désert, Cécile Duby et Lars-Henrick Heckmann pour
leurs conseils et les discussions lors de l’étude préliminaire en qPCR. Merci également à
Olivier Bouchez, Eugénie Robe, Nathalie Marsaud de la plateforme génomique Genotoul, à
Céline Noirot, Claire Hoede, Cristophe Klopp de la plateforme bioinformatique Genotoul,
ainsi que Damien Choine et Thierry Pécot pour leur travail et implication lors de la génération
et l’analyse des données transcriptomiques.
Je tiens aussi à remercier Gervaise Février et Marie-Thérèse Delaroche pour leur
disponibilité et le travail considérable qu’elles réalisent au niveau administratif pour l’unité et
merci à Sonia Azam pour son aide informatique au début de ma thèse.
Un grand MERCI également à l’ensemble du personnel du laboratoire, et tout d’abord
à mes collègues de bureau, Marc Collinet, Sabrina Le Cam et Jessica Côte pour tous ces
moments conviviaux, ces éclats de rire, les p’tits gateaux, les p’tits coups de mains et toutes
ces dicussions plus ou moins scientifiques. Un merci particulier à Jessica pour ton aide et ton
soutien lors de la dernière ligne droite quand le stress d’origine anthropique s’est
particulièrement fait sentir, pour tes conseils pertinents et relectures bénéfiques, un grand
merci!
Merci aussi aux autres collègues pour les bons moments partagés en pause café/clope,
à la cantine, au labo ou à l’extérieur du boulot, je pense à Dominique Huteau, Glenn Suzur,
Guillaume Forget, Julien Tremblay, Yannick Bayona, Caroline Gorzerino, Martine Ollitrault,
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Quentin Rougemont, Anne Tréguier, Jérôme Sawtchuck, Guillaune Bal, Sophie Launey,
Charles Perrier, Valérie Lopez, Fanny Caupos, Frédéric Lemarchand, Anna et Marc
Roucaute, Virginie Ducrot, Arnaud Auber, Ludovic Jégousse et Martine Perdriaux. Petite
dédicace aussi à mes homologues groluscoloques belge, serbe, et allemand Arnaud Giusti,
Alpar Barsi et Elke Zimmer pour tous les bons moments passés en coloc et en colloque
(Quelle descente de drapeaux Nono!), bon courage pour le sprint final ! Yannick, Anne et
Quentin, c’est dans plus ou moins longtemps, courage ! Tenez bon aussi !
Je souhaite aussi remercier mes anciens encadrants de stage, Lars-Henrick Heckmann
en Angleterre, et Michel Grisoni et Hélène Delatte à La Réunion, aux côtés de qui j’ai
beaucoup appris.
Enfin, je souhaite remercier mes proches, en premier lieu ma famille. Un grand merci
à mes parents, Alain et Odile pour votre confiance, votre soutien dans mes choix, et pour tout
ce que vous avez fait pour moi. Merci aussi à mes sœurs, Sophie et Virginie, ainsi qu’à toutes
leurs petites familles, Marco, Jéjé, Hugo, Sido, Yaya, et Juju ; je suis un fiston, frangin et
tonton comblé ! Merci aussi à mes oncles et tantes, cousins et cousines, bref la famille
auxquels s’ajoutent tous mes amis, d’ici ou d’ailleurs, qui m’ont aussi construit et font mon
bonheur. Mon vieux poto Sim, chez qui cette aventure a commencé, lettre et CV sont partis en
bas de chez toi, lors de cette excursion inoubliable au départ de Tel-Aviv. Désolé je n’ai pas
réussi à caser le conflit Sino-Tibétain dans ce manuscrit alors je profite de ces remerciements
pour relever ce défi. Merci aussi à Aurèl, Matthias et Dominique… je n’ai pas abdiqué...
Merci aux « Zafers », Nico D. & Nico P. et Victor pour toutes ces bouffes et sessions
musicales par toujours très mélodiques mais sacrément sympathiques. Continuons à valser !
Merci aussi aux vieux d’la vieille, Camille, Elise, Alex, Vivi, BenH, Gaga, Sam, Audrey, la
clic du Garage à Pau, Annie, aux dallons de la Réunion, Paulo, Alice (et Béné et Cécile de la
Grosse !), Tipichon, Zozo, Mimi, Tomtom, Arthur, Solène, Lucette, Hélène, Denis, FX,
Rodolphe, Caro, Daphné, Pierre, Séverine, Thierry, Leila, Céline, Rachelle, Zébulon,
Vanessa, Coco, Edou, Imane, Fab, la case Kabar Boissy, les tontons de la route, Cyril, Henri,
Pierre-Yves et tous ceux que j’ai pu (par erreur) oublier.
Pour terminer, une petite pensée pour ces « quasi » 20000 limnées, mortes pour la
science, en espérant que leur sacrifice ait contribué à apporter un peu plus de sens au pluriel…
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Sommaire
Résumé
........................................................................................................................ 3
Abstract
........................................................................................................................ 4
Remerciements ........................................................................................................................ 5
Liste des figures ........................................................................................................................ 9
Liste des tableaux ..................................................................................................................... 13
Liste des tables supplémentaires .............................................................................................. 15
Préface
Chapitre I -
...................................................................................................................... 18
Introduction générale..................................................................................... 21
1. Stress environnemental et impact évolutif des polluants sur les populations naturelles............... 25
2. Etude des mécanismes évolutifs intra-spécifiques ......................................................................... 31
3. Séquençage de Nouvelle Génération, l’écotoxicogénomique à la portée des espèces « non
modèles ». ....................................................................................................................................... 37
4. Le modèle biologique : Lymnaea stagnalis.................................................................................... 41
5. Stress d’intérêt : le stress oxydant ................................................................................................. 42
6. Hypothèses, problématique, objectifs et démarche scientifique. ................................................... 47
Chapitre II -
Impacts moléculaires d’un herbicide pro-oxydant chez L. stagnalis: approche
« gènes candidats »........................................................................................ 50
Chapitre III -
Développement de ressources génomiques chez L. stagnalis par
pyroséquençage 454 : marqueurs génétiques microsatellites et séquençage de
transcriptome................................................................................................. 75
Chapitre IV -
Impact évolutif des activités agricoles et anthropiques chez L. stagnalis :
étude de 14 populations naturelles .............................................................. 121
Chapitre V -
Analyse RNAseq (Illumina) de la divergence génétique des réponses
moléculaires au stress entre différentes populations naturelles de L. stagnalis.
..................................................................................................................... 156
Chapitre VI -
Discussion générale..................................................................................... 192
Effet toxique du diquat sur les réponses moléculaires de l’hémolymphe et de la glande digestive. 194
Structure génétique des populations naturelles de L. stagnalis dans le Nord-Ouest de l’Europe... 196
Présence d’une forte variabilité inter-populationnelle des traits quantitatifs à l’échelle phénotypique
(traits d’histoire de vie) et moléculaire (transcriptome) .............................................................. 197
Effets potentiels des pressions historiques d’origine anthropique sur les réponses moléculaires au
stress oxydatif et la fitness des populations .................................................................................. 199
Le type d’habitat comme principal facteur de divergence adaptative et neutre .............................. 200
Présence d’une forte variabilité intra-populationnelle des traits quantitatifs à l’échelle phénotypique
et moléculaire. .............................................................................................................................. 202
Intérêt appliqué de l’étude dans le contexte de l’évaluation du risque écologique ......................... 203
Conclusions et perspectives ................................................................................................... 204
Travaux annexes .................................................................................................................... 207
Références bibliographiques .................................................................................................. 208
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Liste des figures
Introduction générale
Figure 1. Evolution de la démographie et de la diversité génétique d’une population après un
bottleneck. D’après Bickham et al. (2000)…………………………………………………………...p29
Figure 2. Exemple de paternal half-sib mating design. Chaque mâle se reproduit avec plusieurs
femelles non apparentées (d’après Lynch & Walsh 1998)………………………………………….p33
Figure 3. Représentation schématique de différents cas de normes de réaction, d’après Pigliucci
(2001)…………………………………………………………………………………………………p34
Figure 4. Représentation schématique des différents éléments de régulation de l’expression
génique. CDS, séquence codante; Co-A/I, co-activateur/inhibiteur; EBS, Site de fixation de
l’amplificateur de transcription ; EN, amplificateur de transcription, M, modification, Me,
méthylation ; S, stabilisateur d’ARNm; TF, facteur de transcription; TFBS, site de fixation du facteur
de transcription, TM, inhibiteur de traduction; Ub, ubiquitine (d’après Morgan et al., 2007)……….p38
Figure 5. Evolution du nombre des projets de génomiques référencés dans la base de données
GOLD en fonction des groupes phylogénétiques. 20327 projets depuis sa création jusqu’à octobre
2012. B, Bactéries ; A, Archeabactéries ; E, Eucaryotes, M, Mammifères. source :
http://www.genomesonline.org.............................................................................................................p39
Figure 6. Schéma des mécanismes impliqués dans la production, l’élimination des ROS ainsi que
de leurs potentiels effets biologiques. (d’après Luschback 2011).….…..…..…..…..…..…..……...p43
Figure 7. Représentation du dibromure de diquat. (Br2C12H12N2)…………………………....….p45
Chapitre II
Article 1- Impact of the redox-cycling herbicide diquat on transcript expression and
antioxidant enzymatic activities of the freshwater snail Lymnaea stagnalis
Figure 1. Effective concentration of diquat as function of time and nominal concentration…..p63
Figure 2. Effect of diquat on transcript expression of r18s, r28s and cor in haemolymph (A) and
in the gonado digestive complex (B) of Lymnaea stagnalis, as a function of time and diquat
concentration. Transcription levels (mean ± SE) are presented relative to the control (mean ± SE).
Significant differences between snails exposed to diquat and their control counterparts are denoted
with an asterisk (p < 0.05) or a dot (p < 0.1) (ANOVA followed by post hoc test)………………….p65
Figure 3. Effect of diquat exposure for time-course on transcription of hsp40 and hsp70 in
haemolymph of Lymnaea stagnalis, as a function of time and diquat concentration. Transcription
levels (mean ± SE) are presented relative to the control (mean ± SE). Significant differences between
snails exposed to diquat and their control counterparts are denoted with an asterisk (p < 0.05) or a dot
(p < 0.1) (ANOVA followed by post-hoc test)…………………………………………………….…p66
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Figure 4. Effect of diquat exposure for time-course on transcription of gene involved in
antioxidant response in haemolymph of Lymnaea stagnalis, as a function of time and diquat
concentration. Transcript expression levels (mean ± SE) are presented relative to the control (mean ±
SE). Significant differences between snails exposed to diquat and their control counterparts are
denoted with an asterisk (p < 0.05) or a dot (p < 0.1) (ANOVA followed by post-hoc test). ……….p66
Figure 5. Effect of diquat exposure for time-course on transcription of hsp40 and hsp70 in the
gonado digestive complex of Lymnaea stagnalis, as a function of time and diquat concentration.
Transcript expression levels (mean ± SE) are presented relative to the control (mean ± SE). Significant
differences between snails exposed to diquat and their control counterparts are denoted with an
asterisk (p < 0.05) or a dot (p < 0.1) (ANOVA followed by post-hoc test). ……………………...…p67
Figure 6. Temporal antioxidant profiles of transcript expression (A & B) and enzymatic activity
(C & D) in the gonado-digestive complex of L. stagnalis exposed to diquat. Snails were exposed
during 5, 24 or 48 hours to 22.2, 44.4 or 222.2 µg l-1 of diquat dibromide. Transcription was measured
by RT-qPCR and normalized with NORMA-Gene (Heckmann et al., 2011). Transcript expression
levels, as enzymatic activities (mean ± SE) are presented relative to the control (mean ± SE).
with an asterisk (p < 0.05) or a dot (p < 0.1) (ANOVA followed by post-hoc test)…………………p68
Chapitre III
Article 2 - Pyrosequencing-based transcriptomic resources in the pond snail Lymnaea
stagnalis, with a focus on genes involved in molecular response to diquat-induced stress
Fig.1. Distribution of annotation quality according to the database used in BlastX (percent
identity between query and database sequences). Dataset = LsContig V1…………………….……..p89
Fig.2. Number of contigs classified according to the KEGG top categories. ……………………p90
Fig.3. Number of contigs classified according to the subcategories of KEGG organismal systems
category (LsContigV1 annotated contigs)………………………………………….………………..p90
Figure 4. Comparison of the distribution of GO terms among three groups of contigs from
Pondsnail_Contigs_V1. The X-axis shows subgroups of the three top categories of GO. The Y-axis
shows the percentage (log-scale) of matched sequences in three groups of annotated contigs having at
least one GO hit: non-differentially expressed among C and D libraries, overexpressed in D library,
and underexpressed in D Library………………………………………………….…………….……p94
Primer note - Isolation and characterization of three new multiplex sets of microsatellite
markers in the hermaphroditic freshwater snail Lymnaea stagnalis (Mollusca, Gastropoda,
Heterobranchia, Panpulmonata, Hygrophila) using 454-pyrosequencing technology
Figure 1. Geographic origin of Lymnaea stagnalis natural populations (Castricum and Kuinre,
NL) and INRA U3E laboratory cultures (Le Rheu FR, Amsterdam NL, and Sandbjerg DK) used
to describe genetic variability at 9 new microsatellites. Upper-left icon shows the UPGMA
clustering of samples based on pairwise Fst-values…………………………………………..…….p114
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Chapitre IV
Figure 8. Photos du dispositif d’élevage des lignées G1….….….………………………….……p123
Article 3 - Do pesticides influence evolutionary processes in natural populations of non-target
species? A study in the freshwater snail, Lymnaea stagnalis
Figure 1. Location of the 14 populations studied. The number each population is indicated and the 3
different colours corresponds to the 3 environmental categories (white, grey and black for ER0, ER1
and ER2, respectively). See Table 1 for details…………………………………………...……p131
Figure 2. Schematic overview of the common garden experiment (See text for details)…….…p136
Figure 3. Correlation plot between population inbreeding and 15 days fecundity. …….…….p140
Figure 4. Genetic differentiation among the 14 studied populations. (A) Bayesian individual
clustering results with STRUCTURE for k = 2. Coloured bars represent proportions of membership of
each individual to each cluster. (B) Unrooted Neighbour-Joining tree based on Weir and Cockerham
genetic distances among the 14 populations (see Table S2)………………………………………...p141
Figure 5. Individual growth (G1 snails) according to habitat of origin. Gompertz growth curves
were based on 12 dates of measurement, performed on 1154 individuals…………………….……p143
Figure 6. Observed hatching rate depending on the level of anthropization. Plot based on 1537
clutches……………………………………………………………………………………….….….p145
Chapitre V
Figure 9. Photos des sites d’échantillonnage des quatre populations naturelles utilisées pour
l’approche RNAseq……………………………………………………………………………….. p157
Article 4-Genetic variation of transcriptomic expression in Lymnaea stagnalis exposed to a
redox-cycling pesticide
Figure 1. Overview of the scientific approach. On the map are localized the four studied
populations, Unexposed and exposed sites are indicated in white and black, respectively…….……164
Figure 2. Distribution of annotation quality according to the database used in BlastX (percent
identity between query and database sequences). Identity percent is represented on the axis (“20”
stands for 0-20% identity, etc.) ……………………………………………………………………..p171
Figure 3. Numbers of best hit annotations among the 20 most represented species (E-value < 1e5)……………………………………………………………………………………………….……p172
Figure 4. Number of contigs classified according to the KEGG top categories. (12581 contigs,
BLAST threshold > 50% identity)…………………………………………………………………..p173
11
Figure 5. Genetic differentiation among the four studied populations. A, Unrooted Neighborjoining tree based on FST values estimated from 12 SSR loci. B, Bayesian individual clustering results
with STRUCTURE for k=2. C, hierarchical clustering based on the count data of the 8 control
libraries…………………………………………………………………………………………..….p174
Figure 6. Heatmap based on Euclidean distance between samples, as calculated from the
variance stabilising transformation of the count data. * indicates 100% family homology between
replicates……………………………………………………………………………………….……p175
Figure 7. Venn Diagramm of the DE contigs according to the three factors implemented in the
multi-factorial model analyzed in Deseq. The count data dispersion of the 45249 contigs were fitted
with a "local" parameter, adjusted p-value < 0.1. Number of Contigs having annotation in KEGG is
indicated between brackets………………………………………………………………………….p176
Figure 8. Hierarchical clustering based on correlation table of subset of contigs matching in
signalling pathways apoptosis, ubiquitin metiated proteolysis and the cellular process “Folding,
sorting and degradation”. Numbers of contigs is indicated under each corresponding trees. 100%
homology between family replicates is indicated by a point after samples names. Colored frames
differentiate population types according to the historical pesticide pressure, i.e., low (red) or high
(green)……………………………………………………………………………………………….p180
Figure 9. Heatmap of DE contigs detected by t-test statistics and ranged by KEGG processes.
Only annotated contigs presented (p-val < 0.1) a foldchange > |2| for at least one of the four
populations are presented. On the left, foldchange intensity is determined by divided the mean
expression of treated samples by the mean expression of the control samples of the relative population.
On the right, count data of each sample are divided by the mean count obtained from the two controls
of the relative population. …………………………………………………………………………..p181
12
Liste des tableaux
Chapitre II
Article 1- Impact of the redox-cycling herbicide diquat on transcript expression and
antioxidant enzymatic activities of the freshwater snail Lymnaea stagnalis
Table 1. Characteristics of the sequences used for real-time qPCR analysis.…………………...p60
Table 2. Physicochemical parameters as measured under control conditions.………………….p63
Table 3. Results of the normalization procedure based on GeNorm, using ribosomal RNA 28S,
18S and cortactin as reference genes.………………………………………………………………p64
Chapitre III
Table 1. Results of cleaning processes applied to the raw sequences obtained from L. stagnalis C
and D libraries. The number of sequences is given for each library…....….….….…..……………p88
Table 2. Number of genes having at least one significant match (best-hit annotation) in
Pondsnail_Contig_V1, and involved in 13 KEGG pathways selected for their implication in
oxidative and molecular stress. ……………………………………………………………………p91
Table 3. Contig annotations matching with genes involved in apoptosis pathway (ko04210) (IAP
= inhibitors of apoptosis)……………………………………………………………………………..p92
Table 4. List of gene hits (identity > 50%) matching exclusively with contigs for which
Log2FoldChange is either less than - 2 or higher than + 2 between diquat and control conditions
(D<C; D>C), or with contigs with no read under one condition and at least 4 reads in the other
condition. ………………………………………………………………………………………...p95-96
Primer note - Isolation and characterization of three new multiplex sets of microsatellite
markers in the hermaphroditic freshwater snail Lymnaea stagnalis (Mollusca, Gastropoda,
Heterobranchia, Panpulmonata, Hygrophila) using 454-pyrosequencing technology
Table 1. Sequence and multiplex characteristics of 9 microsatellites loci isolated in L.
stagnalis.…………………………………………………………………………………………….p116
13
Table 2. Polymorphism statistics for nine microsatellites loci in L. stagnalis. NA is the total
number of alleles, A allelic richness, n number of individuals, nA number of observed alleles per locus
per population, Ho observed heterozygosity, He expected heterozygosity (Nei 1978). Bolded
characters indicate significant departures from HWE, asterisks the presence of null alleles……….p118
Table 3. Genetic diversity and correlation parameters estimated per population. Hexp = Expected
heterozygosity, unbiased estimate. HWE p-values: p-value of the Hardy-Weinberg test with H1 =
heterozygote deficiency (HD), or with H1 = heterozygote excess (HE), significant value in
bold………………………………………………………………………………………………….p119
Chapitre IV
Table 1. Geographical and ecological characteristics of the sites of origin of the 14 investigated
Lymnaea stagnalis populations and presentation of their respective genetic diversity indices. The
classification of populations in two levels of anthropogenic pressure (AL1 < AL2), two levels of
pesticide risk (PR1 < PR2) and three levels of environmental risk (ER0 < ER1 < ER2) was based on
the percentage of land use categories within a radius of 100 m around the sampled sites. n indicates
the number genotyped individuals, N is the number of alleles, AR is allelic richness (based on samples
of 12 individuals), HE is the unbiased expected heterozygosity, HO is the observed heterozygosity, and
FIS is the inbreeding coefficient (significance is indicated by asterisk). Ne is the effective population
size and estimates of self-fertilization rates are also presented……………………………………..p133
Table 2. Results of the QST-FST comparison for 11 traits studied under a full-sib design. ……p142
Chapitre V
Table 1. Ecological neutral genetic characteristics of four Lymnaea stagnalis populations.
Pesticide exposure risk was based on the percentage of land use categories within a radius of 100 m
around the sampled sites. n indicates the number of genotyped individuals, N is the number of alleles,
AR is allelic richness (based on samples of 12 individuals), HE is the unbiased expected heterozygosity,
HO is the observed heterozygosity, and FIS is the inbreeding coefficient (significance is indicated by
asterisk). Ne is the effective population size and estimations of self-fertilization rates calculated are
also presented………………………………………………………………………………………..p165
Table 2. Organization of the 16 libraries on the Illumina flow-cell and family relatedness within
library………………………………………………………………………………..……………..p167
Table 3. Summary of Illumina sequencing data of the L. stagnalis transcriptome. …..………p170
Table 4. Overview of the preliminary results obtained from Kegg annotation and statistical
analyses conducted per population, using t-test statistics and DEseq. ………………….…….p177
14
Liste des tables supplémentaires
Chapitre III
Table S1 Tentative estimates of EC50s for diquat, using data from Coutellec et al. 2008
(Chemosphere 73:326-336). Average values are given with their bootstrapped 95%CIs, as estimated
under a Hill model, using the EXCELTM macro REGTOX ………………………………………...p98
Table S2. Tissue volume / mass per condition (C = control, D = diquat exposure) and sample,
and corresponding quantity of extracted total RNAs. GDC holds for gonado-digestive complex,
BAP for body anterior part, and BPP for body posterior part…………………………………….…p98
Table S3. Genes with at least one significant match (best-hit annotation) in LsContigV1, and
involved in alanine, aspartate and glutamate metabolism pathway (Ko00250)..........................p99
involved in Ascorbate and aldarate metabolism (KEGG ko00053)….….….…………………..p100
involved in cystein and methionine metabolism pathway (ko00270). …………………………p100
Table S6. Genes with least one significant match (best-hit annotation) in LsContigV1, and
involved in glutathione metabolism pathway (KEGG: ko00480)……………………….………p101
involved in MAPk signaling pathway (KEGG ko0410) …...….…………….....……….…..p102-103
involved in metabolism of xenobiotics by cytochrome P450 (ko00980). ………....….…………p103
involved in oxidative phosphorylation pathway (KEGG Ko00190)…………………..……p104-105
involved in in p53 signaling pathway (KEGG Ko04115)………………………………………...p106
involved in peroxisome pathway (KEGG ko04146)……………………………….…………..….p107
involved in porphyrin and chlorophyll metabolism (KEGG pathway Ko00860)……………….p108
involved in protein processing in endoplasmic reticulum (KEGG pathway ko04141)….…..109-110
involved in retinol metabolism pathway (KEGG pathway ko00830)…………………………....p111
15
Table S15-S16-S17-18 Available on line
(http://link.springer.com/article/10.1007/s10646-012-0977-1)
Table S15. Summary of GO annotation statistics in “Cellular Component” GO category, as
provided by WEGO. Columns 1 and 2: first value refers to non-differentially expressed genes (1),
second and third values (2 and 3) to over and under-expressed genes, respectively. Column 3: P-values
are given for Pearson Chi-square pairwise tests, ordered as 1:2,1:3, 2:3. Ml holds for “meaningless”
(count<5), Na for “not available”. Dataset = Pondsnail_Contigs_V1.
Table S16. Summary of GO annotation statistics in “Molecular Function” GO category, as
(count <5), Na for “not available”. Dataset = Pondsnail_Contigs_V1
Table S17. Summary of GO annotation statistics in “Biological Process” GO category, as
Table S18. KEGG code, abbreviation and name of genes identified in L. stagnalis transcriptome
as putatively regulated by diquat (see Table 4)
Chapitre IV
Table S1. Locus and population genetic characteristics. n indicates the number genotyped
individuals, Na is the number of alleles, AR is allelic richness (based on samples of 12 individuals), HO
is the observed heterozygosity, HE is the unbiased expected heterozygosity, and FIS is the inbreeding
coefficient (significance is indicated in bold characters). ……………………………………..p151-152
Table S2. Pairwise θ values, estimated on the basis of 12 SSR loci (lower diagonal). Upper
diagonal: nominal level for multiple comparisons (adjusted to 0.000549)…………………………p152
Table S3. Mean values of the life history traits obtained for the 14 studied populations…..…p153
Table S4. Effects of fixed factors on life history traits using GLMM (Y ~ covar + Geo + Hab +
Environmental Risk+ (1 | pop / fam))…………………………………………………….………p154
Table S5. Effects of the fixed covariates on Life history traits from the GLMM analyses : Y ~
covar + Geo + Hab + ER + (1 | pop / fam)………………………………………………….…….p155
16
Chapitre V
Table S1. List of gene hits (identity > 50%) in the “Enzymes” and “Cytochrome P450”
categories matching exclusively with contigs for which the Fold-Change is either less than -2 or
higher than +2 between diquat and control conditions (D < C ; D > C) according to a t-test, and/or
considered as DE by DEseq analysis (p < 0.1). Genes in italics were detected by DEseq. Genes in bold
were detected by both methods. Index letters indicate different contigs matching with the same
reference gene.…………………………………………………………….………………….……..p185
Table S2. List of gene hits (identity > 50%) in the “Cellular processes category” matching
exclusively with contigs for which the Fold-Change is either less than -2 or higher than +2 between
diquat and control conditions (D < C ; D > C) according to a t-test, and/or considered as DE by DEseq
analysis (p < 0.1). Genes in italics were detected by DEseq. Genes in bold were detected by both
methods. Index letters indicate different contigs matching with the same reference
gene…………………………………………………………………………………….……………p186
Table S3. List of gene hits (identity > 50%) in the “Environmental Information Processing”
category matching exclusively with contigs for which the Fold-Change is either less than -2 or higher
than +2 between diquat and control conditions (D < C ; D > C) according to a t-test, and/or considered
as DE by DEseq analysis (p < 0.1). Genes in italics were detected by DEseq. Genes in bold were
detected by both methods. Index letters indicate different contigs matching with the same reference
gene…………………………………………………………………………………………..…p187-188
Table S4. List of gene hits (identity > 50%) in the “Genetic Information Processing” category
matching exclusively with contigs for which the Fold-Change is either less than -2 or higher than +2
between diquat and control conditions (D < C ; D > C) according to a t-test, and/or considered as DE
by DEseq analysis (p < 0.1). Genes in italics were detected by DEseq. Genes in bold were detected by
both methods. Index letters indicate different contigs matching with the same reference
gene………………………………………………………………………………………………….p189
Table S5. List of gene hits (identity > 50%) in the “Metabolism” category matching exclusively
gene…………………………………………………………………………………….……………p190
Table S6. List of gene hits (identity > 50%) in the “Organismal system” category matching
gene…………………………………………………………………………….……………………p191
17
Préface
« Si j’admettais l’intervention des impressions sentimentales dans les sciences, je dirais,
comme M. Claparède, que j’aimerais mieux être un singe perfectionné qu’un Adam
dégénéré. » (P. Broca 1870)
18
Nous vivons une époque extraordinaire, bienvenus dans l’Anthropocène ! Que de
chemins parcourus par notre espèce depuis que de lointains ancêtres se dressèrent sur leurs
pattes arrière ! Qui sait, pour cueillir une de ces succulentes pommes africaines peut-être? À
cette époque révolue, il n’y avait encore ni bombe H, ni hypermarché, ni 4x4, ni McDonalds.
Il lui a fallu s’adapter, lentement, émigrer, souvent et tout le temps se réadapter à un paysage
changeur et ses locataires inféodés. À mesure de la prise de conscience de son environnement,
tant écologique que culturel, au rythme de l’acquisition de connaissances empiriques, des
dogmes et des progrès techniques, l’espèce humaine a cherché à se trouver une place, à se
définir, donner du sens à l’improbable, au mystère de la vie. Autant de définitions que de
civilisations, cultures et courants de pensées, entraînant son flot de conflits, échanges,
dominations et révolutions. En occident où la pensée est longtemps restée cloisonnée dans le
carcan catholique, « l’image de Dieu » a senti bon de se placer au centre d’un univers
initialement réduit au système solaire, selon le modèle géocentrique inspiré par Aristote et
Ptolémée. Malgré quelques astronomes visionnaires de l’Inde et de la Grèce antique, ainsi que
de la Perse médiévale, il faudra attendre les travaux de Copernic, Kepler et Galilée, conduits
aux cours des XVIe et XVIIe siècles, pour voir réellement émerger le modèle héliocentrique.
Toutefois, plusieurs décennies et le siècle des Lumières seront encore nécessaires pour qu’il
s’impose devant les critiques et la censure et que l’église reconnaisse définitivement la
trajectoire elliptique de la terre autour du soleil. S’initie alors une effervescence durable et
sans précédent dans de multiples domaines scientifiques notamment en biologie, dont le terme
fut introduit par Jean-Baptiste de Lamarck en 1802. En 1831, Charles Darwin s’engage sur le
Beagle en tant que géologue et naturaliste, dans l’exploration de cette terre sphérique et
nomade. De ses nombreuses observations, découvertes de fossiles et autres spécimens
rapportées de ces cinq années de voyage, il élabore sa théorie sur l’origine des espèces qu’il
publiera en 1859, apportant des éléments d’explication logique pour interpréter les similarités
et la diversité du monde vivant présent et passé. En dépit de son opposition directe avec la
doctrine créationniste ancrée dans le prolongement de la Genèse biblique, la théorie de
l’évolution s’imposa progressivement, appuyée par l’avènement de la génétique Mendélienne
(1865), de la microbiologie puis au gré des avancées technologiques du XXe siècle, par la
découverte de l’ADN, le support universel nécessaire à toute vie sur terre. Entre temps, Edwin
Hubble démontra définitivement en 1924 la multiplicité des galaxies, de quoi chambouler
encore les dogmes et certains fondements philosophiques sur la nature humaine, la descendant
19
de ce piédestal pseudo-divin pour la rendre microscopique et perdue dans un univers toujours
plus grand et indéfini et la considérant sur le plan évolutif au même rang que les autres
espèces sur une planète déjà âgée, aux contours et réserves limités.
Cependant, une certaine interprétation de la loi de la sélection naturelle dont se sont
largement inspirés le modèle d’économie libérale et les dérives scientistes et eugénistes,
tendent à penser que l’anthropocentrisme est toujours bien ancré dans les rouages et
l’inconscient collectif de nos sociétés industrialisées. Certes les progrès médicaux et
technologiques des ces derniers siècles ont permis à notre espèce de passer le cap des sept
milliards d’individus en 2012, démontrant ainsi son incroyable potentiel d’adaptation, de
créativité, et une intelligence remarquable à faire rougir plus d’un DRH, lui permettant
d’ériger bâtisses et monuments démesurés, de mettre au point des moyens de transport et de
communication toujours plus puissants et rapides mais aussi des armes de destructions
capables de tout anéantir.
En 2012 l’UICN, Union International pour la Conservation de la Nature, a recensé
environ
30%
des
espèces
menacées
d’extinction
parmi
les
65000
étudiées
(http://www.iucnredlist.org). Dans le contexte du réchauffement climatique constaté au cours
du siècle dernier et directement imputé aux émissions de gaz à effet de serre résultant des
activités anthropiques, la crainte que de nombreuses espèces n’aient pas le temps de s’adapter
au « changement global » et que n’augmente le taux d’extinction des espèces déjà estimé 100
à 1000 fois supérieur au taux naturel (Pimm et al., 1995), mène à penser que nous assistons
peut-être actuellement à la sixième extinction de masse de l’histoire de la biosphère
(Barnosky et al., 2011) et que l’ère post-industrielle pourrait marquée le début d’une nouvelle
ère géologique : l’Anthropocène (Zalasiewicz et al., 2010; Barnosky et al., 2012).
20
Chapitre I -
Introduction générale
« Quel est la différence entre un pigeon ? »
(Michel Colucci, Qui perd perd, 1978)
21
Contexte socio-économique et historique
Au lendemain de la seconde guerre mondiale, pour subvenir aux besoins alimentaires
d’une population en pleine explosion démographique s’apprêtant à passer de 2.5 milliards à
plus de 6 milliards dans la seconde moitié du XXe siècle (INED, 2013), la révolution
industrielle s’étend aux systèmes agricoles. Des usines d’armement se voient alors recyclées
en usines d’engrais et de produits phytosanitaires, tandis que des tracteurs et ensileuses
remplacent les chars et autres machines de guerre en sortie de chaîne. Les systèmes agraires
traditionnels polyvalents, fonctionnant à la manière d’écosystèmes relativement clos et
autosuffisants, sont remplacés en quelques décennies par de plus grandes exploitations
mécanisées orientées vers la monoculture de variétés à haut rendement, gourmandes en eau et
consommatrices d’intrants (engrais et pesticides) principalement conçus à partir des énergies
fossiles. La mondialisation et les enjeux économiques ont rapidement répandu ce modèle à
travers le globe, généralisant ainsi les risques de pollutions et la modification des habitats liés
à ces pratiques (modification physique des rivières ou prélèvements de leurs eaux,
déforestation, etc.). L’agriculture, combinée aux activités industrielles et domestiques, utilise
plus d'un tiers des ressources d'eau douce accessibles et renouvelables de la Terre (environ
4.430 km3 / an en 2006) et cette consommation intensive est souvent liée à des
contaminations (Schwarzenbach et al., 2010). Environ 140 millions de tonnes d'engrais et
plusieurs millions de tonnes de pesticides sont appliquées chaque année (Schwarzenbach et
al., 2006). D'importantes pertes de biodiversité sont attribuées à l'intensification de
l’exploitation des terres arable au cours des 50 dernières années (Memmott, 2009), de même
que les activités anthropiques sont tenues responsables du déclin de nombreuses populations
d’amphibiens (Bridges et al., 2001).
L’écotoxicologie, une science à finalité environnementale
Pour faire face aux problèmes environnementaux et de santé publique relatifs à
l’utilisation de produits phytosanitaires, émergea une nouvelle discipline à l’interface entre
l’écologie et la toxicologie: l’écotoxicologie, dont le terme fut proposé pour la première fois
en 1969 par le Professeur René Truhaut, lors du Conseil International des Unions
Scientifiques tenu à Stockholm (Molle, 1984). Les sciences environnementales et
l’écotoxicologie jouent aujourd’hui un rôle essentiel dans la gestion des risques
environnementaux, en fournissant des outils pour les décisionnaires chargés de la gestion des
22
écosystèmes et de leur protection face aux potentiels effets indésirables des contaminants
issues des activités humaines. Conscients des risques liés à ces pollutions, notamment dans les
écosystèmes aquatiques, les gouvernements ont progressivement mis en place tout un panel
de législation. Dans l’Union Européenne, diverses directives ont ainsi vu le jour fixant des
seuils de contamination maximale tolérés dans les eaux de consommation (Directive CEE 80778), renforçant successivement les critères d’évaluation toxicologique et écotoxicologique
pour l’homologation et la mise sur le marché des anciennes et des nouvelles molécules
(Directive 79/117/CEE ; Directive 91/414/CEE ; système REACH ; Règlement (CE)
1107/2209), déterminant le cadre de leurs applications dans un contexte de développent
durable (Directive 2009/128/CE) et obligeant les Etats à atteindre des objectifs en matière de
qualité et « bonne santé » des eaux de surface et la préservation des nappes d’eaux
souterraines (Directive 2008/105/CE) ainsi que la mise en place de zones naturelles protégées
pour assurer le maintien de la biodiversité (Natura 2000).
L’Homme et la biodiversité : vers une durabilité des relations
Le concept de biodiversité, dans son sens le plus commun, désigne l’ensemble des
espèces vivantes ainsi que les communautés formées par celles-ci et les différents types
d’écosystèmes (Wilson & Peter, 1988). Chaque écosystème repose sur des relations
complexes entre les éléments biotiques et abiotiques. L’importance écologique de la
biodiversité se manifeste dans les interdépendances qui existent entre les différentes espèces,
assurant des services essentiels à la vie tels que la production de dioxygène et la fixation du
dioxyde de carbone de l’air, la filtration et la purification de l’eau, la pollinisation des plantes,
la décomposition des déchets et le transfert des substances nutritives dans les chaînes
trophiques. D’après la définition de la Convention sur la Diversité Biologique (CBD) de
1993, la biodiversité est définie comme la variabilité entre organismes vivants de toutes
origines, incluant entre autre, les écoystèmes terrestres, marins et autres systèmes aquatiques,
et les complexes écologiques auxquels ils participent ; cette définition inclut la diversité
génétique
intraspécifique,
interspécifique
et
au
sein
des
écosystèmes
(http://www.cbd.int/convention/articles). Le concept de biodiversité est également étroitement
relié à celui de service écosystémique, lui-même défini comme toute activité ou fonction d’un
écosystème bénéfique à l’humanité, incluant non seulement les services écosystémiques
terminaux (qui fournissent un gain ou une perte de bien-être aux populations à travers les
23
biens) mais aussi l’ensemble des processus écologiques qui conduisent à ces services, biens et
valeurs pour l’Homme. La biodiversité est soit considérée comme l’équivalent de l’ensemble
des services écosystémiques, soit comme un service écosystémique (valeur de conservation)
parmi les autres (Mace et al., 2012).
Dans ce contexte, l’Homme tente également de formaliser ses relations avec
l’environnement pour en accroître la durabilité, via l’évaluation du risque écologique, qui
estime la probabilité que des effets écologiques négatifs se produisent comme résultat de
l’exposition à un ou plusieurs agents de stress (Lackey, 1997). L’évaluation du risque repose
sur une stratégie générale de comparaison de concentrations d’exposition et d’effet en 4
étapes : identification du danger, évaluation de la relation « dose-réponse », évaluation de
l’exposition, et caractérisation du risque (Breitholtz et al., 2006).
Que ce soit en termes de services écosystémiques ou d’évaluation du risque écologique,
la biodiversité est donc un élément central à considérer. En particulier, la « variabilité
génétique intraspécifique » représente un niveau d’intérêt actuellement croissant en
écotoxicologie, car sa prise en compte dans l’évaluation du risque est supposée accroître la
pertinence écologique des estimations (Depledge, 1994; Forbes, 1998; Klerks, 2002). La
justification de cette prise en compte repose sur l’importance théorique des effets évolutifs
potentiels des polluants, et sur leur documentation empirique de plus plus riche (voir Bickham
2011 ; Coutellec & Barata 2011, et le numéro spécial d’Ecotoxicology dans son ensemble).
Pourquoi s’intéresser aux impacts évolutifs des polluants
Du point de vue scientifique, les arguments justifiant la prise en compte des effets
évolutifs des polluants peuvent être classés selon quatre critères :
Connaissance fondamentale : les environnements anthropisés constituent des
conditions d’évolution rapide et «en action» qui permettent de tester des hypothèses
fondamentales relatives aux processus micro-évolutifs ;
Précision des effets estimés : la variation génétique est une réalité
biologique qui, jusqu’à présent, a été sciemment ignorée dans les procédures d’évaluation
du risque, pour des raisons de contrôle de variabilité des réponses. Cependant, l’adaptation
évolutive, si elle se produit, a la capacité de modifier les attendus toxicologiques en terme
24
d’effet (courbe dose-réponse) et devrait donc être considérée a priori dans une démarche
d’évaluation ;
Pertinence biologique (i.e., éco-évolutive) des effets estimés : les conséquences
évolutives peuvent être plus dommageables à la fois sur le long-terme et dans un contexte
de stress multiples, que des effets purement démographiques ou des effets limités aux
individus exposés ;
Conservation de la biodiversité et des services qu’elle peut produire : tout
impact sur le niveau de variabilité génétique intraspécifique a des conséquences négatives
en termes de biodiversité, et doit donc pouvoir être considéré a priori, de manière à
pouvoir être estimé.
Dans cette thèse située à l’interface entre la biologie évolutive et l’écotoxicologie,
l’étude du potentiel évolutif des réponses au stress a été menée chez un mollusque
gastéropode hermaphrodite, la limnée des étangs, Lymnaea stagnalis. Elle porte plus
précisément sur les réponses moléculaires et phénotypiques vis-à-vis du stress généré par un
herbicide pro-oxydant (le diquat), chez un organisme non-cible. Ce travail repose sur
l’utilisation de méthodes de génétique quantitative, de génétique des populations et sur
l’intégration de données d’expression transcriptomique. Dans la suite de cette introduction,
nous définirons différents concepts issus de la biologie évolutive et de l’écotoxicologie, ainsi
que les modèles biologique et chimique utilisés, avant de présenter la démarche scientifique et
les hypothèses testées.
1.
Stress environnemental et impact évolutif des polluants sur les populations
naturelles
Les sources de stress environnemental sont multiples, que leur origine soit naturelle ou
anthropique, leur nature biotique ou abiotique. Le stress environnemental résulte des
variations spatiales et temporelles de l’environnement, susceptibles d’entraîner des
modifications biochimiques et physiologiques chez les organismes qui y sont soumis. La
valeur sélective ou fitness, c’est à dire la capacité des individus à transmettre leur génotype
aux générations ultérieures, peut aussi être affectée par le stress environnemental (Hoffmann
& Hercus, 2000; Meyers & Bull, 2002). Le contexte actuel de changement climatique et de
25
changements globaux intensifie les probabilités de mise en place de processus évolutifs au
sein des écosystèmes (Bijlsma & Loeschcke, 2012). En théorie, le stress environnemental
peut interagir avec l’ensemble des forces évolutives majeures et les mécanismes sous-jacents :
dérive génétique au hasard, consanguinité, flux génique, taux de mutation, sélection naturelle
(van Straalen & Timmermans, 2002). Le stress environnemental peut donc avoir des
conséquences évolutives rapides (Hoffmann & Hercus, 2000), en particulier dans les cas de
colonisation de nouveaux milieux, ou d’hétérogénéité environnementale associée à une
structure en métapopulation (Reznick & Ghalambor, 2001). Le stress environnemental peut
aussi accroître le taux même d’évolution phénotypique (Hendry et al., 2008). Pour cette
raison, les concepts et principes évolutifs sont de plus en plus intégrés aux problématiques
relatives aux sciences environnementales et à la biologie de la conservation (Smith &
Bernatchez, 2008; Bijlsma & Loeschcke, 2012).
Dans les milieux anthropisés, les polluants sont des sources potentielles de stress et
d’hétérogénéité environnementale, pouvant avoir diverses conséquences évolutives (Medina
et al., 2007). On peut les classer parmi 4 grands types : l’adaptation (sélection directionnelle
de mutations avantageuses), la dérive génétique au hasard, la mutation (Eeva et al., 2006;
Matson et al., 2006), et les effets trans-générationnels non génétiques (Vandegehuchte &
Janssen, 2011). Ces derniers, bien que transmis de façon non mendélienne classique, peuvent
toutefois avoir des conséquences évolutives (pour une revue détaillée, voir Bonduriansky &
Day 2009 ; voir aussi Mousseau et al., 2009), notamment en modifiant la réponse à la
sélection et sa dynamique (Kirkpatrick & Lande, 1989). Nous nous intéresserons de façon
plus détaillée aux effets de sélection et de dérive, dans un contexte d’exposition aux
contaminants.
1.1. Effets sélectifs, directs et indirects
Face à un stress, la réponse des populations peut reposer sur la plasticité phénotypique
des génotypes ou sur l’adaptation génétique. Cependant, bien que la plasticité elle-même ait
une base génétique et puisse donc être sélectionnée (voir Pigliucci, 2005), nous ne nous
intéressons ici qu’à l’adaptation génétique au sens classique du terme.
Bien que l’adaptation d’une population à un contaminant permette le maintien de la
fitness en présence de ce contaminant, le phénomène sélectif sous-jacent, s’il est directionnel
26
entraîne en théorie une baisse de la diversité génétique (la sélection épuise la diversité), qui
diminue le potentiel adaptatif des populations vis-à-vis de futurs stress environnementaux
(van Straalen & Timmermans, 2002; Jansen et al., 2011).
Les effets sélectifs des polluants sont aujourd’hui documentés par de nombreux cas
rapportés de résistance, par exemple aux métaux (Reznick & Ghalambor, 2001; Morgan et al.,
2007; Bourret et al., 2008) ou aux pesticides (Brausch & Smith, 2009; Coors et al., 2009) au
sein de populations naturelles soumises à des expositions récurrentes ou chroniques de
polluants d’origine industrielle ou agro-chimique. Parmi les substances émises dans
l’environnement par les activités humaines, les pesticides constituent un groupe de molécules
qui demande une attention particulière, car ils sont spécialement conçus pour avoir un effet
biologique négatif. Ils sont ainsi reconnus à juste titre comme des facteurs sélectifs potentiels
majeurs (Jansen et al., 2011).
L’apparition de résistances aux pesticides chez les espèces cibles est un phénomène
répandu, notamment dans le cadre de la lutte contre les adventices (Powles & Yu, 2010), les
moustiques (Tantely et al., 2010), les acariens (Umina et al., 2012) ou les rongeurs (Ishizuka
et al., 2008), pouvant conduire, en réponse, à l’augmentation des concentrations de traitement,
l’utilisation ponctuelle de molécules interdites et nécessitant la recherche perpétuelle de
nouvelles substances.
La résistance se définit comme un changement héritable dans une population, qui se
traduit par une meilleure aptitude à survivre ou à se reproduire en présence d’une condition
environnementale ayant auparavant eu un effet létal sur l’espèce à laquelle appartient cette
population. Pour qu’une résistance puisse évoluer et s’étendre dans une population, plusieurs
critères doivent être vérifiés, même s’ils ne sont pas forcément suffisants. En plus de la
variation génétique (condition de base), il faut notamment que : (i) l’intensité du stress soit
supérieure à la capacité de persistence des populations locales par pure plasticité ; (ii) la durée
d’exposition soit assez longue, relativement au temps de génération de l’espèce considérée ;
(iii) des génotypes résistants pré-existent, même en très faible nombre, et (iv) le flux de gènes
et la mobilité soient réduits.
L’apparition d’une résistance résultant de l’effet sélectif exercé par un contaminant
peut affecter d’autres traits, en raison de corrélation génétique entre ces traits et le trait
répondant à la sélection (Sgro & Hoffmann, 2004). Ces effets peuvent résulter de
déséquilibres de liaison entre gènes controllant les différents traits ou d’effets pléiotropiques.
27
La sélection opérée par un contaminant peut conférer une meilleure tolérance vis-à-vis
d’autres contaminants. On parle alors de résistance croisée ou de co-résistance (e.g., Brausch
& Smith, 2009) pouvant résulter de mécanismes communs de détoxication, telle que la
production accrue de métallothionéines en réponse au cadmium et au cuivre (Morgan et al.,
2007). Les corrélations génétiques peuvent aussi être négatives et se traduire par un coût de la
résistance. Ainsi, la résistance évolutive à un contaminant donné peut comporter un coût en
terme de fitness globale (Duron et al., 2006), ou vis-à-vis d’autres contaminants ou agents de
stress spécifiques (Salice & Roesijadi, 2009). Le coût d’une adaptation se mesure par la
baisse de performance observée chez les génotypes résistants lorsque la pression de sélection
à l’origine de l’adaptation est levée. Il en résulte que l’adaptation rapide à un nouveau stress
environnemental ne garantit pas la persistence des populations sur le long-terme.
Une augmentation de l’allocation d’énergie aux mécanismes de détoxication, si elle
reste physiologiquement constitutive en l’absence de stress, peut expliquer une diminution de
la fécondité des individus adaptés en l’absence de stress, notamment si l’énergie allouées aux
fonctions reproductives est en partie détournée.
La notion de coût de la résistance demeure difficile à démontrer empiriquement. Ainsi,
les exemples de coûts adaptatifs associés à la résistance aux pesticides sont peu nombreux.
Chez la rainette versicolore, Hyla versicolor, une baisse de la survie des têtards les plus
tolérants à l’insecticide Carbaryl a été constatée en l’absence de pollution (Semlitsch et al.,
2000). Toutefois, le fait que ce coût ne s’exprime que sous certaines conditions de stress
(densité individuelle élevée) montre l’importance des interactions entre facteurs de stress, que
ceux-ci aient un effet sélectif ou non. Chez l’adventice Ipomoea purpurea, les génotypes
tolérants au glyphosate présentent une fécondité plus faible en absence de l’herbicide
(Baucom & Mauricio, 2004). De même, la résistance aux pesticides des populations de la
tordeuse à bandes obliques Choristoneura rosaceana conduit à une diminution de la taille et
un allongement du temps de développement au stade nymphal comparativement aux
populations sensibles (Carriere et al., 1994). Le potentiel migratoire des génotypes tolérants
vers des zones non contaminées peut ainsi être limité en cas de diminution de la pression de
sélection, tandis que l’immigration de génotypes sensibles peut entraîner une baisse
progressive de la fréquence des génotypes résistants dans la populations (Mackie et al., 2010).
Cependant, les phénomènes de résistance ne sont pas toujours associés à un coût
(Lopes et al., 2008; Brausch & Smith, 2009), et peuvent même parfois induire une meilleure
28
fitness en l’absence de pollution (Arnaud et al., 2005). Il semblerait que l’existence de coût lié
à la résistance dépende principalement du mécanisme moléculaire impliqué dans l’apparition
de cette résistance. Ainsi, un coût maximal est attendu pour les mécanismes de désactivation
des récepteurs et les mutations structurelles dans les systèmes de détoxication (Taylor &
Feyereisen, 1996). Le coût éventuel peut être réduit si les mécanismes impliqués sont
uniquement sollicités lorsque le polluant est présent.
1.2. Effets non sélectifs : dérive génétique et processus associés
Le stress environnemental peut également avoir des conséquences évolutives de nature
non adaptative sur les populations. Du point de vue des polluants, les impacts évolutifs sont
de plus en plus documentés, et reflètent en effet aussi bien des processus adaptatifs que
l’augmentation des effets de dérive génétique au hasard (Barata et al., 2002; Matson et al.,
2006; Medina et al., 2007; Whitehead et al., 2012). A la base du second type d’effet, les
réductions démographiques potentiellement associées au stress (mortalité, réduction de la
fécondité) peuvent se traduire par une réduction de la diversité génétique des populations, qui
lorsque le stress est levé, ne peut se rétablir en absence de nouvelles mutations ou d’apport de
variation par flux de gènes (immigration), et ce, même si les effectifs de la population sont
reconstitués de façon intrinsèque (Fig 1).
Figure 1. Evolution de la démographie et de la diversité
génétique d’une population après un bottleneck. D’après
Bickham et al. (2000).
29
Ce type d’effet est proportionnel à la réduction démographique (la dérive est d’autant
plus forte que la taille de la population est petite), et concerne en premier lieu la variation
génétique neutre. A l’échelle de plusieurs populations, l’effet de dérive, de par sa nature
aléatoire, conduit en outre à la différenciation accrue des populations (Hartl & Clark, 1997).
Au-delà des effets sur les mutations neutres, la dérive peut aussi affecter les mutations
délétères spontanées, lorsque celles-ci on un faible effet individuel. Ces mutations peuvent,
par dérive soit disparaître, soit se fixer (i.e., se comporter comme si elles étaient neutres), et
conduire dans le second cas à une accumulation du fardeau de dérive locale au fil des
générations (Whitlock et al., 2000). L’importance de ce fardeau va dépendre de la relation
entre l’ampleur de la dérive et l’intensité de la sélection contre ces mutations (s). On
considère cet effet possible dès que |s| < 1/2Ne (Ne étant la taille efficace de la population).
Ainsi, la probabilité de fixation de mutations délétères augmente dans les petites populations,
car l’effet de la sélection contre elles est contrarié par celui de la dérive au hasard qui devient
prédominant. L’effet du stress environnemental, par sa capacité à amplifier l’expression
délétère des mutations (Szafraniec et al., 2001), peut donc être amplifié dans des conditions
qui favorisent l’accumulation de ces mutations ou l’augmentation de leur fréquence sous
forme homozygote, i.e., respectivement la petite taille des populations et la consanguinité.
L’expression accrue de la dépression de consanguinité en conditions de stress est bien
documentée (Armbruster & Reed, 2005; Bijlsma & Loeschcke, 2012), tandis que l’effet du
stress sur l’hétérosis entre populations (meilleure fitness des hybrides, mettant en évidence
l’existence de fardeau dans les populations parentales) semble plus difficile à montrer (mais
voir Coutellec & Caquet 2011).
Les conditions environnementales où les sources de stress sont multiples et
récurrentes, comme par exemple dans un contexte d’application répétée de traitements
pesticides variés (programmes protection des cultures, itinéraires techniques), qui peuvent être
transmis aux pièces d’eau voisines des parcelles traitées par diverses voies (dérive
atmosphérique, ruissellement, drainage), peuvent donc conduire à une accumulation du
fardeau génétique dans les populations d’organismes aquatiques non-cibles. Ces effets
peuvent être amplifiés par les réductions démographiques occasionnées, et par l’isolement des
populations résultant de la dégradation/fragmentation de l’habitat.
30
La conséquence générale de ces effets de dérive est une perte d’adaptabilité génétique
aux changements environnementaux dans les petites populations (Willi et al., 2006), qui peut
à l’extrême conduire à l’extinction de celles-ci. Ce scénario est d’autant plus probable que les
changements environnementaux sont imprédictibles (limitation de la sélection en faveur des
mutations avantageuses) et multiples.
2.
Etude des mécanismes évolutifs intra-spécifiques
L’étude des mécanismes micro-évolutifs repose principalement sur la mesure de
« traits d’histoire de vie » (THV) en lien avec la valeur sélective des populations et / ou sur
l’utilisation de marqueurs neutres. Ces approches renseignent respectivement sur la diversité
génétique additive et de la diversité génétique neutre.
Estimation de la diversité génétique neutre des populations
Les méthodes moléculaires permettant d’estimer la diversité génétique neutre se
focalisent sur le polymorphisme observable dans des régions non codantes du génome, et
n’ayant a priori pas d’impact sur la fitness des individus. Elles utilisent des marqueurs, dits
« neutres » car supposés affectés uniquement par la dérive génétique au hasard, permettant de
différencier les individus au niveau de motifs particuliers telles que les séquences
microsatellites (séquences di- ou tri-nucléotidiques répétées de longueurs variables). Ces
marqueurs moléculaires sont typiquement utilisés en génétique des populations, en raison de
leur caractère codominant et de leur variabilité élevée (Jarne & Lagoda, 1996). L’analyse des
fréquences alléliques et génotypiques permet d’estimer divers indices de diversité génétiques
et paramètres de génétique des populations, tels que l’hétérozygotie attendue sous l’équilibre
de Hardy-Weinberg, la diversité génétique, la richesse allélique, le coefficient de
consanguinité (FIS), la taille efficace des populations (Ne) ou encore la différenciation
génétique des populations (FST). L’analyse de la structure génétique est classiquement fondée
sur l’estimation des indices F de Wright (FST, FIT, FIS) à partir d’une décomposition de la
variance des fréquences alléliques en composantes inter-échantillon, inter-individuelle intraéchantillon, et intra-individuelle (Weir & Cockerham, 1984). Les marqueurs neutres
codominants sont également appropriés pour l’étude des systèmes de reproduction
31
hermaphrodites, notamment via l’estimation des paramètres relatifs à l’autofécondation
(Ritland, 2002; David et al., 2007).
Estimation de la variation génétique quantitative
L’expression phénotypique des traits quantitatifs (codés par plusieurs gènes) est le
résultat de l’influence du génotype et de celle de l’environnement. Cette relation est à la base
de la génétique quantitative, et de ses applications à l’écologie évolutive (Lynch & Walsh,
1998), et en écotoxicologie (Klerks et al., 2011). L’estimation de la part héritable (variance
génétique additive) de ces traits permet d’aborder les questions d’évolution relatives à
l’adaptation, et en particulier d’en inférer le potentiel adaptatif des populations vis-à-vis du
changement environnemental (e.g., Hoffmann & Sgrò, 2011).
Trois types d’approches de génétique quantitative peuvent être utilisés dans l’étude de
l’évolution des résistances. Une première approche d’« évolution expérimentale » consiste à
appliquer la sélection en laboratoire et à déterminer après plusieurs générations si le
développement de résistances est possible au sein d’une population et si tel est le cas, à quelle
vitesse elles apparaissent. Ce type d’approche utilise généralement des espèces modèles
issues de populations de laboratoire à cycle de vie court (e.g., résistance au cadmium chez
Drosophilia melanogaster ; Shirley & Sibly, 1999). Une deuxième approche est de déterminer
si la population présente de la variation génétique pour
la sensibilité vis-à-vis d’un
contaminant, et si elle possède la variation nécessaire pouvant conduire à une réponse à la
sélection. Ce type d’approche, généralement expérimental, repose sur des protocoles de
génétique quantitative classique permettant d’estimer l’héritabilité du ou des traits focaux
(voir ci-dessous). Cependant, ces estimations peuvent également être estimées en milieux
naturel, via l’utilisation de matrices d’apparentement génétique et de variation phénotypique
observée (Ritland, 2000). Un troisième type d’approche vise également à estimer l’action
passée de la sélection sur les populations naturelles, et repose sur la décomposition de la
variance génétique additive des traits d’intérêt en composantes intra et inter-populations, à
partir de protocoles de mesures de normes de réaction en conditions communes controlées
(common garden). Bien qu’encore peu appliqué au contexte écotoxicologique (voir JimenezAmbriz et al., 2007), ce type d’approche semble approprié à la recherche de patrons de
sélection en lien avec l’exposition chronique et historique (multi-génération) à des polluants.
32
L’héritabilité
L’adaptation génétique est le résultat de la sélection agissant sur des traits héritables,
et qui favorise au cours des générations les allèles conférant une meilleure valeur de trait dans
l’environnment considéré. Dans une population, l'héritabilité au sens strict d’un caractère
désigne la proportion de variation génétique additive relative à la variation phénotypique
globale de ce caractère (i.e., qui résulte de l’effet additif des allèles codant pour ce trait, seule
part transmissible à la génération suivante).
L’héritabilité (h²) d’un trait correspond au ratio de sa variance génétique additive (VA)
sur la variance phénotypique totale (VP) observée dans la population (Lynch & Walsh, 1998).
VP comprend la variance génétique (VG) et la variance liée à l’environnement (VE),
théoriquement transmise et non-transmise aux générations suivantes, respectivement. VG
quant à elle, comprend trois composantes : la variance additive (Va), la variance de dominance
(Vd) et la variance d’interaction (Vi).
Ce paramètre peut varier en fonction du trait considéré et est spécifique d’une
population dans un environnement donné (Lynch & Walsh, 1998). Pour estimer au mieux la
part de variance de chacune de ces composantes, le protocole expérimental doit reposer sur
l’élevage ou la culture contrôlé d’un nombre important de lignées d’individus en conditions
standardisées. Le protocole de type half-sib permet de découpler l’effet maternel et l’effet de
dominance, de l’effet famille (Figure 2).
Figure 2. Exemple de paternal half-sib mating design. Chaque mâle se
reproduit avec plusieurs femelles non apparentées (d’après Lynch & Walsh
1998).
33
Il permet de prendre en compte les différents niveaux de relations familiales (pèreprogéniture, mère-progéniture, pleins-frères, demi-frères, intra- et inter famille, etc.) et
l’héritabilité est alors estimée de la façon la plus précise « narrow sense heritability » (h²). Ce
type d’analyse a notamment été testé chez des amphibiens démontrant un certain potentiel
adaptatif chez Rana sphenocephala vis-à-vis d’un stress induit par l’insectide carbaryl
(Bridges et al., 2001), ainsi que chez Hyla versicolor, avec la mise en évidence d’un trade-off
entrainant une baisse de survie des têtards en condition non stressante (Semlitsch et al., 2000).
Cependant, la séparation totale des effets additifs et non additifs, n’est pas toujours réalisable
pour des raisons biologiques (croisements multiples parfois impossibles) ou de faisabilité
technique. Des protocoles plus simples d’estimation peuvent alors être mis en œuvre, (full-sib
design) conduisant à une estimation plus grossière (héritabilité au sens large, « broad sens
heritability », H²), qui tend à surestimer la variance génétique en incluant les effets maternels
et d’environnement commun (Lynch & Walsh, 1998; Pakkasmaa et al., 2003). Plus h² (ou
H²) est élevée pour un trait, plus sa réponse à la sélection sera forte.
Common garden experiment et normes de réaction
Le design experimental en common garden consiste à mesurer la variation
phénotypique en laboratoire ou en milieu semi-naturel, sur des individus F1 issus d’adultes
sauvages échantillonnés dans différentes populations naturelles. Ce protocole peut être enrichi
en imposant plusieurs conditions contrôlées à chacune des lignées étudiées, permettant alors
de mesurer une norme de réaction (Stearns, 1989) vis-à-vis du facteur testé, à l’échelle
familiale (par exemple, témoin vs. contamination par un polluant à différentes
concentrations). Ces plans expérimentaux permettent d’établir des normes de réaction
correspondant à la gamme de phénotypes possibles pour un génotype ou une population dans
des conditions environnementales différentes.
Figure 3. Représentation schématique de différents cas de
normes de réaction, d’après Pigliucci (2001).
34
Des interactions génotype x environnent (G x E) ou de la plasticité phénotypique
peuvent ainsi être mis en évidence (Pigliucci, 2001). Si aucune différence phénotypique n’est
constatée pour un génotype malgré les changements de conditions environnementales, il n’y a
pas de plasticité phénotypique détectée comme c’est le cas pour les deux génotypes
représentés dans la figure 3a. Lorsque les normes de réaction sont parallèles et que les pentes
sont non nulles, la réponse phénotypique induite par le stress est due à la plasticité (b) tandis
que si les pentes sont différentes, les différences phénotypiques observées ont une base
génétique (G x E) pouvant mettre en évidence des processus de sélection passés (c). Ce type
d’approche a notamment été utilisé pour comparer la fitness de populations de Styela plicata,
un invertébré marin soumis à différentes concentrations de cuivres (Galletly et al., 2007), ou
encore chez Lymnaea stagnalis pour tester l’impact de l’exposition parentale à des mélanges
de pesticides sur divers traits d’histoire de vie de la descendance, en fonction d’un gradient de
stress biotique (Coutellec et al., 2011).
Cependant, la divergence entre populations peut simplement résulter d’effets liés à la
dérive génétique au hasard (isolement par la distance). Cette hypothèse peut être testée par
l’approche QST - FST (Leinonen et al., 2008; Whitlock, 2008).
Recherche de patrons de sélection, la comparaison QST vs. FST
Cette méthode permettant de tester l’hypothèse d’adaptation locale entre des
populations issues de milieux contrastés du point de vue d’un stress d’intérêt, consiste à
comparer le niveau de différenciation génétique neutre estimé à partir de marqueurs neutres
(indice FST ; Wright 1952) avec la variation observée pour des traits quantitatifs reflétant la
variation génétique additive globale (QST ; Spitze 1993). Dans la littérature, l’estimation des
valeurs de QST repose principalement sur des mesures de traits d’histoire de vie réalisées sur
la descendance issue de populations naturelles et élevée en conditions de common garden.
L’étude de la descendance de ces populations en laboratoire permet d’écarter des biais
potentiels dus aux effets directs de l’environnement d’origine ainsi que d’éventuels effets
génétiques non additifs et effets maternels (O'Hara & Merila, 2005). Les indices FST et QST
sont comparables car ils sont estimés selon des calculs similaires (Whitlock, 2008).
35
Pour le calcul de QST, l’équation est la suivante:
QST trait =
Vb
(V b + 2V w )
où Vb correspond à la variance génétique entre les populations (between) et Vw à la variance
génétique intra-population (within) (Spitze, 1993; O'Hara and Merila, 2005). Pour chaque
trait, les variances intra- et inter-population sont extraites de l’analyse statistique des traits
basée sur des modèles linéaires mixtes généralisés (GLMM) (Pinheiro & Bates, 2002). Vb est
obtenue directement à partir de la valeur de la composante de variance estimée pour l’effet
population et Vw est obtenue à partir de la composante de variance estimée pour l’effet
famille, qui est multiplié par 2 dans le cadre de designs en full-sib ou par 4 dans le cadre de
designs en half-sib (Lynch & Walsh, 1998).
Dans la méthode développée par Whitlock & Guillaume (2009), la différence observée
entre les valeurs estimées des indices QST et FST à partir des composantes de variance de FST
(Weir & Cockerham, 1984) est comparée par bootstrap avec la distribution théorique attendue
sous l’hypothèse de neutralité (la dérive génétique au hasard est suffisante pour expliquer les
patrons de divergence observés pour les traits quantitatifs). En l’absence de sélection, les
valeurs de QST et de FST sont donc censées être similaires et si la valeur observée n’est pas
significativement différente des valeurs attendues, le test conclut que le trait est
principalement soumis à la dérive génétique (neutralité, FST = QST). En revanche, des écarts
significatifs à la distribution attendue sous l’hypothèse neutre indiquent une sélection vers un
optimum commun aux différentes populations (FST > QST , sélection homogenéisante) tandis
que QST > FST indique que l’effet de la sélection vers différents optima est supérieur à celui de
la dérive génétique, ce qui induit une sélection divergente entre les populations (Spitze, 1993;
O'Hara & Merila, 2005; Whitlock, 2008; Lamy et al., 2012).
Cette approche est classiquement utilisée en biologie évolutive (Evanno et al., 2006;
Johansson et al., 2007; Eroukhmanoff et al., 2009; Nemec et al., 2012; Rogell et al., 2012).
Elle a notamment permis de démontrer chez le gastéropode d’eau douce Galba truncatula,
une divergence adaptative entre des populations naturelles occupant des habitats temporaires
et permanents (Chapuis et al., 2007), ou encore la sélection vers différents optima selon la
pression sélective issue de l’acidification des habitats chez la grenouille des landes Rana
arvalis (Hangartner et al., 2012). Egalement appliquée en écotoxicologie, elle a permis la
36
mise en évidence d’une sélection divergente induite par une pollution au zinc chez Thlaspi
caerulescens (Jimenez-Ambriz et al., 2007).
La recherche de traits d’histoire de vie corrélés à l’évolution de résistance et de coûts
potentiels pour la fitness des génotypes résistants, est importante pour avoir une
compréhension globale des mécanismes évolutifs à l’origine des patrons observés.
L’approche écotoxicogénomique peut alors apporter des explications sur les mécanismes
moléculaires impliqués dans l’acquisition de la résistance, d’éventuels trade-offs et des effets
pléiotropiques.
3.
Séquençage de Nouvelle Génération, l’écotoxicogénomique à la portée des
espèces « non modèles ».
L’écotoxicogénomique
L’écotoxicogénomique correspond au domaine utilisant les techniques permettant
l’exploration
des
génomes
et
des
réponses
transcriptomiques,
protéomiques,
et
métabolomiques dans un contexte écotoxicologique. Elle est définie comme l’étude chez des
organismes non-cibles de l’expression génique et protéique contribuant à la réponse des
organismes non-cibles aux toxiques environnementaux (Snape et al., 2004).
Le terme génomique émergea au milieu des années 80 et se trouve depuis en
perpétuelle évolution. A mesure que la compréhension des processus moléculaires et
cellulaires avance, le champ des problématiques de recherche permettant d’estimer les effets
induit par des toxiques sur les organismes s’élargit. Etudier les réponses individuelles au
stress à un seul niveau de l’organisation biologique ne donne que peu d’informations sur la
façon dont les organismes gèrent la réponse au stress mais l’intégration de réponses à
différents niveaux favorise une compréhension plus globale. Les biomarqueurs moléculaires
correspondent au niveau d’organisation biologique le plus élémentaire dans l’investigation de
la réponse au stress. Les réponses moléculaires sont à la source d’une cascade de réactions
dans l’organisme résultant de l’altération des conditions environnementales. Cette cascade se
répercute ensuite à des niveaux d’organisation biologique plus élevés, depuis la survie et le
succès reproducteur des individus jusqu’aux conséquences au niveau de la population et de la
communauté (Morgan et al., 2007). La connaissance des conséquences phénotypiques du
stress couplée aux composantes transcriptomiques induites ou réprimées rend possible une
caractérisation plus fine des modes d’action des agents stressants, mais aussi des mécanismes
37
pouvant affecter la croissance, la survie et la reproduction (Heckmann et al., 2008).
Cependant, la complexité des processus d’expression génique incite à la prudence dans les
conclusions tant il est difficile de maîtriser tous les facteurs génétiques et épigénétiques de
régulation agissant et interagissant à trois niveaux, la transcription, la traduction, et le
repliement protéique. (Fig.4).
Figure 4. Représentation schématique des différents éléments de régulation de l’expression
génique. CDS, séquence codante; Co-A/I, co-activateur/inhibiteur; EBS, Site de fixation de
l’amplificateur de transcription ; EN, amplificateur de transcription, M, modification, Me,
méthylation ; S, stabilisateur d’ARNm; TF, facteur de transcription; TFBS, site de fixation du facteur
de transcription, TM, inhibiteur de traduction; Ub, ubiquitine (d’après Morgan et al., 2007).
Il y a maintenant une décennie, des chercheurs ont pu mettre en évidence chez D.
melanogaster, qu’en réponse à un stress, la régulation de la réponse transcriptomique
résulterait de modifications physiques locales de la chromatine changeant l’accessibilité et
l’expression de gènes regroupés en clusters, ce qui indiquerait l’existence de « territoires de
transcription » (Spellman & Rubin, 2002; Weitzman, 2002). Disposant du génome complet,
ils ont pu démontrer que certains groupes de gènes physiquement proches sur les
chromosomes, présentaient des profils d’expression comparables en fonction des conditions
environnementales. Ces groupes peuvent compter une dizaine de gènes adjacents voire plus
et de tels clusters d’expression pourrait concerner 20 % du génome codant. Cela montre
comment la génomique peut fondamentalement changer la façon d’appréhender la relation
entre les gènes et leur environnement (van Straalen & Roelofs, 2006).
38
Next Generation Sequencing (NGS)
Grâce aux progrès techniques et informatiques, l’avènement des technologies de
séquençage haut-débit de dernière génération (NGS) a révolutionné les méthodes de
séquençage traditionnel (type Sanger) au début des années 2000 élargissant ainsi la portée de
ce champs disciplinaire aux problématiques phylogéniques, évolutives, écologiques ou
écotoxicologiques. Jusqu’alors restreinte aux espèces modèles du point de vue du génome,
telles que Drosophilia melanogaster, Arabidopsis thaliana, Saccharomyces cerevisiae,
Caenorhabditis elegans, Mus musculus ou encore Escherichia coli, l’acquisition de données
génomiques et transcriptomiques était en plus très longue et coûteuse, nécessitant l’appui de
grands consortiums (van Straalen & Roelofs, 2006). Les NGS regroupant les technologies de
pyroséquençage 454, les systèmes Illumina ou SOLID, associées aux différentes méthodes
statistiques et bioinformatiques d’assemblages, de comptage et d’annotations, ont
considérablement diminué les coûts associés à ces projets, permettant ainsi de les appliquer
aux espèces non-modèles (Wang et al., 2009). Ainsi, d’après la Genomes OnLine Database
(GOLD) créée en 1997, 20327 projets étaient enregistrés en octobre 2012, parmi lesquels
environ 4000 sont déjà complets (http://www.genomesonline.org) (fig.5). Leur nombre en
nette croissance depuis 2008 est à corréler à une diminution du coût de tels projets d’une part
mais également à l’intégration dans GOLD depuis 2009, des projets de séquençage de
transcriptome, de métatranscriptome et de re-séquençage de génome, en plus des projets de
génomique et de métagénomique (Pagani et al., 2012).
Figure 5. Evolution du nombre des projets de génomiques référencés dans la base de
données GOLD en fonction des groupes phylogénétiques. 20327 projets depuis sa création
jusqu’à octobre 2012. B, Bactéries ; A, Archeabactéries ; E, Eucaryotes, M, Mammifères. source :
http://www.genomesonline.org
39
Cette révolution de la génomique n’est pas seulement due aux avancées en biologie
moléculaire. Les progrès des nanotechnologies, notamment grâce à la technologie laser, ont
ouvert les portes d’un « nanomonde ». De plus, des technologies informatiques d’analyse se
sont développées en conséquence pour s’adapter aux procédures d’assemblage basées sur des
programmations complexes et nécessitant de grandes puissances de calcul. Enfin, les
technologies de communication ont joué un rôle primordial pour la consultation des bases de
données génomiques nécessaires aussi bien pour l’assemblage que pour l’annotation.
L’utilisation d’outils tels que les bases de données GO (Gene Onthology) et KEGG (Kyoto
Encyclopedia of Genes and Genomes) permettent de raisonner à plus large échelle en termes
de processus moléculaires, cellulaires, métaboliques et de replacer les résultats obtenus dans
le contexte des voies de signalisation déjà connues et partiellement définies.
Dans une approche transcriptomique utilisant la technologie RNAseq, la génération de
séquences courtes de ~100pb augmente la profondeur d’analyse. l’intérêt est non seulement
de caractériser les gènes exprimés, mais aussi de quantifier leur expression de façon absolue
ou relative (Wang et al., 2009). Elle permet en quelque sorte d’obtenir une photo instantanée
du transcriptome, c'est-à-dire obtenir la quasi-totalité de l’activité transcriptionnelle d’un
génome à un instant t et dans une condition donnée.
Potentiel des NGS en écotoxicologie évolutive
Les possibilités d’investigation de la variation génétique naturelle (à de multiples
niveaux de variation) permises par les NGS, sont une source d’information sans précédents
(Gilad et al., 2009). Cependant, la technologie RNAseq étant relativement récente, peu
d’applications en écotoxicologie et en biologie évolutive ont déjà été publiées, mais de
nombreux projets utilisant les NGS émergent actuellement ou sont en cours d’avancement.
On peut toutefois mentionner l’acquisition récente de ressources transcriptomiques couplées à
deux études d’expression différentielle en présence ou en absence de stress thermique chez la
moule d’eau douce Villosa lienosa (Wang et al., 2012) et chez le copépode Tigriopus
californicus suggérant des divergences génétiques adaptatives entre deux populations
naturelles (Schoville et al., 2012). A noter également l’étude de De Wit et Palumbi (2012)
explorant la structure génétiques entre trois populations naturelles d’ormeaux Haliotis
rufescens, via la recherche de polymorphisme SNPs (Single Nucleotide Polymorphism) le
long d’un gradient thermique.
40
4.
Le modèle biologique : Lymnaea stagnalis
Dans cette thèse, l’étude du potentiel évolutif des réponses au stress est réalisée chez
la limnée des étangs, Lymnaea stagnalis, un gastéropode pulmoné d’eau douce occupant des
habitats lentiques. De par sa large distribution, L. stagnalis est une espèce représentative des
milieux lentiques d’eau douce et de la communauté herbivore invertébrée. Les escargots d’eau
douce sont estimés représenter 20 à 60 % de la biomasse totale de la communauté invertébrée
dans ces milieux où ils jouent un rôle essentiel dans la chaine alimentaire (Habdija et al.,
1995). De telles espèces sont considérées comme étant de bons modèles pour estimer l’impact
évolutif de pollutions agricoles liées à l’usage de pesticide (Coutellec et al., 2011). En effet,
les milieux aquatiques lentiques localisés au sein des paysages agricoles sont susceptibles
d’être exposés de façon récurrente à la contamination par les pesticides, via des processus
variés tels que la dérive durant l’application, le ruissellement et le drainage (Liess, 2002;
Brown & van Beinum, 2009). Les espèces non-cibles occupant ces milieux sont par
conséquent exposées à un risque important d’impact évolutif. Ce risque est d’autant plus
important pour les espèces habitant des mares, fossés ou petits étangs et pour lesquelles le
cycle complet est aquatique (Coutellec et al., 2011). En effet, d’une part l’absence de flux
génique entre populations facilite en théorie les processus adaptatifs locaux, et d’autre part, la
petite taille des populations augmente les effets de dérive génétique (Richards, 2000;
Whitlock et al., 2000). Le patron de sélection opérant sur ces populations naturelles dépend
donc de l’intensité relative de ces forces, sachant que la sélection est moins efficace dans les
petites populations (Keller and Waller, 2002), leur potentiel adaptatif vis-à-vis du
changement environnemental étant limité (Willi et al., 2006).
D’un point de vue taxonomique, L. stagnalis appartient au groupe des gastéropodes
hermaphrodites, sous-ordre des Basommatophores (Escobar et al., 2011). Bien que sa
préférence pour la fécondation croisée ait été démontrée à plusieurs reprises, la dépression de
consanguinité chez cette espèce semble très réduite (Puurtinen et al., 2007; Coutellec &
Caquet, 2011).
L. stagnalis est utilisée comme organisme modèle dans divers domaines de recherche
incluant, l’écologie comportementale (Hoffer et al., 2010), la biologie évolutive (Coutellec
and Caquet, 2011), la parasitologie (Adema et al., 1994), l’écotoxicologie aquatique
(Coutellec & Lagadic, 2006; Coutellec et al., 2011; Byzitter et al., 2012) et la
neurophysiologie (Carter et al., 2006). Au début de ce projet de thèse, les ressources
41
génétiques disponibles pour cette espèce comprenaient deux banques EST produites par
séquençage de type Sanger (Davison & Blaxter, 2005; Feng et al., 2009), ainsi qu’un jeu de
392 séquences nucléotidiques (Genbank) et 15 loci microsatellites (Knott et al., 2003; Kopp
& Wolf, 2007). Un consortium visant à séquencer son génome était également en construction
(voir http://www.lymnaea.org). De plus, la souche Renilys® élevée dans l’unité expérimentale
(U3E) de l’INRA de Rennes est actuellement candidate en tant qu’espèce modèle pour un test
de reprotoxicité standardisé de l’OCDE.
5.
Stress d’intérêt : le stress oxydant
Un rôle évolutif primordial et paradoxal
Il y a environ 2,45 milliards d’années commence une augmentation de la concentration
en dioxygène (O2) dans l’atmosphère initiée par l’activité photosynthétique des premières
cyanobactéries dans les océans, pour se stabiliser en quelques 100 millions d’années au 21%
que nous connaissons actuellement (Sessions et al., 2009). Le stress oxydant généré par ce
nouveau « polluant » a provoqué des ravages considérables sur les premières formes de vies
anaérobies, favorisant la sélection des espèces aptes à développer des systèmes de défense
appropriés à l’aérobiose. Ces systèmes antioxydants ont ensuite évolué au gré de la
complexification d’une partie du monde vivant optant pour la respiration. Grâce à
l’endosymbiose de protéobactéries ancestrales à l’origine des mitochondries, les premiers
organismes eucaryotes ont pu tirer profit de l’apport d’énergie issue du métabolisme de l’O2,
tout en se protégeant de ses dérivés pro-oxydants générés au cours des différentes étapes de sa
réduction (ROS pour reactive oxygen species), pouvant nuire à l’intégrité et aux fonctions des
macromolécules (protéines, phospholipides, ADN, etc.) (Costantini et al., 2010). Cependant,
le rôle des ROS est paradoxal. Bien que toxiques et responsable du vieillissement cellulaire
(Junqueira et al., 2004), leur forte réactivité chimique joue un rôle essentiel dans la
communication intracellulaire, une certaine concentration minimale étant même nécessaire
pour maintenir l’homéostasie et le bon fonctionnement cellulaire (D'Autreaux and Toledano,
2007). Environ 90% des ROS générées au niveau intracellulaire proviennent de la respiration
mitochondriale, les 10 % restants étant produits par des cyclo-oxydases cytosoliques, des
NADPH transmembranaires ou dans les peroxisomes au cours du métabolisme des lipides
(Balaban et al., 2005). Au niveau physiologique, les ROS interviennent aussi dans de
nombreux processus, tels que la régulation de la circulation sanguine, la relaxation des
42
muscles lisses ou les défenses immunitaires (Dröge, 2002) (Fig. 6). Dans des conditions
environnementales stressantes, la perturbation de l’équilibre redox dans les tissus peut être à
l’origine de nombreux dysfonctionnements. Le stress oxydant est une source potentielle de
trade-offs pour la valeur sélective façonnant depuis son apparition les multiples chemins de
l’évolution (Monaghan et al., 2009). Pour se prémunir des cascades réactionnelles
d’oxydation induites par le non-contrôle des ROS, les organismes aérobies ont développé des
systèmes enzymatiques et non-enzymatiques.
Elimination
Production
Chaîne de
Transport
d’électrons
Neutralisants
de faible masse
moléculaire
Oxygénases
Auto-oxydation
Etat d’équilibre
du niveau de ROS
Systèmes
spécifiques
(NADPH-oxydase
NO-synthase)
Effets /
Fonctions
Conséquences
Antioxydants
de forte masse
moléculaire
Détérioration des
constituants
cellulaires
Régulation des
processus
cellulaires
Attaque de
corps étrangers
Lésion cellulaire,
apoptose, nécrose
Adaptation aux
changements internes
et environnementaux
Protection de
l’organisme contre
l’infection
Figure 6. Schéma des mécanismes impliqués dans la production, l’élimination des
ROS ainsi que de leurs potentiels effets biologiques (d’après Luschback 2011).
Les systèmes de défense antioxydants
Les systèmes de défense contre les ROS incluent des enzymes anti-oxydantes telles
que la superoxide dismutase (SOD), qui réduit l’anion superoxyde O2.- en peroxyde
d’hydrogène H2O2, à son tour réduit par la catalase (Cat) ou la glutathion péroxidase (GPx) en
H2O et O2. La GPx, utilisant le glutathion comme co-faceur, participe au cycle du glutathion
impliquant également la glutathion réductase (GRed) et les glutathion S-transferases (GSTs),
43
enzymes de détoxication secondaire (Lushchak, 2011). Selon le tissu et l’espèce, le facteur de
transcription RXR (récepteur X de rétinoïdes) est reconnu pour jouer un rôle dans la réponse
au stress oxydant, notamment en activant des réponses de détoxication de phase II (Kang et
al., 2005), la production de facteurs anti-apoptotiques ou encore l’activation de catalase (Shan
et al., 2008). Toutes ces enzymes et protéines sont donc des biomarqueurs d’effets potentiels
pour l’étude du stress oxydant.
Les dommages causés aux protéines nécessitent l’intervention de protéines chaperons
telles que les Heat Shock Proteins (HSPs) pour préserver leur activité fonctionnelle, prévenir
leur agrégation et faciliter leur dégradation en cas de dommage irréversible (Qiu et al., 2006;
Kim et al., 2007). On peut citer notamment HSP70 qui est la protéine chaperon la plus étudiée
chez les invertébrés, impliquée dans la réponse à de nombreux types de stress, elle est utilisée
comme biomarqueur de toxicité (Lee et al., 2006; Park et al., 2009; Park et al., 2010). De par
leur rôle vital dans la réponse physiologique au stress, les HSPs ont été fortement conservées
au cours de l’évolution. Leurs activités réparatrices diminueraient ou amplifieraient les
variations phénotypiques en fonction de leur quantité et de l’intensité des stress. En assurant
le bon repliement fonctionnel des protéines malgré la présence éventuelle de mutations, les
protéines chaperons contribuent au maintien du polymorphisme génétique dans les
populations quand les conditions du milieu sont normales. C’est en particulier le cas de
HSP90, connue pour réparer lors du développement l’effet de mutations (voir Yeyati et al.
2007). Ces variations augmentent alors la capacité d’expression de caractères phénotypiques
héritables adaptés en cas de changements environnementaux (Rutherford, 2003). En cas de
stress oxydant, elles participent donc activement à la réponse en limitant l’accumulation de
protéines dénaturées potentiellement cytotoxiques. La dégradation et le recyclage des
protéines impactées sont assurés dans des péroxisomes susceptibles de proliférer en cas de
stress (Schrader & Fahimi, 2006; Aiken et al., 2011).
Lorsque l’intensité du stress dépasse le seuil de tolérance des cellules, les processus
apoptotiques constituent souvent le dernier rempart face à la nécrose des cellules qui
engendrent des effets en cascade sur leurs cellules adjacentes. L’apoptose est un mécanisme
de mort cellulaire programmée dont les caractéristiques physiologiques et biochimiques
présentent également de fortes homologies entre les espèces. De l’équilibre redox des cellules
dépendent l’induction ou l’inhibition de ces mécanismes, le système antioxydant et les ROS
agissant à de nombreux points clés dans la signalisation de ces processus (Kannan & Jain,
44
2000; Alberts et al., 2008). Les dysfonctionnements de ses systèmes sont à l’origine de
nombreuses pathologies tels que des cancers, des déficiences immunitaires ou encore des
maladies neuro-dégénératives (Junqueira et al., 2004; Balaban et al., 2005).
La toxicité de nombreux polluants tels certains produits pharmaceutiques, métaux
lourds ou autres pesticides pouvant se retrouver dans les milieux aquatiques, est induite par du
stress oxydant (Scandalios, 2005; Valavanidis et al., 2006).
Le diquat, un herbicide générateur de stress oxydant.
Le diquat est un herbicide non sélectif de la famille des bipyridyles (Fig. 7). Il fait
partie de la première génération d’herbicides synthétiques commercialisés dans les années
1940 (Karuppagounder et al., 2012). Depuis, de
nombreuses formulations contenant cette substance active
seule ou combinée à d’autres agents similaires tels que le
paraquat, sont utilisées en agriculture, notamment pour le
désherbage dans les vignes, le défanage des cultures de
pomme de terre ou encore la récolte de cultures portegraines (betterave, colza, tournesol) (US-EPA, 1995;
Ritter et al., 2000; FAO, 2008; French Ministry of
Agriculture, 2012).
Figure 7. Représentation du
dibromure
de
diquat.
(Br2C12H12N2)
En France, il est commercialisé par Syngenta sous la marque Reglone 2® et utilisé en
association avec l’Agral 90, un adjuvant tensioactif à base de nonylphénols polyéthoxylés. En
moyenne, 90% de sa production est utilisé en Amérique du Nord, Europe, Australie et Japon
bien qu’il soit considéré comme un toxique majeur et suspecté responsable de nombreux cas
de Parkinson parmi les anciens utilisateurs (Karuppagounder et al., 2012). Cependant, selon
des estimations probabilistes des risques et de modélisations de données d’expositions et de
toxicité (Bartell et al., 2000; Campbell et al., 2000), peu d’impacts écologiques du diquat ont
été démontrés sur les invertébrés et les poissons, les principales conclusions indiquant une
dissipation rapide du cation diquat dans l’eau (Ritter et al., 2000). Ainsi, son utilisation pour
le faucardage des étangs est jugée sans risques, autorisée et même recommandée.
Il est établi que la toxicité du diquat et plus généralement celle des herbicides de la
famille des bipyridyles, est élicité par l’induction de stress oxydant (Smith et al., 1985),
45
susceptible de perturber des processus physiologiques chez les organismes aquatiques
(Schultz et al., 1995; Figueiredo-Fernandes et al., 2006; Sanchez et al., 2006). Etant donné
ses propriétés pro-oxydantes, le diquat est très utilisé comme molécule modèle en toxicologie
et en biologie cellulaire dans les tests in vivo et in vitro pour l’étude du stress oxydant chez les
mammifères (Crabtree et al., 1977; Smith et al., 1985; Gallagher et al., 1995; Anton et al.,
2002; Rogers et al., 2006). Ce puissant hépatotoxique est capable d’induire le transfert
d'électrons de NADPH (NADH) vers l’O2, ce qui génère un flux d'O2.-, ainsi que de réduire
les ions ferriques (Fe3+) en ions ferreux (Fe2+), soit indirectement par O2.- et H2O2, soit
directement via les espèces de cations radicaux bipyridil (Smith et al., 1985; Sandy et al.,
1986; Thomas & Aust, 1986). Une étude transcriptomique conduite sur des tissus hépatiques
de truite arc-en-ciel Oncorhynchus mykiss et basée sur des puces à ADN, a montré que le
diquat impactait l’expression de nombreux gènes (Hook et al., 2006).
Chez L. stagnalis, des perturbations sur divers traits d’histoire de vie tels que le taux
d’éclosion, la croissance et la survie des juvéniles et des adultes ont déjà été constatées lors
d’expositions en laboratoire à des concentrations de diquat écologiquement réalistes
(Coutellec et al., 2008; Ducrot et al., 2010). Une induction significative de l’apoptose des
hémocytes a également été démontrée par microscopie à fluorescence après 48h d’exposition
à 44,4 et 222,2 µg/L, tandis que les mesures de certaines activités enzymatiques antioxydantes dans le complex gonado-digestif après deux à 31 jours d’exposition se sont
révélées peu concluantes (Lagadic, 2007).
Ainsi, de par l’utilisation répandue et déjà ancienne de ses propriétés herbicides en
agriculture et dans le contrôle de la végétation aquatique, son pouvoir pro-oxydant, la
connaissance partielle mais croissante de ses mécanismes de toxicité au niveau cellulaire et
les différents effets sub-létaux déjà constatés chez L. stagnalis, le diquat semble être une
molécule très pertinente pour estimer le potentiel évolutif de la réponse moléculaire vis-à-vis
du stress oxydant chez cette espèce.
46
6.
Hypothèses, problématique, objectifs et démarche scientifique.
Le projet se place dans le contexte de l’évaluation du risque écotoxicologique lié à
l’utilisation de pesticides. Cette démarche inclut la recherche de marqueurs d’exposition et
d’effet, ainsi que la mise au point de modèles prédictifs. L’intégration d’une dimension
évolutive à ces questions de recherche est assez récente et reflète la prise de conscience
actuelle de l’impact à long terme de l’exposition des organismes aux polluants, et des
processus adaptatifs et/ou d’extinction qui peuvent se développer au fil des générations.
Hypothèse initiale
Dans cette thèse, nous nous intéressons aux composantes évolutives de la réponse des
populations naturelles de Lymnaea stagnalis vis-à-vis du stress environnemental généré par
les pesticides et autres polluants présents en milieu aquatique. L’objectif est d’explorer les
systèmes moléculaires de défense régulant les effets du stress oxydant chez cette espèce non
cible, ainsi que leur potentiel évolutif. Ce stress pouvant résulter de la toxicité induite par un
grand nombre de xénobiotiques, l’hypothèse générale de ce projet suppose que ce type de
perturbations répétées ou chroniques pourrait entraîner des processus de différenciation
adaptative entre populations naturelles de L. stagnalis situées à proximité des activités
agricoles ou de façon plus générale, à proximité des activités humaines.
Problématique
La recherche de tels patrons sélectifs chez notre espèce modèle passe tout d’abord par
une caractérisation plus fine de ses systèmes moléculaires antioxydants, de leurs éventuels
liens avec les traits relatifs à la valeur sélective, et l’étude de la composante génétique de ces
caractéristiques phénotypiques (incluant les réponses moléculaires) aux niveaux intra et interpopulations.
Ce projet de thèse s’articule autour de quatre questions centrales :
Q1 Existe-t-il une divergence génétique phénotypique et moléculaire entre populations
naturelles génétiquement différenciées du point de vue neutre ?
Q2 L’exposition historique aux stress d’origine anthropique peut-elle entraîner une
divergence adaptative des populations (sélection divergente sur les traits d’histoire de vie) ?
47
Q3 A l’échelle moléculaire, observe-t-on une variation inter-populations dans la réponse au
stress oxydant généré par le diquat ? Si tel est le cas, cette variation résulte-t-elle des
pressions de sélection d’origine anthropique auxquelles sont soumises les populations
impliquées ?
Q4 Quel est le potentiel adaptatif de ces populations naturelles vis-à-vis du stress d’origine
anthropique en général ?
Objectifs
Les enjeux sont doubles. Ils sont cognitifs d’une part, visant à l’acquisition de
connaissances sur les mécanismes populationnels évolutifs chez les organismes exposés de
façon non intentionnelle à des substances xénobiotiques, et de façon plus spécifique, à une
meilleure compréhension des réponses moléculaires antioxydantes chez L. stagnalis.
D’autre part, ce projet a pour finalité de proposer des outils pouvant servir dans le
contexte de l’évaluation du risque écologique, grâce à la recherche d’indicateurs de processus
adaptatifs ou d’extinction et à l’estimation des conséquences à long-terme des expositions aux
pesticides dans les écosystèmes aquatiques situés dans les paysages agricoles.
Démarche scientifique
Dans ce projet, l’étude de la réponse au stress oxydant chez L. stagnalis a été réalisée
via des expositions au diquat, un herbicide reconnu pour son fort pouvoir oxydant.
•
Dans le but de mieux caractériser sa toxicité au niveau moléculaire et définir
les conditions d’expositions (temps x concentration) permettant d’obtenir les réponses les
plus contrastées, une étude préliminaire évaluant par PCR quantitative l’expression de
gènes cibles potentiels parmi les ressources génétiques disponibles, et quelques activités
enzymatiques associées, a été réalisée sur deux tissus, l’hémolymphe et le complexe
gonado-digestif, de la population de laboratoire Renilys®.
•
Sur la base des résultats obtenus lors de cette approche préliminaire ont été
définies les conditions expérimentales des tests d’écotoxicité utilisées dans le reste du
projet de thèse. Afin d’augmenter de façon significative des ressources transcriptomiques
jusque là très limitées pour l’investigation à grande échelle des réponses moléculaires,
48
deux librairies d’ADN complémentaires (ADNc), regroupant les transcriptomes de 3
populations exposées ou non à l’agent oxydant, ont été séquencées lors d’un run de
pyroséquençage 454 (AIP Bioressources INRA, projet EGELys). Une première analyse
transcriptomique d’expression différentielle a été réalisée à cette occasion permettant de
mieux cibler les voies de signalisation potentiellement impactées par le diquat. De plus, en
parallèle de ce projet, de nouveaux marqueurs microcatellite ont été mis au point et font
l’objet d’une primer note.
•
L’étude a ensuite porté sur 14 populations naturelles échantillonnées dans des
milieux contrastés du point de vue de la pression pesticide pour tester l’hypothèse de
divergence adaptative associée aux processus sélectifs locaux, grâce à une analyse
statistique multifactorielle de la variation génétique de 11 traits d’histoire de vie mise en
relation avec la variation génétique neutre basée sur 12 marqueurs microsatellites dans
une approche QST-FST.
•
Enfin, la composante évolutive / adaptative des réponses moléculaires de L.
stagnalis au stress pesticide a été étudiée en comparant l’expression différentielle entre
diquat et condition témoin de différentes familles maternelles issues d’un sous-jeu de 4
populations naturelles. Un total de 16 librairies a été séquencé par RNAseq sur une demie
flow-cell Illumina Hiseq2000. Ici, l’intérêt majeur de l’examen des réponses
(post)génomiques est qu’il informe sur une réponse intégrée à l’échelle des organismes,
les normes de réactions pouvant mettre en évidence d’éventuelles interactions G x E.
Ces différents points font l’objet des quatre chapitres suivants présentés sous la forme de
quatre articles scientifiques, deux déjà publiés, deux en préparation et une note.
Pour terminer, les résultats obtenus permettront de discuter l’influence de l’exposition
chronique aux pesticides sur le potentiel évolutif des populations naturelles étudiées ainsi que
l’intérêt de considérer de telles problématiques dans les procédures d’estimations des risques.
49
Chapitre II -
Impacts moléculaires d’un herbicide prooxydant chez L. stagnalis: approche « gènes
candidats »
50
Ce premier chapitre présente le travail réalisé en première étape de la thèse, et visant à
mieux comprendre les mécanismes moléculaires de réponse sollicités chez Lymnaea stagnalis
lors d’une exposition à la substance active d’un herbicide générateur de stress oxydant, le
dibromure de diquat.
Sur la base des ressources génétiques initialement disponibles chez L. stagnalis, des
marqueurs transcriptionnels ont été mis au point par PCR quantitative pour évaluer l’impact
du diquat sur un ensemble de gènes candidats, codant pour : la catalase (cat), une superoxyde
dismutase cytosolique (Cu / Zn-sod), une glutathion peroxydase sélénium-dépendante (gpx),
une glutathion réductase (gred), le récepteur rétinoïde X des rétinoïdes (rxr), deux protéines
chaperons (hsp40 et hsp70). L’expression de ces gènes a été étudiée dans deux tissus
supposés répondre au stress généré par la substance testée, la glande digestive et
l’hémolymphe, chez des individus issus de la souche INRA Renilys®. Les niveaux de
transcription des gènes codant pour la cortactine (cor) et les sous-unités ribosomiques r18S et
r28s ont aussi été étudiés dans le but initial de normaliser l’expression des gènes cibles. Les
activités enzymatiques de SOD, Gpx, Gred et de la glutathion S-transférase (GST) ont
également été étudiées dans la glande digestive via des méthodes spectrophotométriques et
fluorimétriques. Sur la base de résultats antérieurs issus d’analyses biochimiques menées sur
la même souche de L. stagnalis (Lagadic et al., 2007), nous avons choisi d’explorer les effets
du diquat sur les réponses moléculaires précoces. Nous avons ainsi exposé des individus
pendant des temps relativement courts (5, 24 et 48 heures), à trois concentrations de
diquat considérées comme réalistes du point de vue des doses environnementales (22,2 ;
44,4 et 222,2 µg/L) ; la plus forte de ces concentrations correspond à la dose maximale
d’application recommandée pour le faucardage des étangs (Ritter et al., 2000), et reste proche
des valeurs maximales mesurées dans le milieu naturel après traitement (Emmett, 2002).
Globalement, ce travail a apporté des résultats intéressants sur la toxicité du diquat sur
la limnée. Les valeurs d’expression normalisées selon une méthode basée sur le niveau
d’expression de l’ensemble des gènes étudiés (i.e., NormaGene ; Heckmann et al., 2011), a
mis en évidence une perturbation significative de l’ensemble des gènes cibles, ainsi que des
gènes initialement utilisés comme référence (« gènes de ménage ») selon la méthode classique
basée sur l’expression relative (i.e., GeNorm ; Vandesompele et al., 2002). Dans
l’hémolymphe, le sens des effets diffère selon la concentration et le temps d’exposition: à la
surexpression significative de cor, hsp40, rxr, et sod, après 24 hrs d’exposition à la plus faible
51
concentration, s’oppose une régulation négative de l'expression de la plupart des gènes étudiés
sous la plus forte concentration, surtout après 48 heures d'exposition. Ce résultat tend à
confirmer l’observation antérieure d’une induction massive de processus apoptotiques sous
ces conditions. Dans le tissu de la glande digestive, des réponses plus précoces et
d’amplitudes plus élevées ont été obtenues. Quelle que soit la concentration en diquat, la
transcription de tous les gènes cibles a augmenté de manière significative (excepté pour cat)
après 5 heures d'exposition, avant de revenir aux niveaux des conditions témoin après 24 et 48
heures d’exposition. Ces résultats suggèrent une forte réactivité des réponses moléculaires
vis-à-vis du stress oxydant induite par une perturbation de l’équilibre homéostatique des
espèces réactives de l'oxygène (ROS) dans les hépatocytes. Bien que l’augmentation
d’activité enzymatique de Gred et SOD soient globalement conformes aux niveaux de
transcription des gènes correspondants, nous avons constaté qu’une augmentation de la
transcription n'était pas toujours accompagnée de l'augmentation de l'activité enzymatique
associée. Ce résultat indique un effet post-transcriptionnel du diquat, qui peut concerner la
traduction, le repliement protéique ou l’activation des domaines catalytiques. Dans un
contexte d’utilisation de ces réponses comme biomarqueur, le maintien des activités
enzymatiques à un niveau constitutif en présence de diquat montre que l’absence
d’observation de réponse significative au niveau fonctionnel (activité enzymatique) peut
masquer des effets toxiques plus en amont, et met en évidence un risque de sous-estimation
des effets.
En conclusion, ce travail représente à notre connaissance la première étude démontrant
la mise place, chez un gastéropode d’eau douce, de réponses moléculaires au stress oxydant
induites par la toxicité du diquat, un herbicide utilisé en traitement de cultures mais aussi pour
le contrôle des plantes aquatiques. Les résultats permettent de justifier l’utilisation de cette
molécule dans le cadre de l’objectif général de la thèse, qui porte sur le potentiel évolutif des
réponses moléculaires chez L. stagnalis, en particulier vis-à-vis du stress oxydant. Par
ailleurs, d’un point de vue secondaire plus finalisé, les marqueurs d’expression génique mis
au point constituent des outils utiles pour l’étude du stress oxydant chez L. stagnalis.
Ce chapitre a fait l’objet d’un article accepté en novembre 2012 et publié en janvier
2013 dans la revue Aquatic toxicology (vol 126: 256-265).
52
Article 1
-
Impact of the redox-cycling herbicide diquat on transcript
expression and antioxidant enzymatic activities of the freshwater
snail Lymnaea stagnalis
Anthony Bouétard1§, Anne-Laure Besnard1, Danièle Vassaux1, Laurent Lagadic1, MarieAgnès Coutellec1
1
INRA, UMR INRA-Agrocampus Ouest ESE 0985, Equipe Ecotoxicologie et Qualité des
Milieux Aquatiques. 65 rue de Saint-Brieuc, 35042 Rennes cedex, France.
§
Corresponding author at: Tel : +33 2 2348 5529 ; E-mail : [email protected]
Keywords: Oxidative stress, Pesticides, Molecular response, Haemolymph, Digestive gland,
Pond snail.
53
Abstract
The presence of pesticides in the environment results in potential unwanted effects on
non-target species. Freshwater organisms inhabiting water bodies adjacent to agricultural
areas, such as ditches, ponds and marshes, are good models to test such effects as various
pesticides may reach these habitats through several ways, including aerial drift, run-off, and
drainage. Diquat is a non-selective herbicide used for crop protection or for weed control in
such water bodies. In this study, we investigated the effects of diquat on a widely spread
aquatic invertebrate, the holarctic freshwater snail Lymnaea stagnalis. Due to the known
redox-cycling properties of diquat, we studied transcript expression and enzymatic activities
relative to oxidative and general stress in the haemolymph and gonado-digestive complex
(GDC). As diquat is not persistent, snails were exposed for short times (5, 24, and 48 hrs) to
ecologically relevant concentrations (22.2, 44.4, and 222.2 µg l-1) of diquat dibromide. RTqPCR was used to quantify the transcription of genes encoding catalase (cat), a cytosolic
superoxide dismutase (Cu/Zn-sod), a selenium-dependent glutathione peroxidase (gpx), a
glutathione reductase (gred), the retinoid X receptor (rxr), two heat shock proteins (hsp40 and
hsp70), cortactin (cor) and the two ribosomal genes r18S and r28s. Enzymatic activities of
SOD, Gpx, Gred and glutathione S-tranferase (GST) were investigated in the GDC using
spectrophoto/fluorometric methods.
Opposite trends were obtained in the haemolymph
depending on the herbicide concentration. At the lowest concentration, effects were mainly
observed after 24 hrs of exposure, with over-transcription of cor, hsp40, rxr, and sod, whereas
higher concentrations down-regulated the expression of most of the studied transcripts,
especially after 48 hrs of exposure. In the GDC, earlier responses were observed and the foldchange magnitude was generally much higher: transcription of all target genes increased
significantly (or non-significantly for cat) after 5 hrs of exposure, and went back to control
levels afterwards, suggesting the onset of an early response to oxidative stress associated to
the unbalance of reactive oxygen species (ROS) in hepatocytes. Although increases obtained
for Gred and SOD activities were globally consistent with their respective transcript
expressions, up-regulation of transcription was not always correlated with increase of
enzymatic activity, indicating that diquat might affect steps downstream of transcription.
However, constitutive levels of enzymatic activities were at least maintained. In conclusion,
diquat was shown to affect expression of the whole set of studied transcripts, reflecting their
suitability as markers of early response to oxidative stress in L. stagnalis.
54
Introduction
The presence of pesticides in the environment results in potential unwanted effects on
non-target species. Assessing such effects is a complicated task, in part due to the fact that
they may not be related to the mode of action of the molecules in target species. Freshwater
organisms inhabiting water bodies adjacent to agricultural parcels, such as ditches and
marshes, are good models to test such effects as various pesticides may reach these habitats
through several ways, including aerial drift, run-off, and drainage (Brown and van Beinum,
2009).
Diquat is a non-selective herbicide of the bipyridylium class, which was first
commercially available in the 1940s with the advent of first ever synthetic herbicides
(Karuppagounder et al., 2012). Since then, various formulations containing diquat alone or in
combination with other similar herbicidal agents such as paraquat, have been widely used in
crop protection (potato, banana, vine and other seed crops), and also in aquatic systems for the
control of submersed, floating and emerging weeds (US-EPA, 1995; Ritter et al., 2000; FAO,
2008; French Ministry of Agriculture, 2012). On average, 90% of diquat consumption is
reported in North America, Europe, Australia and Japan, although it is considered as a major
toxic, possibly responsible for cases of Parkinson’s disease (Karuppagounder et al., 2012).
It is now established that the toxicity of diquat is elicited by oxidative stress (Smith et
al., 1985), as this is also the case for a number of xenobiotics, including pharmaceuticals,
metals and pesticides (Scandalios, 2005; Valavanidis et al., 2006). Bipyridyl herbicides
induce the production of reactive oxygen species (ROS), which may impair physiological
processes in aquatic organisms (Schultz et al., 1995; Figueiredo-Fernandes et al., 2006;
Sanchez et al., 2006).
However, little ecological impact on invertebrates and fish was
demonstrated for diquat, as shown by probabilistic risk assessment, exposure and toxicity
data, and modelling of ecological processes (Bartell et al., 2000; Campbell et al., 2000). This
conclusion mainly results from very rapid dissipation of diquat cation in water (Ritter et al.,
2000).
Furthermore, once bound to sediment and soil, diquat is no longer biologically
available. Therefore, due to the minimal risk posed by diquat, the use of this herbicide for
weed control is considered safe and recommended. However, as a consequence, non target
organisms such as freshwater invertebrates are expected to be more often exposed to diquat,
regardless of its dissipation time. For these organisms, adverse effects of diquat are not
clearly characterized yet (Emmett, 2002).
55
Among aquatic invertebrates, Lymnaea stagnalis is a mollusk representative of lentic
habitats, and of the herbivorous community compartment, which may be regularly exposed to
diquat, either when the water is treated directly or when adjacent parcels are treated, as lentic
systems are the ultimate recipient of many chemicals, through various transfer processes
(Brown and van Beinum, 2009). Diquat has been shown to affect various life history traits,
such as hatching rate, juvenile and adult growth, and survival, in L. stagnalis (Coutellec et al.,
2008; Ducrot et al., 2010). Previous investigations on antioxidant enzymatic activities were
conducted in L. stagnalis exposed during two to 31 days to 4.4 to 222.2 µg l-1 diquat,
showing only slight perturbations (Lagadic, 2007). From these results, we hypothesized that
the onset of more contrasted cellular responses might occur earlier, i.e. within the first two
days of exposure. Given its oxidative properties, diquat is widely used as model chemical for
in vivo and in vitro studies of oxidative stress in toxicology and cellular biology using
mammalian models (Crabtree et al., 1977; Smith et al., 1985; Gallagher et al., 1995; Anton et
al., 2002; Rogers et al., 2006). This potent hepatotoxicant is able to mediate the transfer of
electrons from NADPH (NADH) to O2, which generates a flux of superoxide anion (O2.-), as
well as to reduce ferric ions (Fe3+) into ferrous ions (Fe2+), either indirectly by O2.- and H2O2,
or directly by the bipyridil cation radical species (Smith et al., 1985; Sandy et al., 1986;
Thomas and Aust, 1986). In a microarray expression study, diquat was shown to cause large
changes in transcription in rainbow trout liver (Hook et al., 2006). Although most studies
were conducted on vertebrates, the resulting corpus of knowledge is a useful reference for
molecular investigations in less known models such as invertebrates. In this context, we have
recently generated new transcriptomic resources from multi-tissue extracts of snails exposed
to diquat (Bouétard et al., 2012). In the present study, effects of diquat in L. stagnalis were
explored on a set of genes encoding proteins presented hereafter, most of which being
involved in oxidative pathways.
Despite their toxic effects, ROS are natural by-products of aerobic metabolism and are
thought to mediate the toxicity of oxygen. They also act as intracellular signalling molecules,
under mechanisms that are not yet fully understood, even in the most widely studied models,
i.e., mammalian models (Poulsen et al., 2000; D'Autreaux and Toledano, 2007). Defence
systems against ROS include antioxidant enzymes such as superoxide dismutase (SOD),
which reduces O2.- to H2O2, in turn reduced by catalase (Cat) or glutathione peroxidase (GPx)
into H2O and O2. GPx, using glutathione as co-factor, is a protagonist of the glutathione cycle
56
which also involves glutathione reductase (Gred) and the second phase detoxication enzyme
glutathione S-transferases (GSTs) (Lushchak, 2011). Depending on tissues and species, the
transcription factor retinoid X receptor (RXR) is recognized to play different roles in response
to oxidative stress, such as activator for the phase II detoxication induction (Kang et al.,
2005), anti-apoptotic factor, and inhibitor of intracellular ROS generation by up-regulating
catalase activity (Shan et al., 2008). ROS may also induce protein misfolding. Accumulation
of misfolded proteins in stressed cells activates heat shock factors and results in the
expression of Heat shock proteins (Hsps) (Kim et al., 2007). Hsps have the ability to restore
the damaged proteins to their functional three-dimensional structure, to prevent aggregation of
unfolded proteins, or to facilitate their degradation in case of irreversible damage (Qiu et al.,
2006; Grune et al., 2011). Hsp70, which may act with its co-chaperone Hsp40 (Kim et al.,
2007), is the most studied Hsp in aquatic invertebrates, and is commonly used as a biomarker
inducible by pollutants (Lee et al., 2006; Park et al., 2009; Park et al., 2010).
The present study aimed at investigating the mechanisms of diquat toxicity in L.
stagnalis, through the exploration of enzymatic and molecular “candidate” responses which
may be reasonably linked to the mode of action of this herbicide. Thus, RT-qPCR was used
to study the transcription of genes encoding catalase (cat), a cytosolic superoxide dismutase
(Cu/Zn-sod), a selenium-dependent glutathione peroxidase (gpx), glutathione reductase
(gred), retinoid X receptor (rxr), two heat shock proteins (hsp70 and its co-chaperone hsp40),
the ribosomal genes r18S and r28s and the gene encoding cortactin (cor), an actin-binding
protein involved in exocytosis and endocytosis processes (Clark et al., 2007; Sung and
Weaver, 2011). Gene transcription was investigated in the haemolymph and in the gonadodigestive complex (GDC) of snails exposed to diquat in its dibromate form, at concentrations
of 22.2, 44.4 and 222.2 µg l-1 during 5, 24 and 48 hrs. In the GDC, temporal transcript
expression profiles were complemented by measurements of antioxidant enzymatic activities
of SOD, Gpx, Gred and GST.
57
Material and methods
Chemical
Technical diquat dibromate hydrate (6,7-Dihydrodipyridol[1,2-a:2’,1’-c]pyrazidiium
dibromide) was purchased from Cluzeau Info Labo, Sainte Foy la Grande, France. Solutions
were prepared in pure distilled water.
Snails
The Renilys strain of Lymnaea stagnalis (Linné, 1758) (Mollusca, Panpulmonata,
Heterobranchia) is reared at the Experimental Unit of Aquatic Ecology and Ecotoxicology
(U3E, INRA Rennes, France) under controlled conditions (14/10 L / D, 20 ± 1 ° C, pH = 8 ±
1, [O2] > 6 mg l-1, water hardness : 254 mg CaCO3 l-1, regular diet: organic salad) as
previously described (Russo and Lagadic 2004). One day prior to exposure, adult snails (27 ±
1 mm shell length) were isolated from the culture and transferred to their 240 ml exposure
vessels (one snail per vessel). They were fed with 0.5 g of organic lettuce for the 24 first
hours prior exposure. Just before contamination, feces and lettuce leftovers, as well as 5 ml
of water were removed from each vessel, so that they contained 235 ml water.
Exposure conditions
Snails were individually exposed to nominal diquat concentrations of 22.2, 44.4 and
222.2 µg l-1.
Diquat was introduced once as 5 ml stock solution for each exposure
concentration, poured into the 235 ml water. Exposures were carried out in plastic vessels.
Vessel location and contamination order were randomized.
After exposure, physico-chemical parameters (temperature, pH, conductivity and
oxygen saturation) were measured in control vessels, and a water sample from each treatment
was taken and stored at -20°C until HPLC analysis of diquat residues (Laboratoires des
Pyrénées, Lagor, France).
Tissue sampling
Snails were sampled after 5, 24 and 48 hrs of exposure. At each sampling time, three
snails (replicates) per diquat concentration were used. Haemolymph was collected after
gentle stimulation of the foot sole (Russo and Lagadic, 2004), using a micropipette.
58
Individual haemolymph samples were transferred into 300 µL of concentrated lysis buffer
(Norgen Biotech total RNA purification kit, Ontario, Canada) and stored at -80°C.
Immediately after haemolymph collection, animals were frozen in liquid nitrogen. After shell
removal, the tip of the digestive gland was plunged into RNAlater® (Ambion) and stored at 80°C. This tissue sample will be hereafter referred to as gonado-digestive complex (GDC), as
it may include some parts of the ovotestis (this organ is embedded in the digestive gland and
its removal may impair RNA quality). Remaining parts of the body, including proximal part
of the digestive gland, were stored at -80°C for biochemical analysis.
Transcript expression analysis
RNA extraction and cDNA synthesis
RNA was extracted from the haemolymph and GDC, using Norgen Biotek kit
(Ontario, Canada) and RNeasy Plus Mini kit (Qiagen), respectively. RNAs were eluted in 50
µL of RNase-free water.
Concentration and purity of samples were then determined
spectrophotometrically (NanoDrop, Thermo, Fisher Scientific), and the concentrations were
adjusted to 10 ng µl-1 and 20 ng µl-1 for haemolymph and GDC, respectively. After DNase
treatment (DNAse I, Promega), cDNAs were synthesized in a volume of 40 µL using the
iScript RT kit (Bio-Rad), according to the manufacturer’s guideline and then stored at -20 °C
until qPCR analysis.
RT-qPCR analysis and Normalization
Gene
sequences
were
retrieved
from
GenBank
(http://www.ncbi.nlm.nih.gov/genbank/). Primers were designed using Primer 3 (Rozen and
Skaletsky, 1999) (Tab. 1), and synthesized by Sigma-Aldrich (St Quentin Fallavier, France).
Real time quantitative polymerase chain reaction (RT-qPCR) was conducted on the thermal
cycler CFX 96 (Bio-Rad) using EvaGreen® chemistry (Bio-Rad). Each reaction was run in
triplicate (technical replicates) and was done in 20 µl of reaction volume, containing 2 µl of
cDNA, 500 nM primers, and the EvaGreen mix. Amplification was performed under the
following conditions: 98°C for 2 min to activate the DNA polymerase, followed by 40 cycles
at 95°C for 4 sec and 60°C for 10 sec. Dissociation curves were examined at the end of PCR
reactions to check for unspecific amplification and primer dimers.
59
Table 1. Characteristics of the sequences used for real-time qPCR analysis.
Gene name
Catalase
symbol
Accession #
cat
FJ418795
Cu/Zn-Superoxyde
Dismutase
sod
AY332385
Retinoïd X Receptor
rxr
AY846875
Glutathione Reductase
gred
FJ418794
Glutathione Peroxidase
gpx
FJ418796
Heat Shock Protein 40
hsp40
DQ278442
Heat Shock Protein 70
hsp70
DQ206432
Cortactin
cor
AY577343
18S rRNA
r18s
EF489345
28S rRNA
r28s
EF489367
Primer (5' > 3')
F: GCAACAACACCCCAATTTTC
R: TCTGGACGCAGAGTGAAGAA
F: GGTGGGCCATTAGATCAAGA
R: CACTAAGCTGCGACCAATGA
F: TCCTGGTCTGCCTCATTCTT
R: ACCGTAATGTTTGCCAGAGG
F: CTTTTGACTGGAGGCGAATC
R: GATTCCATGGCCCTCAATTT
F: TGTAAACGGGACGGAGATTC
R: GATCTCGTTTTCCCCATTCA
F: GGTCTTGAATCCTGATGGACA
R: CTTTGGGGAAGGTTATTTTGG
F: CAGCTTGAGGGCTACGTCTT
R: GCCATTTCATTGTGTCGTTG
F: ACGAAGCAGCACAACATCAC
R: GGTAACGACACCAGGAATGC
F: CTCCTTCGTGCTAGGGATTG
R: GTACAAAGGGCAGGGACGTA
F: CTCAGGAGTCGGGTTGTTTG
R: TTCCCTCACGGTACTTGTCC
Amplicon
size (bp)
133
141
82
116
118
104
115
112
106
105
Raw qPCR data were analysed using CFX manager (Bio-Rad). For each gene, a
standard curve based on 7 dilutions from an equimolar mix of mRNA samples, was produced
in duplicate to verify amplification efficiency. Transcript expression was normalized using
two different methods. The first one, GeNorm (Vandesompele et al., 2002), is based on the
determination and use of reference genes (here, r18S RNA, r28s RNA and cortactin). This
method is based on the stability of their transcriptions, as indicated by the geometric mean of
the variation observed between them (M) with a threshold value of 0.5 (1.0) for homogeneous
(heterogeneous) tissues (Bustin, 2010). The second one, NORMA-Gene, is a data-driven
normalization method, which does not require reference genes (Heckmann et al., 2011). In
this method, the normalization factor is based on the calculation of the mean expression value
for each replicate across the studied transcripts. It allows reducing systematic and artificial
bias. Therefore, this new method is more robust than those based on reference genes.
60
Enzyme assays
Sample preparation
Proximal part of the GDC was removed from the rest of individuals, weighted and
homogenized in 75 mM Phosphate buffer (KH2PO4, pH 7) in order to obtain a 10% (w/v)
solution. Tissues were homogenised using an ice-cold glass homogenizer with a motordriven Teflon pestle. Homogenates were centrifuged at 10000 g for 5 min at 4°C. Enzyme
assays were performed on the supernatants, and run in triplicate.
Superoxide Dismutase
The activity of superoxide dismutase (SOD; E.C.1.15.1.1) was determined using the
Ransod Kit supplied by the Randox Laboratories (Ardmore, Northern Ireland, UK). Xanthine
and xanthine oxidase were used to generate superoxide anion, which reacts with 2-(4indophenyl)-3-(4-nitro-phenyl)-5-phenyl tetrazolium chloride (INT) to form a red formazan
dye. Changes in the absorbance were determined at 505 nm during the first three minutes of
reaction. Enzyme activity was calculated from a calibration curve prepared with the standard
supplied in the kit. One unit of SOD is defined as the amount that inhibits 50% of the INT
reaction (Beauchamp and Fridovich, 1971; Ukeda et al., 1997). Specific activity was defined
as Units mg-1 protein.
Glutathione Reductase
Glutathione reductase (GR; CE 1.8.1.7) activity was measured at 25°C by monitoring
the rate of production of 5-thio-2-nitrobenzoic acid (TNB) from 5,5'-dithiobis(2-nitrobenzoic
acid) (DTNB) at 412 nm every 9 sec during 70 sec. Production of TNB is coupled with the
reduction of glutathione disulfide by the enzyme.
GR activity was calculated using an
extinction coefficient of 13,600 M-1 cm-1 for DNTB and was expressed as µmol min-1 mg-1
protein.
Glutathione Peroxidase
Glutathione peroxidase (GPx; EC 1.11.1.9) activity was measured with the Ransod Kit
supplied by the Randox Laboratories (Ardmore, Northern Ireland, UK) by monitoring the
continuous decrease in NADPH concentration using cumene - H2O2 as the substrate at
61
340 nm (Flohé and Günzler, 1984). One unit of GPx activity is defined as the amount of
enzyme that oxidizes 1 µmol of NADPH per min and is expressed as Units mg-1 protein.
Glutathione S-Transferase
Glutathione S-transferase (GST; EC 2.5.1.18) activity was measured according to
Habig (1974). Briefly, 5 µL of GSH solution (100 mM in phosphate buffer) and 10 µL of
sample were mixed in 235 µL of 100 mM phosphate buffer (pH 6.5). After adding 5 µL of
100 mM 1 chloro-2,4 dinitrobenzene (CDNB) in ethanol, the change in absorbance at 340 nm
was recorded during 40 sec. One GST Unit was defined as the amount of enzyme needed to
catalyze the formation of 1 µmol of GS-DNB per minute at 25 °C.
GST activity was
expressed as µmol min-1 mg-1 protein.
Protein assay
Protein concentration (mg protein ml-1) in the enzyme extracts was evaluated
according to the Bradford assay (1976) with bovine serum albumin as the standard.
Data analysis
Transcript expression and enzymatic activities were compared between control and
treatment conditions using ANOVA, after checking for normality (Shapiro-Wilk’s test) and
homoscedasticity (Bartlett’s test). When required, data were transformed (logarithmic or
Box-Cox transformations) prior to analysis.
ANOVAs were followed with multiple
comparison tests (Tukey’s post-hoc test, α = 0.05). Analyses were performed using the R
software (Windows version 2.11.1, R Development Core Team, 2006, Vienna, Austria).
62
Results
Exposure characterization
Diquat concentrations in water remained stable during the longest exposure period (48
hrs) and were slightly lower than the nominal ones (Fig. 1), which was consistent with
previous results (Lagadic, 2007).
22.2 µg l-1
44.4 µg l-1
200
222.2 µg l-1
180
[diquat] (µg l-1)
160
140
120
100
80
60
40
20
0
5
24
48
Exposure time (hrs)
Figure 1. Effective concentration of diquat as
function of time and nominal concentration.
Water chemistry measured in control vessels did not reveal abnormal conditions
during exposure (Tab. 2). No mortality was observed during the exposure. However, a
moribund state (no foot-retraction reflex upon mechanic pressure, immobility) was observed
in one snail exposed during 48 hrs to 44.4 µg l-1 of diquat. This individual was discarded
from the analysis.
Table 2. Physicochemical parameters as measured under control conditions.
Time
(hrs)
pH
5
24
48
7 ± 0.05
7.4 ± 0.25
7.5 ± 0.1
Oxygen
saturation
(%)
4.6 ± 0.6
5.6 ± 0.6
6.8 ± 0.2
63
Conductivity
(µS/cm)
Temperature
(°C)
503.3 ± 5.1
451.7 ± 24
526.3 ± 10.6
19.7 ± 0.15
19.8 ± 0.1
19.5 ± 0.1
Normalization and transcription profiles of r18s, r28s and cortactin
Genes encoding rRNA18s, rRNA28s and cortactin were initially selected among a
panel of potential reference genes (including also β-tubulin, actin and thymosin) for the
normalization of target transcripts expression with GeNorm. Indeed, this subset of genes
presented the lowest M-values. However, these were globally above the validation threshold
recommended in the MIQE guideline for homogeneous tissues (0.5). The highest instability
was observed after 5 hrs of exposure in the GDC (M-value = 0.73). However, it was accepted
as this tissue could be considered as heterogeneous (Tab. 3).
Table 3. Results of the normalization procedure based on GeNorm, using ribosomal RNA 28S,
18S and cortactin as reference genes.
r28S
Exposure
time
r18s
cortactin
Mean Value
Tissue
M-Value
CV
M-Value
CV
M-Value
CV
M-Value
CV
Haemolymph
0.38
0.14
0.40
0.16
0.47
0.20
0.42
0.16
GDC
0.71
0.34
0.61
0.18
0.86
0.33
0.73
0.28
Haemolymph
0.56
0.22
0.56
0.21
0.60
0.24
0.57
0.22
GDC
0.63
0.27
0.63
0.24
0.68
0.28
0.65
0.26
Haemolymph
0.47
0.13
0.57
0.27
0.68
0.29
0.57
0.23
GDC
0.61
0.23
0.69
0.35
0.55
0.17
0.62
0.25
5 hrs
24 hrs
48 hrs
Normalization with NORMA-Gene revealed that in both tissues, diquat exposure
significantly impacted the transcription of genes encoding r18s, r28s and cor (Fig. 2), but the
response was modulated both by exposure concentration and time, and evolved differently
according to the tissue. Consequently, only the results from NORMA-Gene analysis will be
presented and discussed hereafter. In the haemolymph of snails treated with 22.2 µg l-1
diquat, transcript expression was similar to the control level, except in the case of cor which
was significantly over-expressed at 24 hrs (Fig. 2a).
64
Control
22.2 µg l-1
44.4 µg l-1
222.2 µg l-1
Normalized
expression ratios
A.
1
.
Normalized
expression ratios
*
*
0
B.
*
2
5h
24h
48h
*
5h
r18s
6
24h
48h
5h
r28s
*
5
4
3
24h
48h
cor
*
*
. *
*
2
.
1
0
5h
24h
48h
5h
24h
48h
5h
24h
48h
Exposure time
Figure 2. Effect of diquat on transcript expression of r18s, r28s and cor in haemolymph (A) and
in the gonado digestive complex (B) of Lymnaea stagnalis, as a function of time and diquat
concentration. Transcription levels (mean ± SE) are presented relative to the control (mean ± SE).
with an asterisk (p < 0.05) or a dot (p < 0.1) (ANOVA followed by post hoc test).
At higher concentrations, diquat induced under-expression regardless of exposure
time, but the decrease was only significant at 222.2 µg l-1 for r18s after 48 hrs (~ 0.25-fold),
and for r28s after 5 and 48 hrs (~0.5- and 0.2-fold, respectively). However, it is to be noted
that cor was down-regulated to a similar level by diquat after 48 hrs, although this was not
statistically significant (see control variation, Fig. 2a). In the GDC, transcript expression of
r18s, r28s and cor were globally up-regulated after 5 hrs of exposure (Fig. 2b). Overtranscription of cor was significant whatever the concentration (~3.2- to 5.1-fold; F = 20.2, P
< 0.001), whereas only at 44.4 µg l-1 in the case of r18s and r28s. After 24 and 48 hrs, most
of the expression levels were close to the control, despite a slight down-regulation of r28s (P
< 0.1) observed in snails exposed to 22.2 µg l-1 for 24 hrs.
65
Transcription profiles of target genes in the haemolymph
The transcription of hsp70 was not significantly affected by the treatments, although
data suggest a shift from up- to down-regulation between 5 hrs and 24-48 hrs of exposure
(Fig. 3).
hsp70
hsp40
Control
22.2 µg l-1
44.4 µg l-1
222.2 µg l-1
Normalized
expression ratios
2.5
2
*
1.5
1
* *
0.5
0
5h
24h
48h
5h
24h
48h
Exposure time
Figure 3. Effect of diquat exposure for time-course on transcription of hsp40 and hsp70 in
haemolymph of Lymnaea stagnalis, as a function of time and diquat concentration. Transcription
levels (mean ± SE) are presented relative to the control (mean ± SE). Significant differences between
snails exposed to diquat and their control counterparts are denoted with an asterisk (p < 0.05) or a dot
(p < 0.1) (ANOVA followed by post-hoc test).
Regarding the transcript expression of hsp40, the lowest concentration of diquat
induced significant increase after 24 hrs of exposure, whereas higher concentrations (44.4 and
222.2 µg l-1) led to an opposite trend, with significant decrease measured after 5 hrs of
Normalized expression ratios
exposure and a non-significant decrease after 48 hrs.
rxr
3
cat
*
*
2.5
Control
22.2 µg l-1
44.4 µg l-1
222.2 µg l-1
sod
2
1.5
1
0.5
.
*
*
0
5h
24h
48h
5h
24h
48h
5h
24h
48h
Exposure time
Figure 4. Effect of diquat exposure for time-course on transcription of gene involved in
antioxidant response in haemolymph of Lymnaea stagnalis, as a function of time and diquat
concentration. Transcript expression levels (mean ± SE) are presented relative to the control (mean ±
SE). Significant differences between snails exposed to diquat and their control counterparts are
denoted with an asterisk (p < 0.05) or a dot (p < 0.1) (ANOVA followed by post-hoc test).
66
Transcription of genes involved in glutathione cycle was too low in the haemolymph
to be reliably inferred from RT-qPCR. Therefore, the study of antioxidant response was
limited to rxr, cat and sod (Fig. 4). Diquat had a similar effect on rxr and sod. Their
transcriptions were significantly up-regulated (2.5 fold) after 24 hrs of exposure to the lowest
concentration, whereas higher concentrations had a down-regulating effect after 48hrs, which
was significant only at 222.2 µg l-1. The transcription of cat also increased at the lowest
concentration (48 hrs, 2-fold change) and decreased at higher concentrations (48 hrs), but this
was not strictly significant (but see ANOVA P-value of 0.01, followed with marginal P-value
of the post-hoc test).
Transcription profiles of target genes and antioxidant enzymatic activities in the gonadodigestive complex (GDC)
As general features, changes in gene transcription occurred earlier and were much
more important in the GDC than in the haemolymph. As also observed for r18s, r28s and
cor, the main perturbation of target gene transcription was induced after 5 hrs of exposure to
diquat, with a global trend to up-regulation (except for rxr at the lowest herbicide
concentration) (Fig. 5 and Fig. 6a, b). Moreover, gene transcription increased in a dose–
dependent manner in the case of heat shock proteins (Fig. 5), gpx and gred (Fig. 6a).
Normalized
expression ratios
hsp70
8
7
6
5
4
3
2
1
0
hsp40
Control
22.2 µg l-1
44.4 µg l-1
222.2 µg l-1
* *
*
**
.
.
*
5h
24h
48h
5h
24h
48h
Exposure time
Figure 5. Effect of diquat exposure for time-course on transcription of hsp40 and hsp70 in the
gonado digestive complex of Lymnaea stagnalis, as a function of time and diquat concentration.
Transcript expression levels (mean ± SE) are presented relative to the control (mean ± SE).
with an asterisk (p < 0.05) or a dot (p < 0.1) (ANOVA followed by post-hoc test).
67
gpx
10
9
8
7
6
5
4
3
2
1
0
rxr
5
*
cat
sod
*
4
*
3
*
.
2
*
* *
1
*
.
*
0
5h
C.
24h
48h
Gpx
5h
24h
*
4
Enzymatic activity ratios
B.
gred
48h
5h
Gred
24h
48h
5h
24h
48h
5h
D.
GST
3
Control
22.2 µg l-1
44.4 µg l-1
222.2 µg l-1
.
2
1
Enzymatic activity ratios
Normalized expression ratios
A.
24h
48h
SOD
3
*
*
2
1
0
0
5h
24h
48h
5h
24h
48h
5h
24h
48h
5h
24h
48h
Figure 6. Temporal antioxidant profiles of transcript expression (A & B) and enzymatic
activity (C & D) in the gonado-digestive complex of L. stagnalis exposed to diquat. Snails were
exposed during 5, 24 or 48 hours to 22.2, 44.4 or 222.2 µg l-1 of diquat dibromide. Transcription
was measured by RT-qPCR and normalized with NORMA-Gene (Heckmann et al., 2011).
Transcript expression levels, as enzymatic activities (mean ± SE) are presented relative to the
control (mean ± SE). Significant differences between snails exposed to diquat and their control
counterparts are denoted with an asterisk (p < 0.05) or a dot (p < 0.1) (ANOVA followed by posthoc test).
Distinct transcription profiles were obtained for cat (up-regulation similar across
concentrations, although not significant due to high variations among replicates, see Fig. 6b)
and for sod (highly significant up-regulation, absence of dose-dependent relation). For all
transcripts, a global return to control levels was observed afterwards, except for the
significant down-regulation of hsp70 and cat at 22.2 µg l-1 (24 hrs), and the trend towards upregulation for hsp40 at 44.4 µg l-1 (48 hrs).
Compared to transcript profiles, enzymatic activities were less responsive to diquat.
Glutathione reductase and SOD activities were consistent with gred and sod overtranscription in the GDC after 5 hrs of exposure (Fig. 6c, d), except for the intermediate
concentration (44.4 µg l-1). A weak but significant increase in GST activity also occurred
after 24 hrs of exposure to the lowest concentration (~1.5-fold, ANOVA P = 0.04, but posthoc tests failed to detect significance). GPx activity was maintained at constitutive levels
whatever the condition.
68
Discussion
In the present study, sublethal effects of the herbicide diquat were studied at the
molecular level, using two different tissues of a non-target species, the pond snail Lymnaea
stagnalis. Due to the redox-cycling property of diquat (Thomas and Aust, 1986), we focused
on a set of genes involved in anti-oxidant defence (rxr, cat, sod, gpx, gred), which are
generally ubiquitous in animal species although their expression can differ between tissues
(Livingstone, 2001). Target gene transcription was thus investigated in the haemolymph, a
tissue in which immune cells are likely to manage the toxic effect of ROS to kill
phagocytosed pathogens in defence mechanisms (Segal and Shatwell, 1997), and in the
digestive gland, as diquat is recognized as a potent hepatotoxicant, due to its pro-oxidant
property (Sandy et al., 1986).
To be relevant to ecotoxicity testing and assessment of
ecotoxicological risk, snails were exposed to ecologically realistic concentrations, ranging
from 22.2 to 222.2 µg l-1.
Results indicate significant responses produced by genes specifically involved in
redox cycling and oxidative stress (rxr, cat, sod, gpx, gred), and by other stress responsive
genes (hsp40 and hsp70). More surprisingly, transcription of genes traditionally considered
as stable (r18s, r28s and cor) were also significantly impacted by diquat.
Indeed,
normalization with NORMA-Gene revealed that this set of transcripts (initially selected as
reference genes for normalization with GeNorm) was mostly down-regulated in the
haemolymph of snails exposed to diquat concentration of 44.4 and 222.2 µg l-1, especially
after 48 hrs of exposure, whereas it was significantly up-regulated as early responses observed
in the GDC. However, it is interesting to note a return to homeostasis in the GDC after 24 hrs
as both normalization methods gave very similar results in this tissue at 24 and 48 hrs. These
results raise the difficulty to find stable reference genes. Under the present conditions, for a
minimum of 5 target genes, NORMA-Gene appears as the most relevant normalization
method. Using this method, we showed that diquat induced significant changes in transcript
expression in both the haemolymph and GDC, yet in different ways.
Hemocyte responses
Generally, opposite trends were observed for transcript expression in the haemolymph
indicating biphasic molecular responses depending on herbicide concentration and exposure
time, as also reported in eels exposed to sediments presenting different levels of pollution
69
(Regoli et al., 2011). At the lowest concentration (22.2 µg l-1), the most contrasted responses
were obtained after 24 hrs of exposure. The significant increase of rxr transcription might
reveal the establishment of an active and organized response in hemocytes. As the ligandactivated transcription factor RXR may occur as homodimers, and heterodimers, it is
recognized to participate in the regulation of many aspects of metazoan life and represents the
crossroad of multiple distinct biological pathways (Szanto et al., 2004).
In rat
cardiomyocytes, activation of the transcription factor RXR has been reported to play an antiapoptotic role in response to oxidative stress. It was also supposed to inhibit the production
of intracellular ROS by up-regulating catalase activity (Shan et al., 2008), which the main
peroxisomal enzyme participating in the elimination of H2O2 (Bilbao et al., 2010).
Consistently, up-regulation of cat transcription was suggested in the present study (48 hrs,
22.2 µg l-1), suggesting possible peroxisome proliferation. In vertebrates, RXR has been
shown to enhance transcription of various peroxisomal enzymes when coupled with the
peroxisome proliferator activated receptor-γ (PPAR-γ) (Bardot et al., 1993; Szanto et al.,
2004). Furthermore, PPAR-γ agonists mediate the transcription of superoxide dismutase
(SOD) and catalase genes (Fong et al., 2010). Although PPARs have not been identified yet
in invertebrates (Canesi et al., 2007), PPAR agonists in these organisms, as well as the
transcription factor RXR, may explain the significant over-transcription of sod observed in
hemocytes after 24 hrs.
Despite the lack of data concerning the enzymatic activity of SOD in haemolymph, its
higher transcription compared to that of catalase is consistent with the mode of action of
diquat which is known to generate more likely superoxide anion (Sandy et al., 1986; Osburn
et al., 2006; Fussell et al., 2011). This might reflect an increased activity of SOD, a faster
turn-over of this enzyme because of its implication in O2- reduction, or the need for
hemocytes to increase sod transcription to maintain its basal activity. Constitutive expression
of cat might be sufficient to handle overproduction of H2O2 resulting from the reduction of
O2.- by SOD. As indicated by the slight up-regulation of hsp40 transcription observed at 24
hrs, accumulation of aggregated oxidized proteins might occur (Glover and Lindquist, 1998).
Furthermore cor up-regulation might reflect alterations of cytoskeleton, cell adhesion, cell–
cell connection (Chou et al., 2010), and/or induction of exocytosis (Sung and Weaver, 2011),
endocytosis (Clark et al., 2007), or autophagy processes in response to potential membrane
lipoperoxidation (Lee et al., 2010).
70
At higher concentrations (44.4 and 222.2 µg l-1), the toxicity of diquat to hemocytes
was more clearly evidenced. Most gene transcriptions were inhibited through time, and
transcript expression significantly dropped down after 48 hrs, under exposure to the highest
diquat concentration.
Significant induction of apoptosis by diquat was previously
demonstrated in L. stagnalis hemocytes after 48 hrs of exposure to concentrations of 44.4 and
222.2 µg l-1 (Lagadic, 2007). Diquat was also shown to induce apoptosis in vitro in murine
embryonic fibroblasts (Ran et al., 2004) and in rat neuronal cells (Zhang et al., 2012). There
is growing evidence that environmental toxicants able to induce alterations in redox
homeostasis and oxidative stress exert both agonistic and antagonistic effects on apoptotic
signaling (Curtin et al., 2002; Franco et al., 2009). Although apoptotic mechanisms may
differ between invertebrates and vertebrates, programmed cell death pathways are highly
conserved processes throughout evolution, with a tight regulation at both transcriptional and
post-translational levels (Kiss, 2010). Low transcription levels of ribosomal RNAs observed
after 48 hrs of exposure to diquat could thus reflect their degradation by apoptotic enzymes as
demonstrated in the yeast Saccharomyces cerevisiae submitted to oxidative stress (Mroczek
and Kufel, 2008).
Decrease of phagocytosis ability and lysosomal fragility have also been reported in L.
stagnalis under exposure to diquat (Lagadic, 2007). This herbicide may thus negatively affect
L. stagnalis immune system, making individuals more sensitive to pathogens and parasitic
attacks, as reviewed by Ellis (2011) for various invertebrate models exposed to different
environmental stressors.
Responses observed in the gonado-digestive complex
In the GDC, gene response to diquat-induced oxidative stress appeared definitely more
pronounced than in the haemolymph. Whatever the transcript studied, significant changes
were induced (except cat for which ANOVA analysis revealed only a trend to overtranscription; P = 0.054). In general, up-regulation was observed early (5 hrs) under all
treatments (except for rxr at 22.2 µg l-1). This confirms the rapid implication of this tissue to
handle diquat toxicity, as also reported in rodents (Burk et al., 1980; Smith et al., 1985; Sandy
et al., 1986; Fu et al., 1999). This is also consistent with the fact that digestive tissues are
considered as the primary tissue counteracting oxidative challenge caused by chemicals,
notably in fishes (Bilbao et al., 2010; Regoli et al., 2011).
71
Strong increases in hsp70 and hsp40 transcription indicated the need for hepatocytes to
refold or degrade damaged proteins (and to protect undamaged ones), and prevent protein
aggregation. HSP40 is known to stimulate the ATPase activity of the HSP70 chaperone
proteins (Qiu et al., 2006) which is also recognized to play a major role in proteasome
activation or inhibition during oxidative stress in murine embryonic fibroblasts (Grune et al.,
2011). Parallel to the present study, the transcriptome-wide analysis of L. stagnalis response
to diquat revealed over-transcription of a gene encoding the homologous protein NEDD4
(Bouétard et al., 2012). As this protein is known to be involved in ubiquitin mediated
proteolysis, this suggests that diquat exposure induces rapid oxidation of cellular proteins,
which are degraded once they have been tagged by ubiquitin. The 10-fold up-regulation of
polyubiquitin measured by RT-qPCR in the liver of rainbow trout exposed to diquat also
supports this hypothesis (Hook et al., 2006).
Marked observed effects of diquat on the activity of hepatic drug-metabolizing and
antioxidant enzymes occurred, at least in part, at the pre-translational level. Although nonstatistically supported, the trend to an early up-regulation of rxr transcription observed in
snails exposed to 44.4 and 222.2 µg l-1 diquat might reflect the set-up of an organized
antioxidant response.
As described above, the transcription factor RXR might enhance
transcription of peroxisomal enzymes such as cat and sod and induce peroxisomal
proliferation (Fong et al., 2010). Albeit pathways involving RXR and those inducing overtranscription of gred and gpx are not simply linked, it is suggested that the dose-dependent
over-transcription observed for these genes involved in glutathione cycle could also be
regulated by such transcription regulators. Glutathione and selenium have been shown to play
an important role in defence against diquat-induced toxicity and lipid peroxidation in plasma,
liver, kidney and lung of rats (Awad et al., 1994).
Also, significant contribution of
glutathione-reductase to face diquat-mediated injuries is reported to prevent renal necrosis in
mice (Rogers et al., 2006). In contrast, the involvement of GST in diquat detoxication has
never been clearly demonstrated, and several genes encoding different GST isoforms were
even down-regulated in liver of rats following diquat intraperitoneal injection (Gallagher et
al., 1995). Consistently, in the present study, increase in GST activity was not observed in
the GDC, despite a slight increase was graphically suggested in snails exposed for 5 and 24
hrs to 22.2 µg l-1 diquat. Significant up-regulation of transcription of gene encoding GSTZ1
was also detected in L. stagnalis at the whole organism scale, after 5 hrs of exposure to 222.2
72
µg l-1 (Bouétard et al., 2012). Several others copies of gst genes have also been obtained in
this transcriptome analysis, so that future investigations can be designed to clarify the role of
glutathione cycle in diquat toxicity mechanisms.
The up-regulation of gred and sod transcription in the GDC of diquat-exposed snails
reflects the need for hepatocytes to mediate of H2O2 and O2.- overproduction due to the
herbicide, whatever its concentration. Regarding the corresponding enzymatic activities,
increase of Gred and SOD activities were in agreement with transcript expression profiles,
except for the intermediate diquat concentration tested (44.4 µg l-1) for which no significant
increase was observed. It is unclear whether the observed effects resulted from a chemicallyinduced decrease in mRNA stability or from a decrease in protein translation and/or
activation. The significant up-regulation of ribosomal RNAs observed after 5 hrs of exposure
to this intermediate concentration of diquat may reflect alterations of translation mechanisms.
As for Gpx activity, mechanisms of folding and enzyme activation of Gred and SOD could be
altered by oxidative stress. Such differences between transcript expression and related
enzymatic activities have also been observed in gills and liver of eels exposed to polluted
sediments (Regoli et al., 2011). However, enzymatic activities remained close to control
levels throughout the experiment, indicating that basal levels of enzymatic activity might be
sufficient to deal with oxidative stress. Nevertheless, in some cases, up-regulation of the
transcription of genes encoding these enzymes may be necessary to maintain or increase this
constitutive activity.
The impact of diquat on transcript expression tended to be negatively related to the
exposure time, although herbicide concentrations in water were stable in the course of the
experiment. These responses should not be interpreted as an absence of toxicity of diquat
after 24 hrs, since toxic effects were clearly evidenced in hemocytes after 48 hrs of exposure
to the two highest concentrations. Possible explanations may be related to the fact that snails
were not fed during the experiment, so that they could not properly cover the maintenance or
the increase of enzyme activities which are energy-demanding. Circadian regulation may also
have interfered with exposure time comparisons. Indeed, such regulation mechanisms are too
often neglected in ecotoxicity tests although they may have a great importance, as
demonstrated in Drosophila melanogaster (Claridge-Chang et al., 2001; Hooven et al., 2009).
Nevertheless, we demonstrated that whatever the concentration, diquat induced significant
changes in gene transcription in both the haemolymph and GDC of L. stagnalis. Transcript
73
expression was shown to evolve on a short time scale, suggesting that snails are able to set up
a balance of phase I and phase II detoxication mechanisms to limit accumulation and toxicity
of reactive metabolites, and to initiate folding and degradation processes involving chaperone
proteins.
Conclusion and Perspectives
In the present study, we confirmed that diquat exerts toxic effects in non-target animal
species. Moreover and for the first time in an aquatic invertebrate, we elucidated part of the
underlying mechanisms at the molecular level. All genes studied here were shown to be
significantly impacted by diquat exposure to realistic environmental concentrations, making
them suitable biomarkers for the detection of oxidative stress in L. stagnalis. Although it is
too early to draw a precise mechanism of response to diquat from the set of studied genes,
hemocytes seemed to be more impacted as compared to the GDC. At the highest diquat
concentrations, possible induction of apoptosis in hemocytes suggests that this herbicide may
induce harmful effect on snail immune system. In the GDC, as expected, the strongest
changes were observed as soon as 5 hrs of exposure reflecting the setting of an active
response to oxidative stress. Transcriptomic resources recently developed in L. stagnalis
(Bouétard et al., 2012a; Sadamoto et al., 2012) will be most useful to elucidate the
mechanisms underlying toxicity of chemicals able to induce oxidative stress and other
molecular alterations such as e.g., endocrine disruption.
Acknowledgements
The project has been carried out with INRA financial support (EFPA Projet Innovant 2010;
AIP Bioressources 2010). AB is granted by the INRA (PhD, Contrat Jeune Scientifique).
The authors thank Marc Collinet and the INRA U3E staff for technical assistance, and LarsHenrik Heckmann for useful advices.
74
Chapitre III -
Développement de ressources
génomiques chez Lymnaea stagnalis par
pyroséquençage 454 : marqueurs
génétiques microsatellites et séquençage
de transcriptome
75
Grâce à leurs capacités d’exploration globale à l’échelle du transcriptome, les
techniques de séquençage de nouvelle génération (NGS) ont révolutionné la façon
d'appréhender les questions biologiques relatives aux réponses des organismes au stress
(Wang et al., 2009). En particulier, l’étude de l'impact des polluants est aujourd’hui permise
pour des organismes sans génome de référence (van Straalen & Feder, 2011) dans des
approches permettant de coupler l’acquisition de nouvelles ressources génétiques à la
quantification relative de la transcription des gènes.
Au début de la thèse, ces ressources génétiques étaient trop limitées chez L. stagnalis
pour entreprendre une quantification directe de la réponse transcriptomique par la technologie
RNAseq (Illumina Hiseq2000) et appréhender la diversité génétique neutre de populations
naturelles. Elles comportaient deux banques ESTs produites par séquençage de type Sanger
(Davison & Blaxter, 2005; Feng et al., 2009) sur le tissu nerveux, un jeu de 392 séquences
nucléotidiques codantes et 15 loci microsatellites (Knott et al., 2003; Kopp & Wolf, 2007),
pour la plupart trop peu polymorphes (voire non amplifiables dans certaines populations),
pour obtenir une résolution suffisante de la structure génétique des populations. Aussi, nous
avons utilisé le séquençage haut-débit (pyroséquençage 454, technologie Titanium) pour
d’une part, développer des nouveaux marqueurs microsatellites à partir d’ADN génomique
(projet EcoMicro) et d’autre part, réaliser un premier séquençage de transcriptome en
condition témoin (C) et de stress oxydant (D). Cette technique était en effet recommandée car,
générant des séquences relativement longues, elle permet un assemblage en contigs également
plus longs, avec comme conséquence positive une meilleure annotation.
Pour augmenter la diversité et la gamme d’expression observée, deux librairies
d’ADNc ont été préparées à partir de l’ensemble des tissus d’individus issus de trois souches
de L. stagnalis génétiquement différenciées (Fst moyen : 0.415*), exposés (D) ou non (C) à
l’herbicide diquat. Nous avons obtenu respectivement, 151.967 et 128.945 séquences de haute
qualité dans les librairies C et D après nettoyage (Pyrocleaner). L'assemblage des lectures a
produit 141999 contigs, dont 124387 étaient des singletons (longueur moyenne des contigs
non-singlets : 761 bp ; singlets : 438 bp). La recherche d’alignements BLASTX sur les bases
de données publiques (SwissProt, Refseq-Prot, Refseq-RNA, TrEMBL) a révélé des
correspondances significatives pour 34,6% des contigs et 21,2% d’annotation protéique.
L’annotation fonctionnelle contre la base KEGG a permis d’identifier plus de 400 gènes
impliqués dans le stress oxydant, les voies de signalisation et de réponse au stress
76
cellulaire/moléculaire telles que l’apoptose ou le métabolisme des xénobiotiques. L’analyse
d’expression différentielle selon la méthode adaptée à des plans expérimentaux sans réplicats
proposée dans le package DEseq (Anders & Huber, 2010) suggèrent une grande diversité
d’effets moléculaires pour le diquat, outre l’effet spécifiquement lié au stress oxydant. De
plus, 3708 potentiels marqueurs SNPs (Single Nucleotide Polymorphism) répartis sur 1342
contigs, ont également été détectés lors de cette étude et pourront s’avérer des outils utiles
pour de futures investigations.
En conclusion, les données de pyroséquençage 454 ont fourni des ressources
transcriptomiques permettant de générer des hypothèses sur les diverses voies moléculaires
impliquées dans la réponse au stress qui seront testées lors de l’analyse de l’expression
différentielle par RNAseq sur des populations naturelles de L. stagnalis. Ces données sont
publiquement accessibles sur la base SRA (lectures brutes) ainsi que sur le contig-browser
mis en place par Génotoul (contigs annotés). Ce travail a été accepté en juillet 2012 et publié
en novembre 2012 dans la revue Ecotoxicology (vol 21: 2222-2234).
De plus, la mise au point à partir des 174 loci à motifs parfaits et primers valides, de
neuf marqueurs microsatellites et de leur amplification/analyse sous forme de 3 sets de
multiplexes, a été réalisée en collaboration avec A.L. Besnard (UMR ESE), et a fait l’objet
d’une Primer Note dans Molecular Ecology Ressources parue en janvier 2013.
77
Article 2
-
Pyrosequencing-based transcriptomic resources in the pond snail
Lymnaea stagnalis, with a focus on genes involved in molecular
response to diquat-induced stress
Bouétard Anthony1*, Noirot Céline2*, Besnard Anne-Laure1, Bouchez Olivier3, Choisne
Damien1, Robe Eugénie3, Klopp Christophe2, Laurent Lagadic1, Coutellec Marie-Agnès1§
1
INRA, UMR INRA-Agrocampus Ouest ESE, Equipe Ecotoxicologie et Qualité des Milieux
Aquatiques. 65 rue de Saint-Brieuc, 35042 Rennes cedex, rance.
2
Genotoul, Plateforme Bioinformatique, INRA Toulouse-Auzeville, Chemin de Borderouge,
31326 Castanet Tolosan, France
3
Genotoul, Plateforme Génomique, INRA UMR0444 LGC Toulouse-Auzeville, Chemin de
Borderouge, 31326 Castanet Tolosan, France
*These authors contributed equally to this work
§
Corresponding author: [email protected]
Keywords: Ecotoxicology, Pesticides, Oxidative Stress, Transcriptomics, Pyrosequencing,
Lymnaea stagnalis.
78
Abstract
Due to their ability to explore whole genome response to drugs and stressors, omicsbased approaches are widely used in toxicology and ecotoxicology, and identified as powerful
tools for future ecological risk assessment and environmental monitoring programs.
Understanding the long-term effects of contaminants may indeed benefit from the coupling of
genomics and eco-evolutionary hypotheses. Next-generation sequencing provides a new way
to investigate pollutants impact, by targeting early responses, screening chemicals, and
directly quantifying gene expression, even in organisms without reference genome. Lymnaea
stagnalis is a freshwater mollusk in which access to genomic resources is critical for many
scientific issues, especially in ecotoxicology. We used 454-pyrosequencing to obtain new
transcriptomic resources in L. stagnalis and to preliminarily explore gene expression response
to a redox-cycling pesticide, diquat. We obtained 151,967 and 128,945 high-quality reads
from control and diquat-exposed L. stagnalis, respectively. Sequence assembly provided
141,999 contigs, of which 124,387 were singletons. BlastX search against public databases
revealed significant match for 34.6 % of the contigs (21.2% protein hits). GO categories were
evenly distributed among differentially and equally expressed contigs between control /
pesticide-exposed snails. KEGG annotation showed a predominance of hits with genes
involved in energy metabolism and circulatory system, and revealed more than 400 putative
genes involved in oxidative stress, cellular/molecular stress and signaling pathways,
apoptosis, and metabolism of xenobiotics. Moreover, new genetic markers (putative SNPs)
were discovered. We also created a Ensembl-like web-tool for contig data-mining
(http://genotoul-contigbrowser.toulouse.inra.fr:9095/Lymnaea_stagnalis/index.html).
This
new resource is expected to be relevant for any genomic approach aimed at understanding the
molecular basis of physiological and evolutionary responses to environmental stress in L.
stagnalis.
79
Introduction
Omics-based approaches are now widely used in toxicological and ecotoxicological
research, and their application to ecological risk assessment and environmental monitoring
programs is a question of current discussion (Snape et al., 2004; Martyniuk et al., 2011;
Villeneuve and Garcia-Reyero, 2011). Recent progress in sequencing technology (NGS, nextgeneration sequencing), Omics, and bioinformatics provides indeed extremely powerful tools
to investigate processes underlying biological response to stressors, for example, through the
framework of system biology. Adverse outcome pathways can be inferred from molecular
interaction networks, and causal / regulatory relationships as well as hypotheses about key
biological processes can be tested with statistical modelling (Perkins et al., 2011). Under this
framework, it is possible to link molecular processes to higher levels of biological
organization, i.e., individual phenotype and population, and to community-level relationships.
At the scale of an ecosystem, changes in ecological functions may also be potentially inferred
from molecular trait-based ecology combined with metagenomic approaches (Raes et al.,
2011). System biology approaches are currently implemented in predictive ecotoxicology
(Watanabe et al., 2011), which appears particularly suited to ecological risk assessment. In
particular, they are appropriate to the study of stressors which mode of action is unknown or
not fully characterized (which is likely for numerous pesticides / non-target species systems),
and also to the study of complex mixtures (as effectively encountered by organisms in their
real environment). Furthermore, Omics tools are not restricted to model-species anymore, and
can now be successfully applied to species of eco-evolutionary or ecotoxicological
significance (Vera et al., 2008; Martyniuk et al., 2011).
NGS allows pollutant impact to be investigated in a completely new and hopefully
more comprehensive manner. First, gene expression is thought to be among the earliest
molecular responses to be triggered in exposed organisms, which makes toxicogenomic
endpoints good candidates as markers of early effects (van Straalen and Roelofs, 2008).
Second, transcriptome-wide approaches allow various chemicals to be screened and
classified, based on the mechanistic description of their mode of action (Garcia-Reyero et al.,
2011; Villeneuve and Garcia-Reyero, 2011). Third, high through-put sequencing techniques
allow direct quantification of expression, even for rare transcripts (Wang et al., 2009).
Compared to microarrays, NGS techniques have their own biases, however sequencing has
been shown to have higher sensitivity and dynamic range, coupled with lower technical
80
variability. Moreover, sequencing has greater ability to provide important information on
various transcriptional characteristics, such as novel transcribed regions, allele-specific
expression, RNA editing, or alternative splicing (Oshlack et al., 2010). Finally, sequencingbased gene expression studies are possible for any species, including those for which no
microarrays exist.
The evolutionary impact of human-induced environmental disturbance to wildlife is a
central issue in conservation biology (Smith and Bernatchez, 2008). Evolutionary responses
to environmental stress may occur rapidly (Hoffmann and Hercus, 2000), and the rate of
evolutionary change can be altered by human factors (Hendry et al., 2008). The question of
long-term impact also represents a serious concern in the field of ecological risk assessment,
especially for xenobiotic substances, since such an impact may have negative consequences
on ecosystem sustainability. Therefore, new assessment criteria have been recently proposed
to account for evolutionary impact, using e.g., population genetics and evolutionary indices or
parameters (Breitholtz et al., 2006; Bickham, 2011; Coutellec and Barata, 2011). The
incorporation of genomic tools into evolutionary issues appears promising in this context, as
hypotheses can be built and tested on a mechanistic basis.
From the above, it is evident that access to genomic resources in non-model species is
critical for ecotoxicological and conservation issues. Among invertebrate models, Lymnaea
stagnalis is a freshwater mollusk representative of lentic habitats, and of the herbivorous
community compartment. It is currently used as a model in various research fields, including
aquatic ecotoxicology (Coutellec et al., 2011; Byzitter et al., 2012), evolution (Coutellec and
Caquet, 2011), behavioral ecology (Hoffer et al., 2010), parasitology (Adema et al., 1994),
and neurophysiology (Carter et al., 2006), all of which are in need for genomic resources.
Current genetic resources are composed of two EST banks based on Sanger sequencing
(Davison and Blaxter, 2005; Feng et al., 2009), and a set of 392 nucleotidic sequences
(Genbank).
A
genome
project
is
under
construction
in
L.
stagnalis
(see
http://www.lymnaea.org). Finally, L. stagnalis is also currently candidate as model species for
standardized toxicity tests (under validation, OECD).
In this context, we developed a project based on the model L. stagnalis, with the main
goal of exploring the evolvability of gene expression responses to pesticides, at the
transcriptome level (de novo RNAseq). Here is presented the first step of the transcriptomic
81
study, in which genomic resources were obtained by pyrosequencing L. stagnalis cDNAs
from various pooled tissues. Laboratory-reared individuals originating from three different
populations were pooled, in order to include some level of genetic diversity and to minimize
the risk of idiosyncratic patterns due to the use of a single isolated lineage. In order to further
broaden the range of expression, two libraries were constructed (i) one from individuals
exposed to a redox cycling herbicide, diquat, and (ii) one from control individuals. Diquat is a
non selective herbicide (chemical class: bipyridylium) widely used to control crops (potato,
cotton and other seed crops), but also submersed and floating weeds (US-EPA, 1995). L.
stagnalis is thus likely to be regularly exposed to diquat, either directly when the water is
treated, or indirectly, since lentic aquatic systems are the ultimate recipient of many
chemicals, through various transfer processes (Brown and van Beinum, 2009). As a redox
cycling molecule, diquat generates ROS (reactive oxygen species) and oxidative stress. ROS
are thought to mediate the toxicity of oxygen, yet they also operate as intracellular signaling
molecules (Mager et al., 2000), under mechanisms that are not yet fully understood, even in
the most widely studied models, i.e., mammalian models (Poulsen et al., 2000; D'Autreaux
and Toledano, 2007). Oxidative stress is known to mediate trade-offs between reproduction,
immunity, lifespan and aging (Monaghan et al., 2009). This feature may thus have important
consequences for fitness-related traits. In L. stagnalis, diquat has been shown to affect
various life history traits (Coutellec et al., 2008; Ducrot et al., 2010), and its effect on several
genes involved in cellular stress (oxidative stress, metabolism of xenobiotics, and chaperone
activity) has been preliminarily explored in haemolymph and digestive gland, to determine
the most contrasted molecular response among 3 exposure time (5, 24 & 48h) and 3
concentrations (22.2, 44.4 & 222.2 µg/L) compaired to controls (Bouétard et al., 2013).
The present study reports on the generation and analysis of EST sequences based on
454 pyrosequencing of two L. stagnalis transcriptomic libraries, stemming from control and
exposure to diquat conditions (short read assembly, contig annotation, functional annotation,
differential expression) and on the creation of an publically accessible genomic resource web
tool, which allows sequence and annotation retrieval, as well as SNP putative location
(http://genotoul-contigbrowser.toulouse.inra.fr:9095/Lymnaea_stagnalis/index.html ; Great
Pond Snail Contig Browser). This tool is expected to be useful for any upcoming genomic
approach developed in L. stagnalis, from single gene expression to complex molecular
pathways and population genomics. In the toxicological context of the study, functional
82
annotation (Gene ontology, KEGG pathways) allowed the identification of a number of genes
potentially useful to investigations on cellular and molecular response to oxidative stress in a
multidisciplinary invertebrate model. We also present preliminary results on differential
expression under diquat exposure.
Material and methods
Biological material
L. stagnalis individuals were taken as adults from the INRA U3E laboratory cultures
(Experimental Unit of Aquatic Ecology and Ecotoxicology, INRA, Rennes). Rearing
conditions have been described earlier (Coutellec et al., 2008). Snails from three different
origins were used: a local population (Le Rheu, France), a culture from VU University
Amsterdam (Netherlands) and a natural population from Sandbjerg (Denmark).
Half of the snails were individually exposed to a diquat solution of 222.2 µg/L for 5
hours, and the other half was maintained under control conditions. This concentration was
chosen as it is close to the maximum recommended application rate (Emmett, 2002), and
because it falls in the range of field concentrations measured after spraying (Emmett, 2002).
Moreover, we applied the concentration we used in previous studies, with the purpose of
comparing results. Several life history traits were indeed shown to be impaired in snails
exposed to this concentration (adult growth, progeny development time and hatching success ;
Coutellec et al. 2008). Exposure time was determined on the basis of preliminary expression
experiments using candidate genes (Bouétard et al., 2013). Three days prior to contamination,
animals were isolated from the culture, placed in exposure vessels, and fed until the last
24hrs. Feces and salad leftovers were removed from the vessels just before contamination. In
order to limit potential stress due to water change, contamination was performed by adding a
small volume (5ml) of stock solution (concentration: 10.66 mg/L) to the initial water volume
(240 ml).
Tissue sampling and processing
Five hours after contamination, haemolymph was collected as described earlier (Russo
and Madec, 2007) and centrifuged (2 min at 4000 rpm). Pellets were homogenized in 350 µL
of lysis buffer (Norgen Biotek) and stored at -80°C. Snails were then quickly plunged into
liquid nitrogen, and their body was rapidly split into three parts: (i) digestive gland and
83
ovotestis (both are intricate and cannot be easily separated), (ii) front part of the body
(including : head, anterior part of the digestive tract and distal reproductive structures, central
nervous system, tegument, mantel and part of the foot), (iii) posterior part of the body
(essentially foot and proximal part o f the reproductive apparatus, except ovotestis). As the
aim was to explore the whole range of transcriptomic expression, all tissues and organs were
included. Each part was stored in RNAlater at -80°C until further processing.
RNA preparation
Total RNA was extracted from haemolymph using Total RNA extraction Kit (Norgen
Biotek), and from solid tissues using TRIzol reagent (Invitrogen). Haemolymph RNA was
extracted from pooled samples taken from 3 to 4 individuals. A total of 16 different
extractions were performed (8 control, 8 diquat exposure conditions). For solid tissues, RNA
was extracted from pooled samples corresponding to 9 individuals. One extraction per
condition per tissue category was performed (i.e., 6 different extractions in total). For each
treatment, the same number of individuals was used. RNA quantity and purity were assessed
with Nanodrop (Thermo, Fisher Scientific). Absorbance ratio values were A260/A280 = 2.04
± 0.07 and A260/A230 = 1.52 ± 0.18 for haemolymph, and A260/A280 = 2.05 ± 0.03 and
A260/A230 = 1.93 ± 0.11 for solid tissues. The amount of tissue used and of total RNAs
obtained from each sample and condition, are available as supplementary material (Table S1).
Total RNAs (45 µg from each pool of solid tissue samples, whole haemolymph RNAs) were
treated with DNAse I (Promega) and purified with the RNeasy Minelute Cleanup Kit
(Qiagen). Absence of genomic DNA was PCR-checked. RNA quality was determined with
Agilent 2100 Bioanalyzer (Agilent Technologies). Messenger RNAs (polyA-tailed) were then
purified using the purification kit Dynabeads® Oligo (dT) 25 (Invitrogen). RNA amounts
were controlled at each step.
cDNA synthesis
This step was performed following Leroux et al. (2010). First strand cDNA was
synthesized and using the SuperscriptTM II RT kit from Invitrogen. An oligodT modified
primer (GAGAGAGAGACTGGAG(T)16VN) containing the GsuI restriction site was used,
in order to cut the polyA tail after synthesis. To avoid internal cleavage by GsuI, methylated
84
dCTP was used (dm5CTP, Fermentas) instead of dCTP (see manufacturer’s protocol). The
Second strand synthesis protocol was used to synthesize the second strand cDNA: 30 µL of
5x Second strand Reaction Buffer 20% (Invitrogen) were added (1x final concentration), 3 µL
of 10mM dNTP mix (0.2 mM final concentration), 10 U of E. coli DNA ligase, 40 U of E.
coli DNA Polymerase, and 2.5 U of E. coli RNAse H (all enzymes from New England
Biolabs). Samples were then incubated at 16°C for 2 hours. After adding 10 U of T4 DNA
Polymerase (New England Biolabs), samples were incubated at 16°C for 5 minutes. EDTA
was used to stop the reaction (30 mM final concentration). Finally, samples were added 5 U
of Ribonuclease I (Fermentas) and incubated for 15 minutes at 37°C. Double-strand cDNAs
were then purified using a phenol-chloroform-isoamyl alcohol extraction (25:24:1) with Phase
Lock Gel (Phase Lock Gel Light, 2ml, 5 Prime), following the manufacturer’s protocol.
Pellets were suspended in 6 µL of DEPC-treated water, and cDNA concentration was
estimated on Nanodrop. Two pools of cDNA (control and exposure conditions) were then
created from an equimolar mixture of the four sample types (haemolymph, GDC, BAP, BBP).
Digestion of cDNAs by GsuI was performed at 30°C for one hour, with 5 U of the enzyme
(Fermentas). Samples were then placed at 65°C for 20 minutes, to stop the reaction.
Library construction and 454 pyrosequencing
For each pool, the amount and quality of cDNA was estimated using a picogreen
dosage (500 and 502 ng of C and D library were used, respectively). DNA was fragmented by
nebulization and adaptors were ligated (GS Rapid Library Prep kit). Emulsion PCR was then
performed using GS FLX Titanium LV emPCR Kit, and the two libraries were pyrosequenced
(each on half a run, as physically separated). Sequencing was performed on the Roche 454
Genome Sequencer FLX, using the GS FLX Sequencing Kit Titanium Reagents XLR70. All
steps were performed following Leroux et al. (2010).
Sequence cleaning and assembly
Raw sequences (reads) were cleaned with the software Pyrocleaner (Mariette et al.,
2011), using the following criteria: (1) complexity/length ratio less than 40 (using a sliding
window approach based on 100 bp sequence length, and a step of 5 bp); (2) duplicate read
removal (see bias associated with pyrosequencing, due to the random generation of duplicate
reads); (3) removal of too long/too short reads (maximum read length= mean read length ±
85
2SD); (4) removal of reads with too many undetermined bases (more than 4%).
Contaminations were discarded by searching hits against the following databases: ecoli536
2011-01-10, genbank_phage178 and Saccharomyces_cerevisiae 2011-01-07. Finally, polyAtailed reads and reads shorter than 100pb were removed from the dataset using Seqclean.
Reads were assembled into contigs with Tgicl (Pertea et al. 2003). External databases
were also included (11697 sequences of L. stagnalis dbEST and 279 sequences from
Genbank, which after cleaning by Seqclean represented 11639 sequences).
Contig annotation
NCBI BlastX or Blastn was used to search for sequence homology between contigs
and the following generalist databases: UniProtKB/Swiss-Prot, UniProtKB/TrEMBL Release
2011_02 of 08-Feb-2011, RefSeq Protein Index Blast of 09-Jan-2011, Pfam Release 24.0 of
Jul-2009, and RefSeq RNA Index Blast of 09-Jan-2011. The cut-off e-value was set to 1e-5.
The following species specific databases were queried with a cut-off e-value of 1e-2: TIGR
Spisula solidissima CLAMGI 1.0, UniGene Aplysia_Californica Build #8, UniGene
Lottia_gigantea Build #2. Finally, we realigned our contigs with the cut-off e-value of 1e-30
(Pondsnail_Contigs_V1 database).
Putative function of unigene elements having at least one significant hit against
generalist databases was determined using Gene Ontology (GO) and its GOslim generic
version (http://www.geneontology.org/GO.slims.shtml). Functional annotation was also
performed using “single best hit” Blast comparison against the manually curated KEGG genes
database, with the program KAAS (KEGG Automatic Annotation Server: http:
www.genome.jp/tools/kaas/). Annotated sequences were further categorized using KEGG
Orthology (KO) and the BRITE hierarchy for protein families, where four top categories were
examined:
metabolism,
genetic
information
processing,
environmental
information
processing, and cellular processes. Pathways relative to organismal systems were also
investigated. Some specific pathways were examined further, due to their link with oxidative
stress, cell and molecular stress and signalling, and apoptosis.
86
Differential expression
As each read could be reassigned to the initial library, i.e. control (C) or diquatexposed (D) pool, contigs were characterized by their relative number of C and D reads.
Singlets and contigs containing only external sequences were excluded. Among the remaining
contigs, some were exclusively composed of C or D reads, the others being a mixture thereof.
Functional annotation (GO terms) was first globally compared among three categories of
annotated contigs: (1) exclusively expressed and over-expressed under diquat exposure (D,
and D>C according to Fisher exact test), (2) exclusively expressed under control condition
and under-expressed under diquat exposure (C, and C>D according to Fisher exact test), and
(3) equally expressed under control and diquat exposure conditions (C=D, Fisher exact test).
Differential expression (DE) was then tested using DESeq package (Anders and
Huber, 2010). The analysis was performed after library normalization (function
estimateSizeFactor), and following the procedure for data without replication. However, due
to a large variance between libraries, the test was extremely conservative (only 6 DE cases).
In order to elaborate some hypotheses to be further tested (using RNAseq on replicated data
from Illumina Hiseq2000; ongoing project), we finally investigated KEGG annotation in
contigs for which Log2Fold-Change fell outside the range [-2; +2] i.e., exhibiting a 4-fold
increased expression under one of the conditions (after normalization). For contigs with no
read in either control or diquat exposure condition, we selected those with at least 4 reads in
the other condition. Then, we discarded annotation hits which were also found in the set of
“non differentially expressed” contigs, as based on the criterion of |Log2FoldChange|<2.
Furthermore, we also excluded all annotations with identity less than 50%.
SNPs detection
Putative SNPs (single-nucleotide polymorphism) were screened for contigs with a
coverage depth of more than seven sequences. If the less frequent allele count was more than
two and 100 % identical for four bases before and after the polymorphic site, we considered
this site as a putative SNP. The putative SNPs were identified among 1362 (7.7%) contigs,
resulting in 3708 sites with putative polymorphism.
87
Results & Discussion
The prime objective of the present study was to increase the genomic resources in
Lymnaea stagnalis and to make them available to the scientific community who uses this
species as model. A Ensembl-like public database (Great pond snail Contig Browser) has
been constructed which includes previously available sequences, and the ones developed in
the present study (141,999 contigs in total). Contigs and annotations can be accessed directly
from the database, and data mining can be performed using the incorporated Biomart tool.
Pyrosequencing and contig assembly of ESTs
Pyrosequencing of the two libraries led to a total of 406,245 raw sequences (after
using Roche quality filters). Table 1 summarizes the effect of cleaning procedures on the final
number of sequences. In total, 30.8 % of the initial reads were discarded from the dataset
(37.1% in library C, 21.6% in library D). Sequence assembly was performed using reads from
the present study and external L. stagnalis nucleotide sequences. A total of 141,999 contigs
was generated, of which 124,387 were singlets and 17,612 non singlets. Mean length was
761bp for non singlet contigs and 438 bp for singlets. Excluding singlets, contig mean depth
was 9.54 reads. The whole set of short reads is submitted to SRA (Sequence Read Archive,
National Center for Biotechnology Information; accession [SRA049626.2]). Assembled
contigs
are
publically
available
at
http://genotoul-
contigbrowser.toulouse.inra.fr:9095/Lymnaea_stagnalis/index.html.
Table 1. Results of cleaning processes applied to the raw sequences obtained from
L. stagnalis C and D libraries. The number of sequences is given for each library.
Sequence type
Initial sequences
Too short / too long sequences
Sequences with too many Ns
Low complexity sequences
Duplicate sequences
Contaminations*
Final sequences
C
D
241449
5631
431
12481
70808
131
151967
164408
2998
194
9816
22333
122
128945
* from Blast against ecoli536, genbank_phage 178 and
Saccharomyces cerevisiae.
88
Annotation
BLAST search led to the annotation of 34.2% of the 141,999 contigs, with significant
E-value (cut-off value set to 1e-5): 4.7 % against SwissProt, 13.8 % against TrEMBL, 2.7 %
against RefSeqProt, and 13% against RefSeqRNA. The mean identity obtained with these 4
annotation databases was 64, 59, 53, and 92%, respectively. Fig.1 illustrates the distribution
of identities according to the database. It is to be noted that high identities (> 90%) stemmed
mostly from matches with Swissprot and RefseqRNA databases. With regard to Swissprot,
this is likely to result from the automatic preference given to this database compared to
frequency
TrEMBL when both produce similar identity values.
0.9
0.8
0.7
0.6
0.5
0.4
0.3
0.2
0.1
0
RefSeqProt
SwissProt
TrEMBL
RefSeqRNA
20
30
40
50
60
70
80
90
100
% identity
Fig.1. Distribution of annotation quality according to the database used in BlastX
(percent identity between query and database sequences). Dataset = LsContig V1.
Functional annotation
General processes
GO annotation was summarized using the GO_slim generic ontology. GO terms
(6,540 in total, ignoring 26 obsolete terms) were mapped to 556 GO_slim keywords and to
113 GO_slim classes. Main categories were represented as follows: biological process
(31.26%), cellular component (33.60%), and molecular function (35.14%).
Second, the KEGG database was also used. KEGG annotation helped identify
predominant processes within each top category (Fig. 2). Energy metabolism was by far the
most represented (metabolism category). In other categories, patterns were less marked.
Nevertheless, translation predominated in genetic information processing, as did signal
transduction in environmental information processing, and cell communication in cellular
processes (although transport and catabolism were also well represented in the latter) (Fig. 2).
89
Considering organismal systems, most contigs matched with GO terms related to the
circulatory system, whereas excretory system, development and environmental adaptation
were the least represented (Fig. 3). It is to be mentioned that without a sequenced genome in
L. stagnalis, observed abundances of GO and KEGG terms cannot be compared to any
reference.
Number of contigs
0
2000
Carbohydrate Metabolism
Energy Metabolism
Lipid Metabolism
Nucleotid Metabolism
Amino Acid Metabolism
Metabolism of Other Amino Acids
Glycan Synthesis and Metabolism
Metabolism of Cofactors and Vitamins
Metabolism of Terpenoids and Polyketides
Biosynthesis of Other Secondary Metabolites
Xenobiotic Biodegradation and Metabolism
Transcription
Translation
Folding, Sorting and Degradation
Replication and Repair
Membrane Transport
Signal Transduction
Signaling Molecules and Interaction
Transport and Catabolism
Cell Motility
Cell Growth and Death
Cell Communication
4000
6000
8000
Metabolism
Genetic Information Processing
Environmental Information Processing
Cellular Processes
Fig.2. Number of contigs classified according to the KEGG top categories.
10000
8000
6000
4000
2000
0
Im
10000
E
De
Ne
Se
Ex
Di
En
Ci
ge
rc
mu nd o
ns
cr
v ir
ve
ula
eto r vou
sti
cr
or
on
l
o
ne
p
ine
v
t
y
s
r
m
o
e
m
y
Sy
Sy
Sy
en
Sy
en
Sy
Sy ry S
s
ste
s
tal
s
t
t
t
s
y
s
e
e
t
t
m
ste
te
em
m
e
m
Ad
m
m
m
ap
ta
ti o
n
Fig.3. Number of contigs classified according to the subcategories of
KEGG organismal systems category (LsContigV1 annotated contigs).
90
12000
Stress response related genes
Genes involved in various molecular pathways were searched for using KEGG
annotations and KEGG pathways. In the context of the study, some pathways were given a
specific attention (Tab. 2; see also detailed annotations per pathway in supplementary Table
S2 to Table S13). Significant homology was found with 403 different genes involved in 13
pathways related to oxidative stress, antioxidant defense mechanisms, molecular and cellular
stress, apoptosis, and metabolism of xenobiotics (Tab. 2).
Table 2. Number of genes having at least one significant match (besthit annotation) in Pondsnail_Contig_V1, and involved in 13 KEGG
pathways selected for their implication in oxidative and molecular
stress.
Molecular pathway
KEGG
pathway
code
Number of
annotations
in
Pondsnail_
Contigs_V1
Alanine, aspartate and glutamate metabolism
Apoptosis
Ascorbate and aldarate metabolism
Cystein and methionin metabolism
Glutathione metabolism
MAP kinase signaling
Metabolism of xenobiotics by cytochrome P450
Oxidative phosphorylation
p53 signaling
Peroxisome
Porphyrin and chlorophyll metabolism
Protein processing in endoplasmic reticulum
Retinol metabolism
ko00250
ko04210
ko00053
ko00270
ko00480
ko04010
ko00980
ko00190
ko04115
ko04146
ko00860
ko04141
ko00830
24
20
8
20
29
56
12
62
19
45
17
76
15
Pathways related to the metabolism of non-enzymatic antioxidant scavengers were
well represented (glutathione: 29 genes, ascorbate and aldarate: 8 genes). Cysteine and
methionine metabolism was represented by 20 genes (supplementary Table S4), among
which, cystathionine-γ-lyase, involved in the synthesis of cysteine, a critical amino-acid for
glutathione synthesis (Diwakar and Ravindranath, 2007). Main enzymes involved in oxidative
stress were represented, such as superoxide dismutases (Cu/Zn and Fe/Mn families),
glutathione peroxidases, glutathione reductase, peroxiredoxins, glutathione S-transferases and
catalase (Chaudière and Ferrari-Iliou, 1999; Wood et al., 2003; Rogers et al., 2006).
Numerous genes involved in the peroxisome pathway had significant matches in the set of L.
91
stagnalis contigs (supplementary Table S10). Significant matches with genes implicated in
apoptosis are listed in Table 3, with representatives of both intrinsic (AIF, EndoG) and
extrinsic (caspase 8, IAP, etc.) pathways (Riedl and Shi, 2004).
Table 3. Contig annotations matching with genes involved in apoptosis pathway (ko04210)
(IAP = inhibitors of apoptosis)
Gene name (enzyme code)
Gene code
KEGG
Apoptosis regulator
Apoptosis regulator BCL2
Ataxia telangectasia mutated family protein (EC 2.7.11.1)
Baculoviral IAP repeat containing protein 2/3/4
cAMP-dependant protein kinase regulator (EC 2.7.11.1)
Caspase 7 (EC 3.4.22.60)
Caspase 8 (EC 3.4.22.61)
Cytochrome C
DNA fragmentation factor, 45 kD, alpha subunit
Endonuclease G (EC 3.1.30.-)
Inhibitor of nuclear factor kappa-B kinase subunit alpha (EC 2.7.11.10)
Interleukin 1 receptor-associated kinase 4 (EC 2.7.11.1)
Myeloid differentiated primary response protein
Neurotrophic tyrosine kinase receptor type 1 (EC 2.7.10.1)
NFKBIA: NF-kappa-B inhibitor alpha
Nuclear factor NF-kappa-B p105 subunit
Phosphatidylinositol-4,5-bisphosphate kinase (EC 2.7.1.153)
Programmed cell death (apoptosis-inducing factor) (EC 1.-.-.-)
Protein phosphatase 3, catalytic subunit (3.1.3.16)
Protein phosphatase 3, regulatory subunit
RAC serine / threonine protein kinase (EC 2.7.11.11)
BAX
BCL2
ATM, TEL1
IAP, BIRC_2_3_4
PRKAR
CASP7
CASP8
CYC
DFF45 (DFFA)
EndoG
IKBA, IKKA, CHUK
IRAK4
MyD88
NRRK, TRKA
I B
NFKB1
PIK3C
PDCD8,AIF
Cn, PPP3C
Cn, PPP3R
PKA
K02159
K02161
K04728
K04725
K04739
K04397
K04398
K08738
K02310
K01173
K04467
K04733
K04729
K03176
K04734
K02580
K00922
K04727
K04348
K06268
K04345
BlastX homology search found matches with other important genes, such as the one
encoding biliverdin reductase, which role in antioxidant defense was recently demonstrated
(Barañano et al., 2002), GADD45, a p53 target gene which is oxidative stress-responsive
(Han et al., 2008), ferritin heavy chain, which reduces ROS production (H2O2) and apoptosis
(Berberat et al., 2003; Aung et al., 2007), or a member of the ataxia telengectasia mutated
protein family (ATM), which in humans regulates cellular response to oxidative stress (Guo et
al., 2010) (see supplementary Table S9 for other p53 target genes). Regarding inflammatory
response and oxidative stress in the gastro-intestinal system, annotations were also obtained
for a set of important genes: oxidative stress-sensitive transcription factor NFkB,
prostaglandin-endoperoxide synthase 2 (COX2, regulated by NFkB), myeloperoxidase (MPO)
and inducible nitric oxide synthase (iNOS). Moreover, these three enzymes have been shown
to be affected by diquat (Anton et al. 2007). Inhibitors of NFkB (IKBKA, Nemo-like kinase
92
NLK) were also identified. With respect to the mitogen-activated protein kinase (MAPK)
cascade triggered by the accumulation of ROS (Son et al., 2011), important genes were
annotated such as Upf1, a regulator of non-sense transcript which has been shown to control
gene expression in response to oxidative stress . Several contigs significantly matched with
the ROS receptor KEAP1 NRF2-associated protein and with cullins (see KEAP1-NRF2
pathway functions in oxidative and environmental stress tolerance (D’Autréaux and Toledano
2007). Nrf2 (which is bound to KEAP1) has been shown to induce a number of enzymes
involved in ROS metabolism, in mice exposed to diquat (Osburn et al., 2006; Sun et al.,
2011). In the MAP kinase signaling pathway, matches with extracellular signal-regulated
kinase (ERK), p38 Map kinase (P38), TGF-beta receptor (TGFBR1) and related enzymes
were identified, with more than 50 annotated genes involved in this pathway (supplementary
Table S6). We also identified significant hits with other various genes of which expression
was previously shown to be affected by diquat: crystallin (CRYAB), calmodulin (CALM),
interleukin1 receptor (Waring et al., 2001). Finally, Pondsnail-Contigs_V1 had significant
hits with 12 genes involved in the metabolism of xenobiotics by cytochrome P450 (KEGG
pathway ko00980; supplementary Table S7).
Globally, the new database provides access to the sequence of a number of new
candidate genes involved in L. stagnalis response to stress.
Differential expression under diquat exposure
Differential expression was estimated by comparing the composition of non-singlet
contigs, in terms of number of reads (RNAseq) from the two libraries (control and exposure to
the herbicide diquat), and using Fisher’s exact test. Excluding singlets, 49.3% of the contigs
were differently expressed among the two libraries. Within these contigs, 46.4% were overexpressed in snails exposed to diquat (53.6% under-expressed). The percentage of annotated
sequences in the two categories was 32.7% and 27.6%, respectively.
GOslim terms were compared among three categories of annotated contigs, i.e., overexpressed under diquat exposure, under-expressed under diquat exposure, equally expressed
among control and exposure conditions. Results were plotted for the main categories using
WeGo plotting tool (Fig. 4). Quantitatively, the various GO categories were evenly
represented among the three groups of contigs, except for organelle part, auxiliary transport
protein, and rhythmic processes (not represented in over-expressed contigs), virion part and
93
synapse part (only represented in under-expressed contigs), and nutrient reservoir and protein
tag (exclusively represented in over-expressed contigs). However, all these categories were
very weakly represented, which might be largely responsible for the observed patterns. More
detailed annotations are provided, including statistical testing, for cellular components,
molecular functions, and biological processes, respectively, as additional files (supplementary
Tables S14, S15, S16).
Figure 4. Comparison of the distribution of GO terms among three groups of contigs from
Pondsnail_Contigs_V1. The X-axis shows subgroups of the three top categories of GO. The Y-axis
shows the percentage (log-scale) of matched sequences in three groups of annotated contigs having at
least one GO hit: non-differentially expressed among C and D libraries, overexpressed in D library,
and underexpressed in D Library.
Analysis of contigs exhibiting |Log2FoldChange| >2 revealed a subset of 94
annotations specific to these contigs. From this set, KEGG annotation identified various
potentially affected pathways, which may be used to elaborate a signature of diquat effect on
transcriptomic expression (Table 4). Several other contigs were excluded from this list, due to
the fact that they shared the same best hit gene with contigs with a Log2FoldChange within [2; 2].
94
Table 4. List of gene hits (identity > 50%) matching exclusively with contigs for which
Log2FoldChange is either less than - 2 or higher than + 2 between diquat and control conditions
(D<C; D>C), or with contigs with no read under one condition and at least 4 reads in the other
condition.
KEGG Process
Cellular Processes
Cell Communication
Cell Growth & Death
Transport & Catabolism
Environmental Information
Processing
Membrane Transport
Signal Transduction
Signaing Molecules &
interaction
KEGG Pathway
D<C
Tight junction [PATH:ko04530]
Apoptosis [PATH:ko04210]
Cell cycle [PATH:ko04110]
Endocytosis [PATH:ko04144]
Lysosome [PATH:ko04142]
Peroxisome [PATH:ko04146]
Phagosome [PATH:ko04145]
ABC transporters [PATH:ko02010]
Solute carrier family [BR:ko02001]
Transporters [BR:ko02000]
MAPK signaling pathway [PATH:ko04010]
Cell adhesion molecules (CAMs) [PATH:ko04514]
Cellular antigens [BR:ko04090]
Glycan binding proteins [BR:ko04091]
GTP-binding proteins [BR:ko04031]
Chaperones and folding catalysts [BR:ko03110]
Folding, Sorting &
a
Degradation
Protein folding and associated processing
Proteasome [BR:ko03051]
SNARE interactions in vesicular transport
[PATH:ko04130]
Ubiquitin mediated proteolysis [PATH:ko04120]
Ubiquitin system [BR:ko04121]
Replication & Repair
Chromosome [BR:ko03036]
DNA replication proteins [BR:ko03032]
Transcription
Basal transcription factors [PATH:ko03022]
Spliceosome [BR:ko03041], [PATH:ko03040]
Translation
Ribosome [PATH:ko03010], [BR:ko03009],
[PATH:ko03008]
RNA transport [PATH:ko03013]
Transfer RNA biogenesis [BR:ko03016]
NFKBIA
ORC4
GM2A
MVK
hcaT
PAK1, PPP3R
L1CAM
CD109
Carbohydrate
Energy
95
RAB11A
AP3D1
FAR
DYNC1H
pstS
SLC28,
SLC25A46
SLC37A3
MAPT, MP1,
PP3R
LGALS9
FKBP2, PIN4
pcm
PSMD9
YKT6
KLHL10
TFIIB
RBMX2,
U2AF2
NHP2
a
Alanine, aspartate & glutamate metabolism
[PATH:ko00250]
Amino acid related enzymes [BR:ko01007]
Arginine and proline metabolism [PATH:ko00330]
Glycine, serine & threonine metabolism
[PATH:ko00260]
Phenylalanine metabolism [PATH:ko00360]
Ascorbate & aldarate metabolism [PATH:ko00053]
Citrate cycle (TCA cycle) [PATH:ko00020]
Inositol phosphate metabolism [PATH:ko00562]
Nitrogen metabolism [PATH:ko00910]
Oxidative phosphorylation [PATH:ko00190]
RAB3B
ENDOG
RHOU
Translation proteins
Metabolism
Amino Acid
D>C
NEDD4
KLHL10 (2)
SIRT5
GINS2
DDX18, PRPF4,
TXNL4A,
RP-L2,
PWP1,
NIP7, NOP2,
cca, NUP62
truA
PUA domain
protein
GOT1
tdh
PRDX6
QARS, GARS,
NARS
DNOS
serA
MIOX
fumA
NDUFS6,
NDUFA4
PTEN
fixA
ATPeVAC39
Table 4. continued.
KEGG Process
Enzyme Families
Glycan
KEGG Pathway
Peptidases [BR:ko01002]
Protein kinases [BR:ko01001]
Glycan Biosynthesis & N-Glycan biosynthesis
a
[PATH:ko00510]
Others
Lipid
Biosynthesis of unsaturated fatty acids
[PATH:ko01040]
Fatty acid metabolism [PATH:ko00071]
Sphingolipid metabolism [PATH:ko00600]
Synthesis & degradation of ketone bodies
[PATH:ko00072]
Cofactors & Vitamins
Folate
biosynthesis [PATH:ko00790]
Lipoic acid metabolism [PATH:ko00785]
Riboflavin metabolism [PATH:ko00740]
Glutathione metabolism [PATH:ko00480]
Terpenoids & Polyketides
Terpenoid backbone biosynthesis [PATH:ko00900]
Nucleotide
Purine [PATH:ko00230] & pyrimidine metabolism
[PATH:ko00240]
Xenobiotics Biodegradation [PATH:ko00627], [PATH:ko00643]
& Metabolism
Organismal Systems
Immune System
Fc gamma R-mediated phagocytosis [PATH:ko04666]
Nervous System
Synaptic vesicle cycle [PATH:ko04721]
D<C
REXO4,
METTL3
D>C
ENPEP
PTK9
DPM3
PECR
ACADS
B4GALT6
bdh
folA
lipA
dut
acyP
ACP1
CARP
ispD
RPB9, RPA12,
RPB2, carB
GSTZ1 (2)
ARPC2
NAPA
This might result from the occurrence of several isoforms associated with alternative splicing
events or subfunctionalization within gene families. However, such hypotheses need further
investigation to be confirmed. To this end, other sequencing methods, which produce a
dramatically higher number of reads (e.g., RNAseq using HiSeq Illumina technology) will be
applied, using the present database as reference transcriptome. The primary objective was to
obtain a set of genomic resources in L. stagnalis with a purpose of annotation, to which 454pyrosequencing was most appropriate, due to the read length produced. The use of two
contrasted conditions, with regard to oxidative stress, allowed broadening the range of
expressed transcripts, i.e., in a qualitative manner (for the same reason, snails from different
original populations were used, and various tissues were pooled). As a preliminary analysis,
we investigated differential expression among the two conditions, however, results need to be
interpreted with caution, in large part because the two conditions were not replicated. On the
other hand, this strategy allowed to detect many genes involved in various stress related
pathways, some of which being detected only under diquat exposure condition. These
annotations provide tools that may thus be particularly useful to further studies focusing on
the molecular basis of stress response in L. stagnalis.
96
SNP detection
Putative SNPs were identified among 1,362 contigs, resulting in 3,708 sites. Transition type
SNPs were slightly predominant (57.23 %), transversions represented 42.45 %, and 0.32% of
the SNPs exhibited three different alleles.
Conclusion
The present paper reports on the generation of 141,999 EST contigs in L. stagnalis,
based on 280,912 new reads and other previously published sequences. Similarity search
against various databases produced significant matches for 34.2% of these contigs. This new
genomic resource is made publically accessible, and provides a unique tool to discover genes
of interest and develop new genetic markers, in an invertebrate species which is a
“multidisciplinary” model for the scientific community, and also a model for future regulatory
procedures to be implemented in ecological risk assessment (toxicogenomics, standardized
tests).
Based on functional annotation, we identified more than 400 putative genes of
importance for the study of oxidative stress and cellular stress response. These newly
identified homologies are expected to be valuable for the study of molecular mechanisms
which underlie the response to chemicals, as exemplified here with a redox-cycling pesticide
and a non-target organism. Oxidative stress was targeted in the present investigation, because
it is indirectly involved in the modulation of various important life history and fitness-related
traits, and may thus affect population evolutionary potential and long-term ability to respond
to environmental contaminants. Sequence polymorphism was detected, providing a number of
new genetic markers suited to genotyping. More generally, the new genomic resource
obtained in L. stagnalis is expected to be a relevant tool for any genomic approach aimed at
understanding the molecular basis of integrative physiological functions. Finally, this new
collection of ESTs constitutes also a fundamental resource for the L. stagnalis Genome
sequencing project
.
97
Acknowledgments
The project has been carried out with INRA financial support (EFPA Projet Innovant 2010;
AIP Bioressources 2010). AB is granted by the INRA (PhD, Contrat Jeune Scientifique). The
authors thank Christophe Plomion for advices and insightful comments on the project, and
Marc Collinet and the INRA U3E staff for technical assistance.
Supplementary Tables
Table S1 Tentative estimates of EC50s for diquat, using data from Coutellec et al. 2008
(Chemosphere 73:326-336). Average values are given with their bootstrapped 95%CIs, as estimated
under a Hill model, using the EXCELTM macro REGTOX
(http://www.normalesup.org/~vindimian/en_index.html).
Trait significantly affected by diquat at
concentration 222.2µg/L
EC50 average (µg/L)
CI 95% (µg/L)
Adult growth
31.89
[23.12 ; 41.72]
Progeny minimum development time
143.47
[118.17 ; 165.37]
Clutch hatching rate
194.48
[157.87 ; 211.11]
Table S2. Tissue volume / mass per condition (C = control, D = diquat exposure) and sample,
and corresponding quantity of extracted total RNAs. GDC holds for gonado-digestive complex,
BAP for body anterior part, and BPP for body posterior part.
Tissue :
Haemolymph
GDC
BAP
BPP
Total RNA: Haemolymph
GDC
BAP
BAP
98
C
D
x ml
600 mg
1040 mg
1019 mg
32.7 µg
2417 µg
514 µg
1062 µg
x ml
609 mg
968 mg
1020 mg
24 µg
2067 µg
852 µg
829 µg
Table S3. Genes with at least one significant match (best-hit annotation) in LsContigV1, and involved in alanine, aspartate and
glutamate metabolism pathway (Ko00250).
Gene code
KEGG
4-aminobutyrate aminotransferase (EC 2.6.1.19)
4-amino-butyrate aminotransferase/(S) 3-amino-2-methylpropionate transaminase (2.6.1.19, 2.6.1.22)
puuE
ABAT
K00823
K13524
Adenylosuccinate lyase (EC 4.3.2.2)
Adelynosuccinate synthase (EC 6.3.4.4)
Argininosuccinate lyase (EC 4.3.2.1)
purB
purA
argH, ASL
K01756
K01939
K01755
Argininosuccinate synthase (EC 6.3.4.5)
Alanine dehydrogenase (EC 1.4.1.1)
Alanine glyoxylate transaminase / (R)-3-amino-2-methylpropionate-pyruvate transaminase (2.6.1.44, 2.6.1.40)
argG
ald
AGXT2
K01940
K00259
K00827
Alanine transaminase (EC 2.6.1.2)
Aspartate aminotransferase, cytoplasmique (EC 2.6.1.1)
GPT, ALT
GOT1
K00814
K14454
Aspartate aminotransferase, mitochondrial (EC 2.6.1.1)
Aspartate carbamoyltransferase catalytic subunit (EC 2.1.3.2)
GOT2
pyrB, PYR2
K14455
K00609
Carbamoyl-phosphate synthase (ammonia) (EC 6.3.4.16)
Carbamoyl-phosphate synthase/aspartate carbamoyltransferase/dihydroorotase (EC 6.3.5.5., 2.1.3.2, 3.5.2.3)
Carbamoyl-phosphate synthase large subunit (EC 6.3.5.5)
CPS1
CAD
carB, CPA2
K01948
K11540
K01955
D-aspartate oxidase (EC 1.4.3.1)
Glucosamine—fructose-6-phosphate aminotransferase (isomerizing) (EC 2.6.1.16)
DDO
glmS
K00272
K00820
Glutamate decarboxylase (EC 4.1.1.15)
Glutamate dehydrogenase (NAD(P)+) (EC 1.4.1.3)
Glutamate synthase (NADPH/NADH) (EC 1.4.1.13, 1.4.1.14)
gadB, gadA; GAD
1.4.1.3
GLT1
K01580
K00261
K00026
Glutamine synthetase (EC 6.3.1.2)
Omega-amidase (EC 3.5.1.3)
glna
NIT2
K01915
K13566
Succinate-semialdehyde dehydrogenase (EC 1.2.1.24)
Succinate-semialdehyde dehydrogenase (NADP+) (EC 1.2.1.16)
1.2.1.24
gabD
K00139
K00135
99
involved in Ascorbate and aldarate metabolism (KEGG ko00053).
Gene code
KEGG
Aldehyde dehydrogenase (NAD+) (EC 1.2.1.3)
1.2.1.3
K00128
Aldehyde dehydrogenase family 7 member A1 (EC 1.2.1.31, 1.2.1.8, 1.2.1.3)
Aldehyde dehydrogenase family 9 member A1 (EC 1.2.1.47, 1.2.1.3)
Gluconolactonase (EC3.1.1.17)
ALDH7A1
ALDH9A1
3.1.1.17
K14085
K00149
K01053
Glucuronosyltransferase (EC 2.4.1.17)
Inositol oxygenase (EC 1.13.99.1)
UGT
MIOX
K00699
K00469
L-ascorbate oxidase (EC 1.10.3.3)
1.10.3.3
K0423
UDPglucose 6-dehydrogenase (EC 1.1.1.22)
UGDH, ugd
K00012
involved in cystein and methionine metabolism pathway (ko00270).
Gene name ( enzyme code)
Gene code
KEGG
1,2-dihydroxy-3-keto-5-methylthionpentene dioxygenase (EC 1.13.11.53, 1.13.11.54)
mtnD, mtnZ, ADI1
K08967
5’-methylthioadenosine phosphorylase (EC 2.4.2.28)
mtaP
K00772
Adenosylhomocysteinase (EC 3.3.1.1)
Aspartate aminotransferase, cytoplasmic (EC 2.6.1.1)
ahcY
GOT1
K01251
K14454
Aspartate aminotransferase, mitochondrial (EC 2.6.1.1)
GOT2
K14455
Betaine-homocysteine S-methyltransferase (EC 2.1.1.5)
Cystathionine beta-synthase (EC 4.2.1.22)
BHMT
CBS
K00544
K01697
Cystathionine gamma-lyase (EC 4.4.1.1)
Cysteine dioxygenase (EC 1.13.11.20)
4.4.1.1
CDO1
K01758
K00456
Cysteine synthase A (EC 2.5.1.47)
DNA(cytosine-5-)-methyltransferase (EC 2.1.1.37)
cysK
DNMT, dcm
K01738
K00558
Enolase-phosphatase E1 (EC 3.1.3.77)
mtnC
K09880
L-serine dehydratase (EC 4.3.1.17)
Methylthioribose-1-phosphate isomerase (EC 5.3.1.23)
sdaA
mtnA
K01752
K08963
O-succinylhomoserine sulfhydrase (EC 2.5.1.-)
S-adenosylmethionine decarboxylase (EC 4.1.1.50)
metZ
speD
K10764
K01611
S-adenosylmethionine synthetase (EC 2.5.1.6)
Spermidine synthase (EC 2.5.1.16)
metK
SRM, speE
K00789
K00797
Thiosulfate/3-mercaptopyruvate sulfurtransferase (EC 2.8.1.1, 2.8.1.2)
TST, MPST
K01011
Tyrosine aminotransferase (EC 2.6.1.5)
TAT
K00815
100
Table S6. Genes with least one significant match (best-hit annotation) in LsContigV1, and
involved in glutathione metabolism pathway (KEGG: ko00480).
Gene name ( enzyme code)
Gene code
KEGG
6 Phosphogluconate dehydrogenase (EC 1.1.1.44)
PGD, gnd
K00033
Aminopeptidase N (EC 3.4.11.2)
Cytosol Leucine aminopeptidase (EC 3.4.11.1)
ANPEP, pepN
LAP3
K11140, K01256
K11142
Isocitrate dehydrogenase (NADP) (EC 1.1.1.42)
IDH1, IDH2
K00031
Gamma-glutamylcyclotransferase (EC 2.3.2.4)
Gamma-glutamyltranspeptidase (EC 2.3.2.2)
GGCT
ggt
K00682
K00681
Glucose-6-phosphate 1 dehydrogenase (EC 1.1.1.49)
G6PD, zwf
K00036
Glutamate-cysteine ligase (EC 6.3.2.2)
Glutamate-cysteine ligase catalytic subunit
GSH1
GCLC
K01919
K11204
Glutamate-cysteine ligase regulatory subunit
GCLM
K11205
Glutathione peroxidase 4b (EC 1.11.1.9))
GPX4
K00432
Glutathione peroxidase 7
Glutathione reductase (NADPH) (EC 1.8.1.7)
GPX7
GSR, gor
K00432
K00383
Glutathione reductase, mitochondrial
GSHR
K00383
Glutathione S-transferase (EC 2.5.1.18)
Glutathione S-transferase alpha-4
Gst
GSTA4
K00799
K00799
Glutathione S-transferase kappa 1
Glutathione S-transferase class omega
GSTK1
GTSO1
K13299
K00799
Glutathione S-transferase class pi
GSTP1
K00799
Glutathione S-transferase class mu
Glutathione S-transferase class sigma
GTSM1
gst-7
K00799
K00799
Glutathione S-transferase microsomal 1
Glutathione synthase (EC 6.3.2.3)
Leucyl aminopeptidase (EC 3.4.1.11)
MGST1
gshB
pepA, CARP
K00799
K01920
K01255
Ornithine decarboxylase (EC 4.1.1.17)
Ribonucleoside-diphosphate reductase subunit M2 (EC 1.17.4.1)
Protein disulfide reductase (glutathione) (thioredoxine domaincontaining protein) (EC 1.8.4.2)
ODC1
RRM2
K01581
K10808
TXNDC12
K05360
Pyrimidodiazepine synthase (EC 1.5.4.1)
1.5.4.1
K00310
Spermidine synthase (EC 2.5.1.16)
SRM, spee
K00797
101
involved in MAPk signaling pathway (KEGG ko0410).
Gene code
KEGG
Activating transcription factor 4
Alpha 1,3-glucosidase (EC 3.2.1.84)
CREB2, ATF4
ATF6
GANAB
K04374
K09054
K05546
Apoptosis regulator BAX
Apoptosis regulator BCL-2
BAX
BCL2
K02159
K02161
Ataxin-3
B-cell receptor-associated protein 31
ATXN3, MJD
BAP31, BCAP31
K11863
K14009
Calnexin
Calreticulin
CANX
CALR
K08054
K08057
Crystallin, alpha B
Cullin 1
DnaJ homolog subfamily A member 1
CRYAB
CUL1, CDC53
DNAJA1
K09542
K03347
K09502
DnaJ homolog subfamily C member 1
Dolichyl-diphosphooligosaccharide-protein glycosyltransferase
(EC 2.4.1.119)
E3 ubiquitin-protein ligase synoviolin (EC 6.3.2.19)
DNAJA2
DNAJC1
DNAJC3
K09503
K09521
K09523
STT3
K07151
SYVN1, HRD1
K10601
Endoplasmic reticulum lectin 1
Endoplasmic reticulum protein 29
ERLEC1, XTP3B
ERP29
K14008
K09586
Eukaryotic translation initiation factor 2-alpha kinase (EC 2.7.11.1)
GTP-binding protein SAR1 (EC 3.6.5.-)
EIF2AK
SAR1
K08860
K07953
Heat shock protein 70kDa 1/8
Heat shock protein 110 kDa
HSPA1_8
HSP110
K03283
K09485
Homology and U-box containing protein 1
Hsp70-interacting protein
STUB1, CHIP, STIP1
HSPBP1
K09561
K09562
Hypoxia upregulated 1
Lectin, mannose-binding 2
HYOU1
LMAN2, VIP36
K09486
K10082
Mannosyl-oligosaccharide alpha-1,2-mannosidase (EC 3.2.1.113)
Mitogen-activated protein kinase5
MAN1
MPA3K5, ASK1
K01230
K04426
Molecular chaperone HtpG
Nuclear protein localization protein 4 homolog
htpG, HSP90A
NPLOC4, NPL4
K04079
K14015
Oligosaccharyltransferase complex subunit alpha (ribophorin I)
Oligosaccharyltransferase complex subunit beta
OST1, RPN1
WBP1
K12666
K12670
Oligosaccharyltransferase complex subunit delta (ribophorin II)
Oligosaccharyltransferase complex subunit epsilon
SWP1, RPN2
OST1, DAD1
K12667
K12668
Oligosaccharyltransferase complex subunit gamma
Parkin
OST3, OST6
PARK2
K12669
K04556
102
Table S7. continued (KEGG ko0410).
Gene code
KEGG
P21-activated kinase 2 (EC 2.7.11.1)
PAK2
K04410
P38 MAP kinase (E 2.7.11.24)
P90 ribosomal S6 kinase (EC 2.7.11.1)
P38
RPS6KA, RSK2
K04441
K04373
Protein kinase A (E C 2.7.11.11)
Protein phosphatas e 1A (EC 3.1.3.16)
PKA
PPM1A, PP2CA
K04345
K04457
Protein phosphatas e 3, catalytic subunit (EC 3.1.3.16)
PPP3C
K04348
Protein phosphatas e 3, regulatory subunit
Protein phosphatas e 5 (EC 3.1.3.16)
PPP3R, CNB
PPP5C
K06268
K04460
Proto-oncogene C-crk
RAC serine/threonine-protein kinase (EC 2.7.11.1)
CRK, CRKII
AKT
K04438
K04456
Rap guanine nucleotide exchange factor (GEF)2
RAPGEF2, PDZGEF1
K08018
Ras-related botulinum toxin substrate 1
Ras-related protein M-Ras
RAC1
MRAS
K04392
K07831
Ras-related protein Rap-1A
RAP1A
K04353
Ras-specific guanine nucleotide-releasing factor 1
Stathmin
RASGRF1
STMN1
K04349
K04381
TAK1-binding protein 1
TGF-beta receptor type-1 (EC 2.7.11.30)
MAP3K7IP1, TAB1
TGFBR1
K04403
K04674
TNF receptor-associated factor 6
TRAF6
K03175
Voltage-dependent calcium channel beta-2
Voltage-dependent calcium channel L type alpha-1, invertebrate
CACNB2
CACNA1N
K04863
K05315
Voltage-dependent calcium channel L type alpha-1C
CACNA1C
K04850
involved in metabolism of xenobiotics by cytochrome P450 (ko00980).
Gene code
KEGG
Aldehyde dehydrogenase NAD(P)+
E.1.2.1.5
K00129
S-(hydroxymethyl)glutathione dehydrogenase/ alcohol dehydrogenase
Carbonyl reductase
frmA, ADH5, adhC
CBR1
K00121
K00079
Cytochrome P450, family 1, subfamily A, polypeptide 1
Cytochrome P450, family 2, subfamily B
Cytochrome P450, family 2, subfamily C
CYP1A1
CYP2B
CYP2C
K07408
K07412
K07413
Cytochrome P450, family 2, subfamily D
Cytochrome P450, family 2, subfamily F
Cytochrome P450, family 3, subfamily A (EC 1.14.14.1)
CYP2D
CYP2F
CYP3A
K07414
K07416
K07424
Duhydrodiol dehydrogenase/D-xylose 1 dehydrogenase (NADP) EC 1.3.1.20.,1.1.1.179
Glutathione S-transferase
DHDH
gst
K00078
K00799
Glucuronyl transferase
UGT
K00699
103
involved in oxidative phosphorylation pathway (KEGG Ko00190).
Gene code
KEGG
Cytochrome c oxidase assembly protein subunit 15
Cytochrome c oxidase subunit 1 (EC 1.9.3.1)
COX15
COX1
K02259
K02256
Cytochrome c oxidase subunit 2
COX2
K02261
COX3
COX4
K02262
K02263
Cytochrome c oxidase subunit 5a
COX5A
K02264
Cytochrome c oxidase subunit 5b
COX5B
K02265
Cytochrome c oxidase subunit 6a
Cytochrome c oxidase subunit 6b
COX6A
COX6B
K02266
K02267
F-type H+-transporting ATPase 54 kD subunit (EC 3.6.3.14)
F-type H+-transporting ATPase Oligomycin sensitivity conferral
protein
F-type H+-transporting ATPase subunit 6
ATPeV54kD, V-type
K02144
ATPeF1O, ATP5O
K02137
ATPeF0F6, ATP5J
K02131
F-type H+-transporting ATPase subunit a
ATPeF0A, MTATP6
K02126
F-type H+-transporting ATPase subunit AC39b
F-type H+-transporting ATPase subunit alpha
ATPeAC39, ATP6D
ATPeF1A, ATP5A1
K02146
K02132
F-type H+-transporting ATPase subunit b
ATPeF0B, ATP5F1
K02127
F-type H+-transporting ATPase subunit beta
ATPeF1B, ATP5B
K02133
F-type H+-transporting ATPase subunit c
ATP2F0C, ATP5G
K02128
F-type H+-transporting ATPase subunit d
F-type H+-transporting ATPase subunit delta
ATPeFD, ATP5H
ATPeF1D, ATP5D
K02138
K02134
F-type H+-transporting ATPase subunit f
ATPeF0F, ATP5J2
K02130
F-type H+-transporting ATPase subunit g
ATPeFG, ATP5L
K02140
F-type H+-transporting ATPase subunit gamma
ATPeF1G, ATP5C1
K02136
Inorganic pyrophosphatase (EC 3.6.1.1)
NADH-quinone oxi doreductase subunit H (EC 1.6.5.3)
ppa
nuoH
K01507
K00337
NADH dehydrogenase (1.6.99.3)
ndh
K03885
NADH dehydrogenase (ubiquinone) 1 alpha subcomplex 4
NDUFA4
K03948
NDUFA6
K03950
NDUFA7
K03951
NADH-dehydrogenase (ubiquinone) 1 alpha subcomplex 11
NADH-dehydrogenase (ubiquinone) 1 alpha subcomplex 13
NDUFB11
NDUFA13
K11351
K11353
NADH dehydrogenase (ubiquinone) 1 alpha/beta subcomplex 1
NDUFAB1
K03955
NADH dehydrogenase (ubiquinone) 1 beta subcomplex 2
NDUFB2
K03958
NADH dehydrogenase (ubiquinone) 1 beta subcomplex 5
NDUFB5
K03961
NADH dehydrogenase (ubiquinone) Fe-S protein 1
NDUFS1
NDUFS2
K03934
K03935
104
Table S9. continued (KEGG Ko00190).
Gene code
KEGG
NDUFS3
K03936
NDUFS4
NDUFS8
K03937
K03941
NDUFV2
K03943
NADH-ubiquinone oxidoreductase chain 1 (EC 1.6.5.3)
ND1
K03878
NADH-ubiquinone oxidoreductase chain 2
ND2
K03879
Succinate dehydrogenase (ubiquinone) flavoprotein subunit (EC
1.3.5.1)
Succinate dehydrogenase (ubiquinone) iron-sulfur subunit
Ubiquinol-cytochrome c reductase core subunit 2
ND4
ND5
K03881
K03883
SDH1, SDHA
K00234
SDHB, SDH2
QCR2, UQCRC2
K00235
K00415
Ubiquinol-cytochrome c reductase cytochrome b subunit
CYTB, petB
K00412
Ubiquinol-cytochrome c reductase subunit 6
QCR6, UQCRH
K00416
Ubiquinol-cytochrome c reductase subunit 7
QCR7, UQCRB
K00417
V-type H+- transporting ATPase subunit A (3.6.3.14)
V-type H+- transporting ATPase subunit B
ATPeVA,ATP6A1
ATPeVB,ATP6B1
K02145
K02147
V-type H+- transporting ATPase subunit C
ATPeVC, ATP6C
K02148
V-type H+- transporting ATPase subunit D
ATPeVD, ATP6M
K02149
V-type H+- transporting ATPase subunit E
ATPeVE, ATP6E
K02150
V-type H+- transporting ATPase subunit F
V-type H+- transporting ATPase subunit G
ATPeVF,ATP6S14
ATPEVG, ATP6G1
K02151
K02152
V-type H+- transporting ATPase subunit H
ATPeVH,ATP6H
K02153
V-type H+- transporting ATPase subunit I
ATPeVI,ATP6NA1
K02154
V-type H+- transporting ATPase 16kDa proteolipid subunit
ATPeVL,ATP6L
K02155
V-type H+- transporting ATPase 21kDa proteolipid subunit
V-type H+- transporting ATPase S1 subunit
ATPeVPF, ATP6F
ATPeVS1,ATP6S1
K03661
K03662
105
involved in in p53 signaling pathway (KEGG Ko04115).
Gene code
KEGG
Apoptosis regulator
Ataxia telangectasia mutated family protein (EC 2.7.11.1)
Caspase 8 (EC 3.4.22.61)
BAX
ATM, TEL1
CASP8
K02159
K04728
K04398
Cyclin B
Cyclin D2
CCNB
CCND2
K05868
K10151
Cyclin G1
Cyclin-dependant kinase 4 (EC 2.7.11.1)
CCNG1
CDK4
K10145
K02089
Cyclin-dependant kinase 6 (EC 2.7.11.1)
Cytochrome C
CDK6
CYC
K02091
K08738
Growth arrest and DNA damage inducible protein
GADD45
K04402
Leucin-rich repeats and death domain containing protein
Phosphatidylinositol-3,4,5-triphosphate3-phosphatase (EC 3.1.3.67)
LRDD, PIDD
PTEN
K10130
K01110
Plasminogen activator inhibitor-1
Ribonucleoside diphosphate reductase subunit M2
(EC 1.17.4.1)
Ring finger and CHY zinc finger domain-containing protein 1
SERPINE1, PAI1
K03982
RRM2 (p53R2)
K10808
PIRH2, RCHY1
K10144
Serine/threonine protein kinase ChK2 (EC 2.7.11.1)
Sestrin
CHK2
SESN
K06641
K10141
Tuberous sclerosis 2 protein (tuberin, tumor growth suppressor)
TSC2
K07207
Tumor protein p53-inducible protein 3 (EC 1.-.-.-)
TP53I3, PIGs
K10133
106
involved in peroxisome pathway (KEGG ko04146).
Gene code
KEGG
2-hydroxyacyl-CoA lyase 1 (EC 4.1.-.-)
HACL1
K12261
HSD17B4
K12405
3-hydroxyacyl-CoA dehydrogenase / 3a, 7a, 12ª-trihydroxy-5b-cholest-24-enoyl-CoA hydratase (EC
1.1.1.35, 4.2.1.107)
Acetyl-CoA acyltransferase 1 (EC 2.3.1.16)
ACAA1
K07513
AcylCoA oxidase (EC 1.3.3.6)
Alanine-glyoxylate transaminase/serine-glyoxylate transaminase/serine-pyruvate transaminase (EC
2.6.1.44, 2.6.1.45, 2.6.1.51)
Alpha-methylacyl-CoA racemase (EC 5.1.99.4)
ACOX1, ACOX3
K00232
AGXT
K00830
AMACR
K01796
ATP-binding cassette, subfamily D (ALD) member 2
ABCD2, ALDL1
K05676
ATP-binding cassette, subfamily D (ALD) member 3
ABCD3, PMP70
K05677
Bile acid-CoA: amino acid N-acetyltransferase (EC 2.3.1.65, 3.1.2.2)
Carnitine O-acyltransferase (EC 2.3.1.7)
BAAT
2.3.1.7
K00659
K00624
Carnitine O-octanoyltransferase (EC 2.3.1.137)
CROT
K05940
Catalase (EC 1.11.1.6)
katE, CAT
K03781
Cu/Zn superoxide dismutase (EC 1.15.1.1)
sodC, SOD1
K04565
D-aspartate oxidase (EC 1.4.3.1)
DDO
K00272
Delta(3,5)-Delta(2,4)-dienoyl-CoA isomerase (EC 5.3.3.-)
D-amino-acid oxidase (EC 1.4.3.3)
ECH1
DAO
K12663
K00273
Dehydrogenase/reductase SDR family member 4 (EC 1.1.-.-)
Enoyl-CoA hydratase/3 hydroxyacyl-CoA dehydrogenase/3,2-trans-enoyl(CoA isomerase
(EC 4.2.1.17, 1.1.1.35, 5.3.3.8)
Fatty-acyl CoA reductase (EC 1.2.1.-)
DHRS4
K11147
EHHADH
K07514
FAR
K13356
Glutathione S-transferase kappa 1 (2.5.1.18)
GSTK1
K13299
Glyceronephosphate O-acyltransferase (EC 2.3.1.42)
GNPAT
K00649
Isocitrate dehydrogenase (EC 1.1.1.42)
IDH1, IDH2, icd
K00031
Long-chain acyl-CoA synthetase (EC 6.2.1.3)
ACSL, fadD
K01897
Mevalonate kinase (EC 2.71.36)
MVK, mvaK1
K00869
N1-acetylpolyamine oxidase (EC 1.5.3.13)
NAD+ diphosphatase (EC 3.6.1.22)
PAOX
NUDT12, nudC
K00308
K03426
Nitric-oxide synthase, inducible (EC 1.14.13.39)
NOS2
K13241
Nucleoside diphosphate-linked moiety X motif19, mitochondrial (EC 3.6.1.-)
NUDT19
K13355
Peroxin10
PEX10
K13346
Peroxin 11B
PEX11B
K13352
Peroxin-13
Peroxin-14
PEX13
PEX14
K13344
K13343
Peroxin-26
Peroxiredoxin1 (EC 1.11.1.15)
Peroxiredoxin 5, atypical 2-Cys peroxiredoxin (EC 1.11.1.15)
PEX26
PRDX1
PRDX5
K13340
K13279
K11187
Peroxisomal 3,2-trans-enoyl-CoA isomerase (EC 5.3.3.8)
PECI
K13239
Peroxisomal membrane protein 2
Peroxisomal trans-2-enoyl-CoA reductase (EC 1.3.1.38)
PXMP2, PMP22
PECR
K13347
K07753
Phosphomevalonate kinase (EC 2.7.4.2)
PMVK
K13273
Phytanoyl-CoA hydroxylase (EC1.14.11.18)
Sarcosine oxidase / L-pipecolate oxidase (EC 1.5.3.1, 1.5.3.7)
K00477
K00306
Sterol carrier protein 2 (EC 2.3.1.176)
PHYH
PIPOX
SLC27A2,
FACVL1, FATP2
SCP2, SCPX
Superoxide dismutase, Fe-Mn family (EC 1.15.1.1)
sodA, sodB, SOD2
K04564
Xanthine dehydrogenase/oxidase (EC 1.17.1.4, 1.17.3.2)
XDH
K00106
Solute carrier family 27 (fatty acid transporter), member 2 (EC 6.2.1.-, 6.2.1.3)
107
K08746
K08764
involved in porphyrin and chlorophyll metabolism (KEGG pathway Ko00860).
Gene code
KEGG
5-aminolevulinate synthase (EC 2.3.1.37)
ALAS
K00643
Adenosylcobionamide-phosphate synthase (EC 6.2.1.10)
cbiB, cobD
K02227
Beta-glucuronidase (EC 3.2.1.31)
Bifunctional glutamyl/prolyl-tRNA synthetase (EC 6.1.1.17, 6.1.1.15)
GUSB
EPRS
K01195
K14163
Biliverdin reductase/flavin reductase (EC 1.3.1.24, 1.5.1.30)
BLVRB
K05901
Cob(l)alamin adenosyltransferase (EC 2.5.1.17)
cobO, btuR
K00798
Cytochrome c heme-lyase (EC 4.4.1.17)
4.4.1.17
K01764
Cytochrome oxidase assembly protein subunit 15
COX15
K02259
Ferritin heavy chain (EC 1.16.3.1)
Ferrochelatase (EC 4.99.1.1)
FTH1
hemH, FECH
K00522
K01772
Glucuronyltransferase (EC 2.4.1.17)
UGT
K00699
Glutamyl-tRNA synthetase (EC 6.1.1.17)
EARS, gltX
K01885
Hydroxymethylbilane synthase (EC 2.5.1.61)
hemC, HMBS
K07149
Porphobilinogen synthase (EC 4.2.1.24)
Uroporphyrin-III C-methyltransferase (EC 2.1.1.107)
hemB, ALAD
cobA
K01698
K02303
Uroporphyrinogen decarboxylase (EC 4.1.1.37)
hemE, UROD
K01599
Uroporphyrinogen-III synthase (EC4.2.1.75)
hemD, UROS
K01719
108
involved in protein processing in endoplasmic reticulum (KEGG pathway ko04141).
Gene code
KEGG
CREB2, ATF4
ATF6
K04374
K09054
Alpha 1,3-glucosidase (EC 3.2.1.84)
Apoptosis regulator BAX
Apoptosis regulator BCL-2
GANAB
BAX
BCL2
K05546
K02159
K02161
Ataxin-3
B-cell receptor-associated protein 31
Calnexin
Calreticulin
ATXN3, MJD
BAP31, BCAP31
CANX
CALR
K11863
K14009
K08054
K08057
Crystallin, alpha B
Cullin 1
CRYAB
CUL1, CDC53
DNAJA1
K09542
K03347
K09502
Dolichyl-diphosphooligosaccharide-protein glycosyltransferase
(EC 2.4.1.119)
E3 ubiquitin-protein ligase synoviolin (EC 6.3.2.19)
Endoplasmic reticulum lectin 1
Endoplasmic reticulum protein 29
DNAJA2
DNAJC1
DNAJC3
K09503
K09521
K09523
STT3
K07151
SYVN1, HRD1
ERLEC1, XTP3B
ERP29
K10601
K14008
K09586
Eukaryotic translation initiation factor 2-alpha kinase (EC 2.7.11.1)
GTP-binding protein SAR1 (EC 3.6.5.-)
Heat shock protein 70kDa 1/8
EIF2AK
SAR1
HSPA1_8
K08860
K07953
K03283
Heat shock protein 110 kDa
Homology and U-box containing protein 1
Hsp70-interacting protein
HSP110
STUB1, CHIP, STIP1
HSPBP1
K09485
K09561
K09562
Hypoxia upregulated 1
Lectin, mannose-binding 2
Mannosyl-oligosaccharide alpha-1,2-mannosidase (EC 3.2.1.113)
HYOU1
LMAN2, VIP36
MAN1
K09486
K10082
K01230
Mitogen-activated protein kinase5
Molecular chaperone HtpG
Nuclear protein localization protein 4 homolog
MPA3K5, ASK1
htpG, HSP90A
NPLOC4, NPL4
K04426
K04079
K14015
Oligosaccharyltransferase complex subunit alpha (ribophorin I)
Oligosaccharyltransferase complex subunit beta
Oligosaccharyltransferase complex subunit delta (ribophorin II)
OST1, RPN1
WBP1
SWP1, RPN2
K12666
K12670
K12667
Oligosaccharyltransferase complex subunit epsilon
Oligosaccharyltransferase complex subunit gamma
Parkin
OST1, DAD1
OST3, OST6
PARK2
K12668
K12669
K04556
109
Table S13. continued (KEGG pathway ko04141).
Gene code
KEGG
Phospholipase A-2-activating protein
Phospholipase A-2-activating protein
Prolactin regulatory element-binding protein
PLAA, DOA1, UFD3
PLAA, DOA1, UFD3
PREB, SEC12
K14018
K14018
K14003
Protein transport protein SEC61 subunit gamma and related proteins
Protein disulfide isomerase A1 (EC 5.3.4.1)
Protein disulfide isomerase A4
SEC61G, SSS1, secE
PDIA1, P4HB
PDIA4, ERP72
K07342
K09580
K09582
Protein disulfide isomerase A6
Protein disulfide isomerase family A, member 3 (EC 5.3.4.1)
Protein kinase C substrate 80K-H
PDIA6, TXNDC7
PDIA3, GRP58
PRKCSH
K09584
K08056
K08288
Protein transport protein SEC61 subunit alpha
Protein transport protein SEC13
SEC61A
SEC13
SEC31
K10956
K14004
K14005
SEC23
K14006
Protein transport protein SEC61 subunit beta
SEC24
SEC61B, SBH2
K14007
K09481
RING-box protein 1
RBX1, ROC1
K03868
SEL1 protein
S-phase kinase-associated protein 1
Translation initiation factor 2 subunit 1
SEL1
SKP1, CBFD
EIF2S1
K14026
K03094
K03237
Translocation protein
Translocation protein SEC62
Translocon-associated protein subunit delta
SEC63
SEC62
SSR4
K09540
K12275
K04571
Ubiquilin
Ubiquitin-conjugating enzyme E2 D/E (EC 6.3.2.19)
Ubiquitin-conjugating enzyme E2 G1
UBQLN, DSK2
K04523
UBE2D_E, UBC4, UBC5 K06689
UBE2G1, UBC7
K10575
Ubiquitin-conjugating enzyme E2 G2
Ubiquitin fusion degradation protein 1
Ubiquitin-protein ligase RNF5
UBX domain-containing protein 1
UBE2G2, UBC7
UBE2J1, NCUBE1,
UBC6
UBE2J2, NCUBE2,
UBC6
UFD1
RNF5
SHP1, UBX1, NSFL1C
K14016
K10666
K14012
UDP-glucose:glycoprotein glucosyltransferase (EC 2.4.1-)
UV excision repair protein RAD23
Translocon-associated protein subunit alpha
HUGT
RAD23, HR23
SSR1
K11718
K10839
K13249
Translocon-associated protein subunit beta
Translocon-associated protein subunit gamma
Transitional endoplasmic reticulum ATPase
SSR2
SSR3
VCP, CDC48
K13250
K13251
K13525
Wolfamin
X box-binding protein 1
WFS1
XBP1
K14020
K09027
Ubiquitin-conjugating enzyme E2 J1 (EC 6.3.2.19)
Ubiquitin-conjugating enzyme E2 J2
110
K04555
K10578
K04554
involved in retinol metabolism pathway (KEGG pathway ko00830).
Gene code
KEGG
All-trans-retinol 13,14 reductase (EC 1.3.99.23)
Cytochrome P450 family 1, subfamily A, polypeptide 1 (EC 1.14.14.1)
RETSAT
CYP1A1
K09516
K07408
Cytochrome P450 family 2, subfamily B
CYP2B
K07412
Cytochrome P450 family 2, subfamily C
Cytochrome P450 family 3, subfamily A
CYP2C
CYP3A
K07413
K07424
CYP4A
CYP26A
K07425
K07437
Dehydrogenase / reductase SDR family member 9 (EC 1.1.-.-)
Glucuronosyltransf erase (EC 2.4.1.17)
DHRS9
UGT
K11149
K00699
Retinol dehydrogenase 10 (EC 1.1.1.-)
RDH10
K11151
Retinol dehydrogenase 16
Retinol dehydrogenase R
RDH16
RDH5
K11154
K00061
Retinol dehydrogenase 11
Retinol dehydrogenase (hydroxysteroid dehydrogenase) (EC 1.1.1.105)
S-(hydroxymethyl) glutathione dehydrogenase/alcohol dehydrogenase (EC
1.1.1.284, 1.1.1.1)
RDH11
HSD17B6
ADH5, fmrA,
adhC
K11152
K13369
K00121
Table S15-S16-S17-18 Available on line
(http://link.springer.com/article/10.1007/s10646-012-0977-1)
Table S15. Summary of GO annotation statistics in “Cellular Component” GO category, as
(count<5), Na for “not available”. Dataset = Pondsnail_Contigs_V1.
Table S16. Summary of GO annotation statistics in “Molecular Function” GO category, as
Table S17. Summary of GO annotation statistics in “Biological Process” GO category, as
Table S18. KEGG code, abbreviation and name of genes identified in L. stagnalis transcriptome
as putatively regulated by diquat (see Table 4)
111
Primer note
-
Isolation and characterization of three new multiplex sets of
microsatellite markers in the hermaphroditic freshwater snail
Lymnaea stagnalis (Mollusca, Gastropoda, Heterobranchia,
Panpulmonata, Hygrophila) using 454-pyrosequencing technology
Anne-Laure Besnard1, Anthony Bouétard1, Didier Azam2 and Marie-Agnès Coutellec1
1
INRA, UMR0985 Ecologie et Santé des Ecosystèmes, INRA/Agrocampus-Ouest, Equipe
EQMA, 65 rue de Saint-Brieuc. 35042 Rennes Cedex, France
2
INRA, U3E, Unité Expérimentale d’Ecologie et Ecotoxicologie Aquatique, 65 rue de
SaintBrieuc. 35042 Rennes Cedex, France
Correspondence: Marie-Agnès Coutellec, INRA, UMR0985 Ecologie et Santé des
Ecosystèmes, INRA/Agrocampus-Ouest, Equipe EQMA, 65 rue de Saint-Brieuc.
35042 Rennes Cedex, France
Fax : +33(0)223485248
E-mail : [email protected]
Keywords: Lymnaea stagnalis, microsatellites, pyrosequencing, evolutionary ecotoxicology,
mating system
Running title: nine new microsatellite loci in L. stagnalis
Abstract
A new set of microsatellite loci was isolated in the pondsnail Lymnaea stagnalis, using 454FLX Titanium pyrosequencing technology. Of these, 174 loci were perfect motifs.
Experimental conditions are described for amplification and multiplex fragment analysis of
nine validated loci. Allelic variation is described in three laboratory cultures and two natural
populations. Genetic variation was low to moderate regardless of sample origin, with four to
eight polymorphic loci per population, whereas genetic differentiation was very high. This
new set of microsatellites will be useful to eco-evolutionary and genetic studies involving L.
stagnalis.
112
Introduction
The freshwater pulmonate gastropod Lymnaea stagnalis is a model species used in
various research fields, including behavioural ecology, evolutionary biology, parasitology,
aquatic ecotoxicology, and neurophysiology (Adema et al., 1994; Carter et al., 2006;
Coutellec and Caquet, 2011; Byzitter et al., 2012; Hoffer et al., 2012). With regard to the
mating system, it belongs to the main group of self-fertile animals, suborder Basommatophora
(Escobar et al., 2011). Although demonstrated as a preferential outcrosser, L. stagnalis shows
very low inbreeding depression (Puurtinen et al., 2007; Coutellec and Caquet, 2011), in
opposition with theoretical expectations (Charlesworth and Charlesworth, 1987). These
characteristics make L. stagnalis a good model to test alternative hypotheses on mating
system evolution, within basommatophorans. Ecologically, L. stagnalis is representative of
freshwater lentic environments and of the herbivorous macroinvertebrate community.
Freshwater snails may represent up to 20—60% of the total abundance and biomass of
macroinvertebrates in some freshwater ecosystems (Habdija et al., 1995) where they play a
major role in the transfer of energy and material across food webs. In this context, L. stagnalis
is widely used for environmental issues and ecological applications (Coutellec et al., 2011).
Finally, transcriptomic resources (Davison and Blaxter, 2005; Feng et al., 2009) have been
recently updated in this species using 454 pyrosequencing (Bouétard et al., 2012).
A total of 15 microsatellite loci were previously available in this species (Knott et al.
2003; Kopp and Wolf 2007, Genbank accessions [EF208747-EF208522]). However, due to
low polymorphism in some populations, most of these loci are not suitable for high-resolution
population genetics studies (e.g., natural estimation of heritability, Ritland, 2000) or parentage
analysis. In order to circumvent this drawback, we isolated new microsatellite markers in L.
stagnalis using pyrosequencing, and present here a set of characterized loci, which can be
amplified and assessed under three multiplexes. The present study also aimed to characterize
the genetic diversity of three laboratory cultures at these loci, and compared it to that of two
natural populations, in order to assess whether culturing reduced variation. This information
may indeed be useful to take into account in experiments designed for eco-evolutionary and
ecotoxicological issues.
113
Materials and method
Biological material
Snails originated from three cultures maintained for several years by the INRA
Experimental Unit of Aquatic Ecology and Ecotoxicology (INRA U3E, Rennes, France):
REN (Renilys® strain, first established in 2000 from Le Rheu ponds) stems from a local
population (Le Rheu, INRA, France), AMS was initiated in 2007 with clutches from a culture
of the Ecology Laboratory of Amsterdam University (V.U., Amsterdam, The Netherlands),
and SAN was settled from a sample of adults and clutches collected in Spring 2010 in
Sønderborg (Sandbjerg Estate, Aarhus Univ. Conference Centre, Denmark). These cultures
have previously been used for transcriptome pyrosequencing (Bouétard et al., 2012;
[SRA049626]). Samples from two natural populations were also used: CAS (Castricum,
52.548°N, 4.618°E) and KUI (Kuinre, 52.794°N, 5.795 °E), collected as adults in July 2011
(see location on Fig.1). DNA was chelex extracted for fragment analysis using non-invasive
sampling of haemolymph tissue, as described earlier (Adema et al., 1994).
SANDBJERG DK
AMSTERDAM NL
CASTRICUM NL
KUINRE NL
RENNES FR
Figure 1. Geographic origin of Lymnaea stagnalis natural populations (Castricum and
Kuinre, NL) and INRA U3E laboratory cultures (Le Rheu FR, Amsterdam NL, and
Sandbjerg DK) used to describe genetic variability at 9 new microsatellites. Upper-left
icon shows the UPGMA clustering of samples based on pairwise Fst-values.
114
DNA isolation
Genomic DNA was extracted from 25 mg of foot fresh tissue, using the DNeasy blood
and tissue Qiagen Kit. DNA quality was assessed on Nanodrop 2000 (Thermo scientific).
Absorbance ratios were 1.90-2.06 at 260 / 280nm and 1.50-2.00 at 260 / 230nm and quality
was estimated on agarose gel 1% with a 1kb SmartLadder.
Library enrichment was performed as described in Malausa et al. (2011), using an
optimization of classical biotin-enrichment methods (Kijas et al., 1994). Total DNA was
enriched for the following motifs: (AG)10, (AC)10, (AAC)8, (AGG)8, (ACG)8, (AAG)8,
(ACAT)6 and(ATCT)6. Each purified library was then pyrosequenced, using the 454 FLX
Titanium Genome Analyser (Roche Applied Science), as described in Malausa et al. (2011).
Sequences were sorted and selected with the software QDD (Meglécz et al., 2010). A total of
1515 valid microsatellite sequences were identified. From these, a subset of 248 loci longer
than 100 bp, containing motifs with at least five repeats, and with satisfying primers (no
tandem-repetition) was obtained, among which 174 loci were pure motif microsatellites (127
di-, 35 tri-, 10 tetra-, and 2 hexanucleotides).
Amplification was tested on 48 of these 174 loci, which were selected according to the
motif type (preference for dinucleotide motifs), amplicon size and annealing temperature.
These markers were tested on individual DNA samples from each laboratory culture (REN,
AMS and SAN). Genomic DNA was chelex extracted from haemolymph, with a proteinase K
solution (10mg/ml), and was heated during 2 hrs at 55°C, then 10 min at 100°C.
Multiplex optimization
Fragment amplification was tested for each locus separately, on a capillary sequencer
(ABI 3130xl) using a nested PCR, with the following set of primers: (1) sequence-specific
forward primer with M13 at the 5’ end, (2) sequence-specific reverse primer, (3) universal
fluorescent-labelled M13 primer. This method allows laser detection of amplification
products without the need to directly label one of the sequence-specific primer (Schuelke,
2000). On the other hand, only post-PCR multiplex is possible under this design. PCR were
performed in 6 µL of reaction volume, containing 0.06U Taq DNA polymerase and PCR
buffer (GoTaq, Promega), 208µM each dNTP’s, 2.17 mM MgCl2, 0.055 µM, 0.055 µM
sequence-specific forward primer with M13 at the 5’ end, 0.55 µM sequence-specific reverse
primer and 0.57 µM universal fluorescent-labelled M13 primer. Touch-down PCR procedure
was applied as follows: initial denaturation at 95°C for 4 min, 20 cycles consisting of a
115
denaturing step at 94 °C for 30 s, a touch-down annealing step from 65°C to 55°C for 30 s,
and an extension step at 72 °C for1 min, then 10 cycles consisting of a denaturing step at 94
°C for 30 s, an annealing step at 55°C for 30 s, and an extension step at 72 °C for 1 min. A
final extension step at 72°C was performed for 5 min.
Each locus was first tested separately, using 6-FAM label. Non amplified loci and loci
for which allele scoring was ambiguous or difficult were eliminated. For validated loci, a
second step was performed, in which loci and dyes were combined in post-PCR multiplexes,
according to size range and amplification signal (peak height) (Tab. 1).
Table 1. Sequence and multiplex characteristics of 9 microsatellites loci isolated in L. stagnalis.
Locus
Motif
Primer sequences (5'-3')
Size (bp) Multiplex Dye
set
GenBank
Accession
EMLS04
(CG)7
GATGAACCCAACCTCGTCAC
CATCCGGGAATGAGAACATC
100-116
EM1
NED
JX287524
EMLS10
(CA)6
GTTCCGACGGTAGGCAGTAA 169-177
TAAGGAAAGGTAGGGGTGGG
EM1
NED
JX287525
EMLS13
(AT)5
TGAAGGGTGACAGCATGGTA
GGTGACGCTCTAAATGGGAA
221-225
EM1
6FAM
JX287526
EMLS05
(GT)7
TTAGGCCAGCTGCAGAGATT
TGAGGCGAATATAGGGTTGG
123-127
EM2
6FAM
JX287527
EMLS21
(TC) 5
CTTAACACGCTTCATTGCAGA 120-122
GGGGGAGAGGAGCAGAGA
EM2
PET
JX287528
EMLS26
(CT) 12
GTCTTCAAGAAGTGCGAGGG
CTACTTTGCCGGTTGGTTTC
98-110
EM3
VIC
JX287529
EMLS29
(CA)10
GGAACTATAGCCGCCCACTC
CGTACACGGAGCCTTGAGTT
120-128
EM3
6FAM
JX287530
EMLS41
(GA)11
TTATCGTTTTTGCTCCCTGC
GCCAGGCTATCTGTCTCCAG
186-198
EM3
NED
JX287531
EMLS45
(AG)7
ACGTGTTATCAACGCCTTCC
215-219
CGTAAAAGACCACGATGTAGT
EM3
6FAM
JX287532
116
Microsatellite scoring and diversity analysis
Allele peaks were scored using GENEMAPPER (Applied Biosystems). The presence of
null alleles was tested with MICROCHECKER (van Oosterhout et al., 2004). Allelic richness,
expected and observed heterozygosities and FIS values were estimated per locus and sample
using the softwares GENEPOP (Rousset, 2008) and GENETIX (Belkhir et al., 2004). Departures
from HWE (heterozygote excess or deficiency) were tested using GENEPOP. Genotypic
linkage disequilibrium was tested using the log-likelihood ratio option in GENEPOP.
Population genetic differentiation (FST) was tested using FSTAT (Goudet, 1995).
Results and Discussion
The presence of null alleles was indicated in 4 loci, all of which were in the SAN
sample (EMLS21, EMLS26, EMLS41, EMLS45, despite the lack of missing data in EMLS26
and EMLS41).
Overall, the observed number of alleles varied from two to six across loci, and mean
allelic richness ranged from 2 to 5.789 (A, Tab. 2), which is rather low for a category of
markers deemed to be hypervariable. Genetic diversity per locus per sample varied from 0 to
0.692. Significant genotypic linkage disequilibrium was observed in 12 out of 91 comparisons
(tests were performed within each sample separately). However, after Bonferroni correction,
only 3 cases remained significant, reflecting a link between EMLS10, EMLS21 and EMLS41
in AMS sample. FIS-values varied widely across loci, and among samples within loci, ranging
from -0.249 (EMSL45 in CAS) to 1.000 (EMLS21 in SAN) (Tab. 2).
Genetic parameters estimated per sample over all nine loci reflected strong
discrepancies between samples (Tab. 3). Within laboratory cultures, the number of
polymorphic loci, mean allelic richness and genetic diversity increased as follows: REN<
SAN < AMS. Furthermore, genetic variability was even higher in AMS than in the two
natural populations CAS and KUI, whereas lower levels were observed in REN and SAN. As
a consequence, genetic diversity was not significantly lower in the group of laboratory strains
relative than in the group of natural populations (allelic richness: 2.108 against 2.434, P =
0.383, expected heterozygosity: 0.290 against 0.349, p = 0.585; test based on 1000
permutations). Observed heterozygosity and Fis values indicated no departure from HardyWeinberg expectations in REN and AMS. However, a surprising significant deficit was
117
indicated at locus EMLS04 in AMS sample, in spite of very similar Ho and He values
(Tab.2).
Table 2. Polymorphism statistics for nine microsatellites loci in L. stagnalis. NA is the total
number of alleles, A allelic richness, n number of individuals, nA number of observed alleles per locus
per population, Ho observed heterozygosity, He expected heterozygosity (Nei 1978). Bolded
characters indicate significant departures from HWE, asterisks the presence of null alleles.
FIS
FIS
(W&C)
(H&R)
0.419
0.649
0.150
0.396
0.633
0.000
0.413
0.507
0.444
0.455
0.045
-0.012
-0.024
0.133
0.048
-0.080
0.024
-0.040
0.039
0.172
0.277
0.202
0.184
0.167
0.046
0.400
-0.025
0.135
0.002
0.081
-0.081
-0.013
0.043
0.161
0.283
0.206
0.190
0.171
0.000
0.000*
0.065
0.000
0.000
0.000
0.189
0.064
0.000
0.000
1.000
-0.017
-
1.036
-0.035
-
3
3
3
2
4
1
1
3
2
1
0.613
0.133*
0.375
0.364
0.546
0.000
0.000
0.719
0.136
0.000
0.573
0.296
0.319
0.359
0.652
0.000
0.000
0.659
0.511
0.000
-0.070
0.554
-0.177
-0.012
0.167
-0.092
0.737
-
-0.084
0.485
-0.101
-0.012
0.254
-0.024
0.768
-
32
30
32
22
22
2
2
4
3
4
0.406
0.033*
0.531
0.409
0.636
0.396
0.155
0.571
0.540
0.692
-0.025
0.788
0.071
0.247
0.083
-0.026
0.813
0.014
0.251
0.024
29
25
32
22
22
1
3
3
3
4
0.000
0.080*
0.719
0.636
0.409
0.000
0.346
0.657
0.513
0.468
0.773
-0.096
-0.248
0.129
0.846
-0.100
-0.131
0.076
Locus
NA
A
Origin
n
nA
Ho
He
EMLS04
4
3.401
EMLS05
4
2.526
REN
SAN
AMS
CAS
KUI
REN
SAN
AMS
CAS
KUI
30
30
32
22
22
30
30
24
21
19
1
2
3
1
2
2
1
1
3
2
0.000
0.467
0.531
0.000
0.091
0.300
0.000
0.000
0.429
0.053
0.000
0.488
0.525
0.000
0.089
0.345
0.000
0.000
0.450
0.053
EMLS10
5
4.346
EMLS13
2
2.000
REN
SAN
AMS
CAS
KUI
REN
SAN
AMS
CAS
KUI
31
30
32
21
19
32
30
32
22
21
2
3
4
3
4
1
2
2
2
2
0.452
0.633
0.156
0.381
0.526
0.000
0.300
0.406
0.364
0.381
EMLS21
2
2.000
REN
SAN
AMS
CAS
KUI
32
29
31
22
22
1
2
2
1
1
EMLS26
6
5.789
EMLS29
3
2.898
REN
SAN
AMS
CAS
KUI
REN
SAN
AMS
CAS
KUI
31
30
32
22
22
32
30
32
22
22
EMLS41
4
3.779
REN
SAN
AMS
CAS
KUI
EMLS45
5
4.231
REN
SAN
AMS
CAS
KUI
118
This pattern was presumably related to the occurrence of one of the three alleles in
homozygous state only (2 genotypes), whereas the two others were close to expectations (see
also the discrepancy between the two estimates of FIS, Tab. 2). SAN exhibited overall
significant heterozygote deficiency (Tab. 3).
Table 3. Genetic diversity and correlation parameters estimated per population. Hexp = Expected
heterozygosity, unbiased estimate. HWE p-values: p-value of the Hardy-Weinberg test with H1 =
heterozygote deficiency (HD), or with H1 = heterozygote excess (HE), significant value in bold.
Origin
Parameter
REN
SAN
AMS
CAS
KUI
4
7
8
7
7
Mean allelic richness
1.556
2.111
2.778
2.222
2.667
Hexp mean
Hexp SD
0.193
0.236
0.282
0.219
0.387
0.253
0.357
0.210
0.338
0.299
Ho mean
Ho SD
0.197
0.247
0.183
0.233
0.393
0.262
0.302
0.213
0.294
0.257
FIS
-0.023
0.343
-0.015
0.158
0.134
HWE p-value HD
0.621
0.000
0.191
0.033
0.023
HWE p-value HE
0.374
1.000
0.808
0.967
0.978
Number of polymorphic loci
Table 2 showed that this deficiency concerned 4 loci (EMLS21, -26, -41 and -45), for which
MICROCHECKER also detected null alleles. Therefore, inbreeding and / or the presence of null
alleles might be responsible of the pattern observed in SAN. Since this strain originated from
only 2 adults and 3 clutches (about 100 eggs each), the apparent fixation may well result from
mating among relatives since the culture was set (2010, i.e., few generations back). The
potential contribution of self-fertilization could not be ruled out, but further investigation is
clearly needed to confirm any hypothesis. Natural populations showed overall significant
heterozygote deficiency, which was due to a single locus in each case (LS29 in CAS,
p<0.001; LS26 in KUI, p = 0.032). Such a pattern is not expected to result from inbreeding.
With regard to the mating system, the general absence of inbreeding is consistent with the
“preferential outcrosser” status of L. stagnalis (Escobar et al., 2011). Population
differentiation was generally strong (mean FST-value = 0.416, 95% CI [0.344; 0.509]), as also
119
reflected by pairwise estimates, which were all highly significant (p < 0.001). Nevertheless,
the UPGMA tree built from these pairwise estimates reflected closer relationships among
three Dutch samples (Fig. 1).
To summarize, the set of markers reported here provide new tools that should be
useful for performing population genetics in L. stagnalis. Strains and populations used in this
study exhibited low variation and high differentiation. This suggests that the loci may be also
suited to parentage analysis, and the use of individuals from different populations may be
advocated. Genetic variability was particularly reduced in two out of three laboratory cultures,
as compared to natural populations. This information is relevant for experimental designs
aimed at testing the influence of genetic variability on other biological performances, such as
the response to environmental pressures. Inbreeding was not detected except in one culture.
Overall, the observed pattern is consistent with the low dispersal ability of the species, its
propensity to occupy small and isolated habitats, and its preference for outcrossing.
120
Chapitre IV -
Impact évolutif des activités agricoles et
anthropiques chez L. stagnalis : étude de
14 populations naturelles
121
A partir de ce chapitre, nous entrons vraiment dans la problématique évolutive de la
thèse. Nous avons cherché à savoir ici, si des expositions répétées aux pesticides sur le long
terme pouvaient induire des processus de divergence adaptative entre populations naturelles
de L. stagnalis.
Un ensemble de 14 populations naturelles ont ainsi été échantillonnées au mois de
juillet 2011 dans le Nord-Ouest de l’Europe, de Bruxelles à Hambourg, en passant par
Amsterdam, dans des étangs, fossés et canaux d’irrigations contrastés du point de vue de leur
pression pesticide. Le niveau d’exposition de ces populations a alors été caractérisé à partir
d’une estimation du pourcentage de la surface occupée par différents types de paysages, i.e.,
landes/forêt, pâture/friche, culture et zone urbaine/résidentielle, dans un rayon de 100m autour
des sites échantillonnés. Nous avons ainsi déterminé deux niveaux d’anthropisation (AL1 <
AL2) et deux niveaux d’exposition aux pesticides (PR1 < PR2), nous permettant de classer les
14 populations dans trois niveaux d’exposition anthropique (ER0 < ER1 < ER2).
L’hypothèse de divergence génétique adaptative a été testée grâce à une approche QST
-FST, basée sur la comparaison entre le niveau de différenciation génétique neutre estimé à
partir de marqueurs neutres (indice FST ; Wright 1952) et la variation génétique estimée pour
des traits quantitatifs (QST ; Spitze 1993). La diversité génétique neutre des populations a été
estimée par le génotypage de 399 adultes capturés (G0) à 12 loci microsatellites. Ces individus
ont été suivis au laboratoire afin d’élever leur progéniture en common garden (Fig. 8) et ainsi
étudier, de l’éclosion à l’âge adulte, 11 traits d’histoire de vie (THV) relatifs à la croissance (n
= 1154 individus de G1) et à la reproduction (492 individus de G1), pour lesquels la variance
génétique a été estimée au sein et entre les populations (QST). Cette étude repose sur un
élevage de lignées réalisé au cours du deuxième semestre 2011, et basé sur environ 15000
individus durant les trois premiers mois, 2828 individus de l’âge de 3 à 6 mois, et enfin 1680
individus marqués individuellement et suivis jusqu’à l’âge de neuf mois. Par ailleurs, 504
individus représentatifs de l’ensemble des lignées ont été exposés pendant 5h à 0 ou 222,2
µg/L de diquat pour l’étude quantitative à l’échelle transcriptomique (RNAseq, objet du
prochain chapitre).
Globalement, les traits d’histoire de vie étudiés ont révélé des patrons de divergence
compatibles avec la sélection divergente (QST > FST ; i.e., taille à l’âge adulte, aptitude à
pondre, délai d’oviposition post-isolement, nombre de pontes pendant les 15 jours de suivie
de fécondité), homogénéisante (QST < FST ; taille à l’éclosion, taux de croissance, survie à
122
l’éclosion), ou avec l’hypothèse neutre (QST = FST ; fécondité totale, taille des pontes). Bien
que les cas de sélection divergente constatés semblent principalement influencés par les
différents types d'habitat et le degré d’isolement génétique des populations, nos résultats
suggèrent qu’une fécondité accrue ait été sélectionnée en réponse à la survie précoce réduite
chez les populations soumises aux pressions anthropiques.
En outre, d’après nos résultats, le niveau de consanguinité des populations apparaît
négativement corrélé avec la fécondité et le taux d'éclosion, ce qui suggère l’existence d’une
dépression de consanguinité plus forte que précédemment estimée chez L. stagnalis. La
grande divergence phénotypique et génétique observée entre populations confirme la
pertinence de prendre en considération la diversité génétique des populations ainsi que
l’impact évolutif potentiel des polluants dans les procédures d'évaluation du risque
écologique. Cette étude sera valorisée sous la forme d’un article en préparation (présenté ciaprès) à soumettre à la revue Evolutionary Applications.
Figure 8. Photos du dispositif d’élevage des lignées G1
123
Article 3
-
Do pesticides influence evolutionary processes in natural
populations of non-target species? A study in the freshwater snail,
Lymnaea stagnalis
Anthony Bouétard, Anne-Laure Besnard, Marc Collinet and Marie-Agnès Coutellec
INRA, UMR 0985 ESE, Ecotoxicology and Quality of Aquatic Environments,
65 rue de Saint-Brieuc, CS84215, 35042 Rennes Cedex, France
E-mail contact: [email protected]
(To be submitted in Evolutionary Applications)
Keywords: Historical pesticide exposure, Life history traits, Population genetic variation,
Evolutionary impact, Qst-Fst comparison, Inbreeding depression, Great pond snail.
124
Abstract
Pollutants present in the environment may affect the evolution of natural populations
in several ways. Fitness reduction can result from direct effects on the germ-line (mutagenic
compounds), or it may be the consequence of a negative impact on genetic diversity, through
directional selection (when the population becomes locally adapted) or through amplification
of random genetic drift (when local demography and dispersal patterns are impaired).
Pollutants may thus affect the evolution of genes under selection as well as neutral regions of
the genome.
The issue of evolutionary impact is therefore conceptually important for
ecotoxicologists and also for ecological risk assessment and management of chemicals. With
respect to human-induced pollutions, some conditions are expected to increase the risk of
genetic change in natural populations. Among such conditions, freshwater lentic habitats
located within agricultural landscapes are likely to be exposed to recurrent contamination by
pesticides, through various modes of transfer from treated fields. Non-target species in these
habitats are thus exposed to a high risk of evolutionary impact, especially when they have low
dispersal ability (e.g., when the whole life cycle is aquatic) or opportunity (weak connectivity
between aquatic ecosystems, e.g., marshes or ditches).
In such a context, the effects of historical exposure to pesticides on population
genetics patterns have been investigated in the freshwater gastropod Lymnaea stagnalis. A
set of 14 populations was sampled from contrasted types of landscape in North-Western
Europe, expected to differ in terms of pesticide pressure, i.e., sites within areas of intensive
agricultural activity and sites distant from such areas. Neutral genetic diversity was estimated
among 12 microsatellites loci and additive genetic variances were assessed among 11 lifehistory traits related to growth and fecundity, from early stage to adulthood. A QST-FST
approach was used to identify the different selection patterns. Although divergent selection
patterns among populations were found to be mainly influenced by the types of habitat and
their respective gene flow potentials, our results suggest that pesticide pressure may induce
evolutionary processes enhancing the ability of snails to reproduce in order to overcome
reduced hatching success. Furthermore, inbreeding was shown to be negatively correlated
with the fecundity and hatching success, suggesting that inbreeding depression in L. stagnalis
might be stronger than previously estimated. Anyway, the high genetic and phenotypic
differences observed within and among populations in this study, confirm the relevance of
125
taking into account population genetics and trans-generational endpoints for ecological risk
assessment.
126
Introduction
Understanding the causes of population divergence is a central goal in evolutionary
biology. This is also becoming a question of importance in environmental sciences, since the
consideration of evolutionary principles is relevant to improve the management of
populations and biological resources (Hendry et al., 2011). Basically, population genetic
divergence may result from both directed and random processes, and from the balance
thereof.
Rapid adaptive responses have been demonstrated under particular situations, such as
heterogeneous environment with a metapopulation structure or colonization of new habitats
(Reznick and Ghalambor, 2001). Human activities may lead to such situations, e.g., through
habitat fragmentation or degradation, due to physical, chemical and biological stressors.
Moreover, the natural rate of evolution can itself be modified by human activities (Hendry et
al., 2008). Among them, pollutants represent a source of environmental stress which may
have the capacity to reduce population fitness and genetic variability, through microevolutionary processes that may involve both random genetic drift and local adaptation
(Medina et al., 2007). Pollutants may also have effects that are passed on to subsequent
generations without being genetically inherited, yet with some influence on offspring
phenotype (Bonduriansky and Day, 2009).
A substantial loss of biodiversity has been shown to be associated with the
intensification of arable agriculture over the last 5 decades (Memmott, 2009). Agriculture,
combined with industrial and domestic activities, uses more than one-third of the Earth’s
accessible renewable freshwater (i.e., approximately 4,430 km3 / year in 2006), and this
intensive consumption is often leading to water contamination (Schwarzenbach et al., 2010).
About 140 million tons of fertilizers and several million tons of pesticides are applied each
year (Schwarzenbach et al., 2006). Natural populations are thus exposed to a variety of
spatially and temporarily variable pressures. For these reasons, the evolutionary impact is
becoming a new challenge for ecological risk assessment of chemicals (Breitholtz et al.,
2006; Bickham, 2011; Coutellec and Barata, 2011). A number of studies have reported cases
of evolved tolerance to metals (Reznick and Ghalambor, 2001; Morgan et al., 2007; Bourret
et al., 2008) or pesticides (Brausch and Smith, 2009; Coors et al., 2009) in natural populations
chronically exposed to industrial or agro-chemical pollutants.
127
These responses may be correlated with a loss of genetic diversity, which may further
impair population adaptive potential to future environmental change (van Straalen and
Timmermans, 2002).
Also, pesticide resistance may imply costs, as demonstrated for
example, in terms of survival under high density in gray treefrog tadpoles tolerant to the
insecticide carbaryl (Semlitsch et al., 2000), seed production in glyphosate-tolerant genotypes
of the weed Ipomoea purpurea (Baucom and Mauricio, 2004), or pupal mass and
development time in pesticide-resistant populations of Choristoneura rosaceana (Carriere et
al., 1994). However, the evolution of costly resistance or tolerance is empirically not as
supported as expected. Indeed, several studies have opposed results, i.e., cross-resistance or
cross-tolerance, such as fairy shrimp populations inhabiting agricultural watersheds showing
higher significant tolerance to Methyl Parathion® 4ec, Tempo® SC Ultra, Karmex® DF and
DDT than populations from playas surrounded grasslands (Brausch and Smith, 2009).
One method now commonly used to test for local adaptation and subsequent
divergence among populations, is the QST-FST approach (Spitze, 1993), which compares the
genetic variance distribution of phenotypic traits (used as fitness components) to the
population genetic structure inferred from neutral marker loci. A quantitative trait is
considered to be exposed to random genetic drift only, when both genetic indices overlap in
their distribution (FST = QST). In other words, selection processes are not required to explain
the observed pattern of population divergence for that trait. Under this model, significant
departure from the null hypothesis may take two forms: FST > QST is indicative of
homogeneous selection, i.e., towards a unique optimum across populations, while FST < QST
reflects divergent selection, and the occurrence of different fitness optima between
populations, i.e., local adaptation (Spitze, 1993; O'Hara and Merila, 2005; Whitlock, 2008;
Lamy et al., 2012).
The approach most often relies on phenotypic measurements made on the laboratoryborn progeny of wild-caught individuals (G1, isofemale lines). Although fitness should be
estimated in the real local environment of populations, fitness estimates remain challenging to
perform in the field, and suffer from inaccuracy, so to circumvent this difficulty, the fitness
performances of individual genotypes or lines are usually measured from life history traits
(used as fitness proxies or components) under common environmental conditions (common
garden, i.e., controlled conditions identical to all populations). The use of G1 individuals
allows limiting the bias in additive genetic variance estimation, that is due to non-additive
128
genetic effects (O'Hara and Merila, 2005), and to direct environmental effects, although more
generations would be required to safely eliminate maternal effects and other transgenerational effects. The use of a common environment allows to assume that populations are
compared under standard conditions, which supports the idea of an “objective” comparison,
although imposed conditions may not be equally similar to (or distant from) local conditions
across the studied populations, especially if local adaptation effectively occurs. However, this
method has been applied in a number of studies, which provide important documentation on
selection patterns (Saint-Laurent et al., 2003; Evanno et al., 2006; Chapuis et al., 2007;
Johansson et al., 2007; Eroukhmanoff et al., 2009; Hangartner et al., 2012), and allow
evaluating its merits (see meta-analyses; Leinonen et al., 2008; Lamy et al., 2012). With
regard to anthropogenic pressure, the QST-FST approach appears promising for the long-term
assessment of ecotoxicological effects (Klerks et al., 2011), however, it has still never been
applied in this field except a case study of adaptation to metal contamination in Thlaspi
caerulescens (Jimenez-Ambriz et al., 2007).
With respect to human-induced pollutions, some conditions are expected to increase
the risk of genetic change in natural populations. Among such conditions, freshwater lentic
habitats located within agricultural landscapes are likely to be exposed to recurrent
contamination by pesticides, through various modes of transfer from treated fields, including
aerial drift, run-off, and drainage (Brown and van Beinum, 2009). Such changes in
environmental conditions can rapidly shift allele frequencies in populations of species having
relatively short generation times (Hoffmann and Willi, 2008). Also, freshwater organisms
occupying these habitats are exposed to a high risk of evolutionary impact, especially when
they have low dispersal ability (entirely aquatic life cycle, e.g., molluscs, crustaceans) or
opportunity (weak connectivity among occupied sites, e.g., ponds or ditches) (Coutellec et al.,
2011). As non-target organisms, these species are also expected to be good models to test
non-intentional evolutionary effects of pesticide exposure. In particular, freshwater snails
belong to an ecologically important group, due to their major role in the transfer of energy
and material across food webs, and because they can represent up to 20-60% of the total
abundance and biomass of macroinvertebrates in some freshwater ecosystems (Habdija et al.,
1995). Among them, the pulmonate gastropod Lymnaea stagnalis is a representative species
129
of freshwater lentic environments and of the herbivorous aquatic macroinvertebrate
community.
In the present study, we tested the hypothesis that pesticide chronic pressure may act
as a selective force and lead to adaptive divergence among a set of 14 natural L. stagnalis
populations, using the QST-FST methodology. Pesticides were considered as a whole, as
contributing to a global, potentially toxic, pressure on non-target organisms (with three levels:
low, medium, and high). Thus, under this hypothesis, directional selection involving
mechanisms of response to a given pesticide are not specifically focused (searched for),
although they obviously contribute to the fitness of populations and genotypes. Rather, we
hypothesize that global chronic exposure to various cocktails of pesticides may drive
populations to an optimum in terms of ability to respond to temporary but recurrent stressful
conditions (including indirect effects at the community level).
The study aimed at addressing the following questions: (i) is agricultural pressure
able, through pesticide chronic exposure, to trigger adaptive processes in non-target
organisms? (ii) what is the relative strength of this pressure type, compared to other
environmental components, including natural and other anthropogenic factors? Indeed, as in
any field study, because populations classified in a given level of pesticide pressure are not
replicates of each other in terms of ecology and evolution, any other relevant environmental
factor should be also accounted for. Under this rationale, we characterized populations in
terms of “other anthropogenic pressure” (roads, urbanized zones), “global pressure” (a
combination of agricultural and non agricultural anthropogenic pressures), and “aquatic
system” (pond, ditch, channel, as physical features expected to affect population genetic
isolation).
130
Materials and methods
Population sampling and characterization
Adult snails were collected from 14 natural populations during the reproductive
period, in a single campaign (5-12 July 2011) (Fig. 1). Locality information and sampling
characteristics are summarized in Table 1.
6
5 7
9
8
4
2
Amsterdam
12
11 10
13
Hamburg
3
14
Brussel
1
Figure 1. Location of the 14 populations studied. The number each population is
indicated and the 3 different colours corresponds to the 3 environmental categories
(white, grey and black for ER0, ER1 and ER2, respectively). See Table 1 for details.
Pesticide and other anthropogenic pressures were estimated from land-use patterns
observed in the immediate surroundings of sampled sites. In the present study, we were not
interested in an instant environmental concentration of pesticides at the time of sampling. On
the contrary, under the tested hypothesis (population adaptive divergence caused by pesticide
pressure), we assume that historical (repeated or chronic) contamination is more relevant as a
potential selective force driving phenotypic evolution in the studied populations. However, it
is very difficult to assess pesticide contamination over a timescale relevant to ecoevolutionary issues, since (1) it would require accurate and long-term monitoring at a local
scale, (2) most importantly, chemical analyses are inherently limited to a pre-selected set of
molecules to be analysed/dosed (e.g., compounds and their metabolites expected to be
present, given some observed agricultural practices or other local environnemental pressures).
Thus, even a strong technical effort on monitoring would not prevent from the possibility that
the molecule(s) effectively responsible for the observed pattern is (are) would be ignored.
Our strategy built on the use of geographic landscape information and agricultural
land-use data. These have proved to be relevant tools to predict the likelihood of water bodies
131
contamination by pesticides (Brown et al., 2007) and for ecological risk assessment of
pesticides in the field (ditches: De Zwart, 2005; ponds: Coors et al., 2009; Orsini et al., 2012).
Moreover, in such aquatic systems, most variation may be explained by close land-use
patterns (within a 100 m radius area centred on the sampling site), as demonstrated for water
quality and vegetation complexity by Declerck et al. (2006). Consistently, we characterized
the environment of each studied population on the following basis: percent coverage by forest
or moorland, pasture, crop (including potatoes, corn, orchards, bulb plants), and urban zone
(Tab. 1). By crossing observations in the field and satellite views from Google Earth with
informations obtained from farmers on their practices, we estimated coverage proportions,
using the software ImageJ (http://rsbweb.nih.gov/ij/).
132
Table 1. Geographical and ecological characteristics of the sites of origin of the 14 investigated Lymnaea stagnalis populations and presentation
of their respective genetic diversity indices. The classification of populations in two levels of anthropogenic pressure (AL1 < AL2), two levels of
pesticide risk (PR1 < PR2) and three levels of environmental risk (ER0 < ER1 < ER2) was based on the percentage of land use categories within a
radius of 100 m around the sampled sites. n indicates the number genotyped individuals, N is the number of alleles, AR is allelic richness (based on
samples of 12 individuals), HE is the unbiased expected heterozygosity, HO is the observed heterozygosity, and FIS is the inbreeding coefficient
(significance is indicated by asterisk). Ne is the effective population size and estimates of self-fertilization rates are also presented.
St atist ical f acto rs
L o calizati o n
G eo g rap hi c
coo rd o n ates
L and u se
R eg io n
clu st er
H ab itat
G en eti c di ver sity in d ices
L evel o f
A n th ro p izati on P esti cid e
En v ir on m en tal
L evel
R isk
R i sk
n
N
AR
HE
HO
FI S
S elf in g
r ate
(R M E S)
N
e
[C I 9 5 % ]
5 .Ca s.
C astri cu m ( A lk m aar, N L)
5 2 °3 3 .17 N ,
4 °3 7 .22 E
1 00 % m o or
W
Po n d
A L1
P R1
ER 0
32
3 .3
2 .6 0
0 .3 4 1
0 .23 1
0 .3 27 *
0
31
[1 9 ; 5 8 ]
9 .Ku i .
K u in re (N o o rd o os tp o ld er, N L)
5 2 °4 7 .67 N ,
5 °4 7 .69 E
9 8% fo r es t, 2 %
u rb an
W
D itch
A L1
P R1
ER 0
25
3 .8
3 .3 3
0 .4 2 8
0 .34 1
0 .2 07 *
0
25
[1 4 ; 5 2 ]
1 3 .H ed .
H eden d o rf (H am b u rg , D )
5 3 °2 9 .47 N ,
9 °3 6 .55 E
9 0% fo r es t, 1 0%
u rb an
E
Po n d
A L1
P R1
ER 0
26
2 .8
2 .3 9
0 .3 1 5
0 .23 5
0 .2 58 *
0
32
[1 8 ; 6 6 ]
1 .O u d .
O ud -H ev erlee (B ru x elles, B )
5 0 °4 9 .39 N ,
4 °3 9 .29 E
5 5% fo r es t, 4 5%
u rb an
W
Po n d
A L1
P R1
ER 0
13
3 .3
3 .2 8
0 .4 1 6
0 .34 5
0 .17 7
40 .6 *
26
[1 3 ; 6 6 ]
7 .S ch .
S ch o or ld am ( A lk m aar, N L)
5 2 °4 3 .13 N ,
4 °4 2 .2 E
9 5% p astu r e, 5 %
u rb an
W
C h an nel
A L2
P R1
ER 1
26
3 .3
2 .8 9
0 .3 2 4
0 .25 2
0 .2 25 *
1 8 .5
24
[1 3 ; 4 7 ]
1 4 .D et.
D etselb erg en (G en t, B)
5 1 °0 3 .29 N ,
3 °4 9 .35 E
3 0% fo r es t, 3 0%
fall o w , 4 0 % u rb an
W
Po n d
A L2
P R1
ER 1
14
2 .5
2 .4 5
0 .3 2 5
0 .21 3
0 .3 53 *
48 .5 *
33
[1 6 ; 1 3 9]
1 0 .Bu x.
B u xt ehu d e (H am b u rg , D )
5 3 °2 9 .63 N ,
9 °4 2 .78 E
8 0% fal lo w , 1 5 %
cro p, 5 % u rb an
E
C h an nel
A L2
P R1
ER 1
27
3 .2
2 .8 4
0 .3 9 1
0 .35 5
0 .09 4
0
22
[1 2 ; 4 3 ]
4 .B aa .
B aarn (U tr echt , N L)
5 2 °1 3 .38 N ,
5 °1 8 .53 E
2 5% p astu r e, 2 5 %
cro p, 5 0 % u rb an
W
D itch
A L2
P R2
ER 2
22
3 .9
3 .5 4
0 .4 4 8
0 .31 2
0 .3 08 *
12
33
[1 8 ; 7 2 ]
2 .O o s.
O ost ein d (B r ed a, N L )
5 1 °3 8 .87 N ,
4 °5 5 .82 E
3 0% p astu r e, 4 0 %
cro p, 3 0 % u rb an
W
C h an nel
A L2
P R2
ER 2
32
4 .8
4 .1 8
0 .5 9 0
0 .48 8
0 .1 75 *
0
198
[1 0 5 ; 6 9 8]
3 .B ie.
B iezen mo rt el (B red a, N L)
5 1 °3 8 .03 N ,
5 °9 .53 E
2 0% fo r es t, 5 %
p as tu re, 7 5% cro p
W
C h an nel
A L2
P R2
ER 2
30
4 .4
3 .6 7
0 .4 5 2
0 .37 7
0 .1 68 *
9 .1
109
[6 4 ; 2 5 9]
6 .P ut .
P u tt en ( A lk m aar , N L)
5 2 °4 5 .81 N ,
4 °3 9 .82 E
3 0% cro p , 7 0%
u rb an
W
D itch
AL 2
P R2
ER 2
32
2 .9
2 .6 2
0 .3 7 0
0 .29 1
0 .2 16 *
1 8 .6
37
[2 2 ; 6 7 ]
8 .E m m .
E m m elo o rd ( N o or d oo st po l der , N L)
5 2 °4 6 .27 N ,
5 °4 8 .26 E
8 0% cro p , 2 0%
u rb an
W
D itch
AL 2
P R2
ER 2
24
3 .7
3 .3 2
0 .4 5 6
0 .34 3
0 .2 52 *
0
36
[2 0 ; 8 0 ]
1 1 .Ko e.
K o en i gsr eich (H am b ur g, D )
5 3 °3 1 .6 N ,
9 °4 6 .29 E
8 5% cro p , 1 5%
u rb an
E
C h an nel
AL 2
P R2
ER 2
27
5 .3
4 .1 7
0 .4 4 4
0 .36 7
0 .1 76 *
9 .5
48
[2 9 ; 9 1 ]
1 2 .Ag a .
A gat hen b u rg ( H am bu rg , D )
5 3 °3 4 .54 N ,
9 °3 2 .82 E
1 00 % cro p
E
D itch
AL 2
P R2
ER 2
25
4 .3
3 .6 9
0 .4 9 1
0 .36 3
0 .2 66 *
30 .7 *
29
[1 7 ; 5 3 ]
133
When available, official data on pesticide detection and quantification in surface water
near the sampled sites were found to be globally consistent with our classification
(http://81.93.58.66/bma_nieuw/begin.html; http://www.milieurapport.be/). Finally, besides
geographical location, populations were classified according to four environmental criteria:
aquatic system (pond, channel, ditch), pesticide pressure (two levels, low vs high), other
anthropogenic pressure (two levels, low vs high), global environmental pressure (three levels,
as a combination of pesticide and other anthropogenic pressures).
Molecular analyses
DNA was chelex extracted from haemolymph or foot tissue from 399 wild-caught
adults (14 to 33 snails per population). Neutral genetic variation was described at 12
microsatellite loci, i.e., A2, A112, B117 (Knott et al., 2003), 2k11 and 2k27 (Kopp and Wolf,
2007), and EMLS04, EMLS13, EMLS21, EMLS26, EMLS29, EMLS41, EMLS45, following
the protocol described in Besnard et al. (2013). Only individuals with less than three missing
genotypes were retained for analysis.
Population neutral genetic structure
Mean allele number (N), allelic richness (AR), expected heterozygosity HE (Nei, 1978),
and observed heterozygosity HO, were calculated with GENETIX 4.05.2 (Belkhir et al.,
2004).
The distribution of neutral genetic diversity within and among populations was
estimated from Weir and Cockerham’s estimators of Wright’s F indices (Weir and
Cockerham, 1984) using FSTAT 2.9.3.2 (Goudet, 2001).
Departures from HWE
(heterozygote excess or deficiency), and linkage disequilibria were tested using GENEPOP
version 4.0.10 (Raymond and Rousset, 1995). The selfing rate was estimated per population
with RMES (David et al., 2007). Effective population size was estimated using the sibship
assignment method, as implemented in the software COLONY 2.0.3.0 (Jones and Wang,
2010) and, assuming inbreeding, male and female polygamic mating systems, and monoecy.
The genotypic dataset was not checked for the presence of null alleles (e.g., using MicroChecker; van Oosterhout et al. 2004), since the method requires HWE (see David et al.,
2007), a condition that was clearly not met in the studied populations (see results section).
Global and pairwise FST-values were computed with FSTAT 2.9.3.2, according to Weir and
134
Cockerham (1984) and the Neighnoor-joining tree was realised with MEGA 5 (Tamura et al.,
2011).
To estimate the number of genetic clusters in our dataset without taking into account
any predefined population, we used STRUCTURE 2.2 (Pritchard et al., 2000). Analyses were
performed assuming an admixture model and a number of genetic clusters (k) from 1 to 16
(15 replicates for each k). Each run started with a burn-in period of 50 000 steps followed by
300 000 Markov Chain Monte Carlo (MCMC) replicates. The most likely number of clusters
was determined using the ∆k statistic (Evanno et al., 2005) using STRUCTURE
HARVESTER (Earl and von Holdt, 2012). We used DISTRUCT to plot Structure output data
(Rosenberg, 2004).
Common garden experiment
Wild-caught adults were brought to the laboratory and reared under standard
conditions at the INRA experimental unit U3E (Rennes, France), as previously described
(Coutellec and Caquet, 2011). Snails were isolated in plastic vessels filled with 1 L dechlorinated charcoal filtered tapwater. They were fed weekly with 1.5 g of organic salad, at
each water renewal. Room temperature was maintained at 20 ± 1°C and the photoperiod was
16L/8D. For a given snail, reproduction was followed during 14 days after the first clutch
laid in the laboratory. A total of 228 snails had reproduced after 3 months (15 July – 15
October, 2011).
The genetic component of phenotypic variation within and among
populations was characterized for various life-history traits (individual growth, reproduction,
hatching success) on the laboratory-born progeny (G1) as illustrated in Figure 2. G1 snails
were reared at 20 ± 1°C, under a 14L/10D photoperiod.
Juvenile growth
From hatching to the age of 56 days, 20 individuals per clutch were reared as groups
(n = 427 families, i.e. on average two clutches per parent). Individual size (maximum shell
length) was measured every two weeks on four randomly chosen juveniles per group, using a
stereomicroscope fitted with an ocular micrometer. Hence, for this period, shell size data
were mean values per group. At the age of 63 days, all snails were measured, and 14 snails
per group were randomly chosen and transferred to 1L plastic vessels in which they were
reared until the age of 119 days (roughly corresponding to the end of juvenile growth).
135
SSR analyses
(12 loci)
14 natural populations sampled
(from contrasted local conditions)
Population 1
Population 14
…
Oviposition
Estimation of
neutral genetic
diversity
Laboratory
G1
d0
d14
d28
d42
d56
Mean size (4
individuals on 20
snails / family)
d63
d98
d119
Mean size (14
individuals / family)
Tank n° 1
Tank n°14
…
Measure of
quantitative
traits
Marking and
transfer in tank
Survey of
individual growth
Estimation of (84 individuals x
additive genetic 14 populations)
variance
=> QST
144 families
reared from
hatching to
adult stage
=> 1176 snails
d2
d2
d1
78
7 individuals x
12 families /
tank
d1
92
15
da
ys
f ec
un
06
dit
27
Growth modelized
with Gomperzt
m odel
y
=> 492 snails
(3 inds / family)
• Number of
clutches and eggs
• Oviposition time
• Ability to lay eggs
• Hatching rate
September to December 2011 January 2012 Febuary - M arch 2012
=> F ST
July to August 2011
Field G0
Figure 2. Schematic overview of the common garden experiment (See text for details)
Individual size was estimated from maximum shell length after 98 days (373 families)
and 119 days (207 families), as measured to the nearest 0.1 mm using a digital calliper.
Water was renewed and snails were fed with organic salad every week. Vessels were moved
at random to limit spatial effect within the rearing room. The water volume was increased
with snail age (75 ml from hatching day -d0- to d63, 750 ml from d63 to d119), as was the
weekly amount of organic salad per group (0.5 g from hatching day -d0- to d42, 1 g from d42
to d63, 3 g from d63 to d98, 6 g from d98 to d119).
At the age of 119 days, 7 snails from each of the 144 sibships of similar age (i.e., 165
clutches laid between 16 July and 17 August, 2011), were randomly chosen and marked
individually with a plastic mark for queen honeybees (model FC075; diameter: 2.3 mm,
weight: 1.8 mg; Ickowicz Apiculture, Bollene, France), as described in Coutellec et al.
136
(2011). Marking has been shown not to affect life-history traits in basommatophorans (Henry
and Jarne, 2007). A total of 1176 snails were marked, which represented 4 to 12 different
maternal families per population. They were transferred to 30 L tanks in which they were fed
twice a week with organic salad (0.5 g per snail), and 2/3 of water was renewed weekly.
Four supplementary individual sizes were measured at the age of 177.71 days (SD ±
5.41), 191.71 days (SD ± 5.41), 205.7 days (SD ± 5.4) and 226.51 days (SD ± 5.56), to obtain
12 size measurements for a total of 1154 snails for which growths were modelled using the
Gompertz’s model. Mean age values reflect the fact that all families were not exactly of the
same age (as using only clutches laid on the same date would have considerably reduced the
number of usable families). The growth curve is given by the equation:
Lt = Ae − be
( −kt)
where Lt is the shell length at time t, A is the asymptotic shell length, b is a scaling factor
related to the shell length at t = 0, and k reflects the growth rate.
Reproduction
Once the first clutches started to be observed in aquaria (20 February 2011), three G1
individuals per family (492 snails in total) were isolated in 200 mL plastic vessels to follow
reproduction during two weeks.
Snails were fed weekly with 1 g of organic salad
immediately after water renewal. Several traits were measured: time to first oviposition (egglaying) under isolation (with a censoring limit of 30 days), ability to lay eggs (as the
proportion of reproductive snails per family), number of clutches laid, clutch size, and
hatching rate.
Statistical analyses
All traits were analyzed with generalized linear mixed effect models (GLMM, Rpackage lme4) (Pinheiro and Bates, 2002), with appropriate error distribution (Gaussian for
normal data, Poisson for count data, and Binomial for proportions). Due to the difficulty to
find L. stagnalis in some specific habitats (e.g., contaminated ponds), the study design did not
allow crossing or nesting all factors, and we tested their potential effects using additively
hypotheses.
When necessary, data were log-transformed or BoxCox transformed, and covariates
were included in the model. The model structure was:
137
Y ~ factor1 + factor2 + factor 3 + covariate1 + covariate2+...+ (1|population/family)
Fixed effects were tested by model simplification and comparison using a log-likelihood ratio
test. Fixed factors were: genetic cluster (see STRUCTURE analysis), aquatic system and
pesticide pressure, other anthropogenic pressure or global pressure. Family was nested within
population, and both were treated as random factors.
Depending on the trait, different
covariates were also tested, including 15 days fecundity, 15 days growth, age, parental size,
clutch size or minimum development time of clutches of wild-caught adults (Tab. S5). All
statistical analyses were performed using R version 2.14.0 (The R Foundation for Statistical
Computing).
QST -FST analysis
Observed values of QST - FST were compared with the QST - FST distribution expected
under the neutral hypothesis, and calculated on the basis of the mean FST value and variance
components (Weir and Cockerham, 1984; Whitlock and Guillaume, 2009). Values were
considered as significantly different when their 95% confidence intervals did not overlap.
Within- and between-population variance components were estimated for each of the 11 traits
using restricted maximum likelihood (REML) methods under a simplified mixed model, i.e.
Y~1+ (pop / fam). After sampling, G0 snails were isolated once arrived in the laboratory.
Regarding their preferential outcrossing mode of reproduction (Knott et al., 2003; Puurtinen
et al., 2007; Coutellec and Caquet, 2011), they were supposed to have stored sperm from
previous mating in the field. So, progeny from G0 snails were more likely to be half-sibs than
full-sibs. However, as we were not able to certify the mating mode, both possibilities were
explored for QST estimations (see also Evanno et al., 2006). QST was computed using the
equation (Spitze, 1993; O'Hara and Merila, 2005):
QST trait =
Vb
(V b + 2V w )
where Vb is the between population genetic variance and Vw the within population genetic
variance. Vb was obtained directly from the estimated variance component for the population
effect and the within population variance (Vw) was estimated using the among family variance
component (Vf) multiplied by 2 in the case of full-sib design or by 4 in the case of half-sib
design (Lynch and Walsh, 1998).
138
Results
Within-population genetic diversity
On 399 individual genotypes, 44 were discarded because of missing or unreadable
data at more than 4 loci. Overall, the observed number of alleles per locus varied from three
to 29, mean allelic richness ranged from 1.1 to 9.6 (AR based on samples of 12 individuals;
see Tab. S1), and genetic diversity per locus per sample varied from 0 to 0.86. Significant
genotypic linkage disequilibrium was observed in 40 out of 656 comparisons (tests were
performed within each sample separately). However, after Bonferroni correction, no case
remained significant. FIS-values varied widely across loci, and among samples within loci,
ranging from -0.238 (EMSL41 in Bux.) to 1.000 (2k27 and B117 in Det; EMLS13 in Hed.;
EMLS21 in Baa.) (Tab. S1). Genetic parameters estimated per sample over all 12 loci
reflected discrepancies between populations (Tab. 1). To the exception of Bux. and Oud., in
which observed heterozygosities (0.355 and 0.345, respectively) and FIS-values (0.094 and
0.177, respectively) indicated no departure from Hardy-Weinberg equilibrium, other
populations were found significantly inbred, as shown by FIS-values.
A significant effect on FIS-value was found for habitat (AMOVA, p = 0.016; KruskalWallis test, p = 0.030), suggesting lower inbreeding in channels (mean FIS = 0.168 SD ± 0.05)
as compared to ditches and ponds (mean FIS = 0.25 SD ± 0.04 and 0.279 SD ± 0.08,
respectively).
However, post-hoc p-values were not significant (Tukey, α = 0.05).
Inbreeding appeared also negatively correlated with some fitness-related traits, i.e., fecundity
measured during 15 days (ρ = - 0.798, p = 0.001, linear regression: r² = 0.710, d.f. = 12, p <
0.001, see Fig. 3), ability to reproduce (ρ = - 0.591, p < 0.05) and clutch hatching rate (ρ = 0.587, p < 0.05).
The rate of self-fertilization was found significantly different from zero in three
populations (Oud.: 40.6%, Det.: 48.5%, and Aga.: 30.7%). The effective population sizes, Ne,
estimated with COLONY, were significantly higher in the most exposed populations
(Kruskal-Wallis test: PR2 > PR1, p = 0.010), mostly because of high values in two channel
populations (Oos.: 198 and Bie.:109).
139
140
120
100
80
60
Fecundity (eggs / 15 days)
0.10
0.15
0.20
0.25
0.30
0.35
FIS
Figure 3. Correlation plot between population
inbreeding and 15 days fecundity.
Bayesian clustering and neutral divergence among populations
Using STRUCTURE, the highest likelihood was obtained for k = 2 clusters, and the
differentiation of individual genotypes into 2 clusters was also confirmed by the presence of a
single peak with the ∆k statistic (Evanno et al., 2005) (Fig. 4A). The two clusters inferred
corresponded to populations from two geographic regions: western (n = 10) and eastern
populations (n = 4). For these reasons, a two geographical levels factor was implemented in
the statistical analyses of life-history traits (see factor “genetic cluster”, as mentioned above).
Population differentiation was generally high (mean FST-value = 0.282, 95% CI [0.24;
0.326]), as also reflected by pairwise estimates, which were all significant (p < 0.01) (Tab.
S2), except between the two geographically close populations Kui. and Emm. (East of The
Netherlands). The Neighbour-joining tree built from these pairwise estimates reflected closer
relationships within the two regions characterised with STRUCTURE (Fig. 4B).
Nevertheless, on the basis of the tree representation, the two populations Cas. and Det. from
ponds sampled in the western part of Netherlands and Belgium, respectively, were
particularly differentiated. More generally, the four pond populations (Cas., Det., Oud. and
Hed.) had longer branches, indicating stronger differentiation among populations in each
cluster.
140
A.
et. ud. os.
D
.
O O
14 1. 2.
ie.
3.B
a
4.B
a.
5.C
.
as
6.P
ut.
.
.
.
.
.
.
i.
ed
oe
ux
ga
ch
m m . Ku
B
K
A
H
.
S
.
.
.
E
.
9
7
10
8.
11
12
13
7.Sch.
B.
4.Baa.
5.Cas.
11.Koe.
6.Put.
12.Aga.
0.05
3.Bie.
10.Bux.
2.Oos.
8.Emm.
13.Hed.
9.Kui.
14.Det.
1.Oud.
Figure 4. Genetic differentiation among the 14 studied populations. (A) Bayesian
individual clustering results with STRUCTURE for k = 2. Coloured bars represent proportions
of membership of each individual to each cluster. (B) Unrooted Neighbour-Joining tree based
on Weir and Cockerham genetic distances among the 14 populations (see Table S2).
Statistical analyses and QST-FST comparisons
Congruent selection patterns were obtained between the two sib designs tested (fullvs half-sib), especially for traits related to fecundity. However, as we do not have exact
information concerning mating in the field, and as estimates of intra- and inter-populations
variance components did not take into account such mating in the model, only results under
the full-sib design are presented (Tab. 2).
141
Table 2. Results of the QST-FST comparison for 11 traits studied under a full-sib design.
number
of G1
snails
number
of
families
Vbetween pop
Hatching size
427
211
1.46E-03 *
Size at 119 days
2855
182
7.09E+00 *** 3.17E+00
Asymptotic size (A)
1154
144
Growth parameter
related to initial size (b)
1154
Parameter related to
growth rate (k)
Expected 95% CI
for Q ST-F ST
under neutrality
p.val
left
p.val
right
Observed
Q ST - F ST
Selection
pattern
0.078
0.922
-0.118
Neutrality
[ -0.146 ; 0.148 ]
0.998
0.002
***
0.246
Divergent
selection
6.81E-03 ***
4.66E-03 *** [ -0.149 ; 0.166 ]
0.957
0.043
0.140
Neutrality
144
1.06E-03
2.82E-03 *** [ -0.159 ; 0.152 ]
0.062
0.938
-0.124
Neutrality
1154
144
1.25E-07 **
3.94E-07 *** [ -0.152 ; 0.157 ]
0.026 .
0.974
-0.145
Neutrality
Ability to lay eggs
492
144
2.70E+00 **
9.52E-11
1
0 ***
0.718
Divergent
selection
Time to oviposition
492
144
2.86E-02 ***
1.67E-02 *** [ -0.147 ; 0.141 ]
0.988
0.012
**
0.179
Divergent
selection
15 days fecundity
492
144
8.22E+02 **
1.13E+03 *** [ -0.151 ; 0.152 ]
0.481
0.519
-0.015
Neutrality
Number of clutches
laid during 15 days
492
144
1.10E-01 ***
3.09E-14
1
0 ***
0.718
Divergent
selection
Number of eggs per
clutch
1537
144
2.95E+00 **
2.94E+00 *** [ -0.149 ; 0.162 ]
0.231
0.769
0.052
Neutrality
Hatching rate
1537
144
3.27E-01 **
1.26E+00 *** [ -0.153 ; 0.159 ]
0.02 **
0.98
-0.167
Homogenizing
selection
Traits
V within pop
3.73E-03 *** [ -0.155 ; 0.165 ]
Significance levels : .P < 0.1, *P < 0.05, **P < 0.01, ***P < 0.001
142
.
[ -0.161 ; 0.153 ]
[ -0.146 ; 0.167 ]
.
Concerning traits related to growth, only size at 119 days was found to be under
divergent selection. Differences were highly significant between populations but none of the
fixed factors tested were explicative (GLMM analyses). Based on the full dataset (1154
individuals), the analysis indicated that the pattern of genetic differentiation in parameters of
the Gompertz model was due to genetic drift only (QST = FST). However, a trend towards
divergent selection was observed for the asymptotic size (A, observed QST-FST = 0.140, CI =
[-0.149 ; 0.166] under neutrality). This relation became highly significant when considering
only the subset of 492 snails used for fecundity traits (results not shown). Whatever the
dataset, habitat was the main factor explaining variation in this parameter. Post-hoc tests
indicated significantly greater asymptotic sizes (p < 0.001) for snails inhabiting ponds (34.78
mm SE ± 0.25) as compared to those from channels (31.05 mm SE ± 0.14) and ditches (30.79
mm SE ± 0.19) (Fig. 5, Tab. S3).
Figure 5. Individual growth (G1 snails) according to habitat of origin. Gompertz
growth curves were based on 12 dates of measurement, performed on 1154 individuals.
The two other Gompertz parameters, i.e., b and k, although showing non significant
departure from the neutral hypothesis with the full dataset, tended to be selected towards a
same optimum (homogenizing selection, especially for k), as reflected by negative values for
observed QST-FST difference, close to the lower bound CI under neutrality. Variation in
growth rate (k) seemed to be mainly explained by population and family (no effect found for
any fixed factor) whereas the parameter relative to the initial size (b) appeared partly
influenced by geography (as reflected by “genetic cluster” factor), habitat, and anthropization
level, whatever the dataset (Tab. S4). The corresponding trait, hatching length (i.e., body size
at hatching), also showed a similar neutral pattern with a weak trend towards homogenizing
143
selection (Tab. 4). A significant effect of geography was also detected via GLMM analysis
and post-hoc test, which indicated that hatching size was greater in eastern populations (1.51
mm, SE ± 0.01) than in western ones (1.44 mm, SE ± 0.01). This was consistent with results
obtained with the parameter b (Tab. S4; W > E, as b is negatively related to size at t0).
Considering traits related to fecundity, the three possible patterns of selection were
observed. First, neutral patterns were found for the total number of eggs laid per snail during
15 days (15 days fecundity) and for the number of eggs per clutch (clutch size). In apparent
contradiction with this finding, GLMM analysis followed by post-hoc test (p < 0.001) clearly
indicated smaller clutches from pond populations (n = 31.11 SE ± 0.8) than from ditch
populations (n = 33.55 SE ± 0.71), themselves being significantly smaller than clutches from
channel populations (42.54 SE ± 0.8). Second, divergent selection was shown to act on three
traits, i.e., ability to lay eggs, time to oviposition, and number of clutches laid during 15 days.
According to the GLMM analyses, the number of clutches varied mainly according to
population (no fixed effect) whereas habitat was the main fixed factor acting on reproduction
ability and on time to oviposition. The proportion of reproductive (G1) snails was lower in
pond populations (80.56% SE ± 1.66) than in ditch (97.22% SE ± 0.26) and channel
populations (98.33% SE ± 0.11) (post-hoc test: p < 0.001). Snails from pond populations also
laid their first clutch significantly later than snails from the other two types of aquatic system
(8.9 days SE ± 0.8 vs. 4.4 days SE ± 0.3 and 4.8 days SE ± 0.3, respectively) (post-hoc test: p
< 0.01). Furthermore, a trend to higher ability to reproduce was also noted in snails stemming
from populations with a higher risk of pesticide exposure. Last, clutch hatching rate (i.e., G2
survival at hatching) appeared to be submitted to homogenizing selection (Tab. 4). However,
despite this general trend towards the same optimum (highest survival) significant differences
were noted between aquatic systems and levels of environmental risk, with higher hatching
rates from channel populations (95.9% SE ± 0.46) as compared to those from ditches and
ponds (93.9 % SE ± 0.61 and SE ± 0.7, respectively) (post-hoc test: p < 0.05), and
independently in the less anthropized water bodies (96.1 % SE ± 0.53 vs. 94% SE ± 0.43 for
AL2) (p < 0.001) (Fig. 6).
144
20 40 60 80
0
Hatching rate (%)
AL1
AL2
Figure 6. Observed hatching rate depending on
the level of anthropization. Plot based on 1537
clutches.
Discussion
The hypothesis that human activities may induce divergent selection patterns in
Lymnaea stagnalis was addressed using a common garden experiment, involving life history
traits and a set of natural populations stemming from contrasted environments, with respect to
pesticide pressure. Different patterns of selection were suggested by the data. Compared to
the level of anthropization and the pesticide risk related to agricultural activities, habitat type
appeared as the main factor of population genetic divergence.
Globally, between-population genetic variation was strong and significant, except for
two early expressed traits, i.e., growth parameter b and size at hatching, for which the analysis
consistently concluded to homogenizing selection. For these traits, low QST values were
related to a high family effect. This finding supports results previously obtained in another
freshwater gastropod (Galba truncatula; Chapuis et al., 2007), as the expression of maternal
effects operating early in life, and which may result from the transfer of epigenetic,
cytoplasmic or somatic factors, nutrients, or extra-organismal environment from parents to
offspring (Bonduriansky and Day, 2009), and tend to recede with age (Wolf, 2000).
However, our experimental design (G1-based design) did not allow disentangling these effects
from true genetic effects. Indeed, as wild-caught adults were likely to have stored sperm from
previous mating in the field, a full-sib design was assumed, under which both maternal effects
and dominance effects cannot be separated from true additive genetic effects (Leinonen et al.,
145
2008). Moreover, in the present experiment, an additional source of family variation may be
related to the fact that the seven first measurements are mean sizes based on 4 or 14 snails that
were randomly selected each date (depending on age, see Fig. 2 & methods). Despite low
variability, these two traits were the only ones significantly influenced by the East-West
geographical structure, with smaller size at hatching to the East. Also, the growth parameter b
was significantly influenced by habitat type and anthropization level, suggesting larger
individual size at hatching in the most exposed channels and ditches. The largest hatchlings
were observed in families from Baarn population, whereas this population exhibited the
smallest size at age 119 days and the smallest asymptotic size (see Tab. S3). Both of these
traits were suspected to be under divergent selection. GLMM analysis revealed that later traits
related to growth were mainly influenced by the type of habitat, with larger snails from pond
populations than from ditches and channels. In contrast, regarding the growth rate parameter
k, only population and family effects were significant, and this trait was shown to tend to be
under homogenizing selection. As experimental conditions may act strongly on growth rate
(Coutellec et al., 2011), standard rearing conditions applied in the present study, with limited
amounts of food, might explain the homogenizing trend observed. One may expect that
genetic variation in growth rate could express with a larger magnitude under ad libitum
conditions (potential growth under optimal energetic conditions). However, this may on the
other hand be counterbalanced by the fact that stressful conditions (here, limited food) are
likely to increase phenotypic variation (Holloway et al., 2003). These results reflect the
critical dependency of life-history expression upon direct environmental (and thus
experimental) conditions, and the potential bias this may result in, when estimating
evolutionary patterns from heritability, especially when the traits are used as fitness proxies
(fitness vs. laboratory estimates of heritability; see Ritland 2000).
Concerning traits related to fecundity, patterns of divergent selection were found to act
on the ability of snails to lay eggs, indicating a lower ability in snails from pond populations,
as well as a trend to higher reproductive ability in populations from sites more exposed to
pesticides. Accordingly, divergent selection was shown to act also on the time to first clutch
after isolation from grouping conditions, with a trend to longer times in snails from pond
populations. Two populations (Cas. and Det.) showed particularly low reproductive ability.
Overall, neutral genetic distances (pairwise FST) indicated that pond populations were
particularly differentiated which is consistent with population isolation and weak habitat
146
connectivity, added to the low dispersal potential of the species. Although migratory birds are
suspected to participate to dispersion in freshwater gastropods (e.g., Radix balthica;
Pfenninger et al., 2011) and in other invertebrate taxa (Frisch et al., 2007), this has never been
demonstrated in L. stagnalis, whose clutches are among the largest in this community (which
may limit passive aerial dispersal). Be that as it may, such events are likely to be rare and
have little evolutionary impact on populations. Consistently, genetic diversity was lower and
inbreeding higher in populations from ponds, and to a lesser extent, in those from ditches.
Compared to channels, these aquatic systems are likely to imply stronger population isolation,
even among ditches. Indeed, despite the fact these habitats may belong to large interconnected
networks, gene flow is expected to be limited, due to frequent drought. A similar influence of
habitat was emphasized in another freshwater snail (Physa acuta; Escobar et al., 2008), with a
stronger differentiation among pond populations than among river ones. Habitat
fragmentation has also been shown to negatively impact body size in tadpoles of the common
frog, Rana temporaria, and to correlate with low genetic diversity and high differentiation
(Johansson et al., 2007). Although in the present study, ultimate body size (Gompertz’
parameter A) was found to be larger in ponds, the relation between size and fitness is not
necessarily positive in L. stagnalis, possibly due to energetic trade-offs between growth and
reproduction (Zimmer et al., 2012), whereas in amphibians, the size of tadpoles at
metamorphosis is a critical trait which determines individual’s fitness in terms of survival and
future reproductive success (Bridges and Semlitsch, 2000).
Population inbreeding (FIS) was found negatively related to fecundity, in terms of
ability to reproduce, and of number of eggs laid over a given period once oviposition started
(G1 adults isolated from grouping conditions). This result is consistent with the occurrence of
inbreeding depression in inbred populations, which translated into reduced fecundity in
laboratory reared G1 snails, under outcrossing.
Although inbreeding depression associated to self-fertilization is low in L. stagnalis
(Puurtinen et al., 2004a; Puurtinen et al., 2004b; Coutellec and Lagadic, 2006; Puurtinen et
al., 2007), inbreeding indices (FIS) determined in the present study seem directly correlated to
reduced fecundity and lower ability to reproduce. This suggests that inbreeding depression in
the field might be higher than empirically demonstrated in laboratory, and could confirm that
random drift load might be strong in small and isolated natural populations (Coutellec and
Caquet, 2011).
147
Regarding the mating system, selfing rates were generally not significant (not different
from zero), in accordance with the status of preferential outcrosser of L. stagnalis (Puurtinen
et al., 2007; Coutellec and Caquet, 2011; Escobar et al., 2011). However, large differences
were found among populations, without link to anthropogenic pressure. Indeed, significant
self-fertilization rates were found in two pond populations (Oud. and Det.) and one ditch
population (Aga.), which represented each of the three levels of environmental pressure, as
defined in the present study.
Despite the apparent link between inbreeding and habitat type (isolation), and the
correlation of inbreeding with reduced fecundity, clearly neutral pattern were found for total
fecundity and for the mean number of eggs per clutches, indicating that differences observed
within and among populations were due to random genetic drift only. However, habitat effect
was revealed by GLMM analysis suggesting that clutches from ditch populations were
smaller than those from channels and larger than those from ponds. In contrast, the number of
clutches laid during the 15 day-survey was found to highly diverge among populations. As
for total fecundity, none of the fixed factors was significant. More than the high variance
among populations, the extremely weak variance observed within populations explains the
high QST value for this trait. Although heterogeneous selection cannot be excluded, the fact
that habitat type (connected vs. closed) explains largest part of variance for most of the
studied traits, can also reflect a differential impact of drift rather than different selection
regimes (Escobar et al., 2008).
Regarding the last studied trait, i.e., the hatching success observed on clutches laid by
G1 snails, significant differences between habitats were found indicating higher hatching rates
in channel populations, although homogenizing selection was found to act on this trait. This
selection pattern toward the best hatching rates was not surprising as this trait is related to
survival. However, the negative correlation between hatching rates and inbreeding may be
another proof of the non-negligible inbreeding depression occurring in natural populations of
L. stagnalis as expected by theory for predominant outcrosser and ever demonstrated in other
highly outcrossing basommatophorans (Coutellec-Vreto et al., 1998; Jarne et al., 2000;
Escobar et al., 2011) although low inbreeding depression were observed in laboratory under
self-fertilization controlled mating (Coutellec and Lagadic, 2006; Puurtinen et al., 2007;
Coutellec and Caquet, 2011). Despite the lack of channels (i.e., habitat with high gene flow
potential), significantly higher hatching rates were found in the less anthropized regions,
148
suggesting negative evolutionary impact of human activities on hatching success.
Interestingly, a similar result was already observed on the hatching rate of the clutches laid by
G0 snails (720 clutches, p = 0.007). Coupled with the reduced reproductive ability in
populations from the less exposed regions, these were the main life-history traits impacted by
human-induced
pollution.
This
might
highlight
evolutionary
response
enhancing
performances in fecundity in the most exposed populations to overcome early mortality
induced by chemicals.
Although the number of natural populations studied was close to the minimum number
recommended for QST – FST approaches (Goudet and Buchi, 2006), and GLMM analysis can
manage unbalanced designs (Pinheiro and Bates, 2002), the lack of ponds with a high
expectation of contamination by pesticides and the miss of channels in the less anthropized
regions may impede in part statistical analysis of phenotypic traits and hierarchical analyses.
It underscores the need of a well equilibrate sampling design for such analyses. However, we
currently try to apply analysis method based on G-matrix comparison, treating traits together
and allowing hierarchical patterns (Chapuis et al., 2008; Martin et al., 2008; Ovaskainen et
al., 2008; Ovaskainen et al., 2011). Our present results suggest that to investigate impact of
human and more specifically agricultural activities on micro-evolutionary processes occurring
in natural populations, sampling animals from comparable habitats could permit to discard
some confounding effects. Anyway, the strong differences observed among and within
populations whatever the studied traits, reflect high genetic and phenotypic diversity in
natural populations. Both types of effects are expected to affect ecotoxicity testing (Barata et
al., 2000; Coutellec and Lagadic, 2006; Coutellec et al., 2011). Thus, population adaptation
to chemical stress alters the dose-response curve expected from non-adapted populations.
Toxicity tests that ignore the genetic attributes of populations or cohorts (i.e., genetic
diversity, inbreeding level) should not be used to extrapolate from the laboratory to the field
(Bridges et al., 2001; Frankham, 2005; Nowak et al., 2007). Moreover, with regard to nongenetic trans-generational effects, exposed individuals may not be the right ones to focus on,
as effects may only be detected after one or several generations.
Arguments for the
incorporation of population genetics and trans-generational endpoints into ecological risk
assessment are thus now clearly identified (Breitholtz et al., 2006).
149
Conclusion and perspectives
The investigation of evolutionary impact of human activities on natural populations of
Lymnaea stagnalis has revealed potential impacts on the ability of snails to reproduce and on
the hatching success of the progeny of wild-caught snails reared in common garden
conditions. However, divergent selection patterns among populations revealed for some of
the studied traits were found to be mainly influenced by the different types of habitat and their
respective gene flow potentials.
Nevertheless, the high differences within and among
populations observed in this study confirm the need to take into account such population
genetics and trans-generational endpoints for ecotoxicological risk assessment. However,
fundamental work is still needed before concrete recommendations can be made.
Additionally, as a companion study of the present work, a quantitative analysis of genetic
divergence in gene expression has been conducted at the transcriptomic level (RNAseq) in
response to a challlenge by oxidative stress (using the redox-cycling herbicide diquat). This
approach is currently conducted on a subset of these populations (Cas., Put., Kui. and Emm.)
and might possibly highlight stronger divergences in stressful conditions between populations
with respect to their historical exposure to pesticides. The whole dataset and analysis thereof
are expected to give a clearer picture of the evolutionary impact of pesticides on non-target
organisms.
Acknowledgments
This work has been carried out with the financial support from the INRA (EFPA
Projet Innovant 2010; AIP Bioressources 2010). AB is granted by the INRA (PhD, Contrat
Jeune Scientifique). The authors thank the INRA U3E staff (INRA, Rennes) and more
particularly Maïra Coke for her technical assistance. Our thanks are also due to Thierry
Caquet, Sabrina Le Cam, Jessica Côte for their help, constructive discussions and advices in
statistics, and also Laurent Lagadic for reviewing the last version of manuscript. The authors
finally thank Benoît Heurtault and Gaëlle Méheut (from groovy-banana.com), Elise Petitpas
and Arnaud Giusti for their hospitality and logistic support during the sampling period.
150
Supplementary tables
Table S1. Locus and population genetic characteristics. n indicates the number genotyped
individuals, Na is the number of alleles, AR is allelic richness (based on samples of 12 individuals), HO
is the observed heterozygosity, HE is the unbiased expected heterozygosity, and FIS is the inbreeding
coefficient (significance is indicated in bold characters).
Locus
Overall
1.Oud
2.Oos
3.Bie
4.Baa
5.Cas
6.Put
7.Sch
8.Emm 9.Kui
2k11
n
Na
AR
Ho
He
F IS
10.Bux
11.Koe
12.Aga
13.Hed
336
14
7.633
0.530
0.822
0.356
13
5
4.85
0.615
0.683
0.103
31
8
6.13
0.903
0.770
-0.176
30
8
6.56
0.567
0.790
0.286
21
3
2.93
0.571
0.529
-0.084
30
4
3.62
0.433
0.481
0.1
32
3
2.99
0.563
0.569
0.012
25
4
3.35
0.480
0.545
0.121
23
4
3.94
0.609
0.565
-0.079
A112
n
Na
AR
Ho
He
F IS
349
29
9.261
0.332
0.759
0.562
13
7
6.84
0.769
0.812
0.055
32
12
8.88
0.406
0.818
0.507
29
8
6.29
0.448
0.794
0.44
19
8
6.91
0.316
0.676
0.539
32
1
1.00
0.000
0.000
_
31
2
1.99
0.290
0.252
-0.154
26
2
1.46
0.039
0.039
0
2k27
n
Na
AR
Ho
He
F IS
349
5
3.919
0.338
0.664
0.491
13
4
3.85
0.385
0.345
-0.121
31
4
3.97
0.581
0.698
0.17
29
3
2.96
0.483
0.402
-0.206
22
3
2.99
0.318
0.555
0.432
31
4
2.88
0.032
0.211
0.849
30
4
3.04
0.200
0.403
0.508
n
Na
AR
Ho
He
F IS
345
14
7.914
0.481
0.817
0.411
12
5
5.00
0.417
0.801
0.491
31
7
6.68
0.581
0.820
0.295
30
11
7.79
0.633
0.682
0.072
21
7
6.31
0.524
0.797
0.348
32
9
4.86
0.344
0.464
0.262
B117
n
Na
AR
Ho
He
F IS
339
27
9.627
0.434
0.806
0.462
12
3
3.00
0.417
0.540
0.236
32
7
5.46
0.406
0.695
0.419
27
4
2.88
0.259
0.294
0.119
20
6
5.53
0.600
0.724
0.175
EMLS04
n
Na
AR
Ho
He
F IS
351
5
2.778
0.202
0.317
0.361
13
2
2.00
0.154
0.148
-0.043
31
3
2.63
0.323
0.368
0.125
30
1
1.00
0.000
0.000
_
EMLS13
n
Na
AR
Ho
He
F IS
354
4
2.133
0.271
0.470
0.423
13
2
1.92
0.077
0.077
0
32
2
2.00
0.438
0.508
0.141
EMLS21
n
Na
AR
Ho
He
F IS
352
3
1.101
0.003
0.009
0.666
13
1
1.00
0.000
0.000
_
32
1
1.00
0.000
0.000
_
14.Det
24
3
2.48
0.250
0.228
-0.1
25
5
4.54
0.480
0.607
0.212
25
8
6.49
0.480
0.700
0.319
19
8
7.48
0.474
0.868
0.461
26
3
2.99
0.423
0.597
0.295
12
3
3.00
0.500
0.594
0.165
24
5
4.70
0.125
0.719
0.829
25
9
7.33
0.440
0.816
0.466
27
3
2.70
0.444
0.545
0.188
27
10
6.36
0.444
0.743
0.406
25
9
7.25
0.160
0.764
0.794
26
3
2.46
0.423
0.497
0.151
13
4
4.00
0.846
0.699
-0.222
26
3
2.96
0.308
0.456
0.33
23
3
2.90
0.391
0.484
0.195
25
3
2.73
0.360
0.411
0.126
27
4
3.14
0.333
0.486
0.318
27
3
2.84
0.519
0.469
-0.108
25
3
2.94
0.480
0.581
0.177
26
3
2.70
0.231
0.275
0.162
14
2
2.00
0.000
0.349
1
29
5
3.83
0.207
0.698
0.707
24
6
4.51
0.500
0.582
0.144
23
7
5.70
0.478
0.627
0.241
25
7
4.91
0.480
0.629
0.24
27
6
4.86
0.630
0.699
0.101
26
9
7.37
0.808
0.789
-0.024
25
7
5.56
0.440
0.692
0.369
26
4
3.45
0.423
0.635
0.338
14
4
3.86
0.071
0.516
0.866
31
6
5.04
0.161
0.623
0.744
32
4
3.80
0.281
0.514
0.457
21
5
4.47
0.238
0.547
0.571
22
5
4.33
0.500
0.613
0.188
24
5
4.96
0.583
0.769
0.245
27
7
6.45
0.704
0.787
0.107
26
17
11.49
0.808
0.858
0.06
25
9
6.89
0.560
0.779
0.285
26
7
5.47
0.462
0.646
0.289
14
3
2.97
0.000
0.265
1
22
2
2.00
0.409
0.384
-0.068
32
1
1.00
0.000
0.000
_
30
1
1.00
0.000
0.000
_
25
2
1.48
0.040
0.040
0
24
2
1.99
0.250
0.223
-0.122
25
3
2.22
0.120
0.117
-0.029
27
2
2.00
0.370
0.425
0.13
27
2
2.00
0.185
0.373
0.508
25
2
2.00
0.440
0.393
-0.123
26
3
2.46
0.539
0.520
-0.037
14
1
1.00
0.000
0.000
_
30
2
2.00
0.367
0.463
0.21
22
2
1.91
0.136
0.130
-0.05
32
2
2.00
0.375
0.437
0.143
32
2
2.00
0.563
0.476
-0.185
26
4
3.43
0.308
0.434
0.296
24
2
2.00
0.458
0.503
0.09
24
2
2.00
0.333
0.422
0.214
27
1
1.00
0.000
0.000
_
27
2
1.84
0.037
0.107
0.658
25
2
2.00
0.320
0.327
0.02
26
2
1.72
0.000
0.075
1
14
2
2.00
0.071
0.198
0.649
29
1
1.00
0.000
0.000
_
22
2
1.80
0.000
0.089
1
31
1
1.00
0.000
0.000
_
32
1
1.00
0.000
0.000
_
25
1
1.00
0.000
0.000
_
24
2
1.50
0.042
0.042
0
25
1
1.00
0.000
0.000
_
27
1
1.00
0.000
0.000
_
27
1
1.00
0.000
0.000
_
25
1
1.00
0.000
0.000
_
26
1
1.00
0.000
0.000
_
14
1
1.00
0.000
0.000
_
A2
151
Table S1. (continued)
Locus
Overall
1.Oud
2.Oos
3.Bie
4.Baa
5.Cas
6.Put
7.Sch
8.Emm 9.Kui
EMLS26
n
Na
AR
Ho
He
F IS
10.Bux
11.Koe
355
8
5.934
0.468
0.787
0.406
13
5
4.99
0.615
0.726
0.158
32
5
4.50
0.656
0.682
0.038
30
6
5.51
0.800
0.777
-0.03
22
5
4.47
0.546
0.650
0.164
32
2
2.00
0.313
0.347
0.101
32
4
2.90
0.188
0.231
0.19
26
4
3.81
0.462
0.472
0.023
24
4
3.95
0.542
0.713
0.244
EMLS29
n
Na
AR
Ho
He
F IS
348
4
3.105
0.112
0.280
0.6
13
2
2.00
0.154
0.271
0.442
32
2
2.00
0.406
0.396
-0.025
29
3
2.47
0.069
0.164
0.584
22
2
1.55
0.046
0.046
0
28
2
2.00
0.143
0.486
0.71
32
2
2.00
0.281
0.289
0.028
26
1
1.00
0.000
0.000
_
EMLS41
n
Na
AR
Ho
He
F IS
355
6
4.669
0.378
0.694
0.456
13
2
2.00
0.462
0.517
0.111
32
3
2.95
0.594
0.590
-0.007
30
3
3.00
0.533
0.666
0.202
22
3
2.80
0.136
0.529
0.746
32
3
3.00
0.406
0.548
0.261
32
4
3.93
0.500
0.585
0.147
EMLS45
n
Na
AR
Ho
He
F IS
347
9
3.794
0.378
0.507
0.256
13
2
1.92
0.077
0.077
0
32
4
3.97
0.563
0.732
0.234
30
3
2.64
0.367
0.396
0.075
21
4
3.32
0.143
0.264
0.464
32
4
2.75
0.563
0.499
-0.131
31
3
2.92
0.419
0.418
-0.003
12.Aga
13.Hed
14.Det
25
4
3.87
0.520
0.637
0.186
27
4
3.39
0.482
0.405
-0.192
27
3
2.96
0.482
0.514
0.064
25
3
2.74
0.600
0.548
-0.098
26
3
1.92
0.077
0.076
-0.01
14
2
2.00
0.286
0.254
-0.13
24
1
1.00
0.000
0.000
_
25
1
1.00
0.000
0.000
_
26
1
1.00
0.000
0.000
_
26
3
2.46
0.192
0.298
0.359
25
3
2.45
0.080
0.220
0.64
26
1
1.00
0.000
0.000
_
14
3
2.86
0.071
0.405
0.829
26
4
3.39
0.308
0.367
0.163
24
5
4.64
0.458
0.740
0.386
25
4
3.99
0.640
0.678
0.058
27
2
2.00
0.407
0.331
-0.238
27
3
2.87
0.259
0.297
0.129
25
2
1.99
0.280
0.246
-0.143
26
1
1.00
0.000
0.000
_
14
2
1.86
0.071
0.071
0
23
4
3.78
0.348
0.410
0.154
23
4
3.20
0.261
0.243
-0.078
25
4
3.47
0.360
0.425
0.156
27
2
2.00
0.407
0.409
0.003
26
3
2.39
0.192
0.180
-0.068
25
2
2.00
0.520
0.481
-0.083
25
3
2.48
0.240
0.456
0.479
14
3
2.86
0.643
0.553
-0.17
Table S2. Pairwise θ values, estimated on the basis of 12 SSR loci (lower diagonal). Upper
diagonal: nominal level for multiple comparisons (adjusted to 0.000549).
1.Oud.
2.Oos.
3.Bie.
4.Baa.
5.Cas.
6.Put.
7.Sch.
8.Emm.
9.Kui.
10.Bux.
11.Koe.
12.Aga.
13.Hed.
14.Det.
1.Oud 2.Oos
*
0.129
0.180 0.124
0.274 0.148
0.405 0.247
0.335 0.188
0.285 0.202
0.143 0.116
0.211 0.135
0.415 0.283
0.349 0.245
0.293 0.209
0.475 0.330
0.348 0.230
3.Bie
*
*
0.158
0.291
0.154
0.200
0.180
0.231
0.330
0.229
0.231
0.418
0.290
4.Baa 5.Cas
*
*
*
*
*
*
*
0.319
0.219 0.305
0.302 0.365
0.170 0.347
0.187 0.384
0.300 0.464
0.223 0.382
0.232 0.416
0.423 0.548
0.404 0.478
6.Put
*
*
*
*
*
0.146
0.240
0.266
0.352
0.231
0.294
0.462
0.364
7.Sch 8.Emm
*
*
*
*
*
*
*
*
*
*
*
*
*
0.194
0.249 0.041
0.404 0.335
0.290 0.237
0.306 0.206
0.455 0.364
0.440 0.382
152
9.Kui 10.Bux
*
*
*
*
*
*
*
*
*
*
*
*
*
*
NS
*
*
0.369
0.287 0.138
0.262 0.147
0.412 0.212
0.395 0.519
11.Koe 12.Aga 13.Hed 14.Det
*
*
*
*
*
*
*
*
*
*
*
*
*
*
*
*
*
*
*
*
*
*
*
*
*
*
*
*
*
*
*
*
*
*
*
*
*
*
*
*
*
*
*
0.081
*
*
0.247 0.149
*
0.449 0.419 0.597
Table S3. Mean values of the life history traits obtained for the 14 studied populations.
Level of
Environmental
Risk
ER0
ER1
W
Region cluster
W
E
Pond
ER2
W
E
Canal
Pond
E
W
Canal
Ditch
Habitat
Ditch
Pop #
9. Kui
1. Oud
5. Cas
13. Hed
14. Det
7. Sch
10. Bux
2. Oos
3. Bie
11. Koe
12. Aga
4. Baa
6. Put
8. Emm
Asymptotique
size (1154i)
31.3 (0.4)
31.3 (0.3)
33.6 (0.3)
34.5 (0.3)
39.7 (0.5)
31 (0.2)
29.8 (0.2)
32 (0.3)
32.1 (0.3)
30.5 (0.3)
30.8 (0.3)
27.7 (0.5)
30.8 (0.3)
33.3 (0.4)
size at 119 days
(mm)
18 (0.1)
18.8 (0.2)
18.5 (0.2)
17.9 (0.1)
18.8 (0.2)
17.5 (0.1)
18.2 (0.1)
18.3 (0.1)
18.2 (0.1)
17.4 (0.1)
17.8 (0.1)
17 (0.3)
17.9 (0.1)
18.4 (0.1)
parameter b
(1154i)
2.93
(0.02)
3.07
(0.04)
3.11
(0.02)
2.98
(0.02)
3.3 (0.03)
3 (0.02)
2.93
(0.02)
3.05
(0.02)
2.99
(0.02)
2.93
(0.02)
2.92
(0.02)
2.83
(0.03)
3.05
(0.02)
2.96
(0.02)
hatching lenth
(427 clutches)
1.5 (0.02)
1.4 (0.03)
1.4 (0.02)
1.5 (0.02)
1.4 (0.03)
1.5 (0.02)
1.4 (0.02)
1.4 (0.02)
1.4 (0.02)
1.5 (0.02)
1.5 (0.03)
1.6 (0.05)
1.4 (0.03)
1.5 (0.04)
egg laying
ability rate (%)
100
100
58.3
94.4
69.4
100
97.2
97.2
100
97.2
97.2
91.7
97.2
100
oviposition
time (days)
3.8 (0.4)
2.7 (0.3)
15.6 (1.8)
5.1 (0.9)
12.3 (1.9)
5.5 (0.4)
3.9 (0.8)
5.1 (0.8)
5.4 (0.5)
4.3 (0.8)
3.2 (0.5)
6.8 (1.6)
4.6 (0.8)
4.4 (0.4)
total fecundity
101.8
(8.2)
149.5
(5.9)
51.4 (9.6)
107 (9.3)
55.7
(11.2)
99.3 (9.4)
144.1
(11.4)
145.1
(14.6)
143.2
(8.5)
118 (7.3)
110.6 (7)
105.2
(12.9)
111.1
(7.6)
112.1
(8.3)
number of
clutches
3 (0.2)
5.8 (0.2)
1.2 (0.2)
2.9 (0.3)
2 (0.4)
2.1 (0.2)
3.8 (0.3)
3.3 (0.3)
2.8 (0.2)
3.4 (0.2)
3.7 (0.2)
4.1 (0.5)
2.9 (0.2)
2.9 (0.2)
number of eggs
per clutch
34.6 (1.4)
26.2 (0.9)
45.4 (2.8)
36.5 (1.6)
27.7 (1.9)
47.1 (2.7)
38 (1.1)
44.3 (1.8)
50.8 (2.1)
36.2 (1.3)
30.6 (1.2)
26.1 (1.6)
37.4 (1.8)
39.5 (1.7)
hatching rate
(%)
97.5 (0.7)
97.5 (0.6)
94 (2.2)
92.6 (1.6)
85.1 (2.4)
95.4 (1.4)
96.4 (1.1)
97.1 (0.5)
92.4 (1.5)
97.4 (0.5)
95 (1)
83 (2.7)
96.7 (0.6)
96 (0.5)
153
Table S4. Effects of fixed factors on life history traits using GLMM (Y ~ covar + Geo + Hab + Enviromental Tisk + (1 | pop / fam)).
P- value significance level
Traits \ Factors
number
number of
of
Geography
observations
families
Habitat
Pesticide
Risk
Anthropization
level
Level of
Environmental
Risk
Growth-related traits / parameters
Hatching size
427
211
*
E>W
ns
ns
ns
ns
Size at 119 days
2855
182
ns
ns
ns
ns
ns
Asymptotic size (A)
1154
144
ns
*
P>C=D
ns
ns
ns
initial size (b)
1154
144
*
W>E
**
P>C>D
ns
**
AL1 > AL2
*
ER0 = ER1 > ER2
growth rate (k)
1154
144
ns
ns
ns
ns
ns
ns
**
P<D=C
RP1 < RP2
ns
ns
.
Fecundity –related traits
Ability to lay eggs
492
144
.
Time to oviposition
(from a 15-day follow-up)
492
144
ns
P>D=C
ns
ns
ns
Total fecundity
492
144
ns
ns
ns
ns
ns
Number of clutches
492
144
ns
ns
ns
ns
ns
Clutch size
1537
144
ns
**
P<D<C
ns
ns
ns
Hatching rate
1537
144
ns
*
P=D<C
ns
*
AL1 > AL2
*
ER0 > ER1 = ER2
Significance levels : .P < 0.1, *P < 0.05, **P < 0.01, ***P < 0.001
Table S5. Effects of the fixed covariates on Life history traits from the GLMM analyses : Y ~ covar + Geo + Hab + ER + (1 | pop / fam).
Traits
Hatching lenght
number of number of
observations families
427
211
pop
family
age
<0.05
<0.001
_
15 days growth
during fecundity
monitoring
Parent
size
clutch
size
development
time of
parental
clutch
<0.1
<0.01
<0.001
(from a 15-day follow-up)
Traits relative to fecundity
Traits relative to growth
_
size at 119 days
2855
182
<0.001
<0.1
_
_
<0.001
_
_
Asymptotic size
1154
144
<0.001
<0.001
_
_
NS
_
_
parameter (b) related to
initial size
1154
144
NS
<0.001
_
_
NS
_
_
growth rate parameter
(k)
1154
144
<0.01
<0.001
_
_
NS
_
_
Ability to lay eggs
492
144
<0.01
NS
_
<0.001
_
_
_
Oviposition time
492
144
<0.001
<0.001
_
<0.001
_
_
_
15 days fecondity
492
144
<0.01
<0.001
_
<0.001
_
_
_
Number of clutches
during 15 days
492
144
<0.001
NS
_
<0.001
_
_
_
Number of eggs per
clutches
1537
144
<0.01
<0.001
<0.001
<0.001
_
_
_
Hatching rate
1537
144
<0.01
<0.001
<0.05
<0.001
_
_
_
155
Chapitre V -
Analyse RNAseq (Illumina) de la divergence
génétique des réponses moléculaires au
stress entre différentes populations
naturelles de Lymnaea stagnalis.
156
Dans le chapitre précédent, nous avons constaté que la pression exercée par les activités
anthropiques, et plus spécifiquement par l’utilisation de pesticides, pouvait impacter certains
traits d’histoire de vie au sein des populations naturelles de L. stagnalis (i.e., augmentation de
la capacité à se reproduire, baisse de la survie à l’éclosion). Dans le présent chapitre, nous
avons cherché à savoir si, sur le long-terme, ces perturbations environnementales pouvaient
induire au sein de ces populations, une divergence liée à l’adaptation locale au niveau des
réponses transcriptomiques au stress oxydant. Pour cela, 504 individus G1 (~9 mois)
représentatifs de l’ensemble des lignées ont été exposés pendant 5h à 0 ou 222,2 µg/L de
dibromure de diquat, pour étudier et comparer en RNAseq (Illumina 2000), le transcriptome
exprimé dans la glande digestive aux niveaux intra- et inter-populations.
Initialement, l’objectif était de tester l’hypothèse de sélection divergente en appliquant
une approche QST-FST sur ces mêmes 14 populations naturelles. L’idée était de considérer les
données d’expression génique à la manière des traits phénotypiques quantitatifs dans
l’estimation des QST et de les analyser au moyen de matrice G. Malheureusement, en raison du
coût associé aux techniques de séquençage de nouvelle génération (NGS), même s’il tend à
diminuer au fil des ans, nous avons dû nous limiter à un total de 16 librairies sur une demie
flow-cell Illumina Hiseq2000 (assurant a priori un minimum de 20 millions de séquences par
librairie). La composante évolutive des réponses moléculaires de L. stagnalis au stress
pesticide a ainsi été étudiée en comparant l’expression différentielle entre diquat et condition
témoin de différentes familles maternelles issues d’un panel de quatre populations naturelles
échantillonnées en Hollande. Ces populations proviennent de fossés situés en bordure de
champs cultivés (Putten, Emmeloord) ou de milieux préservés tels que la réserve d’eau
potable de Castricum ou dans la forêt de Kuinre à proximité d’une réserve naturelle (Fig. 9).
Castricum
Emmelord
Putten
Kuinre
Figure 9. Photos des
sites d’échantillonnage
des quatre populations
naturelles utilisées pour
l’approche RNAseq.
157
Les données de séquençage ont été générées, assemblées et annotées à la plateforme
génomique et bioinformatique de l’INRA de Toulouse (Genotoul) et nous sont parvenues à la
fin du mois de novembre 2012. A partir d’un nombre initial de 632464 contigs, la procédure
d’assemblage suivie de filtration des contigs pour lesquels mappent moins de une lecture sur
1000000, a conduit à un total de 48381 contigs, dont 26958 ont un blast hit contre Swissport,
Refseq-Prot ou Refseq-RNA et 12581 contigs ont une annotation fonctionnelle dans KEGG
(> 50% hits). En raison du temps imparti, nous présentons ci-après les résultats préliminaires
de l’analyse d’expression différentielle (méthodes statistiques test-t et DEseq). Globalement,
ces résultats suggèrent une forte différenciation de la réponse transcriptomique entre les
populations étudiées et présentent une forte analogie avec les résultats de l’analyse de
différenciation génétique neutre. La variation intra-population est également très prononcée et
démontre l’influence non négligeable de l’effet famille sur la réponse aux toxiques. Ces
analyses préliminaires suggèrent donc un potentiel d’évolution adaptative important pour ces
réponses (interactions génotype×environnement) et les réponses observées semblent
cohérentes avec une toxicité du diquat induite par le stress oxydant, régulant la transcription
de gènes impliqués dans de multiples voies de signalisation (e.g., apoptose, phosphorylation
oxydative, système ubiquitine, métabolisme des xénobiotiques, etc.). Bien que des analyses
qualitatives à l’échelle de ces processus et voies cellulaires spécifiques semblent indiquer une
plus faible sensibilité au sein des populations historiquement exposées, l’analyse statistique ne
permet pas pour le moment d’affirmer avec certitude la présence de réponses adaptives
communes, à cause notamment de la forte variabilité des réponses transcriptomiques au sein
des populations.
Des méthodes statistiques plus appropriées à l’analyse de ce type de données sont
actuellement en cours de développement (Lurin & Balzergue, en préparation), et seront
prochainement appliquées afin d’optimiser l’analyse d’expression différentielle. L’analyse des
réseaux de gènes impliqués dans ces réponses sera également réalisée pour enfin mettre
l’ensemble de ces résultats en relation avec les indices de diversité génétique neutre et les
traits d’histoire de vie précédemment étudiés sur ces mêmes lignées en condition de common
garden. La recherche de corrélation permettra de mettre en évidence d’éventuels trade-offs et
mécanismes pléiotropiques.
158
Article 4
-
Genetic variation of transcriptomic expression in Lymnaea
stagnalis exposed to a redox-cycling pesticide
Anthony Bouétard1, Claire Hoede2, Thierry Pécot4, Anne-Laure Besnard1, Olivier Bouchez3,
Nathalie Marsaud3, Marc Collinet1, Céline Noirot2, Marie-Agnès Coutellec1§
1
INRA, UMR 0985 ESE, INRA/Agrocampus Ouest,
Aquatic Ecotoxicology
65 rue de Saint-Brieuc
35042 Rennes, France
2
INRA, Genotoul Genomics Platform and
3
INRA, Genotoul Bioinformatics Platform,
Toulouse Midi Pyrénées
Toulouse-Auzeville, Chemin de Borderouge
3
Genotoul, Plateforme Génomique, INRA UMR0444 LGC Toulouse-Auzeville,
Chemin de Borderouge
4
Comprehensive Cancer Center
Ohio State University
Columbus, Ohio 43210, USA
§
Corresponding author:
Marie-Agnès Coutellec
INRA, UMR INRA-Agrocampus Ouest ESE 0985
65 rue de Saint-Brieuc, 35042 Rennes cedex, France
+33 2 2348 5529
[email protected]
159
Abstract
Population response to stress may have a genetic component which, if additive, is the
basis for adaptive evolution to local conditions. Apart from monogenic resistance, adaptive
processes have been traditionally investigated through phenotypes at quantitative traits.
However, more elementary responses may also entail a heritable component. This is true for
gene expression, which results from various molecular interactions. We investigated the
evolutionary potential of transcriptomic expression induced by a pro-oxidant herbicide,
diquat, using lines from four natural populations of the pond snail Lymnaea stagnalis.
Populations stemmed from contrasted environments (close to vs distant from agricultural
zones), which allowed testing the influence of historical exposure to putative environmental
stressors. These populations were significantly differentiated at neutral genetic markers
(global FST) and showed significant genetic divergence at several life history traits. In the
laboratory, G1 individuals were exposed during 5 hours to diquat vs control conditions. RNA
was extracted from hepatic tissue. RNAseq (Illumina Hiseq2000) analysis was based on 16
cDNA libraries, i.e., two replicates (each based on three different families) per population ×
two exposure conditions (diquat, control). Read assembly was performed combining FLASH,
Trans-AByss and MIRA, then reads count were done using bwa. After filtering contigs the
less represented, 48321 contigs were obtained, 26958 of which had a blast hit against
Swissprot, Refseq-Prot or Refseq-RNA and 12581 had blast hits against KEGG database.
Diquat-induced differential expression was compared across genetic origins using
DESeq package and compared to a Student t-test performed within each population. Overall,
preliminary results indicate a strong differentiation of transcriptomic responses between
populations and exhibit a strong analogy with neutral genetic differentiation. Variations
within each population seem also very pronounced demonstrating the non-negligible
influence of family effects on the response to toxic. Analyzes suggest a potential for adaptive
evolution in these responses (G x E interactions). These responses were globally consistent
with toxicity of diquat-induced oxidative stress and concerned various signaling pathways
(e.g., apoptosis, oxidative phosphorylation, ubiquitin mediated proteolysis, xenobiotic
metabolism, etc.). Although present analyses do not permit to conclude yet with certainty on
the presence of common adaptive responses, qualitative analyzes across some of cellular
processes of interest suggest a lower sensitivity in populations historically exposed to high
pesticide pressure.
160
Introduction
Understanding the genetic basis of population response to environmental change is a
major issue in fundamental evolutionary biology (Pigliucci, 2006), which has also gained
increasing interest in more finalized contexts, mainly environmental sciences and
conservation biology (Hoffmann and Parsons, 1997; Hedrick and Kalinowski, 2000;
Hoffmann and Hercus, 2000; Hoffmann and Willi, 2008a; Smith and Bernatchez, 2008).
Similarly (although more recently), in the current context of environmental crisis which
combines ever-expanding anthropogenic alterations with the effects of climate and global
changes, ecotoxicologists have started to express their concern towards the long term and
evolutionary impact of pollutants and other human-induced environmental disturbances
(Depledge, 1994; Bickham et al., 2000; Medina et al., 2007). Consistently, a growing corpus
of knowledge is currently being provided to the scientific community, which builds on both
natural case-study documentation (Lind and Grahn, 2011) and experimental approaches
(Barata et al., 2002; Coutellec et al., 2011; Jansen et al., 2011; Costa et al., 2012). These new
datasets, by producing cumulative evidences for the multi-generational impact of pollutants,
set out relevant and timely arguments to support the need to incorporate an evolutionary
dimension into ecological risk assessment (Breitholtz et al., 2006; Coutellec and Barata,
2011). To this end, reliable tools are required which may be directly borrowed from
population genetics and quantitative genetics (Bickham, 2011; Klerks et al., 2011), and also
benefit from new advances in genomics (Martyniuk et al., 2011; van Straalen and Feder,
2011).
Population response to stress may have a genetic component which, if additive, is the
basis for adaptive evolution to local conditions (Bridges et al., 2001; Carvajal-Rodriguez et
al., 2005). Evolutionary processes related to adaptation have been traditionally investigated
through integrated responses that involve polygenic characters, such as quantitative traits
(e.g., life-history traits, physiological functions). However, besides this level of phenotypic
integration, more elementary responses may also entail a heritable genetic component. This is
particularly true for gene expression, which results from genetic interactions between the
encoding gene itself and other regulatory genes. Genetic variation in gene expression has been
rarely quantified (Roelofs et al., 2008). The genetic component of transcriptomic responses to
environmental stress has been investigated at the level of natural populations in only a few
studies, reflecting either divergent (Schoville et al., 2012) or convergent patterns (Whitehead
et al., 2012). Inferring evolutionary adaptive patterns associated to environmental change
161
focuses mainly on the search for genes or gene sets under positive selection (see Hoffmann
and Willi, 2008). However, one possible drawback of the strategy is that it can bias estimation
towards genes of major effects, at the expense of adaptive processes based on quantitative
tolerance responses, which may involve more numerous genes of smaller effect, and whose
genetic basis may be more complex to assess.
Although tolerance to pollutants can be attributable to monogenic mutations in some cases,
such as advantageous mutations on gene promoter regions, as described in the soil arthropod
Orchesella cincta, as increased constitutive expression of metallothionein gene in populations
from metal contaminated sites (van Straalen and Roelofs, 2011; Costa et al., 2012), historical
exposure to toxicants may also trigger different molecular processes which may altogether
contribute to adaptation.
Freshwater organisms inhabiting water bodies adjacent to agricultural parcels, such as
ditches and marshes, are expected to be good models to test local evolutionary effects of
pesticides (e.g., Coutellec et al. 2011). This is particularly true for species with a fully aquatic
life cycle (e.g., molluscs and crustaceans). Indeed, various pesticides that are repeatedly
applied under culture protection programs may non-intentionally reach these habitats through
several ways, including aerial drift, run-off, and drainage (Brown and van Beinum, 2009) and
may impact non-target organisms. Moreover, water bodies located in agricultural landscapes
have hydro-physical characteristics that also promote local evolutionary processes, due to
limited habitat connectivity and permanence (lentic and close or frequently drained habitats).
In theory, local evolutionary processes may involve both random genetic drift and selection.
Genetic variation of small population inhabiting freshwater lentic habitats located close to
agricultural parcels may have a predominant effect because of low (or frequently reduced)
population size and limited gene flow, whereas larger population sizes would be needed for
local adaptation to occur (Willi et al., 2006).
The present study investigates transcriptomic responses of the freshwater pulmonate
gastropod Lymnaea stagnalis exposed to a redox-cycling herbicide, diquat. Ecologically, L.
stagnalis is representative of freshwater lentic environments and of the holarctic herbivorous
macroinvertebrate community. Freshwater snails may represent up to 20—60% of the total
abundance and biomass of macroinvertebrates in some freshwater ecosystems (Habidja et al.
1995) where they play a major role in the transfer of energy and material across food webs.
The bypiridyl herbicide diquat was chosen as model pesticide to test the hypothesis
that agricultural activities may induce evolutionary changes in natural population of L.
stagnalis. First, diquat belongs to the first class of synthetic herbicides ever commercialized
162
in the 1940s (Karuppagounder et al., 2012) and is still widely used, mostly in industrial
countries, in crop protection (potato, banana, vine and other seed crops) and also floating and
emerging weeds control (US-EPA, 1995; Ritter et al., 2000; FAO, 2008; French Ministry of
Agriculture, 2012). Therefore, populations inhabiting farmland ditches or ponds may be
regularly exposed to diquat, for many successive generations. Second, as a number of
xenobiotics, including pharmaceuticals, metals and pesticides, the toxicity of diquat is elicited
by oxidative stress (Smith et al., 1985). Due to its pro-oxidant properties, diquat is used as
model chemical in medical toxicology and cellular biology (Smith et al., 1985; Sandy et al.,
1986; Thomas and Aust, 1986; Gallagher et al., 1995). Oxidative stress results from the
unbalanced redox status in cells inducing cascade damages on protein, cellular membrane and
DNA integrity by the increase of uncontrolled reactive oxygen species (ROS) (Dröge, 2002;
Monaghan et al., 2009). Despite their toxic nature, ROS are natural by-products of aerobic
metabolism that mediate the toxicity of oxygen, and are expected to act in intracellular
signalling transduction (Poulsen et al., 2000; D'Autreaux and Toledano, 2007). Defence
systems against ROS, including antioxidant enzymes such as superoxide dismutase (SOD),
catalase (Cat), glutathione peroxidase (GPx) or glutathione reductase (Gred), a regulator
factor (retinoid X receptor), and chaperones such as Hsp70 and Hsp40, have been investigated
through qPCR and biochemical analyses on a L. stagnalis laboratory strain (Renilys®, INRA).
Results showed a significant impact of diquat on these candidate genes, which was however
not necessarily followed by changes in activity of the corresponding enzymes. Transcriptomic
resources have been recently increased in L. stagnalis (Bouétard et al., 2012; Sadamoto et al.,
2012), providing tools for genomewide investigations of molecular mechanisms of stress
response. These new resources were applied in the present study, together with a set of new
microsatellite markers (Besnard et al., 2013), to address the following nested issues: (i) is
there genetic variation in the molecular ability to handle oxidative stress? This hypothesis
was tested by using G1 families from four natural populations of L. stagnalis, (ii) if occurring,
is genetic variation mainly driven by random forces or selectively directed? To tackle this
question, we compared the pattern of population variation inferred from neutral markers (FST)
to that obtained from a whole transcriptome expression analysis of the digestive gland tissue,
as inferred from RNAseq analysis. (iii) finally, if population divergence has a genetic
component, we aimed at testing the hypothesis that historical exposure to anthropogenic
stress may act as a selective force that shapes gene expression variation and evolution.
To do so, four natural populations were selected to represent two contrasted
environmental conditions with respect to anthropogenic pressures (reference vs. exposed to
163
high risk of contamination by pesticides). These populations belong to a group of 14 natural
populations previously sampled in July 2011, for the purpose of a companion study (Bouétard
et al in prep.), which investigated the evolutionary impact of historical pesticide exposure on
fitness-related traits, through a QST-FST approach. In the present context, RNAseq (Illumina
Hiseq2000) was used to assess transcriptomewide differential expression induced by diquat,
using G1 snails representing several families per population, and a total of 16 libraries.
Coupled with the neutral genetic diversity, we have used these datasets to search for selective
patterns between population types that could be indicative of an evolutionary impact of
historical exposure to anthropogenic stress. (Fig. 1)
Figure 1. Overview of the scientific approach. On the map are localized the four studied
populations, Unexposed and exposed sites are indicated in white and black, respectively.
164
Materials and methods
Snails, study sites and laboratory breeding
The study populations of L. stagnalis represent a subset of 14 natural populations
whose genetic variation has been previously characterized at neutral markers and life-history
traits, in a FST-QST approach (Bouétard et al. in prep). The four study populations were
selected using two criteria: geographic location (~100km maximum distance, northern part of
The Netherlands) and pesticide pressure (two reference and two exposed populations). Two
populations were sampled in the western side of the Ijsselmeer (Castricum – Cas., 52°33.17N
/ 4°37.22E ; Putten – Put., 52°45.81N / 4°39.82E) and the other two on the eastern side
(Kuinre – Kui., 52°47.67N / 5°47.69E ; Emmeloord – Emm., 52°46.27N / 5°48.26E).
Populations Cas. and Kui. were considered as unexposed with respect to the pesticide
pressure, whereas Put. and Emm. were close to intensive agricultural areas (Fig. 1.). Details
on sample sites characterization and on population neutral genetic diversity are given in
Table_1.
Laboratory-born G1 snails from wild-caught adults (Summer 2011) were reared under
standard conditions, as explained earlier (Bouétard et al. in prep).
Table 1. Ecological neutral genetic characteristics of four Lymnaea stagnalis populations.
Pesticide exposure risk was based on the percentage of land use categories within a radius of 100
m around the sampled sites. n indicates the number of genotyped individuals, N is the number of
alleles, AR is allelic richness (based on samples of 12 individuals), HE is the unbiased expected
heterozygosity, HO is the observed heterozygosity, and FIS is the inbreeding coefficient
(significance is indicated by asterisk). Ne is the effective population size and estimations of selffertilization rates calculated are also presented.
Localization
Castricum
Putten
(Alkmaar)
Population
features
Abbreviation
Cas.
Land use
100% moor
Habitat
Pesticide Risk
n
N
AR
Genetic
HE
diversity
HO
indices
FIS
N e [CI 95%]
Self-fertilization
rate (%)
Kuinre
Emmeloord
(Noordoostpolder)
Put.
Kui.
Emm.
Pond
30% crop,
70% urban
Ditch
98% forest,
2% urban
Ditch
80% crop,
20% urban
Ditch
unexposed
exposed
unexposed
exposed
32
3.3
2.60
0.341
0.231
0.327 *
31 [19 ; 58]
32
2.9
2.62
0.370
0.291
0.216 *
37 [22 ; 67]
25
3.8
3.33
0.428
0.341
0.207 *
25 [14 ; 52]
24
3.7
3.32
0.456
0.343
0.252 *
36 [20 ; 80]
0
18.6
0
0
165
Molecular analyses
DNA was chelex extracted from haemolymph or foot tissue from 113 wild-caught
adults (24 to 32 snails per population). Neutral genetic variation was described at 12
microsatellite loci, i.e., A2, A112, B117 (Knott et al., 2003), 2k11 and 2k27 (Kopp and Wolf,
2007), and EMLS04, EMLS13, EMLS21, EMLS26, EMLS29, EMLS41, EMLS45
(Molecular Ecology Resources Primer Development et al., 2013), following the protocol
described in Besnard et al. (2013). Only individuals with less than three missing (unreadable)
genotypes were retained for analysis.
Population neutral genetic diversity and differentiation
Mean allele number (N), allelic richness (AR), expected heterozygosity HE (Nei, 1978),
and observed heterozygosity HO, were calculated with GENETIX 4.05.2 (Belkhir et al.,
2004). The distribution of neutral genetic diversity within and among populations was
estimated from Weir and Cockerham’s estimators of Wright’s F indices (Weir and
Cockerham, 1984) using FSTAT 2.9.3.2 (Goudet, 2001). The selfing rate were estimated per
population with RMES (David et al., 2007). Effective population size was estimated using the
sibship assignment method, as implemented in the software COLONY 2.0.3.0 (Jones and
Wang, 2010) and, assuming inbreeding, male and female polygamic mating systems, and
monoecy.
To estimate the number of genetic clusters in our dataset without taking into account
any predefined population, we used STRUCTURE 2.2 (Pritchard et al., 2000). Analyses were
performed assuming an admixture model and a number of genetic clusters (k) from 1 to 4 (15
replicates for each k). Each run started with a burn-in period of 50 000 steps followed by
300 000 Markov Chain Monte Carlo (MCMC) replicates. The most likely number of clusters
was determined using the ∆k statistic (Evanno et al., 2005) using STRUCTURE
HARVESTER (Earl and von Holdt, 2012). DISTRUCT was used to plot Structure output data
(Rosenberg, 2004).
Diquat exposure and tissue sampling
Among the 144 G1 snails used in the present study (i.e., 3 snails per family, 12
families per population, 4 populations), 48 snails of age 205.8 ± 5.7 days (and of size 26.7 ±
2.44 mm), were used for transcriptomics. Half of the snails were individually exposed to a
diquat solution of 222.2 µg/L for 5 hours, and the other half was maintained under control
conditions as described earlier (Bouétard et al., 2012). After the exposure period, snails were
166
quickly killed into liquid nitrogen, and their shell removed. The tip of the digestive gland was
then cut and plunged into RNAlater® (Ambion), and stored at -80°C as previously described
(Bouétard et al., 2013).
RNA isolation and sample preparation
Total RNAs were extracted from 48 individual samples using 1 mL of TRIzol®
reagent (Invitrogen) according to manufacturer’s instructions on portions of GDC varying
from 10 to 22.5 mg of stabilized tissues. RNA quantity and purity were assessed with
Nanodrop (Thermo, Fisher Scientific). Absorbance ratio values were A260/A280 = 1.97 ±
0.07 and A260/A230 = 1.9 ± 0.26. Total RNAs (30 µg from each GDC samples) were treated
with DNAse I (Promega) and purified with the RNeasy Minelute Cleanup Kit (Qiagen).
Following purification and quantification (Nanodrop), RNA samples were pooled to
constitute 16 different libraries. The design included two replicates per treatment × population
combination. Each library contained a pool of equal amounts of RNA (4 µg per sample) from
three samples from a same population and submitted to a same treatment (C or D). The three
samples composing each library stemmed from different families.
Table 2. Organization of the 16 libraries on the Illumina
flow-cell and family relatedness within library.
FlowCell \ Pop
Cas.
Put.
Emm.
Kui.
lane 1
C-R1*
D-R1#
C-R1*
D-R1*
lane 3
D-R1*
C-R1#
D-R1*
C-R1*
lane 2
D-R2#
C-R2#
D-R2#
C-R2#
lane 4
C-R2#
D-R2#
C-R2#
D-R2#
* and # indicate 100% and 66% of families homology between replicates, respectively
Initially, we intented to cross family sets with treatment, by creating replicates with
the same sets of families under each treatment (e.g., library1 = fam1C + fam2C + fam3C,
library2 = fam1D + fam2D + fam3D), in order to have a finer estimate of the evolutionary
potential at the intra-population level. Unfortunately, because of natural pre-treatment
mortality or accidental shell injuries (e.g., apex broken), total homology was only met for
three of the eight pairs of family sets (e.g., total homology between CasR1C and CasR1D),
the other five having 66% of homology (i.e., two identical families on three, e.g., CasR2C and
CasR2D) (Tab. 2). After pooling, RNA quality was determined for each library using Agilent
2100 Bioanalyzer (Agilent Technologies).
167
Libraries preparation and Illumina sequencing
Library sequencing was organized in a half run comprising 4 lanes as indicated on
Figure 2. To do so, total RNA libraries were firstly prepared for sequencing using the
Illumina TruSeq RNA Sample Preparation Kit according to the TruSeq protocol. Fragments
of cDNA were tagged with Truseq indices 1 to 4 supplied in the kit and amplified with 15
cycle of polymerase chain reaction (PCR). PCR products were purified using the E-Gel
SIZESELECT 2% Invitrogen and the range of fragments around 300pb were excised and
purified according to the manufacturer’s protocol. Their quality was checked with Agilent
2100 Bioanalyzer and concentrations were assessed by qPCR, indicating an average size of
275bp and concentration of 0.64 to 36.64 nM. The 16 libraries were then diluted to
homogenize concentrations at 13 pM and pooled in four samples corresponding to the four
lanes. Sequencing was carried on using a HiSeq 2000 instrument with 100-bp paired-end
reads at the platform Genome and Transcriptome (GeT) at the Genotoul INRA institute
(Toulouse, France).
De novo Assembly
The de Bruijn graph method was used as a primary algorithm in the RNA-seq
assembly. As the insert size between the two mapped reads was small, the computational tool
FLASH was used to improve assembly on all libraries (Magoč and Salzberg, 2011). Multistep assembly method was applied using sequential analysis with the de novo assembler
Trans-AByss (Robertson et al., 2010) and the meta-assembler MIRA (Chevreux et al., 2004).
Multiple k-mer size was used (25, 35, 45, 55, 65, 75) for assembly. A filtration step was
applied to remove the less represented contigs, i.e., those for which less than one read on one
million mapped (7% reads were removed).
Gene annotation
The final Trans-AByss assembly contigs were used as queries against the National
Center for Biotechnology Information (NCBI) BlastX or Blastn (version 2.2.25), to search for
homologies in the generalist databases: UniProtKB/SwissProt, Refseq Protein Index Blast,
Refseq RNA Index Blast and PDBaa database. The cut-off e-value was set to 1e-5. The
following species specific databases were queried with a cut-off e-value of 1e-10:
SadamotoContigs.fasta, Unigene Lottia gigantea Build #2, TIGR Spisula solidissima Build
#1.
Finally,
we
realigned
our
contigs
(Pondsnail_Contigs_V1 database).
168
with
the
cut-off
e-value
of
1e-30
Functional annotation was performed using ‘‘single best hit’’ Blast comparison against
the KEGG genes database, with the program KAAS (KEGG Automatic Annotation Server:
http://www.genome.jp/tools/kaas/) requiring 50% identity. Annotated sequences were further
categorized using KEGG Orthology (KO) and the BRITE hierarchy for protein families,
where four top categories were examined: metabolism, genetic information processing,
environmental information processing, organismal systems and cellular processes.
Differential Expression
The list of contigs defined after assembly with Trans-AByss and MIRA was used as
reference against which reads were mapped for the analysis of gene expression. In order to
identify potentially meaningful group of librairies in the global count dataset and in subdatasets specific to target pathways, we performed a hierarchical clustering based on the
correlation table of count data in R (command : heatmap(cor(log10(countdata+1)))).
Differential expression (DE) was tested using two statistical methods. Firstly, DE
analysis was performed with the DESeq package (Anders and Huber, 2010). This method has
been specifically developed for count data with high dispersal, and is based on a negative
binomial error distribution. The analysis was performed after library normalization (function
estimateSizeFactor), using the binomial test (function fitNbinomGLMs) fitted with “local
dispersion parameter” in the following generalized linear model:
Y ~ population + condition + condition * population
Because of a large variance between populations, analyses of DE induced by diquat exposure
were also conducted within each population separately on the basis of the normalized count
data, as determined from the 16 libraries. P-values were adjusted for multiple testing using the
false discovery rate controlling procedure (Benjamini and Hochberg, 1995).
Secondly, significance of the expression fold change was also assessed using a t-type
test statistic from which heatmap of DE were built (Pécot, unpublished method). For data
visualization (Fig. 9 & Tab. S1-6), DE contigs were filtered using the followind threshold
parameters, p < 0.1 and weighted proportions of fold-change superior to 2 or inferior to -2.
169
Results & discussion
Short-read de novo sequencing and assembly
Raw data
After removing adaptor sequences, low-quality bases, too short reads, Illumina
sequencing conducted on the four lanes of the flowcell generated ~1,253 billion 100 bp reads
(Tab. 3).
Table 3. Summary of Illumina sequencing data of the L. stagnalis transcriptome.
lane
1
2
3
4
Number of
sequences
284,921,410
224,170,090
360,222,272
383,613,024
Full sequences
length (bp)
28,777,062,410
22,641,179,090
36,382,449,472
38,744,915,424
Average
read length
(bp)
100
100
100
100
Library
Insert
length
(bp)
Number of
sequences in
the final
dataset
Cas. C r.1
135
38,650,624
Put. D r.1
152
108,699,656
Emm. C r.1
163
43,753,934
Kui. D r.1
161
49,538,816
Cas. C r.2
145
35,552,536
Put. D r.2
151
52,704,702
Emm. C r.2
167
63,262,874
Kui. D r.2
159
37,795,962
Cas. D r.1
165
90,885,446
Put. C r.1
163
51,662,226
Emm. D r.1
148
113,077,600
Kui. C r.1
187
47,112,660
Cas. D r.2
163
75,965,736
Put. C r.2
147
59,249,150
Emm. D r.2
159
97,552,048
Kui. C r.2
176
93,236,350
De novo assembly
After filtration of the less represented contigs (i.e., those for which less than one read
on one million mapped), the combined assembly using Trans-AByss and Mira generated a
total of 48,321 contigs (~101 billions bases) corresponding to a dataset of ~1,070 billion reads
among the 16 libraries. Contigs length varied from 100 bp to 29,437 bp with a mean value of
2,086.84 bp and depth was comprised between 0 and 174,143 reads with a mean value of
111.97 reads. N50 (statistical index of length N where 50% of all bases in the sequences are
in a sequence of length L > N) sizes at 3,349 bp and N90 at 1,200 bp.
170
All contigs <200 bp were removed from differential expression analysis because there
are known to give poor downstream results (Feldmeyer et al., 2011; Wang et al., 2012). We
decided to remove also the few contigs >20,000 bp, as there annotations were unreliable (low
% coverage query and % hit identity). Such extremely long contigs might reflect artefactual
chimeras. Finally, a dataset of 45249 contigs totalizing ~1.060 billions 100 bp reads were
selected for further differential expression analysis.
Gene annotation
Among the 45,249 contigs selected, 26,040 (57.55 %) had a significant BLAST hit
against the generalist databases, i.e, 8,425 (18.62 %) against UniprotKB/Swiss-Prot, 16,087
(35.55%) against RefSeq protein, and 1,528 (3.38%) against RefSeq RNA. Figure 2 indicates
the distribution of identities according to the database.
0.9
RefSeqProt
relative frequency
0.8
0.7
RefSeqRNA
0.6
SwissProt
0.5
0.4
0.3
0.2
0.1
0
20
30
40
50
60
70
80
90
100
% identity
Figure 2. Distribution of annotation quality according to the database used in BlastX
(percent identity between query and database sequences). Identity percent is represented on the
axis (“20” stands for 0-20% identity, etc.)
171
The highest numbers of best annotation hits were obtained with the acorn worm Saccoglossus
kowalevskii (7845 contigs) and the amphioxus Branchiostoma floridae (6640 contigs),
belonging respectively to phyla Hemichordata and Chordata (Fig. 3). The high phylogenetic
distance between L. stagnalis and these species highlights the lack of (and critical need for)
relevant genomic resources among invertebrates, and particularly in molluscs.
Figure 3. Numbers of best hit annotations among the 20 most represented species (E-value < 1e-5)
The search of functional annotations on KEGG database allowed to classify 12581
contigs within the 5 top process categories. As previously observed with the preliminary
approach based on 454 pyrosequencing (Bouétard et al., 2012), the mostly represented
category was metabolism (6245 annotations), followed with genetic information processing,
(4165 annotations), environmental information processing (3262 annotations), organismal
systems (2626 annotations) and cellular processes (2293 annotations) (Fig. 4).
172
Figure 4. Number of contigs classified according to the KEGG top categories. (12581 contigs,
BLAST threshold > 50% identity).
173
Comparison between neutral and transcriptomic diversity
Cas.
A
005
Kui.
Emm.
Put.
B
Cas.
Put.
Emm.
Kui.
C
P
E
Ca Ca
K
K
E
P
s.
s. ut. r ut. r mm mm u i. r ui. r
rep re p ep
. re . re e p ep
ep
1
2
1
1
2
p1
p2 2
Figure 5. Genetic differentiation among the four studied populations. A, Unrooted Neighborjoining tree based on FST values estimated from 12 SSR loci. B, Bayesian individual clustering results
with STRUCTURE for k=2. C, hierarchical clustering based on the count data of the 8 control
libraries.
As observed in the companion article investigating neutral genetic diversity among 14
natural populations, the four presently studied populations were significantly differentiated on
the whole (FST = 0.280, p = 0.001), although pairwise FST-values revealed that it was not the
case between Kui. and Emm. (Fig. 5a). Although in the previous Bayesian analysis involving
14 populations, these four populations belonged to the same western cluster, STRUCTURE
here segregates two groups (Cas. and Put. vs. Emm. and Kui.) consistent with geographical
location (Fig. 5b).
Regarding the hierarchical clustering obtained from the correlation table restricted to
eight control samples, it is worth noting that the corresponding tree had a very similar
topology compared with the tree based on neutral markers. To our knowledge, it is the first
evidence of such a RNAseq-based clustering of natural populations, if we except the case of
natural populations of carrion crow Corvus corone and their hybrids with the hooded crow
Corvus cornix, which is consistent with morphological phenotypic divergence, whereas no
structure is observed with neutral markers (Wolf et al., 2010).
174
As suggested by the non-significant neutral differentiation (FST) between Emm. and
Kui., gene flow might be possible through the network of temporary connected ditches. This
hypothesis was confirmed by similar transcriptomic responses produced by these two
populations, which are 2.5 km apart. By contrast, the two other populations Cas. and Put.,
separated by ~25 km, appeared strongly differentiated. Because of the low dispersal ability of
L. stagnalis, genetic drift seems to play a predominant role in population differentiation.
However no apparent link between the level of neutral genetic diversity and variance in
transcriptomic response was observed under control conditions.
Aside from local selective processes associated to chronic environmental stress,
background selection and factors influencing the expression of deleterious mutations (of
genomewide distribution), may become predominant under multi-stress and relatively
unpredictable conditions. Indeed, stressful conditions have been shown to exacerbate the
effect of spontaneous deleterious mutations (Szafraniec et al., 2001). Therefore, conditions
under which deleterious accumulate or express at homozygous state (or do both), are expected
to lead to a more severe impact of stress. Clearly, this is the case in small and inbred
populations, where both local drift load and inbreeding depression can occur (Whitlock et al.,
2000). Increased inbreeding depression under stressful conditions is empirically well
supported (Armbruster and Reed, 2005; Fox and Reed, 2011; Bijlsma and Loeschcke, 2012),
whereas the effect of environmental stress on heterosis is less documented (but see e.g.,
Coutellec and Caquet 2011). Our four populations differing in neutral genetic variability as
well as in level of inbreeding, the relation between transcriptomic expression variation and
neutral variation could be investigated at this scale, under oxidative stress used as stressful
condition.
Differential expression analysis
When considering treated libraries, populations were still grouped according to the
geographical pattern (Fig. 6). Interestingly, more variation occurred in Emm. than in Kui.,
however, whatever the degree of homology between family sets, correspondence between
related family groups was respected. Such a proximity between family replicates was
observed for most C-D pairs although the topology was slightly less clear in Put., possibly
because only 66% of families were identical among C and D conditions for both treatment
replicates (R1 and R2) (see Tab. 2).
175
Cas. diquat2
Cas. control2
Cas. diquat1*
Cas. control1*
Emm. diquat2
Emm. control2
Kui. diquat2
Kui. control2
Kui. diquat1*
Kui. control1*
Emm. diquat1*
Emm. control1*
Put. diquat1
Put. control2
Put. diquat2
Put. control1
Figure 6. Heatmap based on Euclidean distance between samples, as calculated from the
variance stabilising transformation of the count data. * indicates 100% family homology between
replicates.
According to the generalized linear mixte model approach conducted with DEseq,
6157 contigs on the 45249 tested were significantly influenced by a population effect,
confirming that population was the main factor shaping molecular responses among the 16
libraries (Fig. 7). Most of the 631 contigs detected to be significantly impacted by diquat
exposure were shown to be also influenced by population (108 contigs) and by their
interaction (505 contigs). The significant interaction detected between population and
treatment for 542 contigs indicates that populations have strongly different responses and
reaction norms to face oxidative stress induced by diquat. In other words, the effect of diquat
is mainly expressed through its interaction with population.
Diquat
Population
n=631
(84)
n=6157
(979)
108
(14)
9
(2)
9
(3)
505
(65)
5518
(896)
26
(4)
2 (0)
Population x Diquat n=542 (72)
176
Figure 7. Venn Diagramm of the DE
contigs according to the three factors
implemented in the multi-factorial
model analyzed in Deseq. The count
data dispersion of the 45249 contigs
were fitted with a "local" parameter,
adjusted p-value < 0.1. Number of
Contigs having annotation in KEGG is
indicated between brackets.
Indeed, only 9 contigs were found to be differentially impacted by diquat exposure in
all populations, including genes coding for ABCA4 implicated in membrane transport and the
helicase SKI2 degrading RNA. The analysis of diquat-induced DE appeared very
conservative, despite the fact that the significance threshold was set to 0.1. Due to the extreme
influence of (population) origin on molecular responses, we investigated DE induced by
diquat for each population separately using both a t-test and DEseq.
Comparison of statistical methods
Table 4. Overview of the preliminary results obtained from Kegg annotation and statistical
analyses conducted per population, using t-test statistics and DEseq.
Number of
contigs (&
related gene)
Total dataset
Number of contig DE according
Number of contig DE according
to t-test (p < 0.1)
to DEseq (p < 0.1)
FoldChange > |2|
D>C
D <C
D >C
D<C
total
total
45249
1211
523
132
100
125
166
688
168
218
113
189
592
287
41
43
60
153
311
43
96
53
122
12581 (4161)
170
80
20
20
19
21
90
24
28
11
27
90
49
6
12
6
27
42
7
10
6
19
2293 (595)
47
52
4165 (1548)
44
6245 (2023)
75
2626 (668)
58
30
8
8
5
9
34
10
7
6
11
21
4
9
2
6
40
11
11
5
13
34
6
13
2
13
22
3262 (983)
18
4
5
5
4
20
4
3
7
6
23
8
4
6
5
35
9
13
5
8
25
4
8
6
7
10
2
3
3
2
16
2
7
2
7
9
2
1
1
5
30
4
6
3
17
20
4
5
0
11
12
1
3
2
6
17
2
3
2
10
17
2
6
4
5
20
4
5
3
8
16
3
3
2
8
Cas.
Put.
Emm.
Kui.
Kegg annotated contigs dataset
Cas.
Put.
Emm.
Kui.
Kegg processes
Cellular processes
Cas.
Put.
Emm.
Kui.
Environmental Information Processing
Cas.
Put.
Emm.
Kui.
Cas.
Put.
Emm.
Kui.
Metabolism
Cas.
Put.
Emm.
Kui.
Organismal Systems
Cas.
Put.
Emm.
Kui.
177
33
25
50
36
Globally, DEseq was more conservative than the t-test analysis with almost half less
contigs considered as DE (p < 0.1) (i.e., 592 / 1211), and this, although no threshold foldchange was applied on DEseq results (Table 4). This result tends to corroborate the fact that
DEseq is a too conservative method for DE analyses, as revealed by a recent comparative
analysis with microarrays and qPCR on A. thaliana (in this study, half of the contigs found as
non DE with DEseq were actually highly DE, according to microarrays and qPCR; Balzergue,
com. pers.). A method to overcome this bias is currently under development by the authors
(Lurin & Balzergue, unpublished). However, in the present study, we found consistent results
among both methods for 326 contigs. The proportion of annotated contigs in KEGG was
similar and close to 15 % for both DE lists.
Although at the scale of the whole dataset, ABCA4 and SKI2 appeared impacted by
diquat exposure whatever the population, this response was less global in analyses conducted
per population (see Fig. 9 and Tab. S1-5). Indeed, both statistical methods (t-test and DEseq)
indicated that ABCA4 was significantly up-regulated in the two populations of the eastern
cluster only. Consistently, ABCA4 has been shown to be significantly sollicitated in natural
populations of the marine bivalve Modiolus modiolus inhabiting sites next to wastewater
outfall (Veldhoen et al., 2009; Veldhoen et al., 2011). Concerning SKI2, opposite responses
were observed in the western cluster, down-regulation occurring in Cas. whereas it was upregulated in Put.. The homologue of SKI2 in the yeast Saccharomyces cerevisiae, SUV3, is
known to be a critical component of mitochondrial degradosome and its downregulation can
be linked with the induction of apoptosis through AIP (apoptosis-inducing factor)- and
caspase- dependent pathway (Szczesny et al., 2007).
Regulation of apoptosis
SKI2 might have different functions according to species or tissue, as regarding the
results of statistical analyses conducted population per population, over-transcription of the
inhibitor apoptosis proteins, IAP, BIRC proteins in Cas. as also observed in Emm. suggested
that both induced an active anti-apoptotic response. In Emm., the down-regulation of the
transmembrane death receptor FAS and TRAILR was consistent with this hypothesis. In
opposition, the decreased transcription of BIRC proteins and TNFα receptor, as well as the
up-regulation Caspase7 might reveal induction of apoptosis in Put., presumably through the
intrinsic pathway. In Kui., the response was less clear, with on the one hand, a decrease of Fas
ligand and Casp7 transcription, which suggest anti-apoptotic response, and on the other hand,
down-regulation of apoptosis inhibitor BIRC and up-regulation of TRAILR (Fig. 9, Tab. S2).
178
Several contigs matching with NOTCH behave in opposite ways in this population whereas
once again in Cas. and Emm., similar up-regulation were observed. As NOTCH proteins
regulate many processes such as cellular interactions, proliferation, cell fate or apoptosis
(Theodosiou et al., 2009), its regulation by oxidative stress is not surprising.
Metabolism
Globally, most DE contigs were implicated in metabolism (Fig. 9, Tab. S5), notably in
oxidative phosphorylation, as shown by the up-regulation of several cytochrome oxidases
such as COX3, COX1 or CYTB, as well as several NADH-ubiquinone oxidoreductases (e.g.,
ND1, ND4 and ND5) indicating probable perturbations in the energy metabolism of
mitochondria. These effects appeared stronger in Cas. than in other populations.
Considering active response against oxidative stress, further genes encoding phase II
detoxication enzymes seemed to be sollicitated such as CYP2U ou CYP3A (Cytochrome
P450 family) in Cas. and Kui., respectively. Additionally, glutathione metabolism was
apparently involved in diquat toxicity pathway in Put. and Kui., as revealed by the upregulation of GST in Put. and the GCLC in Kui.. In Emm. surprisingly, no up-regulation was
noted in this pathway, however we can mention the down-regulation of the Xanthine
dehydrogenase XDH, which is recognized to play a role in oxidative stress. As Xanthine
oxygenase (XO), which is able to generate ROS, can be formed from XDH by reversible
sulfhydryl oxidation (Harris et al., 1999), the under-expression measured for XDH may
reflect a strategy to decrease intracellular sources of ROS.
Taken together, these results are consistent with molecular responses to diquat
toxicity. However, it seems difficult from the present statistical analyses, to identify common
molecular responses within the two studied types of population, and thus to detect any
putative evolutionary influence of historical exposure to pesticide. Another approach could be
to not focus on DE contigs only, but compare populations at a larger scale such as pathways
integrating all expressed contigs.
179
Potential impact of historical pesticide exposure
Figure 8. Hierarchical clustering based on correlation table of subset of contigs matching in
signalling pathways apoptosis, ubiquitin metiated proteolysis and the cellular process “Folding,
sorting and degradation”. Numbers of contigs is indicated under each corresponding trees. 100%
homology between family replicates is indicated by a point after samples names. Colored frames
differentiate population types according to the historical pesticide pressure, i.e., low (red) or high
(green).
When comparing topologies of correlation trees based on contigs implicated in
signalling pathways, apoptosis, and ubiquitin mediated proteolysis, or at the level of the
cellular processes related to folding, sorting and degradation functions (Fig. 8), the strong
divergence of molecular responses among population appears still clear. However, the
isolation of one of the two replicates was observed in Put. (C/D-R1), which seem to be mainly
due to the absence of reads for several contigs.
However, similar patterns were generally observed whatever the subset of contigs, and
differences appeared between populations originated from historically exposed or unexposed
sites. Indeed we can see that diquat seems to explain more variance than family compositions
in population Kui and Cas.. Although we could expect more adapted responses in more
exposed populations Put. and Emm., such results could be also interpreted as a lower
sensitivity of these populations to diquat exposure. However, it is not possible yet to conclude
with statistical confidence for such evolutionary impacts.
180
181
Figure 9 (previous page). Heatmap of DE contigs detected by t-test statistics and ranged by
KEGG processes. Only annotated contigs presented (p-val < 0.1) a foldchange > |2| for at least one of
the four populations are presented. On the left, foldchange intensity is determined by divided the
mean expression of treated samples by the mean expression of the control samples of the relative
population. On the right, count data of each sample are divided by the mean count obtained from the
two controls of the relative population.
Within-population variation and evolutionary potential
Initially the aim was to test a QST-FST approach with multivariate G matrix analyses,
considering reads count as quantitative traits in the process. Unfortunately, because of the cost
of the RNAseq run, we were too limited in the number of population and family replicates.
Nevertheless, family variance appeared very high (Fig. 9). The relative proximity between
treated-untreated family replicates dataset tends to indicate that the family effect is strong,
which is also supported by the fact that inbreeding was significant in all populations.
As replicates between conditions were genetically related, we have tested the
procedure for data without replication, as proposed in DEseq to analyse DE per replicate, i.e.,
regardless of the intra-population variability (Anders and Huber, 2010). This procedure is not
recommended, as biological replication is the basis for any statistical test, but in this case, we
simply wanted to test the method performance in detecting differential expression. Data have
not been fully analyzed yet but a preliminary look indicates a higher number of DE contigs
than obtained with replication. Thus, taking into account sample relatedness would seem
totally relevant for a finer understanding of implicated molecular mechanisms in this kind of
toxigenomic approach. Our results emphasize a limit to the definition of biological replication
when genetic variation is accounted for. Indeed, biological replication would be relevant in
the context of RNAseq, as long as individuals from the same genetic line are used, which on
the other hand limits the scope of findings to this genetic line.
Despite low replication and the impossibility to conduct appropriate estimations of
additive genetic variances, the apparent strong family effect may be interpreted as a high
evolutionary potential of the species. Moreover, except for the genetically undifferentiated
populations Emm. and Kui., the effect of random genetic drift may be too strong to identify
selective patterns induced by the historical pesticide pressure. This is also strengthened by the
fact that selection needs large population size to operate (see Keller and Waller, 2002). The
comparison of non differentiated populations at neutral markers (such as eastern populations
in the present study), may thus be more relevant in this respect. Also, focusing on large
populations (as inferred from Ne) might increase the chances of identifying divergent
182
selection patterns. In the respect, no conclusion can be drawn from our study, since Ne-values
were very similar across populations.
Taking into account the low dispersal ability of L. stagnalis, transcriptomic expression
differences observed among populations are a good example of evolutionary processes
continuously occurring in nature among allopatric populations and possibly leading in the
long term to speciation.
The present results are poorly consistent with previous ones based on qPCR and using
the laboratory strain ReniLys® (Bouétard et al., 2013). Considering the observed differences
of population responses, this discrepancy is not surprising. A strong neutral genetic
differentiation between this strain and another laboratory strain from Amsterdam has been
previously demonstrated (Besnard et al., 2013). According to the present statistical analysis,
HSPs genes were not found to be highly regulated by diquat, whereas up-regulation was
expected. It is possible that constitutive HSPs expression might be naturally higher in natural
populations than in laboratory cultures, because of multiple sources of environmental stress
and cyclic environmental variation. Seasonal regulations of the anti-oxidant system have
notably been shown to occur in snail Helix aspersa (Ramos-Vasconcelos et al., 2005). Such
effects could thus hamper the comparison of ecotoxicity tests performed on different lines or
origins. In the present study, further qPCR analyses are clearly required to confirm observed
patterns. Nevertheless, such differences highlight the difficulty to assess the tolerance of a
species in regard to a toxicant by studying only one population, be it a laboratory strain or not.
To conclude, deeper analyses are needed before assessing with confidence that
historical pesticide pressure of a population could be responsible of the transcriptomic
differences observed among populations, but the dataset seems powerful and this preliminary
investigation is encouraging. Data should be refined by searching possible redundant contigs
by multiple alignments within the present dataset as with newly published sequenced.
Moreover, investigation of SNPs could reveal interesting polymorphism which might be
relevant to explain DE patterns. Research of correlation with the life history traits studied in
the precedent chapter should be promising when DE analyses will be updated with more
appropriate statistical methods currently developed (Lurin & Balzergue, unpublished).
Anyway, present analysis indicates that transcriptomic variations appeared very strong among
and within populations suggesting a relatively high evolutionary potential of this species.
Therefore, these results constitute another proof that such evolutionary issues are relevant in
the context of ecological risk assessment (Breitholtz et al., 2006; Coutellec and Barata, 2011).
183
Acknowledgments
The project has been carried out with INRA financial support (EFPA Projet Innovant
2010; AIP Bioressources 2010). AB is granted by the INRA (PhD, Contrat Jeune
Scientifique). The authors thank Christophe Plomion, Moumen Bouziane for advices,
exchange and insightful comments on the project. The authors thank the INRA U3E staff
(INRA, Rennes) and more particularly Maïra Coke for technical assistance, Thierry Caquet,
Sabrina Le Cam and Jessica Côte for their help and constructive discussions. The authors
finally thank Benoît Heurtault and Gaëlle Méheut (from groovy-banana.com), Elise Petitpas
and Arnaud Giusti for their hospitality and logistic support during the sampling period.
184
Table S1. List of gene hits (identity > 50%) in the “Enzymes” and “Cytochrome P450”
categories matching exclusively with contigs for which the Fold-Change is either less than -2 or
higher than +2 between diquat and control conditions (D < C ; D > C) according to a t-test, and/or
considered as DE by DEseq analysis (p < 0.1). Genes in italics were detected by DEseq. Genes in bold
were detected by both methods. Index letters indicate different contigs matching with the same
reference gene.
Cas.
KEGG category
[Br:ko00199] Cytochrome P450
CYP2 family
CYP3 family
[Br:ko01000] Enzymes
Oxidoreductases
Acting on the CH-OH group of donors
Oxygenases acting on single donors with O2
Acting on paired donors with incorporation of O2
Acting on the CH-NH2 group of donors
Acting on CH or CH2 groups
Acting on NADH or NADPH
Acting on a heme group of donors
Transferases
Glycosyltransferases
Transferring alkyl or aryl groups, other than methyl
groups
Transferring phosphorus-containing groups
Transferring sulfur-containing groups
Hydrolases
Acting on ester bonds
Glycosylases
Acting on peptide bonds (peptidases)
Acting on acid anhydrides
gene code
D<C
K07422
K07424
K00088
K08683
K13370
K00461
K00502
K07424
K00280
K00106
K00344
K03878
K03881
K03883
K02256
K00699
K03766
K07968
K10799
K15261
K00799
K06125
K00889
K02327
K02677
K04427
K05099
K08892
K04742
K00412
K01057
K01104
K01175
K06776
K01350
K07188
K08624
K14073
K10147
K05970
K01176
K01181
K01229
K05349
K12047
K01301
K01349
K02857
K04397
K08621
K09614
K13050
K13289
K12599
K15255
Put.
D>C
D<C
Kui.
D>C
D<C
Emm.
D>C
D<C
D>C
CYP2U
CYP3A
guaB
HSD17B10
HSD17B8
ALOX5a/b
ALOX5c
TPH1/2
CYP3A
LOXL2/3/4
XDHa/b
qor a
ND1b
ND4
ND5
qorb
ND1a
COX1a/b/c
UGT
B3GNT5
B4GALT3
TNKS
PARPa
PARP b /c /d
GST
COQ2
PIP5K
POLD1
CPKC
TAK1
MET
FRK
CHST12
CYTB a /b
PGLS
E3.1.3.48b
E3.1.3.48a
E3.1.-.PTPRK
PTPRK
PRTN3
HSL
ADAMTS9
PNLIPa
PNLIPb
PNLIPc
PPM1D
SIAE
amyA
xynA
LCT b
bglXa
MGAM
LCTa
bglXc
bglXb
E3.4.17.21
FURIN
RHBDL
CASP7b
CORINa
CASP7a/c
ADAMTS6a
ADAMTS6b
CORINb
PCSK9
CTSA
SKI2b
SKI2a
PIF1
Lyases
Carbon-nitrogen lyases
Phosphorus-oxygen lyases
Isomerases
cis-trans-Isomerases
Intramolecular oxidoreductases
Ligases
Forming carbon-nitrogen bonds
Forming phosphoric-ester bonds
K01754
K08042
K12323
K12735
K12663
tdcB
ADCY2
ANPRA
PPIL4
ECH1
K10624
K10645
K11204
K01974
RBBP6
MIB
GCLC
rtcA
185
Table S2. List of gene hits (identity > 50%) in the “Cellular processes category” matching
methods. Index letters indicate different contigs matching with the same reference gene.
Cas.
KEGG
processes
KEGG pathway
[Path:ko04520]
Adherens junction
gene code
D<C
Put.
D>C
D<C
Kui.
D>C
K04393
MET
K02677
CPKC
ACTB/G1
K05692
K03900
VWF
Cell communication
K04393
CDC42
K04725
XIAP,BIRC4
K05099
MET
K05635
K06236
LAMC1
COL1AS
K06252
[Path:ko04540]
[Path:ko04530]
Gap junction
Tight junction
TN
K16060
BIRC2/3e
K02677
CPKC
K07374
TUBAb/c
K07375
TUBB
K04420
MAP3K2 a
K08042
ADCY2
K02677
BIRC2/3b/d
TUBAa
MAP3K2 c/d
MAP3K2 b
CPKC
CDC42
K05692
ACTB/G1
K10352
Apoptosis
BIRC2/3e
BIRC2/3a /c
K04393
[Path:ko04210]
MYHa
MYH b /c
K03158
TNFR1a/b
K04389
FASLa/b/c
K04390
FAS
K04397
CASP7b
CASP7a/c
K04722
[Path:ko04111]
Cell cycle
TRAILR
K04725
XIAP,BIRC4
K16060
BIRC2/3 e
BIRC2/3b/d
K16061
BIRC7/8i
BIRC7/8f/g/h
BIRC2/3e
BIRC2/3a /c
SMC2
K12575
Meiosis
K12575
[Path:ko04114]
Oocyte meiosis
p53 signaling pathway
K08042
[Path:ko04115]
TRAILR
K06674
[Path:ko04113]
SLK19
SLK19
ADCY2
K04390
FAS
Cell
motility
K10147
[Path:ko04810]
Regulation of actin
cytoskeleton
K00889
PPM1D
PIP5K
ACTB/G1
K05692
K04393
[Path:ko04144]
Endocytosis
K00889
CDC42
PIP5K
Transport & catabolism
K03283
HSPA1/8
K04393
CDC42
K05099
MET
K12482
RABIP4
K15053
[Path:ko04142]
Lysosome
K13289
[Path:ko04146]
Peroxisome
K00106
K12663
[Path:ko04145]
Phagosome
D>C
TAK1
K05099
Cell growth & death
D<C
ACTB/G1
K04427
Focal adhesion
D>C
CDC42
K05692
[Path:ko04510]
D<C
Emm.
CHMP7
CTSA
XDHa/b
ECH1
K06560
MRC
K05692
ACTB/G1
K06563
CD209
K07374
TUBAb/c
K07375
TUBB
TUBAa
186
HSPA1/8
Table S3. List of gene hits (identity > 50%) in the “Environmental Information Processing”
category matching exclusively with contigs for which the Fold-Change is either less than -2 or higher
than +2 between diquat and control conditions (D < C ; D > C) according to a t-test, and/or considered
as DE by DEseq analysis (p < 0.1). Genes in italics were detected by DEseq. Genes in bold were
detected by both methods. Index letters indicate different contigs matching with the same reference
gene.
Cas.
Membrane
transport
KEGG
KEGG
processes pathway
D<C
D>C
D<C
Kui.
D>C
[Path:ko02010] ABC transporters
[Br:ko02001] Solute carrier family
[Br:ko02000]
[Path:ko04020]
[Path:ko04020]
[Path:ko04012]
[Path:ko04340]
[Path:ko04066]
[Path:ko04390]
[Path:ko04010]
Signal transduction
gene
code
Put.
[Path:ko04150]
[Path:ko04064]
[Path:ko04330]
[Path:ko04070]
[Path:ko04151]
[Path:ko04350]
[Path:ko04370]
[Path:ko04310]
K05644
K05038
K14611
K14687
Transporters
K05644
K08175
Two-component system K00412
Calcium sig. path.
K02677
K08042
ErbB sig. path.
K02677
Hedgehog sig. path.
K06233
HIF-1 sig. path.
K02677
Hippo sig. path.
K16507
K05692
MAPK sig. path.
K02677
K04420
K03158
K03283
K04389
K04390
K04393
K04427
K09291
mTOR sig. path.
K02677
K07204
NF-kappa B sig. path.
K03158
K03161
K04427
K04725
K16060
Notch sig. path.
K02599
K06058
Phosphatidylinositol sig. K00889
K02677
path. system
PI3K-Akt sig. path.
K03900
K04079
K04389
K05099
K05635
K06236
K06252
K07204
TGF-beta sig. path.
K04658
VEGF sig. path.
K02677
K04393
Wnt sig. path.
K02677
K04427
K03068
D<C
Emm.
D>C
D<C
ABCA4
D>C
ABCA4
SLC6A5S
SLC23A1/2
SLC31A2
ABCA4
ABCA4
NAGLT1
CYTB a/b
CPKC
ADCY2
CPKC
LRP2a/b/c
CPKC
PCDH16/23
ACTB/G1
CPKC
MAP3K2 a
MAP3K2 c/d
MAP3K2 b
TNFR1a/b
HSPA1/8
HSPA1/8
FASLa/b/c
FAS
CDC42
TAK1
TPRa
TPRb
CPKC
RAPTOR
TNFR1a/b
TNFSF5
TAK1
XIAP,BIRC4
BIRC2/3 e
NOTCHc
BIRC2/3a/c
NOTCHb/d
DTX
BIRC2/3b/d
NOTCH f
NOTCHa/e
PIP5K
CPKC
VWF
HSP90A
FASLa/b/c
MET
LAMC1
COL1AS
TN
RAPTOR
NOG
CPKC
CDC42
CPKC
TAK1
LRP5/6
[Path:ko04514]
187
Table S3. “Environmental Information Processing” category. (continued)
Cas.
KEGG
KEGG
processes pathway
gene
code
D<C
Put.
D>C
[Path:ko04514] Cell adhesion molecules K03161
K06550
(CAMs)
[Br:ko04516]
Signaling molecules & interaction
[Br:ko04090]
[Br:ko04050]
[Path:ko04060]
K06759
Cell adhesion molecules K05635
K05637
& their ligands
K06240
Cellular Antigen
K02599
K03158
K04389
K04390
K04550
K04722
K05099
K06550
K06560
K06563
K06702
Cytokine receptors
K05099
Cytokine-cytokine
receptor interaction
D>C
D<C
Emm.
D>C
D<C
D>C
TNFSF5
L1CAM
CNTN1
NOTCHc
NOTCHb/d
LAMC1
LAMA1-2
LAMA3-5
NOTCHa/e
NOTCH f
CD120a/b
CD178a/b/c
CD95
CD91b
CD91c
CD91a
CD261-2-3-4
CD261-2-3-4
MET
CD171
CD206-208
CD209-99
CDw93
MET
TNFR1a/b
K03158
K03161
K04389
K04390
K04722
K05099
[Path:ko04512] ECM-receptor
K03900
K05635
interaction
K06236
K06252
[Br:ko01020] Enzyme-linked receptors K05099
[Br:ko04091] Glycan Binding Proteins K06560
K06563
[Br:ko04031] GTP-binding proteins
K04393
K07912
[Br:ko04040] Ion Channels
K04825
K04914
K05038
K05185
K05185
[Path:ko04080] Neuroactive ligand-
D<C
Kui.
TNFSF5
FASLa/b/c
TRAILR
FAS
TRAILR
MET
VWF
LAMC1
COL1AS
TN
MET
MRC
CD209-99
CDC42
RAB24
SCNN1B
KCNK3
SLC6A5S
GABRE
GABRE
receptor interaction
188
Table S4. List of gene hits (identity > 50%) in the “Genetic Information Processing” category
both methods. Index letters indicate different contigs matching with the same reference gene.
Cas.
KEGG
KEGG
processes pathway
gene
code
D<C
Put.
D>C
D<C
Kui.
D>C
D<C
Emm.
D>C
Transcription
Replication &
repair
Folding, sorting & degradation
[Path:ko04141] Protein processing in
endoplasmic reticulum
[Path:ko03018]
[Path:ko04120]
[Br:ko03110]
[Br:ko04121]
[Path:ko03410]
[Br:ko03400]
[Path:ko03030]
[Path:ko03440]
[Path:ko03430]
[Path:ko03420]
[Br:ko03041]
[Path:ko03040]
[Br:ko03000]
Translation
[Path:ko03010]
[Br:ko03011]
[Path:ko03013]
[Path:ko00970]
[Br:ko03016]
K03283
K04079
HSP90A
K14000
RRBP1
K09542
CRYAB
RNA degradation
K12599
SKI2a
XIAP,BIRC4
Ubiquitin mediated
K04725
BIRC2/3b/d
K16060
BIRC2/3 e
proteolysis
K16061
BIRC7/8i
BIRC7/8f/g/h
LCTa
K01229
Chaperones & folding
K01349
FURIN
catalysts
K03283
K09542
CRYAB
K03900
K04079
HSP90A
K12172
K13289
Ubiquitin system
K10268
K10272
FBXL6
K10314
K10461 KLHL24/35a
K10470
K10473
K10314
K06058
Base excision repair
K02327
DNA repairs & recombinationK02327
proteins
DNA replication
K02327
K15255
PIF1
Homologous recombination K02327
Mismatch repair
K02327
Nucleotide excision repair
K02327
Spliceosome
K03283
K12735
PPIL4
K13122
FRG1
K03283
Transcription factors
K09206
K09228
K09250
CNBP
K09266
SRY
K09431
ETV1
Ribosome
K02872
K02949
K02990
RP-S6
Ribosome
K02872
K02949
K02927
K02990
RP-S6
RNA transport
K09291
K12172
Aminoacyl-tRNA biosynthesisK01867
WARS a/b
Transfer RNA biogenesis
K15433
RLLM1
189
D<C
D>C
HSPA1/8
HSPA1/8
SKI2b
BIRC2/3e
BIRC2/3a/c
LCT b
HSPA1/8
HSPA1/8
VWF
RanBP2
CTSA
FBXL2/20
FBXO39
KBTBD2a
KLHL24/35 b
KBTBD2b
KBTBD2 b
KBTBD5/10
FBXO39
DTX
POLD1
POLD1
POLD1
POLD1
POLD1
POLD1
HSPA1/8
HSPA1/8
HSPA1/8
HSPA1/8
KLF5
KRABa/c
KRABb
RP-L13Ae
RP-S11eb/c
RP-S11ea
RP-S11eb/c
RP-S11ea
RP-L13Ae
RP-L40e a/b
TPRa
TPRb
RanBP2
WARS c
Table S5. List of gene hits (identity > 50%) in the “Metabolism” category matching exclusively
with contigs for which the Fold-Change is either less than -2 or higher than +2 between diquat and
control conditions (D < C ; D > C) according to a t-test, and/or considered as DE by DEseq analysis (p
< 0.1). Genes in italics were detected by DEseq. Genes in bold were detected by both methods. Index
letters indicate different contigs matching with the same reference gene.
Cas.
KEGG
pathway
KEGG processes
Amino acid metabolism
Biosynthesis of other 2
metabolites
nd
Carbohydrate metabolism
[Path:ko00260]
[Path:ko00380]
[Path:ko00290]
[Path:ko00280]
Glycan biosynthesis &
metabolism
[Path:ko00940] Phenylpropanoid biosynthesis
[Path:ko00053] Ascorbate & aldarate metabolism
[Path:ko00052] Galactose metabolism
K05349
K00699
K01229
K12047
K00889
K00699
K01057
K00699
K01176
K05349
K12047
K01179
K01027
K02256
K02262
K03881
K00412
K03878
K03883
K00412
K04742
Inositol phosphate metabolism
Pentose & glucuronate interconversions
Pentose phosphate path.
Starch & sucrose metabolism
[Path:ko00650] Butanoate metabolism
[Path:ko00190] Oxidative phosphorylation
[Path:ko00910] Nitrogen metabolism
[Path:ko00920] Sulfur metabolism
Glycosaminoglycan biosynthesis:
[Path:ko00532]
- Chondroitin sulfate / dermatan sulfate
[Path:ko00533]
- Keratan sulfate
[Path:ko00601] Glycosphingolipid biosynthesis: lacto &
neolacto series
[Br:ko00536]
[Path:ko00510]
[Path:ko00514]
[Path:ko00513]
[Path:ko00072]
[Path:ko00590]
[Path:ko00561]
[Path:ko00564]
[Path:ko00565]
[Path:ko00591]
[Path:ko00140]
[Path:ko00130] Ubiquinone & other terpenoid-quinone
biosynthesis
Metabolism of other amino [Path:ko00460] Cyanoamino acid metabolism
acids
[Path:ko00480] Glutathione metabolism
[Br:ko01006] Prenyltransferases
[Path:ko00230] Purine metabolism
[Path:ko00240] Pyrimidine metabolism
Xenobiotics biodegradation [Path:ko00627] Aminobenzoate degradation
& metabolism
[Path:ko00982] Drug metabolism - cytochrome P450
[Path:ko00983] Drug metabolism - other enzymes
[Path:ko00980] Metabolism of xenobiotics by cytochrome
P450
Put.
D>C
D<C
D>C
D<C
TPH1/2
HSD17B10
OXCT
XDHa/b
bglXc
bglXa
LCTa
LCT b
MGAM
bglXb
UGT
PIP5K
UGT
PGLS
UGT
amyA
bglXa
MGAM
E3.2.1.4
OXCT
bglXc
bglXb
COX1a /b/c
COX3 a/b
ND4
CYTB a/b
ND1b
ND5
ND1a
CYTB a/b
CHST12
B4GALT3
B3GNT5
B4GALT3
FUT1/2
UGT
B3GNT5
B4GALT3
TN
B4GALT3
B4GALT3
B4GALT3
OXCT
ALOX5a/b
CYP2U
PNLIPc
ALOX5c
PNLIPb
PLA2G a PLA2G b /c/d/e
CYP3A
UGT
CYP3A
HSD17B8
UGT
UGT
CYP3A
COQ2
K05349
K00799
K11204
190
D>C
Emm.
tdcB
K00699
K00699
K07424
K06125
K06125
K00088
K00106
K02327
K08042
K12323
K02327
K07424
K00699
K00799
K07424
K00088
K00106
K00699
K07424
K00699
K00799
K07424
D<C
Kui.
tdcB
K04742 CHST12
K07968
K03766
K07968
K00718
Glycosyltransferase
K00699
K03766
K04742 CHST12
K07968
Heparan sulfate / heparin binding proteins K06236 COL1AS
K06252
N-Glycan biosynthesis
K07968
Other types of O-glycan biosynthesis
K07968
Various types of N-glycan biosynthesis
K07968
Synthesis and degradation of ketone bodies K01027
Arachidonic acid metabolism
K00461
K07422
Glycerolipid metabolism
K14073 PNLIPa
Glycerophospholipid metabolism
K01047
Ether lipid metabolism
K01047
Linoleic acid metabolism
K07424
K01047
Steroid hormone biosynthesis
K00699
K07424
K13370
Metabolism of cofactors & [Path:ko00860] Porphyrin metabolism
vitamins
[Path:ko00830] Retinol metabolism
Metabolism of terpenoids
& polyketides
Nucleotide metabolism
D<C
K01754
K00502
K01754
K08683
K01027
K00106
[Br:ko01003]
Lipid metabolism
Glycine, serine & threonine metabolism
Tryptophan metabolism
Valine, leucine & isoleucine biosynthesis
Valine, leucine & isoleucine degradation
[Path:ko00232] Caffeine metabolism
[Path:ko00562]
[Path:ko00040]
[Path:ko00030]
[Path:ko00500]
Energy metabolism
gene
code
bglXc
bglXa
bglXb
GST
GCLC
COQ2
guaB
XDHa/b
POLD1
ADCY2
ANPRA
POLD1
CYP3A
UGT
GST
CYP3A
guaB
XDHa/b
UGT
CYP3A
UGT
GST
CYP3A
D>C
Table S6. List of gene hits (identity > 50%) in the “Organismal system” category matching
methods. Index letters indicate different contigs matching with the same reference gene.
Cas.
KEGG
processes
KEGG pathway
gene
code
D<C
Put.
D>C
D<C
Circulatory
system
[Path:ko04260] Cardiac muscle contraction
[Path:ko04270]
Development
[Path:ko04360]
[Path:ko04320]
[Path:ko04380]
[Path:ko04976]
[Path:ko04973]
[Path:ko04975]
Digestive system
[Path:ko04971]
[Path:ko04978]
[Path:ko04972]
[Path:ko04974]
[Path:ko04970]
[Path:ko04977]
[Path:ko04672 ]
K02256
K00412
K02262
Vascular smooth muscle contraction K02677
K01047
K08042
K12323
Axon guidance
K04393
K05099
K06550
K06820
K06841
Dorso-ventral axis formation
K02599
K02184
K04427
Osteoclast differentiation
K03158
Bile secretion
K08042
Carbohydrate digestion & absorption K01176
K01229
K12047
Fat digestion & absorption
K14073
K01047
Gastric acid secretion
K02677
K08042
Mineral absorption
K14735
Pancreatic secretion
K02677
K01047
K08042
K14073
Protein digestion & absorption
K06236
K08132
K16628
Salivary secretion
K02677
K08042
Vitamin digestion & absorption
K14073
K14616
Intestinal immune network for IgA
K03161
Endocrine system
production
[Path:ko04920] Adipocytokine sig. path.
[Path:ko04912] GnRH sig. path.
[Path:ko04910] Insulin sig. path.
[Path:ko04916] Melanogenesis
[Path:ko03320] PPAR sig. path.
[Path:ko04914] Progesterone-mediated oocyte
maturation
[Path:ko04614] Renin-angiotensin system
Kui.
D>C
D<C
Emm.
D>C
D<C
D >C
COX1a/b/c
CYTB a/b
COX3 a/b
CPKC
PLA2G a PLA2G b/c/d /e
ADCY2
ANPRA
CDC42
MET
L1CAM
PLXNA
NOTCH c
SEMA5
NOTCHa /e
NOTCHb /d NOTCH f
FMN2
TNFR1
TAK1
ADCY2
amyA
LCTa
PNLIP a
PNLIPc
LCT b
MGAM
PNLIPb
CPKC
ADCY2
HEPH
CPKC
ADCY2
PNLIP a
COL1AS
PNLIPb
PNLIPc
COL12Ab
COL12Aa
COL7A
CPKC
ADCY2
PNLIP a
PNLIPc
CUBNa
PNLIPb
CUBNb
TNFSF5
K03158
K04420 MAP3K2 a
K04393
K08042 ADCY2
K07188
K07204
K02677
K08042 ADCY2
K08770
K04079 HSP90A
K08042 ADCY2
K13289
K01283
191
TNFR1a/b
MAP3K2 c/d
MAP3K2 b
CDC42
HSL
RAPTOR
CPKC
UBC a/b
CTSA
ACE
Chapitre VI - Discussion générale
192
Dans cette thèse, nous nous sommes intéressés au potentiel adaptatif de L. stagnalis
vis-à-vis du stress provoqué par les activités anthropiques, avec un intérêt particulier pour les
impacts évolutifs liés à l’usage des pesticides. Dans les paysages agricoles, les milieux
lentiques occupés par cette espèce sont susceptibles d’être contaminés de façon récurrente par
les produits phytosanitaires (Brown & van Beinum, 2009). La question initiale de ce projet
était de savoir si ce type de perturbations répétées pouvait entraîner des processus microévolutifs de différenciation adaptative au sein des populations naturelles chez une espèce non
cible. D’une façon plus générale, nous avons cherché à mieux comprendre les bases
génétiques de la réponse aux changements environnementaux au sein des populations
naturelles.
La question de l’impact évolutif des polluants sur les écosystèmes est d’intérêt
croissant en écotoxicologie et représente un défi majeur pour les futures procédures
d’évaluation du risque écologique. Le travail réalisé présente donc un intérêt à la fois en
termes de résultats de recherche originale (documentation d’effets) et d’application à
l’évaluation environnementale.
Le sujet était articulé autour de quatre questions majeures :
Q1 Existe-t-il une divergence génétique phénotypique et moléculaire entre populations
naturelles génétiquement différenciées du point de vue neutre ?
Q2 L’exposition historique aux stress d’origine anthropique peut-elle entraîner une
divergence adaptative des populations (sélection divergente sur les traits d’histoire de vie) ?
Q3 A l’échelle moléculaire, observe-t-on une variation inter-population dans la réponse au
stress oxydant généré par le diquat ? Si tel est le cas, cette variation résulte-t-elle des
pressions de sélection d’origine anthropique auxquelles sont soumises les populations
impliquées ?
Q4 Quel est le potentiel adaptatif de ces populations naturelles vis-à-vis du stress d’origine
anthropique en général ?
193
Les résultats obtenus dans cette étude répondent à ces différentes questions en mettant en
évidence :
Un effet toxique du diquat sur les réponses moléculaires produites par l’hémolymphe
et la glande digestive (approche toxicologique classique)
Une forte structure génétique des populations de L. stagnalis dans le Nord-Ouest de
l’Europe
Une forte variabilité inter-population des traits quantitatifs, à l’échelle phénotypique
(traits d’histoire de vie) et moléculaire (expression génique)
Un impact évolutif potentiel des pressions d’origine anthropique sur la fitness et les
réponses moléculaires au stress oxydatif
Le type d’habitat comme principal facteur de divergence adaptative et neutre (échelle
phénotypique)
Une forte variabilité intra-population des traits quantitatifs à l’échelle phénotypique
(traits d’histoire de vie) et moléculaire (expression génique)
La nécessité de prendre en compte de tels facteurs génétiques dans les procédures
d’évaluation du risque écologique
Effet toxique du diquat sur les réponses moléculaires de l’hémolymphe et de la glande
digestive
Dans une première approche toxicologique classique, nous avons testé la pertinence
d’une molécule toxique modèle, l’herbicide diquat, utilisée comme xénobiotique générateur
de stress oxydant sur notre modèle biologique, L. stagnalis. Sur la base des ressources
génétiques initialement disponibles chez cette espèce, ont été développés des marqueurs
transcriptionnels pour quantifier par PCR quantitative, l’impact de l’herbicide sur l’expression
des gènes codant pour un certain nombre de gènes candidats [catalase (cat), une superoxyde
dismutase cytosolique (Cu / Zn-sod), une glutathion peroxydase sélénium-dépendante (gpx),
une glutathion réductase (gred), le récepteur X des rétinoïdes (rxr), deux protéines chaperons
(hsp40 et hsp70)], ainsi que pour la cortactine (cor) et les sous-unités ribosomiques r18S et
r28s.
L’exploration des réponses moléculaires précoces (5, 24 et 48 heures) d’individus
exposés à trois concentrations de diquat considérées comme réalistes du point de vue des
doses environnementales (22,2 ; 44,4 et 222,2 µg/L) a été réalisée sur deux tissus,
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l’hémolymphe et la glande digestive. Les activités enzymatiques de Cat, SOD, Gpx, Gred et
GST ont également été mesurées dans les tissus hépatiques.
Globalement, les résultats obtenus sont cohérents avec l’observation antérieure d’une
induction massive de processus apoptotiques dans les hémocytes après 48 heures d’exposition
au deux plus fortes concentrations (sous-expression de la plupart des gènes) tandis que la plus
faible concentration entraîne une surexpression significative de cor, hsp40, rxr, et sod après
24 heures. Dans la glande digestive (GD), nous avons constaté une forte réactivité vis-à-vis du
stress oxydant. En effet, dès 5 heures d’exposition, la sur-expression quasi-générale des gènes
étudiés dans les hépatocytes suggère une perturbation de l’équilibre homéostatique induite par
la génération d’espèces réactives de l'oxygène (ROS), avant un retour à un niveau
d’expression constitutive après 24 et 48 heures. De plus, les résultats indiquent un effet posttranscriptionnel du diquat, qui peut concerner la traduction, le repliement protéique ou
l’activation des domaines catalytiques. Dans un contexte d’utilisation de ces réponses comme
biomarqueur, le maintien des activités enzymatiques à un niveau constitutif en présence de
diquat montre que l’absence de réponse significative au niveau fonctionnel (activité
enzymatique) peut masquer des effets toxiques plus en amont, pouvant ainsi conduire à une
sous-estimation des effets.
A notre connaissance, cette étude est la première démontrant au niveau moléculaire, la
toxicité du diquat chez un gastéropode d’eau douce, induisant des effets compatibles avec
ceux observés sur des modèles murins dans le cadre d’étude du stress oxydant (Crabtree et al.,
1977; Smith et al., 1985; Gallagher et al., 1995; Anton et al., 2002; Rogers et al., 2006).
Sur la base de ces résultats, nous avons choisi les conditions expérimentales permettant
d’obtenir les réponses les plus contrastées (i.e., 5 heures, 222.2 µg/L), afin de générer par
pyroséquençage 454 des ressources transcriptomiques (141999 contigs) nécessaires à de
futures analyses d’expression différentielle à l’échelle globale (RNAseq). Une analyse
exploratoire conduite à cette occasion a suggèré une grande diversité d’effets moléculaires
pour le diquat, outre l’effet spécifiquement lié au stress oxydant. Sur la base de ces résultats,
cinq nouveaux marqueurs qPCR permettant d’étudier la transcription de gènes impliqués dans
la réponse au stress (hsp 90, gst), dans les processus apoptotiques (endoG, dj1) la chaîne
respiratoire (cytC) ont été mis au point (Barbu, 2011).
L’herbicide diquat s’est donc avéré un modèle pertinent pour l’étude ultérieure de
l’impact évolutif du stress d’origine anthropique, de par ses effets oxydatifs sub-létaux,
représentatifs de la toxicité d’un grand nombre de substances et éléments présents dans les
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milieux aquatiques, tels que pesticides, métaux, ou encore produits pharmaceutiques
(Scandalios, 2005; Valavanidis et al., 2006). Les tests classiques d’écotoxicité sont
généralement menés sur une seule lignée, souche ou origine génétique, pour limiter la
variation intra-traitement, et de fait, négligent la variabilité génétique intra-spécifique.
Pourtant, la variation génétique est une réalité biologique, et l’existence de variabilité inter et
intra-populationnelle dans la réponse des espèces à différents stress environnementaux et
anthropiques a été mise en évidence chez de nombreuses espèces, e.g., d’amphibiens (Bridges
& Semlitsch, 2000; Semlitsch et al., 2000; Matson et al., 2006; Hangartner et al., 2012), de
mollusques (Gnatyshyna et al., 2011; Veldhoen et al., 2011), de crustacés (Brausch & Smith,
2009; Coors et al., 2009; Jansen et al., 2011), d’arthropodes (van Straalen and Roelofs, 2011;
Costa et al., 2012), ou encore de plantes (Keane et al., 2005). Dans le contexte
d’écotoxicologie évolutive du travail présenté, l’herbicide diquat a donc été utilisé pour
comparer les réponses moléculaires au stress de plusieurs populations naturelles
échantillonnées dans des milieux contrastés en terme de pression anthropique chronique.
Structure génétique des populations naturelles de L. stagnalis dans le Nord-Ouest de
l’Europe
L’étude de l’impact évolutif des stress d’origine anthropique sur les populations
naturelles de L. stagnalis a porté sur 14 populations échantillonnées dans le nord de l’Europe,
entre Bruxelles, Amsterdam et Hambourg, en juillet 2011. Les analyses de structure basées
sur le génotypage à 12 loci de 355 individus ont mis en évidence (i) une consanguinité
généralement élevée des populations, des effectifs efficaces homogènes, une variation des
systèmes de reproduction allant de l’absence d’autofécondation à des taux intermédiaires, et
(ii) la différenciation de deux groupes principaux (Est et Ouest) discriminant les quatre
populations allemandes des autres, en cohérence avec un modèle d’isolement par la distance,
et donc d’une structure neutre. Globalement, les populations sont fortement différenciées (FST
global
= 0.282). Etant donné l’écologie de l’espèce, sa capacité de dispersion limitée et les
dimensions de l’aire d’échantillonnage, de telles différences ne sont pas étonnantes. Des
valeurs de FST par paires particulièrement élevées ont été constatées entre la plupart des
populations d’étangs étudiées. Ce résultat est compatible avec l’absence de flux de gène dans
ce type d’habitat fermé, comme déjà observé chez d’autres gastéropodes d’eau douce, tels que
Physa acuta (Escobar et al., 2008).
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Compte tenu de la forte différenciation génétique et de la faible capacité de dispersion
de L. stagnalis, l’hypothèse d’adaptation locale des populations étudiées est possible. Nous
avons ainsi cherché à déterminer à cette échelle le niveau de variation génétique de traits liés
à la fitness et à la sensibilité au stress oxydant, et à évaluer l’impact évolutif potentiel lié aux
pressions historiques d’exposition au stress d’origine anthropique.
Présence d’une forte variabilité inter-populationnelle des traits quantitatifs à l’échelle
phénotypique (traits d’histoire de vie) et moléculaire (transcriptome)
L’étude en common garden des traits d’histoire de vie, mesurés sur la descendance des
individus échantillonnés en milieu naturel a permis de constater de fortes disparités entre
populations, ceci pour la plupart des traits étudiés, que ceux-ci soient relatifs à la croissance
ou à la fécondité. L’influence de la localisation géographique (Est/Ouest, déterminée par
STRUCTURE) semble n’avoir que peu d’influence sur les traits étudiés, excepté pour deux
traits liés à la taille à l’éclosion (taille mesurée et paramètre du modèle de Gompertz).
L’hypothèse de divergence génétique adaptative testée grâce à l’approche QST-FST, a mis en
évidence des processus de sélection divergente entre les populations étudiées (QST > FST) pour
plusieurs traits, tels que la taille à l’âge adulte, l’aptitude à pondre, le délai d’oviposition, ou
encore le nombre de pontes produites en 15 jours.
Les traits relatifs à la fécondité et à la taille des pontes ne semblent pas soumis à la
sélection divergente malgré une influence significative de la population (GLMM,
comparaison de modèles successifs, avec et sans le facteur aléatoire « population »). Le test a
estimé que la variance inter-population ne diffère pas des valeurs attendues sous l’hypothèse
neutre, ce qui indique un effet exclusif de la dérive génétique (QST = FST). Des travaux
réalisés sur les amphibiens ont mis en évidence une variation des valeurs de QST en fonction
des conditions environnementales, suggérant que des conditions stressantes pouvaient faciliter
la mise en évidence de patrons de sélection divergente entre populations (Palo et al., 2003;
Hangartner et al., 2012). Dans le cadre de la thèse, l’exploration des réponses
transcriptomiques en condition de stress oxydant à partir d’un sous-ensemble de quatre
populations nous a permis dans une certaine mesure de tester cette hypothèse.
Bien que l’approche QST-FST initialement prévue à l’échelle transcriptomique n’ait pu
être réalisée en raison du nombre limité de réplicats et de populations étudiées, une forte
différenciation de la réponse transcriptomique inter-populations a été mise en évidence, aussi
bien en condition stressante qu’en condition témoin. De plus, une forte analogie entre les
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niveaux de différenciation génétique neutre et les différences de réponse transcriptomique a
été constatée. A notre connaissance, ce type de concordance n’a encore jamais été révélée à
une telle échelle. Les observations mettent en exergue des processus évolutifs propres à
chaque population. L’analyse préliminaire de l’expression différentielle entre conditions
expérimentales a en effet confirmé que l’origine de la population expliquait une bien plus
grande part de variance que le toxique lui-même. Le stress oxydant étant à la fois un
processus moléculaire naturel et un état pouvant résulter de l’exposition à des sources variées
de stress environnemental, il n’est probablement pas étonnant d’observer une si grande
divergence entre populations. Il est vraisemblable que des réponses plus convergentes aient
pu être observées dans le cas d’une molécule dont le mode d’action est plus ciblé, bien que
d’un point de vue adaptatif, les réponses produites par des populations isolées sont le reflet de
processus évolutifs indépendants propres à chaque population (cf. Morgan et al. 2007).
Absence de relation entre le système de reproduction et les facteurs environnementaux
testés
Certains traits mesurés sur les G1, comme l’aptitude à se reproduire, la fécondité
estimée, le taux d’éclosion des pontes, montrent une corrélation négative avec le degré de
consanguinité des populations. Ce résultat est cohérent avec ceux précédemment obtenus dans
un ensemble de populations différentes de la même espèce (Puurtinen et al., 2004b), et
supporte l’hypothèse d’existence de dépression de consanguinité dans les populations
naturelles. Le système de reproduction de L. stagnalis est classé dans les « allofécondants
préférentiels » (Escobar et al., 2011), et ce, en dépit de la faible dépression de consanguinité
observée expérimentalement, entre descendance d’auto- vs allo-fécondation (Coutellec &
Lagadic, 2006 ; Puurtinen et al., 2007 ; Coutellec & Caquet, 2011). Dans la présente étude,
l’analyse du système de reproduction basée sur la distribution de l’hétérozygotie multi-locus
au sein de la population (RMES), ne montre pas de relation particulière entre d’une part le
taux d’autofécondation et la consanguinité globale, et d’autre part le taux d’autofécondation et
la valeur des traits d’histoire de vie mesurés. De plus, ce taux n’est affecté par aucun des
facteurs environnementaux testés, même si on peut noter que deux des trois valeurs de taux
d’autofécondation significatifs sont observés en étang, où l’on observe également une plus
grande divergence génétique (arbre basé sur les valeurs de FST). Il semble donc que dans les
populations étudiées, la consanguinité biparentale (fécondation croisée entre apparentés) joue
un rôle important dans la structure globale, et contribue majoritairement au fardeau de
mutations délétères dans les populations.
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Une hypothèse connexe à ce travail était relative à l’influence du système de
reproduction dans la réponse au stress environnemental, et nous nous attendions en particulier
à une relation positive entre le taux d’autofécondation des populations et le niveau de stress
environnemental caractérisé, pour des raisons liées à l’assurance de reproduction (chances
réduites de rencontre de partenaire, ou de partenaire en état de s’accoupler) et d’allocation
énergétique en condition de stress. Sur la base du jeu de données et des conditions
environnementales choisies, l’étude n’a pas permis de mettre en évidence une telle relation. Il
est probable que cet échec soit lié à la faible puissance du plan statistique, et qu’un
échantillonnage plus conséquent soit nécessaire (en nombre de populations par type de
milieu) pour approfondir cette question.
Effets potentiels des pressions historiques d’origine anthropique sur les réponses
moléculaires au stress oxydatif et la fitness des populations
Les deux approches utilisées dans cette thèse pour évaluer l’impact évolutif des
pressions anthropiques sur les populations de L. stagnalis ont mis en évidence des effets au
niveau de la fitness (baisse de survie à l’éclosion, aptitude accrue à se reproduire), ainsi que
des résultats préliminaires compatibles avec un processus de sélection divergente au niveau
transcriptomique. D’après l’étude des traits d’histoire de vie mesurés en common garden, il
semblerait en effet que les perturbations répétées des milieux aquatiques par les polluants
puissent avoir un impact évolutif diminuant la survie à l’éclosion. Dans ces populations, il est
possible qu’une fécondité accrue ait été sélectionnée en réponse à la survie précoce réduite.
Concernant l’étude du transcriptome en condition de stress oxydant, les analyses
qualitatives de classification hiérarchique basées sur les corrélations relatives aux contigs
impliqués dans l’apoptose, la protéolyse impliquant le système de l’ubiquitine, ou à plus large
échelle, les processus de repliement, de transport et de dégradation protéique, suggèrent une
plus faible sensibilité des populations exposées aux pesticides. Cependant, l’analyse
statistique de l’expression différentielle n’a pas permis pour le moment de faire ressortir des
réponses communes aux deux populations. L’effet du diquat s’exprime principalement via son
interaction avec le facteur population (542 contigs), ce qui suggère une réponse différente
pour chacune des quatre populations. Seuls neufs contigs semblent influencés par le
traitement quelle que soit la population (e.g., dont deux contigs annotés, SKI2, ABCA4), bien
que l’analyse indépendante de ces transcrits par population suggère un impact différentiel.
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Bien que préliminaires, ces résultats démontrent toutefois une forte base génétique de la
réponse de L. stagnalis au stress oxydant induit par le diquat.
L’analyse statistique des données d’expression différentielle fait l’objet d’un
développement actuellement important. Ainsi, parmi les méthodes disponibles, DEseq nous
est apparue comme la plus intéressante, car elle se base sur une distribution binomiale
négative, adaptée aux données de comptage présentant une très forte dispersion
(caractéristique des données de RNAseq). Cependant, une étude comparative (microarray,
qPCR, RNAseq) récemment menée chez un modèle éprouvé (A. thaliana) a montré les limites
de cette méthode, et son incapacité à détecter l’expression différentielle pour une certaine
catégorie de contigs. Une amélioration de la méthode DEseq est en cours de développement
(Lurin and Balzergue), et devrait permettre d’obtenir des résultats statistiquement plus fiables,
nécessaire à l’identification des voies moléculaires réellement sollicitées par le diquat et de
leur caractère commun au niveau de l’espèce. Cependant, compte tenu de l’hétérogénéité des
pressions environnementales entre les différentes localités, i.e., la diversité des polluants, de
leur toxicité, mode d’action et effets synergiques potentiels, il est peu probable que
l’évolution adaptative de tolérance aux stress anthropiques résulte de sélection pour les
mêmes gènes. D’après les analyses préliminaires, la diversité des mécanismes de réponse au
stress induit par le diquat semble aller dans le sens de cette hypothèse. Il apparait en effet plus
pertinent de réfléchir à l’échelle des voies moléculaires sollicitées dans ces réponses. Une
analyse des réseaux de gènes impliqués permettrait d’approfondir ces questions, moyennant
une meilleure connaissance du génome de l’espèce (Horvath, 2011). Au vue de la diversité
des réponses produites, les processus micro-évolutifs en action semblent spécifiques des
caractéristiques écologiques locales des milieux occupées par ces populations. Dans ce
contexte, une approche méta-transcriptomique impliquant l’étude de plusieurs taxa pourrait
également s’avérer pertinente pour tester l’hypothèse de mécanismes moléculaires communs
vis-à-vis des mêmes conditions environnementales (Garcia-Reyero & Perkins, 2011), à la
condition toutefois que la composante phylogénique des réponses mesurées puisse être prise
en compte (Carew et al., 2011).
Le type d’habitat comme principal facteur de divergence adaptative et neutre
Bien que la présente étude suggère un effet de l’exposition chronique aux pesticides
sur la fitness des populations, la confrontation des résultats obtenus par l’approche QST-FST et
l’analyse statistique en modèle linéaire mixte a révélé que le type d’habitat constituait le
principal facteur de la divergence observée. D’une façon générale, les étangs se distinguent
200
des fossés et des canaux d’irrigation. Les populations inféodées aux étangs semblent
composées d’individus plus grands, moins aptes à se reproduire et produisant des pontes plus
petites, avec un délai d’oviposition post-isolement plus long et une survie à l’éclosion moins
importante. Dans une moindre mesure, la taille des pontes et la survie à l’éclosion semblent
également plus faibles dans les populations de fossés que dans celles occupant les canaux.
La comparaison de ces patrons de divergence avec ceux issus de la diversité génétique
neutre suggère un rôle important de l’isolement des populations et donc de la dérive
génétique. En effet, le flux génique est en théorie plus faible entre étangs isolés qu’entre
fossés connectés temporairement ou entre canaux plus permanents.
Concernant l’étude des réponses moléculaires observées en condition de stress
oxydant, il est impossible d’évaluer l’impact du type d’habitat et du niveau de diversité
génétique étant donné le plan expérimental utilisé.
Bien que l’analyse GLMM puisse gérer des plans expérimentaux déséquilibrés
(Pinheiro & Bates, 2002), l’absence d’étangs en zone agricole et de canaux d’irrigation en
zone non anthropisée constitue un facteur limitant dans l’étude présentée. En effet, il eut été
intéressant de tester les hypothèses de divergence adaptative dans un cadre hiérarchique, i.e.,
permettant de prendre en compte simultanément les différents facteurs suspectés d’agir sur la
structure des populations, comme la région, la pression anthropique, le type d’habitat
(Goudet, 1995). De même, un test de l’hypothèse de sélection aurait été intéressant à
développer à l’échelle de l’ensemble des traits étudiés (Chapuis et al., 2008; Martin et al.,
2008; Ovaskainen et al., 2008; Ovaskainen et al., 2011). Une tentative d’application de la
méthode développée par Martin et al. (2008) a cependant montré des résultats ambigus (non
présentés ici), probablement liés au fait que l’ensemble des traits ne suivent pas le même
modèle de sélection (cf. analyses univariées présentées). L’application d’une méthode
bayésienne récemment développée (Ovaskainen et al. 2011), pour laquelle un outil statistique
sera bientôt disponible (Ovaskainen, comm. pers.), pourrait se révéler intéressante dans le
présent contexte. D’une façon plus globale, ces résultats confirment l’importance de la prise
en compte des caractéristiques des habitats dans l’étude de l’effet de la sélection sur les
organismes (Johansson et al., 2007), ainsi que dans l’estimation des effets toxiques sur le
terrain.
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Présence d’une forte variabilité intra-populationnelle des traits quantitatifs à l’échelle
phénotypique et moléculaire.
L’étude des traits d’histoire de vie et des réponses transcriptomiques a mis en évidence
la présence d’une forte variabilité au niveau intra-populationnel. Cette variabilité n’a toutefois
pu être évaluée statistiquement qu’à travers l’expression des traits d’histoire de vie (pas de
réplicat intra-famille pour l’étude RNAseq). L’effet famille s’est révélé significatif pour la
plupart des traits étudiés, excepté pour l’aptitude à la reproduction et le nombre de pontes
déposées par individu pendant les 15 jours de suivi de la fécondité. Ces effets famille étaient
même supérieurs aux effets population pour les traits relatifs à la taille à l’éclosion, au taux de
croissance, la fécondité totale, le nombre d’oeufs par ponte et le taux d’éclosion.
Concernant les traits précoces, i.e., la taille à l’éclosion et le paramètre b du modèle de
croissance de Gompertz, l’approche QST-FST suggère des patrons de sélection homogénéisante
(QST < FST) entre les populations. Des résultats similaires ont déjà été rapportés chez un autre
gastéropode d’eau douce, Galba truncatula (Chapuis et al., 2007). Une contribution
artéfactuelle des effets maternels aux patrons observés ne peut être écartée dans la présente
étude, car le plan expérimental fondé sur des lignées de type full-sib (pas de connaissance de
l’origine paternelle) ne permet pas de faire la distinction entre effets maternels et effets
génétiques. Cependant, le patron de sélection détecté est la sélection homogénéisante, ce qui
suggère l’absence d’une part, d’influence environnementale locale sur les performances
« maternelles » à ce niveau, et d’autre part une forte canalisation ou l’existence d’une
contrainte évolutive importante sur le trait « taille individuelle à l’éclosion » (Lamy et al.,
2012).
Afin d’estimer plus précisément le potentiel évolutif à l’échelle intra-populationnelle
des réponses biochimiques et moléculaires de L. stagnalis vis-à-vis du stress chimique, une
étude d’héritabilité basée sur un protocole de génétique quantitative de type full-sib nested in
half-sib design (Lynch & Walsh, 1998), a été initié en octobre 2009 par Marie-Agnès
Coutellec et Marc Collinet. Ce plan expérimental a reposé sur les croisements des 40 familles
issues de la souche de laboratoire Renylis®. L’élevage et l’exposition à l’herbicide diquat ont
été réalisés dans des conditions similaires à celles décrites précédemment (voir chapitre V).
Les mesures d’activité enzymatique sont partiellement analysées mais l’analyse d’expression
génique n’a quant à elle, pas pu être réalisée dans le cadre de la thèse, par manque de temps.
Néanmoins, nous disposons d’échantillons d’hémolymphe et de glandes digestives conservés
dans le RNAlater (Ambion) à -80°C pour 309 individus. Ce jeu d’échantillons permettra
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d’étudier à l’échelle individuelle, la relation entre transcription et expression fonctionnelle de
gènes candidats, et d’estimer l’héritabilité des réponses au stress généré par le diquat.
La variation intra-population peut perturber l’observation et la révélation statistique
des effets toxiques lors des procédures d’évaluation du risque écologique (Barata et al., 2000;
Coutellec & Lagadic, 2006; Coutellec et al., 2011). Les résultats obtenus lors de l’exploration
des réponses transcriptomiques, même s’ils ne sont pas soutenus statistiquement, vont dans ce
sens (patrons de réponse propre à chaque réplicat) ; et suggèrent un potentiel adaptatif élevé
pour ces réponses (interactions génotype-environnement).
Intérêt appliqué de l’étude dans le contexte de l’évaluation du risque écologique
Les tests d’écotoxicité préconisés par l’OCDE, faisant référence dans les procédures
d’homologation des produits phytosanitaires, font l’objet de certaines critiques, notamment
liées au fait qu’ils se basent sur un nombre limité d’espèces (Frampton et al., 2006), et que ces
tests utilisent principalement des organismes issus de souches d’élevage et négligent la
diversité génétique intra-spécifique (Breitholtz et al., 2006). La toxicité estimée ainsi n’est
donc pas forcément représentative des effets à l’échelle de l’espèce. Les populations
élevées/cultivées en laboratoire pendant de nombreuses générations sont souvent sujettes à
une perte de diversité génétique (par sélection et / ou dérive) invalidant ainsi l'extrapolation
des résultats à l’échelle de populations naturelles (Frankham, 2005; Nowak et al., 2007).
Ainsi, la souche de laboratoire Renylis® utilisée dans cette étude (chapitre II), est très peu
polymorphe, comparativement aux populations naturelles étudiées ici (Besnard et al., 2013).
Dans leur ensemble, les résultats présents appuient le bien-fondé de la prise en compte de la
diversité génétique dans les procédures d’évaluation du risque écologique.
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Conclusions et perspectives
204
Les ressources génomiques générées dans ce projet par pyroséquençage 454 et
RNaseq (Illumina 2000) constituent une base solide pour de futures investigations à l’échelle
transcriptomique, et utile à l’ensemble de la communauté scientifique travaillant sur ce
modèle biologique ou sur des espèces proches. D’un point de vue finalisé, les nouvelles
données
offrent
aussi
la
possibilité
d’élaborer
des
outils
de
type
« marqueur
écotoxicogénomique » (biopuces), qui, dans le cadre d’analyses de routine, restent
financièrement plus avantageux que le RNAseq (Ekblom & Galindo, 2011).
Toutefois, bien que ces techniques de séquençage de nouvelle génération présentent
un potentiel indéniable pour l’étude des réponses moléculaires chez les espèces non modèles,
de nombreux mécanismes de régulation restent insondables, bien que théoriquement
exploitables à partir du présent jeu de données. En effet, l’absence de génome de référence est
un frein à la compréhension, par exemple, des mécanismes d’épissage alternatif et
l’identification de différentes isoformes fonctionnelles issues d’une même séquence codante,
et peut biaiser en partie les résultats de l’étape d’assemblage. Par ailleurs, en termes d’outils
de cartographie, cette lacune tend à limiter la portée des approches de recherche de QTL pour
des traits d’intérêt (cartographie dense ; e.g., Tarres et al., 2009). Cependant, les marqueurs
SNPs détectés dans le cadre présent d’acquistion de séquences de transcrits, même s’ils ne
sont pas représentatifs de l’ensemble du génome et restent sans information cartographique
physique, peuvent s’avérer intéressants dans une perspective de génomique des populations
(Nielsen, 2005) ainsi que de recherche de QTL de traits impliqués dans les réponses au stress
environnemental.
Un projet de séquençage du génome de L. stagnalis est en cours, mais l’acquisition
d’un véritable génome annoté représente un travail de longue haleine. Dans un premier temps
et dans le cadre du présent projet, les données d’assemblage seront à affiner, par un travail de
recherche d'éventuels contigs redondants via des alignements multiples de contigs de même
meilleure annotation, ainsi qu’avec les nouvelles séquences publiées (Sadamoto et al., 2012).
Ces données seront ensuite analysées par les méthodes statistiques récentes les plus adaptées
au RNAseq, en commençant par celle permettant de corriger le biais lié à l’utilisation d’une
distribution binomiale négative (Lurin & Balzergue). Les résultats attendus permettront
l’estimation plus précise de l’impact moléculaire du diquat, et de tester l’hypothèse
actuellement suggérée par les analyses réalisées, d’une plus faible sensibilité au diquat pour
les populations des milieux pollués. Etant donné les informations obtenues sur certaines voies
cellulaires et moléculaires d’intérêt, telles que l’apoptose, l’analyse de réseaux de gènes
permettrait d’approfondir ces questions (Horvath, 2011). Une étape postérieure de validation
205
par PCR quantitative peut être envisagée, notamment en relation avec les transcrits
« candidats » déjà étudiés sur la souche Renilys® (chapitre II).
Ces nouveaux résultats seront également à mettre en relation avec les données
phénotypiques mesurées sur les mêmes lignées, en conditions de common garden (chapitre
IV). Ainsi, nous chercherons à mettre en évidence d’éventuels trade-offs ainsi que des effets
pléiotropiques (Zera & Harshman, 2001), pouvant être à l’origne de coût en termes de fitness
dans les populations les plus tolérantes au stress. Ces effets pourront être estimés grâce à des
analyses basées sur les matrices de corrélation génétique. Par ailleurs, le test des hypothèses
relatives au modèle de sélection sera approfondi en appliquant les méthodes multivariées
actuelles, qui permettent de considérer les traits dans leur ensemble (matrices G), et pour
certaines de prendre en compte l’apparentement génétique des populations (Martin et al.,
2008; Ovaskainen et al., 2008; Ovaskainen et al., 2011). Enfin, dans le contexte
écotoxicologique, les données seront explorées à la lumière de nouveaux concepts relatifs à la
toxicité, tel que celui d’Adverse Outcome Pathway (AOP), qui permet de relier un événement
moléculaire initiateur à des effets phénotypiques intégrés (physiologie, traits liés à la fitness)
par recherche de réseaux d’interactions (moléculaire, biologique), et de les mettre ensuite en
relation avec des modèles populationnels (Ankley et al., 2010). Dans cette optique, les effets
post-transcriptionnels du diquat mis en évidence sur la souche Renylis® (Chapitre II), peuvent
justifier le développement d’approches protéomiques, ainsi que des analyses fonctionnelles
(activité enzymatique). La disponibilité des échantillons de glande digestive issus des
individus étudiés en RNAseq, devrait également permettre dans ce cadre d’étudier les effets
toxiques du diquat à différentes étapes de l’expression protéique.
Enfin, dans une perspective éco-évolutive plus large, l’importance évolutive des
conditions environnementales locales (telles qu’elle est suggérée par les données du présent
travail), pourrait être testée dans une approche méta-transcriptomique. En effet, l’étude des
réponses produites par différents taxa présent dans un milieu donné pourrait s’avérer
pertinente pour tester l’hypothèse de mécanismes moléculaires communs (Garcia-Reyero et
al., 2011), à condition de pouvoir prendre en compte la composante phylogénétique de ces
réponses (Carew et al., 2011).
206
Travaux annexes
Colloques
• 20ème Colloque annuel de la SETAC, Séville, 23-27 mai 2010 [poster]
• Journée Des Doctorants du Caren, Rennes 28 juin 2010 [poster]
• Congrès Ecologie 2010, Montpellier 2-4 septembre 2010 [poster]
• 21ème Colloque annuel de la SETAC, Milan 15-19 mai 2011 [poster]
• 13ème Congrès de l’ESEB, Tübigen 20-25 août 2011 [poster]
• 6ème Colloque mondial de la SETAC, Berlin 20-24 mai 2012 [communication orale]
Enseignement
• Vacation (9h de TD et 24h de TP d’immunologie) à IUT Laval en mars avril 2012
• Encadrement stagiaire IUT : Hélène Barbu, avril à juin 2011
Formations
• Formation INRA « prise de parole en public », Paris, 27-29 janvier 2010.
• Workshop « Evolutionary potential in natural populations », Sandjberg, 11-14 avril
2010 [poster et synthèse bibliographique en anglais notée par V. Loeschcke]
• Short course «The discovery of mechanism based biomarkers using “omics”
technologies : a bioinformatics perspective» , Sevilla, 23 mai 2010.
• Conférence « 8e journée de la plate-forme Bio-informatique : techniques NGS »,
Rennes, INRIA 26 octobre 2010.
• Séminaire des doctorants du département EFPA de l’INRA, Lyon 10-12 janvier
2011 [poster]
• Modules « Alignements de séquences et SNP » dispensé par la plateforme
BioInformatique de la Génopole INRA de Toulouse, le 03 octobre 2012.
Publications hors contexte thèse
• Delatte H., Bagny L., Brengue C., Bouétard A., Paupy C., Fontenille D. The
invaders: phylogeography of dengue and chikungunya viruses Aedes vectors, on the
South West islands of the Indian Ocean. Infect Genet Evol. (2011) 11(7):1769-81.
• Bouétard A., Lefeuvre P., Gigant R., Bory S., Pignal M., Besse P., Grisoni M.
Evidence of transoceanic dispersion of the genus Vanilla based on plastid DNA
phylogenetic analysis. Molecular Phylogenetics and Evolution (2010) 55(2):621630.
• Delatte H., Desvars A., Bouétard A., Bord S., Gimonneau G., Vourc’h G., Fontenille
D. Blood-feeding behavior of Aedes albopictus, vector of chikungunya on La
Réunion. Vector-Borne and Zoonotic Diseases (2009) 10:249-258.
• Heckmann LH., Sibly RM., Connon R., Hooper HL., Hutchinson TH., Maund SJ.,
Hill CJ., Bouétard A., Callaghan A. Systems biology meets stress ecology: Linking
molecular and organismal stress responses in Daphnia magna. Genome Biology
(2008) 9(2):R40.
• Heckmann LH., Bouétard A., Hill C., Sibly R., Callaghan A. A simple and rapid
method for preserving RNA of aquatic invertebrates for ecotoxicogenomics.
Ecotoxicology (2007) 16(6):445-7.
• Lauzon K., Zhao X., Bouétard A., Delbecchi L., Paquette B., Lacasse P.
Antioxidants to prevent bovine neutrophil-induced mammary epithelial cell damage.
Journal of Dairy Science (2005) 88:4295-303.
207
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