Nouvelle Méthode De Dépistage De Phytopathogènes Fongiques Et De Plantes Au Potentiel Envahissant Par Métabarcodage

Nouvelle méthode de dépistage de phytopathogènes fongiques et de plantes au potentiel envahissant par métabarcodage

Thèse

Emilie Tremblay

Doctorat en microbiologie Philosophiæ doctor (Ph. D.)

Québec, Canada

NOUVELLE MÉTHODE DE DÉPISTAGE DE PHYTOPATHOGÈNES FONGIQUES ET DE PLANTES AU POTENTIEL ENVAHISSANT PAR MÉTABARCODAGE

Thèse

ÉMILIE D. TREMBLAY

Sous la direction de :

Claude Lemieux, directeur de recherche Guillaume Bilodeau, codirecteur de recherche

Résumé

Les dommages causés par les pathogènes des plantes sont une menace redoutable pour l’environnement, la diversité, ainsi que pour une partie considérable des ressources naturelles forestières et agronomiques telles que les arbres, les plantes et les récoltes. Les endroits situés à proximité des ports d’importation pour le commerce international et des sites de décharge de déchets verts sont considérés comme étant à risque pour l’introduction d’organismes exotiques et indésirables tels que des insectes, des phytopathogènes et des plantes envahissantes. Bien qu’il existe plusieurs méthodes développées et validées selon des standards établis visant à détecter certains genres préoccupants ou espèces ciblées, la plupart d’entre elles sont mal adaptées aux analyses à grande échelle ou sont limitées en ce qui concerne le nombre d’organismes différents pouvant être détectés simultanément. L’objectif principal de ce projet était de développer une méthode de détection nouvelle, plus rapide, à haut débit, hyper sensible et couvrant de plus grandes superficies afin de contribuer à l’amélioration des méthodes de dépistage et de lutte contre les phytopathogènes et les espèces envahissantes. Le projet a tiré avantage d’enquêtes en entomologie d’envergure nationale et préétablies par l’Agence Canadienne d’Inspection des Aliments en réutilisant les liquides de préservation provenant de pièges à insectes. Ces pièges, en plus de pièges à spores et de pièges à granules de pollen issus du butinage par des abeilles, ont été utilisés pour recueillir des échantillons environnementaux à travers le Canada. Le développement d’un pipeline bio-informatique adapté aux types d’organismes recherchés a permis de supporter et d’analyser efficacement les grandes charges de données produites par la plateforme de séquençage de nouvelle génération (SNG) Ion Torrent. De plus, la conception d’amorces de fusion a conféré un pouvoir de multiplexage significatif aux analyses. Le pipeline intégré au métabarcodage a permis d’effectuer la biosurveillance d’entités phytopathogènes fongiques et oomycètes, de plantes envahissantes ainsi que de localiser des régions géographiques d’intérêt où des organismes indésirables ont été trouvés. Les résultats suggèrent l’existence de pathosystèmes entre des insectes xylophages et des maladies fongiques n’ayant jamais été reportés auparavant. De plus, certains pathogènes fongiques et leurs plantes hôtes ont été trouvés dans les échantillons de granules de pollen, et les espèces de plantes identifiées par SNG corroboraient avec l’identification visuelle des plantes ayant été effectuée sur le terrain. Certains des résultats obtenus par métabarcodage ont été validés avec des tests qPCR spécifiques à certaines espèces cibles, ce qui a confirmé le pouvoir et la sensibilité de cette nouvelle méthode. Par exemple,

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de minimes quantités de propagules d’espèces de Phytophthora spp. ont été obtenues. Plusieurs espèces faisant partie de genres préoccupants ont été détectées, dont les champignons phytopathogènes Heterobasidion annosum s.s., H. abietinum/H. parviporum, Leptographium spp., Ophiostoma spp., Gremmeniella spp. et Geosmithia spp. et les oomycètes phytopathogènes Peronospora spp., Pythium spp. et Phytophthora spp. Ces résultats prometteurs indiquent que des organismes de réglementation de partout dans le monde pourraient ajouter cette méthode de métabarcodage à leur boîte à outils servant à faire la biosurveillance et la détection d’espèces réglementées. Dans le cas où des zones nécessitant des enquêtes approfondies étaient localisées selon les résultats de métagénomique, des tests qPCR ou tout autre test validé demeurent essentiels, surtout lorsqu’il s’agit d’identifier des espèces critiques comme des ravageurs réglementés. En outre, comme les technologies de séquençage évoluent continuellement, elles produisent des données dont la qualité s’améliore constamment, et ce, à moindre coût. Par conséquent, il est anticipé que la qualité des bases de données sur lesquelles repose le métabarcodage se perfectionne du même coup, permettant également d’augmenter la capacité de résolution de la nouvelle méthodologie décrite.

Abstract

Damage caused by plant pathogens represents a devastating threat to the environment, diversity, and a significant part of natural forest and agronomic resources such as trees, plants, and crops. Areas that are in close proximity to international trade ports and green waste disposal facilities are considered high-risk introduction sites for exotic and unwanted organisms such as insects, phytopathogens, and invasive plants. Although there are many standard methods developed to detect numerous specific genera of concern or target species, most are ill-suited for large-scale screening, or are limited in the number of different organisms that can be assessed at a time. The main objective of this project was to develop a new detection method which is fast, high-throughput, highly sensitive, and targets vast survey areas, in order to contribute in the improvement of the methods for screening and battling of phytopathogens and invasive species. Spore traps, insect traps, and honeybee-foraged pollen clusters were used to collect environmental samples across Canada. The project took advantage of entomology surveys conducted by the Canadian Food Inspection Agency by reusing preservative fluids from those insect traps. The development of a bioinformatics pipeline customized for the types of organisms screened allowed for the handling and efficient analysis of the large data loads produced with the Ion Torrent next-generation sequencing (NGS) platform. Additionally, the design of fusion primers conferred the analyses a significant multiplexing power. Integrating the pipeline to metabarcoding allowed for the biosurveillance of fungal and oomycete phytopathogens, as well as invasive plants, and pinpointing geographical regions of concern where unwanted species were found. Results suggest the existence of wood-boring insects and fungal diseases pathosystems never previously reported. In addition, certain fungal pathogens and their plant hosts were detected from the pollen cluster samples, and the plants species identified by NGS corroborated the records of the visual plant inspections performed in the field. Some of the metabarcoding results were validated with some species-specific qPCR assays, which confirmed the power and the sensitivity of this new method. For example, very low levels of some Phytophthora species propagules could be detected. Multiple species within genera of concern were identified, including the plant pathogenic fungi Heterobasidion annosum s.s., H. abietinum/H. parviporum, Leptographium spp., Ophiostoma spp., Gremmeniella spp., and Geosmithia spp., and the oomycetes Peronospora spp., Pythium spp., and Phytophthora spp. These promising results indicate that regulatory agencies across the world could combine our new metabarcoding approach to their regulated species monitoring and detection toolbox

for biosurveillance and screening. In the case where areas requiring further inquiries are pinpointed based on the metagenomics results, qPCR or alternate validated assays remain essential, especially to resolve the identification of critical species such as regulated pests. Furthermore, given that constantly evolving sequencing technologies yield increasing quality data continually, and at reduced costs, it is anticipated that the quality of the databases on which metabarcoding relies will improve at the same time, therefore increasing the resolving capacity of the new method described.

Table des matières

Résumé ...... iii Abstract ...... v Table des matières ...... vii Liste des figures ...... x Liste des tableaux ...... xii Remerciements ...... xv Avant-propos ...... xix Introduction ...... 1 Chapitre 1: Revue de littérature ...... 3 1.1.1 Champignons phytopathogènes ...... 5 1.2 Oomycètes ...... 6 1.2.1 Oomycètes phytopathogènes ...... 7 1.3 Moyens de propagation des agents responsable de maladies fongiques des plantes ...... 8 1.3.1 Facteurs environnementaux abiotiques ...... 8 1.3.2 Facteurs anthropogéniques ...... 8 1.3.3 Insectes vecteurs ...... 9 1.3.3.1 Abeilles ...... 10 1.4 Rôle de l’Agence Canadienne d’Inspection des Aliments (ACIA) ...... 11 1.5 Identification des pathogènes ...... 14 1.5.1 Morphologie et culture ...... 14 1.5.2 Biologie moléculaire ...... 15 1.5.3 Marqueurs génétiques ...... 17 1.5.3.1 L’espaceur transcrit interne (ITS) ...... 17 1.5.3.2 L’espaceur ATP9-NAD9...... 18 1.5.4 Le séquençage et son évolution ...... 18 1.5.4.1 Séquençage par terminaison de chaîne (Sanger) ...... 18 1.5.4.2 Séquençage à haut débit ...... 19 1.5.5 Contexte et choix de la méthode de séquençage dans le cadre du projet ...... 20 1.6 Bio-informatique ...... 21 1.7 Métagénomique et métabarcodage ...... 22 1.8 Cueillette d’échantillons environnementaux ...... 24 1.8.1 Pièges à spores ...... 24

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1.8.2 Pièges à insectes ...... 25 1.9 Hypothèses et objectifs du projet ...... 25 1.9.1 Hypothèses ...... 26 1.9.2 Objectifs ...... 26 1.10 Figures ...... 29 Chapitre 2: Screening for exotic forest pathogens to increase survey capacity using metagenomics . 32 2.1 Résumé ...... 34 2.2 Abstract ...... 35 2.3 Introduction ...... 36 2.4 Materials and methods ...... 40 2.5 Results ...... 48 2.6 Discussion ...... 52 2.7 Acknowledgements ...... 59 2.8 Tables ...... 60 2.9 Figures ...... 66 2.9 Supplementary Materials ...... 68 2.10 Supplementary Tables ...... 69 2.11 Supplementary Figures ...... 82 Chapitre 3: Next generation sequencing to investigate existing and new insect associations with phytopathogenic fungal propagules ...... 92 3.1 Résumé ...... 94 3.2 Abstract ...... 95 3.3 Introduction ...... 96 3.4 Materials and methods ...... 99 3.5 Results ...... 104 3.6 Discussion ...... 109 3.7 Acknowledgements ...... 116 3.8 Tables ...... 117 3.9 Figures ...... 125 3.10 Supplementary Materials ...... 128 3.11 Supplementary Tables ...... 130 3.12 Supplementary Figures ...... 150 Chapitre 4: High-resolution biomonitoring of plant pathogens and bee-foraged plant species using metabarcoding of pollen clusters content collected from a honeybee hive ...... 155 4.1 Résumé ...... 157

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4.2 Abstract ...... 158 4.3 Introduction ...... 159 4.4 Materials and methods ...... 161 4.5 Results ...... 166 4.6 Discussion ...... 173 4.7 Acknowledgements ...... 179 4.8 Tables ...... 180 4.9 Figures ...... 189 4.10 Supplementary Materials ...... 197 4.11 Supplementary Tables ...... 202 4.12 Supplementary Figures ...... 213 Conclusion ...... 221 Rappel des hypothèses de recherche ...... 221 Principaux résultats ...... 221 Contributions ...... 227 Perspectives et considérations futures ...... 229 Bibliographie ...... 233

Liste des figures

Figure 1.1 Schéma de la structure de a) l’ADN ribosomique et des régions ITS1 et ITS2 chez les champignons et les oomycètes et b) de l’ADN mitochondrial des Phytophthora spp. au niveau des gènes ATP9 et NAD9. Les encadrés blancs représentent les gènes, les encadrés bleus représentent les région intergéniques, les flèches montrent les points d’ancrage de quelques amorces populaires utilisées pour cibler les régions intergéniques illustrées, la boîte grise représente une sonde nucléique spécifique au genre Phytophthora spp. (Bilodeau et al. 2014) et la boîte noire représente une sonde nucléique spécifique à P. ramorum (Bilodeau et al. 2014). Figures adaptées de Larena et al. (1999); Bilodeau et al. (2014)...... 29 Figure 1.2 Processus biochimique du séquençage Sanger impliquant a) des désoxyribonucléotides (forme naturelle) et des nucléotides terminateurs, les didésoxyribonucléotides (groupement hydroxyle remplacé par un atome d’hydrogène) qui, lorsque ajoutés de manière aléatoire, c) préviennent la polymérisation. c) Des séquences de longueurs différentes marquées avec un fluorochrome spécifique au ddNTP sont alors générées et d) lues avec un laser optique afin de déduire la séquence. Figure adaptée de Kircher and Kelso (2010); McGovern (2015)...... 30 Figure 1.3 Schéma simplifié du fonctionnement des plates-formes de séquençage à haut débit les plus populaires en 2013 a) Ion Torrent, b) pyroséquençage 454 et c) Illumina. Figure adaptée de Mardis (2008); Voelkerding et al. (2009); Life Technologies (2010)...... 31 Figure 2.1 Sampling sites in A the Canadian West Coast and B, Eastern Canada. Adapted from Google Earth...... 66 Figure 2.2 Bioinformatic pipeline and tools used for next-generation sequencing analysis...... 67 Figure 3.1 Upset plot to visualize the type of trees from which traps were suspended. The intersection size number represents the number of times a specific tree combination was found (similar to a Venn diagram), and the set size number corresponds to the number of samples surrounded by a specific type of tree. Most samples were collected from traps placed in forested areas comprising more than one tree species...... 126 Figure 3.2 Venn diagram of a) fungal species shared or unique to the semiochemical type employed in insect traps, b) fungal species shared or unique to the semiochemical type employed in insect traps after species subtraction, c) oomycete species shared or unique to the semiochemical type employed in insect traps, d) oomycete species shared or unique to the semiochemical type employed in insect traps after species subtraction. All were obtained by amplifying the ITS1 genic region...... 127 Figure 4.1 For each pollen pellet sample collected in 2017, abundance of operational taxonomic unit of fungi detected by the analysis of sequences (ITS1) using the bidirectional fusion primers (ITS1F and ITS2) at the a) phylum (top 5), b) class (top 10), c) order (top 10), d) family (top 15), e) genus (top 15), and f) species (top 30) Levels. n.b. Only the DPS07’s reverse tagged primer (i.e., ITS2) data was analyzed as the ITS1F was discarded because of a suspected contamination...... 192 Figure 4.2 For each pollen pellet sample collected in 2017, abundance of oomycete operational taxonomic units detected by analysis of sequences (ITS1) using the bidirectional fusion primers (Omup and Omlo) at the a) order, b) family, c) genus, and d) species (top 30) levels. . 194

Figure 4.3 For each sample collected in 2017, abundance of operational taxonomic units detected from the analysis of plant sequences (ITS2) using the bidirectional fusion primers (ITS4 and SPL) at the a) order (top 10), b) family (top 15), c) genus (top 15), and d) species (top 30) levels...... 196

Liste des tableaux

Table 2.1 Presence or absence of amplification as detected by gel electrophoresis using barcoded polymerase chain reaction (PCR) products from 398 environmental samples from targeted organisms, and the percentages of positive reactions obtained...... 60 Table 2.2 Presence or absence of amplification as detected by gel electrophoresis using barcoded polymerase chain reaction (PCR) products from 10 mock samples from targeted organisms, and the percentages of positive reactions associated compared with the expected results...... 60 Table 2.3 Organism sequences and operational taxonomic units (OTU) counts produced from Ion Torrent Personal Genome Machine next-generation sequencing data output...... 62 Table 2.4 Saturation values (sequence count) reached through rarefaction curves generated according to the trap or sample types and observed species number by trap or sample types. .. 63 Table 2.5 Comparison of fungal species of interest found in environmental samples and mock samples using next-generation sequencing (NGS) and quantitative polymerase chain reaction (qPCR)...... 64 Table 3.1 Presence or absence of amplification as detected by gel electrophoreses using barcoded PCR products from 108 environmental insect samples by targeted organisms, and the percentages of positive reactions obtained...... 117 Table 3.2 Operational Taxonomic Units: fungal identification proportion (%) by semiochemical type at the Phylum taxonomic level using the ITS1 genic region...... 118 Table 3.3 Operational Taxonomic Units: fungal identification proportion (%) by semiochemical type at the genus taxonomic level using the ITS1 genic region...... 119 Table 3.4 Operational Taxonomic Units: fungal identification proportion (%) by semiochemical type at the species taxonomic level (top 10 species) using the ITS1 genic region...... 120 Table 3.5 Operational taxonomic units: oomycete identification proportion (%) by semiochemical type at the Phylum and Genus taxonomic levels using the ITS1 genic region. . 122 Table 3.6 Operational Taxonomic Units: oomycete identification proportion (%) by semiochemical type at the species taxonomic level (top 10 species) using the ITS1 genic region...... 123 Table 4.1 List of fungal and oomycetes genera of importance in forest and/or agriculture phytopathology that were targeted and screened using the custom Perl script metaResultExtractor.pl (Tremblay et al. 2018). Notorious species within the target genus are also listed, most of which are regulated by the Canadian Food Inspection Agency and/or the United States Department of Agriculture - Animal and Plant Health Inspection Service...... 181 Table 4.2 Summary of amplification bands obtained following electrophoresis ran on the amplicons generated by PCR with fusion primers. The ITS1 was amplified from fungi and oomycetes, the ITS2 was amplified from plants, and the ATP9-NAD9 was amplified from Phytophthora spp. + means visual band obtained. – means no visual band obtained. (f) = forward fusion primer used. (r) = reverse fusion primer used...... 187

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Table 4.3 Spearman’s rank correlation (rho) tests (α = 0.05) between the true alpha diversity (Shannon index) of fungi (ITS1), oomycetes (ITS1), and plants (ITS2), and rainfall (total, in mm) or temperature (= T°, average, in °C) recorded. S = significant correlation. NS = Non- significant correlation...... 188

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À mon père Luc, pour m’avoir enseigné la persévérance et le travail sans répit À ma mère Lise, qui m’a appris à être une femme forte, fière et dévouée À ma sœur Marilyn, ma complice, ma rivale, qui veille à ce que je grandisse avec dignité À ma grand-mère Lucille, qui m’a toujours inspirée de par son amour passionné pour la vie Et à toi Conrad, mon bel allié, sur qui j’ai su compter à chaque instant

«It required the plague of the potato disease and the example of the Irish famine finally to focus attention upon the fundamental problem—the relation of the mildew to the sick potato plant, of the smut and rust fungi to the infected grain—the problem of parasitism»

Lewis Ralph Jones

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Remerciements

Dans la naïveté du début de la vingtaine, je suis partie de ma province natale, seule, avec le peu de bagages (mon chihuahua et quelques manuels) et de compétences en anglais que j’avais, pour venir habiter à Ottawa. Réalisant à peine l’ampleur de ce que je m’apprêtais à entamer, l’enthousiasme ne manquait point à ce moment.

Guillaume Bilodeau, qui avait accepté de me prendre sous son aile en tant que co-directeur, m’a toujours supportée, poussée, encouragée et défendue. Guillaume, je te remercie d’avoir cru en moi. J’ai parfois eu l’impression que tu croyais plus que moi-même que j’y arriverais au bout du compte. Merci de m’avoir donné tous les outils, les conseils et les réseaux nécessaires pour ce projet et, en partie, pour ma carrière. Merci d’avoir donné la chance à la si petite fille que j’étais alors, car j’ai beaucoup grandi et appris au cours de mon passage dans ton laboratoire. Ton ambition, ta générosité, ta persévérance, ta patience, mais surtout ta passion pour la science sont des qualités contagieuses et on ne peut plus inspirantes. Merci d’avoir insisté pour que je me lance dans le passage accéléré au doctorat. Tu es motivant et c’est ce qui m’a permis de terminer l’un des plus grands accomplissements de ma vie à ce jour.

Également, je remercie Claude Lemieux qui a accepté de me superviser pendant ce voyage. Claude, malgré la distance physique, j’ai toujours senti que vous étiez là pour moi et que vous feriez tout pour m’aider, me guider et m’encourager. Vous êtes une personne exceptionnellement positive et j’ai toujours été reconnaissante de vous avoir comme directeur. Vous êtes extrêmement professionnel, respectueux et vous démontrez un intérêt et un souci des autres remarquables. Tout ce que vous m’avez apporté—outils, conseils, personnes ressources, etc.—était utile, succint, enrichissant, constructif et agréable. Merci de m’avoir acceptée en tant que candidate au doctorat malgré qu’on ne se connaissait que très peu alors. Je vous dois tout mon respect et vous admire.

Je tiens aussi à remercier les autres membres de mon comité d’encadrement, Jean Bérubé et Louis Bernier, qui ont aussi apporté beaucoup au projet tout au long de mon cheminement. L’expertise et la sagesse que vous m’avez apprises sont des outils fondamentaux que j’utiliserai durant toute ma carrière.

Je remercie aussi Caroline Duchaine, qui a été ma directrice de maîtrise. J’apprécie sincèrement ta présence tout au long de mon doctorat.

Je veux également remercier tous ceux qui m’ont aidée dans le processus de rédaction, incluant mes plus proches collaborateurs: Troy Kimoto, Marc-Olivier Duceppe, Marie-José Côté, Marie-Claude Gagnon, Jean Bérubé, Claude Lemieux, Guillaume Bilodeau et Graham B. Thurston. Notamment, rédiger une publication scientifique n’est pas si simple, et le faire en anglais l’est encore moins. Réviser le travail anglais d’une nouvelle étudiante francophone, je n’en doute point, requiert aussi beaucoup de patience. Vous m’avez toutefois souvent fait la remarque de mes progrès, eh bien c’est grâce à vous tous! Mon ami et collègue Ian King m’a aussi appris beaucoup de subtilités de la langue de Shakespeare et je lui en suis reconnaissante.

Je voudrais également remercier Marie-José Côté pour m’avoir offert la chance de contribuer, de près ou de loin, à des projets de recherches à plusieurs reprises. Entre autres, j’ai beaucoup apprécié contribuer à un domaine d’études autre que la phytopathologie, soit les plantes.

Merci à tous les organismes qui m’ont offert le support financier nécessaire à la réalisation de mon projet de recherche : l’Agence Canadienne d’Inspection des Aliments (ACIA) et sa Stratégie de Partenariat de Recherche (RPS), les Fonds de Développement de Technologies (TD) mandatés de l’ACIA et l’Initiative de Recherche et Développement en Génomique (GRDI) de l’ACIA. J’espère sincèrement que ce projet apportera une contribution positive à l’ACIA… (et aux arbres).

Merci à tous les organismes qui m’ont décerné des bourses afin de m’encourager à poursuivre mon projet de recherche et mes études aux cycles supérieurs: les fonds de soutien à la réussite de la Faculté des Études Supérieures (FESP) de l’Université Laval, la Société de Protection des Forêts contre les Insectes et Maladies (SOPFIM), Ressources Naturelles Canada (RNCan), la Société de Protection des Plantes du Québec (SPPQ), ainsi que le centre d’information Bioinformatics.ca. Chaque fois, les prix reçus sont venus réchauffer mon cœur parce qu’ils m’ont rappelé que les problèmes sur lesquels je travaillais comptent et valent la peine d’être considérés. Merci à Richard Hamelin et ses projets Genome Canada et BioSAFE pour m’avoir permis d’assister à

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une conférence internationale, cette opportunité m’a donné la chance d’élargir mon réseau, mais aussi de goûter aux joies et à l’intensité qui règnent dans de tels événements.

Par-dessus tout, merci à ma tendre famille. J’ai la chance d’entretenir une excellente relation avec chacun de vous, malgré les centaines kilomètres qui nous séparent. Merci à ma maman, Lise, de m’avoir supportée pratiquement tous les dimanches soirs, qui ont parfois été un réel défi moral. Merci de m’avoir préparé des petits pots, cuisiné des biscuits et d’avoir enrichi ma garde-robe pour remplacer mes vieux chandails troués dans mes moments de désespoir (vestimentaires). Merci à mon père Luc pour son support inépuisable. Te rendre fier a toujours été une motivation insatiable. Merci de m’avoir appris, avec le patinage artistique, qu’après être tombée, on doit se relever, se resserrer le chignon, et enchaîner avec la prochaine pirouette: c’était un excellent conditionnement aux montagnes russes que peuvent parfois être les études aux cycles supérieurs. Merci à mes parents pour m’avoir fourni une majeure partie des ressources nécessaires à mon bien-être tout au long de mes, disons-le, longues études. Merci à ma sœur Marilyn pour m’avoir écoutée et porté conseil à maintes reprises, mais aussi pour son support avec de nombreux documents écrits.

Je veux aussi remercier mes amis les plus proches, qui m’ont toujours encouragée et écoutée : Allison, Sabrina, Maryline, Caroline, Mélissa G., Mélissa S., Stéphanie, Raymond, Dana, Marie-Claude, Marc-Olivier et Lisa. Tout au long du processus, j’ai su que je pouvais compter sur vous et je l’apprécie profondément.

Merci aussi à mon plus grand amour, mon partenaire, mon meilleur ami, Conrad Hutter. Au cours de mes études aux cycles supérieurs, tu as fait preuve d’une patience inouïe et d’une force incroyable. Tu ne m’as jamais laissé couler, tu as célébré mes avancements avec moi, et essuyé mes larmes d’autres fois. Tu m’as aussi beaucoup appris à propos des champignons, du fonctionnement à l’ACIA et des compétences informatiques (surtout avec Microsoft Excel). Grâce à toi, je suis devenue une meilleure personne, tout spécialement en termes de patience. Tu as cru en mon potentiel et aussi, tu m’as permis de réaliser des accomplissements personnels plus immédiats durant mon projet, ce qui m’a gardée motivée pour tout le reste : demi-marathons, compétitions de slalom et barrières géantes en ski alpin, compétitions nationales de canoë de sprint, et j’en passe. Il est très clair pour moi que cette aventure a été beaucoup plus agréable avec toi et ta formidable famille à mes côtés.

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Les défis que la vie pourra désormais nous lancer ne m’inquiètent guère puisque, ensemble, nous avons traversé toutes ces étapes en partageant nos buts futurs et les plaisirs quotidiens. Enfin, Bruno et Rose, merci pour votre soutien, vos encouragements et l’énergie positive que vous dégagez en tout temps. Je me sens toujours la bienvenue chez vous, vous êtes une deuxième famille pour moi et vous êtes si souvent venus à ma rescousse, spécialement dans les moments où le temps manquait pour remplir un besoin essentiel: manger.

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Avant-propos

Cette thèse de doctorat comporte cinq chapitres, plus précisément, une introduction générale (Chapitre 1), un article scientifique publié (Chapitre 2), deux articles scientifiques soumis, à ce jour, à des revues scientifiques, donc possiblement en attente d’être publiés (Chapitres 3 et 4), et une discussion générale en conclusion (Chapitre 5). Je suis la première auteure de ces trois articles scientifiques.

Le Chapitre 2, intitulé Screening for exotic forest pathogens to increase survey capacity using metagenomics, est un article accepté pour publication dans la revue Phytopathology en date du 17 juin 2018; doi : 10.1094/PHYTO-02-18-0028-R.

L’article scientifique de ce chapitre présente le concept éprouvé d’une nouvelle approche pour aider les efforts de biosurveillance du gouvernement Canadien. Cette approche permet de dépister des espèces de champignons et d’oomycètes pathogènes de plantes qui représentent un risque majeur pour la biodiversité, l’environnement et les ressources forestières et économiques. La méthode présentée traite plus d’échantillons à la fois, comparativement aux méthodes plus traditionnelles utilisées par les intervenants en phytopathologie. La technologie de séquençage à haut débit ainsi que le pipeline (étapes séquentielles) bio-informatique développé justifient ce rendement plus efficace. Différents types d’échantillonneurs ont été utilisés et placés dans plusieurs provinces canadiennes. Des résultats intéressants ont été obtenus de chacun des types d’échantillonneurs, ce qui a mené à une analyse plus approfondie des résultats des pièges à insectes dans le chapitre 3.

Les coauteurs du chapitre 2 sont Marc-Olivier Duceppe, Jean A. Bérubé, Troy Kimoto, Claude Lemieux et Guillaume Bilodeau. Plus précisément, G. Bilodeau a conceptualisé et supervisé ce projet pilote. Il a collaboré avec T. Kimoto afin d’obtenir des échantillons de pièges à insectes provenant de partout au Canada. T. Kimoto a planifié les protocoles opérationnels, il a mené les enquêtes en entomologie et il a géré les échantillons d’insectes ramassés partout au pays. Enfin, il a révisé cet article. J. Bérubé a fourni les pièges à spores et mené les enquêtes utilisant ces pièges. Il a également révisé cet article. M.-O. Duceppe a contribué au développement du pipeline bio- informatique avec É. Tremblay, ainsi qu’à la révision de ce manuscrit. É. Tremblay a effectué les

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analyses formelles au laboratoire et en bio-informatique. Elle a procédé à la collecte d’échantillons à l’aide des pièges JB à Ottawa (Ontario) et à Aylmer (Québec). Elle a fait le développement et l’optimisation de la méthodologie décrite. C. Lemieux a supervisé les travaux et révisé cet article.

Le chapitre 3, qui s’intitule Next-generation sequencing to investigate existing and new insect associations with phytopathogenic fungal propagules, est un article scientifique qui a été soumis, en octobre dernier, à la revue scientifique Journal of Fungi dans le cadre d’un numéro spécial intitulé Fungal-Insect Interactions.

Cet article traite tous les échantillons de liquides préservateurs de pièges à insectes recueillis pendant trois saisons estivales avec l’approche développée dans le chapitre 2. Quatre différents composés sémiochimiques, des phéromones attirant différents groupes d’insectes xylophages, ont été utilisés. L’analyse des échantillons récoltés suite à l’utilisation de chacun de ces composés sémiochimiques a permis de détecter des champignons et des oomycètes modérément pathogènes, des champignons entomopathogènes, ainsi que de nombreux champignons qui font perdre de la valeur au bois. Des pathosystèmes potentiellement nouveaux ont aussi été observés. Chacun des jeux de données des composés sémiochimiques contenaient aussi des espèces retrouvées dans un seul de ces leurres. De plus, la capture accidentelle d’autres insectes, dont plusieurs pollinisateurs, a mené, au chapitre 4, à l’analyse du contenu des granules de pollen récoltés par des abeilles.

Les coauteurs du chapitre 3 sont Jean A. Bérubé, Guillaume J. Bilodeau et Troy Kimoto. Comme cet article a utilisé les mêmes échantillons que ceux ramassés pour le chapitre 2, les rôles de T. Kimoto et G. Bilodeau demeurent les mêmes pour cet article. Il est à noter que T. Kimoto a participé à la rédaction de cet article. De plus, J. Bérubé a contribué à l’analyse des données et à la révision de l’article.

Le chapitre 4 est également un article scientifique qui a été soumis à une revue scientifique le 7 novembre 2018. L’article porte le titre suivant : High-resolution biomonitoring of plant pathogens and plant species using metabarcoding of pollen pellet contents collected from a honey bee hive. Toujours en utilisant la méthode développée au chapitre 2, ce chapitre décrit l’utilisation des pièges à granules

de pollen pour évaluer le potentiel des abeilles à contribuer à la surveillance d’agents phytopathogènes et de plantes au potentiel envahissant.

Les coauteurs du chapitre 4 sont Marc-Olivier Duceppe, Graham B. Thurston, Marie-Claude Gagnon, Marie-José Côté et Guillaume J. Bilodeau. Tous les auteurs ont contribué à l’analyse formelle, à la validation des données et à la révision de l’article. M.-O. Duceppe a également participé aux analyses bio-informatiques. G. Thurston a ramassé les échantillons et a pris soin des abeilles de la ruche, il a procédé aux observations des plantes en floraison sur le terrain, il apporté du support quand est venu le temps des analyses, il a révisé l’article et il a conceptualisé le projet avec M.-C. Gagnon. Quant à M.-C. Gagnon et G. Bilodeau, ils ont supervisé ce projet. M.-C. Gagnon, M.-J. Côté et G. Bilodeau ont fourni les ressources et le matériel nécessaires à la réalisation de ce projet.

Durant mes études supérieures, j’ai collaboré à plusieurs projets. De ce fait, je suis coauteure des articles scientifiques publiés suivants :

 Bengtsson-Palme, J., Richardson R. T., Meola M., Wurzbacher C., Tremblay, É. D., Thorell, K., Kanger, K., Eriksson, K. M., Bilodeau, G. J., Johnson, R. M., Hartmann, M., Nilsson, R. H. (2018). Metataxa2 Database Builder: enabling taxonomic identification from metagenomics or metabarcoding data using any genetic marker. Bioinformatics, DOI: bty482, https://doi.org/10.1093/bioinformatics/bty482.

 Bérubé, J. A., Gagné, P. N., Ponchart, J. P., Tremblay, É. D., Bilodeau, G. J. (2018). Detection of Diplodia corticola spores in Ontario and Québec based on HighThroughput Sequencing (HTS) methods. Canadian Journal of Plant Pathology, DOI: 10.1080/07060661.2018.1498394.

 Roe, A., Torson, A., Bilodeau, G. B., P, Blackburn, G., Cui, M., Cusson, M., Doucet, D., Griess, V., Lafond, V., Paradis, G., Porth, I., Prunier, J., Srivastava, V., Tremblay, E., Uzunovic, A., Yemshanov, D., and Hamelin, R. (2018). Biosurveillance of forest insects: part I—integration and application of genomic tools to the surveillance of non-native forest insects. Journal of Pest Science: 1-20.

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 Bilodeau, P., Roe, A. D., Bilodeau, G., Blackburn, G. S., Cui, M., Cusson, M., Doucet, D., Griess, V. C., Lafond, V. M., Nilausen, C., Paradis, G., Porth, I., Prunier, J., Srivastava, V., Stewart, D., Torson, A. S., Tremblay, E., Uzunovic, A., Yemshanov, D., and Hamelin, R. C. (2018). Biosurveillance of forest insects: part II—adoption of genomic tools by end user communities and barriers to integration. Journal of Pest Science: 1-12.

Cette recherche était initialement un projet maîtrise. J’ai effectué un passage accéléré au doctorat suite à la première rencontre annuelle que j’ai eue avec le comité d’encadrement en décembre 2014. Cette transition vers le troisième cycle a occasionné le changement d’un des co-directeurs; Caroline Duchaine s’est retirée et c’est Claude Lemieux qui a alors poursuivi la supervision de mon projet avec Guillaume Bilodeau.

Enfin, parmi les nombreux congrès, réunions et autre événements auxquels j’ai participé et où j’ai présenté mon projet de recherche, les plus importants étaient :

 le congrès International de Pathologie des Plantes (Boston, 2018);  la réunion annuelle de la Société Canadienne de Phytopathologie de l’Est de l’Ontario (Ottawa, 2018);  la réunion annuelle de la Société Américaine de Phytopathologie, division Nord-Est (Ville de Québec, 2017);  la série de séminaires en recherche sur les plantes de l’ACIA (Ottawa, 2016);  le Café des sciences de l’ACIA (Ottawa, 2016);  la rencontre annuelle de la Société Canadienne de Phytopathologie de l’Est de l’Ontario (Ottawa, 2016);  la réunion annuelle de la Société de Protection des Plantes du Québec (Sainte-Anne-de- Beaupré, 2015);

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Introduction

L’introduction d’organismes indésirables ou envahissants dans un environnement peut causer des conséquences néfastes, voire irréversibles au sein d’un écosystème. Il s’agit d’un phénomène de plus en plus observé, et ce, pour plusieurs raisons, la principale étant l’augmentation constante du commerce international entre les pays à travers le monde entier. Au Canada, de nombreuses ressources naturelles (ex. grains, plantes horticoles, plantes ornementales, arbres forestiers, etc.) se sont retrouvées menacées ou ont été décimées dans le passé. Des organismes de réglementation exigent des certifications phytosanitaires strictes et spécifiques à certains produits afin de prévenir ou de réduire les dommages occasionnés. À ce jour, des méthodes standards classiques sont couramment utilisées pour identifier les organismes indésirables tels que des plantes envahissantes ou des champignons et des oomycètes phytopathogènes. Cependant, étant donné la vitesse à laquelle ces organismes ravageurs peuvent se propager, il y a un besoin urgent. Une méthode permettant le criblage plus rapide de ceux-ci, à plus grande échelle, et ce, pour un plus grand nombre d’échantillons à la fois est désormais nécessaire.

Cette thèse prouve que l’utilisation de la technologie du séquençage à haut débit permet de faire la détection primaire d’organismes d’intérêt (c.-à.-d. au potentiel phytopathogène) à partir de différents types de pièges installés dans des endroits jugés à risque. La méthode testée a rapporté des résultats prometteurs, c’est pourquoi elle a également été appliquée à la recherche de microorganismes phytopathogènes possiblement transportés par des insectes vecteurs. Un projet pilote a aussi été effectué afin d’évaluer le contenu de granules de pollen pour rechercher des microorganismes indésirables de même que des plantes envahissantes possiblement transportés par des abeilles lors des activités de butinage.

En ce qui concerne le séquençage, il faut dire que les méthodes à haut débit sont attrayantes en raison de leur pouvoir d’analyse. Par contre, elles produisent des charges de données considérables, donc l’interprétation et le traitement de celles-ci représentent un réel défi. Différents marqueurs génétiques peuvent servir à identifier les organismes contenus au niveau du genre et, parfois, de l’espèce. Cependant, l’obtention de résultats plus facilement interprétables doit passer par des analyses bio-informatiques puissantes et complexes. Le traitement bio-informatique consiste à

lier une séquence de différentes étapes à effectuer pour lancer différents logiciels à l’aide de scripts exécutant, ensuite, des opérations en boucles ou en parallèle qui allègent et accélèrent la tâche.

Cette revue de la littérature présentera d’abord les principaux organismes à l’étude et certaines caractéristiques spécifiques clés de leur métabolisme. Ensuite, il sera question du rôle de l’Agence Canadienne d’Inspection des Aliments et d’une vue d’ensemble des méthodes traditionnelles, moléculaires et plus nouvelles qui permettent de détecter des organismes d’intérêt. Puis, le rôle déterminant de la bio-informatique dans cette étude sera expliqué. Différents types de pièges permettant de capturer des entités phytopathogènes seront également présentés. Finalement, les hypothèses et objectifs spécifiques de cette thèse seront exposés.

Chapitre 1: Revue de littérature

1.1 Champignons Le règne des Fungi comprend l’une des plus larges variétés d’espèces sur terre (Feofilova 2001). Les champignons remplissent des rôles écologiques essentiels, notamment celui du recyclage de la matière organique. La paroi cellulaire fongique est constituée principalement de chitine qui forme généralement une structure tubulaire appelée hyphe (Bowman and Free 2006; Stephenson 2010; Carris et al. 2012). La plupart des champignons sont des organismes filamenteux formant un mycélium et qui se reproduisent le plus souvent au moyen de spores disséminées dans l’environnement. Bien que la plupart d’entre eux se reproduisent de manière asexuée, certains peuvent, ou vont occasionnellement le faire de manière sexuée (Stephenson 2010). Selon le groupe fongique et certains facteurs environnementaux imposés, incluant le stress hydrique, le froid et la chaleur, les champignons peuvent produire différents types de structures pour survivre, se disséminer, ou se reproduire—de manière sexuée ou asexuée—, telles des conidiospores (spores asexuées à paroi fine), des chlamydospores (spores asexuées à paroi épaisse issues du gonflement d’hyphes et conçue pour la survie à plus long terme) et des méiospores (spores sexuées) (Kendrick 1985; Prescott et al. 1995; Agrios 2005).

À première vue, les champignons ressemblent aux plantes en raison de leurs structures morphologiques comparables, or, ils ont un métabolisme différent puisqu’ils ne peuvent produire leur propre énergie. En d’autres termes, contrairement aux plantes, ils ne sont pas photosynthétiques. Hétérotrophes, les champignons obtiennent plutôt leurs nutriments à partir de sources de carbone provenant d’autres organismes comme les plantes et les animaux. La digestion du substrat, qui est extracellulaire, est possible grâce au relâchement d’enzymes vers la source d’aliments afin d’accéder aux nutriments essentiels. Les champignons qui se multiplient sur de la matière organique morte sont appelés saprotrophes, tandis que ceux qui utilisent des organismes vivants sont des parasites ou des endophytes. Quant aux champignons endophytes, ils peuvent entretenir une relation symbiotique ou parasitique avec une plante hôte à l’intérieur de laquelle ils prolifèrent (Stephenson 2010; Carris et al. 2012).

Les principaux groupes de champignons sont les ascomycètes, les basidiomycètes, les chytridiomycètes, les zoopagomycètes et les mucoromycètes (Alexopoulos 1996; Blackwell et al. 2006; Webster and Weber 2007; The Royal Botanic Gardens: Kew 2018). Cependant, la

classification taxonomique des champignons est très complexe. Autrefois, cette dernière était basée sur les caractéristiques phénotypiques et morphologiques. Aujourd’hui, la taxonomie des champignons repère et utilise des marqueurs génétiques et d’autres méthodes de biologie moléculaire—les réactions en chaîne par polymérase (PCR), les PCR en temps réel (qPCR), le séquençage de nouvelle génération (SNG) et la métagénomique, méthodes décrites dans les sections 1.5.2 à 1.5.4.—qui ont apporté précision, mais aussi complexité et changements constants (Hebert et al. 2003; Yang and Rothman 2004; Shendure and Ji 2008; Shokralla et al. 2012; Katoch and Kapoor 2014; Reina 2017).

1.1.1 Champignons phytopathogènes

Une partie des champignons parasites de plantes sont aussi des pathogènes (Stephenson 2010; Carris et al. 2012). Le règne Fungi est d’ailleurs celui qui inclut le plus grand nombre d’espèces phytopathogènes (Knogge 1996; Carris et al. 2012). En fait, 70% des maladies rencontrées chez les plantes sont à caractère fongique, ce qui représente une véritable menace écologique et économique, car des pertes de récoltes alimentaires, de diversité et de ressources forestières majeures peuvent en découler (Conners 1967; Agrios 2005; Bilodeau et al. 2012; Dean et al. 2012). Les ascomycètes ainsi que les basidiomycètes comprennent la majorité des espèces phytopathogènes (Doehlemann et al. 2017). Les champignons peuvent nuire, voire causer la mort des plantes de diverses façons, notamment en interférant avec leur croissance, ou encore en produisant des toxines (Wolpert et al. 2002; Carris et al. 2012; Ward et al. 2012; Redkar et al. 2015). Les infections fongiques peuvent affecter toutes les parties de la plante, notamment les racines, le système vasculaire, les feuilles et les fruits (Carris et al. 2012). Des exemples typiques de maladies causées par des agents phytopathogènes sont les chancres, les rouilles, les charbons, les pourritures racinaires et les rabougrissements (Vánky 2002; Tisserat et al. 2009; Ward et al. 2012; Redkar et al. 2015). Les champignons phytopathogènes emploient toutes sortes de stratégies pour s’attaquer aux plantes. Par exemple, alors que certains champignons arriveront à contourner le système immunitaire des plantes, d’autres induiront une croissance accrue de l’hôte, ce qui formera des tumeurs (Ward et al. 2012; Redkar et al. 2015). Alors que les champignons biotrophes infectent des tissus vivants, les champignons nécrotrophes, quant à eux, doivent absorber leurs nutriments à

travers des tissus ou des cellules de l’hôte dont ils ont causé la mort afin d’assurer leur croissance (Carris et al. 2012; Doehlemann et al. 2017).

Certains champignons sont limités à un hôte en particulier, mais d’autres, comme le champignon hautement polyphage Botrytis cinerea, responsable de la moisissure grise, infectent une vaste gamme de plantes, un aspect qui est souvent associé aux champignons particulièrement dévastateurs (Rodriguez et al. 2009; Giraud et al. 2010; Dean et al. 2012). La coévolution hôte- pathogène entraîne souvent des événements d’adaptation permettant à un pathogène d’infecter plus d’un groupe de plantes (Norton and Carpenter 1998; Rundle and Nosil 2005). En général, la reconnaissance des effecteurs propres aux phytopathogènes par les plantes se fait au moyen de récepteurs, les protéines R, ce qui déclenche une réaction immunitaire chez la plante, qui elle, relâchera alors des molécules antimicrobiennes (ex. éliciteurs) (Bent and Mackey 2007). Typiquement, à la fin d’un cycle d’infection, les champignons vont produire des spores afin de se disperser et de survivre à plus ou moins long terme dans l’environnement (Madden 1997; Andanson 2010; Carris et al. 2012; Vági et al. 2013).

1.2 Oomycètes

Les oomycètes, tout comme les champignons, sont des organismes filamenteux formant des hyphes et un mycélium. Ils peuvent aussi se reproduire de manière asexuée et sexuée. Ils sont souvent appelés pseudo-champignons parce qu’ils se nourrissent de manière hétérotrophe en tant que saprotrophes ou en tant que parasites, et parce qu’’ils produisent des spores (Fry and Grunwald 2010; Stephenson 2010). D’ailleurs, la production de zoospores flagellées et motiles à l’intérieur des sporanges est l’une des caractéristiques typiques de ces eucaryotes qui vivent principalement dans des environnements humides (e.g. cours d’eau) ou dans le sol (Fry and Grunwald 2010; Stephenson 2010).

La paroi cellulaire des oomycètes est principalement composée de glucanes (Fry and Grunwald 2010). Comme c’est le cas pour les champignons, les mécanismes de défense des plantes contre les oomycètes sont basés sur des interactions entre les gènes R (plante), reconnaissants les effecteurs des agents phytopathogènes (Fry and Grunwald 2010; Stephenson 2010). Or, malgré les

ressemblances morphologiques et physiologiques entre les champignons et les oomycètes, les analyses phylogénétiques faites sur ces derniers démontrent que les oomycètes sont tout à fait distincts des « vrais » champignons (White and Dighton 2017).

1.2.1 Oomycètes phytopathogènes

Les oomycètes sont aussi responsables de ravages importants chez les plantes. En effet, plusieurs espèces sont considérées menaçantes puisqu’elles possèdent un large éventail d’hôtes, ce qui favorise leur propagation. Les genres les plus fréquemments rapportés en phytopathologie sont Phytophthora sp., Pythium sp., Plasmopara sp., Hyaloperonospora sp., et Peronospora sp. (Fry and Grunwald 2010; Kamoun et al. 2015). Les maladies les plus communément observées sont les mildious, les fontes des semis, les rouilles blanches et les pourritures racinaires (Couture 2008; Carris et al. 2012). Plusieurs espèces de Phytophthora spp. sont particulièrement redoutables parce qu’elles peuvent manipuler le système immunitaire des plantes. Par exemple, les Phytophthora spp. peuvent interférer avec la transcription génique des végétaux grâce à un facteur de transcription silenceur, un phénomène qui peut empêcher l’expression (répression) de gènes de défense chez la plante hôte, ou même causer la mort cellulaire (van West et al. 1999; Fry and Grunwald 2010; Sherwood et al. 2010; Stephenson 2010; Kamoun et al. 2015; Mafurah et al. 2015). La manipulation du système immunitaire des plantes par les oomycètes phytopathogènes est possible grâce à des effecteurs tels que les RXLR (Arg-any amino acid-Leu-Arg) et les CRN (crinkler, crinkling and necrosis inducing protein). Ces molécules sont aussi connues pour favoriser une adaptation rapide par l’acquisition de pathogénicité (Mafurah et al. 2015).

Phytophthora ramorum est un exemple notable d’oomycète phytopathogène. En plus d’être responsable de graves dommages causés aux chênes rouges en Californie, il peut infecter plus d’une centaine d’espèces végétales et provoquer de multiples symptômes selon l’espèce touchée (Rioux et al. 2006; Bilodeau et al. 2009; Kristjansson and Miller 2009). De façon similaire, durant les années 1840, Phytophthora infestans a infecté et ruiné la quasi-totalité des cultures de pommes de terre, causant, du coup, la Grande Famine en Irlande (de Bary 1876; Goss et al. 2014).

1.3 Moyens de propagation des agents responsable de maladies fongiques des plantes

1.3.1 Facteurs environnementaux abiotiques

Les champignons et les oomycètes se propagent naturellement sur de courtes et sur de longues distances, un phénomène qui assure la survie de plusieurs espèces (Gage et al. 1999; West and Kimber 2015). Les champignons produisent d’immenses quantités de spores qui se dispersent ensuite dans l’environnement afin de se propager (Brown and Hovmøller 2002). La dispersion par l’air, par l’eau, par le le vent, de même que par des vecteurs pour certains groupes (ex. Ophiostomaceae) sont les principaux moyens empruntés par les champignons et les oomycètes pour envahir un nouvel environnement (Card et al. 2007; Fröhlich-Nowoisky et al. 2009; Fry and Grunwald 2010). De plus, certains types de spores de champignons et d’oomycètes peuvent résister à de nombreux facteurs environnementaux, et ce, sur une longue durée (West and Kimber 2015). De surcroît, lorsqu’un organisme est introduit dans un endroit d’où il n’est pas indigène, il devient exotique et peut acquérir un potentiel envahissant s’il entre en contact avec un hôte compatible et que les conditions sont permissives (Allen and Humble 2002).

1.3.2 Facteurs anthropogéniques

Les spores d’espèces phytopathogènes, y compris celles d’espèces envahissantes ou exotiques, peuvent se propager via l’exportation de plantes ornementales non-sensibles à la maladie via des emballages de bois transportés par cargos ou par le mouvement des insectes ou des animaux (Andanson 2010; Vettraino et al. 2015). De plus, le commerce international du bois est directement lié aux problèmes de forêts décimées par des maladies exotiques, puisque l’introduction accidentelle d’espèces exotiques au potentiel envahissant provient de ces vecteurs de transport (Vettraino et al. 2015). Cette dispersion des spores dans l’environnement par les champignons et par les oomycètes, quoique bien avantageuse pour la propagation de ces microorganismes, représente toutefois un risque élevé d’infection pour les plantes. L’établissement de ces espèces peut aussi être facilité par l’absence ou le délai de réponse des moyens de défense chez les plantes indigènes et par le manque d’organismes compétiteurs sur un terrain nouveau (Allen and Humble 2002). Les monocultures sont des terrains propices à la multiplication des espèces nouvellement arrivées puisqu’il s’agit de grandes superficies où la biodiversité est faible (Brown and Hovmøller 2002). Le déplacement de plantes ou encore de matériel de plantation d’un continent à l’autre entraîne aussi le mouvement et la propagation des maladies (Card et al. 2007; Vettraino et al. 2015). La section 1. 4

élabore davantage sur les moyens utilisés pour lutter contre ces invasions indésirables. Il sera question de la gestion des risques phytosanitaires et d’exemples marquants d’introductions dévastatrices de champignons et d’oomycètes au Canada.

1.3.3 Insectes vecteurs

Le transport des agents phytopathogènes par des insectes vecteurs est un phénomène complexe et souvent mal compris. Par contre, certains pathosystèmes sont mieux connus en raison des dommages qu’ils entraînent ou qu’ils ont causés dans le passé (Huang 2003; Maixner 2005; Teale et al. 2011; Kanzaki and Giblin-Davis 2016).

Les mécanismes de transmission, par des insectes vecteurs d’entités phytopathogènes incluant les bactéries, les champignons et les virus, se divisent en deux catégories, soit circulatoire et non- circulatoire. Pour être transmis de manière circulatoire, les phytopathogènes doivent entrer dans l’hémocœle des insectes vecteurs, une composante aux fonctions comparables à celles du sang chez les vertébrés. Il est fréquent que ces microorganismes soient transmis par la salive. Pour ce qui est des vecteurs non-circulatoires, les pathogènes sont transmis par des structures corporelles telles les mycanges chez les scolytes. Les mycanges sont des structures spécialisées qui permettent aux insectes vecteurs d’emmagasiner et de transporter des champignons symbiotiques (Batra 1963; Levieux et al. 1991). Les entités phytopathogènes sont ainsi transmises lorsque, entre autres, des insectes s’alimentent sur une plante (Eigenbrode et al. 2018).

Il a été démontré que les agents phytopathogènes manipulent directement ou indirectement leurs insectes vecteurs afin de favoriser leur dissémination (McArt et al. 2014; Eigenbrode et al. 2018). Par exemple, McLeod et al. (2005) ont démontré que les ormes infectés par les champignons reponsables de la maladie hollandaise de l’orme (Ophiostoma novo-ulmi et O. ulmi) vont produire une molécule organique volatile (sémiochimique) attirant les insectes xylophages. Cette maladie a dévasté des millions d’ormes pratiquement partout en Amérique du Nord suite à son introduction au Canada en 1945 (Allen and Humble 2002). Un moyen connu de prolifération et de propagation de cette maladie est que les larves de scolytes (exotiques et indigènes du continent nord-américain) ayant été en contact physique avec le champignon contaminent, de manière subséquente, des arbres sains lorsqu’elles se nourrissent sous l’écorce des ormes (Allen and Humble 2002). Un autre

exemple répertorié seulement à l’extérieur du Canada, est la maladie des mille chancres du noyer (Geosmithia morbida), transportée par le scolyte des pousses du noyer (Pityophthorus juglandis), originaire du Sud-Ouest des États-Unis. Bien connue en raison des ravages qu’elle a causés et qu’elle pourrait induire advenant son introduction au pays, cette maladie a engendré la perte, voire l’élimination, du noyer noir dans certains états américains très près du Canada (ex. Indiana et Ohio) (Tisserat et al. 2009; Juzwik et al. 2015; Juzwik et al. 2016).

1.3.3.1 Abeilles

Plusieurs chercheurs ont étudié les abeilles butineuses pour leur capacité à transmettre des entités phytopathogènes par le transport de granules de pollen recueillis d’une plante à l’autre. Les agents phytopathogènes soupçonnés d’être transmis par le pollen (avec, ou sans insecte pollinisateur) qui ont été les plus étudiés sont les virus (Card et al. 2007; Roberts et al. 2018). Par exemple, le virus de la marbrure du bleuetier (Vaccinium corymbosum) peut se propager au moyen du butinage par les abeilles qui transportent du pollen contaminé (Childress and Ramsdell 1987). Cependant, beaucoup de résultats non concluants ont aussi été rapportés quant à l’association entre les abeilles et certaines infections virales chez les plantes (Card et al. 2007). Il s’agit d’une controverse qui nécessite des recherches plus approfondies. D’autres auteurs ont aussi démontré que les abeilles peuvent agir en tant que vecteurs de bactéries. En l’occurrence, la brûlure bactérienne (Erwinia amylovora) affecte des arbres fruitiers (pommiers, poiriers, etc.) (Johnson et al. 1993; McArt et al. 2014; Pattemore et al. 2014), et le complexe bactérien Pseudomonas syringae affecte, entre autres, les kiwis (Actinidia deliciosa) (Pattemore et al. 2014). Il a même été démontré que les abeilles, lors de leur retour au sein de la colonie, peuvent infecter d’autres butineuses de la ruche, qui elles, pourraient alors potentiellement infecter de nouvelles plantes lors des activités de pollinisation (Pattemore et al. 2014).

Il existe très peu d’études scientifiques qui démontrent la transmission de phytopathogènes fongiques par butinage. Stelfox et al. (1978) ont montré que le champignon de la brûlure de la tête Sclerotinia sclerotiorum, infectant les crucifères (Brassica sp.), peut être transmis lors des activités de butinage des abeilles. Également, Huang (2003) a montré que l’abeille découpeuse (Megachile rotundata) peut induire la verticillose (Verticillium albo-atrum) de la luzerne (Medicago sativa) par

contacts accidentels. Il a aussi été démontré que les champignons phytopathogènes (e.g. Ustilago spp.) peuvent modifier la période de floraison de leur plante hôte pour augmenter les chances de visites d’un insecte pollinisateur vecteur (Jennersten 1988; Jennersten and Kwak 1991; Lara and Ornelas 2003; McArt et al. 2016). Certains champignons plus agressifs (ex. Uromyces pisi) peuvent même induire la production de structures à partir des tissus végétatifs de la plante, imitant les fleurs (c.-à.-d. pseudofleurs), dans le but d’attirer les insectes butineurs et, ainsi favoriser leur propagation (Roy 1993; Pfunder and Roy 2000; McArt et al. 2016).

1.4 Rôle de l’Agence Canadienne d’Inspection des Aliments (ACIA)

En plus de veiller à la protection et à la sécurité des aliments au Canada, l’ACIA joue un rôle très important dans le maintien de la santé des plantes, de l’environnement, des ressources essentielles à la santé humaine, de la biodiversité et de la santé économique du pays (Allen and Humble 2002; Canadian Food Inspection Agency 2018b). La mission de l’ACIA est d’assurer la qualité des aliments, donc de prévenir la propagation de pathogènes responsables de ravages majeurs. À titre d’organisme de réglementation, elle se doit de protéger les produits forestiers, les grains, les cultures, les plantes horticoles, les pommes de terre et les semences qui sont exportés, importés ou vendus au pays. Pour ce faire, des réglementations, des certifications, de même que des normes strictes sont établies au pays (Canadian Food Inspection Agency 2018b). L’ACIA travaille également en partenariat avec d’autres organismes gouvernementaux comme Ressources Naturelles Canada, Agriculture et Agroalimentaire Canada, Service Canadien des Forêts et Santé Canada pour la protection des ressources (Natural Resources Canada 2017).

Bien que les raisons de protéger l’intégrité des aliments soient évidentes, l’importance de la protection des plantes peut sembler plus subtile. L’une des principales raisons pour lesquelles l’ACIA réglemente les importations et les exportations de matériel végétal est que l’introduction de plantes envahissantes et d’organismes nuisibles pour les plantes peut causer des dommages irréversibles suite à leur propagation, et ce, au Canada comme ailleurs (Liebhold et al. 1995; Wallner 1996; Allen and Humble 2002). Les espèces envahissantes sont des espèces exotiques qui s’acclimatent de manière permanente suite à leur introduction dans un environnement où elles prolifèrent. Elles sont responsables de changements importants dans les écosystèmes puisqu’elles peuvent mobiliser les

ressources essentielles à d’autres espèces indigènes (Cronk 1995). La loi sur la protection des végétaux a donc pour but de prévenir l’importation et l’exportation d’ennemis des plantes, entre autres, en planifiant des moyens de lutte (e.g. mise en quarantaine, destruction de matériel infecté, interdiction de mouvement du matériel contaminé et restriction des activités) applicables au besoin (The Minister of Justice (Canada) 1990; Agence Canadienne d'Inspection des Aliments 2018a). Une liste de ravageurs, de maladies (ex. insectes, champignons et nématodes) et de plantes exotiques, indigènes ou ayant un potentiel envahissant est établie par l’ACIA (Canadian Food Inspection Agency 2013b; Ressources Naturelles Canada 2018). En constante évolution, cette liste élabore le statut de chacune des espèces y figurant, tout en spécifiant les mesures à prendre pour le traitement du matériel végétal dont il est question (e.g. fruits, plantes ornementales, noix, grains, etc.). Des exemples notables de maladies causées par des champignons exotiques qui ont occasionné des dommages forestiers et économiques irréversibles au pays incluent le chancre européen du mélèze (Lachnellula wilkommi), le chancre du noyer cendré (Ophiognomonia clavigignenti-juglandacearum) et la maladie hollandaise de l’orme (O. novo-ulmi et O. ulmi), expliquée plus en détail dans la section 1.3.3. Le chancre scléroderrien (Gremmeniella abietina), plus spécifiquement la race européenne (plus agressive), malheureusement présent au Canada (Lachance 1979; Laflamme 1987), a été responsable de dommages immenses dans l’état de New York, causant la mort de près de 90% des pins rouges et des pins sylvestres matures à certains endroits (Agence Canadienne d'Inspection des Alimnets 2012). Une maladie encore non-répertoriée au Canada, mais notable pour ses dommages irréversibles, est la maladie des mille chancres (Geosmithia morbida) (discutée dans la section 1.3.3) transportée par un insecte vecteur (Tisserat et al. 2009). En Nouvelle-Écosse, un exemple récent d’introduction d’un insecte indésirable réglementé est le puceron lanigère de la pruche (Adelges tsugae). La presence de cet insecte a de graves conséquences sur l’environnement, puisque des habitats terrestres et aquatiques naturels en souffrent grandement (Canadian Food Inspection Agency 2018c). Considérant l’ampleur de l’industrie du bois en Amérique du Nord, il importe de mentionner les conséquences que l’économie subit suite à de telles épidémies. Aux États-Unis, par exemple, les coûts associés aux impacts des maladies envahissantes s’élèvent à des milliards de dollars par année (Pimentel et al. 2000; Pimentel 2002) et plus spécifquement au Canada, l’ACIA estime ces coûts annuels à 20 milliards de dollars pour le secteur forestier et à 2,2 milliards de dollars pour les plantes envahissantes du secteur agricole (Canadian Food Inspection Agency 2014b).

Un problème associé à l’introduction de certaines plantes est le potentiel d’une espèce à devenir envahissante. Par exemple, les plantes envahissantes peuvent envahir des zones agricoles, laissant derrière elles des pertes considérables. Tout comme pour les organismes phytopathogènes, l’ACIA réglemente et effectue la surveillance de ces espèces listées en exerçant un contrôle strict sur les importations, sur la manipulation et sur les déplacements de ces végétaux au pays (Canadian Food Inspection Agency 2013b; Agence Canadienne d'Inspection des Aliments 2018b). À titre d’exemple, le kudzu (Pueraria montana), une plante envahissante provenant de l’Asie qui a été introduite aux États-Unis et en Ontario au cours de la dernière décennie, s’avère désormais un problème majeur. La principale raison est que la croissance parfois qualifiée d’incontrôlable de cette vigne grimpante est possible dans à peu près n’importe quel environnement (forêts, bordures de routes, champs agricoles, etc.) (Agence Canadienne d'Inspection des Aliments 2016).

De plus, certaines plantes, bien que présentes au pays, font également partie de la liste des espèces réglementées puisqu’elles sont des hôtes alternes de certains champignons pathogènes. Ainsi, le transport, la vente et la reproduction, en territoire canadien, de l’épine-vinette japonaise (Berberis thunbergii) sont interdits parce qu’il s’agit d’un hôte alterne de la rouille noire des céréales (Puccinia graminis) (Canadian Food Inspection Agency 2014c; Berlin et al. 2017).

Avec la pression constante et grandissante exercée par l’introduction d’espèces exotiques et envahissantes au Canada, l’ACIA bénéficierait à développer des outils diagnostiques de haute performance. Le Canada étant l’un des plus grands pays au monde, l’ACIA a besoin de méthodes de surveillance pouvant supporter l’analyse d’un plus haut débit d’échantillons afin d’enquêter sur l’ensemble de sa superficie (ou, du moins, sur les zones hautement à risque). Pour ce faire, il lui faut un outil qui lui permette de détecter un organisme indésirable présent en très faible quantité, (e.g. quelques spores), avant même qu’il ne puisse s’établir.

Les méthodes d’enquêtes phytosanitaires présentement utilisées par l’ACIA incluent des inspections visuelles sur le terrain afin d’identifier des signes de la présence d’organismes phytpathogènes (Agence Canadienne d'Inspection des Aliments 2018a). La recherche visuelle de plantes envahissantes est faite, en majorité, dans des zones environnantes des installations de

manutention des semences et des grains (e.g. les fossés et les terres à proximité et les sites de compostage de déchets), puisque ces lieux sont souvent associés à l’introduction de mauvaises herbes indésirables. Les abords des chemins de fer sur lesquels des trains transportent des grains sont également des endroits ciblés où l’ACIA fait la surveillance de plantes envahissantes (Agence Canadienne d'Inspection des Aliments 2018f). Le personnel de l’ACIA applique aussi différentes méthodes d’échantillonnage. Par exemple, des inspecteurs utilisent des pièges contenant des attractifs chimiques pour attirer et identifier des insectes indésirables. La collecte de matériel provenant d’un hôte suspect ou d’échantillons de sols est également une méthode courante. Le personnel de l’ACIA pratique aussi l’élevage, par exemple, suite à l’échantillonnage d’un hôte soupçonné d’être infecté par un insecte indésirable. Cette méthode de surveillance consiste à fournir un environnement propice à l’émergence de larves dans le but de les identifier (Agence Canadienne d'Inspection des Aliments 2018a).

Il est à noter que les laboratoires de diagnostic de l’ACIA détiennent un mandat de libre accès sur les outils issus des projets de recherche accomplis par des chercheurs de l’ACIA. Un exemple pertinent d’une technologie couramment utilisée par le laboratoire de diagnostic de phytopathologie est le test qPCR (duplex) de Bilodeau et al. (2009), qui dépiste la présence du genre Phytophthora et l’espèce P. ramorum.

1.5 Identification des pathogènes

1.5.1 Morphologie et culture

Les méthodes d’identification des champignons et des oomycètes phytopathogènes évoluent constamment. Historiquement, la morphologie et la mise en culture étaient utilisées pour classifier, identifier et effectuer le diagnostic des agents phytopathogènes (Guarro et al. 1999; Hyde et al. 2011). Les observations microscopiques de structures particulières comme des spores ne fournissent cependant qu’une information limitée et requièrent une expertise poussée (Turin et al. 2000; Raja et al. 2017). Bien qu’il soit souvent possible d’identifier des champignons jusqu’au niveau taxonomique de la famille par morphologie et par culture, ce processus n’est pas aussi simple lorsqu’il s’agit de déterminer le genre et l’espèce, en particulier lorsque de nombreuses espèces sont en jeu (Turin et al. 2000; Geiser 2004; Raja et al. 2017). En effet, certaines caractéristiques

morphologiques peuvent facilement être confondues par l’analyste en raison d’événements d’hybridation, d’évolution convergente—un phénomène conférant des caractéristiques semblables entre des lignées évolutives différentes—,des complexes d’espèces et, notamment, des formes asexuées et sexuées d’un même microorganisme (Raja et al. 2017).

En outre, de nombreux champignons et oomycètes sont incultivables (Bindslev et al. 2002). En fait, il est fréquent qu’un champignon ne sporule pas lorsqu’il est mis en culture ou qu’il ne croisse que sous sa forme asexuée. Il arrive aussi que les aspects phénotypiques servant à l’identification varient, soient inconsistants et qu’ils dépendent de l’environnement dans lequel les champignons et les oomycètes se développent (Guarro et al. 1999; Turin et al. 2000; Raja et al. 2017). De surcroît, de nombreux champignons et oomycètes ont un taux de croissance lent, ce qui, combiné au fait que la méthode ne peut supporter qu’un nombre limité d’échantillons à la fois, rend le processus d’identification inefficace. Dans le cas d’une introduction, le faible débit de ces méthodes est donc particulièrement désavantageux pour l’ACIA puisqu’elle doit réagir le plus rapidement possible afin d’atténuer les dégâts (Guarro et al. 1999; Turin et al. 2000).

1.5.2 Biologie moléculaire

Les méthodes de biologie moléculaire permettent de pallier de nombreuses limitations des méthodes de morphologie et de culture en plus d’être ultra-sensibles, spécifiques et rapides (Yang and Rothman 2004). Ces techniques sont généralement basées sur la détection de fragments d’ADN spécifiques à certains groupes (e.g. genre, espèces, individus, etc.). La PCR est un exemple des plus populaires encore aujourd’hui, puisqu’elle permet de résoudre de nombreuses questions phylogénétiques et épidémiologiques (Hebert et al. 2003). Étant basée sur l’amplification répétitive de certaines séquences d’ADN, la PCR permet de développer des tests très sensibles. La PCR requiert des amorces dont les séquences spécifiques s’hybrident de part et d’autre à une séquence de nucléotides d’intérêt dans le génome étudié. La séquence localisée entre ces deux amorces est ensuite répliquée par une polymérase. Les amorces à PCR sont habituellement ancrées dans des régions conservées, qui encadrent la région amplifiée. Cette dernière est, pour sa part, hypervariable et permet donc de distinguer de nombreux taxons d’intérêt (Erlich 1989). La section 1.5.3 présente deux régions couramment utilisées pour différencier de nombreuses espèces de champignons et

d’oomycètes. La PCR permet de traiter un plus grand volume d’échantillons à la fois, en plus de générer des résultats avec une meilleure précision et une plus grande sensibilité que les méthodes visuelles. D’ailleurs, de nombreux tests diagnostiques sont issus de la PCR. Toutefois, la nécessité de valider les résultats au moyen d’une étape de visualisation des produits (électrophorèse sur gel) affecte l’efficacité en raison des étapes ajoutées au travail (Yang and Rothman 2004).

Afin de remédier à cet inconvénient, d’autres méthodes dérivées de la PCR ont ensuite connu un succès significatif. La PCR en temps réel (qPCR), en particulier, a apporté un progrès majeur aux limitations de la PCR traditionnelle, puisque l’amplification et la détection des produits sont effectués en simultané (Yang and Rothman 2004). L’utilisation de sondes nucléiques émettant de la fluorescence, des fluorochromes (e.g. TaqMan et molecular beacon), permet aussi de quantifier le produit amplifié en temps réel, compte tenu que la quantité de lumière émise est proportionnelle à l’amplification. Les sondes nucléiques permettent de détecter la présence d’espèces cibles au moyen de séquences spécifiques à une partie unique de l’ADN (Dorak 2006; Lamarche et al. 2014; Lamarche et al. 2017; Zitnick-Anderson et al. 2018). L’utilisation de fluorochromes à spectres d’excitation/d’émission uniques permet de combiner plusieurs tests, notamment dans le but de distinguer certains groupes taxonomiques (Dorak 2006). De nombreux tests qPCR multiplexes ont été développés pour dissocier l’ADN des différentes espèces dans un échantillon donné (Yang and Rothman 2004; Schena et al. 2006; Park et al. 2013; Bilodeau et al. 2014). Par exemple, Bilodeau et al. (2014) ont créé un test permettant de vérifier la présence du genre Phytophthora et de l’espèce P. ramorum grâce aux deux sondes nucléiques respectives impliquées qui ciblent l’espaceur des gènes Adenosine triphosphate synthase subunit 9 et nicotinamide adenine dinucleotide dehydrogenase subunit 9 (ATP9-NAD9) (voir la section 1.5.3.2 pour davantage de détails).

Enfin, s’il est vrai que la PCR, plus particulièrement la qPCR, constitue une avancée majeure dans le diagnostic des phytopathogènes fongiques et oomycètes, il existe désormais de nouvelles techniques de biologie moléculaire encore plus puissantes, soit le séquençage et ses méthodes dérivées. C’est ce qui sera présenté dans la section 1.5.4.

1.5.3 Marqueurs génétiques

1.5.3.1 L’espaceur transcrit interne (ITS)

L’ITS est une région génomique codante faisant partie de l’ADN ribosomique (ADNr). Elle se retrouve chez tous les organismes vivants libres. Comme il l’est illustré à la Figure 1.1a), l’ADNr comprend trois régions hautement conservées. Deux régions hypervariables, l’ITS1 et l’ITS2, intercalent les sous-unités 18S, 5.8S et 28S (Boyer et al. 2001; Calonje et al. 2009; Poczai and Hyvönen 2010; Edger et al. 2014). Le terme ITS réfère à la sous-unité 5.8S et ses deux sous- régions flanquantes, ITS1 et ITS2. La popularité de la région ITS pour effectuer des études d’évolution, de phylogénie, de spéciation, de diversité, d’épidémiologie, etc. est attribuable à ses nombreuses caractéristiques: (i) l’ADNr est présent en multiples copies, ce qui permet de détecter un organisme présent, même en faible quantité, (ii) la région ITS est d’une longueur modérée, rendant possible de nombreuses méthodes de biologie moléculaire basées sur la PCR, bien que sa longueur varie d’un organisme à l’autre, (iii) elle comporte des régions hypervariables permettant la différenciation jusqu’au niveau de l’espèce dans certains cas, (iv) elle possède des régions flanquantes conservées, facilitant l’ancrage d’amorces universelles (Figure 1.1a), et (v) la séquence d’un grand nombre d’espèces est disponible publiquement (Boyer et al. 2001; Calonje et al. 2009; Poczai and Hyvönen 2010; Edger et al. 2014; Nilsson et al. 2014). Cependant, la région ITS comporte également certains désavantages, le principal étant le risque de mal identifier certains groupes parce qu’il arrive que les séquences intraspécifiques varient beaucoup (Coleman 2003; Bellemain et al. 2010; Poczai and Hyvönen 2010).

À ce jour, il existe une multitude d’outils diagnostiques et d’études qui prennent avantage de la région ITS en entier ou de l’une ou l’autre de ses sous-régions (ITS1 ou ITS2) pour effectuer l’identification de différents champignons, de plantes et d’oomycètes (Gardes and Bruns 1993; Cooke et al. 2000; Chen et al. 2010; Nilsson et al. 2010; Ihrmark et al. 2012; Bazzicalupo et al. 2013; Bengtsson-Palme et al. 2013; Edger et al. 2014; Li et al. 2018). D’ailleurs, l’ITS a été proposé comme marqueur génétique (code à barre) officiel des champignons (Seifert 2009; Schoch et al. 2012).

1.5.3.2 L’espaceur ATP9-NAD9

Comme il est difficile d’identifier certains groupes taxonomiques de plantes (Hilpold et al. 2014; López-Alvarado et al. 2014), de champignons (Schoch et al. 2012; Lamarche et al. 2014; Lamarche et al. 2015) et d’oomycètes (Schena and Cooke 2006; Bilodeau et al. 2014) à une résolution subgénérique avec l’ITS, d’autres régions génétiques ont été identifiées afin de pallier cette limitation. Par exemple, la tubuline β, l’ARN-polymérase B II (RPB2) et la sous-unité 1 de la cytochrome oxydase (CO1) sont des régions alternatives fréquemment choisies pour le développement de marqueurs génétiques permettant l’identification spécifique de plusieurs champignons et d’oomycètes (Martin and Tooley 2003; Kroon et al. 2004; Robideau 2011; Schoch et al. 2012). Pour le genre Phytophthora sp. (oomycète), par contre, certaines espèces ne peuvent être identifiées avec la région ITS. Entre autres, certains Pythium spp. ont engendré des réactions croisées avec les Phytophthora spp. lors d’essais moléculaires (Martin et al. 2012). Par conséquent, il a été démontré que l’espaceur ATP9-NAD9 peut s’avérer plus robuste et précis que l’ITS pour identifier les Phytophthora spp. (Martin et al. 2012; Bilodeau et al. 2014; Miles et al. 2017). Tout comme l’ITS, la région ATP9-NAD9 est une région hautement variable localisée entre deux régions génétiques hautement conservées: la sous-unité 9 de l’ATP-synthase et la NADH-déshydrogénase 9 (Figure 1.1b) (Bilodeau et al. 2014). Puisque la région ATP9-NAD9 se situe dans l’ADN mitochondrial, elle se retrouve en multiples copies dans le génome (Paquin et al. 1997; Wattier et al. 2003). La synthénie de cette région est aussi hautement conservée par les Phytophthora spp. (Bilodeau et al. 2014). La séquence de cette région est disponible publiquement pour de plus en plus d’oomycètes, ce qui est possiblement lié au fait que la longueur moyenne (environ 300 paires de bases [pb]) de l’ATP9-NAD9 est idéale pour développer des tests moléculaires basés sur la PCR (Bilodeau et al. 2014).

1.5.4 Le séquençage et son évolution

1.5.4.1 Séquençage par terminaison de chaîne (Sanger)

En 1980, Frederick Sanger a affirmé qu’améliorer nos connaissances sur les séquences d’ADN pourrait contribuer significativement à notre compréhension du monde vivant (Sanger 1980). En effet, l’ordre des acides nucléiques dans l’ADN comprend de l’information relative à l’évolution, à l’hérédité et aux fonctions biochimiques des organismes vivants, ce qui est essentiel à l’explication de nombreux phénomènes biologiques (Heather and Chain 2016).

Le principe derrière la PCR se rapproche de celui du séquençage par terminaison de chaîne (Sanger). Or, la principale différence entre une réaction de séquençage et une PCR est qu’une fraction des désoxyribonucléotides (dNTPs) est remplacée par des didésoxyribonucléotides (ddNTPs), des dNTPs modifiés qui empêchent l’addition de dNTPs supplémentaires et auxquels une molécule rapporteuse est attachée (Figures 1.2 a-b) (Brown and Brown 2005; Shendure and Ji 2008; Reina 2017). Dans le cas du séquençage, comme les bases (dNTPs ou ddNTPs) seront prises au hasard par la polymérase, des événements ponctuels d’interruption de la polymérisation surviendront et génèreront des séquences de longueurs variables (Figure 1.2c). La séquence du brin d’ADN peut ensuite être déterminée au moyen d’une électrophorèse séparant les produits amplifiés selon leur taille et détectant les molécules rapporteuses uniques à chacun des ddNTPs (Figure 1.2d) (Brown and Brown 2005; Shendure and Ji 2008; Reina 2017).

La méthode de séquençage Sanger a rapidement évolué. Elle peut maintenant (i) détecter des molécules rapporteuses fluorescentes et (ii) supporter la séparation des fragments d’ADN par une électrophorèse à capillaires. Ces deux avancées permettent notamment d’analyser jusqu’à 96 échantillons simultanément (Shendure and Ji 2008; Reina 2017). Néanmoins, le séquençage Sanger est toujours utilisé pour des applications dont les besoins sont à plus petits volumes parce qu’il est simple, facile à utiliser et relativement peu coûteux comparé aux nouvelles méthodes disponibles.

1.5.4.2 Séquençage à haut débit

Les méthodes de séquençage à haut débit (high-throughput sequencing [HTS]) représentent une révolution technologique majeure en biologie moléculaire. En effet, le séquençage de deuxième génération a apporté bon nombre d’avantages par rapport au séquençage Sanger. D’abord, ce qui différencie les deux technologies est que la méthode Sanger nécessite une analyse par électrophorèse. En revanche, les méthodes de séquençage de deuxième génération permettent d’identifier les bases en temps réel. Cette méthode innovatrice de séquençage massivement parallèle a considérablement augmenté la quantité d’échantillons pouvant être traités lors de chaque réaction (Heather and Chain 2016).

Le HTS est conçu pour générer des millions de séquences par analyse. Il est désormais possible de séquencer plusieurs échantillons hautement complexes tels que des échantillons environnementaux mixtes (sol, fèces, eau, sédiments, etc.) dans une même réaction. Le séquençage direct d’échantillons permet également d’éviter les étapes d’isolement et de culture (Shokralla et al. 2012). De façon chronologique, les séquenceurs qui ont connu le plus de popularité sont le pyroséquençage par la plateforme 454 (Roche), puis les plateformes Solexa (Illumina Genome Analyzer), SOLiD (Applied Biosystems), Ion Torrent (Life Technologies) et Illumina (Illumina) (Shendure and Ji 2008; Holtgrewe 2010; Robison 2010; Heather and Chain 2016). Les technologies de séquençage 454, Ion Torrent et Illumina sont présentées dans la Figure 1.3.

Les plateformes de séquençage à haut débit évoluent à une vitesse fulgurante depuis la dernière décennie. De nouveaux séquenceurs et de nouveaux réactifs sont constamment lancés afin d’améliorer la longueur, la qualité et le nombre de séquences produites, puis de réduire les coûts par échantillon et le temps requis par ronde de séquençage (Shendure and Ji 2008; Loman et al. 2012; Jünemann et al. 2013).

1.5.5 Contexte et choix de la méthode de séquençage dans le cadre du projet

En 2012, la plateforme Ion Torrent avait déjà rattrapé le 454 en termes de longueur de fragments. Effectivement, les deux séquenceurs permettaient d’atteindre des longueurs de séquençage d’environ 400 pb. L’Ion Torrent avait aussi dépassé le 454 au niveau du débit. En ce qui a trait à l’Illumina, la compagnie avait déjà lancé sa technologie Nextera pour séquencer avec le MiSeq—offrant alors un plus haut débit et une meilleure qualité de séquences. Or, le « paired-end (PE) », une technologie qui séquence à partir des deux extrémités du fragment d’ADN pour améliorer la qualité des lectures, limitait la longueur des fragments à 1 x 150 pb, car la technologie PE Nextera 2 x 250 pb n’était pas encore disponible (Loman et al. 2012; Jünemann et al. 2013; Heather and Chain 2016). Le temps de procédure du 454 était beaucoup plus long que celui de ses deux compétiteurs et son coût d’opération était plus élevé également. De plus, le point faible de la technologie du Ion Torrent était la qualité inférieure de ses séquences, plus particulièrement avec les séquences homopolymères (Quail et al. 2012; Pillai et al. 2017).

La plate-forme Ion Torrent Personal Genome Machine (PGM) permettait d’atteindre i) un plus haut débit de séquençage par rapport au 454 et ii) une longueur de séquençage contigüe entre 350 et 400 pb, contrairement au MiSeq qui ne se limitait encore qu’à 150 pb. De plus, seul l’Ion Torrent offrait la méthode des amorces de fusion. Cette méthode de préparation des banques de séquençage permet aux utilisateurs d’attacher un identifiant unique, un code à barres, en plus de pouvoir effectuer de manière bidirectionnelle une amplification spécifique de plusieurs régions génétiques grâce aux amorces de fusion, elles aussi spécifiques aux organismes ciblés (Thermofisher 2012).

En plus de l’instrument Ion Torrent (PGM), l’Ion Torrent S5, lancé quelques années plus tard, peuvent être couplés à l’Ion Torrent Chef, un robot automatisant la préparation des banques de séquençage. En utilisant une puce Ion 530, l’Ion Torrent S5 peut de générer beaucoup plus de séquences par rapport à l’Ion Torrent PGM, soit un total pouvant théoriquement atteindre 20 millions de séquences par ronde effectuée. De surcroît, le temps de processus de l’Ion Torrent S5 est beaucoup plus court, offrant une ronde de séquençage pour des fragments de 400 pb de moins de cinq heures, comparativement aux huit heures nécessaires au Ion Torrent PGM pour effectuer la même opération (Hertke 2015).

1.6 Bio-informatique

À l’origine, la bio-informatique repose sur le développement d’ordinateurs puissants pouvant reconnaître et déterminer les séquences et les structures de différentes molécules biologiques, notamment l’ADN, afin d’évaluer les fonctions et l’évolution des organismes. De façon générale, les analyses bio-informatiques nécessitent une très grande puissance de calcul pour pouvoir compléter leurs tâches dans un délai raisonnable. La bio-informatique est un outil qui s’avère désormais indispensable pour traiter les quantités phénoménales de données générées, entre autres, avec les avancements des technologies de SNG (Diniz 2017). La bio-informatique est également essentielle (i) pour transformer les données brutes issues d’un instrument en formats interprétables par différents logiciels, (ii) pour ordonner la vaste quantité de données produite et (iii) pour faciliter la visualisation, l’interprétation et la démonstration de résultats statistiquement significatifs (Luscombe et al. 2001; Diniz 2017).

La communauté scientifique utilise plusieurs bases de données pour centraliser et rendre accessibles les séquences d’ADN. Il existe deux types fondamentaux de bases de données ouvertes au public: (i) celles dans lesquelles le public ajoute librement du matériel sans passer par des étapes d’édition de contenu, par exemple la base de données nucleotides du National Center for Biotechnology Information (NCBI), et (ii) celles qui sont soumises à un processus d’édition de contenu (e.g. UNITE (Kõljalg et al. 2005)) afin (iia) que des séquences de haute qualité uniquement soient sélectionnées et (iib) que les identifications des séquences déposées soient validées avant d’être rendues publiques. Un système universel d’identification des séquences est habituellement utilisé afin de normaliser le matériel contenu et de faciliter son utilisation. Typiquement, les bases de données non-éditées contiennent beaucoup plus de séquences, mais la qualité de leur contenu est grandement variable, parfois même médiocre. Les bases de données qui ont subi une étape d’édition contiennent généralement beaucoup moins de séquences en raison du temps et des ressources requis pour traiter tous les dépôts. De plus, les fragments inutiles ou de mauvaise qualité sont retirés de ces bases de données.

En comparant les séquences obtenues par SNG avec celles contenues dans les bases de données, il devient possible d’identifier les taxons présents, puis d’étudier l’évolution grâce à certaines portions conservées ou encore des variations observées dans les séquences d’ADN au sein d’une population donnée, par exemple. Un score obtenu à l’aide d’un algorithme prenant en considération les insertions et les délétions (indels) ainsi que la longueur de l’alignement permet d’évaluer la ressemblance entre les séquences comparées (Diniz 2017).

1.7 Métagénomique et métabarcodage

La métagénomique est une méthode d’analyse de la diversité taxonomique d’échantillons environnementaux plus ou moins complexes (e.g. sol, air, eau, etc.) qui est indépendante des méthodes de culture. Cette méthode s’avère une révolution en microbiologie, car elle a permis d’étudier des organismes incultivables. Du même coup, elle a mené à la compréhension de davantage de phénomènes se produisant dans un environnement donné (Handelsman 2004; Sherwood et al. 2010). Il est à noter que la majorité des organismes présentement classifiés et non-

classifiés sont incultivables (Rappé and Giovannoni 2003; Handelsman 2004; Riesenfeld et al. 2004).

L’accès aux séquences d’ADN des organismes est très pertinente lorsque ces derniers sont présents en quantité infime ou lorsque des espèces proches ne sont pas dissociables à l’aide des méthodes traditionnelles (Tringe and Rubin 2005; Majaneva et al. 2015). L’analyse à base d’amplicons de régions génétiques hypervariables (e.g. l’ITS), le métabarcodage, est fréquente en métagénomique. Le métabarcodage, tout comme la métagénomique, permet d’évaluer la présence de multiples espèces et de multiplexer plusieurs échantillons en simultané. Dans une situation d’analyse de tels échantillons en multiplex, les analyses par métabarcodage s’avèrent plus rapides que l’étude de génomes entiers puisque seulement une portion du bagage génétique de chacun des taxons est amplifiée (amplicon) (Illumina Inc. 2015).

Les unités taxonomiques opérationelles (OTU) sont une façon de regrouper des séquences phylogénétiquement proches selon un seuil de similarité entre les fragments d’ADN. Bien qu’il y ait des exceptions, un seuil de 97% de similarité est habituellement admis, puisqu’il correspond généralement au pourcentage de variabilité intraspécifique (Kemler et al. 2013; Nicolas et al. 2013; Abdelfattah et al. 2015; Dorado-Morales et al. 2015; Vettraino et al. 2015).

Afin de supporter la charge de données générées et d’alléger les analyses bio-informatiques, la méthode de regroupement d’OTU, ou OTU clustering, est fréquemment utilisée pour effectuer les analyses de séquences obtenues par métabarcodage. Parmi les outils bio-informatiques les plus populaires (Bengtsson-Palme et al. 2015; Majaneva et al. 2015; Callahan et al. 2016; Escudié et al. 2017) on retrouve les logiciels Mothur (Schloss et al. 2009), QIIME (Caporaso et al. 2010) et UPARSE (Edgar 2013). Or, ces outils ne pouvaient directement servir à l’analyse des données générées avec des amorces de fusion amplifiant des séquences d’organismes non-modèles (ex. champignons et oomycètes). Les données brutes obtenues, organisées par identifiant unique (code à barre) et par région génétique amplifiée, ne pouvaient pas être démultiplexées (c.-à-d. triées selon les codes à barres et les régions génétiques ciblées) par les logiciels susmentionnés. Cela étant, il était impossible de conserver une traçabilité des régions étudiées afin d’analyser les séquences avec une base de données correspondant aux organismes étudiés. De plus, ces logiciels étaient limités

quant aux organismes qu’ils pouvaient traiter car les bases de données intégrées contenaient principalement des séquences bactériennes. Enfin, le format des résultats obtenus n’était pas directement interprétable, c’est pourquoi des étapes supplémentaires étaient requises pour générer des figures et des tableaux plus significatifs et plus compréhensibles.

1.8 Cueillette d’échantillons environnementaux

1.8.1 Pièges à spores

L’étude du matériel biologique en suspension dans l’air est depuis plusieurs années une source convoitée d’information. C’est notamment le cas lorsqu’il est question du pollen, mais aussi des agents phytopathogènes tels que les virus, les bactéries et les champignons. Beaucoup de chercheurs se sont intéressés à l’aérobiologie pour comprendre les méthodes de propagation de ces microorganismes, ce qui a mené au développement de diverses méthodes d’échantillonnage adaptées à différentes contraintes (espace, coût, durée, etc.). Les méthodes classiques de récolte comprennent le bras rotatif de type « rotorod », l’échantillonneur Andersen, le piège à spore Hirst et ses méthodes dérivées incluant le piège Burkard et les séparateurs à cyclones. Des méthodes passives sont encore utilisées fréquemment pour faire la cueillette de bioaérosols. Parmi ces méthodes, on retrouve le collecteur de pluie JB (J. L. Johnson, communication personnelle, 2018) qui consiste en un entonnoir au fond duquel un filtre récupère et concentre les particules transportées par la pluie et le vent (West and Kimber 2015). Les pièges ramassant les volumes de pluie sont spécialisés pour recueillir des spores voyageant sur de longues distances ou encore disséminées par éclaboussement (Aizenberg et al. 2000; Barnes et al. 2006; West and Kimber 2015). La rouille du soya est un exemple notoire de maladie surveillée au moyen de pièges à spores passifs (Hambleton et al. 12-14 December 2007; Aizenberg et al. 2000; von Qualen and Yang 2006). Bien qu’elles soient moins avancées au niveau technologique, les méthodes de collection passives représentent une façon simple, peu coûteuse et accessible de faire la cueillette d’échantillons à distance et sur de longues durées.

1.8.2 Pièges à insectes

Différents pièges sont conçus pour récolter différents groupes d’insectes. Pour attirer des groupes spécifiques d’insectes, certains pièges ont des caratérisiques visuelles, d’autres chimiques, et d’autres une combinaison des deux.

Les pièges visuels utilisent des couleurs, des formes ou de la lumière pour attirer certains insectes. Par exemple, des pièges avec lampes à rayons ultraviolets (UV) permettent de capturer des insectes volants indésirables à l’intérieur d’un bâtiment (Rentokil Inital), tandis que des pièges avec diode électroluminescente (DEL) permettent d’attraper des phlébotomes vecteurs de maladies humaines ou animales (Cohnstaedt et al. 2008). De plus, des pièges collants de couleurs spécifiques permettent d’attirer certains insectes (ex. cicadelles) vecteurs d’agents phytopathogènes (Thein et al. 2011).

Les pièges chimiques, quant à eux, contiennent des molécules qui attirent les insectes ciblés (phéromones [intraspécifique] et kairomones [interspécifique]) (Ginzel 2010). Ces molécules sont des composés sémiochimiques qui améliorerent la spécificité des pièges. Parmi ces derniers, on retrouve deux pièges à spongieuse européenne (Lymantria dispar), attirant les mâles adultes, soit celui de type Delta avec adhésif collant, et celui de type « carton de lait » avec bande insecticide (Agence Canadienne d'Inspection des Aliments 2018c). On retrouve aussi le piège à scarabée japonais (Popillia japonica) avec ailettes et entonnoir (de type Catch-Can ou tout autre équivalent) qui utilise deux leurres spécifiques, dont une pour les mâles (Agence Canadienne d'Inspection des Aliments 2018d). À ce sujet, l’ACIA utilise des pièges avec entonnoirs multiples pour effectuer la surveillance d’insectes envahissants en forêt. Ces derniers incluent aussi de multiples molécules attirant différents groupes de longicornes (Agence Canadienne d'Inspection des Aliments 2018e).

1.9 Hypothèses et objectifs du projet

Ce projet de doctorat a pour but de contribuer à la biosurveillance et à la protection des ressources naturelles, particulièrement des arbres forestiers parce qu’ils sont une ressource économique importante au Canada, en développant un outil de dépistage fiable et sensible qui peut être combiné aux méthodes actuellement utilisées par les organismes de réglementation tels que

l’ACIA. Plus spécifiquement, le projet a pour but d’augmenter la superficie des zones d’échantillonnage dans les régions à risque d’introduction d’espèces phytopathogènes et de plantes exotiques à potentiel envahissant au Canada. Le projet a aussi pour but de développer une méthode d’analyse rapide, à haut débit et supportant un très grand volume d’échantillons à la fois afin d’accélérer l’application des mesures de traitement phytosanitaires requises lorsqu’elles sont requises. Dans le but d’évaluer le potentiel du métabarcodage à détecter des indices (ex. espèces ou genres d’intérêt en phytopathologie), différents échantillons ont été recueillis dans plusieurs régions du pays à l’aide de diverses méthodes de collecte.

1.9.1 Hypothèses

1. Le métabarcodage est une technique hypersensible qui permet de faire l’identification primaire des espèces de champignons, d’oomycètes et de plantes dans un échantillon environnemental ainsi que de détecter des cibles en particulier. 2. L’étude des bioaérosols et des insectes vecteurs permet d’augmenter la couverture actuelle d’échantillonnage des régions à risque, de dépister et de suivre la présence et l’étendue des espèces exotiques et à potentiel envahissant. 3. Les insectes sont des vecteurs de champignons et d’oomycètes pouvant servir à dépister et à suivre l’occurrence et le développement des espèces exotiques et à potentiel envahissant. 4. La surveillance d’une population sentinelle d’abeilles permet de dépister et de suivre l’évolution des champignons, des oomycètes et des plantes exotiques et à potentiel envahissant.

1.9.2 Objectifs

Le premier objectif de cette thèse est de combler une lacune dans la détection précoce de certaines espèces (champignons, oomycètes et plantes) indésirables et d’en atténuer les ravages plus tôt et plus rapidement en prenant avantage i) du pouvoir d’analyse à haut débit qu’offre le SNG et ii) en tirant avantage d’un réseau d’échantillonnage pré-établi à plusieurs endroits au Canada pour couvrir une grand étendue géographique et ensuite identifier des régions géographiques d’intérêt (c.-

à-d. détection d’agents phytopathogènes) requérant davantage d’échantillonnage et de tests pour confirmer la présence d’organismes indésirables. Cet objectif général est développé aux chapitres 2, 3 et 4.

Puisqu’une nouvelle méthode à haut débit est nécessaire pour améliorer les enquêtes conduites au pays, le deuxième objectif du projet, présenté dans le chapitre 2, est de démontrer que l’utilisation combinée de i) la métagénomique, basée sur le SNG et sur des analyses bio-informatiques sur mesure (pipeline), et ii) de différents types de pièges placés dans des endroits à risque a le potentiel de dépister des champignons et des oomycètes indésirables. Il est à noter que nous avons recyclé les liquides préservatifs utilisés dans les pièges à insectes que des inspecteurs de l’ACIA employaient dans le cadre d’enquêtes entomologiques réalisées dans un réseau de sites de collecte préétabli. Dix-sept agents phytopathogènes (champignons et oomycètes) indigènes, déjà introduits ou exotiques dont certains sont réglementés par l’ACIA étaient ciblés. De plus, des échantillons de sol ainsi que des échantillons inoculés de concentrations d’ADN d’espèces connues (c.-à.-d. mocks) ont été testés à des fins de validation.

Le troisième objectif de cette thèse est de comparer des résultats obtenus par métagénomique avec des tests validés selon des standards établis (essais qPCR avec sondes spécifiques aux espèces ciblées). Le chapitre 2 présente cet aspect du projet.

Comme les données obtenues des liquides des pièges à insectes étaient chargées en OTU, le quatrième objectif de cette thèse, présenté dans le chapitre 3, est d’approfondir les analyses en évaluant les données selon chacun des quatre différents composés sémiochimiques utilisés dans les pièges à insectes dans le but de déterminer si certains insectes attirés étaient vecteurs d’entités phytopathogènes. Le projet a tiré avantage du réseau d’échantillonnage préétabli par l’ACIA dans plusieurs provinces canadiennes à proximité des régions à risque.

Le cinquième objectif, décrit au chapitre 4, est d’évaluer le potentiel des abeilles, en tant que population sentinelle, à faire la cueillette d’échantillons environnementaux via les granules de pollen accumulés lors du butinage pour aider à dépister la présence de champignons, d’oomycètes et de plantes indésirables dans les environs de la ruche étudiée. L’inventaire visuel des plantes

fleurissantes environnantes a permis de comparer la présence d’espèces de plantes avec celles identifiées par métabarcodage.

Le sixième objectif est d’évaluer, à partir d’échantillons de granules de pollen, les variations des communautés de champignons, d’oomycètes et de plantes en fonction du temps de la saison et des conditions climatiques enregistrées. Le fait que des insectes pollinisateurs aient contribué à la présence de tels agents phytopathogènes a ainsi été exploré dans le cadre de cet objectif.

1.10 Figures

Figure 1.2 Processus biochimique du séquençage Sanger impliquant a) des désoxyribonucléotides (forme naturelle) et des nucléotides terminateurs, les didésoxyribonucléotides (groupement hydroxyle remplacé par un atome d’hydrogène) qui, lorsque ajoutés de manière aléatoire, c) préviennent la polymérisation. c) Des séquences de longueurs différentes marquées avec un fluorochrome spécifique au ddNTP sont alors générées et d) lues avec un laser optique afin de déduire la séquence. Figure adaptée de Kircher and Kelso (2010); McGovern (2015).

Figure 1.3 Schéma simplifié du fonctionnement des plates-formes de séquençage à haut débit les plus populaires en 2013 a) Ion Torrent, b) pyroséquençage 454 et c) Illumina. Figure adaptée de Mardis (2008); Voelkerding et al. (2009); Life Technologies (2010).

Chapitre 2: Screening for exotic forest pathogens to increase survey capacity using metagenomics

Screening for exotic forest pathogens to increase survey capacity using metagenomics

Tremblay, É. D1.; Duceppe, M.-O1.; Bérubé, J. A.2; Kimoto, T.3; Lemieux, C., and G. J. Bilodeau1

1Canadian Food Inspection Agency (CFIA), 3851 Fallowfield Road, Ottawa, Ontario, K2H 8P9, Canada;

2Natural Resources Canada, Laurentian Forestry Centre, 1055 Du P.E.P.S. Street, P.O. Box 10380 Québec, Québec, G1V 4C7, Canada;

3CFIA, 4321 Still Creek Dr, Burnaby, British Columbia, V5C 6S7, Canada;

4Institut de Biologie Intégrative et des Systèmes, 1030 avenue de la Médecine, Québec, Québec, G1V 0A6, Canada.

Article accepté pour publication dans la revue Phytopathology le 17 Juin 2018, doi: 10.1094/PHYTO-02-18-0028-R.

2.1 Résumé

Les activités anthropiques ont des incidences majeures sur l’environnement, et ce, au niveau mondial. Les ressources naturelles canadiennes sont menacées par la propagation d’agents pathogènes fongiques, un phénomène qui est renforcé par la pratique d’activités agricoles et par le commerce international. Certains champignons sont introduits dans de nouveaux environnements et, quand ils s’y établissent, ils peuvent causer des épidémies et entraîner d’importants déclins forestiers. Ce chapitre démontre que la combinaison d’une stratégie de collecte d’échantillons d’ampleur nationale au séquençage de nouvelle génération (SNG) (c.-à-d. la métagénomique) permet de dépister des espèces exotiques envahissantes de manière rapide et complète. Cette méthode de recherche peut aider à orienter des organismes de réglementation lorsqu’il est question de la phytopathologie. D’ailleurs, plusieurs espèces réglementées ont été surveillées grâce au traitement d’échantillons de terrain recueillis sur une période de trois ans (2013-2015) près de zones à risques élevés à travers le Canada. Quinze rondes de séquençage sur la plateforme Ion Torrent ont été requises afin de traiter les 398 échantillons, qui eux, ont permis de produire 45 millions de séquences. Le dépistage d’unités taxonomiques opérationnelles de champignons et d’oomycètes à partir du séquençage à haut débit utilisant des amorces conçues sur mesure et spécifiques à l’espaceur transcrit interne 1 des champignons et des oomycètes a été effectué. Également, des amorces spécifiques aux Phytophthora spp. avec code à barres ont été utilisées pour amplifier l’espaceur de la région génomique adenosine triphosphate synthase subunit 9 - nicotinamide adenine dinucleotide dehydrogenase subunit 9 spacer. De nombreuses espèces de Phytophthora ont été détectées par SNG et confirmées avec des tests qPCR spécifiques aux espèces. L’espèce ciblée Heterobasidion annosum s.s. a pu être détectée par métagénomique seulement. Nous avons démontré qu’en utilisant une variété de méthodes d’échantillonnage et le SNG—dont les résultats ont été validés avec des tests qPCRs—il est possible d’augmenter la puissance des enquêtes de dépistage des espèces cibles parce que la sensibilité de détection est supérieure, le temps de manipulation est réduit de même que les coûts associés. Cette amélioration permet ainsi d’aider les organismes de réglementation à identifier les points d’entrée. De fait, la méthode développée représente un atout considérable pour la gestion des maladies des plantes, puisque la détection précoce et la prévention sont des étapes non négligeables si on veut réduire les dommages.

2.2 Abstract

Anthropogenic activities have a major impact on the global environment. Canada’s natural resources are threatened by the spread of fungal pathogens, which is facilitated by agricultural practices and international trade. Fungi are introduced to new environments and sometimes become established, in which case they can cause disease outbreaks resulting in extensive forest decline. Here we describe how a nationwide sample collection strategy coupled to next-generation sequencing (NGS) (i.e., metagenomics) can achieve fast and comprehensive screening for exotic invasive species. This methodology can help provide guidance to phytopathology stakeholders such as regulatory agencies. Several regulated invasive species were monitored by processing field samples collected over 3 years (2013 to 2015) near high-risk areas across Canada. Fifteen sequencing runs were required on the Ion Torrent platform to process 398 samples that yielded 45 million reads. High-throughput screening of fungal and oomycete operational taxonomic units using customized fungi-specific ribosomal internal transcribed spacer 1 barcoded primers was performed. Likewise, Phytophthora-specific barcoded primers were used to amplify the adenosine triphosphate synthase subunit 9-nicotinamide adenine dinucleotide dehydrogenase subunit 9 spacer. Several Phytophthora species were detected by NGS and confirmed by species-specific quantitative polymerase chain reaction (qPCR) assays. The target species Heterobasidion annosum sensu stricto could be detected only through metagenomics. We demonstrated that screening target species using a variety of sampling techniques and NGS—the results of which were validated by qPCR—has the potential to increase survey capacity and detection sensitivity, reduce hands-on time and costs, and assist regulatory agencies to identify ports of entry. Considering that early detection and prevention are the keys in mitigating invasive species damage, our method represents a substantial asset in plant pathology management.

2.3 Introduction

Among all the microorganisms, the kingdom Fungi contains the largest number of phytopathogenic species (Knogge 1996; Carris et al. 2012). Many species members of the phyla Eumycota and Oomycota newly introduced in a given country are considered important threats to plants because they can cause major harvest losses (Mecteau et al. 2002; Bilodeau et al. 2012; Vettraino et al. 2015) and ecological damage (Allen and Humble 2002; Loo 2009). Local trees and crops may not have acquired the ability to recognize these newly introduced pathogens and, therefore, their defense mechanisms may not be triggered appropriately (Raj and Dentino 2002). Although some fungi are harmless in their native environment, they can switch to a phytopathogenic state when in contact with a nonnative host with which they have not coevolved and, therefore, may cause significant ecological damage in their new environment (Redman et al. 2001; Pirofski and Casadevall 2012; Bérubé and Nicolas 2015). Typical symptoms associated with fungus- and oomycetes-incited diseases include root rots, cankers, wilting, and foliar and shoot damage. The genus Phytophthora stands out as the most significant phytopathogenic oomycete because of its broad host range and its substantial impact on the agriculture and forest industries (Kamoun et al. 2015). A notorious example of such devastating oomycete is Phytophthora infestans (Mont.) de Bary, the causal agent of potato blight, considering it was demonstrated to be responsible for the Great Famine in Ireland between 1845 and 1852 (Ó'Gráda 1995; Goss et al. 2014).

One remarkable characteristic of most fungal and oomycete species is long-distance spore dispersal (Tsitsigiannis et al. 2004; Fry and Grunwald 2010). This feature allows for broad dissemination throughout an environment and becomes a considerable risk of tree and plant material infection following the dispersal of exotic fungal spores by wind, rain, or human-mediated distribution over kilometers or even across continents (Brown and Hovmøller 2002; Tsitsigiannis et al. 2004; Fröhlich-Nowoisky et al. 2009; Fry and Grunwald 2010; West and Kimber 2015). In addition, movement and introduction of many fungi into new areas can also be facilitated by insect vectors or by the transport of live ornamental plants, firewood, wood products, and wood packaging material. An infamous example of phytopathogenic spores vectored by the elm bark beetle is Ophiostoma novo- ulmi Brasier, the causal agent of Dutch elm disease (Allen and Humble 2002). Dutch elm disease affected millions of trees in North America after it was spread by the movement of beetle-infested

firewood (Allen and Humble 2002; Negrón et al. 2005). The fact that many exotic insects are routinely intercepted in imported wood material in Canada and the United States demonstrates even more how alarming the risk associated with the introduction of such unwanted species is (Dick 1998; Haack et al. 2014; Vettraino et al. 2015, G. S. Thurston, personal communication).

In Canada, surveys for disease vectors (e.g., wood-boring insects) and for fungal diseases are actively conducted at ports of entry and urban areas (Canadian Food Inspection Agency 2011, 2013a, 2014a). These areas are considered at high risk for the introduction of nonindigenous species due to their exposure to increasing volumes of international commodities and proximity to potential hosts, transportation networks, and commercial activities (Liebhold et al. 2012; Bullas-Appleton et al. 2014; Eschen et al. 2015; Canadian Food Inspection Agency 2017a). Detection surveys in these areas should represent an important component of an overall strategy for any given country to prevent the introduction and spread of invasive plant pests and to mitigate economic and environmental impacts associated (Hamelin 2012).

Many regulatory agencies currently use morphology, culturing, and established molecular assays to identify fungal and oomycete species of concern; however, these reference detection methods are considered slow, with low throughput, and often arduous (Guarro et al. 1999; Turin et al. 2000; Goud and Termorshuizen 2003; Tavanti et al. 2005; Feng et al. 2014). In addition, because many fungi can’t be cultured (i.e., obligate parasites or slow-growing or complex growth media requirements), reliance on molecular methods or sequence analysis is required (Bindslev et al. 2002). To date, there are several molecular assays based on polymerase chain reactions (PCR) (e.g., real-time PCR [qPCR]), conventional PCR, and isothermal amplification) and sequencing available for identification of fungal species (Schena et al. 2006; Aroca et al. 2008; Bilodeau et al. 2009; Lievens et al. 2009; Capote et al. 2012). Because molecular assays are highly specific and very sensitive when targeting multiple-copy DNA sequence regions (Gardes et al. 1991; Martin et al. 2012), they are commonly used for species screening by regulatory agencies. For example, the Canadian Food Inspection Agency (CFIA) uses qPCR assays to detect Escherichia coli O157:H7, Salmonella spp. and Listeria monocytogenes (Canadian Food Inspection Agency 2017c; Government of Canada 2017), and the United States Department of Agriculture uses dual qPCR assays to screen for P. ramorum (United States Department of Agriculture 2012). However, qPCR

assays only focus on a limited number of species at a time (Schena et al. 2006; Aroca et al. 2008; Bilodeau et al. 2009; Lievens et al. 2009; Capote et al. 2012).

The constant evolution of DNA analysis technologies allows researchers to study microbial communities and promotes direct use of field samples. Not only does metagenomics have the advantage of testing several samples in one run but also this method, using next-generation sequencing (NGS) technology, can be used to screen for a large spectrum of organisms in a given environmental sample (Fierer et al. 2007). Furthermore, NGS allows detection of low-abundance species (i.e., few sequences present) that may remain undetected by other conventional detection methods (Lindahl et al. 2013; Prigigallo et al. 2016). NGS has been applied successfully in research for fungi (Bérubé and Nicolas 2015; Leboldus et al. 2015; Miller et al. 2016; Malacrinò et al. 2017), oomycetes (Adhikari et al. 2013; Prigigallo et al. 2016), and macroinvertebrate biomonitoring in water (Carew et al. 2013; Kermarrec et al. 2014). More specifically, the Ion Torrent NGS technology has been successfully used for community studies on fungal endophytes (Kemler et al. 2013) and oomycetes (Lyon et al. 2016).

Compared with other genic regions, the internal transcribed spacer (ITS) ribosomal intergenic region—currently considered the official barcode region for fungi (Schoch et al. 2012)—is often used to identify fungi and oomycetes because its interspecific and intraspecific variation rate can differentiate more species and is more reliable for PCR purposes (Bilodeau et al. 2009; Seifert 2009; Bellemain et al. 2010; Schoch et al. 2012). However, the ITS region is not variable enough to resolve some Phytophthora species. Using the mitochondrial DNA spacer adenosine triphosphate synthase subunit 9-nicotinamide adenine dinucleotide dehydrogenase subunit 9 (ATP9-NAD9) as an alternative marker can overcome this limitation, especially because sequences for this region are available for most of the current 123 official species (Kang 2006; Kox et al. 2007; Martin et al. 2012; Bilodeau et al. 2014; Miles et al. 2017).

This article presents a new metagenomics procedure to aid regulatory agencies in screening for fungal spores collected from air and insect vectors. Using an existing surveillance network and various sampler types, the approach innovates by analyzing the preservative fluids from CFIA’s insect traps. The objectives of this proof of concept were to (i) using metagenomics, develop a new

screening procedure for invasive or alien plant pathogens from air samples and insect traps near ports of entry and high risk areas in Canadian urban environments; (ii) provide guidance to phytopathology stakeholders; and (iii) compare and verify metagenomics results using previously validated qPCR assays that target fungal and oomycete species of interest.

2.4 Materials and methods

Target species. In 2012, the CFIA, in collaboration with the Canadian Forest Service and the Pest Management Regulatory Agency, drafted a plant pathogen research priority list that included 100 native, naturalized, and nonindigenous species. From that list, 17 phytopathogens, counting some pests regulated in Canada, were picked for this project. The target species list included species from the Phytophthora genus, ophiostomatoid fungi, and other fungi causing root rot and foliar diseases such as Bretziella fagacearum (Ceratocystis fagacearum), C. polonica, C. laricicola, Chrysomyxa abietis, Gremmeniella abietina, Geosmithia morbida, Gymnosporangium fuscum, G. yamadae, Melampsora pinitorqua, Ophiostoma ulmi (i.e., O. ulmi sensu stricto), O. novo-ulmi, and Heterobasidion annosum sensu stricto. H. annosum sensu stricto is part of the species complex H. annosum sensu lato, which includes the following species: H. irregulare, H. parvivorum, H. annosum sensu stricto, H. abietinum and H. occidentale (Garbelotto and Gonthier 2013; Lamarche et al. 2017). Not all target species are currently found in Canada.

Sample collection. In total, 398 environmental samples were processed. In addition, 10 mock samples inoculated with various target and closely related species and a blank sample consisting of molecular-grade water were analyzed (Caporaso et al. 2011; Ihrmark et al. 2012; Singer et al. 2016). Sampling occurred over 3 years (summer 2013, 2014, and 2015) at strategically selected sites across Canada (Supplementary Table 2.S1; Fig. 2.1). Sites included commercial and industrial zones, wooded urban areas, and municipal green-waste disposal facilities (Ryall et al. 2015). Four different sampling methods to survey the presence of phytopathogens were used (Supplementary Figure 2.S1).

First sampling method: Spore traps. The Johnson and Barnes (JB) rainfall collectors (J. L. Johnson, personal communication) consisted of a funnel appended with a filter down its throat where particles were collected on a 0.45-µm filter membrane (Fisher Scientific, Waltham, Mass., USA) and were processed as previously described by Barnes et al. (2006); (2009) Hambleton et al. (12-14 December 2007); and Szabo (2007), except that filter membranes used were made of cellulose nitrate instead. The original JB collector (funnel diameter: 44 cm and filter diameter: 45 mm) and a smaller version, the JB mini (funnel diameter: 27 cm and filter diameter: 25 mm), were used. Spores

and rain accumulated and were then collected on the surface of the cellulose membrane filter. Collections were made once every 2 weeks in Québec and Ontario and samples were stored at 4°C until processed. Then, one-half of each filter was put in 1.4 ml of Tris buffer, heated at 65°C, sonicated at 40 kHz, and centrifuged for 2 min (10,000 rpm). The filter was then discarded and the remaining solution was centrifuged at 10,000 rpm for 30 s. The liquids were then dehydrated at low temperature before being extracted. The other half of the filter was kept at -20°C as a back-up.

Second sampling method: Preservative fluids from insect traps. Inspectors (CFIA) across Canada (British Columbia, New Brunswick, Newfoundland and Labrador, Ontario, and Québec) performed entomology surveys using Multiple Funnel Traps (Synergy Semiochemicals Corporation, Burnaby, BC, Canada), an insect collection device consisting of stacked funnels baited with semiochemicals (Supplementary Table 2.S2). Preservative fluids (50 to 1,000 ml of food-grade propylene glycol) from those insect traps’ collection cups were filtered with a 0.45-µm filter paper. Filter paper halves were processed as described above for JB collectors.

Third sampling method: Rotary arm sampler. The rotary arm sampler (Phytopdata Inc., Sherrington, QC, Canada) was a spinning apparatus rotating 5 minutes every hour and prepared following the protocol of Fall et al. (2015). Its two vertical metal rods (1.65 x 20 mm) were dipped in a silicone grease (Versilube ® G697, Novagard Solutions, Cleveland, OH, USA) to facilitate spore adhesion from impacting air samples (Bérubé et al. 2017b). Samples were collected weekly in the province of Québec.

Fourth sampling method: Soil samples from agricultural fields. To initially validate the ability of our metagenomics approach to detect DNA, eight soil samples were included in our first NGS run. Six sandy loam samples were collected in the summer of 2011 from St. Rock, Alberton, PEI. They originated from yearly crop rotations of ryegrass and strawberry spread over 3 years. Samples were collected prior and after soil fumigation. We also included two peat samples collected in Vancouver, BC in 2011. Soil samples were sieved and subsampled into two 500-mg parts due to constraints of lysing matrix kit. Parts were put back together at the elution step to obtain 1-g samples.

Extraction, purification, and quantification of DNA. The 0.45-μm paper filters halves from the JB collectors and the insects traps were processed as described by Barnes et al. (2009), except the FastDNA kit for soil (MP Biomedicals, Santa Ana, CA, USA) was used for DNA extraction. The same kit was used for soil samples. Previous work comparing DNA extraction methods and kits suggested that the FastDNA kit for soil was the most effective at recovering DNA from complex matrices such as environment samples when coupled with subsequent purification for PCR inhibitor removal (Bilodeau and Robideau 2014)(G. Bilodeau, unpublished). The recovery of spores and the DNA extraction from the rotary arm samples were done as described by Lamarche et al. (2017) and using the QIAmp DNA Micro kit (Qiagen, Hilden, NW, Germany). Purification of extracted DNA with magnetic beads (Bio-Nobile, Östernäsvägen, Finland) was done on all sample types to remove potential PCR inhibitors. The Qubit 2.0 fluorometer (Life Technologies, Carlsbad, CA, USA) was used to quantify DNA. A new extraction was performed for samples with a final DNA concentration lower than 0.1 ng/μl. All DNA samples were standardized at a final concentration of 0.1 ng/μl for NGS library preparation.

Fusion PCR primers and design. Ion Torrent technology requires the use of fusion primers to generate tagged amplicons (Thermofisher 2012). Therefore, using the Ion Torrent’s fusion primers, we designed specific PCR primer sets for fungi, oomycetes, and Phytophthora spp. This was accomplished by combining already existing primers that targeted the regions of interest (i.e., ITS1 or ATP9-NAD9) to the Ion Torrent fusion primers, which were appended with the sequencing adapters A and TrP1 [P1]). These adaptors were attached to the 5’ tail of the designed oligos, next to the sequence of the already existing primers used to amplify our region of interest (ITS1 or ATP9-NAD9) (Thermofisher 2012). Unique sequence ID tags were also added to the designed oligos. Doing so allowed for each PCR product (i.e., amplicon), to be tagged with a two-part barcode sequence. The first part revealed the target organism amplified—namely eumycete or oomycete using their respective ITS1 signature and, specifically, Phytophthora spp. from which the ATP9-NAD9 region had been amplified following a positive oomycete’s ITS1 amplification in order to gain specificity— and the second part indexed each sample for traceability purposes. As shown for the three indexed examples presented in Supplementary Table 2.S3, 48 ITS1 barcodes were appended bidirectionally (96 barcodes total) for both fungi and oomycetes, and 16 forward barcodes were designed to label the Phytophthora spp. ATP9-NAD9 amplicons. More specifically, three bidirectional PCR examples

are illustrated in Supplementary Figure 2.S2, where amplicons for the three studied regions (i.e., ITS1 fungi, ITS1 oomycete, and ATP9-NAD9 Phytophthora) and three different samples indexed are presented. Typically, the authors refer to those PCR by calling the forward (yellow) general primer (e.g., ITS1F, ITS2, and so on) name throughout the article. These newly designed barcoded primers generated amplicons from which distinguishing of the ITS1 region from fungi and oomycete amplicons and the ATP9-NAD9 region from Phytophthora amplicons was possible, and, thus, for multiple field samples pooled in a single sequencing run. The Ion Amplicon Library Preparation Fusion Method was followed (Life Technologies) (Thermofisher 2012) using the alternate master mixes presented below.

PCR conditions. PCR amplifications were done with our newly designed fusion primers and sequencing adaptors A and P1 attached to generate 350- 400-bp long amplicons. In order to target general fungal species, bidirectionnal (reverse and forward directions) amplification with ITS1F (Gardes and Bruns 1993) and ITS2 (White et al. 1990) primers was done. Similarly, general oomycetes species were targeted using bidirectional PCR amplification with fusion primers OOM- LO5.8S47 (de Cock et al. 2002; Bilodeau et al. 2007) and OOM-UP18S67 (C.A. Lévesque, personal communication). Amplification of the ATP9-NAD9 mitochondrial region from Phytophthora spp. was performed using fusion primers PhyGATP92FTail and PhyG-R6Tail (Bilodeau et al. 2014).

For fungi and oomycetes, PCR targeting the ITS1 region consisted of a 25-µl PCR volume with 1× PCR buffer, 1 mM MgCl2, 0.25 mM dNTP, 0.50 mM forward and reverse primers, 0.04 U of Platinum Taq polymerase (Life Technologies), and approximately 0.1 ng of DNA. The PCR targeting the ATP9-NAD9 region from Phytophthora spp. had a volume of 25 µl including 6 mM MgCl2, 0.5 mM both forward and reverse primers, 1× 5 PRIME RealMasterMix Probe with and without Rox (Fisher Scientific, Waltham, Mass., USA), and approximately 0.1 ng of DNA. Cycling conditions for ITS1 PCR were 3 min at 95°C; 30 cycles for 30 s at 95°C, 30 s at 52°C, and 1 min at 72°C for each cycle; and 10 min at 72°C. For ATP9-NAD9, PCR cycling conditions were 35 cycles with 15 s at 95°C and 90 s at 53°C for each cycle. Samples were run on a 1.5% agarose gel and visualized with a Gel Doc XR+ Gel Documentation System (Bio-Rad Laboratories, Inc., Hercules, CA, USA) to confirm DNA amplification. Smaller DNA fragments (approximately 100 bp) were removed by doing a purification

with Agencourt AMPure XP magnetic beads at a 0.7:1 beads/DNA ratio (Agencourt Bioscience, Beverly, MA., USA) (Edwards 2012).

High-throughput sequencing. The Ion Universal Library Quantitation qPCR Kit (Life Technologies) was used to determine the concentration of each bidirectional barcoded sample library, which were then pooled at an equimolar concentration of 16 pM. The Ion Personal Genome Machine (Ion PGM) Template OT2 400 Kit (Life Technologies) was initially used for template preparation for runs 1 to 3. Runs 4 to 15 used the Ion PGM Hi-Q OT2 Kit for template preparation. On the Ion PGM sequencer, enriched particles from run 13 were loaded onto a 316v2 chip which can generate 2 to 3 million reads per run. The rest of the runs were loaded onto 318v2 chips, which can generate 4 to 5.5 million reads per run. Both the Ion PGM Sequencing 400 Kit and the upgraded Ion PGM Hi‑Q Sequencing Kit were used (Life Technologies). Samples were demultiplexed and exported (FASTQ format) using the PGM server’s built-in plugins.

NGS data analysis. The bioinformatics pipeline used to analyse the Ion Torrent sequences is summarized in Figure 2.2. Data processing and analyses methods are further detailed below.

Raw files manipulation and filtering: FASTQ files were converted into FASTA and quality score (QUAL) files using fastqutils (Breese and Liu 2013). Sequences were trimmed based on their length and quality scores (ambiguous bases) using the trim.seqs command in Mothur (version 1.37.2) (Schloss et al. 2009) and the following parameters: minlength = 120, maxambig = 0 and maxhomop = 8.

Metadata tables and operational taxonomic unit generation: Metadata tables were compiled using a custom bash script (script available upon request; metagnomics.sh) which output them in a QIIME-compatible format (version 1.7.0) (Caporaso et al. 2010). Metadata tables were then validated using the check_id_map.py script in QIIME. After this, QIIME tags were appended to the FASTA files using the add_qiime_labels.py script. The ITSx software (version 1.0.11) (Bengtsson- Palme et al. 2013) was used next to extract the ITS1 regions from fungi and oomycete samples. Operational taxonomic units (OTU) were then picked with the pick_open_reference_otus.py and pick_otu.py QIIME scripts and merged together with a parameter file. In order to avoid (i)

overestimating biodiversity by generating too many OTUs or (ii) omitting emerging species with a too stringent setting, an OTU threshold of 97% was kept because it roughly corresponds to intraspecies variability level (Kemler et al. 2013; Nicolas et al. 2013; Abdelfattah et al. 2015; Dorado-Morales et al. 2015; Vettraino et al. 2015). Contrary to the usual practice of discarding singletons (Nilsson et al. 2011; Mundry et al. 2012), the latter were kept to promote rare species detection.

Assignment of OTU taxonomy. The UNITE database (version 31.01.2016) (Kõljalg et al. 2005) was used to infer fungal species identity based on ITS1 sequence data (Schloss et al. 2009), whereas the National Center for Biotechnology Information (NCBI) nucleotide database was used to infer oomycete species identity based on ITS1 sequence data. A custom database of ATP9-NAD9 Phytophthora spp. sequences (NCBI accessions JF771616.1 to JF772053.1 and JQ439009.1 to JQ439486.1) (Bilodeau et al. 2014) was used to infer Phytophthora species identity. This region offers an interesting alternative barcode region to the ITS, given that our database included sequences of 140 different species or hybrids, which represents the majority of the 123 currently described species (Kang 2006). Using QIIME, representative sets of sequences were selected (pick_rep_set.py) and taxonomic information was assigned (assign_taxonomy.py) to the OTUs.

OTU tables: With a modified script (parse_nonstandard_chars.py) (Walters 2015), non- ASCII characters coming from the UNITE database were removed prior to generating OTU tables (make_otu_table.py) with QIIME. The tables, initially in the Biological Observation Matrix format, were then converted into a tab-delimited values format (TSV).

Statistics: Using the R (version 3.1.3) (R Core Team) package RAM (version 1.2.1.3) (Chen et al. 2016), species richness was calculated (Jost 2006; Cobey et al. 2013; Magurran 2013). In addition, to compare the different sampling methods and to determine if the diversity of communities was accurately represented based on the sampling and NGS protocols, rarefaction curves were done and used to normalize data and observe the sequencing depth (i.e., sufficient sampling to obtain an accurate assessment on species diversity per sample, which translates in curve saturation/plateau) according to the trap type (Star et al. 2013; David et al. 2014; Abdelfattah et al. 2015). Rarefaction curves were done with the alpha_rarefaction.py script in QIIME, where the median was set as the maximum rarefaction depth and 10 was set as the minimum value.

Species query. A custom Perl script was written to screen the species or genus targeted in this project (script available upon request; metaResultExtractor.pl). The script extracted the sample ID, sequence counts for a specific query, metadata details (city, province, collection date, lure, and trap type), and FASTA sequences, and ouput the data in a TSV file. A separate file with BLAST alignments performed using the NCBI (nucleotide) database was also generated by this script. All ITS1 sequences from which a target fungal genus could be inferred using the UNITE database were then realigned with the NCBI (nucleotide) database using MAFFT (version 6.240) (Katoh et al. 2002) in order to fill potential gaps (i.e., missing species) induced by UNITE. Alignments results were then visualized and edited in BioEdit (version 7.2.5) (Hall 1999) and compared to those done with UNITE.

In an attempt to resolve closely related species, phylogenetic reconstructions were performed in Geneious (version 10.1.2) (Kearse et al. 2012). Consensus trees were built with the neighbor- joining method and the Jukes-Cantor genetic distance model. The resampling method consisted of 1,000 bootstraps that were used to generate a consensus tree with a 70% support threshold. Pythium vexans (oomycete) and H. linzhiense (fungi) were used as outgroup species or genera for the ITS1, whereas Plasmopara viticola was used for the ATP9-NAD9 region. FigTree (Version 1.4.3) (Rambaut 2016) was used to visualize the generated phylogenetic trees. Pythium vexans (outgroup) was picked because this genus’s ITS sequence is so close to Phytophthora spp. that its lack of variability may not allow dissociation of Phytophthora from Pythium, as opposed to other, less closely related oomycetes genera. Although a species within the same genera was required to enable dissociation of the species within the species complex H. annosum sensu stricto, P.viticola was the oomycete with the most similar sequence to the genus Phytophthora amongt all oomycetes in the NCBI, though the ATP9-NAD9 is highly variable and contains recombinant events.

Validation by species-specific qPCR assays. In order to validate NGS results, samples were also screened with species-specific qPCR assays. All assays were run on a ViiA 7 Real-Time PCR System (Life Technologies) and consisted of 50 cycles of 15 s at 95°C and 90 s at 60°C per cycle (Supplementary Table 2.S4). All samples and controls were run in duplicate. Positive controls were included to validate specificity, as well as negative controls with a closely related species and no-template controls.

B. fagacearum, Ceratocystis polonica, C. laricicola, Gremmeniella abietina EU Race, and Geosmithia morbida assays. Assays consisted of a 10-µl reaction. Each qPCR well contained 0.6 µM both forward and reverse primers, 0.1 µM of TaqMan probe (FAM), 1× 5 PRIME PRIME RealMasterMix Probe with and without Rox, and approximately 0.1 ng of DNA (Lamarche et al. 2014; Lamarche et al. 2015) (J. Lamarche, Personal communication). Some primers and probes shared by our collaborators at the time were later redesigned prior to publication by Lamarche et al. (2015).

Ophiostoma ulmi sensu lato, O. ulmi sensu stricto, O. novo-ulmi, O. himal-ulmi and H. annosum sensu stricto assays. The 10-µl reactions consisted of 0.6 µM both forward and reverse primers, 1 µM of TaqMan probe (FAM), 1× PRIME PRIME RealMasterMix Probe with and without Rox, and approximately 0.1 ng of DNA (Lamarche et al. 2017) (J. Lamarche, Personal communication).

Phytophthora genus and species assay. The reactions were done in a 25-µl volume, where each reaction was filled with 6 mM MgCl2, 0.50 µM both PhyGATP92FTail and PhyGR6Tail primers, 0.05 µM both ATP9PhyG2probeR (FAM) and Pramnad9sp1F (VIC) probes, 1× PRIME PRIME RealMasterMix Probe with and without Rox, and approximately 0.1 ng of DNA (Bilodeau et al. 2014).

Validation using mock-inoculated samples. Ten mock-inoculated samples were produced to compare NGS and qPCR results under controlled conditions. Environmental sample EM14S49 was chosen to create mock samples because no target species were detected in it and to ensure proper simulation of associated sequencing errors such as per-base errors (single-nucleotide substitution) and homopolymers (Kunin et al. 2010; Caporaso et al. 2011; Salipante et al. 2014). Sample EM14S49 was collected from a JB mini spore trap in Ottawa, ON on 31 July 2014. With a concentration of approximately 0.01 ng/µl, DNA from target species, closely related species, and other unrelated fungal species were spiked in variable amounts (i.e., 0 to 4 µl) into the mock sample (Supplementary Figure 2.S3), then topped up with water at a final volume of 40 µl. MOCS01 was a negative control (water); therefore, no additional DNA was spiked. All mock samples were subjected to both species-specific qPCR assays and NGS following the same protocol that was used on environmental samples.

2.5 Results

Amplicons. Following PCR reactions and electrophoresis done on the 398 environmental samples, bands were observed for both barcoded primer directions in 83% of samples using fungus primers and 7.8% of samples using oomycete primers. The fungus universal ITS2 barcoded primer yielded a higher frequency of amplification (89% positive) than the fungus universal barcoded ITS1F (85% positive), with 355 and 339 bands obtained, respectively (Table 2.1). Oomycetes showed a fairly low but consistent amplification rate between the two oomycete barcoded universal primers with 8.8% (forward) and 9.8% (reverse). From the oomycetes ITS1 amplicons (both barcoded directions), 36% were positive for Phytophthora spp. following the subsequent amplification of the ATP9-NAD9 region (Table 2.1). Mock samples showed similar results, with an amplification rate of 90 and 100% for the universal ITS1F barcoded primer and the universal ITS2 barcoded primer, respectively (Table 2.2). Oomycete amplification percentages for forward and reverse barcoded primers revealed values of 83 and 100%, respectively. Finally, the ATP9-NAD9 unidirectional barcoded primer set gave an amplification rate of 100% for Phytophthora spp.

NGS. In all, 13 of the 15 NGS runs performed were successful (Supplementary Table 2.S5). Those 13 runs had an average of 77 samples and 3.5 million reads per run. Due to problems that occurred throughout the process, all samples from runs 5, 6 and 10 were sequenced again. Further analysis confirmed that the results from run 6 and the rerun samples (run 7) complemented each other because, though the data from run 6 was proportionally reduced, its contents was not compromised. In total, 45,068,371 reads and 1,151 amplicons originating from the total 398 samples were processed. The highest number of reads obtained from a run totaled 5,980,692, whereas the lowest count was 308,794.

Fungus and oomycete diversity. Processing the NGS data through the developed bioinformatics pipeline (Fig. 2.2) showed that OTU and amplicon numbers (including singletons) were the highest in fungi (3,386 ITS1 OTUs), compared with oomycetes (82 ITS1 OTUs) and Phytophthora spp. (7 ATP9-NAD9 OTUs). Comparison of the average number of sequences per OTU ranging from 18,145 to 31,381 for the organism and region studied is provided in Table 2.3. The blank sample (water) contained no oomycetes OTU but it did contain different fungal ascomycetes

and basidiomycetes species in low concentrations (<50 counts): Aureobasidium sp., Candida sp., Cladosporium sp., Dothideomycetes sp., Microbotryomycetes sp., Mortierella sp., Neonectria sp., Phoma sp., Rhodotorula sp., Taphrina sp., Tremellomycetes sp., Trichocomaceae sp., and uncultured fungus.

Rarefaction curves were done on fungus and oomycete NGS samples but no rarefaction curve could be done for Phytophthora spp. samples due to the insufficient number of sequences. In the rarefaction curves generated for each sample, sequence saturation per sample was reached for all trap types. For the majority of sample types used, the optimal sequence number (saturation) in terms of number of species detected by sample versus sequencing depth was approximately 5,000 (Table 2.4; Supplementary Figures 2.S4 and 2.S5), with the exception that there were no oomycetes detected from the rotary arm sampler. The number of species detected per trap type after normalization was approximately 500 for fungi and 35 to 40 for oomycetes (Table 2.4).

Target species identification. In an attempt to minimize database-dependant misidentifications, a BLAST-based custom script (metaResultExtractor.pl) that reviews all of the UNITE identification using the NCBI nucleotide database was developed. Though it confirmed that no target species were found except for the Heterobasidion genus, the script was especially useful in proofreading identification of closely related Heterobasidion species because it differentiated those from the H. annosum species complex.

Following potential target species detections using the NGS analysis workflow, phylogenetic trees were built to visualize the clusters formed with reference and sample sequences so that we could easily see which samples contained the H. annosum sensu stricto specifically. The phylogenetic tree built with H. annosum sensu lato (ITS1) sequences revealed one targeted species, H. annosum sensu stricto, in 7 environmental samples (11 when including replicates of the samples) from the provinces of Québec and Ontario (Table 2.5; Supplementary Figure 2.S6; Supplementary Table 2.S6). Spore trap samples EM14S57, EM15r52, EM15S52, EM15rS76, EM15S76, EM15rS78, and EM15S78, and rotary arm samples JAB15rtrS42, JAB15rtrS51, JAB15rtrS60, and JAB15rtrS68 were clustered with the H. annosum sensu stricto reference sequences. Sequences clustering with H. abietinum/H. parviporum were from soil samples EM13S45 and EM13S46 and insect trap sample

EM15rS82 and all came from British Columbia. With the exception of one soil sample from Prince Edward Island (EM13S39), all samples that were clustered with the H. irregulare reference sequences, (namely EM13S34, EM13S38, EM15S14, EM15S50, EM15S53, EM15rS62, EM15S71, JAB14S06, JAB14S37, and JAB14S46) were from Québec or Ontario and from either spore or insect traps. H. annosum sensu stricto spiked in mock samples MOCS02, MOCS04, MOCS05, MOCS06, and MOCS07 was also detected by NGS. Likewise, a phylogenetic tree was built using the ITS1 region to infer relationships between Phytophthora spp. using Pythium vexans as outgroup (data not shown). There were Phytophthora spp. detected with oomycete ITS1 amplicons but the ITS region did not always allow identification below the genus taxonomic level. In-depth species analysis with this region was not conclusive. In spite of that, many sequences appeared to be members of defined clades previously described by Blair et al. (2008) and later updated (F.N. Martin, J.E. Blair, and M. D. Coffey, unpublished data) (Kang 2006). These unidentified sequences were very similar to the sequences of currently known species (90 to 97 % identity) but differences (mismatches) between the observed sequences suggested they were distinct species. All samples containing Phytophthora material were more or less closely related to the following species: Phytophthora infestans, P. aff. infestans, P. citrophthora, P. cactorum, P. dreschleri, P. nicotianae, P. cactorum x nicotianae, and some Phytophthora spp. related to clades 1a, 1b, 1c, and 5 (Blair et al. 2008) (Kang 2006) (F.N. Martin, J.E. Blair, and M. D. Coffey, unpublished data).

P. syringae was inferred based on the clusters formed after constructing a phylogenetic tree using the ATP9-NAD9 mitochondrial spacer region (Supplementary Figure 2.S7). Most interestingly, there were several sequences that obtained no hit from the sequence alignment with either the NCBI or the custom database. When aligned together, the BLAST hits obtained that were the closest to Phytophthora spp. were significantly different (81 to 87 % identity).

Many other fungal species closely related to the targeted species were detected with the NGS workflow in environmental samples: Ceratocystis adiposa, Ceratocystis sp., C. manginecans, Geosmithia sp., H. annosum sensu lato species complex (H. irregulare and H. abietinum/parvivorum), Melampsora sp., Ophiostoma nigrocarpum and Ophiostoma sp. (Table 2.5). The majority of identified fungi belonged to Ascomycota (59.7%), Basidiomycota (24.7%), unidentified (11.7%), and unclassified below kingdom (2.9%). Although Phytophthora spp. obtained with the ITS1

amplicons equaled 6.14%., most of the oomycetes genera found were Peronospora spp. (27.3%), and Pythium spp. (40%) and a few were Hyaloperonospora spp. (1.04%), Plasmopara spp. (0.26%), Saprolegnia spp. (0.25%), and Basidiophora spp. (0.32%).

The DNA from most target species (B. fagacearum, C. fimbriata, G. morbida, Geosmithia sp., H. annosum sensu stricto, H. annosum sensu lato, O. ulmi, O. novo-ulmi, O. himal-ulmi, O. floccosum, O. tetropii, and P. ramorum) spiked into the 10 mock samples could be detected by NGS. However, some species that were added to the mock samples were not detected: C. polonica, C. laricicola, G. abietina EU race, and G. abietina NA race.

qPCR. In the presence of the targeted DNA or positive controls, all qPCR assays performed on mock samples were positive in at least one of the two replicates, except for the assays targeting C. polonica, G. abietina and Geosmithia morbida. These specific qPCR assays could not detect the pathogens in either mock or environmental samples.

qPCR assays performed on environmental samples screening for the pathogens B. fagacearum, C. laricicola, H. annosum sensu stricto, O. himal-ulmi, O. novo-ulmi, and O. ulmi sensu stricto generated no signal for any of these pathogens, with the exception of the positive controls. The qPCR duplex assay screening for Phytophthora spp. revealed several species that were different from P. ramorum. For the environmental samples, comparable results between the output of the Ion Torrent data and qPCR assays were observed, (i.e., no detection of B. fagacearum, C. laricicola, O. ulmi s.s., O. himal-ulmi, and O. novo-ulmi). The NGS workflow showed the presence of H. annosum sensu stricto in a few samples from Québec and Ontario (Table 2.5) despite the fact that it could not be detected by qPCR.

2.6 Discussion

Pros and cons of using NGS. This proof of concept introduces a method to collect, process, and screen a high number of samples for exotic or invasive species. First, the network of samplers and the recycling of semiochemical fluids used in high-risk points of pathogen entry represent an efficient way of gathering data at a national scale in order to help diagnostics and screening efforts in the future. Plus, all the different sampling methods yielded sufficient sequencing depth per sample based on the material collected in new areas studied (Fig. 2.1; Table 2.4). These results suggest accurately represented communities. Second, the method uses custom-designed fusion primers combined with the Ion Torrent sequencing technology to monitor exotic fungi and oomycetes from multiple environmental samples simultaneously. Such a primer design approach allowed for sequencing up to 208 samples in a single run, including multiple intergenic or intergenic-flanking regions. Third, bidirectional amplification and sequencing increased sequencing coverage of amplicons, led to more robust initial species inference using databases, and yielded longer reads (350 to 400 bp) compared with the Illumina MiSeq paired-end technology. Fourth, the bioinformatics analysis workflow allows for processing millions of NGS reads from multiple pooled samples in just few days. Finally, validation by species-specific qPCR assays strengthened the robustness of the metagenomics procedure. To our knowledge, this is the first time that fusion primers and the Ion Torrent NGS technology were used together to screen for the presence of fungi and oomycetes in air samples. The combination of the different technologies used in this new method is a fast and efficient way to evaluate information on the biota of a given location. It also has the potential to guide stakeholders for surveillance purposes such as site selection, prevention, and follow-up surveys for deeper inquiries of nonindigenous species.

This article demonstrates that, when jointly used with qPCR, NGS can be used to significantly accelerate the diagnostic process by locating regulated fungal and oomycete species directly from environmental samples and narrow down the number of downstream testing required. This represents a major asset over the current standardized methods (culturing, microscopy, qPCR from cultivated isolates, and so on) by significantly increasing throughput and drastically reducing the number of fungal isolations and culturing needed for formal identification. In addition, unlike qPCR

assays, the approach developed leaves only a few samples to further assess and could detect most target and closely related species using one method rather than different protocols for each species.

The importance of mock and blank samples. DNA from several target species, congeneric species, and other unrelated species as well as one environmental sample were initially added to the 10 mock samples. The analysis of spiked samples confirmed that most pathogenic and nonpathogenic oomycete and fungal target species could be detected and identified, even in minute concentrations, by both NGS and qPCR methods. Indeed, most of the species spiked in the mock samples were persistently detected between different but comparable samples. To mimic natural conditions and add complexity to the mock samples, an environmental sample was added. The use of DNA isolates extracted from pure cultures to inoculate mock samples did not exactly emulate natural fungal and oomycete behaviour but the environmental sample spiked reproduced similar conditions. Mock 1 was made with an environmental sample to ensure that its contents could be known and used as a baseline for analysis. Although fungi were detected in the blank sample (water), those were all species commonly found in either soil, water, or air or as plant endophytes. Those OTUs were found in very low quantity (<50 counts) and likely originated from either airborne fungal sources from the laboratory during sample processing (PCR) or were ubiquitous to the indoor environment and, thus, hard to avoid despite the controlled environment and instruments used (i.e., laminar flow hood cabinet cleaned after each use) at all times during the experiment (Khan and Karuppayil 2012; Cuadros-Orellana et al. 2013; Lindahl et al. 2013). Because none of the organisms found in the blank sample were within the same genera as our targeted species, we refuted the possibility of cross contamination with the other mock samples.

PCR outputs. In fungi, the slightly higher number of bands observed on gel when using the universal ITS2 barcoded primer versus the universal ITS1F barcoded primer may be explained by the fact that the ITS2 primer is not fungi specific, as opposed to the ITS1F primer (Table 2.1). These extra bands could originate from the presence of pollen and other nonfungal environmental contaminants in the samples. For oomycete PCR products, the lesser number of bands on gel observed compared with fungi are likely due to the fact that most oomycetes are soilborne organisms and, thus, less likely to be found in aerial samples. In addition, many more fungal species are described than oomycetes (Hawksworth and Rossman 1997; Rossman and Palm 2006; Blackwell

2011). Although the OTU numbers between oomycetes and fungi varied considerably, the comparison of the average number of sequences per OTU (Table 2.3) proved not to be significantly different regardless of the organism and region studied. Nevertheless, oomycete ITS1 amplicons were useful for narrowing down the number of samples to process through NGS and to target the ATP9-NAD9 region from Phytophthora spp.

NGS outputs: fungi. Fungi produce aerial spores and are ubiquitous in the environment, which explains why all environmental samples contained fungal material, primarily Ascomycota and Basidiomycota (Tables 2.1 and 2.5). Because this project mainly used air and insect traps, it could also explain the type of fungal species found in the blank sample. The target species H. annosum sensu stricto was detected by NGS in environmental samples along with H. abietinum/parviporum and H. irregulare, all species of economic concern related to conifers in North America. The location where species belonging to the H. annosum sensu lato species complex were found corresponded to areas where annosus root rot was previously detected by conventional surveys near Harrington, Gatineau, and Ottawa (Laflamme and Blais 1995). Indeed, our sites were within 50 to 500 km from south Ontario’s red pine (Pinus resinosa) plantations identified by Laflamme and Blais (1995). The detection of H. irregulare through our approach, though not surprising because this species is native to North America and occurs in eastern Canadian forests (Garbelotto and Gonthier 2013; Bérubé et al. 2017a; Lamarche et al. 2017), could be very useful for pest management. H. annosum sensu stricto and H. abietinum/H. parviporum are present in Europe and Asia (Woodward et al. 1998; Garbelotto and Gonthier 2013; Lamarche et al. 2017) but were also recently reported in the United States (Colorado, New Mexico, Nebraska [H. annosum sensu stricto only] and Arizona [H. annosum sensu stricto only]) (Centre for Agriculture and Biosciences International 1910; Worrall et al. 2010). For that reason, our potential spore material detection in Canada leading to formal confirmation is concordant considering the geographical proximity and, therefore, should be taken seriously as a warning to a potential increasing threat. Follow-up sampling and screening for symptomatic trees would be required to further inquire and confirm the status of H. annosum sensu stricto and H. abietinum/H. parviporum presence in Canada. It is also possible that Heterobasidion species previously detected in North America were misidentified for many years because the species complex concept is relatively recent. Again, such results demonstrate the importance of maintaining surveillance of this genus despite the fact that it is not among the CFIA’s regulated pests.

NGS outputs: Oomycetes. Compared to fungi, only a few environmental samples contained oomycetes ITS1 OTUs (Tables 2.1, 2.3, and 2.5), and even fewer contained Phytophthora ATP9- NAD9 OUT; however, because most oomycetes, including Phytophthora spp., are soil and water organisms, this was an expected result from air and insect trap samples. NGS validated that the oomycete primers used (ITS1) were oomycete-specific because only Oomycota taxa were detected; namely Peronospora spp., Phytophthora spp., Pythium spp., Hyaloperonospora spp., Plasmopara spp., Saprolegnia spp., and Basidiophora spp. The ITS1 amplicons generated with oomycete-specific primers revealed the presence of Phytophthora spp. in many samples but, as anticipated, no accurate identification below the genus level was possible. Notwithstanding, there were several ITS1 samples from which Phytophthora spp. such as Phytophthora syringae, Phytophthora species closely related to P. infestans, P. aff. infestans, P. citrophthora, P. cactorum, P. dreschleri, P. nicotianae, and P. cactorum x nicotianae were found. For detected species related to clades 1a, 1b, 1c and 5 (Kang 2006; Blair et al. 2008), identification was not conclusive in most cases because sequence alignments with well-known species had lower levels of similarity (approximately 90%). Furthermore, numerous sequences of Phytophthora ATP9-NAD9 amplicons were actually quite different (81 to 87 % identity) compared with any other known Phytophthora sequences found in NCBI or the custom database, suggesting that they could be new species, genera, or hybrids because our samples originated from atypical oomycete environments. The low similarities observed could also be the result of biases introduced by the Ion Torrent technology itself (Quail et al. 2012), or by the lower temperature the PCR was set at. As explained by Martin et al. (2012), our knowledge of the genus Phytophthora and the number of species described is just starting to expand with new technologies and approaches such as ours but is promising to be helpful in managing and better understanding impacts of exotic species on natural resources. For instance, the highly variable and recombinant nature of the ATP9-NAD9 spacer sequences made alignments and construction of phylogenetic trees more challenging but Phytophthora species or clade inference was still possible in most cases.

NGS versus qPCR. Similarities were observed between NGS and qPCR results, showing robustness of our NGS workflow. Because NGS results could be validated with species-specific qPCR assays on both environmental and mock samples, it would be very useful to obtain or develop qPCR assays for the missing targeted species such as additional Phytophthora spp. The importance

of using species-specific qPCR assays in combination with NGS is highlighted by the fact that potentially new and damaging species were detected only by high-throughput sequencing. Other nonphytopathology-related scientists have actually reported results similar to ours where—just as was observed here with Heterobasidion species— metagenomics could detect some species which couldn’t be caught by qPCR (Ladetto et al. 2014; Goodrich et al. 2016). As opposed to qPCR results, which are limited to presence or absence and quantities, NGS can also provide immediate and complex sequence information such as taxonomic evolution and species proximity.

Proper species identification when dealing with regulated pests is essential because misidentifications could have severe consequences for international trade and policies. For that reason, when discovering regulated species by NGS, it is currently necessary to complement our method with validated species-specific qPCR assays, which are considered reference standards (Derveaux et al. 2010; Adamski et al. 2014). Part of this necessity is due to the fact that metagenomics analysis relies on the use of incomplete and partially curated databases. Taxonomic profiling of environmental samples using marker genes relies on the availability of high-quality databases. The fungal database UNITE was used to assign fungal taxonomy to the ITS bioinformatics workflow. Though it is a manually curated (i.e., high-quality) database, UNITE only has a limited number of sequences available. As a result, many fungal species are not present and can’t be identified. Another challenge when using databases to infer the taxonomic composition of NGS sequences is the constant risk of misidentification. Because some species are genetically very similar (i.e., have very low divergence in the marker gene used), closely related species can be incorrectly identified (present or not in the database). In an attempt to avoid misidentification, a script (metaResultExtractor.pl) was developed to cross-validate the UNITE taxonomic assignments using another database (i.e., NCBI nucleotides). Analyses showed that the genus and species, when available in the database, were always inferred correctly using UNITE (data not shown). However, when species identification was not possible in UNITE, BLAST results from the NCBI (nucleotides) database produced better alignment scores due to the higher sequence number in the database. Because there is currently no curated public oomycete database available, the taxonomic assignment of oomycete ITS1 amplicons has to rely on the NCBI (nucleotides) database. Considering that the ITS can’t be used to distinguish Phytophthora spp., our alternative ATP9-NAD9 custom database, which represents most described species, was subsequently used. By aligning the oomycete ITS1

sequences with those in the NCBI nucleotide database using BLAST, we could filter which samples had the genus Phytophthora, and then align the ATP9-NAD9 amplicons with the sequences from our custom database (ATP9-NAD9) to properly identify Phytophthora spp. However, sequence alignment differences still occurred at times. This could be due, in part, to the recent addition of sequences of newer species in the NCBI database, or because of the presence of incorrectly assigned sequences within the database. The interspecies genetic diversity may be lower than the error rate of the NGS technology, which could be another possible factor in misidentification. Despite its higher error rate (1.71%) compared with the Illumina MiSeq (0.80%) (Quail et al. 2012), the Ion Torrent sequencing platform was chosen because, at the time, it could produce longer reads (up to 400 bp, the actual size of the ITS1 region in fungi and oomycetes and the ATP9-NAD9 region in Phytophthora spp.), an appreciable asset when doing molecular identifications. Comparatively, the Illumina MiSeq platform only yielded sequence data up to 150 bp when the project was initiated in 2013 (Quail et al. 2012).

Three species—C. polonica (mocks 2, 3, 9, and 10), Gremmeniella abietina EU race (mocks 2, 3, 5, and 6), and Geosmithia morbida (mocks 2 and 3) —were not detected by either qPCR or NGS from the spiked mock samples. We could likely not detect them by NGS using the ITS1 region as it lacks sufficient variation to resolve those specific species (Lamarche et al. 2014; Lamarche et al. 2015). As for the qPCR assays, although the β-tubulin and RNA polymerase B II genes—those alternate genes targeted by the qPCR assays—can achieve species resolution due to their higher variation rates (Lamarche et al. 2014; Lamarche et al. 2015), they also failed to detect species, most likely because they are single-copy genes and, therefore, less sensitive (Ayliffe et al. 2001; Aguileta et al. 2008; Schmitt et al. 2009; Bellemain et al. 2010). In comparison, the ITS region is found in multiple copies in fungal and oomycete genomes (up to 251 copies/cell) (Ayliffe et al. 2001; Aguileta et al. 2008; Schmitt et al. 2009; Bellemain et al. 2010), a tremendous advantage when working with minute quantities of DNA and with mixed samples. This advantage alone overcomes the fact that some species can’t be resolved with the ITS or other regions.

In this article, we presented a fast and high-throughput NGS-based method to monitor and evaluate fungal phytopathogenic material in environmental samples originating from air and insect traps. Validated with qPCR assays, NGS data analyzed with a powerful customized bioinformatics pipeline outputs taxonomic composition of samples which can be used as a highly relevant tool for

regulatory agencies, organisations, and countries. Although limited by database quality, we clearly showed that our NGS method can simultaneously and efficiently process large numbers of field- collected samples and has the potential for screening for almost all fungal and oomycete species, including previously uncharacterized species. Additional testing, culture validation, Koch’s postulates, and follow-up surveys in the field to seek for symptomatic trees shall still be performed, especially in situations where regulatory actions are necessary.

More importantly, this new method was able to detect spores of Heterobasidion spp. and Phytophthora spp. in a specific environment, despite the fact that it could not provide information on the extent of establishment or a disease’s status. Used as a screening tool, this protocol identified high risk-areas (Gatineau, QC; Ottawa, ON; and Vancouver, BC) for species of interest and allowed for analysing on a national scale. Although the ITS1 is unable to resolve certain species, it can be applied within a broader surveillance program or at specific locations to pinpoint samples that require further investigation. Validated species-specific assays (qPCR), when available, are still required to confirm the presence of species from NGS data.

2.7 Acknowledgements

The authors would like to thank the anonymous reviewers of our manuscript for their helpful comments. We thank Julie Dubé for the rotary-arm spore sample processing and our field collaborators who accepted to host and operate the rotating-arm spore collectors: Normand Bérubé, Jean-Francois Pépin, Jacques Turcotte, Réjean Ouellet, Martin Lepage, Pascale St-Laurent, Robert Morisset and Alexander Bates. We would like to thank Ron Neville, Lucie Gagné, Erin Bullas- Appleton, CFIA inspection staff and Dr. Richard Wilson who provided us with insect trap samples. Thanks to Drs. Philippe Tanguay and Richard Hamelin who provided samples and DNA isolates. Thank you to Miranda Newton and Hadil Sayed for offering support with sample processing. Thank you to all of those who helped with the bioinformatics analysis, namely: Ahmed Abdelfattah, Emily Giroux, Dr. Wen Chen, Iyaad Kandalaft, Dr. Guillaume Nicolas and Christine Lowe. Thanks to Josyanne Lamarche and Drs. Philippe Tanguay and Richard Hamelin for sharing qPCR assays. Thanks to Dr. Louis Bernier for his support and advices. This project was funded by the CFIA RPS project OLF-P-1304, GRDI fundings (QIS and CFIA OLF-P-1411) and Genome Canada and Genome BC through a Large Scale Applied Research Program (LSARP2112; Genome Canada grant) for the TAIGA collaboration project http://taigaforesthealth.com CFIA-RPS and CFIA-TD projects and GRDI- CFIA mandated.

2.8 Tables

Organism Target regionb Positive PCR Total PCR Positive (%)c Primer used to append barcodesa ITS1F-Forward Fungi ITS1 339 398 85 ITS2-Reverse Fungi ITS1 355 398 89 ITS1-Forward Oomycete ITS1 35 398 8.8 ITS2-Reverse Oomycete ITS1 39 398 9.8 ATP9-NAD9-Forward Phytophthora spp. ATP9-NAD9 17 47 36 Total … … 785 1,639 Average: 45.7 aEach PCR included a set of primers but bidirectional sequencing required one primer per direction to append the sample and organism index (i.e., barcode). bITS1 = internal transcribed spacer 1 and ATP9-NAD9 = adenosine triphosphate synthase subunit 9-nicotinamide adenine dinucleotide dehydrogenase subunit 9 spacer. cPercentage of the number of positive PCR over the total number of reactions done.

Table 2.2 Presence or absence of amplification as detected by gel electrophoresis using barcoded polymerase chain reaction (PCR) products from 10 mock samples from targeted organisms, and the percentages of positive reactions associated compared with the expected results.

Target Primers used to append barcodesa Organism Positive PCR Expected positive Positive (%)c regionb ITS1F-Forward Fungi ITS1 9 10 90 ITS2-Reverse Fungi ITS1 10 10 100

ITS1-Forward Oomycete ITS1 6 6 100 ITS2-Reverse Oomycete ITS1 5 6 83 ATP9-NAD9-Forward Phytophthora spp. ATP9-NAD9 6 6 100 Total … … 36 38 Average: 94.6 aEach PCR included a set of primers but bidirectional sequencing required one primer per direction to append the sample and organism index (i.e., barcode). bITS1 = internal transcribed spacer 1 and ATP9-NAD9 = adenosine triphosphate synthase subunit 9-nicotinamide adenine dinucleotide dehydrogenase subunit 9 spacer. cIPercentage of the number of positive PCR—over the total number of reactions done—that were expected to be positive because the contents of inoculated samples were known.

1 Table 2.3 Organism sequences and operational taxonomic units (OTU) counts produced from Ion Torrent Personal Genome Machine next- 2 generation sequencing data output.

Number of Number of sequence per OTUa Organism Sequence Media Variance SEc P valued and region Samples OTUb Min Max Mean SD s n 16,199,59 46,466,035,5 Fungi (ITS1) versus Fungi ITS1 708e 3,386 1 963,469 22,880 7,384 68,166 1171.45 6 6 Oom (ITS1), P = 0.8049 Oomycete Fungi (ITS1) versus 69e 82 1,251,295 1 100,134 18,145 3,054 26,881 722,588,161 2968.51 (Oom) ITS1 ATP9-NAD9, P = 0.9408 Phytophthor a spp. 5,552,783,28 28164.7 Oom (ITS1) versus 22 7 690,387 1 353,323 31,381 6,692 74,517 ATP9- 9 8 ATP9-NAD9, P = NAD9 0.8723 aMinimum (Min), maximum (Max), and mean per sample library; minimum equals 1 because singletons were kept for rare species detection. SD = standard deviation. bIncluding singletons. cStandard error. dCalculated from an analysis of variance using the Tukey honestly significant difference posthoc test at a confidence interval of 95% (http://statpages.info/). P values were used to compare OTU means between fungi (ITS1) versus oomycetes (ITS1), fungi (ITS1) versus Phytophthora spp. (ATP9-NAD9), and oomycete (ITS1) versus Phytophthora spp. (ATP9-NAD9) in order to determine whether or not the sequence number per OTU significantly varied per region. Note that a P value below 0.05 indicates a significant difference between the datasets compared. eSample amplicons bidirectionnally (forward and reverse) generated.

Table 2.4 Saturation values (sequence count) reached through rarefaction curves generated according to the trap or sample types and observed species number by trap or sample types.

Organism (ITS1), Trap or sample typea rarefaction datab JB Spore Trap Insect Trap Rotary arm Soil Fungi Sequence saturation reachc ~4,000 >~4,000 >~5,000 >~5,000 Species Number ~500 ~500 ~400 <~600 Oomycete Saturation reach >~6,000c >~10,000 N/A >~2,000 Species number ~35 ~35 N/A ~40 aJB = Johnson and Barnes and N/A = insufficient s=data to produce a curve. bAmplicons from the internal transcribed spacer 1 (ITS1) intergenic region. Not done for the adenosine triphosphate synthase subunit 9-nicotinamide adenine dinucleotide dehydrogenase subunit 9 spacer (Phytophthora spp.) due to an insufficient number of sequences obtained. cIn number of sequence per species. dWith the exception of three samples library.

Table 2.5 Comparison of fungal species of interest found in environmental samples and mock samples using next-generation sequencing (NGS) and quantitative polymerase chain reaction (qPCR).

Detection methodsa Environmental samples Mocks samples Species qPCR NGS qPCR NGS Target species Bretziella fagacearum (Ceratocystis fagacearum)b x Species in genus   C. laricicolab x Species in genus  Species in genus C. polonicab x Species in genus x Species in genus Geosmithia morbidab x Species in genus x  Gremmeniella abietina EU raceb x x  Genus Heterobasidion annosum sensu strictob x    Melampsora pinitorquac N/A Genus N/A, NI NI Ophiostoma novo-ulmic x Species in genus   O. ulmib x Species in genus   Phytophthora ramorumb Genus Species in genus   Phytophthora spp.b     Closely related species C. adiposac N/A  N/A NI C. fimbriatab N/A Species in genus N/A Species in genus C. manginecansc N/A  N/A  Ceratocystis sp.b N/A  N/A Genus Gremmeniella abietina NA raceb x x N/A Genus Geosmithia puttirellib x Species in genus N/A Species in genus Geosmithia sp.b N/A  N/A Genus H. annosum sensu latod    

H. irregulareb N/A  N/A  Melampsora sp.c N/A  N/A NI O. floccosumb N/A Species in genus N/A  O. himal-ulmib x Species in genus   O. tetropiib N/A Species in genus N/A  O. nigrocarpumc N/A  N/A NI Ophiostoma sp.b N/A  N/A Genus Phytophthora spp.b     aSpecies in genus = detection o species within the target genus and Genus = detection of genus. NA = absence of or missing qPCR assay for this species or genus and NI = species or genus not included in the mock samples. Symbols: x indicates no detection of the target by the method and  indicates a detection of the target by the method. bSpecies added in the mock samples. cSpecies or genus not included in the mock samples. dH. irregulare was spiked to include Heterobasidion annosum sensu lato.

2.9 Figures

Figure 2.1 Sampling sites in A the Canadian West Coast and B, Eastern Canada. Adapted from Google Earth.

Figure 2.2 Bioinformatic pipeline and tools used for next-generation sequencing analysis.

2.9 Supplementary Materials

2.10 Supplementary Tables

Supplementary Table 2.S1 Details associated with samples collected from 2013 to 2015. Trap Type Lure or sample type Number of sample Canadian province(s) UHR_E_APb 39 C6C8 36 Insecta Propylene glycolc 4 BC, NB, NL, NS, ON, PEI and QC General Longhorn 17 Pine Sawyer 16 JB Spored Rain 51 ON and QC JB Spore minie Rain 157 ON and QC Soilf Soil, no lure 8 BC and PEI Rotary armg Aerial, no lure 70 QC Total 398 7 aBaited insect trap consisting of stacked funnels. bFor insect traps semiochemicals composition, see Supplementary Table 2.S2. cThis collection fluid (i.e., preservative) was used only once throughout all samples, so no lure-specific analyses were done using these data. dOriginal JB rainfall collector used as a spore trap. eSmaller version of the original JB spore. fSoil samples from pre- and post-fumigated fields used for validating the NGS protocol. gAir sampling rotating device made of a metal rod and using a silicone grease for particle adhesion.

Supplementary Table 2.S2 Chemical composition of the semiochemicals used to bait specific types of insects in the insect traps. Semiochemical Chemical composition UHR_E_AP Ultra-high release ethanol and ultra-high release alpha-pinene Ultra-high release ethanol, (E)-6,10-dimethyl-5,9-undecadien-2-ol (E-fuscumol) and (E)-6,10-dimethyl-5,9-undecadien-2- General Longhorn yl (E-fuscumol acetate) 2-undecyloxy-1-ethanol (monochamol), ultra-high release ethanol, ultra-high release alpha-pinene and racemic 2-methyl- Pine Sawyer 6-methylene-7-octen-4-ol (ipsenol)

C6C8 Racemic 3-hydroxyhexan-2-one (K6), racemic 3-hydroxyoctan-2-one (K8) and ultra-high release ethanol

Supplementary Table 2.S3 Barcoded fusion primers designed for organism’s multiplexing and their associated partner primer appended with the Ion Torrent sequencing adaptors (A and P1) for selective amplification of Fungi, oomycetes and Phytophthora spp. and specific region (ITS1 and ATP9-NAD9). Target Primer sequenceb General Primer organism Barcode Primer partnera primer direction and identifier Sequencing General primer key Barcode reference region adaptor sequencec ITS1FAB-1 CTAAGGTAAC ITS1FAB-2 TAAGGAGAAC GATCTTGGTCATT (White et ITS2-P1 TAGAGGAAGTAA al. 1990) ITS1FAB-3 AAGAGGATTC Fungi ITS1d ITS2AB-1 CTAAGGTAAC (Gardes GATGCTGCGTTCT and ITS1F-P1 ITS2AB-2 TAAGGAGAAC TCATCGATGC Bruns 1993) ITS2AB-3 AAGAGGATTC

Omup18S6 CCATCTCATCCCT CTAAGGTAAC 7AB-1 (Bilodeau GCGTGTCTCCGA TCAG et al. Omup18S6 e GATCTCGCCATTT Forward Omlo5.8S47-P1 C TAAGGAGAAC 2007; 7AB-2 AGAGGAAGGT Decock Omup18S6 AAGAGGATTC 2012) Oomycete 7AB-3 ITS1 Omlo58S47 CTAAGGTAAC AB-1 (C.A. Levesque Omlo58S47 GATATTACGTATC Omup18S67-P1 TAAGGAGAAC , Personal AB-2 GCAGTTCGCAG communi Omlo58S47 AAGAGGATTC cation) AB-3 Phytophth PhyGATP9 PhyG-R6-P1 CTAAGGTAAC GATCCTTCTTTAC (Bilodeau

ora spp. 2F-AB-1 AACAAGAATTAAT et al. ATP9- PhyGATP9 G 2014) TAAGGAGAAC NAD9f 2F-AB-2 PhyGATP9 AAGAGGATTC 2F-AB-3 ITS2AB-1, ITS2AB-2, GATCTTGGTCATT (White et ITS1F-P1 ITS2AB-3, TAGAGGAAGTAA al. 1990) Fungi ... ITS1 ITS1FAB-1, (Gardes ITS1FAB-2, GATGCTGCGTTCT and ITS2-P1 ITS1FAB-3, TCATCGATGC Bruns ... 1993) (Bilodeau

Omlo58S47AB-1, CCACTACGCCTC et al. Omup18S6 Omlo58S47AB-2, GATCTCGCCATTT CGCTTTCCTCTCT 2007; 7-P1 Omlo58S47AB-3, None AGAGGAAGGT ATGGGCAGTCGG Decock Reverse ... g Oomycete T 2012) ITS1 (C.A. Omup18S67AB-1, Levesque Omlo5.8S4 Omup18S67AB-2, GATATTACGTATC , Personal 7-P1 Omup18S67AB-3, GCAGTTCGCAG communi ... cation) Phytophth PhyGATP92F-AB-1, (Bilodeau ora spp. PhyG-R6- PhyGATP92F-AB-2, GATATACATAATTC et al. ATP9- P1 PhyGATP92F-AB-3, ATTTTTATA 2014) NAD9 ... aPrimer partner’s identification name. Primers listed are to be used with those listed in the column “Barcode identifier” when performing bidirectional fusion primer sequencing. bNote that the full primer sequence is spread over multiple columns including “Sequencing adaptor”, “Key”, “Barcode” and “General primer sequence”. Therefore, users shall include all nucleotides given in those columns in that specific order. cThis column presents the sequences of already existing primers which became part of the fusion primers in this project. Their specific sequence is used to target specific regions

such as ITS1 in fungi, ITS1 in oomycetes and ATP9-NAD9 in Phytophthora spp. dInternal transcribed spacer 1. eNucleotide sequence of the sequencing (Ion Torrent ) adaptor A1. fAdenosine triphosphate synthase subunit 9-nicotinamide adenine dinucleotide dehydrogenase subunit 9 spacer. gNucleotide sequence of the sequencing (Ion Torrent ) adaptor P1.

Supplementary Table 2.S4 Species-specific qPCR assays primers and probes used for NGS results validations. Target Primer / Name Sequence 5'→ 3' Author gene Probe C. laricicola Claricicola_F451 β-tubulina Forward GCCCGCATCATGTTT Lamarche, et al., Claricicola_R538 Reverse GACGCTTGAGCGG 2014 Claricicola_T505RC Probe 6-FAMb-TGTGCCTGCTC/ZEN/TGATTCAT/IABkFQc C. polonica Cpolonica_F527 β-tubulin Forward GCGTCCACGCCACAAT Lamarche, et al., Cpolonica_R761 Reverse CCTGAACACCAATTATGTTATATC 2014 Cpolonica_T575 Probe 6-FAM/TATATTGTATGATGAGACTAGACGATGCGG/3IABkFQ C. fagacerum Cfagacerum_F315 EF1d Forward GTCTGTAGAAGGGGG Lamarche, et al., Cfagacearum_R406 Reverse CTCCATTCTTTACTACAACC 2015 Cfagacearum_T357 Probe 6-FAM-AGAAGTAAC/ZEN/TGGACAACCGTCT-IABkFQ G. abietina (EU race) Lamarche, et al., Gabietina_F2b e Forward GGCGCGGTCTTC RPB2 2015

Gabietina_R4 Reverse TCGGTCTATGGAATTTTTGAAATTTA Lamarche, Personal

Gabietina_T3 Probe 6-FAM-ACTTCGGAATAGACCACGATCGATACTACT-IABkFQ communication G. morbida GeosBtF_440 β-tubulin Forward TTCTGACCGCACGA Lamarche, Personal GeosBtR_585 Reverse ACGGCAGGAAGGT communication GeosBtT_738-773 Probe 6-FAM-AATAGGCTGGACAGGAAGA/IABkFQ H. annosum s.s. Hannosum_ss_4A/T_ Forward GCGAAACACGCAGC F435 ITSf Lamarche, et al., Hannosum_ss_R501 Reverse GTCGGGTTCTTTTGAC 2016 Hannosum_ss_irregul Probe 6-FAM/TTCCGAGCC/ZEN/GCGTCTTCT/3IABkFQ are_T464RC

O. ulmi s.l. allulmiF_583-597 EF1 Forward CAGGCCTGCAGCATA Lamarche, Personal allulmiR_728-746 Reverse ATAGGCCCGCTTTTC communication allulmiT 646-660 Probe 6-FAM/CCTCACCAA/ZEN/CCC CTC/3IABkFQ O. ulmi s.s. ulmiF_602-645 EF1 Forward CACATTCGCAGACTCG Lamarche, Personal ulmiR_717-743 Reverse CACTTTTCTCTCTCTCTTTT communication ulmiT_676-703rev Probe 6-FAM/TCTTACTCA/ZEN/TCACCCTCAT/3IABkFQ O. novo-ulmi novulmiF_702-726 EF1 Forward GAATGAGAGAGAGACG Lamarche, Personal novulmiR_811-825 Reverse GACTCGTCTCGTTCG communication novulmiT_743-768 Probe 6-FAM/TCTATTGCT/ZEN/CAACTGCATTGA/3IABkFQ O. himal-ulmi himulmiF_730-750 EF1 Forward CACATTCGCAGACTCG Lamarche, Personal himulmiRok_893-911 Reverse GAATGAGAGAGAGACG communication himulmi_T808-835 Probe 6-FAM/AGACGAGTC/ZEN/GAAGATGAA/3IABkFQ P. ramorum PhyG_ATP9_2FTail ATP9- Forward AATAAATCATAACCTTCTTTACAACAAGAATTAATG PhyG-R6_Tail NAD9 Reverse AATAAATCATAAATACATAATTCATTTTTATA ATP9_PhyG2_probe Bilodeau, et al., 2014 Probe 6-FAM/AAAGCCATCATTAAACARAATAAAGC/3IABkFQ R Pram_nad9sp_1F Probe CAL560g-ACGTTACGTCTAGACTTGTATTATGCATTG-BHQ-1h aBeta-tubulin gene. b5’6-FAM (Fluorescin) fluorescent dye. c3’Iowa Black dark quencher. dElongation factor 1 gene. eRNA polymerase B II. fInternal transcribed spacer. g5’CAL_Fluor Orange 560 dye. hBlack-hole quencher 1 dye.

Supplementary Table 2.S5 Ion Torrent PGM runs summaries and averages per run. Run Number of Number of Chip Date number sequences samples version 2014-02-24 1 5,980,692 95 318 2014-04-30 2 3,364,235 77 318 2015-03-10 3 5,458,126 77 318 2015-10-20 4 3,052,115 95 318 2016-01-27 5 2a 95 318 2016-01-28 6 308,794 75 318 2016-03-17 7 4,448,566 98 318 2016-03-31 8 3,071,368 30 318 2016-04-20 9 3,837,095 91 318 2016-05-31 10 10a 87 318 2016-06-13 11 3,985,832 87 318 2016-06-14 12 4,145,528 87 318 2016-07-27 13 1,293,631 23 316b 2016-08-24 14 3,732,534 98 318 2016-10-13 15 2,389,855 36c 318 Total 15 45,068,371 1151 2 Average 3,466,797 77 aSequencing was redone. Value not included in the total average calculations. bA 316 chip was used because less samples were processed in that case. cThis sequencing run was dedicated to mock samples only and processed exactly as all environmental samples.

Supplementary Table 2.S6 Detection of species of interest with NGS and species relationships inferred between samples and references through phylogenetic trees. Phytophthora spp. Type of Heterobasidion Phytophthora spp. from alignment from alignment of Sample ID Origin sample annosum complexa with the ITS1 region the ATP9-NAD9 spacer EM13S08 Insect trap Penticton, BC P. infestans

EM13S09 Insect trap Quebec, QC P. infestans

EM13S11 Spore trap Ottawa, ON P. infestans

EM13S17 Insect trap St-John, NB P. infestans

P. aff. infestans, Phytophthora sp. Mount Pearl, related to clade 1cb, Phytophthora sp. EM13S22 Insect trap P. syringae NL related to clade 1b, close to P. nicotianaec ThamesFord, EM13S28 Insect trap P. infestans ON EM13S30 Spore trap Ottawa, ON P. infestans

EM13S34 Spore trap Ottawa, ON H. irregulare

St-Nicephore, EM13S38 Insect trap H. irregulare QC EM13S39 Soil Alberton, PEI H. irregulare

H. EM13S45 Soil Vancouver, BC P. syringae abietinum/parviporum Phytophthora sp. related to clade 1a, H. EM13S46 Soil Vancouver, BC close to P. cactorum, close to P. abietinum/parviporum nicotianae x cactorum, P. citrophthora Spore trap EM14S57 Gatineau, QC H. annosum s.s. P. syringae mini EM14S63 Insect trap Warwick, ON

EM14S64 Insect trap Warwick, ON P. syringae

EM14S85 Insect trap Toronto, ON P. syringae

Mississauga, EM14S87 Insect trap P. syringae ON Mississauga, EM14S88 Insect trap P. syringae ON EM14S89 Insect trap Caldeon, ON P. syringae

EM14S91 Spore trap Ottawa, ON P. syringae

EM15rS49 Insect trap Valleyfield, QC Phytophthora sp. related to clade 5 EM15rS52 Spore trap Ottawa, ON H. annosum s.s.

P. aff. infestans, Phytophthora sp. related to clade 1c, Phytophthora sp. EM15rS61 Insect trap Plessiville, QC related to clade 1b, Phytophthora sp.

related to clade 5 close to P. nicotianae, St-Tite-des- EM15rS62 Insect trap H. irregulare Caps, QC EM15rS71 Insect trap Sorel, QC Phytophthora sp. related to clade 5

EM15rS76 Spore trap Ottawa, ON H. annosum s.s.

Spore trap EM15rS78 Gatineau, QC H. annosum s.s. mini H. EM15rS82 Insect trap Vernon, BC abietinum/parviporum EM15S14 Spore trap Gatineau, QC H. irregulare

EM15S32 Insect trap Whitby, ON

Spore trap EM15S35 Ottawa, ON P. infestans mini EM15S42 Spore trap Ottawa, ON Phytophthora sp. related to clade 5

EM15S49 Insect trap Valleyfield, QC Phytophthora sp. related to clade 5

EM15S50 Insect trap Chertsey, QC H. irregulare

EM15S52 Spore trap Ottawa, ON H. annosum s.s.

EM15S53 Spore trap Gatineau, QC H. irregulare

P. aff. infestans, Phytophthora sp. related to clade 1c, Phytophthora sp. EM15S61 Insect trap Plessiville, QC related to clade 1b, close to P. nicotianae Spore trap EM15S66 Ottawa, ON P. infestans P. syringae mini Phytophthora sp. related to clade 1a, Phytophthora sp. related to clade 5 EM15S71 Insect trap Sorel, QC H. irregulare close to P. cactorum, close to P. nicotianae x cactorum EM15S76 Spore trap Ottawa, ON H. annosum s.s.

Spore trap EM15S78 Gatineau, QC H. annosum s.s. mini Spore trap JAB14S06 Quebec, QC H. irregulare mini Spore trap JAB14S37 Quebec, QC H. irregulare mini Spore trap JAB14S46 Quebec, QC H. irregulare mini JAB15rtrS4 Rotary arm Harrington, QC H. annosum s.s. 2 JAB15rtrS5 Rotary arm Harrington, QC H. annosum s.s. 1 JAB15rtrS5 Ste-Clotide-de- Rotary arm P. syringae 6 Horton, QC JAB15rtrS6 Rotary arm Harrington, QC H. annosum s.s. 0 JAB15rtrS6 Rotary arm Harrington, QC H. annosum s.s. 8 JAB15S09 Spore trap Montreal, QC Close to P. drechsleri

mini

Close to P. kelmania, close to P. cryptogeae, close to P. P. ramorum, P. pseudocryptogeae, close to P. kelmania, P. MOCS02 Mock Laboratory H. annosum s.s. richardiae, close to P. erythroseptica, syringae, P. close to P. sansomea, close to P. aff. kernoviae sansomeana, close to P. syringae, close to P. austrocedrae, P. syringae

Close to P. drechsleri, close to P. syringae, close to P. austrocedrae, P. P. kelmania, P. syringae, close to P. austrocedrae, MOCS04 Mock Laboratory H. annosum s.s. syringae, P. close to P. richardiae, close to P. kernoviae erythroseptica, close to P. cryptogea, close to P. pseudocryptogea Close to P. sansomea, close to P. aff. MOCS05 Mock Laboratory H. annosum s.s. P. kelmania sansomeana MOCS06 Mock Laboratory H. annosum s.s.

P. ramorum, P. kelmania, P. syringae, P. MOCS07 Mock Laboratory H. annosum s.s. Close to P. drechsleri kernoviae, Phytophthora sp. related to clade 8a

P. ramorum, P. Close to P. drechsleri, close to P. kelmania, P. richardiae, close to P. erythroseptica, syringae, MOCS09 Mock Laboratory close to P. cryptogea, close to P. Phytophthora sp. pseudocryptogea, P. sansomea, related to clade 8a close to P. aff sansomeana,

MOCS10 Mock Laboratory P. kernoviae, close to P. morindae P. kernoviae aThis species complex, formerly called Heterobasidion annosum, now includes several Heterobasidion species namely, but not only: H. annosum sensu stricto, H. irregulare, H. abietinum and H. parviporum (Garbelotto and Gonthier 2013; Lamarche et al. 2016). bWithout aligning perfectly with any species available within the databases, the sequences highly resembled members of the mentioned clade described by (F. N.Martin, J. E. Blair, and M.D.Coffey, unpublished data; Blair et al. 2008), suggesting either unsufficient sequencing resolution or new/previously undescribed species. cBoth tree-resolution and sequences alignment were not conclusive at the species level.

2.11 Supplementary Figures

Supplementary Figure 2.S1 a) JB sampler mechanic simplified b) Insect Multiple Funnel Trap simplified c) Rotary arm sampler simplified.

Supplementary Figure 2.S2 Example of three (B1, B2 and B3) barcoded PCR reaction primers set-up used to respectively amplify bidirectionnally the ITS1 intergenic region (amplicon) from fungi and oomycete and unidirectionally the ATP9-NAD9 region (amplicon) from the Phytophthora species. The Ion Torrent sequencing adaptors A and TrP1 (P1) are also appended to the generated amplicons during this process. See Supplementary Table 2.S3 to obtain sequences primer sequences examples.

Supplementary Figure 2.S3 Proportions of species DNA added to generate each mock-inoculated control sample and of the environmental sample added as a background.

Supplementary Figure 2.S4 Rarefaction curve for each sample (sequences per sample by observed species) of fungi in a) insect trap samples, b) JB collector samples, c) Rotary arm samples and d) soil samples.

Supplementary Figure 2.S5 Oomycete rarefaction curve for each sample (sequences per sample, by observed species) in a) JB collector samples, b) insect trap samples and c) soil samples.

Supplementary Figure 2.S6 Consensus tree resampled with 1000 bootstraps and 70% support threshold for Heterobasidion spp. generated using ITS1 sequence alignment with reference sequences and one outgroup species (H. linzhiense). Consensus tree was built with the neighbor-joining method and the Jukes-Cantor genetic distance model.

Supplementary Figure 2.S7 Consensus tree resampled with 1000 bootstraps and 70% support threshold for Phytophthora spp. generated using ATP9-NAD9 sequence alignment with reference sequences and one outgroup species (Plasmopara viticola). Consensus tree was built with the neighbor-joining method and the Jukes-Cantor genetic distance model.

Chapitre 3: Next generation sequencing to investigate existing and new insect associations with phytopathogenic fungal propagules

Next-generation sequencing to investigate existing and new insect associations with phytopathogenic fungal propagules

Tremblay, É. D.1, Kimoto, T.2, Bérubé, J. A.3, and G. J. Bilodeau1

1Canadian Food Inspection Agency (CFIA), 3851 Fallowfield Road, Nepean, ON, K2H 8P9;

2CFIA, 4321 Still Creek Dr, Burnaby, BC, V5C 6S7;

3Natural Resources Canada, Laurentian Forestry Centre, 1055 Du P.E.P.S. Street, P.O. Box 10380 Québec, QC, G1V 4C7;

Article soumis pour publication dans le numéro spécial Fungal-Insect Interactions de la revue Journal of Fungi, le 15 octobre 2018.

3.1 Résumé

Comprendre les interactions écologiques est une étape clé dans la gestion de la phytopathologie. Bien que les entomologistes utilisent surtout les méthodes moléculaires traditionnelles et les caractéristiques morphologiques pour identifier les ravageurs, le séquençage de nouvelle génération est une approche de plus en plus empruntée par les scientifiques étudiant les champignons et les oomycètes phytopathogènes. Ces organismes infectent parfois les plantes et les insectes. C’est pourquoi les interactions qui se produisent entre tous ces organismes nécessitent une attention particulière. Comme le nombre d’insectes exotiques introduits au Canada croît toujours, une stratégie pour dépister ces insectes est déjà mise en place par l’Agence Canadienne d’Inspection des Aliments (ACIA). Toutefois, aucun plan n’est présentement établi afin d’enquêter sur les espèces de champignons et d’oomycètes phytopathogènes qui interagissent avec les insectes. Des analyses par métagénomique ont été effectuées sur les liquides préservatifs provenant de pièges à insectes de l’ACIA installés à différents endroits au Canada. Puis, à l’aide de l’instrument Ion Torrent PGM et des amorces de fusion qui permettent (i) de multiplexer des régions génétiques et (ii) d’ajouter des identifiants unique aux amplicons, le profilage des communautés a été fait selon les différents composés sémiochimiques utilisés dans les pièges à insectes ainsi que selon les différentes régions où ces pièges ont été installés. Des amplicons ont été générés à partir de l’espaceur transcrit interne 1 (pour les champignons et les oomycètes) et l’espaceur des gènes adenosine triphosphate synthase subunit 9 et nicotinamide adenine dinucleotide dehydrogenase subunit 9 (pour les Phytophthora). Malgré que des liens explicites entre les organismes n’aient pu être établis, certains champignons (e.g. Leptographium spp. et Meria laricis) et oomycètes modérément pathogènes détectés (surtout des Peronospora spp. et des Pythium spp.) ont été identifiés comme étant uniques à un des composés sémiochimiques. La levure entomopathogène Candida michaelii a également été détectée. Ce projet a démontré notre capacité à dépister des espèces indésirables de manière plus rapide, à plus grande échelle, et à plus haut débit comparativement aux méthodes de diagnostic traditionnelles. De plus, des modifications mineures à cette approche pourraient la rendre compatible avec d’autres volets de la phytopathologie.

3.2 Abstract

Understanding ecological interactions is a key in managing phytopathology. Although entomologists rely mostly on both traditional molecular methods and morphological characteristics to identify pests, next-generation sequencing is becoming the go-to avenue for scientists studying fungal and oomycete phytopathogens. These organisms sometimes infect plants together with insects. There are many relationships yet to be discovered and much to learn about how these organisms interact with one another. Considering the growing number of exotic insect introductions in Canada, a high- throughput strategy for screening those insects is already implemented by the Canadian Food Inspection Agency (CFIA). However, no plan is deployed to investigate the phytopathogenic fungal and oomycete species interacting with insects. Metagenomics analysis was performed on the preservative fluids from CFIA’s insect traps across Canada. Using the Ion Torrent PGM technology and fusion primers for multiplexing and indexing, community profiling was conducted on the different semiochemicals used in the insect traps and the various areas where these traps were placed. Internal transcribed spacer 1 (fungi and oomycetes) and adenosine triphosphate synthase subunit 9- nicotinamide adenine dinucleotide dehydrogenase subunit 9 spacer amplicons were generated. Although direct links between organisms could not be established, moderately phytopathogenic fungi (e.g., Leptographium spp. and Meria laricis) and oomycetes (mainly Peronospora spp. and Pythium spp.) unique to every type of semiochemical were discovered. The entomopathogenic yeast Candida michaelii was also detected. This project demonstrated our ability to screen for unwanted species faster and at a higher scale and throughput than traditional pathogen diagnostic techniques. Additionally, minimal modifications to this approach would allow it to be used in other phytopathology fields.

3.3 Introduction

The Era of Globalization has dramatically and consistently increased international cargo shipments since 1970 (Hulme 2009). Solid wood packaging material (SWPM) such as pallets, crates, and boxes are used to transport products all over the world. Bark and wood-boring insects, such as bark beetles, longhorned beetles, woodwasps, jewel beetles, weevils, and ambrosia beetles are often intercepted in SWPM (Haack 2001, 2006; Brockerhoff et al. 2006a; Haack et al. 2010). Even with the implementation of International Standards for Phytosanitary Measures (e.g., ISPM No. 15), which states the need to treat wood products shipped abroad in order to prevent the spread of insects and diseases, live wood-boring insects are still intercepted in SWPM at Canadian and American borders (Gerson et al. 4-6 December 2012; Haack et al. 2014). Emerald ash borer (Agrilus planipennis), brown spruce longhorned beetle (Tetropium fuscum), sirex woodwasp (Sirex noctilio), and pine shoot beetle (Tomicus piniperda) are just a few examples of species recently introduced and established in Canadian forests (Hendrickson 2002; Smith et al. 2002; Ryan et al. 2012; Herms and McCullough 2014; Li et al. 2015).

The transmission of exotic phytopathogenic propagules, an important threat to forest health, figures among the many issues associated with the introduction of exotic insects in Canada, especially because some of these wood-boring insects proliferate within common North American tree species such as pine and spruce (Levieux et al. 1989; Krokene and Solheim 1998; Allen and Humble 2002; Natural Resources Canada 2016). Aside from killing or damaging trees, insects can also transmit different phytopathogenic species (e.g., fungal spores) to their respective plant host. A noteworthy example is the fungus Ophiostoma ulmi, one of the causative agents of Dutch elm disease, transported to elm trees by bark beetles (Levieux et al. 1989). This fungus has devastated North American forests and also occurs in Asia and Europe (Allen and Humble 2002; Negrón et al. 2005). Researchers have also previously reported potential links between insect and arthropod excreta and Phaeoacremonium spp. in grapevine infection cycle (Kubátová et al. 2004; Epstein et al. 2008; Moyo 2013). There are also numerous yeasts associated with insects. More specifically, Candida spp. and Cladosporium spp. have been linked with bark and rove beetles (Callaham and Shifrine 1960; Nguyen et al. 2006; Klimaszewski et al. 2013; Waalberg 2015; Zhu et al. 2017). Oomycetes contain numerous plant pathogens responsible for considerable damage to the

environment as well (Allen and Humble 2002; Mecteau et al. 2002; Loo 2009; Bilodeau et al. 2014; Vettraino et al. 2015). Their propagative structures can remain viable for further plant infection even after ingestion and defecation by invertebrates (Hyder et al. 2009). For instance, chlamydospores of Phytophthora ramorum can still infect leaves after passing through the digestive tract of snails (Hyder et al. 2009). Oomycetes have also been associated with indirect interactions (positive or negative) with insects. For instance, while ants can transport P. palmivora and P. megakarya to cocoa trees, which can subsequently become infected (Webber and Gibbs 16-17 September 1987), oviposition of the moth Spodoptera littoralis is enhanced after P. infestans modifies the volatile compounds emitted by the host plant (Franco et al. 2017). In contrast, the reproductive output by aphids is inversely proportional to the level of Phytophthora infection (Pratt et al. 1982; Ellsbury et al. 1985; Lazebnik et al. 2017). Insects can also benefit from mutualistic relationships with plant pathogens that overwhelm the plant’s defenses (e.g., thousand cankers disease), or induce a plant’s cell suicide response (Krokene and Solheim 1998; Hulcr and Dunn 2011; Kanzaki and Giblin-Davis 2016). Contrarily, the damage caused by insects can indirectly predispose plants to microbial attacks. For example, in addition to the maize crops losses caused by the African pink stem borer (Sesamia calamistis) and the false codling moth (Thaumatotibia leucotreta), it was observed that aflatoxin (produced by Aspergillus spp.) concentrations were proportional to the number of insects that came in contact with these crops after storage (Hell et al. 2000; Miller et al. 2007; Barroso et al. 2017). Many bark and ambrosia beetles even rely on a fungal symbiosis to fulfill their nutrition needs (Kirisits 2007).

While there are numerous studies of insects transmitting plant viruses (Adams et al. 2009; Pinheiro et al. 2015; Tolin et al. 2016) and bacteria (Hogenhout et al. 2008; Orlovskis et al. 2015), there is a need for additional research on associations between forest insects and microorganisms. Although there has been research on pinewood nematode (Teale et al. 2011; Kanzaki and Giblin- Davis 2016), ophiostomatoid fungi (Webber and Gibbs 16-17 September 1987; Levieux et al. 1989; Krokene and Solheim 1998; Roe et al. 2018), and their insect vectors, there are likely more associations to be discovered, including transmission by vectors. For instance, additional fungal species never previously found to be associated with insect species may also be unexpectedly transmitted in this way.

The Canadian Food Inspection Agency (CFIA) conducts annual surveys using traps baited with semiochemicals to detect nonnative wood-boring insects in high-risk areas such as industrial and commercial zones (Canadian Food Inspection Agency 2010; Douglas et al. 2013; Bullas-Appleton et al. 2014; Canadian Food Inspection Agency 2015a). Semiochemicals are communication chemicals to induce inter- or intraspecific interactions between organisms (e.g., decaying trees produce a kairomone that attracts bark beetles) (Gullan and Cranston 2014). They have been extensively studied for their ability to attract specific insects (Brockerhoff et al. 2006b; Silk et al. 2007; Pajares et al. 2010; Teale et al. 2011; Ray et al. 2015; Ryall et al. 2015; Sweeney et al. 2016; Petrice et al. 2018), especially for monitoring particular groups of beetles and the microorganisms associated with them. With the advances associated with next-generation sequencing (NGS), scientists have used the power of metagenomics for the diagnosis of phytoviruses (Adams et al. 2009), fungi, and oomycetes (Tremblay et al. 2018), and the detection of exotic fungi on asymptomatic live plant material imported into Canada (Bérubé and Nicolas 2015). In addition to its high-throughput sequencing capacity and high sensitivity, NGS also allows for the analysis of hundreds of environmental samples in a fraction of the time compared with traditional methods (Barba et al. 2014; Wu et al. 2015; Aylward et al. 2017). Taking advantage of a well-established, nationwide entomological survey that uses preservative fluids within insect traps, this project aimed to use a metagenomics approach to screen for the presence of potentially phytopathogenic oomycetes and fungi in order to fill in a gap in plant pathogens detection. The approach could potentially help forest pathology stakeholders to orientate surveys for disease monitoring and management at a large scale. In attempting to partially decipher complex tree infection processes (e.g., insect-fungi) by extracting additional and valuable information from the insect trap samples, the project also addressed the potential for wood-boring insects to actively, or accidentally carry phytopathogenic propagules into baited insect traps. Evaluation of commonalities between the areas of collection or the specific semiochemicals used and the respective fungal or oomycete diversity were also evaluated.

3.4 Materials and methods

Insect traps: During the summers of 2013 to 2015, CFIA inspectors installed traps at 41 sites in industrial and commercial zones, landfills, and SWPM disposal facilities (Supplementary Figure 3.S1). These areas are end points for international SWPM and dunnage, and are considered high-risk areas for the introduction of nonnative pests. At each site, 12-unit funnel traps with wet collection cups (Synergy Semiochemicals Corporation, Burnaby, BC, Canada) were suspended between trees. Each trap’s collection cup was positioned at approximately 30 to 200 cm above ground, according to the height of the understory vegetation. Depending on local temperatures, baited traps were placed in forested areas between March and April, and taken down at the end of September. Resource limitation within the operational program prevented the usage and handling of the extra 12-unit traps, as a negative control (e.g., un-baited traps). To overcome this situation, assessment of the fungal communities (background noise) in the sampled areas was performed using Johnson and Barnes (JB) rainfall collectors (J. L. Johnson, personal communication). The samplers were used, as previously done by Barnes et al., Szabo et al. and Hambleton et al. (Hambleton et al. 12-14 December 2007; Barnes et al. 2006; Szabo 2007; Barnes et al. 2009). More specifically, the JB spore collectors installed in the same location as the insect traps were used to dissociate the species collected by spore impaction (in both insect and spore traps), from the species caught through insect movement (only in insect traps). These JB samplers, employed as the negative controls, collected spores in suspension, in air and rainfall contents, at the surface of a membrane placed down the throat of the funnel-shaped traps. The JB spore collectors had a mesh screen to prevent insects from falling and clogging the funnel-shaped samplers. Collected fluids from the insect traps and the filter membranes from the spore traps were kept and analyzed, separately.

Semiochemicals: In 2013 and 2014, CFIA inspectors placed six traps per site. Half the traps were baited with one combination of lures, while the other half was baited with a different set. Inspectors attached one lure type to each trap with each lure dispensed from individual release devices. Additional details pertaining to the semiochemicals (chemical composition, purity, packaging, and release rate) used in this project are provided in Supplementary Material 3.S1.

The first semiochemical combination (C6C8) contained ethanol, as well as aggregation pheromones of some longhorned beetles in the Cerambycinae subfamily (ex. Neoclytus sp.) (Ray et al. 2015). The

second semiochemical combination (UHR_E_AP) contained ethanol and alpha-pinene, which are attractive to a wide range of bark and wood-boring insects (Brockerhoff et al. 2006b; Douglas et al.

2013; Petrice et al. 2018). Traps baited with C6C8 were suspended between coniferous or broadleaf trees, whereas traps containing UHR_E_AP were primarily placed between coniferous trees. In 2015, CFIA inspectors implemented two new semiochemical sets in order to target different insect taxa. The first semiochemical (i.e., General Longhorn) was attractive to longhorned beetles in the Spondylidinae (Silk et al. 2007) and Lamiinae subfamilies (Mitchell et al. 2011) but, could also capture various bark and ambrosia beetles, due to the addition of ethanol (Sweeney et al. 2016). The second semiochemical set (Pine Sawyer) was attractive to Monochamus (longhorned beetles) species from North America, Europe, and Asia (Pajares et al. 2010; Teale et al. 2011; Ryall et al. 2015) but the inclusion of ethanol and alpha-pinene also makes it attractive to bark and ambrosia beetles.

The semiochemical sets were deployed in different areas depending on the forest type. In British Columbia, each site had four traps baited with the Pine Sawyer lures, and two traps baited with the General Longhorn. In Ontario and Quebec, 75% of the sites were in broadleaf forests or mixed forests, and all six traps were baited with General Longhorn lure. The remaining sites, composed primarily of coniferous trees, were baited with the Pine Sawyer lure. In the Atlantic provinces (i.e., New Brunswick, Newfoundland and Labrador, Nova Scotia, and Prince Edward Island), each site had three traps with Pine Sawyer lure and three traps with General Longhorn lure. Traps baited with the Pine Sawyer lure were suspended between two coniferous trees, whereas traps baited with the General Longhorn lure were placed between coniferous or broadleaf trees. Collected fluids from each trap (i.e., insect traps and air samplers) were kept and analyzed separately. Lures were replaced approximately every 90 days.

Trapping fluid (200 to 300 mL of USP/FCC grade 1,2-propanediol (propylene glycol) (Fisher Scientific, Hampton, NH, USA), (Denatonium benzoate or Bitrex® = [Benzyl-diethyl (2,6- xylylcarbamoyl methyl) ammonium benzoate]) (Sigma-Aldrich, Saint-Louis, Mo., USA), and PhotoFlo 200 (surfactant) (Fisher Scientific) were poured into the collection cups of each trap, and traps were spaced at least 25 m from each other (Hayes et al. 2008; Government of British Columbia 2016). All of the solution from each trap was collected every two to three weeks. The contents were poured

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onto fine-gauged sieves and insects were removed because only the collection fluid was analysed in this study. The liquids were then filtered on 0.45-μm cellulose paper filters and stored at 4°C until processed. The filter papers were cut in half to preserve a section as a back-up, while the other half was put in Tris buffer, heated at 65°C and sonicated (40 kHz). The solution containing the DNA was then centrifuged (10,000 rpm, 2 min) and extracted with the FastDNA kit for soil (MP Biomedicals, Santa Ana, CA, USA). To remove PCR inhibitors, purification of the extracted DNA was done using magnetic particles (Bio-Nobile, Östernäsvägen, Finland).

Then, PCR was performed bidirectionally to amplify DNA and add unique identifier (barcodes) to each sample using Ion Torrent PGM fusion primers. Detailed sequences for fungi and oomycete fusion primers, as well as PCR cycling and parameters can be found in Tremblay et al. (Tremblay et al. 2018). Fusion primers allowed for multiplexing environmental samples and generating internal transcribed spacer 1 (ITS1) fungi and oomycete amplicons. In addition, when an oomycete band was visualized by electrophoresis, another PCR targeting the adenosine triphosphate synthase subunit 9-nicotinamide adenine dinucleotide dehydrogenase subunit 9 (ATP9-NAD9) spacer was performed to allow for the proper downstream resolution of Phytophthora species, considering that it is better-suited than the ITS to resolve species within this genus (Kox et al. 2007; Martin et al. 2012; Bilodeau et al. 2014; Miles et al. 2017). Products were visualized on a 1.5% agarose gel with a Gel Doc XR+ Gel Documentation System (Bio-Rad Laboratories, Inc., Hercules, CA, USA). Primer-dimers and other smaller sized fragments (<100bp) were removed with Agencourt AMPure XP magnetic beads at a 0.7:1 beads:DNA ratio (Agencourt Bioscience, Beverly, MA, USA) (Edwards 2012). Sequencing libraries were quantified with the Ion Universal Library Quantitation qPCR Kit, (Life Technologies, Carlsbad, CA, USA), and then pooled at the equimolar concentration of 16 pM. The Ion Personal Genome Machine (PGM) Template OT2 Kit 400 bp (Life Technologies) and the Ion PGM sequencer (Life Technologies) were used to perform NGS (Thermofisher 2012).

Bioinformatics: The raw data output from the sequencer was analysed with the pipeline previously described by Tremblay et al. (Tremblay et al. 2018). FASTQ files were converted into sequence (FASTA) and quality score (QUAL) files using fastqutils (Breese and Liu 2013). Quality trimming based on sequence quality and length was done with Mothur (version 1.37.2) (Schloss et al. 2009) using the trim.seqs function parameters minlength = 120, maxambig = 0, and maxhomop = 8.

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ITS extraction was done with ITSx (version 1.0.11) (Bengtsson-Palme et al. 2013). Operational taxonomic unit (OTU) tables were generated with QIIME (version 1.7.0) (Caporaso et al. 2010). Taxonomic assignment was done using the UNITE database (version 31.01.2016) (Kõljalg et al. 2005) for fungi and the National Center for Biotechnology Information (NCBI) nucleotide database for oomycetes. The resolution power of the ATP9-NAD9 region for Phytophthora species relies on a custom-built database including the majority of all currently described Phytophthora species (NCBI accessions numbers JF771616.1 to JF772053.1 and JQ439009.1 to JQ439486.1) (Kang 2006; Bilodeau et al. 2014). To evaluate species alpha diversity, evenness, and the proportion of different organisms within the sample types, statistical analyses were done with R (version 3.1.3) (R Core Team) using the RAM package (version 1.2.1.3) (Chen et al. 2016). This package was also used to evaluate common elements among datasets (Venn diagrams), to assess similarities through Principal Coordinates Analysis (PCoA) plots, and to generate sampling maps. To visualize the distribution of the type of trees that were used to hang traps at the collection sites, a data aggregation plot was built using UpsetR version 1.3.3 (Conway et al. 2017) in R.

Species subtraction. Species subtraction hereafter refers to the dataset excluding species that were commonly detected in both the control and insect traps. Insect trapping procedures were already established by the CFIA biologists and sites were selected by CFIA inspectors prior to this project (Canadian Food Inspection Agency 2017b). Although CFIA entomologists screened and identified all wood-boring insects, due to time constraints, they only reported the non-indigenous insects captured during the survey. Considering the physical design of the insect traps, the passive collection of fungal spores suspended in air was inevitable. In an attempt to extract unique features associated with insects attracted to the different types of semiochemicals, NGS data from JB collector air samples, collected in similar context as insect traps (i.e., same areas, sampling time, sites, year, season, and so on), were used to identify which fungal OTU were solely found in insect traps. Although the JB collectors were passive spore samplers, through species subtraction it was possible to determine which fungi or oomycetes species were unique to the insect traps and may be associated with an insect vector.

Following BLAST alignments of the OTUs with the respective reference databases, the remaining species were screened against a text-formatted database to determine their fungal biological

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functions (Tedersoo et al. 2014), and the resulting file was parsed to identify fungi of interest, mostly those known to be plant pathogens and wood-decay fungi. Ectomycorrhizal fungi, saprotrophs, mycoparasites, lichenized fungi, and most yeast were discarded.

To assess the depth of sequencing and diversity of the subtracted and negative control data (i.e., spore trap), and to compare them with the original insect trap data, rarefaction curves were generated using the functions “diversity” and “rarefy” from the R package Vegan (version 2.5-2) (Oksanen et al. 2018). This step was performed only on fungal data as the oomycete dataset was too small to obtain relevant rarefaction curves.

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3.5 Results

Samples. A total of 108 samples originating from British Columbia, New Brunswick, Newfoundland and Labrador, Nova Scotia, Ontario, Prince Edward Island and Quebec were collected over three years (2013 to 2015). From those samples, 39 were baited with UHR_E_AP, 36 with C6C8, 17 with General Longhorn, and 16 with Pine Sawyer (Supplementary Table 3.S1). As well, Figure 3.1 presents the type of trees to which sample traps were hung for sampling. However, the data do not fully represent the type of forest surrounding sampling sites because traps were only suspended from two trees.

PCRs. Fungal amplicons (ITS1) generated a visible band via gel electrophoresis in ≥83% of samples, whereas oomycete amplicons (ITS1) were observed in only 11% of samples (Table 3.1). Forty percent of oomycete amplicons generated bands for Phytophthora spp. following the PCR amplification of the ATP9-NAD9 spacer (Table 3.1).

Fungal OTUs, prior to species subtraction. At the phylum level, the most abundant fungi for all semiochemical treatments were members of the Ascomycota division, followed by Basidiomycota, unidentified OTU, OTU “unclassified below kingdom”, Chytridiomycota, Zygomycota, and then Glomeromycota (Table 3.2). A few Rozellomycota members were associated with all lures except for General Longhorn (Table 3.2). Ascomycetes and basidiomycetes were evenly distributed between the samples baited with UHR_E_AP and C6C8 semiochemicals but, there were larger proportions of ascomycetes compared with the proportion of basidiomycetes found in the General Longhorn and Pine Sawyer-baited samples (Table 3.2). At the genus level, the most abundant fungal OTU remained unidentified regardless of the semiochemical analysed but, Phoma sp., Leptographium sp., and Mycosphaerella sp., while below 5% in proportion, are genera that include many forest pathogens (Table 3.3). The UHR_E_AP and C6C8 semiochemicals contained higher percentages of Rhodotorula sp. and Cystobasidium sp. compared with the General Longhorn and Pine Sawyer semiochemicals, which only contained traces of these genera. Instead, the latter two semiochemicals had more Cryptococcus sp. and Leptographium sp. All four semiochemicals had low amounts of Epicoccum sp.

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Analysis of the top ten species in relation to lure type revealed a high number of “unidentified fungal OTU” and “OTU unclassified below genus” in all four attractants (Table 3.4). Other species, including Rhodotorula mucilaginosa, Cystobasidium slooffiae, Leptographium piriforme, Cladosporium exasperatum, and Aureobasidium pullulans were frequent across the different semiochemical treatments (Table 3.4).

From the 2439 different species OTUs detected prior to the species subtraction, 1057 (43%) were common to all semiochemical types, 228 species (9%) were unique to the UHR_E_AP semiochemical, 112 (4.6%) were unique to General Longhorn, 105 (4.3%) were unique to Pine

Sawyer, and 118 (4.8%) were unique to the C6C8 semiochemical (Figure 3.2a).

To visualize sampling depth, examples of the rarefaction curves obtained for the spore traps (negative control) and their respective original insect dataset, and subtracted insect dataset are shown in Supplementary Figure 3.S2. The spore trap data demonstrated the highest sequencing depth as saturation was obtained for all of the samples tested with a sequence number per sample ranging between approximately 5,000 and 25,000. The lowest species number obtained by all samples tested was just below 150. In contrast, only part of the samples from both the original insect and the subtracted data reached saturation in their respective rarefaction curves.

Fungal OTUs, after species subtraction. A total of 1,527 species remained once the species detected in the negative control were discarded, of which 368 species (approximately 25%) were common to all semiochemical types, 220 species (approximately 14%) were unique to UHR_E_AP, 109 (7.1%) were unique to General Longhorn, 99 (6.5%) were unique to Pine Sawyer, and 116 (7.6%) were unique to the C6C8 lure (Figure 3.2b). Following the approach of Tedersoo et al. (Tedersoo et al. 2014) to investigate fungal species functions, the data revealed the occurrence of some species of moderate concern in each of the semiochemical treatments (Supplementary Table 3.S2). In Pine Sawyer-baited samples, three rot fungi and six phytopathogenic fungi (including Ambrosiella ferruginea, Leptographium sp., and Phaeoacremonium inflatipes) were detected. From the General Longhorn samples, five rot fungi and eight phytopathogenic causal agents, including Echinodontium tinctorium, Siroccocus conigenus, and Pucciniastrum circaeae were detected. The

C6C8 semiochemical treatment featured unique fungi of interest as well: a single white rot, the yeast

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Candida michaelii, which is associated with the gut flora of handsome fungus beetles (Endomychidae), and eight phytopathogens including Phyllosticta minima, Podosphaera clandestina, and Ciborinia Whetzelii. Finally, the UHR_E_AP semiochemical had six wood-decay fungi along with thirteen pathogenic fungi including Colletotrichum fructi, Podosphaera leucotricha, and Strelitziana mali.

Oomycetes OTUs, prior to species subtraction. At the phylum level, BLAST alignments of the OTU sequences with the ones in the NCBI (nucleotides) database showed that OTUs from ITS1 amplicons using oomycete primers predominantly aligned with Oomycota sequences. However, the

UHR_E_AP (26.1%) and C6C8 (26.6%) semiochemicals had moderate percentages of OTU which could not be identified below the kingdom taxonomic rank (Table 3.5). On the other hand, the majority of sample sequences from traps baited with General Longhorn (99.9%) and Pine Sawyer (96.9%) corresponded to Oomycota members (Table 3.5). Among all genera detected that could be identified,

Peronospora spp. was the most abundant in all semiochemical treatments, except for C6C8, where Phytophthora spp. was the most abundant identified genus (Table 3.5). Prior to species subtraction, of the total 54 different OTUs, there were 21 species detected in all semiochemical types but, few were unique to each treatment (Figure 3.2c). Pythium monospermum was unique to traps baited with the UHR_E_AP semiochemical. Pythium oligandrum was unique to traps baited with the Pine Sawyer semiochemical. Five species were unique to traps baited with the General Longhorn semiochemical (Peronospora sp. UPS F-119986, P. flava, P. sparsa, Pythium carolinianum, and Phytophthora spp.).

No unique species were recovered from traps baited with the C6C8 semiochemical (Figure 3.2c).

Within the top ten most abundant species identified, all semiochemicals except C6C8 (0.67%) had a high percentage of Peronospora manshurica (Table 3.6). The UHR_E_AP (26.1%) and C6C8 (26.6%) semiochemicals had a considerably higher number of OTUs unclassified below genus compared with General Longhorn (traces) and Pine Sawyer (3.02%).

Oomycetes OTUs, after species subtraction. Following species subtraction, fifteen species remained, from which no unique oomycete species were detected in either the Pine Sawyer or UHR_E_AP baited traps (Figure 3.2d). Eleven species were unique to General Longhorn including

Peronospora sp., one was unique to C6C8 (Pythium sp. CAL-2011e), and three species were shared with the latter two semiochemicals (Supplementary Table 3.S3).

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Phytophthora spp. OTUs, prior to species subtraction: No Phytophthora sp. were unique to the insect traps, or any semiochemical treatment. Additionally, ATP9-NAD9 OTUs from the original dataset generated prior to the species subtraction could only recover Phytophthora spp. from traps baited with the C6C8 and UHR_E_AP semiochemicals. Phytophthora cryptogea could only be detected from traps baited with the UHR_E_AP semiochemical, whereas P. foliorum, Phytophthora sp. “kelmania” (Kang 2006) and P. syringae were associated with both C6C8 and UHR_E_AP lures.

Diversity: fungi. The fungal species evenness (Shannon index (Jost 2013a)) was very similar among all four semiochemicals, with median relative values ranging between 0.5 and 0.75 (Supplementary Figure 3.S3a). The true diversity median values for fungi (Shannon index, per unit of number of species (Kéfi 2012; Jost 2013a)) were also evenly distributed among all semiochemical types, ranging between approximately 20 and 40 units of number of species (Supplementary Figure 3.S3b).

Diversity: oomycetes. Oomycete species evenness (Shannon index) revealed a notable variation among the different semiochemicals (Supplementary Figure 3.S3c). While species from the

C6C8 (median relative value of approximately 0.45) and the UHR_E_AP (median relative value of approximately 0.49) lures were evenly distributed, species from Pine Sawyer (median relative value of approximately 0.15) and the General Longhorn-baited traps (median relative value of approximately 0.2) were much less evenly distributed. These differences help explain the true diversity (Shannon) variation also observed in Supplementary Figure 3.S3d.

Areas of collection. After discarding fungal and oomycete species that were most likely passively or accidentally captured in the insect traps, we observed certain community aggregations associated with geographic regions. The sampling sites were split in three areas of Canada: West Coast (i.e., British Columbia), Eastern Canada (i.e., Quebec and Ontario) and the Atlantic Region (i.e., New Brunswick, Newfoundland and Labrador, Nova Scotia, and Prince Edward Island). The PCoA plot generated at the order level for fungi (Supplementary Figure 3.S4a) demonstrated a clear similarity (i.e., clustering) among OTU data from the West Coast, a clustering trend for Eastern Canada data, and a lack of similarity within the Atlantic Region. At the class level, however, the OTUs

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clustered more clearly based on the geographic region (Supplementary Figure 3.S4b). Such observation was not possible for oomycetes because not enough species remained following the species subtraction (data not shown). Nevertheless, there were oomycete species unique to the Eastern zone, and aside from Saprolegnia sp. SAP1 and Hyaloperonospora cochleariae, all others were either Peronospora spp. (P. farinosa, P. sparsa, Peronospora sp. UPS F-119986, P. viciae, and Peronospora sp. isolate 079405,59), or Pythium species (Pythium aff. hypogynum, Pythium sp. CAL- 2011f, Pythium sp. AvdB-2012, Pythium sp. P19300/1/3, and Pythium sp. BP2013k). One species— Pythium sp. BG02— was unique to the West Coast data but, there were no species uniquely associated with the Atlantic Region.

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3.6 Discussion

This study demonstrated that the described metagenomics approach to investigate insect trap fluids makes it possible to detect airborne, or insect-vectored fungal species even at very low abundances (i.e., ≤10 OTUs). A unique aspect of this study is that despite the fact that the CFIA has used insect traps in its national survey for many years, this is the first time that the formerly discarded trap preservative fluids have been analyzed, and the information extracted was highly valuable.

The project aimed to expand knowledge on phytopathogenic fungi and oomycetes by studying insect trap fluid samples, and this was assessed using the subtracted species datasets. More specifically, the project searched for associations between phytopathogenic fungi and insects, and results, though ambiguous in certain cases, surely showed that there may be some novel insect- fungal relationships and effect of lure type that deserve further inquiry. Traditional methods such as cloning and culturing make it difficult for scientists to screen species at a larger scale but, our metagenomics approach now provides the opportunity to do so. Indeed, this project can provide a good overall assessment of the communities without having to isolate all organisms for identification purposes. This is a tool that regulatory agencies and other stakeholders could use for primary screening and disease monitoring. This approach is essentially a general detection survey for nonindigenous pathogens that partially fills a large gap that is a key step in the battle against invasive pathogens. By taking advantage of an established survey conducted by CFIA inspectors, other than shipment of fluid samples to the lab, no additional resources were required, thereby making this a cost-effective surveillance method. Just as air trap samplers are used to capture various pathogens in a given environment, our results suggested that insect traps can actively and passively gather worthy information from the environment.

Despite the fact that using ITS1 for fungal communities profiling does not always allow for resolution below the genus level for certain organisms (Raja et al. 2017), like it was presented in Supplementary Table 3.S2, species subtraction filtered the data from background noise and highlighted numerous species with the potential to damage trees or other plants. Species detected in control traps were least likely to be vectored by insect and were discarded, revealing several potentially phytopathogenic entities remaining that were more likely to be insect-transmitted. For

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instance, species unique to the various semiochemical types were from genera that include important plant pathogens. Among others, Phoma glomerata, the causal agent of blight, leaf spots, and fruit rot of many plants, was uniquely detected in C6C8 samples, whereas Mycosphaerella areola (mildew)— despite the fact that it typically infects wheat leaves (Linde et al. 2002; Goodwin and Kema 2014), field peas (Gossen et al. 2014), and cruciferous vegetables (Kennedy et al. 1999)—was only detected in General Longhorn-baited trap samples. This semiochemical is an aggregation pheromone for longhorned beetles in the Spondylidinae (Silk et al. 2007) and Lamiinae subfamilies (Mitchell et al. 2011). Similarly, the genera Mortierella and Phyllosticta (including, P. minima: the causal agent of leaf spot in maple), which also contain numerous phytopathogenic species (Société de Protection des Plantes du Québec 2003), were recovered from C6C8 semiochemical, which is an aggregation pheromone for longhorned beetles in the Cerambycinae subfamily.

Data collected from Pine Sawyer samples revealed fungi typically associated with insects such as Ceratocystidaceae, Ambrosiella spp., and Leptographium spp. (French and Roeper 1972; Jacobs and Wingfield 2001; Kim et al. 2011a). Detection of Leptographium, a genus that includes causal agents of blue stain in conifers, was not unexpected given its known association with bark beetles (Jacobs and Wingfield 2001; Jacobs et al. 2004). For example, L. piriforme is vectored by Tomicus piniperda (exotic), as well as other native bark beetles species (Greif et al. 2006). Interestingly, the proportion of Leptographium species detected in the General Longhorn and Pine

Sawyer semiochemical traps prior to species subtraction were greater than for C6C8 and UHR_E_AP. This could suggest an association with particular insect groups or taxa. Because the phylogenetic analysis of Leptographium for species inference is usually done using a combination of at least three genic regions (Paciura et al. 2010), more sequencing data, or alternate standardized assay (e.g., qPCR) would be required in order to validate down to the species level in this case (e.g., L. piriforme). Also, two genera of considerable importance (i.e. plant pathogen genera) were observed solely within the Pine Sawyer lure subtracted data: Taphrina (specifically T. padi, the causal agent of cherry fruit deformation) and Phellinus (P. ferrugineovelutinus is the causal agent of wood rot in alder and maple). Given that some of the aforementioned fungal groups detected were less likely to actually encounter wood-boring insects, the presence of genera such as Phoma spp., Taphrina spp., and Mycosphaerella spp. could also be explained by the occurrence of other insects caught into the

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insect traps. In fact, flies, wasps, bees, dragonflies, moths, and other insects, which are regularly found by the CFIA inspectors in the traps (Troy Kimoto, personal communication), could have incidentally come in contact with infected plants prior to be captured, and, therefore, transport fungal propagules. For instance, honey bees are known vectors of bacteria, viruses, and fungus during foraging activities (Stelfox et al. 1978; Johnson et al. 1993; Card et al. 2007; McArt et al. 2014; Pattemore et al. 2014).

Likewise, some insects attracted to UHR_E_AP may be involved in the transmission of powdery mildews (Erysiphe spp.) and anthracnoses (Colletotrichum spp.) considering that these known pathogenic fungi were not detected in any other semiochemical treatment. However, because the addition of ethanol makes this lure attractive to a wide range of insects, there could be another reason for the detection of such pathogens in those samples, possibly being (i) the forest type or province of origin or weather conditions given that mildew-causing agents are wind and water dispersed (Hau and De Vallavieille-Pope 2006), or (ii) the capture of other insects including pollinators.

Similarly, the General Longhorn semiochemical (mainly attracts longhorned beetles and bark beetles) was associated with important phytopathogenic genera, including Pucciniastrum (among others, the rust P. circaeae), Sirococcus (specifically S. piceicola, and S. conigenus, two shoot blight causal agents), and Trametes (reduces wood value as a decaying agent) (Société de Protection des Plantes du Québec 2003). Additionally, while some Verticillium species are pathogenic to insects (Bidochka et al. 1999), other phytopathogenic Verticillium species are transported by jewel beetles (e.g. Agrilus spp.) and bark beetles (Tiberi et al. 2016). For example, in Europe, V. dahliae is transported to Quercus spp. by the bark beetle Scolytus intricatus (does not occur in North America) and the ambrosia beetle Anisandrus dispar (F.) (Tiberi et al. 2016). Therefore, the presence of V. isaacii (wilt in multiple plants) in samples baited with C6C8 may be due, in part, to ethanol contained in the lure because it is highly attractive to ambrosia beetles. However, not all Verticillium species can be resolved using the ITS1 region (Inderbitzin et al. 2011; Inderbitzin and Subbarao 2014) so, the detection of V. isaacii using ITS1 was not conclusive at this point. Still, alignments of ITS1 sequences were done for the potential V. isaacii OTU with reference sequences (data not shown) and revealed a

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100% match with V. isaacii, V. tricorpus, and V. klebahnii, which was expected. For the abovementioned reasons, complementary tests shall be performed to validate sensitive data.

There were similarities in communities between traps baited with UHR_E_AP and C6C8 (Tables 3.3, 3.4 and 3.6), and between the communities detected in Pine Sawyer and General Longhorn-baited samples. In contrast, differences occurred between the communities detected by the two former lure types versus the latter two. Such variation could be due to the fact that there were much fewer UHR_E_AP and C6C8 samples compared with the number of Pine Sawyer and General Longhorn samples, rendering comparison between the datasets unbalanced. The main reason for this difference is because the two former lures were used only during one season (2013), whereas the latter two were used for two collecting seasons (2014 and 2015). Forest type, chemical composition, and seasonal weather (temperature and rain) may also have influenced the communities retrieved.

Compared with fungi, the lower number of identifiable oomycetes OTUs in this study may be explained by the fact that the number of taxonomically described oomycetes is much lower (Hawksworth and Rossman 1997; Rossman and Palm 2006; Blackwell 2011). One outstanding aspect of the oomycete analysis is that, following the species subtraction, most remaining species were unique to the General Longhorn samples (Supplementary Table 3.S3) and none were recovered from the Pine Sawyer semiochemical. There were no Phytophthora species remaining after species subtraction but, Pythium (broad host range, mainly affecting roots or leaves) and Peronospora species (broad host range, mainly causing mildews) were dominant. Once again, this could contribute in demonstrating novel observations between plants, insects, and oomycetes.

The ITS1 Phytophthora OTUs (i.e., non-subtracted data) revealed Phytophthora foliorum (Rhododendron spp. and Azalea leaf blight (Donahoo et al. 2006; Canadian Food Inspection Agency 2015b)), Phytophthora sp. “kelmania” (affects gerbera (Rahman et al. 2015) and Christmas trees (McKeever and Chastagner 2016)), and P. syringae (has wide host range causing numerous diseases (Farr and Rossman 2018)) but, because this intergenic spacer does not contain sufficient variation for species resolution, unlike ATP9-NAD9, those identifications were not conclusive. In contrast, the ATP9-NAD9 region allowed identification of P. cryptogea (numerous hosts causing

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different diseases (Farr and Rossman 2018)), P. foliorum, Phytophthora sp. “kelmania”, and P. syringae but, these were discarded following species subtraction. This might indicate that Phytophthora-insects associations are not as frequent compared with associations involving other oomycetes such as Pythium spp.

Following species subtraction, there was a number of fungal species found that can degrade timber, some of which were associated with specific lures (Supplementary Table 3.S2). Despite the fact that these fungi are avirulent or not highly virulent, they can still damage or stain wood, thereby reducing timber marketability. The methodology used here appears to have the capacity to detect more harmful organisms if they had been present, because genera containing virulent pathogens were detected. The collection areas also seemed to have a role when profiling the fungal communities as OTUs aggregated to geographic region (Supplementary Figure 3.S4).

Considering that the spore trap samples yielded the most sequences per sample compared with both the insect traps and the subtracted insect trap datasets, it refutes the possibility of having mistakenly discarded OTUs due to spore trap undersampling. Because the control samples were highly diverse, it is more likely that the species remaining after subtraction are actually unique to the insect traps. There are logically fewer species in the subtracted data versus the original one, which is likely why those remaining had a rarefaction curve approaching saturation. Interestingly, the fact that only a part of the original insect data was sequenced deeply enough (i.e., rarefaction curve saturation) suggests that, for future NGS runs, sequencing fewer multiplexed fusion primer samples at a time would probably yield a more representative diversity analysis. In contrast, it appears that it was beyond sufficient for the spore traps, meaning more samples could be tested at once.

Given that numerous yeasts are commonly associated with insects (Gonzalez 2014), the presence of Aureobasidium sp., Candida sp., Cladosporium sp., Cystobasidium sp., Cryptococcus sp., Hannaella sp., Kluyveromyces sp., Rhodotorula sp., Torulaspora sp., and Wickerhamomyces sp. in the unsubtracted dataset was expected (Table 3.4). Candida spp. are natural biocontrols agents of fruit and vegetable pests (El-Ghaouth et al. 1998; Droby et al. 2002; Gonzalez 2014), and are also associated with bark beetles (Callaham and Shifrine 1960; Nguyen et al. 2006; Waalberg 2015; Zhu et al. 2017). Following species subtraction, our results showed the presence of Candida

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michaelii only in the samples baited with the C6C8 semiochemical, which primarily attracts longhorned beetles, but the addition of ethanol makes it attractive to bark beetles as well. Furthermore, the fact that the entomopathogenic species Colletotrichum nymphaeae was retrieved from the samples is promising for entomologists, because they could consider using our method to screen for either insect pathogens, or new biological pest controls. As a matter of fact, beneficial fungal endophytes have been studied for their ability to help plant’s defenses (Porras- Alfaro and Bayman 2011). For example, Beauveria bassiana infection has reduced populations of emerald ash borer (Agrilus planipennis) (Liu and Bauer 2008; Augustyniuk-Kram and Kram 2012). The gut-associated species detected (Candida michaelii [handsome fungus beetle]) demonstrated the robustness of our metagenomics approach in studying fungus-insect associations regardless of the niche they occupy. Plus, this high-throughput method is more efficient compared with traditional assays because many sites could be sampled simultaneously due to the high volume of samples processed at a time, thus increasing the likelihood of detecting new nonnative fungal species.

Although some of the fungal genera or species that were detected in this project are already reported to be affiliated with insects, the results suggest that there may be other fungal and oomycetes species transported to potential host trees by insects. Based on the unique fungal and oomycete species detected within a given lure, our data suggests that there may be previously unrecorded associations between insects, fungi, and oomycetes. Each of the different semiochemicals employed in this study was attractive to a certain range of insect group or deme but, inevitably, passive or incidental collection of other insects (e.g., pollinators) contaminated by phytopathogens which they came across, though they would otherwise never transmit it to the plant host, occurred. The ethanol added also increased the chances of such event to happen.

All considered, making a direct link between the fungal species detected based on the semiochemical, the collection area, and the insects caught is very complex when solely using the presented approach. Nevertheless, if an organism of potential interest was detected, this method would provide stakeholders with location data that would narrow the target area for follow-up surveys. These targeted surveys would involve searching for symptomatic hosts, collecting samples, and performing validated low throughput assays. It is important to note that because the material studied

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consisted of airborne material rather than symptomatic trees, the detection of a given species does not automatically translate in the occurrence of diseased trees. Despite the fact that this method may lack in providing forest stakeholders with a definitive answer, the observations shed light on new potential insect-pathogen associations. Yet, the approach to quickly screen species at a large geographic scale would be highly useful to (i) efficiently sample the environment for the presence of any new fungi or (ii) to obtain primary data in order to provide guidance to those who monitor and manage phytopathogens over large jurisdictions. Finally, future research could examine the fungal communities associated with specific wood-boring insects to determine if there are undiscovered relationships with these organisms and their host trees.

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3.7 Acknowledgements

The authors would like to thank Ron Neville, Lucie Gagné, Erin Bullas-Appleton, CFIA inspectors, and Dr. Richard Wilson who provided us with insect trap samples. Thank you to Miranda Newton and Hadil Sayed for their help with sample processing. Thank you to all of those who helped with the bioinformatics analysis, including: Dr. Ahmed Abdelfattah, Emily Giroux, Dr. Wen Chen, Dr. Marc-Olivier Duceppe, Iyaad Kandalaft, Dr. Guillaume Nicolas, Catherine Brown, Patrick Gagné, and Christine Lowe. Thanks to Drs. Louis Bernier and Claude Lemieux for their support and advices. Thanks to Dr. Bryan Brunet for sharing knowledge in entomology. Thank you to Drs. Marie-Claude Gagnon and Ian King for their advice and or helping editing this article.

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3.8 Tables

Table 3.1 Presence or absence of amplification as detected by gel electrophoreses using barcoded PCR products from 108 environmental insect samples by targeted organisms, and the percentages of positive reactions obtained.

Primer used to append Target Positive Total Organism Positive (%)c barcodesa regionb PCR PCR ITS1-Forward Fungi 90 108 84 ITS1-Reverse Fungi 98 108 91 ITS1 ITS1-Forward Oomycete 12 108 11 ITS1-Reverse Oomycete 12 108 11 Phytophthora ATP9-NAD9 ATP9-NAD9- forward 8 20 sp. 40 Total … … 220 452 Average: 47 aEach PCR included a set of primers but, as presented by Tremblay et al. (Tremblay et al. 2018), bidirectional sequencing required one primer per direction to append the sample and organism index (i.e., barcode). bITS1 = internal transcribed spacer 1 and ATP9-NAD9 = adenosine triphosphate synthase subunit 9-nicotinamide adenine dinucleotide dehydrogenase subunit 9 spacer. cPercentage of the number of positive PCR over the total number of reactions done.

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Table 3.2 Operational Taxonomic Units: fungal identification proportion (%) by semiochemical type at the Phylum taxonomic level using the ITS1 genic region.

Semiochemicala UHR_E_AP C6C8 General Longhorn Pine Sawyer Phylum Ascomycota 39.6 41.6 63.9 68.5 Basidiomycota 39.5 40.9 24.2 23.0 Unidentified OTU 17.5 14.2 9.3 5.6 OTU unclassified below kingdom 3.1 3.1 2.4 2.4 Chytridiomycota 0.3 0.1 0.1 0.2 Zygomycota 0.1 0.1 0.1 0.2 Glomeromycota tracesb 1.0 traces traces Rozellomycota traces 0.5 absent traces aUHR_E_AP = Ultra-high release ethanol and ultra-high release alpha-pinene.

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Table 3.3 Operational Taxonomic Units: fungal identification proportion (%) by semiochemical type at the genus taxonomic level using the ITS1 genic region.

Semiochemicala UHR_E_AP C6C8 General Longhorn Pine Sawyer Genus Unidentified OTU 40.5 34.2 29.5 30.0 Rhodotorula 10.0 11.0 tracesb 3.2 Cystobasidium 7.5 5.5 traces traces Cryptococcus 4.3 4.1 5.5 6.1 OTU unclassified below family 3.1 3.1 2.4 2.4 Alternaria 2.9 traces traces traces Epicoccum 2.5 5.2 3.3 2.2 Phoma 2.2 3.2 traces traces Scopuloides 1.8 traces traces traces Verticillium 1.8 2.2 traces traces Hannaella traces 2.3 traces traces Wickerhamomyces traces 1.8 traces traces Aureobasidium traces traces 5.3 traces Leptographium traces traces 5.2 4.4 Cladosporium traces traces 3.9 7.3 Neurospora traces traces 3.2 traces Kluyveromyces absent traces 3.0 traces Torulaspora traces traces 2.3 traces Candida traces traces traces 8.0 Mycosphaerella traces traces traces 3.1 Geopyxis traces traces traces 2.0 aUHR_E_AP = Ultra-high release ethanol and ultra-high release alpha-pinene.

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racemic 2-methyl-6-methylene-7-octen-4-ol (ipsenol). bBelow 0.01% or not in the top 10 for this semiochemical.

Table 3.4 Operational Taxonomic Units: fungal identification proportion (%) by semiochemical type at the species taxonomic level (top 10 species) using the ITS1 genic region.

UHR_E_A

Semiochemicala P C6C8 General Longhorn Pine Sawyer Species fungi sp. 17.5 14.2 9.3 5.6 Rhodotorula mucilaginosa 8.2 8.5 tracesb 2.7 Cystobasidium slooffiae 6.4 3.3 traces traces Ascomycota sp. 5.3 4.5 2.8 5.9 OTU unclassified below genus 9.6 3.1 2.4 2.4 Epicoccum nigrum 2.5 5.2 3.3 2.2 Alternaria alternata 2.5 traces traces traces Scopuloides hydnoides 1.8 traces traces traces Verticillium dahliae 1.8 2.2 traces traces Cystobasidium pinicola traces 2.2 traces traces Hannaella luteola traces 1.9 traces traces Wickerhamomyces anomalus traces 1.8 traces traces Leptographium piriforme traces traces 5.2 4.4 Aureobasidium pullulans traces traces 5.1 traces Cladosporium exasperatum traces traces 3.9 6.9 Neurospora terricola traces traces 3.2 traces Kluyveromyces wickerhamii absent absent 2.9 traces Torulaspora delbrueckii absent traces 2.3 traces Candida sp. traces traces traces 7.9 Mycosphaerellaceae sp. traces traces traces 3.0

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aUHR_E_AP = Ultra-high release ethanol and ultra-high release alpha-pinene.

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Table 3.5 Operational taxonomic units: oomycete identification proportion (%) by semiochemical type at the Phylum and Genus taxonomic levels using the ITS1 genic region.

Semiochemicala UHR_E_AP C6C8 General Longhorn Pine Sawyer Phylum Oomycota 73.9 73.4 99.9 96.9 OTU unclassified below kingdom 26.1 26.6 0.01 3.02 Genus Peronospora 38.7 8.55 64.1 9.39 OTU unclassified below family 26.1 26.6 0.01 0.30 Phytophthora 18.4 46.5 16.3 absent Pythium 13.1 14.4 17.5 0.14 Hyaloperonospora 1.90 2.68 0.87 0.15 Plasmopara 1.72 absent 0.13 absent Basidiophora 0.13 1.34 1.07 0.01 Saprolegnia absent absent tracesb traces aUHR_E_AP = Ultra-high release ethanol and ultra-high release alpha-pinene.

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Table 3.6 Operational Taxonomic Units: oomycete identification proportion (%) by semiochemical type at the species taxonomic level (top 10 species) using the ITS1 genic region.

Semiochemicala UHR_E_AP C6C8 General Longhorn Pine Sawyer Species Peronospora manshurica 26.7 0.67 61.3 84.9 OTU unclassified below genus 26.1 26.6 tracesb 3.02 Phytophthora sp. 18.04 46.2 16.3 traces Peronospora aestivalis 8.20 2.82 1.36 1.47 Pythium sp. CAL-2011e 4.61 11.4 NAc NA Pythium hypogynum 4.14 1.63 traces traces Peronospora alta 2.11 4.59 0.002 3.16 Pythium sp. BG01 1.80 NA 17.1 NA Plasmopara viticola 1.72 NA traces traces Hyaloperonospora brassicae 1.42 traces 0.75 0.39 Hyaloperonospora parasitica traces 2.43 0.001 1.10 Basidiophora entospora traces 1.34 1.07 traces Pythium catenulatum NA 0.32 0.23 traces Peronospora polygoni 0.01 traces 0.49 2.95 Peronospora variabilis traces traces 0.40 0.83 Peronospora sepium traces traces 0.19 traces Pythium sp. 3862 traces traces traces 0.96 Peronospora arthurii traces NA traces 0.29 aUHR_E_AP = Ultra-high release ethanol and ultra-high release alpha-pinene.

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cNot applicable or not in the top 10 for this semiochemical.

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3.9 Figures

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Figure 3.1 Upset plot to visualize the type of trees from which traps were suspended. The intersection size number represents the number of times a specific tree combination was found (similar to a Venn diagram), and the set size number corresponds to the number of samples surrounded by a specific type of tree. Most samples were collected from traps placed in forested areas comprising more than one tree species.

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Figure 3.2 Venn diagram of a) fungal species shared or unique to the semiochemical type employed in insect traps, b) fungal species shared or unique to the semiochemical type employed in insect traps after species subtraction, c) oomycete species shared or unique to the semiochemical type employed in insect traps, d) oomycete species shared or unique to the semiochemical type employed in insect traps after species subtraction. All were obtained by amplifying the ITS1 genic region.

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3.10 Supplementary Materials

Supplementary Material 3.S1 Semiochemicals’ composition and additional details.

Each lure set (i.e., C6C8, UHR_E_AP, General Longhorn, and Pine Sawyer) was placed on separate traps.

First semiochemical set: C6C8.

The first combination (C6C8) consisted of racemic 3-hydroxyhexan-2-one (C6) (Bedoukian Research

Inc., Danbury, CT, USA), racemic 3-hydroxyoctan-2-one (C8) (Bedoukian Research Inc.), and ultra- high release ethanol (UHR EtoH), where each chemical was placed within individual release devices.

C6 and C8 were verified 99% pure by gas chromatography–mass spectrometry (GC-MS) by the Canadian Forest Service, and loaded into polyethylene pouches by Contech Inc (Delta, BC, Canada).

Each pouch contained 1.4 g of either C6 or C8. The release rates (at 20°C) were 20 mg/d for C6, and

25 mg/d for C8. Two C6 and two C8 pouches (semiochemicals) were both placed on a trap to obtain cumulative release rates of 40 to 50 mg/d.

Second semiochemical set: UHR_E_AP. The second semiochemical set (UHR_E_AP) consisted of UHR EtoH and UHR alpha-pinene. The two chemicals were loaded into separate release devices and placed on an insect trap. The UHR ethanol (95% purity, 121.5 g loaded/pouch) and UHR alpha-pinene [95% (-) enantiomer, 172 g/pouch] lures (Contech Inc., Delta, BC, Canada) had release rates (at 20°C) of 275 mg/d and 2 g/d, respectively. The UHR ethanol and UHR alpha-pinene chemicals were exactly the same throughout this project.

Third semiochemical set: General Longhorn. The lure set consisted of UHR EtoH, (E)-6,10-dimethyl-5,9-undecadien-2-ol (E-fuscumol) and (E)- 6,10-dimethyl-5,9-undecadien-2-yl (E-fuscumol acetate). Both E-fuscumol and E-fuscumol acetate were synthesized by Bedoukian Research Inc. and placed into polyethylene bubble caps by Contech Inc.; 130 mg/bubble cap of E-fuscumol (release rate = 1 mg/d), and 200 mg/bubble cap of E-fuscumol acetate (release rate = 2 mg/d).

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Fourth semiochemical set: Pine Sawyer. The Pine Sawyer lure set included four separate components consisting of 2-undecyloxy-1-ethanol (monochamol), UHR EtoH, UHR alpha-pinene and racemic 2-methyl-6-methylene-7-octen-4-ol (ipsenol). Monochamol (99.3% purity, 0.025 g/bubble cap) and racemic ipsenol (>99% purity, 0.04 g/bubble cap) were also purchased from Contech Inc. and had release rates (20°C) of 0.2 mg/d and 0.4 mg/d, respectively.

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3.11 Supplementary Tables

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1 Supplementary Table 3.S1: Summary of the samples collected from 2013 to 2015.

2 Semiochemicala Number of samples Canadian provinces UHR_E_AP 39

C6C8 36 British Columbia, New Brunswick, Newfoundland and Labrador, Nova General Longhorn 17 Scotia, Ontario, Prince Edward Island, and Quebec Pine Sawyer 16 Total: 108 7 Canadian provinces

aUHR_E_AP = Combination of two semiochemicals (ultra-high release (UHR) ethanol and UHR alpha-pinene) that attract a wide range of bark

and wood-boring insects (Brockerhoff et al. 2006b; Douglas et al. 2013; Petrice et al. 2018).

C6C8 = Combination of three semiochemicals (racemic 3-hydroxyhexan-2-one, racemic 3-hydroxyoctan-2-one, and UHR ethanol). The first two chemicals are aggregation pheromones of some longhorned beetles in the Cerambycinae subfamily (Ray et al. 2015) but, the addition of UHR ethanol increases attraction to other wood-boring insects. General Longhorn = Combination of three semiochemicals (UHR ethanol, (E)-6,10-dimethyl-5,9-undecadien-2-ol (E-fuscumol), and (E)-6,10- dimethyl-5,9-undecadien-2-yl (E-fuscumol acetate)) used to attract Spondylidinae, Lamiinae, and Scolytinae beetles (Silk et al. 2007; Mitchell et al. 2011; Sweeney et al. 2016). Pine Sawyer = Combination of four semiochemicals (2-undecyloxy-1-ethanol (monochamol), UHR ethanol, UHR alpha-pinene, and racemic 2- methyl-6-methylene-7-octen-4-ol (ipsenol)) used to attract longhorned beetles (Monochamus) and bark and ambrosia beetles due to the added ethanol and alpha-pinene (Pajares et al. 2010; Teale et al. 2011; Ryall et al. 2015).

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5 Supplementary Table 3.S2: Exotic and native fungal species of interest that are unique to a semiochemical (i.e., post species subtraction), and grouped

6 by the potential damage (= trophic status of concern) associated with those fungi. Also included is a risk level scale in terms of virulence.

7 Identifications based on the ITS1 sequences obtained.

Semiochemicala Presence Known damage Pine General Risk levelc Known host(s) References b C6C8 UHR_E_AP status sawyer Longhorn plant pathogen galleries and wounds caused conifers and (Alamouti et Ambrosiella ferruginea x N, C 2 by insect vector deciduous trees al. 2009) (mycangia) (Ginns 1986; Callan anthracnose and aspen and Ciborinia whetzelii x N, C 2 1998; ink spot disease cottonwood Holst- Jensen et al. 2004)

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(Damm et over 30 plant al. 2009; Colletotrichum fructi anthracnose x N 2 genera Diao et al. 2017) grapevine, (Velho et al. pepper, black 2014; Liu anthracnose, locust, et al. 2016; leaf spot, and strawberry, Nasehi et Colletotrichum bitter rot x N 2 water lily, apple, al. 2016; nymphaeae crab apple and Yamagishi protea et al. 2016) (Mascarin insect pathogen citrus orthezia et al. 2016) (Crous et al. 2007; Devriesia americana unknown x N 2d unknown Kim et al. 2011b) (Crous et al. 2009b; Devriesia strelitziicola death of leaves x E 2 Strelitzia spp. Schoch et al. 2009; Li

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et al. 2013; Quaedvlieg et al. 2014) (Braun and Populus spp. Cook 2012; Erysiphe adunca x N, C 2 and willow Tedersoo et al. 2014) Calystegia spp. (Farr and Erysiphe convolvuli x N, C 2 and Convolvulus Rossman spp. 2018) mustard, (Amano Erysiphe cruciferarum x N, C 2 cabbage, bok powdery mildew 1986) choy, and turnip (Fu et al. soybean and 2015; Erysiphe diffusa x N 2 legumes Baiswar et al. 2016) (Vajna et al. 2004; Erysiphe elevata x N 2 flowering trees Denchev 2008)

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(Braun and Cook 2012; numerous plants Tedersoo et Golovinomyces in the x E 2 al. 2014; depressus Asteraceae Farr and family Rossman 2018) (Conners 1967; wild basil, nettle, Amano Neoerysiphe x N, C 2 white turtlehead, 1986; galeopsidis and mint Ginns 1986; Choi et al. 2016) serviceberry, (Conners hawthorn, purple 1967; loosestrife, crab Ginns Podosphaera x N, C 2 apple, apricot, 1986; clandestina cherry, plum, Société de peach and Protection spirea des Plantes

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du Québec 2003) (Amano 1986; Ginns 1986; Podosphaera apple, and crab x N, C 2 Société de leucotricha apple Protection des Plantes du Québec 2003) (Takamatsu et al. 2010; Braun and Podosphaera lini x E 2 flax Cook 2012; Farr and Rossman 2018) (Braun et Podosphaera negeri x E 2 flowering shrubs al. 2006; Braun and

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Cook 2012) (Maloy 1967; Etheridge and Craig 1976; Echinodontium heart rot and hemlock, fir, and Ginns x N, C 3 tinctorium brown stringy rot cedar 1986; Société de Protection des Plantes du Québec 2003) (Jacobs and Wingfield blue stain and conifer and 2001; Leptographium sp. x N, C 1? sapstain hardwood Jacobs et al. 2004; Greif et al. 2006;

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Paciura et al. 2010; Kim et al. 2011a; Yin et al. 2015) (Ginns 1986; Lirula macrospora x N, C 3 spruce Müller et al. 2001) (Myren needle cast 1984; Société de Meria laricis x N, C 3 larch Protection des Plantes du Québec 2003) (Crous et al. 2003; eyespot of Mollisia dextrinospora x E 2 cereals Johnston et cereal al. 2010; Kiyuna et

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al. 2018) (McGuire and Crandall Mycosphaerella areola areolate mildew x N 3 cotton 1967; Farr and Rossman 2018) (Crous et al. 1996; Kubátová et al. 2004; Quercus spp., Aroca et al. Phaeo-acremonium Nectandra spp., 2008; wilt and decline x N 1? inflatipes whitebeam, vine, Mohammad and quince i and Sharifi 2016; Farr and Rossman 2018) Phoma glomerata blight, leaf spots, x N, C 2 over 80 different (Morgan-

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and fruit rot plants Jones 1967; Hosford 1975; Ginns 1986; Thomidis et al. 2011; Farr and Rossman 2018) (Garibaldi et al. 2012; shrubs, fir and Pucciniastrum circaeae rust x E 2 Farr and Circaea spp. Rossman 2018) (Ginns 1986; leaf spot and Septoria gladioli x N, C 2 flowers and corn French hard rot 1989; Société de

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Protection des Plantes du Québec 2003) (Société de Protection des Plantes du Québec pine, spruce, fir, Sirococcus conigenus x N, C 2 2003; and hemlock Konrad et shoot blight al. 2007; Rossman et al. 2008) (Rossman et al. 2008; Sirococcus piceicola x N, C 2 spruce Walker et al. 2010) Stagonospora (Crous et x E 3 grass pseudopaludosa al. 2013) leaf spot Teratosphaeria (Crous et x E 2 eucalytpus xenocryptica al. 2009a;

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Hunter et al. 2011) (Bissett and Darbyshire 1984; Ginns 1986; Société de Protection Phyllosticta minima x N, C 3 maple des Plantes du Québec 2003; Mcelrone et al. 2005; Tedersoo et al. 2014) (Zhang et al. 2009; Strelitziana mali sooty blotch x E 3 apple and vine Gleason et al. 2011; Crous et al.

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2012) (Mix 1949; Mułenko et Taphrina padi fruit deformation x E 2 cherry al. 2008; Eriksson 2014) artichoke, tomato, spinach, (Inderbitzin lettuce, et al. 2011; Verticillium isaacii vascular wilt x N 2 cauliflower, Gurung et eggplant, al. 2015) pepper, and strawberry insect gut associated handsome (Suh et al. Candida michaelii N/Ae x N N/A fungus beetle 2005) loss of wood value (Vlasák et Donkioporia al. 2010; white rot x U 5 decaying wood albidofusca Tedersoo et al. 2014)

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(Tedersoo Melastiza chateri x U 5 decaying wood et al. 2014) (Zhao and Perenniporia luteola x U 5 decaying wood Cui 2013) (Agustini et al. 2014; Phlebiopsis sp. x N, C 5 decaying wood Health Canada 2014) (Conners 1967; Ginns 1986; Bezalel et al. 1996; Pleurotus ostreatus x N, C 5 decaying wood Callan 1998; Société de Protection des Plantes du Québec

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2003) (Tedersoo Pluteus eludens x U 5 decaying wood et al. 2014) (Tedersoo Pluteus phlebophorus x U 5 decaying wood et al. 2014) (Tedersoo Ramaria pinicola x U 5 decaying wood et al. 2014) (Farr and Steccherinum Rossman ; x N, C 5 decaying wood oreophilum Ginns 1986) (Overholts 1953; United States Department Trametes cubensis x N 5 decaying wood of Agriculture 1960; Justo and Hibbett

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2011; Tedersoo et al. 2014) (Volk et al. 1994; Karasiński Antrodia albobrunnea x N, C 5 decaying wood and Niemelä 2016) brown rot (Miettinen and Larsson Sidera lunata x E 5 decaying wood 2011; Tedersoo et al. 2014) (Conners 1967; decaying Ginns Diatrype disciformis beech barkspot x N, C 3 hardwood trees 1986; Blackwell and Jones

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1997) (Gilbertson Hyphodontia et al. 2002; x E 5 decaying wood microspora Unterseher et al. 2005) (Hanlin 1966; other wood rots Minter et al. 2001; Phellinus x N, C 3 maple and alder Société de ferrugineovelutinus Protection des Plantes du Québec 2003) aUHR_E_AP = Ultra-high release ethanol and ultra-high release alpha-pinene.

C6C8 = Racemic 3-hydroxyhexan-2-one (K6), racemic 3-hydroxyoctan-2-one (K8), and ultra-high release ethanol. General Longhorn = Ultra-high release ethanol, (E)-6,10-dimethyl-5,9-undecadien-2-ol (E-fuscumol), and (E)-6,10-dimethyl-5,9-undecadien-2-yl (E-fuscumol acetate). Pine Sawyer = 2-undecyloxy-1-ethanol (monochamol), ultra-high release ethanol, ultra-high release alpha-pinene, and racemic 2-methyl-6-methylene-7-octen-4-ol (ipsenol). b N = the organism is native or reported to be present in North America, C = the organism is native or reported to be present in Canada, E = the organism is not reported or present in North America (exotic), and U = unknown status because information is lacking for Canada and North America. cRisk associated with the organism on a 1 to 5 scale. 5 = a riskless saprophyte fungus, 4 = a saprophyte fungus capable of causing damages to plants 3 = a weakly-virulent pathogenic fungus, 2 = a moderate virulent pathogenic fungus but common in Canada, and 1 = a highly-virulent pathogenic fungus.

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dAssumption based on the impact of species within the same genus. eNot applicable.

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9 Supplementary Table 3.S3: Unique oomycete species detected in the different

10 semiochemicals after proceeding with species subtraction and using the ITS1 genic region.

Semiochemicala

General Longhorn C6C8 C6C8 and General Longhorn

Species

Peronospora farinosa Pythium sp. CAL-2011e Pythium sp. BG01

Peronospora sp. isolate 079405,59 Pythium sp. P3862

Peronospora sp. UPS F-119986 No blast hit

Peronospora sparsa

Peronospora viciae

Pythium aff. hypogynum

Pythium sp. AvdB-2012

Pythium sp. BP2013k

Pythium sp. CAL-2011f

Pythium sp. P19300/1/3

Saprolegnia sp. SAP1

Total: 11 1 3

a General Longhorn = Ultra-high release ethanol, (E)-6,10-dimethyl-5,9-undecadien-2-ol (E-fuscumol), and (E)- 6,10-dimethyl-5,9-undecadien-2-yl (E-fuscumol acetate). C6C8 = Racemic 3-hydroxyhexan-2-one (K6), racemic 3-hydroxyoctan-2-one (K8), and ultra-high release ethanol.

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3.12 Supplementary Figures

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Supplementary Figure 3.S1: Sampling sites in Canada; a) West Coast, b) the Atlantic Region, and c) Eastern Canada. Adapted from Google Earth.

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Supplementary Figure 3.S2: Rarefaction curves (number of sequences obtained for each species) for a) spore trap samples and their respective b) insect trap samples (original data), and c) insect trap samples (subtracted data) to visualize sequencing depth.

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Supplementary Figure 3.S3: fungal species a) evenness (Shannon) and b) true diversity (Shannon) by semiochemical type, and oomycete species c) evenness (Shannon) and d) true diversity (Shannon) by semiochemical type. The ITS1 sequences were used.

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Supplementary Figure 3.S4: Principal Coordinate Analysis of the fungal Operational Taxonomic Unit (ITS1) found in three Canadian geographic regions; the West Coast (BC), the Atlantic Region (PEI, NL, NS, and NB), and Eastern (QC and ON) at the a) order and b) class taxonomic ranks.

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Chapitre 4: High-resolution biomonitoring of plant pathogens and bee- foraged plant species using metabarcoding of pollen clusters content collected from a honeybee hive

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High-resolution biomonitoring of plant pathogens and plant species using metabarcoding of pollen pellet contents collected from a honeybee hive

Émilie D. Tremblay1, Marc-Olivier Duceppe1, Graham B. Thurston2, Marie-Claude Gagnon1, Marie-

José Côté1 and Guillaume J. Bilodeau1

1Canadian Food Inspection Agency, 3851 Fallowfield Road, Ottawa, Ontario, K2H 8P9,

[email protected], [email protected], Marie-

[email protected], [email protected], [email protected];

2Canadian Food Inspection Agency, 1400 Merivale Road, Tower 1, Ottawa, Ontario, K1A 0Y9,

[email protected]

Keywords: metabarcoding, pollen, honeybee, next-generation sequencing, plant pathogens

Cet article a été soumis pour publication dans une revue scientifique le 7 novembre 2018.

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4.1 Résumé

L’industrie de l’apiculture est présente partout au Canada et d’autant plus dans les provinces des Prairies où se trouve la plus grande proportion des ruchers. Du point de vue national, le nombre de ruchers suit une tendance à la hausse depuis quelques années. Parallèlement, le nombre d’incidents phytosanitaires environnementaux et agronomiques augmente, entre autres, en raison de l’introduction d’organismes exotiques qui sont déplacés par l’entremise du commerce international, l’un des principaux moyens d’introduction d’espèces étrangères au potentiel envahissant. C’est pourquoi les organismes de réglementation ont un intérêt particulier à développer des outils permettant de faire la détection plus rapide des espèces indésirables afin d’accélérer l’application de moyens de lutte contre les maladies introduites. Cette étude avait pour but d’évaluer le potentiel du contenu des granules de pollen ramassés par des abeilles—une méthode d’échantillonnage active, simple, peu coûteuse et couvrant un rayon de quelques kilomètres—afin de surveiller des organismes ravageurs incluant des oomycètes et des champignons envahissants. Le métabarcodage, qui évalue et fait le suivi des échantillons environnementaux mixtes à l’aide du SNG, a été utilisé en tant qu’outil primaire de biosurveillance. L’étude a utilisé la région génétique ITS1 pour cibler les champignons et les oomycètes, et l’espaceur de gène ATP9-NAD9 pour cibler spécifiquement les espèces de Phytophthora spp. Également, l’analyse par métabarcodage basée sur l’ITS2 des plantes sur lesquelles les abeilles ont butiné a été effectuée. Elle a permis d’évaluer si certaines de ces plantes visitées avaient un potentiel envahissant. Plusieurs plantes détectées par SNG correspondaient aux hôtes respectifs de certains des agents phytopathogènes aussi détectés de cette manière. De même, des espèces de plantes phylogénétiquement proches d’espèces indésirables ayant un potentiel envahissant ont été trouvées. Voici quelques exemples de genres comprenant des espèces phytopathogènes trouvés dans les échantillons de pollen : Fusarium spp., Gremmeniella spp., Ophiostoma spp., Peronospora spp., Phytophthora spp. et Pythium spp. Des corrélations entre les volumes de pluie, la température enregistrée et la diversité des communautés fongiques, oomycètes et végétales ont aussi pu être établies. La possibilité d’utiliser les granules de pollen recueillis par des abeilles dans le but d’étudier les phytopathogènes et les plantes envahissantes dans un environnement donné est démontrée. Ce protocole a un potentiel prometteur en tant qu’outil complémentaire de biosurveillance des organismes indésirables lorsqu’il est combiné à d’autres méthodes d’enquête utilisant des pièges à spores.

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4.2 Abstract

The Canadian beekeeping industry is spread across the country, with the greatest proportion of managed honeybee colonies occurring in the Prairie Provinces. Nationally, the number of beekeepers has recently been trending upwards. Simultaneously, agronomic and environmental plant pest incidents are increasing due to a number of factors, including the introduction of exotic organisms through international trade, a major pathway for the introduction of potentially invasive alien species and quarantine pests. Therefore, regulatory agencies are interested in developing high- throughput tools to achieve earlier detection of unwanted species in order to expedite application of mitigating measures to limit the impacts of their introduction. This study evaluates the potential of pollen pellet contents collected by honeybees to monitor plant pests using metabarcoding, a next- generation sequencing (NGS) approach for monitoring complex environmental samples. The study used the ITS1 intergenic region to target oomycetes and fungi, the ATP9-NAD9 spacer to specifically target Phytophthora species, and the ITS2 region to target plant species. From the NGS results, a number of plants detected corresponded to known hosts of certain pathogens or species closely- related potentially invasive plant species. Genera including phytopathogenic species found in the pollen samples comprised Fusarium spp., Gremmeniella spp., Ophiostoma spp., Peronospora spp., Phytophthora spp., and Pythium spp. Correlations between rainfall volumes, temperature, and the diversity of the fungal, plant, and oomycete communities were also established. The potential for using honeybee-collected pollen pellets to study phytopathogens in a given environment is demonstrated here, and this protocol could offer a promising complementary tool for the surveillance of phytopathogens or unwanted plants with previously described air and insect sampling methods.

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4.3 Introduction

The use of living organisms to investigate environmental changes is an approach that is well established. For example, nematode communities have been used to biomonitor environmental conditions in soils (Bongers and Ferris 1999), protozoans to assess changes within a given soil ecosystem (Wodarz et al. 1992), and macroinvertebrates have been used as bioindicators for freshwater ecosystems (Wodarz et al. 1992). More recently, such biomonitoring analyses have been successfully applied using a next-generation sequencing (NGS) approach (Carew et al. 2013; Mordecai et al. 2015). NGS has been used to screen for the presence of fungal propagules present in high-risk environments using various types of air traps (Bérubé et al. 2017a; Bérubé et al. 2018; Tremblay et al. 2018). Such approach allowed to address the need for additional monitoring methods for ecological damage mitigation, most of which are caused by either fungi or oomycetes (Knogge 1996; Fry and Grunwald 2010; Carris et al. 2012; Kamoun et al. 2015). As a matter of fact, exotic or invasive plant pathogens ravaging Canadian natural and agricultural resources such as forests and crops are also increasing at an alarming rate because of anthropogenic activities including international trade and climate change (Allen and Humble 2002; Mecteau et al. 2002; Hulme 2009; Bilodeau et al. 2012; Vettraino et al. 2015; Bellard et al. 2018; Roe et al. 2018).

Pollinators play a very important biological role for human nutrition as they contribute to the production of about one third of human food (crops, seeds, fruits) (McGregor 1976; Aizen et al. 2009). Pollinators also support the preservation of floral biodiversity, as their foraging activities improve the sexual reproduction of many wild plant species (Klein et al. 2007). For generalist pollinators, including honeybees (Apis mellifera L.), pollen is an essential dietary component foraged from a diverse array of plant species as the main source of proteins, lipids, minerals, and vitamins (Herbert 1992). Prior to being transported back to the colony, pollen grains are groomed from the body of a foraging worker and packed into pollen carrying appendages (corbicula), a specialized structure located on the honeybee’s rear leg (Bees Matter 2015; Cornman et al. 2015). The corbicular pollen pellets can be collected from returning foraging bees by means of a pollen trap (Cundill 1991; Bush 1992).

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Traditionally, honeybee pollen content, an indicator of their pollinating activities, was investigated by microscopy (Synge 1947; Louveaux et al. 1978; De Vere et al. 2017). Later, PCR amplification and sequencing of DNA became standard methods for this purpose (Petersen et al. 1996). More recently, metabarcoding—a biodiversity assessment method to mass-amplify and sequence variable regions in the genome of the target species (i.e., DNA barcodes) from environmental samples (Bell et al. 2016)—is replacing microscope and PCR -based methods to investigate plant species foraged by honeybees. Metabarcoding is faster and offers higher throughput, sensitivity, and resolution (Cornman et al. 2015; Keller et al. 2015; Kraaijeveld et al. 2015; Richardson et al. 2015a; Richardson et al. 2015b; Sickel et al. 2015; Smart et al. 2017).

Honeybee colonies can contain thousands of foraging individuals and bees are known to perform their foraging activities within several kilometers of their colony (Beekman and Ratnieks 2000). It has been reported previously that foraging honeybees can indirectly accumulate airborne plant pathogenic material such as viruses (Childress and Ramsdell 1987; Card et al. 2007; Roberts et al. 2018) and bacteria (Johnson et al. 1993; McArt et al. 2014; Pattemore et al. 2014) into the corbicular pollen pellets during foraging flights, or directly from contaminated plant flowers. However, limited information on fungal spores transported by honeybees while foraging or incidentally caught during flight has been reported. We hypothesised that honeybee-foraged pollen pellets could be an active, simple, and effective matrix for monitoring plants or plant species over a few kilometers radius from a colony. Accordingly, the primary objective of this study was to examine the potential for using honeybees as sentinels for surveillance of plant pathogens by performing metabarcoding on pollen pellet contents collected from the hive. The second objective was to assess which plants, including potentially invasive species, could be detected by NGS from the pollen pellet samples, and corroborate those detections with visual assessment of the flowering plant species recorded in the hive vicinity over a specific time period. The third objective was to examine possible correlations between fungal and oomycetes diversity and environmental factors such as temperature and rain volumes.

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4.4 Materials and methods

Sample collection. In the summer of 2017, pollen samples were collected from one honeybee (Apis mellifera L.) colony in an apiary (Kinburn, ON, Canada) containing thirteen double brood chamber colonies. The queen bees in these colonies were daughters (uncontrolled mating) of 2015 queens from a hygienic breeding program (Highlands Honey, Perth, ON, Canada). Six of the colonies in the apiary, including the pollen trapping colony, were visually inspected by provincial bee inspectors (Ontario Ministry of Agriculture, Food and Rural Affairs) in early June 2017. Those colonies were found to be free from the pests listed in Supplementary Material 4S1. The honeybees were suspected of foraging mostly within a five kilometer radius of the apiary (Supplementary Figure 4S1). This foraging area included cultivated fields, fragmented forest, old fields, roadways, ornamental gardens, rivers, and streams. The predominant agricultural plants cultivated in the surrounding fields were corn, soybean, wheat, alfalfa, and hay.

A dancing bee pollen trap (Dancing Bee Equipment, Port Hope, ON, Canada) was used to collect the pollen pellets carried by the returning workers (Supplementary Figure 4S2a-b). The trap functioned by dislodging the pollen pellets from the corbicula of returning foraging bees as they passed through a grate just big enough for their bodies to wiggle in. The dislodged pollen pellets fell into a collecting tray located beneath the grate, which was protected from light and rain (Supplementary Figure 4S2c). To limit the impact on brood rearing, and to avoid depriving the colony of essential nutrients, the pollen trap was activated no longer than three days at a time (except for the sample collected May 21 2017, which had the trap activated for five days).

Every few weeks, immediately after turning off the pollen trap (i.e., removing grate from trap), pollen samples were collected and frozen in individual plastic bags until delivery to the laboratory. The pollen collection tray was cleaned in the apiary prior to activating the pollen trap for each successive sample. In addition, flowering observations were recorded from the local flora throughout the pollen sampling period based on visual assessment (Supplementary Table 4S1). Weather data (i.e., precipitation volume and temperature) was retrieved from the Environment Canada website, sorted by sampling schedule, and compiled in Supplementary Table 4S2.

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DNA Extraction. Subsampling was done on each of the pollen samples. DNA was extracted from seven pollen samples using the DNeasy Plant Mini Kit (QIAGEN, Hilden, NW, Germany) following the supplier’s protocol. The extracted DNA was then quantified to validate the success of the extraction using a Nanodrop ND-1000 instrument (Life Technologies, Carlsbad, CA, USA), and then diluted in water to 10 ng/µl.

PCR using fusion primers. The hypervariable internal transcribed spacer (ITS) regions within the ribosomal DNA loci were selected for fungi, oomycete, and plant amplification. In addition, the adenosine triphosphate synthase subunit 9-nicotinamide adenine dinucleotide dehydrogenase subunit 9 spacer (ATP9-NAD9) (mitochondrial) was selected specifically to distinguish Phytophthora spp. (Martin et al. 2012).

PCR were done on each pollen sample to append a unique sample index and amplify a specific genic region. Primers used were specific for the targeted organism and genic region (i.e., plants [ITS2], fungi [ITS1], oomycetes [ITS1], and Phytophthora spp. [ATP9-NAD9]). Except for the plant- specific (ITS2) PCR, the above-mentioned fusion primers protocol and the PCR oligonucleotide sequences are all described by Tremblay et al. (2018). Briefly, there was a bidirectional fusion primer set to amplify specifically the ITS1 region from fungi (forward primer = ITS1F (Gardes and Bruns 1993) and reverse primer = ITS2 (White et al. 1990)), another set to amplify the ITS1 from oomycetes (forward primer = OOM-UP18S67 (C. A. Lévesque, personal communication) and reverse primer = OOM-LO5.8S47 (Man in 't Veld et al. 2002; Bilodeau et al. 2007)), and a unidirectional fusion primer set (forward primer = PhyGATP92FTail and non-tagged reverse primer = PhyG-R6Tail (Bilodeau et al. 2014)) for Phytophthora species’ ATP9-NAD9 amplification. Given that the bidirectional primer set (forward primer = ITS4 (White et al. 1990) and reverse primer = 58SPL (M.-J. Côté, personal communication)) for the amplification of plants’ ITS2 was not previously described, Supplementary Table 4S3 provides the sequences of a few ITS2 fusion primers.

The fungal and oomycete ITS1 region PCR (25 µl) consisted of PCR buffer (1×), MgCl2 (1 mM), dNTPs (0.25 mM), forward and reverse primers (0.50 mM each), Platinum Taq polymerase (0.04 U) (Life Technologies), and a final DNA concentration of approximately 0.1 ng. Cycling conditions

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consisted of 3 min at 95°C; 30 cycles of 30 s at 95°C, 30 s at 52°C, and 1 min at 72°C for each cycle, and then 10 min at 72°C.

The plant ITS2 region PCR (20 µl) consisted of PCR buffer (1×), MgCl2 (2 mM), dNTPs (0.4 mM), forward and reverse primers (0.67 mM), HotStar Taq polymerase (0.55 U) (QIAGEN), and a final DNA concentration of 0.5 ng. Cycling conditions were 1 cycle of 15 min at 95°C; 40 cycles of 30 s at 94°C, 30 s at 56°C, and 1 min 15 s at 72°C, and one final cycle of 10 min at 72°C.

The ATP9-NAD9 region PCR (25 µl) consisted of 5 PRIME RealMasterMix Probe with and without Rox (Fisher Scientific, Waltham, MA., USA), MgCl2 (6 mM), forward and reverse primers (0.5 mM), and a final DNA concentration of approximately 0.3 ng. The 40 PCR cycles were 15 s at 95°C and 90 s at 53°C.

Samples were loaded and run on a 1.5 % agarose gel, and the electrophoresis products were visualized on a Gel Doc XR+ Gel Documentation System (Bio-Rad Laboratories, Inc., Hercules, CA, USA) to validate the success of DNA amplification. To remove DNA fragments of ≥500 bp and of ≤100 bp respectively, consecutive purifications were done on the PCR products using Agencourt AMPure XP magnetic beads at 0.7:1 and then 1.4:1 beads/DNA ratios (Agencourt Bioscience, Beverly, MA, USA) (Edwards 2012).

NGS. Bidirectional libraries for each indexed sample and region were quantified with the Ion Universal Library Quantitation qPCR Kit (Life Technologies). The oomycete (ITS1 and ATP9-NAD9) and fungal (ITS1) libraries were then pooled in two separate libraries normalized at the respective concentrations of 100 pM and 120 pM. The plant material libraries (ITS2) were pooled into two equimolar concentrations of 80 pM and 100 pM. The Ion Chef Instrument (Life Technologies) was used for template preparation and loading onto Ion 530v1 chips, which were run on the Ion S5 NGS System (Life Technologies). Reagents used were the Ion 520 & Ion 530 Kit-Chef (Life Technologies). Following the sequencing run, a FASTQ file was generated for each sample and demultiplexed by genic region using the S5 built-in software.

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Databases. Four different databases, corresponding to the four amplified genic regions, were used to infer genus or species identity. To screen for fungal species (ITS1), the UNITE database (Kõljalg et al. 2005, version 01.12.2017) was used. Oomycetes were screened using an internally generated “OmDB” database, consisting of all oomycete ITS sequences present in the National Center for Biotechnology Information (NCBI) nucleotide database downloaded on 3 March 2018. As previously described by Tremblay et al. (2018), a custom Phytophthora species database (“PhyDB”) was built by gathering ATP9-NAD9 sequences from Phytophthora spp. retrieved from i) the NCBI (nucleotide) (NCBI accessions JF771616.1 to JF772053.1 and JQ439009.1 to JQ439486.1) and ii) sequences described by Bilodeau et al. (2014). All plant ITS2 sequences contained in the NCBI (nucleotides) database on 16 August 2018 were downloaded to create a local “PlantsDB” database for further analysis of plant sequences.

Data processing. Each of the four NGS datasets (i.e., fungi, oomycete, Phytophthora spp., and plant) was processed as described by Tremblay et al. (2018). Briefly, sequences were trimmed (length, quality) with Mothur (version 1.37.2) (Schloss et al. 2009) and then processed using ITSx (version 1.0.11) (Bengtsson-Palme et al. 2013) (except for the ATP9-NAD9 region), and QIIME (version 1.9.1) (Caporaso et al. 2010). The generated OTU tables included taxonomic assignments at the species or genus level, when possible, based on a 0.97 OTU cut-off following alignment with the respective database (i.e., UNITE, OmDB, PhyDB, and PlantsDB). Community profiling was then performed with the R (version 3.1.3) (R Core Team 2013) package RAM (version 1.2.1.3) (Chen et al. 2016). Species true diversity within the studied community (α) was assessed using the Shannon index. True diversity index were given in units of number of species in order to unify the data for each sample, therefore diversity index were converted to effective numbers of species prior to be treated as true diversities (Jost 2013c, b). Abundance bar plots were generated to visualize the distribution of selected taxa for levels of interest by bidirectional sample or collection date. Pairwise Venn diagrams were generated to assess the number of shared or unique genera by collection date for each target organism (i.e., plant, oomycete, and fungi). Using the “cor.test” function in R, Spearman’s rank correlation (rho) tests were run on the fungal (ITS1), oomycetes (ITS1), and plants (ITS2) datasets to assess potential correlation between alpha diversity and rainfall volumes or temperature recorded.

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Genus- or species-specific query. The custom Perl script metaResultExtractor.pl (Tremblay et al. 2018) was used to screen for fungal and oomycete genera of interest as pathogens to the forestry and agricultural industries, which are listed in Table 4.1. Similarly, the data were screened for the presence of invasive plant species, selected from the Canadian Phytosanitary requirement D-12-01 (Canadian Food Inspection Agency 2013b), and several closely-related species (Supplementary Material 4S2). A primary search to validate that all regulated species had ITS2 sequences in Genbank was performed. If species were detected based on the results obtained with the metagenomics pipeline using Genbank ITS plant sequences, additional local BLAST (version 2.2.26+, e-value = 0.0001) were performed using an internal database containing curated sequences of the invasive species and several closely-related species in order to confirm identities using reference barcode sequences from herbarium voucher samples. Plus, if a sequence alignment displayed a high score alignment with plant species within the target list when using the internal database, those specific sequences were BLASTed again in the NCBI database because several native or cosmopolitan species were not included in the PlantsDB database.

Screening of Phytophthora genus by qPCR. Based on the results obtained from the ATP9- NAD9 NGS data, an additional test to confirm the presence of the genus Phytophthora in the pollen samples was performed on a ViiA 7 Real-Time PCR System (Life Technologies). The assay, developed by Bilodeau et al. (2014), consisted of 50-cycles of 15 s at 95°C and 90 seconds at 60°C per cycle, and used PhyG_ATP9_2FTail (AATAAATCATAACCTTCTTTACAACAAGAATTAATG) (forward) and PhyG-R6_Tail (AATAAATCATAAATACATAATTCATTTTTATA) (reverse) primers, as well as the ATP9_PhyG2_probeR (5-FAM-AAAGCCATCATTAAACARAATAAAGC-3-IABkFQ) probe to target the genus Phytophthora (ATP9-NAD9). The genus-specific qPCR (25 µl) consisted of 5

PRIME RealMasterMix Probe with and without Rox (Fisher Scientific), MgCl2 (6mM), forward and reverse primers (0.50 µM), the genus-specific probe (0.05 µM), and a final DNA concentration of approximately 0.3 ng. All samples and controls were run in duplicate. Positive control P. ramorum Werres, De Cock & Man in 't Veld PR-11-001 (source: Niklaus Grünwald, USDA-ARS) was included to generate a standard curve (four 1 in 10 serial dilutions) as well as water for a no template control.

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4.5 Results

PCR output. As shown in Table 4.2, for each of the seven pollen pellet samples, six to seven (if the oomycete PCR generated a visual band, the Phytophthora ATP9-NAD9 PCR was subsequently performed) assays were performed for a total of 49 PCR (excluding controls) with 86 % of the assays generating a visible band on gel. More specifically, all bidirectional ITS PCR targeting fungi (ITS1 region amplified with fusion primers ITS1F and ITS2) and plants (ITS2 amplified with fusion primers 58SPL and ITS4) produced amplicons visible on gel. Oomycete-specific bidirectional reactions (ITS1) were generally successful as the forward primer products (Omup) had bands in 71 % of the cases, and the reverse one (Omlo) in 86 % of the cases. The unidirectional ATP9-NAD9 PCR products presented a lower proportion (43 %) of bands on gel. Sample DPS07 amplified with the indexed fungal-specific forward primer (ITS1F) appeared contaminated with the PCR positive control Verticillium dahlia kleb. because >99 % of its OTU contents was V. dahliae (data not shown), and the abundance of contained fungal material compared to any of the other samples was much higher (data not shown). Therefore, sample DPS07 ITS1F fungal data was discarded from the study. However, the sequences from the same sample obtained using the reverse indexed ITS2 primer (n.b. not the ITS2 genic region) was not compromised and could therefore be used for the whole analysis of fungal OTUs.

Fungi-specific data analysis: abundance and diversity. The top five most common OTUs detected in all fungal-amplified samples were the three fungal phyla Ascomycota, Basidiomycota, and Mortierellomycota, and the two other most common detections were “OTU that belongs to kingdom fungi” and “OTU with no BLAST hit” (Figure 4.1a). Samples collected in mid-June until end of June (DPS05 and DPS06) presented a higher proportion of Basidiomycota compared to all other samples. Fungal abundance results were obtained at the taxonomic levels of class (Figure 4.1b, top ten), order (Figure 4.1c, top ten), family (Figure 4.1d, top fifteen), genus (Figure 4.1e, top fifteen), and species (Figure 4.1f, top thirty). The ten most abundant results at the genus taxonomic level (Figure 4.1e) were the fungal genera Aspergillus spp. (most were collected mid- to end of May, and early to mid- June [DPS01, DPS03, and DPS04), Itersonilia spp. (most were recovered from mid- to end of June [DPS05 and DPS06]), Alternaria spp. (generally found equally in all collection dates, but less so in early and mid-June [DPS03 and DPS04]), Mycosphaerella spp. (found in May, and from mid-June

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and onward [DPS01, DPS02, DPS05, DPS06, and DPS07]), Filobasidium spp., Epicoccum spp., and, Vishniacozyma spp. (most of those three genera were found after mid-June, but a certain proportion was also found late in May [DPS2, DPS05, DPS06 and DPS07]), and the OTUs not classified down to the genus level “OTU that belongs to kingdom fungi”, ), “OTU with no BLAST hit” (both generally found equally in all collection dates, but much lesser during mid-June [DPS04]), and “OTU that belongs to phylum Ascomycota” (detected in May, mid-June, and onward [DPS01, DPS02, DPS05, DPS06, and DPS07). The abundance analysis at the species level potentially revealed species of interest including A. alternata (Fr.) Keissl., B. graminis (DC.) Speer, (both mostly detected in May, and from mid-June to mid-July [DPS01, DPS02, DPS05, DPS06, and DPS07]), F. sporotrichioides Sherb. (found late in May and in June [DPS02, DPS05, and DPS06]), I. perplexans Derx (present during the whole collecting period, but significantly higher during mid-May and the month of June [DPS01, DPS03, and DPS04]), and V. dahliae (generally detected with a low abundance in all samples) but, the ITS1 is known to fail to resolve certain taxa below the genus level (Zitnick- Anderson et al. 2018).

The temporal abundance results (combinated bidirectional datasets) performed at the genus level (top 25) showed a higher abundance of Aspergillus spp. in May and during the first half of the month of June compared with the rest of the season. Sample collected on June 13th (DPS04) consisted almost only of this genus (Supplementary Figure 4S3a). Although there was still an important part of the fungal material which was Aspergillus spp., Itersonilia spp. abundance increased for the second half of the month of June. On the other hand, many genera such as Alternaria spp., Epicoccum spp., Filobasidium spp., Mycosphaerella spp., Verticillium spp., and Vishniacozyma spp. were detected throughout the whole collection season, except for DPS04. The sample collected on July 12 (DPS07) contained approximately half of the OTU abundance compared to the other samples.

The Shannon true diversity (α) varied throughout the summer given that the mean of the units of number of fungal species detected was approximately 30 for mid-May and jumped to approximately 80 at the end of the same month (Supplementary Figure 4S4a). Then, values dropped below ten for the first half of the month of June but increased to approximately 50 for the second half of June and in July (Supplementary Figure 4S4a).

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Oomycete-specific data analysis: abundance and diversity. Results from the oomycete (ITS1) bidirectional dataset analysis performed at the order level on oomycetes (ITS1) revealed that the most abundant orders were Peronosporales (commonly found in all samples) and Pythiales (the majority were found in May ([DPS01 and DPS02]), in addition to the unclassified “OTU with no BLAST hit” (mostly in May and July [DPS01, DPS02, and DPS07]) (Figure 4.2a). From the analysis performed at the family level, the majority of the OTUs obtained corresponded to Peronosporaceaea (present in all samples) and Pythiaceae (retrieved in May (DPS01 and DPS02]), and the unclassified “OTU with no BLAST hit”, detected in May and July ([DPS01, DPS02, and DPS07]) (Figure 4.2b). When analyzing oomycete data at the genus level, the most abundant results included Peronospora spp. within all samples (with a much lesser proportion in mid-June [DPS04]), Hyaloperonospora spp. (also generally detected in all seven samples but, much less so early in June and in July [DPS03 and DPS07]), Plasmoverna spp. (detected mostly during end of May and mid-June [DPS02 and DPS04]), Plasmopara spp. (mostly detected late in May and in July [DPS02 and DPS07]), Pythium spp. (in May [DPS01 and DPS02], and Bremia spp. (early in June [DPS03]) (Figure 4.2c). Some of the genera detected included notorious phytopathogenic organisms, such as Hyaloperonospora spp., Peronospora spp., Plasmopara spp., Plasmoverna spp. Pseudoperonospora spp., and Pythium spp. The genus Phytophthora was barely detected; hence no Phytophthora species appeared in Figure 4.2d. Of the top thirty oomycetes species detected, most were either Peronospora spp. or Hyaloperonospora spp. (Figure 4.2d). Because all metagenomics and qPCR results from the ATP9- NAD9 data screening for Phytophthora species remained inconclusive (see results section “Validation of the presence of Phytophthora spp. by genus-specific qPCR”), further statistical analyses were not performed on the ATP9-NAD9 dataset.

The oomycete temporal abundance data obtained from the combined fusion primers datasets at the genus level (all genera) revealed a high concentration of Peronospora spp. during May and early June (DPS01, DPS02, and DPS03), which decreased from June 13 until the 28th of the same month, and then spiked again in July (Supplementary Figure 4S3b). Hyaloperonospora spp. was also abundant throughout the whole sampling season. While Plasmoverna spp. abounded earlier in the season, Plasmopara spp. was detected mostly during mid-July. Most of the Pythium spp. material was detected in May.

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The Shannon true diversity of oomycetes species found using the ITS1 region showed that approximately fourteen units of number of oomycetes species were in the sample of May 17th (Supplementary Figure 4S4b). This value dropped to about eight for the second half of the month, and again for the first sample collected in June, because the value was approximately four units of species. The values increased again during the rest of the summer but, remained below ten from mid-June onwards.

Plant-specific data analysis: abundance and diversity. Similar plant abundance levels were observed for both fusion primer direction sequenced (i.e., 58SPL and ITS4) but, different orders dominated in the various samples. While there was a majority of Asterales, Dipsacales, and Rosales in samples collected from mid-May to early June (DPS01, DPS02, and DPS03), Fabales dominated from mid-June until mid-July (DPS04, DPS05, DPS06, and DPS07) (Figure 4.3a). The same pattern was observed at the family level, where Adoxaceae and Asteraceae were more abundant from mid- May to early June but, they were replaced by Boraginaceae and Fabacea which made up the majority for the rest of the sampling season (Figure 4.3b). At the genus level, Sambucus spp., Taraxacum spp., and Quercus spp. comprised the majority of plants found in May (DPS01 and DPS02), whereas Viburnum spp. and Rhamnus spp. predominated early in June (DPS03), and from mid-June onwards Trifolium spp., Symphytum spp., Echium spp., and Melilotus spp. were in the majority (DPS04, DPS05, DPS06, and DPS07) (Figure 4.3c). The top thirty most abundant species, all of which are angiosperms, are listed in Figure 4.3d. Considering that Chen et al. (2010) previously showed that when using BLAST and ITS2, species could be correctly identified in only 92.7% of cases, the sequences potentially assigned to Taraxacum spp. and Trifolium spp. (flowering plants) were not evaluated below the genus level.

After performing the more general temporal abundance analysis at the genus level (top 25) using the combined bidirectional datasets, each sampling date comprised a different abundance of plant genera over the study period (Supplementary Figure 4S3c). A high proportion of Sambucus spp. (elderberry) was contained in mid-May sample DPS01. During the same time period, Taraxacum spp. was also present in a considerable amount but, literally dominated later in May (DPS02). Then, Rhamnus spp. and Viburnum spp. comprised almost the entire sample collected early in June

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(DPS03). A substantive portion of the remaining samples collected from mid-June to mid-July were Trifolium spp. The month of June also included a significant amount of Symphytum spp. (DPS04) and Echium spp. (DPS05). In addition, late June and mid-July samples DPS06 and DPS07 displayed high proportion of Melilotus spp.

Throughout the collection season, the diversity of plants diminished with incidental peaks (Supplementary Figure 4S4c). Samples collected in May and mid-June (DPS01, DPS02, and DPS04) had averages of units of number of species > 10 but, early June, mid- June, and July samples (DPS03, DPS05, and DPS07) had averages that dropped below 7.5. A small species number increase was observed in the sample collected at the end of June (DPS06) with a value > 8.

Primary screening of targeted genera and further validation of species. The genus screening using the script metaResultExtractor.pl showed the detection of OTUs, not all among the most abundant ones, within the following fungal genera: Alternaria spp., Colletotrichum spp., Diaporthe spp., Fusarium spp., Gremmeniella spp., Hymenoscyphus spp., Ophiostioma spp. Pseudopeziza spp., Rosellinia spp., Sclerotinia spp., Phoma spp., Venturia spp., and Verticillium spp. With regards to targeted oomycetes, as mentioned above, the genera Peronospora spp., Phytophthora spp., and Pseudoperonospora spp. were also detected by NGS using the custom Perl script. Although the first general screening of invasive plant species (using NCBI [nucleotide]) reported the potential presence of six species closely-related to invasive or regulated plant species (namely Centaurea Phrygia L., C. stoebe L., C. virgata Lam., Echium vulgare L., Sinapis arvensis L., and Vitis riparia Michaux), the alignment results using the PlantsDB followed by the final BLAST back into the NCBI (nucleotide) could only confirm the identity of E. vulgare because unambiguous scores of ≥99% were obtained with both databases. Using PlantsDB, the second screening could not confirm the presence of Vitis riparia, because no significant alignment was recovered. Additionally, because the sequences of C. stoebe and C. virgata are identical in the ITS2 PlantsDB, the identification of the environmental sequences which aligned with either of those species could not be resolved. This was observed also within the NCBI (nucleotide) database because sequences hit multiple species including C. diffusa Lam., C. nigra L., C. stoebe, and C. virgata with equally high scores. Likewise, even if the alignment with PlantsDB suggested the presence of S. arvensis in the

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dataset, once realigned with the NCBI, sequences matched other Brassiceae (e.g. Brassica carinata Braun) members with high scores.

Furthermore, no significant alignment was recovered from the BLAST performed on the NGS sequences and the invasive plant species reference sequences Centaurea iberica Trevir. ex Spreng., C. solstitatis L., and Echium plantagineum L. Similarly, no significant alignment was obtained when using reference sequences of other closely-related Sinapis species.

Correlation between diversity and rain or temperature. There was a moderate negative correlation (rho = −0.3767) between the rainfall volume and the fungal diversity (alpha), and a slightly positive correlation (rho = 0.1274) between the average temperature recorded and the fungal diversity (Table 4.3). Inversely, there was a moderate negative correlation (rho = −0.2215) between the oomycete diversity and the temperature monitored, and a moderate positive correlation (rho = 0.2304) between the rainfall volume and the oomycete diversity. Similarly, a strong negative correlation (rho = −0.6645) between plant diversity and temperature, and a slightly positive correlation (rho = 0.0089) between plant diversity and rainfall volume were observed. Except for the plant diversity-temperature which had a P value < 0.05 and was therefore significant, all correlations were not significant because they displayed P values > 0.05, (Table 4.3).

Venn plots. Fungal genera found in the samples were split by month (i.e., May, June, and July) (Supplementary Figure 4S5a). A total of 504 different genera were detected. There were 238 genera shared throughout the whole summer, 90 unique to the month of May, 36 unique to the month of June, and 25 unique to the month of July. The same analysis for the oomycete data (ITS1) showed that, of the sixteen genera detected, twelve were commonly found regardless of the time of the season, one was unique to the month of July, two genera were unique to the month of May, and one was detected in June and July only (Supplementary Figure 4S5b). Of the 99 plant genera identified, 33 were commonly found in each collection month (Supplementary Figure 4S5c, while twelve genera were exclusive to the month of May, eleven were unique to June, and eighteen were solely detected in July.

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Validation of the presence of Phytophthora spp. by genus-specific qPCR. The Phytophthora genus-specific qPCR assay performed on all of the seven environmental samples generated a late signal (>35 threshold cycle) for samples DPS04, DPS06, and DPS07 (Supplementary Table 4S4). The standard curve generated had an efficiency value of 97.2 %, a slope of −3.39, and an R2 of 0.981. No amplification was observed in the negative control wells.

Plant flowering observations compared with NGS detection. Most of the observed flowering plant species surrounding the apiary were detected via the metagenomics pipeline for the corresponding collection period for which the beekeeper made observations (Supplementary Table 4S1). In a number of cases, species that were not observed flowering by the beekeeper during an alternate time period were still detected by the NGS. For example, Rhamnus cathartica L. (European buckthorn) was observed to bloom during the second half of May, but the metagenomics approach reported its presence during the whole sampling season, at low (<10 OTUs) or moderate (11>99 OTUs) levels (Supplementary Table 4S1). On the other hand, some plants that were visually assessed were not detected using NGS, namely Triticum aestivum L. (common wheat) and Anethum graveolens L. (dill) (Supplementary Table 4S1).

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4.6 Discussion

This study has employed an innovative metagenomics approach to demonstrate that the pollen pellet samples collected by honeybees can provide valuable information about the diversity of plants and plant pathogens surrounding an apiary. Although the study included only seven samples and focused on a single bee colony studied over one season, the outcome is promising for surveillance purposes, as honeybees carry more than just pollen while foraging (Shaw 1990). Indeed, several genera including phytopathogenic species in forestry and agriculture were detected by the NGS workflow, including Alternaria spp., Colletotrichum sp., Diaporthe spp., Fusarium spp., Gremmeniella sp., Ophiostoma spp., Peronospora spp., Phoma spp., Pythium spp., and Verticillium spp.

Despite the fact that species-level resolution is not always possible using the ITS region (Lamarche et al. 2014), this multiple-copy region is universally found in plant, fungus, and oomycete genomes (Ayliffe et al. 2001; Aguileta et al. 2008; Bellemain et al. 2010), and is readily amplified and detected thus making it more sensitive than other single-copy genes. For instance, some of the detected genera were found in very low abundance (i.e., less than ten counts) using the ITS1 region, which demonstrates the detection sensitivity of NGS. Relevant examples of fungal species within some of the detected genera that were not successfully resolved using the ITS1 include Alternaria species (Peever et al. 2004), Gremmeniella species (Lamarche et al. 2015), Verticillium species (Inderbitzin et al. 2011; Inderbitzin and Subbarao 2014), Colletotrichum species (Choi et al. 2011), Fusarium species (Zitnick-Anderson et al. 2018), and Phytophthora species (Martin et al. 2012; Bilodeau et al. 2014). Although the ITS1 region sequence revealed minute levels of Phytophthora OTUs and the qPCR assay generated a weak signal from a few of the samples, the NGS results from the ATP9-NAD9 region typically used to resolve Phytophthora species were inconclusive. The main reasons for such ambiguous analysis may be: (i) the fact that some species within the PhyDB database comprised only one sequence, (ii) the occurrence of some non-specific amplification of the ATP9-NAD9 region with other oomycetes within the samples, mainly Peronospora spp., causing false positive amplification. This aberration was likely due to the high concentration of MgCl2 combined with the lower temperature (i.e., 53°C) at which the initial PCR were run in order to enhance amplification of rare material in the environmental samples. However, the qPCR assay, which used the same primers as the fusion PCR to amplify the ATP9-NAD9 region, was run at the recommended

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annealing temperature of 60°C (Bilodeau et al. 2014), and could still detect the presence of minimal amount of Phytophthora species in the same ambiguous samples (i.e., DPS04 [June 13 2017], DPS06 [June 28 2017], and DPS07 [July 12 2017]) based on the results obtained with NGS. In parallel, the NGS analysis performed on the oomycete ITS1 amplicons, although not variable enough for accurate species inference in some cases, also revealed several Phytophthora species in those same samples. As the ITS1 allowed assessing general oomycetes contents, the ITS1 amplicons obtained also narrowed down the number of samples to subsequently amplify with the ATP9-NAD9 specific primers and, thus, reducing processing costs as less samples needed to be tested and therefore less fusion primers were required.

A number of the Phytophthora species OTUs obtained with the NGS approach have already been reported or are known to have been introduced in Ontario (e.g., P. cryptogeae Pethybr. & Laff., P. infestans (Mont.) de Bary, and P. erythroseptica Pethybr.) (Farr and Rossman 2018). A few Phytophthora spp. that are not native to North America have also been detected. However, the uncertainty of the identities of the Phytophthora spp. results suggests the need for a complementary and more accurate method to support results from the metagenomics analysis (e.g., species-specific qPCR assays). Nonetheless, metabarcoding was an efficient pre-screening approach to narrow down the number of species of potential interest. Considering that NGS results suggested that very little Phytophthora material was detected in the pollen pellets, this could be related to the fact that identification was not clear at times, or honeybees may not be a good monitoring tool for this particular genus. On the other hand, there could have been very little Phytophthora spp. material which still got detected, in which case, it would confer a highly sensitive detection power to our method.

As expected, the honeybee-collected pollen pellets also contained many of the plant species and their respective pathogens that were visually observed to be flowering within the bee foraging range of the apiary throughout the pollen trapping time points. For instance, the abundance of fungal genus Alternaria spp. during all the collecting season was expected because this genus includes several pathogenic species of a number of crops (Thomma 2003). Though not of concern in terms of plant infection, the abundance of Epicoccum nigrum Link (Figure 4.1f) is likely due to the fact that it is a very common fungal genus found in soil, air, and insects (Schol-Schwarz 1959). More specifically,

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several plant species that were detected with metagenomics are actual hosts for the fungal and oomycete organisms also detected by NGS in parallel. Supplementary Table 4S5 summarizes a number of examples of host-pathogen combinations (fungi and oomycetes) detected by NGS, and for which the plant was recorded based on the visual assessment. For most cases, the time period over which the host and the pathogen were detected (by NGS or visual assessment) overlapped. Based on these combinations, given that parsnip’s abundance was very low and that dill could not be detected at all by metabarcoding, and because the fungus I. perplexans is known to spread by aerial dissemination of spores (McGovern et al. 2006), it is possible that bees and their pollen pellets also incidentally came in contact with plant pathogens suspended in air while flying instead of by foraging contaminated plants. Though highly speculative, assuming that there actually was incidental contact between the bees and propagules during flights, the samples collected (i) could contain entities that originated beyond the foraging area, and (ii) may have the power to provide additional valuable information even from plant species that are not necessarily being foraged on because of bees preferences for certain plants. Evaluation of air contends with a spore trap would be useful for comparison with pollen pellets.

In addition, the detection of the oomycete Plasmoverna anemones-ranunculoidis (Săvulescu and Săvulescu 1951; Constantinescu et al. 2005) (Figure 4.2d) corresponded in timing with the visual flowering record of common buttercup (Ranunculus acris L.), a species within the same family (i.e., Ranunculaceae) as the known plant host of P. anemones-ranunculoidis namely the yellow woodland anemone (Anemone ranunculoides L.) (Supplementary Table 4S1). Considering that the Ranunculaceae member Anemonastrum canadense (Linnaeus) Mosyakin (formerly Anemone canadensis) was also detected with NGS (Figure 4.3d), and although A. ranunculoides is not known to be established in Ontario (Brouillet et al. 2010), the results suggest that, though it has not been recorded previously, either of those plant species could be hosts for Plasmoverna. anemones- ranunculoidis (Savul. & O. Savul.) Constant., Voglmayr, Fatehi & Thines. However, the literature on hosts for P. anemones-ranunculoidis is limited, and the confirmation for those possibly new host- pathogen relationships exceed the scope of the current study. Alternatively, the ITS1 and ITS2 regions may not allow the proper classification of the oomycete or plants in this case.

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Results showed seasonal occurrence associated with the fungi, plants, and oomycetes detected. The correlation tests obtained using the Spearman rho factor showed correlations among the datasets analysed (Table 4.3). Moderately positive correlations were found between fungal diversity and temperature, oomycete diversity and rainfall volume, and plant diversity and rainfall volumes. Moderately negative correlations were also obtained from the Spearman test run on fungal diversity and rainfall volumes, oomycete diversity and temperature, and plant diversity and temperature. Although the only strong and significant correlation from this small set of samples was between plant diversity and temperature, it is likely that testing more samples could display significant correlation factors given more data points. The inversely-correlated rainfall amount to the oomycete diversity aligns with previous reports of proliferating oomycetes in higher rainfall volumes (Erwin and Ribeiro 1996; Kim and Jeun 2006; Kato et al. 2013).

The temporal change in plant, fungal, and oomycete diversity throughout the season is likely related to the local weather and plant phenology. Honeybee foraging is affected by local weather conditions, with reduced foraging occurring in rainy conditions and cool temperatures (Szabo 1980; Puškadija et al. 2007; Tuell and Isaacs 2010). Rainfall volumes, splashing, and wind are also recognized to influence the dissemination of fungal and oomycete spores depending on the intensity of the precipitation and/or storm (Huber et al. 1996; Madden 1997; Patel 2008).

The reduced fungal abundance from July’s sample DPS07 was due to the fact that the ITS1F’s data was discarded because of the suspected contamination with the positive control V. dahliae and, therefore, only the ITS2 direction data was analyzed (Figure 4.1). In addition, the abundance retrieved with both direction primers for all three organisms (plants, fungi, and oomycetes, Figures 4.1, 4.2, and 4.3) resembled one another, except there was a higher abundance obtained with the primer ITS1F than with the primer ITS2 in the fungal samples DPS05 and DPS06 (Figure 4.1). Those two samples had the highest amount of basidiomycetes, which suggest that the ITS1F primer may favor basidiomycetes over ascomycetes, which aligns with previous scientific findings (Gardes and Bruns 1993). For that reason, it appears that, although the abundance barplots generated for each bidirectional sample plots may look very similar to the actual temporal barplots at first glance, the former is actually more informative on the primer influence on data, whereas the latter gives a better

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overall idea of the communities evolution throughout a sampling season regained when the bidirectional data of a given sample are combined.

After running the metaResultExtractor.pl script to scan for genera of interest among the NGS data, for instance Gremmeniella spp., Fusarium spp., Peronospora spp., and Ophiostoma spp., it became obvious that regulated targets are rare among the most abundant genera or species. Not only does this observation show the importance of using both the complementary results obtained with the separate analyses in this study, in combination with standardized validated approaches but, it also demonstrates the high sensitivity of NGS on mixed samples.

Even if the results suggest that phytopathogenic species such as Alternaria alternata, Verticillium dahliae, Fusarium sporotrichioides, Globisporangium rostratum (E.J. Butler) Uzuhashi, Tojo & Kakish., Plasmopara viticola (Berk. & M.A. Curtis) Berl. & De Toni, and Phytophthora spp. are potentially found via bee foraging activities, again, they should be interpreted cautiously until identification of such organisms can be confirmed with a combinatory standardized method (e.g., qPCR) due to the limited resolution power of the ITS1 for certain groups (Peever et al. 2004; Inderbitzin et al. 2011; Martin et al. 2012; Inderbitzin and Subbarao 2014; Taheri et al. 2017; Zitnick-Anderson et al. 2018). For instance, the elongation factor 1α is better suited for identification of Fusarium sporotrichioides (Taheri et al. 2017; Zitnick-Anderson et al. 2018), and the trnH-psbA spacer is gaining in popularity for assisting plant identification (Kress and Erickson 2007; Bolson et al. 2015).

One main limitation of metabarcoding is that it relies on the accuracy and inclusiveness of the databases employed (Nilsson et al. 2006; Nilsson et al. 2014). For example, as there are far fewer described oomycetes compared to plants and fungi, this limitation may subsequently influence the diversity of genera detected (Hawksworth and Rossman 1997; Rossman and Palm 2006; Blackwell 2011). Plus, any genic region, including ITS and ATP9-NAD9, has its pros and cons for identification; these include copy number, sufficient variability for species resolution, gene marker universality, and availability of a large variety of reference sequences (Edger et al. 2014; Lamarche et al. 2014; Lamarche et al. 2015). Any given bioinformatics pipeline also introduce biases in the final taxonomic assignments and abundance used throughout the process (Majaneva et al. 2015). For example, here

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we clustered OTUs based on 97% similarity. Although this approach reduces the number of singletons and eases the data processing, it also increases the risk of misidentification, especially so for plants. Though, to overcome such limitation, when a sequence identified was within a critical genus (e.g.: regulated or invasive), further manual and visual assessments were done on the sequences which aligned with a good match to a reference sequence in the PlantsDB by aligning these extracted sequences in the NCBI again.

With regards to the plant data, more specifically the OTUs recovered that were closely-related to invasive species, the same constraint applies considering that the ITS has been proven to be limiting to properly identify Centaurea spp. in general (Hilpold et al. 2014; López-Alvarado et al. 2014). Therefore, this study cannot identify the Centaurea spp. detected solely based on the ITS2 data. For that reason, based on local alignment performed using the reference PlantsDB, and then NCBI again, it was not possible to validate the presence of C. stoebe and C. virgata. Though, because C. stoebe is common in Canada, unlike C. virgata (Brouillet et al. 2010), it is likely that the actual identity of all of the C. stoebe and C. virgata sequences aligning with sequences in the NCBI (nucleotide) were C. stoebe. Similarly, given that both S. arvensis and E. vulgare are very common weed species in Canada, there is a chance that their identification is accurate in this case. Although it was previously reported that more than one genic marker is needed to resolve Sinapis spp. (Warwick and Sauder 2005), and that the whole ITS was used by Zhu et al. (2014) to reliably distinguish closely-related Echium species, the results obtained can narrow down the area and number of samples for further inspection. More importantly, the ITS2 sequences of E. vulgaris and its regulated congener E. plantagineum can easily be differentiated when using the PlantsDB, followed by NCBI workflow, as done for the Echium spp. sequences obtained to confirm the absence of E. plantagineum.

Finally, considering that beekeeping is practiced worldwide, and that collecting foraged pollen pellets from a honeybee colony is relatively easy and inexpensive, we believe that regulatory agencies from across the world would benefit from adding our metagenomics workflow—using samples collected by honeybees—to their monitoring tools to scan larger, high-risk areas at a higher- throughput rate. In consideration of the diversity and potential significance of the taxa identified in this study, the innovative and low cost research presented here has all the potential to become a very effective and in-demand tool for plant pathogens and invasive plant species surveillance.

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4.7 Acknowledgements

The authors would like to thank Alexandre Blain, Louise Pope, and Debbie Shearlaw for offering their support with sample processing. This project was funded by the CFIA Genotyping and Botany Laboratory, the CFIA Molecular Identification Research Laboratory (CFIA Technology and Development (TD) funds and Genomic Research Development Initiative (GRDI)), and the CFIA Pathogen Identification Research Laboratory (GRDI-mandated OLF-P-1411 and TD OLF-P-1606).

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4.8 Tables

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Table 4.1 List of fungal and oomycetes genera of importance in forest and/or agriculture phytopathology that were targeted and screened using the custom Perl script metaResultExtractor.pl (Tremblay et al. 2018). Notorious species within the target genus are also listed, most of which are regulated by the Canadian Food Inspection Agency and/or the United States Department of Agriculture - Animal and Plant Health Inspection Service.

Included species of concern in phytopathology Genus screened for Organism Scientific name and authority Common name Reference (Woudenberg et al. 2015; Canadian Food Inspection black spot of Japanese Agency 2018a; United States Alternaria sp. Fungus Alternaria gaisen Nagano1,2 pear Department of Agriculture - Animal and Plant Health Inspection Service 2018) (Canadian Food Inspection Anisogramma sp. Fungus Anisogramma anomala (Pk) Müller1 eastern filbert blight Agency 2018a; Jeger et al. 2018) (De Beer et al. 2017; Canadian Food Inspection Agency 2018a; Bretziella sp. Bretziella fagacearum (Bretz) Z.W.deBeer, Fungus oak wilt United States Department of (Ceratocystis sp.)3 Marinc., T.A.Duong & M.J.Wingf.1 Agriculture - Animal and Plant Health Inspection Service 2018) (Cao et al. 2017; United States Department of Agriculture - Chrysomyxa sp. Fungus Chrysomyxa abietis (Wallr.) Unger3 spruce rust Animal and Plant Health Inspection Service 2018) Ciboria sp. Ciboria batschiana (Zopf) N.F. Buchw. (Schroder 2002; Canadian Fungus acorn rot, chestnut rot (Sclerotinia sp.) (Sclerotinia pseudotuberosa)1 Food Inspection Agency 2018a) Colletotrichum panacicola Nakata & (Park et al. 2015; Canadian Colletotrichum sp. Fungus ginseng anthracnose Takimoto1,3 Food Inspection Agency 2018a;

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United States Department of Agriculture - Animal and Plant Health Inspection Service 2018) (Han et al. 2015; Canadian Food Inspection Agency 2018a; Coniella diplodiella (Speg.) Petr. & Syd.1,3 Coniella sp. Fungus dieback, white rot United States Department of

Agriculture - Animal and Plant Health Inspection Service 2018) (Kobayashi and Sakuma 1982; Canadian Food Inspection Agency 2018a; United States Diaporthe tanakae Tak. Kobay. & Sakuma1,3 European pear dieback Department of Agriculture - Animal and Plant Health Diaporthe sp. Fungus Inspection Service 2018) (Phomopsis sp.) (Akgül et al. 2014; Canadian Diaporthe neoviticola Udayanga, Crous & Food Inspection Agency 2018a; K.D. Hyde1,3 United States Department of (Phomopsis viticola (Sacc.) Sacc.)3 Agriculture - Animal and Plant Health Inspection Service 2018) (Brook 1973; Canadian Food Inspection Agency 2018a; Elsinoe sp. Fungus Elsinoe ampelina Shear1,3 anthracnose United States Department of Agriculture - Animal and Plant Health Inspection Service 2018) Fusarium oxysporum4 f. sp. cannabis (Canadian Food Inspection Fusarium sp. Fungus fusarium wilt Noviello & W.C. Snyder1,3 Agency 2018a) Geosmithia morbida M. Kolarík, E. Geosmithia sp. Fungus thousand cankers disease Freeland, C. Utley & Tisserat (Hadziabdic et al. 2014) (Jeger et al. 2017; Canadian Gremmeniella sp. Gremmeniella abietina (Lagerb.) Morelet1 scleroderris canker Food Inspection Agency 2018a) Fungus Gremmeniella sp. Gremmeniella abietina var. abietina scleroderris canker (Kaitera et al. 1998; Canadian (Lagerb.) Morelet1 (European race) Food Inspection Agency 2018a)

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(Holevas et al. 2000; Canadian Food Inspection Agency 2018a; Guignardia sp. Fungus Guignardia baccae (Cavara) Jacz.1,3 black rot of grape United States Department of Agriculture - Animal and Plant Health Inspection Service 2018) (Tao et al. 2017; Canadian Food Inspection Agency 2018a; Gymnosporangium Gymnosporangium yamadae Miyabe ex G. Fungus Japanese apple rust United States Department of sp. Yamada1-2 Agriculture - Animal and Plant Health Inspection Service 2018) Root rot / fomes fungus / Heterobasidion sp. Fungus Heterobasidion annosum (Fr. : Fr.) Bref. butt rot (Lamarche et al. 2017) Hymenoscyphus fraxineus (T. Kowalski) (Baral et al. 2014; Canadian Hymenoscyphus sp. Fungus European ash dieback Baral, Queloz & Hosoya1 Food Inspection Agency 2018a) (Yde-Andersen 1979; Canadian Lachnellula sp. Fungus Lachnellula willkommii (R. Hartig) Dennis1 European larch canker Food Inspection Agency 2018a) (Vialle et al. 2011; Nguyen et al. 2016; United States Melampsora sp. Fungus Melampsora pinitorqua Rostr.1,3 pine twisting rust Department of Agriculture - Animal and Plant Health Inspection Service 2018) (Côté et al. 2004; Canadian Food Inspection Agency 2018a; Monilinia polystroma (G.C.M. Leeuwen) Asiatic brown fruit rot United States Department of Kohn1,2 Agriculture - Animal and Plant Health Inspection Service 2018) Monilinia sp. Fungus (Zhu et al. 2016; Canadian (Monilia sp.) Food Inspection Agency 2018a; Monilinia yunnanensis (M.J. Hu & C.X. Luo) United States Department of brown rot Sandoval-Denis & Crous1,3 Agriculture - Animal and Plant Health Inspection Service 2018)

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(Côté et al. 2004; Canadian Food Inspection Agency 2018a; Monilinia fructigena Honey ex Whetzel1,3 brown rot United States Department of Agriculture - Animal and Plant Health Inspection Service 2018) (Lee et al. 2006; Canadian Food Inspection Agency 2018a; Monilinia mali (Takah.) Whetzel1,3 apple blossom blight United States Department of Agriculture - Animal and Plant Health Inspection Service 2018) (Comeau et al. 2014; Canadian Ophiostoma sp. Fungus Ophiostoma novo-ulmi Brasier1 Dutch elm disease Food Inspection Agency 2018a) (Brasier 1988; Canadian Food Ophiostoma sp. Fungus Ophiostoma ulmi (Buisman) Nannf.1 Dutch elm disease Inspection Agency 2018a)

(Perez et al. 2003; Canadian Food Inspection Agency 2018a; Peronospora hyoscyami f.sp. tabacina Peronospora sp. Fungus tobacco blue mold United States Department of (Adam) Skalicky1,3 Agriculture - Animal and Plant Health Inspection Service 2018) (Carnegie 1984; Canadian Food Inspection Agency 2018a; Phoma exigua var. foveata (Foister) Phoma sp. Fungus potato gangrene United States Department of Boerema1,3 Agriculture - Animal and Plant Health Inspection Service 2018) (Bilodeau et al. 2009; Canadian Food Inspection Agency 2018a; Phytophthora ramorum Werres, De Cock & Phytophthora sp. Oomycete sudden oak death United States Department of Man in 't Veld1,2 Agriculture - Animal and Plant Health Inspection Service 2018) Pseudoperonospora Pseudoperonospora cannabina (G.H. Otth) (McPartland and Cubeta 1997; Oomycete downy mildew of hemp sp. Curzi1 Canadian Food Inspection

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Agency 2018a) angular leaf scorch Pseudopeziza sp. Fungus Pseudopeziza tracheiphila Müll.-Thurg.1 disease / grapevine red (Pearson et al. 1991; Canadian fire disease Food Inspection Agency 2018a) Puccinia coronata Corda1,3 crown rust of oats (Berlin et al. 2017; Canadian Food Inspection Agency 2018a;

1,3,5 United States Department of Puccinia graminis Pers. (Pers.) black stem rust of cereals Agriculture - Animal and Plant Health Inspection Service 2018) Puccinia sp. Fungus (Whipps 1993; Canadian Food Inspection Agency 2018a; Puccinia horiana Henn.1,2 chrysanthemum white rust United States Department of Agriculture - Animal and Plant Health Inspection Service 2018) (Arjona-Girona et al. 2017; Rosellinia sp. Fungus Rosellinia necatrix Prill.1 white root rot Canadian Food Inspection

Agency 2018a) Stromatinia sp. Stromatinia cepivora (Berk.) Whetzel1 (Scott 1956; Canadian Food Fungus white rot of onion (Sclerotium sp.) (Sclerotium cepivorum) Inspection Agency 2018a) Synchytrium endobioticum (Schilb.) (Hampson 1993; Canadian Synchytrium sp. Fungus potato wart Percival1 Food Inspection Agency 2018a) Thecaphora solani (Thirum & M.J. O'Brien) (Mordue 1988; Canadian Food Thecaphora sp. Fungus potato smut Mordue1 Inspection Agency 2018a) Tilletia controversa J. G. Kühn1 dwarf bunt of wheat Tilletia sp. Fungus (Vánky 2002; Canadian Food Tilletia indica Mitra1 karnal bunt of wheat Inspection Agency 2018a) Urocystis agropyri (Preuss) A.A. Fisch. (Purdy 1965; Canadian Food Urocystis sp. Fungus flag smut of wheat Waldh.1 Inspection Agency 2018a) (Ishii and Yanase 2000; Venturia sp. Fungus Venturia nashicola S. Tanaka & S. Yamam.1 Asian pear scab Canadian Food Inspection

Agency 2018a) 1Species regulated in Canada.

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2One or more additional species within the genus are regulated in the United States of America, including this one. 3One or more different species is regulated in the United States of America, excluding this one. 4Species regulated in the United States of America. 5A subspecies is regulated in the United States of America.

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Table 4.2 Summary of amplification bands obtained following electrophoresis ran on the amplicons generated by PCR with fusion primers. The ITS1 was amplified from fungi and oomycetes, the ITS2 was amplified from plants, and the ATP9-NAD9 was amplified from Phytophthora spp. + means visual band obtained. – means no visual band obtained. (f) = forward fusion primer used. (r) = reverse fusion primer used.

Amplicon fusion primers fungi ITS1F oomycete oomycete Phytophthora spp. plants plants Sample fungi ITS2 (r) (f) Omup (f) Omlo (r) ATP9-NAD9 (f) 58SPL (f) ITS4 (r) DPS01 + + - - - + + DPS02 + + - + - + + DPS03 + + + + - + + DPS04 + + + + + + + DPS05 + + + + - + + DPS06 + + + + + + + DPS07 + + + + + + + % success 100 100 71 86 43 100 100 % overall success 86

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Fungal diversity Oomycete diversity Plant diversity T° rain T° rain T° rain p-value 0.6782 0.2045 0.4467 0.4282 0.0095 0.9760 rho 0.1274 −0.3767 −0.2215 0.2304 −0.6645 0.0089 Correlation Yes. NS. Yes. NS. Yes. NS. Yes. NS. Yes. S. Yes. NS.

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4.9 Figures

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Figure 4.1 For each pollen pellet sample collected in 2017, abundance of operational taxonomic unit of fungi detected by the analysis of sequences (ITS1) using the bidirectional fusion primers (ITS1F and ITS2) at the a) phylum (top 5), b) class (top 10), c) order (top 10), d) family (top 15), e) genus (top 15), and f) species (top 30) Levels. n.b. Only the DPS07’s reverse tagged primer (i.e., ITS2) data was analyzed as the ITS1F was discarded because of a suspected contamination.

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Figure 4.2 For each pollen pellet sample collected in 2017, abundance of oomycete operational taxonomic units detected by analysis of sequences (ITS1) using the bidirectional fusion primers (Omup and Omlo) at the a) order, b) family, c) genus, and d) species (top 30) levels.

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4.10 Supplementary Materials

Supplementary Material 4S1 list of the pathogens proved to be absent from the honeybee colonies in this study.  American foul brood (Paenibacillus larvae)| bacterium;  European foul brood (Melissococcus plutonius)| bacterium;  Sacbrood virus (SBV);  Chalkbrood disease (Ascosphaera apis)| fungi;  Small hive beetle (Aethina tumida)| insect;  Wax moths (Galleria mellonella and Achroia grisella)| insect;  Varroa mite (Varroa destructor)*| arthropod;

*Samples taken (alcohol wash) were found to be within the economic treatment threshold of ≤ 2 mites per 100 bees (Kozak et al. 2014) for that time of the year.

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Supplementary Material 4S2 list of the plant species included in the PlantsDB.

 Aegilops cylindrica  Brachyelytrum erectum  Aegilops ovata  Brachyelytrum  Aegilops tauschii septentrionale  Aegilops triuncialis  Camelina alyssum  Aegilops ventricosa  Camelina microcarpa  Ageratum conyzoides  Camelina rumelica  Alopecurus aequalis  Camelina sativa  Alopecurus arundinaceus  Capsella bursa-pastoris  Alopecurus carolinianus  Carduus nutans  Alopecurus geniculatus  Carlina vulgaris  Alopecurus japonicus  Cenchrus ciliaris  Alopecurus myosuroides  Cenchrus echinatus  Alopecurus pratensis  Cenchrus longispinus  Alternanthera brasiliana  Cenchrus myosuroides  Alternanthera philoxeroides  Cenchrus pauciflorus  Alternanthera pungens  Cenchrus spinifex  Alternanthera sessilis  Cenchrus tribuloides  Amphicarpaea bracteata  Centaurea calcitrapa  Andropogon gerardii  Centaurea diffusa  Andropogon hallii  Centaurea diluta  Arthraxon hispidus  Centaurea iberica  Astragalus canadensis  Centaurea melitensis  Bidens pilosa  Centaurea nicaeensis  Boschniakia hookeri  Centaurea phrygia  Boschniakia rossica  Centaurea repens  Bothriochloa ischaemum  Centaurea solstitialis  Bothriochloa saccharoides  Centaurea stoebe

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 Centaurea sulphurea  Echinochloa colona  Centaurea virgata  Echinochloa crus-galli  Conopholis americana  Echinochloa esculenta  Crupina crupinastrum  Echinochloa frumentacea  Crupina intermedia  Echinochloa muricata  Crupina vulgaris  Echinochloa walteri  Cuscuta approximata  Echium italicum  Cuscuta campestris  Echium plantagineum  Cuscuta chinensis  Echium vulgare  Cuscuta epithymum  Elaeagnus commutata  Cuscuta gronovii  Epifagus virginiana  Cuscuta indecora  Eriochloa villosa  Cuscuta polygonorum  Froelichia gracilis  Cuscuta umbrosa  Galega officinalis  Cyperus esculentus  Glycyrrhiza lepidota  Cyperus longus  Gomphrena globosa  Cyperus odoratus  Halogeton glomeratus  Cyperus rotundus  Hesperostipa comata  Cyperus setigerus  Hesperostipa curtiseta  Cyperus sphacelatus  Hesperostipa spartea  Desmodium rotundifolium  Kochia scoparia  Dichanthelium commutatum  Leersia virginica  Dioscorea batatas  Lepidium alyssoides  Dioscorea bulbifera  Lepidium appelianum  Dioscorea dodecaneura  Lepidium campestre  Dioscorea floridana  Lepidium draba  Dioscorea japonica  Lepidium ruderale  Dioscorea polystachya  Lepidium sativum  Dioscorea sansibarensis  Lepidium virginicum  Dioscorea villosa  Menispermum canadense

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 Microstegium vimineum  Persicaria bungeana  Milium vernale  Persicaria longisetum  Miscanthus sinensis  Persicaria meisneriana  Nassella neesiana  Persicaria perfoliata  Nassella tenuissima  Persicaria sagittata  Nassella trichotoma  Portulaca grandiflora  Neslia paniculata  Portulaca oleracea  Nigella arvensis  Psathyrostachys juncea  Nigella damascena  Pseudoroegneria spicata  Nigella hispanica  Pueraria lobata  Orobanche cernua  Pueraria montana  Orobanche crenata  Raphanus confusus  Orobanche cumana  Raphanus raphanistrum  Orobanche hederae  Raphanus sativum  Orobanche ludoviciana  Salsola tragus  Orobanche minor  Schizachyrium scoparium  Orobanche ramosa  Senecio flaccidus  Orobanche uniflora  Senecio inaequidens  Oryzopsis hymenoides  Senecio jacobaea  Parthenocissus  Senecio madagascariensis quinquefolia  Senecio pterophorus  Pascopyrum smithii  Senecio viscosus  Paspalum dilatatum  Senecio vulgaris  Paspalum distichum  Sinapis alba  Paspalum notatum  Sinapis arvensis  Paspalum paniculatum  Sinapis flexuosa  Paspalum quadrifarium  Sinapis hispida  Paspalum scrobiculatum  Sinapis incana  Paspalum urvillei  Solanum elaeagnifolium  Persicaria arifolia  Solanum ellipticum

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 Solanum hindsianum  Strophostyles helvola  Solanum houstonii  Suaeda calceoliformis  Sorghastrum nutans  Suaeda maritima  Sporobolus vaginiflorus  Tribulus terrestris  Striga asiatica  Vigna unguiculata  Striga aspera  Vitis riparia  Striga forbesii  Volutaria crupinoides  Striga gesnerioides  Zygophyllum fabago  Striga hermonthica

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4.11 Supplementary Tables

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Supplementary Table 4S1 Comparison of the visual flowering observations of plant species observed by the beekeeper (V) in the surrounding areas of the apiary throughout the pollen collection period and their detection by the metagenomics workflow at the species

(Ms) or solely the genus (Mg) level. Nomenclature based on the Database of Vascular Plants of Canada (Brouillet et al. 2010). (N.B. blue = 10 counts and less, purple= between 11 and 99 counts, orange = 100 counts and up, when species is present, priority over genus is given.)

Sampling schedule Surrounding plant's identification May June July 01- 16- 01- Scientific name Common name Family 16-31 15 30 15 Capsella bursa-pastoris (L.) Medik. common shepherd's purse Brassicaceae V

Rhamnus cathartica L. European buckthorn Rhamnaceae VMs Ms Ms Ms Frangula alnus Mill. (Rhamnus glossy buckthorn Rhamnaceae VM M M M frangula L.) g g g g

Thlaspi arvense L. field pennycress Brassicaceae VMs

Viola sororia Willd. woolly blue violet Violaceae VMg Berberis thunbergii D.C. Japanese barberry Berberidaceae V

Fraxinus americana L. white ash Oleaceae VMg M g Prunus virginiana L. chokecherry Rosaceae VMs Ms M s M s

Malus pumila Mill. common apple Rosaceae VMs Ms Siberian crabapple / sweet Malus baccata (L.) Borkh. Rosaceae VM M M M crabapple s s s s Viburnum L. spp. viburnum Adoxaceae VMg Mg Mg Mg Crataegus L. spp. hawthorn Rosaceae VMg Mg Mg Mg Cornus sericea L. red-osier dogwood Cornaceae V Rubus occidentalis L. black raspberry Rosaceae VMg M g M g M g Quercus macrocarpa Michx. bur oak Fagaceae VMg Mg Mg Mg

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Hesperis matronalis L. dame’s rocket Brassicaceae VMs Ms Ms Syringa vulgaris L. common lilac Oleaceae VMs Ms Ms M s Sambucus L. spp. elderberry Adoxaceae VMs Ms Ms Ms Tragopogon dubius Scop. yellow goatsbeard Asteraceae VMg Mg Sorbus L. spp. mountain-ash Rosaceae Mg VMg M g M g Lonicera L. spp. honeysuckle Caprifoliaceae Mg VMg Vitis riparia Michx. riverbank grape Vitaceae V M s Vicia cracca L. tufted vetch Fabaceae M g VMs Ms M s Securigera varia (L.) Lassen purple crown-vetch Fabaceae Ms V Ms Ms Juglans nigra L. black walnut Juglandaceae Mg VMg Physocarpus (Camb.) Raf. spp. ninebark Rosaceae V Spiraea L. spp. spiraea Rosaceae V

Trifolium pratense L. red clover Fabaceae M g V Mg M s M g Trifolium repens L. white clover Fabaceae Ms VMs Ms Ms Leucanthemum vulgare Lam. oxeye daisy Asteraceae Ms VMs Ms Ms Ranunculus acris L. common buttercup Ranunculaceae Mg V Mg Mg Geum aleppicum Jacq. yellow avens Rosaceae V M g Hieracium L. spp. hieracium Asteraceae V

Echium vulgare L. common viper's bugloss Boraginaceae M s VMs M s M s Hypericum perforatum L. common St. John’s-wort Hypericaceae V Ms Ms Prunella vulgaris L. common self-heal Lamiaceae V

Melilotus officinalis (L.) Lam. yellow sweet-clover Fabaceae M s Ms VM s M s Melilotus albus Medik. white sweet-clover Fabaceae Ms Ms VMs Ms Silene vulgaris (Moench) Garcke bladder campion Caryophyllaceae V Galium mollugo L. smooth bedstraw Rubiaceae VMg

Sinapis arvensis L. corn mustard Brassicaceae M s VMs Rhus typhina L. staghorn sumac Anacardiaceae Ms M s VMs M s Medicago sativa L. alfalfa Fabaceae Ms Mg VMg Ms

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Triticum aestivum L. common wheat (winter) Poaceae V Plantago major L. common plantain Plantaginaceae V Campanula rapunculoides L. creeping bellflower Campanulaceae V Linaria vulgaris Mill. butter-and-eggs Plantaginaceae VCg

Erigeron L. spp. erigeron Asteraceae M g VMg VM g Asclepias syriaca L. common milkweed Apocynaceae V

Lotus corniculatus L. garden bird's-foot trefoil Fabaceae M g V Mg Cichorium intybus L. wild chicory Asteraceae VMg Achillea millefolium L. common yarrow Asteraceae V

Thalictrum pubescens Pursh tall meadow-rue Ranunculaceae M g VMg Paeonia L. spp. paeonia Paeoniaceae Mg V Phleum pratense L. common timothy Poaceae VMg

Solanum americanum Mill. American black nighsthade Solanaceae M g VMg Daucus carota L. wild carrot Apiaceae V Pastinaca sativa L. wild parsnip Apiaceae VMg

Papaver rhoeas L. corn poppy Papaveraceae M g V Anethum graveolens L. dill Apiaceae V

Cirsium arvense (L.) Scop. Canada thistle Asteraceae M s M s VMs Medicago lupulina L. black medick Fabaceae Mg M g Ms VMg Lythrum salicaria L. purple loosestrife Lythraceae V

Potentilla L. spp. cinquefoil Rosaceae M g M g M g VMg Solanum lycopersicum L. tomato Solanaceae Mg VMg Sonchus arvensis L. field sow-thistle Asteraceae VMs Sonchus asper (L.) Hill prickly sow-thistle Asteraceae VMg Sonchus oleraceus L. common sow-thistle Asteraceae VMg Hemerocallis fulva (L.) L. orange daylily Xanthorrhoeaceae V

Verbascum thapsus L. common mullein Scrophulariaceae M s VMs Tilia americana L. basswood Malvaceae Ms M s VMs

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Triticum aestivum L. common wheat (spring) Poaceae V Clematis L. spp. clematis Ranunculaceae V broad-leaved enchanter's Circaea canadensis (L.) Hill Onagraceae V nightshade

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Supplementary Table 4S2 Maximum, minimum and average of daily temperatures; and total rainfall volumes recorded for the days of collection (2017) for each sample. Data retrieved from Environment Canada. Temperature(°C) Sample Month Day Total rain (mm) Max Min Mean 17 31 12 21.5 0 18 30 20 25 0.8 May 19 16.5 8.5 12.5 0 PS01 20 18.5 5 11.8 0 21 17 9 13 14.2 Sum 15 Average 22.6 10.9 16.8 27 23 9.5 16.3 0 May 28 25 11 18 6.8 PS02 Sum 6.8 Average 24 10.3 17.2 PS03 2 15 8.5 11.8 Traces PS04 13 25 21 23 0 June 17 27 17 22 Traces 18 31 20 25.5 0.6 PS05 19 27 20.5 23.8 3 Sum 3.6 Average 28.4 19.2 23.8 PS06 June 28 23.5 12 17.8 3 12 22 16 19 8 July 13 23.5 12.5 18 7.5 PS07 Sum 15.5 Average 22.8 14.3 18.5

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Supplementary Table 4S3 Examples of barcoded fusion primers designed for sample multiplexing and their associated partner primer appended with the Ion Torrent sequencing adaptors (A and P1) (Thermofisher 2012) for selective amplification of the plant ITS2 genic region using fusion primers.

Target Primer sequenceb Primer organi General primer directi sm Barcode identifier Primer partnera General reference on and Sequencing adaptor key Barcode primer c region sequence CCTGCCATTCG ITS4-AB-81 TCCTCCGCT C (White et al. 58SPL-F-P1-V2 TATTGATATG ITS4-AB-82 TTGGCATCTC 1990) C Plant ITS4-AB-83 CCATCTCATCCCTG CTAGGACATTC TCAG ITS2d CGTGTCTCCGACe CCTGCCATTCG

Forward 58SPL-F- AB-81 C TTTGAACGC (M.-J. Côté, 58SPL-F- AB-82 ITS4-P1 TTGGCATCTC AAGTTGCGC Personal C communication) 58SPL-F- AB-83 CTAGGACATTC 58SPL-F-V2-AB-81, TCCTCCGCT 58SPL-F-V2-AB-82, (White et al. ITS4-P1 TATTGATATG 1990) 58SPL-F-V2-AB-83, CCACTACGCCTCC C Plant ... GCTTTCCTCTCTAT None

ITS2 ITS4-AB-81, GGGCAGTCGGTGA Reverse ITS4-AB-82, T TTTGAACGC (M.-J. Côté, 58SPL-F-P1-V2 AAGTTGCGC Personal ITS4-AB-83, C communication) ...

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aPrimer partner’s identification name. Primers listed are to be used with those listed in the column “Barcode identifier” when performing bidirectional fusion primer sequencing. bNote that the full primer sequence is spread over multiple columns including “Sequencing adaptor”, “Key”, “Barcode” and “General primer sequence”. Therefore, users shall include all nucleotides given in those columns in that specific order. cThis column presents the sequences of already existing primers which became part of the fusion primers in this project. Their specific sequence is used to target the ITS2 genic region in plants. dInternal Transcribed Spacer 2. eNucleotide sequence of the sequencing (Ion Torrent ) adaptor A1. fNucleotide sequence of the sequencing (Ion Torrent ) adaptor P1.

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Supplementary Table 4S4 Amplification results from the genus-specific qPCR assay targeting Phytophthora spp. including a standard curve.

Well Sample Name Task C a C Mean C SDb Quantity (ng/µL) Position t t t A1 DPS01 Unknown Undetermined

B1 DPS01 Unknown Undetermined

C1 DPS02 Unknown Undetermined

D1 DPS02 Unknown Undetermined

E1 DPS03 Unknown Undetermined

F1 DPS03 Unknown Undetermined

G1 DPS04 Unknown 40.536 40.244 0.414 <0.001c H1 DPS04 Unknown 39.951 40.244 0.414 <0.001c A2 DPS05 Unknown Undetermined

B2 DPS05 Unknown Undetermined

C2 DPS06 Unknown 38.875 38.875 <0.001c

D2 DPS06 Unknown Undetermined 38.875

E2 DPS07 Unknown 37.009 36.011 1.410 <0.001c F2 DPS07 Unknown 35.014 36.011 1.410 <0.001c No template G2 Water Undetermined control No template H2 Water Undetermined control A3 P. ramorum Standardd 20.344 20.549 0.289 0.600 B3 P. ramorum Standard 20.753 20.549 0.289 0.600 C3 P. ramorum 1/10 Standard 23.795 24.047 0.357 0.060 D3 P. ramorum 1/10 Standard 24.299 24.047 0.357 0.060 E3 P. ramorum 1/100 Standard 27.315 27.556 0.340 0.006 F3 P. ramorum 1/100 Standard 27.796 27.556 0.340 0.006 G3 P. ramorum Standard 29.743 30.686 1.334 0.001

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1/1000 P. ramorum H3 Standard 31.629 30.686 1.334 0.001 1/1000 aThreshold cycle. Threshold line value: 17,146 bStandard deviation. cTraces. dStandard curve’s efficiency = 97.2%, slope = −3.39, Y-intercept = 19.87, error = 0.191, and R2 = 0.981.

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Supplementary Table 4S5 Fungal and oomycete pathogens and their respective host plant(s) detected by both flowering visual assessment and metabarcoding (NGS) and their respective time period. Time period Pathogen Plant host(s) Visual record NGS detection References Plant host Pathogen Fungi Botrytis caroliniana (Li et al. 2012b; Li Rubus sp. Late in May Mid-June May to July X.P. Li & Schnabel et al. 2012a) wild parsnip (Pastinaca sativa L.) Early in July Itersonilia spp. Early in July Late in June (Koike et al. 2006) dill (Anethum graveolens L.) Not detected Blumeria graminis Poaceae including Phleum pratense (Farr and Rossman Early in July Early in July May and June (DC.) Speer L.1 2018) Oomycetes

alfalfa (Medicago sativa L.) (Müller 2010) (Girilovich et al. Peronospora aestivalis black medick (M. lupulina L.) Late in June May to July June and July 2003) Syd. white sweet-clover (Melilotus albus (Alaka and Rao Late in June May to July Medik.) 1998) (Adamska 2001; P. mayorii Gäum tufted vetch (Vicia cracca L.) Early in June May to July June and July Voglmayr 2003) white sweet-clover (Melilotus albus) Late in June May to July P. meliloti Syd. May to July (Gaponenko 1972) M. officinalis (L.) Lam. Late in June May to July Hyaloperonospora common sheperd’s purse (Capsella (Constantinescu parasitica (Pers.) Mid-May Not detected May and June bursa-pastoris (L.) Medik.) and Fatehi 2002) Constant. 1Only the genus was detected by NGS.

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4.12 Supplementary Figures

Supplementary Figure 4S1 Location of the apiary used in this study (Kinburn, Ontario, Canada) showing a 5km radius of the surrounding area, which likely encompasses much of the pollen foraging surface sampled. Adapted from Google Maps.

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Supplementary Figure 4S2 Pollen trap and tray used to collect pollen pellets from returning foraging bees: a) top and side views - displaying the pollen collection tray and grate b) top view - displaying the bee escape cones and c) side cutaway view - displaying the simplified paths taken by the returning forager bees when going through the pollen trap and/or exiting the bee hive.

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Supplementary Figure 4S3 Temporal abundance of operational taxonomic units in samples using combined bidirectional data at the genus level: a) top 25 fungi (ITS1) detected, b) all oomycetes (ITS1) detected, and c) top 25 plants (ITS2).

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Supplementary Figure 4S4 True diversity (α) (Shannon, in units of number of species) of a) fungi (ITS1), b) oomycetes (ITS1), and c) plants (ITS2) by collection date.

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Supplementary Figure 4S5 Number of different genera detected in the samples and split by month of collection: a) fungi, b) oomycetes, and c) plants.

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Conclusion

Rappel des hypothèses de recherche

Principaux résultats

L’objectif général (premier objectif) de ce projet de recherche était d’améliorer les méthodes de surveillance et de détection d’organismes exotiques ou envahissants actuellement utilisées par les organismes de réglementation. Plus précisément, des plantes indésirables et des maladies causées par des champignons et des oomycètes affectant les arbres forestiers ainsi que d’autres plantes agricoles constituaient le centre d’intérêt de cette thèse. En mettant à profit les fluides de conservation d’insectes des enquêtes en entomologie préétablies par l’ACIA, l’étude a bénéficié d’une étendue d’ampleur nationale: des échantillons provenants de sept provinces au total ont été traités. Il s’agit d’une innovation notable, car ces liquides étaient simplement jetés auparavant. Les échantillonneurs à spores ont aussi contribué à répertorier certaines espèces de champignons et d’oomycètes au Québec et en Ontario.

Les premier et deuxième objectifs ont été atteints grâce au potentiel d’une nouvelle approche basée sur le métabarcodage qui a permis d’évaluer les champignons et les oomycètes présents dans l’air—principalement via les pièges à spores—et par des insectes vecteurs en réutilisant les liquides

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des pièges à insectes. Cette approche a permis de repérer rapidement des zones à risque ainsi que des endroits nécessitant davantage d’attention. Cette méthodologie indépendante des étapes de culture a permis d’analyser les échantillons directement, un avantage majeur par rapport aux techniques de détection traditionnelles actuellement utilisées par l’ACIA. Maintenant, il est possible de traiter plus d’échantillons, et ce, plus rapidement.

En ce qui concerne le deuxième objectif, non seulement le développement d’un pipeline bio- informatique construit sur mesure a permis d’analyser les données de SNG bidirectionelles provenant de centaines d’échantillons à la fois, mais encore, grâce à la technologie des amorces de fusion, chacun des échantillons comprenait un identifiant unique et pouvait inclure jusqu’à trois régions génétiques cibles dans une même ronde de séquençage. Nous croyons d’ailleurs qu’il s’agissait de la première étude utilisant des amorces de fusion pour faire la biosurveillance de champignons et d’oomycètes par métabarcodage. Le pipeline bio-informatique développé peut supporter des charges de données massives, car il simplifie leur interprétation en (i) organisant et en traitant des centaines d’échantillons en quelques jours seulement et (ii) en convertissant les données brutes en résultats présentés sous une forme intelligible (tableaux, figures, …). De plus, pour tous les types d’échantillons et dans le but de chercher spécifiquement des Phytophthora spp., la présélection générale des oomycètes à l’étape des PCR (fusion) a réduit considérablement le nombre d’échantillons à partir desquels la région ATP9-NAD9 devait ensuite être amplifiée parce que seule une fraction de ceux-ci contenait du matériel oomycète.

La sensibilité accrue de l’approche a été démontrée quand, par exemple, cette dernière a permis de détecter des espèces dont l’abondance était minime (e.g. ≤10 OTU au total). Le SNG a même rapporté la présence d’Heterobasidion spp. dans certains échantillons alors que les résultats du test qPCR étaient négatifs. Ce cas particulier indique que le SNG peut être plus sensible que certains tests qPCR dans une situation où l’ADN recherché est en très faible abondance. Dans le cadre du deuxième objectif, l’une des dix-sept espèces indésirables recherchées a été identifiée. Le projet a permis de détecter la présence de propagules de l’espèce H. annosum s.s., de même que l’espèce phytopathogène du même genre H. abietinum/H. parviporum. Il s’agit possiblement d’un premier signalement au Canada. Cette découverte importante est un exemple significatif du pouvoir et de la pertinence de la méthode de métabarcodage. De plus, cette dernière indique que d’autres

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organismes exotiques ou envahissants sont probablement présents au pays, mais qu’ils n’ont encore jamais été rapportés parce que les méthodes de dépistage actuelles en sont incapables ou sont simplement limitées par leur faible débit. De surcroît, il est encore impossible de déterminer si ces maladies affligent les végétaux parce que ces phytopathogènes ont été découverts à partir d’échantillons environnementaux et non à partir de plantes symptomatiques. Étant donné que ces champignons sont une réelle menace pour le pays, ce projet fait ressortir l’importance d’utiliser des méthodes plus perfectionnées à des fins de validation. De fait, la détection primaire par métabarcodage peut contribuer à éviter les dégâts.

En complétant le deuxième objectif, le développement du pipeline bio-informatique a illustré le besoin pressant de bases de données publiques plus complètes et plus robustes que celles actuellement disponibles. Bien qu’il existe des bases de données fiables et de qualité, la résolution au niveau de l’espèce avec l’approche bio-informatique développée demeure incertaine dans certains cas. À titre d’exemple, l’identité des séquences de Centaurea spp. (plante), de Verticillium spp. (champignon) et de certains Phytophthora spp. (oomycète) n’a pas été confirmée au niveau taxonomique subgénérique. Donc, l’utilisation de l’ITS, quoique parmi les meilleures options disponibles pour procéder à l’identification des plantes et des champignons, ne parvient pas à faire l’identification spécifique dans tous les cas. Le complexe d’espèces H. annosum sensu lato représente un cas typique pour lequel l’attribution de la taxonomie est difficile en raison de la grande proximité des espèces de son groupe. Dans un même ordre d’idée, la région ATP9-NAD9 (plutôt que l’ITS) était utilisée pour identifier les espèces du genre Phytophthora spp., or l’identification de certains OTU au niveau de l’espèce est demeurée ambiguë dans certains cas. Cette difficulté de plus en plus fréquente montre l’influence directe de l’évolution constante des bases de données publiques sur les analyses par métabarcodage, la région ATP9-NAD9 étant présentement considérée comme la mieux adaptée pour résoudre les Phytophthora spp. vu l’ordre des gènes qui s’y retrouvent. Plus spécifiquement, de plus en plus de séquences de Peronospora spp. sont ajoutées dans les bases de données, mais leur région ATP9-NAD9 est similaire à celle des Phytophthora spp. (comparativement à celle des Pythium spp.), à tel point qu’il devient très difficile de séparer les Peronospora spp. des Phytophthora spp. D’un côté, ce problème pourrait possiblement être résolu en perfectionnant la spécificité des sondes nucléiques utilisées dans le test qPCR ou en identifiant une nouvelle région génomique suffisamment variable pour permettre la dissociation de ces espèces d’oomycètes. D’un

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autre côté, les échantillons provenaient d’endroits et de types de pièges n’ayant possiblement jamais été utilisés pour recueillir des oomycètes auparavant, il pourrait donc s’agir de nouvelles espèces. Un travail ardu de description de ces nouveaux organismes s’avérerait alors nécessaire. Le même constat s’applique aux nombreux OTU de champignons incultivables ou non-identifiés obtenus tout au long du projet.

La classification spécifique de certains groupes d’organismes est particulièrement complexe, par contre la majorité des groupes de champignons et de plantes peuvent être identifiés avec la région ITS. À titre d’exemple, la plante indigène Echium vulgare a pu être identifiée par métabarcodage de la région ITS2, puisque la base de données interne développée par Marie-José Côté (communication personnelle, 2018) permettant d’identifier les plantes comprenait suffisamment de variations interspécifiques. Également, la résolution étant plus simple aux niveaux taxonomiques supérieurs, les résultats obtenus par métabarcodage des séquences ITS1 (champignons et oomycètes) ont rapporté une majorité d’ascomycètes et de basidiomycètes. Cette observation a révélé les organismes contenus, cultivables ou incultivables, dans les environnements évalués, et ce, sans égard à la méthode de collection utilisée. Il importe toutefois de mentionner que l’une des amorces utilisées pour amplifier les champignons affectait l’abondance des taxons selon la direction de séquençage (bidirectionnel) effectuée. En effet, l’amorce ITS1F (Gardes and Bruns 1993) amplifie de manière préférentielle l’ADN des basidiomycètes par rapport aux ascomycètes (Bellemain et al. 2010). Comme l’amorce ITS2 (White et al. 1990) n’est pas spécifique aux champignons, cette tendance vers les basidiomycètes n’était pas observée lors du séquençage bidirectionnel effectué avec cette amorce de fusion.

En somme, tel que mentionné tout au long de la thèse, plus d’une région génétique peut être requise pour confirmer la phylogénie des OTU de manière rigoureuse. De plus, il est à noter que le taux d’erreur intrinsèque de la technologie de l’Ion Torrent (1.71%) (Quail et al. 2012) excède parfois le pourcentage de variation entre deux espèces proches, ce qui complexifie l’attribution taxonomique des OTU.

Quoi qu’il en soit, les limites rencontrées lors de l’identification de certaines espèces ont été pour la plupart surmontées grâce aux tests qPCR conçus pour valider l’identité de plusieurs espèces

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réglementées ou faisant partie de la liste des espèces ciblées. En l’occurrence, certains tests qPCR utilisés exploitent d’autres régions génétiques comme la sous-unité β de la tubuline (Ceratocysis laricicola, C. polonica et Geosmithia morbida) ou encore le facteur d’élongation α 1 (C. fagacearum, Ophiostoma ulmi et O. novo-ulmi). Ainsi, pour atteindre le deuxième et le troisième objectif, il a fallu élaborer et analyser en parallèle des mocks, ce qui a permis de reproduire des échantillons et des données comparables à ce qui avait été recueilli dans les endroits à risque. Comme différents genres de champignons et d’oomycètes d’intérêt étaient inoculés aux mocks, la capacité de détection de la méthode développée a été confirmée. De plus, il a été possible de dissocier la plupart des champignons et des oomycètes par l’analyse métagénomique et la validation subséquente des résultats à l’aide des tests qPCR. Le doute quant aux risques de contaminations croisées possibles a aussi pu être exclu parce que seuls quelques champignons—dont aucun ne faisait partie des genres ciblés—ont été trouvés dans les échantillons témoins négatifs. Tout compte fait, il importe de mentionner que, contrairement à l’ITS, les régions génomiques alternatives ne se répètent pas en de multiples copies, donc les tests qPCR s’en retrouvaient moins sensibles.

Le quatrième objectif visait à évaluer le potentiel d’insectes xylophages en tant que vecteurs de phytopathogènes. Puisque les liquides provenant des pièges à insectes contenaient des OTU distincts selon les quatre composés sémiochimiques utilisés pour attirer certains groupes d’insectes xylophages, cet objectif a aussi été atteint. À titre d’exemple, des champignons phytopathogènes, des champignons capables de dégrader le bois (pourritures et taches) et des oomycètes phytopathogènes en quantité supérieure aux attentes initiales ont été identifiés. En outre, la présence de certains champignons et oomycètes causant des maladies telles que la galle du prunier, des mildious et des mildious des crucifères dans les liquides à insectes était surprenante compte tenu qu’il y avait peu de chances que ceux-ci aient été transportés par des insectes xylophages en raison des plantes hôtes qu’ils affectent. Il est difficile d’identifier la cause exacte de telles incidences, or il est possible que (i) les pièges à insectes recueillent des particules présentes dans l’air en quantité suffisamment abondante pour être détectée ou encore (ii) que des insectes non-xylophages, par exemple des pollinisateurs, capturés dans les pièges à insectes soient attirés en raison de l’éthanol ajouté dans les composés sémiochimiques (agent de conservation) (Moeck 1970; Kirkland and Kelsey 2015), et qu’ils contribuent ainsi à la biodiversité observée. Comme les échantillons de granules de pollen ont aussi révélé des espèces fongiques et oomycètes qui n’ont probablement pas

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été recueillies lors d’activités de butinage, la théorie du contact accidentel lors de déplacements est renforcée.

Il va sans dire, la taxonomie attribuée à certains des OTU uniques à un composé sémiochimique suggère l’observation de nouveaux liens potentiels (pathosystèmes) entre certains groupes d’insectes et de champignons ou d’oomycètes. D’ailleurs, la comparaison des courbes de raréfactions issues des pièges à insectes (post-soustraction) aux courbes des pièges à spores placés aux mêmes sites d’échantillonnage montre que la profondeur de séquençage était bel et bien atteinte avec les pièges à spores JB (témoins négatifs). Cette observation démontre que la soustraction des espèces communément retrouvées dans les deux pièges (spores et insectes) faite sur les données n’induit pas de biais. En effet, l’hypothèse d’un possible sous-échantillonnage avec les pièges à spores a pu être réfutée, puisque les données relatives au piège JB étaient saturées en nombre de séquences par espèce au préalable. Pour ces raisons, il est fort possible que certains des OTU restants dans les pièges à insectes aient été transportés par des insectes vecteurs.

Le cinquième objectif était d’utiliser les abeilles en tant que population sentinelle via les granules de pollen récoltés afin de répertorier des propagules de champignons, d’oomycètes et de plantes indésirables. Ainsi, les analyses par métabarcodage de ces granules de pollen ont non seulement permis de détecter de nombreuses espèces fongiques, oomycètes et végétales, mais elles ont aussi établi des liens entre les microorganismes recherchés et les plantes hôtes que ces derniers affectent. Les granules de pollen constituent notamment le seul type d’échantillon qui a permis d’approfondir les analyses relatives aux interactions parasite-hôte. De plus, l’inventaire visuel des plantes en fleurs concordait fréquemment avec les données de SNG. Par exemple, la détection du pathogène Plasmoverna anemones-ranunculoides et de la plante Anemonastrum canadense au cours de la même période de temps laisse croire qu’il s’agit d’une plante jusqu’ici jamais identifiée en tant qu’hôte de cet oomycète. Des étapes de validation supplémentaires seraient toutefois requises pour confirmer cette hypothèse (ex.: postulats de Koch). Les analyses de diversité réalisées en fonction des conditions météorologiques et du moment de la saison sont également exclusives aux échantillons de pollen. Différentes corrélations (positives ou négatives) entre la diversité respective des champignons, des oomycètes et des plantes et les averses de pluie ou encore la température enregistrée ont été observées, ce qui a permis d’atteindre le sixième objectif du projet. Bien que la

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plupart des corrélations n’étaient pas statistiquement significatives, il est fort probable que l’augmentation du nombre d’échantillons influencerait les données, ce qui fournirait sans doute des corrélations statistiquement significatives. Le nombre d’échantillons de pollen était effectivemnent limité dans le cadre de ce projet.

Contributions

Ce projet de doctorat montre que le SNG, jumelé à diverses méthodes de collecte d’échantillons, peut dépister une immense quantité d’organismes, et ce, à l’échelle nationale. De plus, chacune des méthodes de collecte d’échantillons utilisées dans ce projet est simple et peu coûteuse. Le pipeline bio-informatique a un rôle significatif dans l’ensemble de l’approche décrite, car il permet de traiter une immense charge de données très rapidement. Le métabarcodage, pour sa part, permet d’analyser des groupes spécifiques en l’espace de quelques jours seulement. Ultimement, les réalisations de ce projet (c.-à-d. les méthodes de collecte combinées au métabarcodage) pourraient guider les enquêtes menées par les organismes de réglementation, entre autres pour identifier les secteurs à risque nécessitant une analyse approfondie. De plus, cet outil pourrait facilement être adapté afin de faire l’analyse primaire d’autres organismes d’intérêt, notamment en agriculture ou en entomopathologie.

Le protocole développé a suscité de l’intérêt assez rapidement après sa conception. Des chercheurs scientifiques l’ont d’ailleurs utilisé jusqu’à maintenant. Plus concrètement, un transfert de protocole officiel a été fait avec Agriculture Canada (Bourlaye Fofanna) et avec University of British Columbia (Richard Hamelin). Marie-Josée Côté l’a également adopté et modifié pour faire la recherche d’espèces de plantes contaminantes dans des lots de semences. En collaboration avec Philippe Tanguay et Danny Rioux (Ressources Naturelles Canada), nous avons procédé à des analyses bio-informatiques primaires de plusieurs échantillons d’oomycètes récoltés dans des cours d’eau au Québec. Toutefois, les résultats sont encore trop préliminaires pour être présentés à ce jour.

De plus, une partie de mes résultats a été mise à contribution dans un projet de recherche de Jean Bérubé. Il a découvert, pour la première fois au Canada, la présence de spores de Diplodia

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corticola, un champignon causant des chancres aux chênes et qui n’avait été répertorié jusque-là qu’en Europe, en Afrique et dans plusieurs états américains (Bérubé et al. 2018). Ce cas particulier montre, une fois de plus, que beaucoup d’informations supplémentaires peuvent être extraites d’un jeu de données obtenu par SNG.

Dans un même ordre d’idées, j’ai pris l’initiative d’entrer en contact avec Johan Bengtsson- Palme afin de lui suggérer l’amélioration d’un de ses outils bio-informatiques (ITSx). Ce partenariat avait pour but de permettre l’utilisation d’autres régions génétiques que l’ITS, spécifiquement l’ATP9- NAD9, pour faire l’identification taxonomique. Suite à cette collaboration, G. Bilodeau et moi avons pris part, en tant que coauteurs, à la rédaction d’un article scientifique qui décrit le nouvel outil Metaxa2 Database Builder (Bengtsson-Palme et al. 2018).

Les résultats obtenus suite au séquençage des liquides de pièges à insectes ont aussi suscité de l’intérêt, notamment en ce qui a trait à la biosurveillance d’insectes exotiques par des méthodes de génomique. Par exemple, certains de nos résulats ont été intégrés à la première partie d’un article de revue (Bilodeau et al. 2018; Roe et al. 2018), ce qui a permis à G. Bilodeau et moi d’être coauteurs de ces deux publications distinctes.

La méthodologie développée et sa principale partie intégrante, soit le pipeline bio- informatique,est un un outil de dépistage primaire fort utile pour pratiquer la biosurveillance. La méthode constitue aussi une base sur laquelle des outils encore plus rigoureux, plus spécifiques et mieux adpatés pourraient émerger et ainsi s’ajouter aux techniques de détection des organismes de réglementation. Chaque technique de collecte rapporte une variété d’organismes différents et uniques.

Dans le cadre de ce projet, des cas qui n’étaient rapportés qu’aux États-Unis jusqu’à maintenant ont été localisés avec précision. Il s’agit de la présence de propagules de H. annosum s.s., soit dans les villes d’Ottawa et de Gatineau, ainsi que de H. abietinum/H. parviporum à Vancouver. Cette découverte démontre l’importance de développer des méthodes plus sensibles et à plus haut débit, de même que le besoin de retourner échantillonner à ces endroits afin de recueillir du matériel supplémentaire et rechercher des plantes symptômatiques.

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Enfin, la détection potentielle de microorganismes entomopathogènes est une ouverture prometteuse qui suggère qu’il est possible de découvrir de nouveaux organismes au potentiel de biocontrôle, une contribution qui s’ajouterait aux moyens de lutte contre les insectes ennemis des plantes.

Perspectives et considérations futures

La portée de cette recherche dans l’avenir repose en partie sur l’accès à des meilleures bases de données. Or, cette tâche représente un travail commun qui requiert énormément de temps et de ressources, entre autres lorsqu’il est question de bases de données publiques telles que la base de données nucleotides du NCBI. Par contre, ce projet a fait voir que la construction de plus petites bases données rigoureuses et comportant des séquences soumises à un pocessus d’édition de contenu représente une solution partielle. La base de données UNITE en est un exemple pertinent, puisque le nombre de séquences qui y sont déposées augmente constamment, ce qui enrichit aussi la variété des organismes qui y sont contenus. L’accès à des bases de données contenant des séquences fiables et de haute qualité facilite et accélère grandement le processus d’identification. La base de données comprenant des séquences ITS2 (plantes) et celle qui contient des séquences de la région ATP9-NAD9 (Phytophthora spp.) sont deux exemples de petites bases de données locales et précises ayant du potentiel d’exploitation. Il est à noter que la base de données des séquences d’ATP9-NAD9 se limite aux Phytophthora spp. pour deux raisons principales : (i) encore très peu de séquences de la région ATP9-NAD9 sont disponibles pour d’autres oomycètes et (ii) la synthénie de cette région, bien que conservée chez les Phytophthora, est extrêmement variable pour ce qui est des autres organismes tels que les plantes et les Pythium spp. (Bilodeau et al. 2014).

Il serait également avantageux de développer des tests qPCR spécifiques aux espèces d’intérêt pour lesquelles aucun test n’est encore disponible. Effectivement, ces tests aideraient à corroborer les résultats des analyses métagénomiques. Par exemple, la rouille japonaise des poires (Gymnosporangium yamadae) réglementée par l’ACIA et la rouille courbeuse du pin (Melampsora pinitorqua) sont deux espèces ciblées qui causent des maladies ayant un potentiel menaçant pour

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les ressources naturelles canadiennes. Des séquences associées au genre Melampsora sp. ont d’ailleurs été détectées suite aux analyses des séquences obtenues par SNG. Or, il a été impossible de confirmer l’espèce vu l’absence d’un test qPCR spécifique et parce qu’il n’y avait que deux séquence ITS de M. pinitorqua dans la base de données nucleotides (NCBI) et aucune dans UNITE.

Faire un suivi aux emplacements où des organismes indésirables—tels que des champignons et des oomycètes phytopathogènes et des plantes envahissantes—ont été découverts serait aussi très important afin de repérer la présence de végétaux symptômatiques ou de l’envahissement par une plante. Les cas les plus notables de ce projet de doctorat sont sans doute les spores d’Heterobasidion annosums s.s. détectées en Ontario (ex. Ottawa) et au Québec (ex. Gatineau) et les spores de H. abietinum/H. parviporum découvertes en Colombie Britannique (e.g. Vancouver), puisqu’il pourrait s’agir d’une première détection à ces endroits. À ce propos, il serait aussi pertinent de prélever des échantillons, d’effectuer des tests validés et des postulats de Koch sur ces derniers afin d’évaluer, plus spécifiquement, si les forêts sont affectées et l’étendue des dommages s’il y a lieu. Cela pourrait être fait par des inspecteurs d’organismes de réglementation lors des recherches visuelles aux endroits mentionnés ci-haut.

Il serait aussi intéressant de faire des analyses SNG en tenant compte des profondeurs de séquençage obtenues pour chacun des types de pièges. Par exemple, les pièges à spores JB ont montré leur capacité à permettre l’analyse d’un plus grand nombre d’échantillons par ronde de séquençage puisque les courbes de raréfaction (nombre de séquence par nombre d’espèces observées) étaient saturées. En revanche, les échantillons des pièges à insectes n’atteignaient pas toujours le plateau de saturation, suggérant que la profondeur de séquençage n’était pas obtenue avec ces pièges. Il serait donc pertinent de réduire le nombre d’échantillons par ronde de séquençage pour les pièges à insectes.

Étant donné que ce projet ne portait que sur une portion des organismes contenus dans les données, c’est-à-dire principalement sur les champignons et sur les oomycètes phytopathogènes, encore beaucoup d’informations pourraient être extraites des jeux de données produits. L’approfondissement de l’étude faite sur des nombreux OTU non identifiés et incultivables pourrait

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permettre de détecter de nouveaux organismes jamais rapportés auparavant et ayant des fonctions écologiques encore inconnues.

Dans sa forme actuelle, l’exécution du pipeline requiert des connaissances en programmation informatique. Plusieurs des langages intégrés au pipeline (bash, Perl, Python et R) nécessitent un minimum d’expertise pour être utilisés. C’est pourquoi le pipeline serait beaucoup plus accessible aux utilisateurs futurs s’il était intégré à une interface visuelle et sous Windows, un système d’exploitation plus populaire que Linux. La plateforme Galaxy (Afgan et al. 2018) est un outil avec un grand potentiel, entre autres parce qu’elle est beaucoup plus intuitive, parce qu’elle est offerte en source libre (Open Source), et parce qu’elle se spécialise dans l’élaboration d’étapes séquentielles.

Les technologies de SNG évoluant à une vitesse fulgurante, de nouveaux instruments de séquençage (ex. Minion de Oxford Nanopore) ont été lancés depuis le début du projet. Ces instruments plus rapides permettent d’obtenir des séquences plus longues et de meilleure qualité, des caractéristiques qui offriraient certainement des résultats plus informatifs et plus faciles à résoudre. De plus, la technologie de séquençage en aveugle d’un génome entier (shotgun sequencing) mentionnée dans notre article de Roe et al. (2018) représente aussi une approche avec un potentiel prometteur puisqu’elle permet de faire l’étude de multiples fragments d’un génome pour y détecter des variations uniques (Sherwood et al. 2010). Comme le projet dans cet article scientifique (bioSAFE; http://www.biosafegenomics.com) se concentre spécifiquement sur les ravageurs forestiers exotiques, notre approche pourrait servir de base à partir de laquelle des outils encore plus puissants pourraient être développés.

L’expérience faite sur les abeilles butineuses a permis d’obtenir une tonne de résultats valables en phytopathologie et à propos d’interactions insectes-microorganismes fongiques et oomycètes. De plus, il semble que les pièges à insectes utilisés contenaient aussi des séquences d’ADN d’organismes susceptibles d’avoir été transportés par d’autres insectes pollinisateurs plutôt que par les insectes xylophages. Par conséquent, il serait intéressant de faire une analyse similaire à celle décrite dans cette thèse sur différents insectes pollinisateurs et leur potentiel de contribution à la biosurveillance des maladies des plantes et des plantes envahissantes.

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Mis à part ceux provenant de pièges à insectes, la majorité des échantillons ont été recueillis en Ontario et au Québec. Il serait donc pertinent d’installer des pièges à spores, des échantillonneurs à bras rotatif et des pièges à granules de pollen dans les autres provinces où des collectes d’insectes ont été faites, afin de mieux évaluer les forces et les faiblesses de chacun des types de pièges pour un endroit donné. De plus, l’industrie de l’apiculture s’étendant partout au Canada avec le plus grand nombre de ruches dans les provinces des prairies, l’accès à ces ruches pourrait permettre de faire la biosurveillance à faible coûts et sur des milliers de kilomètres grâce au métabarcodage d’échantillons recueillis avec des pièges à pollen. Il pourrait s’agir d’un outil très efficace pour la détection primaire d’agents pathogènes des céréales.

Enfin, l’ensemble des nouvelles connaissances acquises, le pipeline bio-informatique développé, les bases de données locales et les méthodes de piégeage vont certainement servir à l’ACIA puisqu’elle pourrait éventuellement intégrer le SNG à ses méthodes d’enquêtes.

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