Nouvelle méthode de dépistage de phytopathogènes fongiques et de plantes au potentiel envahissant par métabarcodage
|Authors:||Tremblay, Émilie D.|
|Advisor:||Lemieux, Claude; Bilodeau, Guillaume|
|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.|
|Document Type:||Thèse de doctorat|
|Open Access Date:||11 April 2019|
|Collection:||Thèses et mémoires|
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