Screening for Exotic Forest Pathogens to Increase Survey Capacity Using Metagenomics

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 spp. 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.

High‐resolution biomonitoring of plant pathogens and plant species using metabarcoding of pollen pellet contents collected from a honey bee hive

“…The custom Perl script metaResultExtractor.pl (Duceppe & Tremblay, ; Tremblay et al, ) was used to screen for fungal and oomycete genera of interest as pathogens to the forestry and agricultural industries, which are listed in Table . Similarly, the data were screened for the presence of invasive plant species, selected from the Canadian Phytosanitary requirement D‐12‐01 (Regulated Plant Pests) (Canadian Food Inspection Agency, ), and several closely related species (Supplementary Information S2).…”

“…(ATP9-NAD9). Except for the plant-specific (ITS2) PCR, the above-mentioned fusion primers protocol and the PCR oligonucleotide sequences were all previously described by Tremblay et al (2018). Briefly, we used a bidirectional fusion primer set to amplify specifically the ITS1 region from fungi (forward primer = ITS1F, Gardes & Bruns, 1993 and reverse primer = ITS2, White, Bruns, Lee, & Taylor, 1990), another set to amplify the ITS1 from oomycetes (forward primer = OOM-UP18S67, C. A. Lévesque, pers.…”

“…Oomycetes were screened using an internally generated "OmDB" (Duceppe & Tremblay, 2019a 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" (Bilodeau & Tremblay, 2019a was built by gathering ATP9-NAD9 sequences from Phytophthora species retrieved from (a) the NCBI (nucleotide) (NCBI accession numbers JF771616.1 to JF772053.1 and JQ439009.1 to JQ439486.1) and (b) sequences described by Bilodeau et al (2014).…”

“…The generated operational taxonomic unit (OTU) tables included taxonomic assignments at the species (putative identification) or genus levels, when possible, based on a 0.97 OTU cut-off following alignment with the respective database (i.e., UNITE, OmDB, PhyDB, and PlantsDB). As previously described in Tremblay et al (2018), a fungal OTU threshold of 97% prevents biodiversity overestimation and rare species omission and is generally accepted as the fungal intraspecific variability level (Abdelfattah, Malacrinò, Wisniewski, Cacciola, & Schena, 2018;Abdelfattah, Nicosia, Cacciola, Droby, & Schena, 2015;Kemler et al, 2013;Nicolas, Ponchart, & Berube, 2013;Vettraino et al, 2015). In addition, following Mainali et al (2017)'s previous work, a minimum count of 100 reads was set as the threshold for assuming the presence of a species or OTU within the data.…”

“…More recently, such biomonitoring analyses have been successfully applied using a high-throughput sequencing (HTS) approach (Carew, Pettigrove, Metzeling, & Hoffmann, 2013;Mordecai et al, 2015). HTS has been used to screen for the presence of fungal propagules present in high-risk environments using various types of spore traps (Bérubé, Dubé, & Potvin, 2017;Bérubé, Gagné, Ponchart, Tremblay, & Bilodeau, 2018;Tremblay et al, 2018). Such approaches address the need for additional monitoring methods for ecological damage mitigation, much of which is caused by either fungi or oomycetes (Carris, Little, & Stiles, 2012;Fry & Grunwald, 2010;Kamoun et al, 2015;Knogge, 1996).…”