Using genome sequencing, protein biotyping, and CRISPR diagnostics to tackle the identification of plant pests

Plant pests impact trade, crop production, human health, and the environment. To detect these species, it is important to develop molecular identification tools that are efficient, sensitive, and reliable. We used plants from the genus Amaranthus to start developing these tools. Some species in this group are regulated in Canada (A. tuberculatus, also known as tall waterhemp) or in the U.S. (A. palmeri, also known as palmer amaranth), due to their invasiveness, herbicide resistance, and impact on crops like corn and soybean.

We assembled 28 chloroplast genomes and nuclear ribosomal DNA regions corresponding to 15 Amaranthus species to find molecular fingerprints (DNA barcodes) to distinguish target species of concern. Additionally, we implemented a bioinformatics pipeline that allowed us to discover unique fixed alleles in the nuclear genome from accessions representing five Amaranthus species.

Using chloroplast polymorphic regions, we adapted CRISPR-Cas12 gene-editing technology to design fluorescent assays to identify Amaranthus species. We achieved a 90% accuracy rate in identifying A. palmeri in a batch of 60 blind samples. We also performed preliminary testing of this technology to identify the causal agent of sudden oak death (Phytophthora ramorum). Most P. ramorum isolates obtained for preliminary testing were correctly identified. The proposed CRISPR-Cas12 enzymatic assay provides results in less than 30 minutes without lab equipment, and we are currently working on a method that may streamline DNA extraction to fluorescent detection in under an hour.

Finally, we applied Matrix Assisted Laser Desorption/Ionization (MALDI) protein biotyping to determine if single Amaranthus seeds could provide consistent and distinctive protein spectra for species classification. After generating a protein spectra database of 16 Amaranthus species, testing two batches of 15 and 60 blind samples resulted in 100% and 87% correct species identification, respectively.