Using plant virology high throughput sequencing data in screening for other potential plant pathogens

The Euphresco project Plant health Health bioinformatics Bioinformatics network Network (PHBN) initiated a community effort to re-analyze existing RNA-seq datasets intended for virus detection. High throughput sequencing (HTS), mainly RNA-seq of plant samples is often used by plant virologists searching for potential virus infections. Re-analyzing those datasets for the potential presence of non-viral pathogens or pests could be useful for wider phytopathological communities.
Eight datasets available at Agricultural Institute of Slovenia were analyzed by first mapping the data against a given rRNA database based on SILVA. The datasets came from different plant species (soybean, grapevine, garlic, and Rubus spp.) and only in three datasets plant virus sequences were confirmed. All the results were sent to ILVO for evaluation of the obtained results and four datasets were selected for more detailed analysis. Two analyses were done on these selected samples: 1) meta-assembly followed by best-hit taxonomic classification using diamond blastx against Uniprot and 2) using direct taxonomic classification of the reads using Kraken2 against the complete Genbank non-redundant Nucleotide BLAST database. The resulting taxonomic classifications were checked for the presence of non-viral plant pests and pathogens and the plausibility of their presence was evaluated. The analyzed Rubus dataset showed a clear evidence of aphid infestation (Aphis gossypii, 3063 reads per million (rpm)), while one of the soybean datasets indicated a presence of mites (Tetranychus urticae, 345 rpm). In the two soybean datasets, Ttraces of oomycete reads (Phytophthora spp.) were also found, at 295 and 276 rpm respectively. The garlic dataset contained reads attributed to Fusarium spp. (726 rpm). These results and fungal sequences were also found in analyzed datasets confirming that those RNA-seq datasets used for plant virus detection can be used not only for virus but also for detection of to find traces of other plant pathogens as well, although the exact species identification sometimes remains unclear. This work will hopefully improve and refine our knowledge on microbiome associated with viral infections in plants.