PT  - JOURNAL ARTICLE
AU  - Marco E. Mechan Llontop
AU  - Parul Sharma
AU  - Marcela Aguilera Flores
AU  - Shu Yang
AU  - Jill Pollock
AU  - Long Tian
AU  - Chenjie Huang
AU  - Steve Rideout
AU  - Lenwood S. Heath
AU  - Song Li
AU  - Boris A. Vinatzer
TI  - Strain-level identification of bacterial tomato pathogens directly from metagenomic sequences
AID  - 10.1101/777706
DP  - 2019 Jan 01
TA  - bioRxiv
PG  - 777706
4099  - http://biorxiv.org/content/early/2019/09/23/777706.short
4100  - http://biorxiv.org/content/early/2019/09/23/777706.full
AB  - Routine strain-level identification of plant pathogens directly from symptomatic tissue could significantly improve plant disease control and prevention. Here we tested the Oxford Nanopore Technologies (ONT) MinION™ sequencer for metagenomic sequencing of tomato plants either artificially inoculated with a known strain of the bacterial speck pathogen Pseudomonas syringae pv. tomato (Pto), or collected in the field and showing bacterial spot symptoms caused by either one of four Xanthomonas species. After species-level identification using ONT’s WIMP software and the third party tools Sourmash and MetaMaps, we used Sourmash and MetaMaps with a custom database of representative genomes of bacterial tomato pathogens to attempt strain-level identification. In parallel, each metagenome was assembled and the longest contigs were used as query with the genome-based microbial identification Web service LINbase. Both the read-based and assembly-based approaches correctly identified Pto strain T1 in the artificially inoculated samples. The pathogen strain in most field samples was identified as a member of Xanthomonas perforans group 2. This result was confirmed by whole genome sequencing of colonies isolated from one of the samples. Although in our case, metagenome-based pathogen identification at the strain-level was achieved, caution still needs to be exerted when interpreting strain-level results because of the challenges inherent to assigning reads to specific strains and the error rate of nanopore sequencing.