# Detecting “invisible” Phytophthora lineages in publicly available sequencing data

## TL;DR

This study introduces a new method to detect hard-to-study microbes, like Phytophthora, in sequencing data, revealing new species and host interactions.

## Contribution

A novel pipeline for assembling ITS sequences from genomic and metagenomic data is introduced, improving detection of elusive microbial lineages.

## Key findings

- The pipeline identified 733 full ITS sequences from 104 datasets, including sequences from known and novel Phytophthora lineages.
- New potential host-pathogen interactions were detected, with implications for agriculture and human health.
- The method enables improved sensitivity and precision in detecting 'invisible' microbial eukaryotes.

## Abstract

Our understanding of microbial eukaryotic diversity is limited by biases induced by cultivation and DNA-amplification. Microbial lineages which are challenging or impossible to culture and develop universal metabarcoding primers for can be considered “invisible.” These “invisible” microbes can however be detected in genomic and metagenomic sequencing datasets. This study introduces a new pipeline for targeted assembly of internal transcribed spacer (ITS) sequences from genomes and metagenomes (https://github.com/tage-ro/denim), which provides advantages in sensitivity and precision over comparable marker-gene assembly software. It further shows how publicly sequencing datasets can be screened for the genus Phytophthora, which includes economically and ecologically devastating plant pathogens. Analysis of 104 sequencing datasets resulted in 733 full ITS sequences, 1626 ITS1 sequences and 2191 ITS2 sequences associated with a variety of eukaryotic lineages. Phytophthora ITS sequences associated with known species in clades 1, 2, 4, 6, 7 and 8 were assembled, along with sequences only distantly related to known taxa. In addition, it provided potential indications of new pathogen-host interactions, with potential impacts on agriculture and human health. This study presents a new approach towards discovering and detecting “invisible” microbes, thus expanding our understanding of microbial eukaryotic diversity. Moreover, it allows detection and monitoring of new host–microbe interactions, and characterizing the geographic distribution of cultured and uncultured microorganisms.

Graphical Abstract