Looking beyond virus detection in RNA sequencing data : lessons learned from a community-based effort to detect cellular plant pathogens and pests

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creativework.keywords - en

Phytopathogenic microorganisms

Micro-organismes phytopathogènes

Diagnostics

Diagnosis

High-Throughput Nucleotide Sequencing

creativework.keywords - fr

Séquençage à haut débit

Métagénomique

Metagenomics

Séquençage de l'ARN

RNA sequencing

dc.contributor.author

Haegeman, Annelies

Foucart, Yoika

De Jonghe, Kris

Goedefroit, Thomas

Al Rwahnih, Maher

Boonham, Neil

Candresse, Thierry

Gaafar, Yahya Z. A.

Hurtado-Gonzales, Oscar P.

Kogej Zwitter, Zala

Kutnjak, Denis

Lamovšek, Janja

Lefebvre, Marie

Malapi, Martha

Mavrič Pleško, Irena

Önder, Serkan

Reynard, Jean-Sébastien

Salavert Pamblanco, Ferran

Schumpp, Olivier

Stevens, Kristian

Pal, Chandan

Tamisier, Lucie

Ulubaş Serçe, Çiğdem

van Duivenbode, Inge

Waite, David W.

Hu, Xiaojun

Ziebell, Heiko

Massart, Sébastien

dc.date.accepted

2023-05-27

dc.date.accessioned

2024-08-16T19:14:07Z

dc.date.available

2024-08-16T19:14:07Z

dc.date.issued

2023-05-29

dc.date.submitted

2023-05-10

dc.description.abstract - en

High-throughput sequencing (HTS), more specifically RNA sequencing of plant tissues, has become an indispensable tool for plant virologists to detect and identify plant viruses. During the data analysis step, plant virologists typically compare the obtained sequences to reference virus databases. In this way, they are neglecting sequences without homologies to viruses, which usually represent the majority of sequencing reads. We hypothesized that traces of other pathogens might be detected in this unused sequence data. In the present study, our goal was to investigate whether total RNA-seq data, as generated for plant virus detection, is also suitable for the detection of other plant pathogens and pests. As proof of concept, we first analyzed RNA-seq datasets of plant materials with confirmed infections by cellular pathogens in order to check whether these non-viral pathogens could be easily detected in the data. Next, we set up a community effort to re-analyze existing Illumina RNA-seq datasets used for virus detection to check for the potential presence of non-viral pathogens or pests. In total, 101 datasets from 15 participants derived from 51 different plant species were re-analyzed, of which 37 were selected for subsequent in-depth analyses. In 29 of the 37 selected samples (78%), we found convincing traces of non-viral plant pathogens or pests. The organisms most frequently detected in this way were fungi (15/37 datasets), followed by insects (13/37) and mites (9/37). The presence of some of the detected pathogens was confirmed by independent (q)PCRs analyses. After communicating the results, 6 out of the 15 participants indicated that they were unaware of the possible presence of these pathogens in their sample(s). All participants indicated that they would broaden the scope of their bioinformatic analyses in future studies and thus check for the presence of non-viral pathogens. In conclusion, we show that it is possible to detect non-viral pathogens or pests from total RNA-seq datasets, in this case primarily fungi, insects, and mites. With this study, we hope to raise awareness among plant virologists that their data might be useful for fellow plant pathologists in other disciplines (mycology, entomology, bacteriology) as well.

dc.description.fosrcfull - en

This article belongs to the Special Issue "High-Throughput Sequencing Applied to Plant Virus and Viroid Detection."

dc.description.fosrcfull-fosrctranslation - fr

Cet article fait partie du numéro spécial "High-Throughput Sequencing Applied to Plant Virus and Viroid Detection."

dc.identifier.citation

Haegeman, A., Foucart, Y., De Jonghe, K., Goedefroit, T., Al Rwahnih, M., Boonham, N., Candresse, T., Gaafar, Y. Z. A., Hurtado-Gonzales, O. P., Kogej Zwitter, Z., Kutnjak, D., Lamovšek, J., Lefebvre, M., Malapi, M., Mavrič Pleško, I., Önder, S., Reynard, J.-S., Salavert Pamblanco, F., Schumpp, O., Stevens, K., Pal, C., Tamisier, L., Ulubaş Serçe, Ç., van Duivenbode, I., Waite, D. W., Hu, X., Ziebell, H., & Massart, S. (2023). Looking beyond virus detection in RNA sequencing data : lessons learned from a community-based effort to detect cellular plant pathogens and pests. Plants, 12(11), Article 2139. https://doi.org/10.3390/plants12112139

dc.identifier.doi

https://doi.org/10.3390/plants12112139

dc.identifier.issn

2223-7747

dc.identifier.uri

https://open-science.canada.ca/handle/123456789/2845

dc.language.iso

en

dc.publisher

MDPI

dc.rights - en

Creative Commons Attribution 4.0 International (CC BY 4.0)

dc.rights - fr

Creative Commons Attribution 4.0 International (CC BY 4.0)

dc.rights.openaccesslevel - en

Gold

dc.rights.openaccesslevel - fr

Or

dc.rights.uri - en

https://creativecommons.org/licenses/by/4.0/

dc.rights.uri - fr

https://creativecommons.org/licenses/by/4.0/deed.fr

dc.subject - en

Health and safety

Science and technology

dc.subject - fr

Santé et sécurité

Sciences et technologie

dc.subject.en - en

Health and safety

Science and technology

dc.subject.fr - fr

Santé et sécurité

Sciences et technologie

dc.title - en

Looking beyond virus detection in RNA sequencing data : lessons learned from a community-based effort to detect cellular plant pathogens and pests

dc.type - en

Article

dc.type - fr

Article

local.acceptedmanuscript.articlenum

2139

local.article.journalissue

11

local.article.journaltitle

Plants

local.article.journalvolume

12

local.pagination

1-20

local.peerreview - en

Yes

local.peerreview - fr

Oui

local.requestdoi

No

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