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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dc.authoridTamisier, Lucie/0000-0002-9231-2997
dc.authoridDe Jonghe, Kris/0000-0003-1763-5654
dc.authoridGaafar, Yahya/0000-0002-7833-1542
dc.authoridHurtado-Gonzales, Oscar/0000-0002-9561-7016
dc.authoridSchumpp, Olivier/0000-0002-2070-2144
dc.authoridKogej Zwitter, Zala/0000-0002-8146-2906
dc.authoridReynard, Jean-Sebastien/0000-0002-2337-107X
dc.contributor.authorHaegeman, Annelies
dc.contributor.authorFoucart, Yoika
dc.contributor.authorDe Jonghe, Kris
dc.contributor.authorGoedefroit, Thomas
dc.contributor.authorAl Rwahnih, Maher
dc.contributor.authorBoonham, Neil
dc.contributor.authorCandresse, Thierry
dc.date.accessioned2024-11-07T13:35:32Z
dc.date.available2024-11-07T13:35:32Z
dc.date.issued2023
dc.departmentNiğde Ömer Halisdemir Üniversitesi
dc.description.abstractHigh-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.sponsorshipBelgian Federal Public Service of Health, Food Chain Safety and Environment (FPS Health) through the contract [RI 18_A-289]; Euphresco project Plant Health Bioinformatics Network (PHBN) [2018-A-289]; COST (European Cooperation in Science and Technology) Action [FA1407]; Slovenian Research Agency [P4-0072, L7-2632, P4-0165]
dc.description.sponsorshipThis research was partially funded by the Belgian Federal Public Service of Health, Food Chain Safety and Environment (FPS Health) through the contract RI 18_A-289 and by the Euphresco project Plant Health Bioinformatics Network (PHBN) (2018-A-289). This research benefited from COST (European Cooperation in Science and Technology) Action FA1407 (DIVAS). This research was partially financed by Slovenian Research Agency (project and core financing grants No. P4-0072, L7-2632 and P4-0165).
dc.identifier.doi10.3390/plants12112139
dc.identifier.issn2223-7747
dc.identifier.issue11
dc.identifier.pmid37299118
dc.identifier.scopus2-s2.0-85161589306
dc.identifier.scopusqualityQ1
dc.identifier.urihttps://doi.org/10.3390/plants12112139
dc.identifier.urihttps://hdl.handle.net/11480/16556
dc.identifier.volume12
dc.identifier.wosWOS:001006442400001
dc.identifier.wosqualityQ1
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.indekslendigikaynakPubMed
dc.language.isoen
dc.publisherMdpi
dc.relation.ispartofPlants-Basel
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/openAccess
dc.snmzKA_20241106
dc.subjectplant pathogen
dc.subjectdiagnostics
dc.subjecthigh-throughput sequencing
dc.subjectmetagenomics
dc.subjectmetatranscriptomics
dc.subjectRNA-seq
dc.titleLooking beyond Virus Detection in RNA Sequencing Data: Lessons Learned from a Community-Based Effort to Detect Cellular Plant Pathogens and Pests
dc.typeArticle

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