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Record W4408989423 · doi:10.1101/2025.03.28.645015

Exploring the mycovirome: novel and diverse mycoviruses in <i>Botrytis cinerea</i>

2025· preprint· en· W4408989423 on OpenAlexaffabout
Sarah C Drury, Abdonaser Poursalavati, Pierre Lemoyne, Dong Xu, Peter Moffett, Odile Carisse, Hervé Van der Heyden, Mamadou L. Fall

Bibliographic record

VenuebioRxiv (Cold Spring Harbor Laboratory) · 2025
Typepreprint
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicPlant and Fungal Interactions Research
Canadian institutionsCégep Saint-Jean-sur-RichelieuUniversité de SherbrookeAgriculture and Agri-Food Canada
Fundersnot available
KeywordsMycovirusBotrytis cinereaAdornmentBotanyBiologyArtAesthetics

Abstract

fetched live from OpenAlex

Abstract Botrytis cinerea is a necrotrophic fungal pathogen that causes significant economic losses to many crops, including vegetables, fruits, and ornamental plants. The management of B. cinerea is difficult due to a rise in fungicide resistance. Harnessing mycoviruses that cause reduced virulence (hypovirulence) in B. cinerea is a promising alternative. Over 100 mycoviruses have been identified in Botrytis spp. to date, including several hypovirulence-inducing mycoviruses. This research aimed to further explore, for the first time in Canada, the mycovirome of B. cinerea and identify potential hypovirulence-inducing mycoviruses. Isolates of B. cinerea were collected from fruits and vegetables in the province of Quebec. Fitness and pathogenicity criteria, including sclerotia production, colony morphotype, and lesion size were evaluated. A double-stranded RNA (dsRNA) extraction protocol tailored to the detection of mycoviruses was used to sequence dsRNA from 45 isolates with low fitness/pathogenicity, and an in-house bioinformatics workflow was used to profile the mycovirome. Mycoviruses were identified in 44/45 isolates. Most of these had positive single-stranded RNA or dsRNA genomes, and a small number had negative single-stranded RNA, single-stranded DNA, or reverse transcriptase RNA genomes. Following deep analysis of RNA-dependent RNA polymerase and replication initiation proteins, a total of 62 unique contigs were identified belonging to new strains of mycovirus species. Furthermore, four putative novel mycovirus species belonging to Endornaviridae , Botybirnaviridae , Peribunyaviridae , and Bunyavirales taxa were identified. Several mycovirus species positively and/or negatively co-occurred with B. cinerea isolates collected from strawberry or raspberry. This study revealed a high degree of diversity in the mycovirome of B. cinerea. Species accumulation curve analysis indicated that, with the number of isolates characterized, we were unable to capture the full extent of expected diversity. Nevertheless, we identified potential hypovirulence-inducing mycoviruses, including Botrytis cinerea mitovirus 1, Botrytis cinerea hypovirus 1, and Botrytis porri botybirnavirus 1. Some of these novel mycoviruses belonged to taxa known to produce viral particles, which can be an interesting feature for their use as biocontrol agents (BCA). Importance This study provides the first comprehensive profiling of mycoviruses infecting Botrytis cinerea in Canada, a significant step in understanding how these viruses can naturally limit crop disease. Due to growing resistance against conventional fungicides, new biological methods to control B. cinerea are crucial. By profiling mycoviruses in fungal samples collected in Quebec, we identified several novel viruses that appear to reduce the pathogenicity of B. cinerea . These viruses, known as hypovirulence-inducing mycoviruses, could be used to develop biocontrol agents (BCA), offering a more sustainable disease management alternative. Notably, we found virus families with extracellular potential, which may enable easier application as BCAs in agriculture. This research not only broadens the understanding of fungal virology but also holds promise for innovative, eco-friendly approaches to managing Botrytis cinerea in Canada.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.268
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.053
GPT teacher head0.265
Teacher spread0.212 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designBench or experimental
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations3
Published2025
Admission routes2
Has abstractyes

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