Characterization of Five Novel Mitoviruses in the White Pine Blister Rust Fungus Cronartium ribicola
Bibliographic record
Abstract
The white pine blister rust (WPBR) fungus Cronartium ribicola (J.C. Fisch.) is an exotic invasive forest pathogen causing severe stem canker disease of native white pine trees (subgenus Strobus) in North America. The present study reports discovery of five novel mitoviruses in C. ribicola by deep RNA sequencing. The complete genome of each mitovirus was determined by rapid amplification of cDNA ends (RACE) and reverse transcriptase-polymerase chain reaction (RT-PCR). A single open reading frame (ORF) encoding a putative RNA-dependent RNA polymerase (RdRp) was detected in each of the viral genomes using mitochondrial genetic codes. Phylogenetic analysis indicated that the C. ribicola mitoviruses (CrMV1 to CrMV5) are new putative species of the genus Mitovirus. qRT-PCR and RNA-Seq analyses revealed that viral RNAs were significantly increased in fungal mycelia in cankered pine stems compared to expression during two different stages of spore development, suggesting that viral genome replication and transcription benefit from active growth of the host fungus. CrMVs were widespread with relatively high levels of minor allele frequency (MAF) in western North America. As the first report of mitoviruses in the Class Pucciniomycetes, this work allows further investigation of the dynamics of a viral community in the WPBR pathosystem, including potential impacts that may affect pathogenicity and virulence of the host fungus.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".