Annotation and functional prediction of RNA helicases in Ustilago maydis
Bibliographic record
Abstract
RNA helicases are conserved enzymes found in both prokaryotes and eukaryotes. They function in all aspects of RNA metabolism and are known to influence various cellular and metabolic processes. In addition, they have been implicated in certain cancers and diseases. Studies on RNA helicases in fungi indicate their conserved roles in RNA metabolism and suggest that their dysregulation can affect fungal growth. However, the roles of RNA helicases in fungal plant pathogenesis remain underexplored, despite increasing knowledge of how RNA helicases modulate gene expression and disease progression. We used the basidiomycete plant pathogen Ustilago maydis as a model to identify 46 RNA helicases. We review the roles of RNA helicases in RNA metabolism, cellular growth and homeostasis, and metabolism. We then utilized available U. maydis transcriptome data and current research to hypothesize potential functions of RNA helicases in fungal plant pathology. These roles include influencing cell growth, modulating stress response, contributing to virulence and disease progression, and regulating fungal spore dormancy and germination. Understanding the roles of RNA helicases in gene regulation may aid in developing strategies to mitigate disease spread in fungal plant pathogens.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".