Comprehensive transcriptome profiling reveals abundant long non‐coding <scp>RNAs</scp> associated with development of the rice false smut fungus, <i>Ustilaginoidea virens</i>
Bibliographic record
Abstract
Long non-coding RNAs (lncRNAs) play an important role in biological processes but regulation and function of lncRNAs remain largely unelucidated, especially in fungi. Ustilaginoidea virens is an economically important fungus causing a devastating disease of rice. By combining microscopic and RNA-seq analyses, we comprehensively characterized lncRNAs of this fungus in infection and developmental processes and defined four serial typical stages. RNA-seq analyses revealed 1724 lncRNAs in U. virens, including 1084 long intergenic non-coding RNAs (lincRNAs), 51 intronic RNAs (incRNAs), 566 natural antisense transcripts (lncNATs) and 23 sense transcripts. Gene Ontology enrichment of differentially expressed lincRNAs and lncNATs demonstrated that these were mainly involved in transport-related regulation. Functional studies of transport-related lncRNAs revealed that UvlncNAT-MFS, a cytoplasm localized lncNAT of a putative MFS transporter gene, UvMFS, could form an RNA duplex with UvMFS and was required for regulation of growth, conidiation and various stress responses. Our results were the first to elucidate the lncRNA profiles during infection and development of this important phytopathogen U. virens. The functional discovery of the novel lncRNA, UvlncNAT-MFS, revealed the potential of lncRNAs in regulation of life processes in fungi.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".