Exploring links between antisense RNAs and pathogenesis in Ustilago maydis through transcript and gene characterization
Bibliographic record
Abstract
Biotrophic basidiomycete plant pathogens cause billions of dollars in losses to cereal crops annually. The model for this group of fungi is the corn smut pathogen Ustilago maydis. Annotation of its genome identified antisense RNAs (asRNAs) complementary to over half of the coded mRNAs, some of which are present at high levels in teliospores but detected at very low levels or not at all in other cell types, suggesting they have a function in the teliospore or during teliospore formation. Expression of three such asRNAs (as-UMAG_02150, ncRNA1, and as-UMAG_02151) is controlled by two adjacent genomic regions. Deletion of these regions increased transcript levels of all three asRNAs and attenuated pathogenesis. This study investigated the reason for this marked reduction in pathogenesis by: (1) using deletion analyses to assess the involvement of genes, complementary to the asRNAs, in pathogenesis; (2) determining that one of the linked genes encodes a putative xylitol dehydrogenase; and (3) identifying and functionally characterizing asRNAs that could influence expression of protein-coding genes. The results presented suggest that the influence of the asRNAs on pathogenesis occurs through their action at unlinked loci.
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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".