Analysis of enriched transcripts induced during velvetleaf -<i>Colletotrichum coccodes</i>interaction
Bibliographic record
Abstract
We used suppression subtractive hybridization (SSH) and differential screening (DS) to generate cDNA libraries enriched for up-regulated host and pathogen genes following inoculation of velvetleaf leaves with the mycoherbicide Colletotrichum coccodes. A total of 116 expressed sequence tags (EST), representing preferentially expressed genes, were analyzed, of which 89 sequences were assigned putative functions including transcription and translation, energy, cell growth and maintenance, and oxidative stress and defense. The EST sequences were assembled into 20 contigs and 29 singletons/singlets. Except for contig 26 that had the highest hit to fungal Histone 4, all other sequences had top hits to plant sequences. Genes related to oxidative stress and defense formed the largest category corresponding to nine contigs and three singlets (or 48% of the ESTs). The metallothioneins (MT) type 3 proteins were represented by six contigs, three singletons, and two singlets. We also identified genes for the ethylene response element binding (EREB) protein, WRKY, and bZIP proteins, which are likely to play roles in transcription, as well as genes for proteins such as ascorbate peroxidase and reticuline oxidase. The up-regulation of seven selected genes were validated by quantitative real-time PCR. Relative to uninfected velvetleaf leaves, these genes were significantly induced by the presence of C. coccodes on velvetleaf leaves.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".